Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51564, first published .
Smartphone-Based Hand Function Assessment: Systematic Review

Smartphone-Based Hand Function Assessment: Systematic Review

Smartphone-Based Hand Function Assessment: Systematic Review

Review

1School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2KITE - Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

3Department of Occupational Science and Occupational Therapy, University of Toronto, Toronto, ON, Canada

4Library and Information Services, University Health Network, Toronto, ON, Canada

5Department of Rehabilitation Medicine, Hubei Province Academy of Traditional Chinese Medicine Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China

Corresponding Author:

Yuxin Zhang, MSc

School of Mechanical Science and Engineering

Huazhong University of Science and Technology

1037 Luoyu Road, Hongshan District

Wuhan, 430074

China

Phone: 86 17381571416

Email: 953385493@qq.com


Background: Hand function assessment heavily relies on specific task scenarios, making it challenging to ensure validity and reliability. In addition, the wide range of assessment tools, limited and expensive data recording, and analysis systems further aggravate the issue. However, smartphones provide a promising opportunity to address these challenges. Thus, the built-in, high-efficiency sensors in smartphones can be used as effective tools for hand function assessment.

Objective: This review aims to evaluate existing studies on hand function evaluation using smartphones.

Methods: An information specialist searched 8 databases on June 8, 2023. The search criteria included two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to human studies in the English language and excluded conference proceedings and trial register records. Two reviewers independently screened all studies, with a third reviewer involved in resolving discrepancies. The included studies were rated according to the Mixed Methods Appraisal Tool. One reviewer extracted data on publication, demographics, hand function types, sensors used for hand function assessment, and statistical or machine learning (ML) methods. Accuracy was checked by another reviewer. The data were synthesized and tabulated based on each of the research questions.

Results: In total, 46 studies were included. Overall, 11 types of hand dysfunction–related problems were identified, such as Parkinson disease, wrist injury, stroke, and hand injury, and 6 types of hand dysfunctions were found, namely an abnormal range of motion, tremors, bradykinesia, the decline of fine motor skills, hypokinesia, and nonspecific dysfunction related to hand arthritis. Among all built-in smartphone sensors, the accelerometer was the most used, followed by the smartphone camera. Most studies used statistical methods for data processing, whereas ML algorithms were applied for disease detection, disease severity evaluation, disease prediction, and feature aggregation.

Conclusions: This systematic review highlights the potential of smartphone-based hand function assessment. The review suggests that a smartphone is a promising tool for hand function evaluation. ML is a conducive method to classify levels of hand dysfunction. Future research could (1) explore a gold standard for smartphone-based hand function assessment and (2) take advantage of smartphones’ multiple built-in sensors to assess hand function comprehensively, focus on developing ML methods for processing collected smartphone data, and focus on real-time assessment during rehabilitation training. The limitations of the research are 2-fold. First, the nascent nature of smartphone-based hand function assessment led to limited relevant literature, affecting the evidence’s completeness and comprehensiveness. This can hinder supporting viewpoints and drawing conclusions. Second, literature quality varies due to the exploratory nature of the topic, with potential inconsistencies and a lack of high-quality reference studies and meta-analyses.

J Med Internet Res 2024;26:e51564

doi:10.2196/51564

Keywords



Background

Hand function assessment is crucial in determining the extent of functional loss in patients and the outcome of surgical and rehabilitative procedures. Subtle changes in hand function could be a good predictor for the early detection of certain neuromuscular degeneration diseases, such as Parkinson disease (PD), which could help take preventive measures to reduce the severity of the illness [Hathaliya JJ, Modi H, Gupta R, Tanwar S, Sharma P, Sharma R. Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity. Comput Electr Eng. Jul 2022;101:107946. [FREE Full text] [CrossRef]1]. However, most current hand function assessments are conducted in a clinical context with the intensive involvement of rehabilitation professionals. Clinical evaluation requires frequent visits and long-duration treatment sessions [Moral-Munoz JA, Zhang W, Cobo MJ, Herrera-Viedma E, Kaber DB. Smartphone-based systems for physical rehabilitation applications: a systematic review. Assist Technol. Jul 04, 2021;33(4):223-236. [FREE Full text] [CrossRef] [Medline]2]. Hand function is usually assessed using standard questionnaires, such as the Michigan Hand Outcome Questionnaire and Disability of the Arm, Shoulder, and Hand Index [Fowler NK, Nicol AC. Functional and biomechanical assessment of the normal and rheumatoid hand. Clin Biomech (Bristol, Avon). Oct 2001;16(8):660-666. [FREE Full text] [CrossRef] [Medline]3]. These measurements are subjective and could result in different assessment results across different test scenarios and medical professionals [Fiems CL, Miller SA, Buchanan N, Knowles E, Larson E, Snow R, et al. Does a sway-based mobile application predict future falls in people with Parkinson disease? Arch Phys Med Rehabil. Mar 2020;101(3):472-478. [FREE Full text] [CrossRef] [Medline]4]. Clinical outcomes based on a rating scale are often insensitive to subtle hand function changes and do not support the provision of timely feedback [Yang K, Xiong WX, Liu FT, Sun YM, Luo S, Ding ZT, et al. Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. Ann Transl Med. Mar 2016;4(5):90. [FREE Full text] [CrossRef] [Medline]5]. As such, a hand assessment tool that can overcome the clinical assessment drawbacks of inconvenience, high cost, and imprecision [Hathaliya JJ, Modi H, Gupta R, Tanwar S, Sharma P, Sharma R. Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity. Comput Electr Eng. Jul 2022;101:107946. [FREE Full text] [CrossRef]1,Yang K, Xiong WX, Liu FT, Sun YM, Luo S, Ding ZT, et al. Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. Ann Transl Med. Mar 2016;4(5):90. [FREE Full text] [CrossRef] [Medline]5] and automatically evaluate hand function over time would benefit patients.

Smartphones are equipped with advanced technologies, such as touchscreens, accelerometers, and gyroscopes, which can be used for measuring and evaluating hand function [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6]. The application of smartphones in clinical hand dysfunction assessments can exploit built-in sensors (such as accelerometers and gyroscopes) to collect real-time hand movement data with convenience and at low cost [Zwar N, Harris M, Griffiths R, Roland M, Dennis S, Powell Davies G, et al. A systematic review of chronic disease management. Australian Primary Health Care Research Institute. 2006. URL: https://unsworks.unsw.edu.au/entities/publication/9a36a75a-ba4b-44c0-a5d5-91ace271b0ad [accessed 2024-04-29] 7]. Smartphones can precisely monitor and analyze a patient’s hand condition for dysfunction assessment using machine learning (ML) and artificial intelligence algorithms [Zuo KJ, Guo D, Rao J. Mobile teledermatology: a promising future in clinical practice. J Cutan Med Surg. Nov 01, 2013;17(6):387-391. [CrossRef] [Medline]8]. Moreover, the smartphone-based hand dysfunction assessment can be designed according to clinical criteria to improve the system’s reliability and validity [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9-Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11]. Despite recent advances in smartphone-based hand function assessment [Gopal A, Hsu WY, Allen DD, Bove R. Remote assessments of hand function in neurological disorders: systematic review. JMIR Rehabil Assist Technol. Mar 09, 2022;9(1):e33157. [FREE Full text] [CrossRef] [Medline]12,Mourcou Q, Fleury A, Diot B, Franco C, Vuillerme N. Mobile phone-based joint angle measurement for functional assessment and rehabilitation of proprioception. Biomed Res Int. 2015;2015:328142. [FREE Full text] [CrossRef] [Medline]13], no systematic reviews have been conducted to provide a holistic perspective on how smartphones can be applied to hand function assessment.

Although other technologies, such as wrist-worn or finger-worn sensors, smartwatches, and specialized keyboards, also show potential for automated hand function assessment, they typically focus on simple physiological data collection with limited data processing capabilities and display of basic information [González-Cañete FJ, Casilari E. A feasibility study of the use of smartwatches in wearable fall detection systems. Sensors (Basel). Mar 23, 2021;21(6):2254. [FREE Full text] [CrossRef] [Medline]14-Rovini E, Galperti G, Lorenzon L, Radi L, Fiorini L, Cianchetti M, et al. Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device. Int J Interact Des Manuf. Dec 14, 2023;18(1):591-607. [CrossRef]16]. However, smartphones offer more extensive data acquisition, accurate data processing, and richer data display options, providing a more comprehensive technological solution [Creagh AP, Simillion C, Scotland A, Lipsmeier F, Bernasconi C, Belachew S, et al. Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape test. Physiol Meas. Jun 19, 2020;41(5):054002. [CrossRef] [Medline]17,Park YM, Kim CH, Lee SJ, Lee MK. Multifunctional hand-held sensor using electronic components embedded in smartphones for quick PCR screening. Biosens Bioelectron. Sep 15, 2019;141:111415. [CrossRef] [Medline]18]. Moreover, considering the widespread availability and user-friendly nature of smartphones [Talwar Y, Karthikeyan S, Bindra N, Medhi B. Smartphone - a user-friendly device to deliver affordable healthcare - a practical paradigm. J Health Med Inform. 2016;7(3):1-7. [FREE Full text] [CrossRef]19], directing research efforts toward smartphone-centric studies can enhance innovation and application possibilities. This approach not only aligns with the current prevalence of smartphones but also extends a broader scope for future technology transfer and development specific to hand function assessment. Therefore, focusing on smartphone research can lead to more innovation and application possibilities, offering a broader scope for future technology transfer and development. As such, the main goal of this review was to synthesize the present ways in which smartphones are applied in hand function assessment and the extent to which hand function evaluation is achieved using smartphones. It aimed to explore the system development guidelines for the future application of smartphones in hand function assessment.

Research Questions

The research questions (RQs) were as follows: (1) What types of hand dysfunctions are studied, and what assessment inventory tools are used? (2) How are smartphones applied in clinical practice in hand function assessment? (3) What sensors are integrated into smartphones to collect hand function data? (4) What statistics or ML algorithms are used for hand function assessment?


This systematic review is reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (

Multimedia Appendix 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

DOC File , 92 KBMultimedia Appendix 1).

Information Sources and Search Strategy

An information specialist (JB) developed and executed a comprehensive search strategy. The following electronic databases were searched: MEDLINE(R) ALL (Ovid), Embase and Embase Classic (Ovid), CENTRAL (Ovid), Scopus, Compendex (Engineering Village), INSPEC (Engineering Village), IEEE Xplore, and ACM Digital Library. The search strategy was first developed in MEDLINE ALL (Ovid) in consultation with the research team. Search terms were also sourced from a previously published review [Weisel KK, Fuhrmann LM, Berking M, Baumeister H, Cuijpers P, Ebert DD. Standalone smartphone apps for mental health-a systematic review and meta-analysis. NPJ Digit Med. 2019;2:118. [FREE Full text] [CrossRef] [Medline]20]. The search strategy was then adapted into other databases.

Search strategies included the use of text words and subject headings related to two major concepts: (1) smartphone or mobile phone or mHealth and (2) hand function or function assessment. Searches were limited to English-language papers. When possible, searches were also limited to human studies and excluded conference proceedings and trial register records. No date limits were applied. All searches were conducted on June 8, 2023. The complete search strategies for each database are provided in

Multimedia Appendix 2

Search strategy.

DOCX File , 23 KBMultimedia Appendix 2.

Study Selection

The studies were imported into Covidence (Veritas Health Innovation) after eliminating duplicates using EndNote (Clarivate). Title and abstract screening and full-text screening were completed by 2 researchers (YZ and YF) independently based on the same inclusion and exclusion criteria. Any disagreement was first discussed and solved by the 2 researchers. Otherwise, a third researcher (BY) was involved to ensure that an agreement was reached.

Neurocognition is evaluated as an independent criterion in clinical hand assessments [Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disorder Society UPDRS Revision Task Force. Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. Nov 15, 2008;23(15):2129-2170. [CrossRef] [Medline]21]. Therefore, neurocognitive studies were excluded from this review to focus specifically on aspects related to hand motor control and dysfunction. Although cognitive functions play a significant role in hand motor control, the primary aim of this review was to narrow its scope and focus on the specific factors directly related to the mechanics and dysfunction of the hand, with particular focus on methods and techniques for using smartphones in assessment. Neurocognitive research often involves specialized equipment and methods, for example, neuroimaging techniques such as functional magnetic resonance imaging or electroencephalogram, which may not be practical for assessing hand function in smartphone-related contexts.

After the screening stage, the research quality of selected studies was evaluated using the Mixed Methods Appraisal Tool, a tool designed for the systematic mixed research review evaluation phase [Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The mixed methods appraisal tool (MMAT) version 2018 for information professionals and researchers. Educ Inf. Dec 18, 2018;34(4):285-291. [CrossRef]22]. The quality assessment was completed by one researcher and checked by another researcher. A conflict that arose regarding the assessment was discussed between the 2 researchers, and an agreement was reached.

The inclusion and exclusion criteria used for the screening process are presented in Textbox 1.

Textbox 1. The inclusion and exclusion criteria used for the screening process.

Inclusion criteria

  • Technology: using smartphone sensors
  • Study focus: hand function screening, including hand movement assessment and hand performance measurement
  • Clinical assessment: measurement of motor function–related criteria, such as grip strength, posture, and degree of freedom
  • Study design: peer-reviewed academic studies
  • Language: English

Population: human participants

Exclusion criteria

  • Technology: not using a smartphone for hand function assessment
  • Study focus: health management and neurocognitive studies
  • Clinical assessment: qualitative, non–peer-reviewed, and nonacademic studies
  • Study design: systematic reviews, literature reviews, case reports, and letters
  • Language: non-English

Population: nonhuman participants


Overview

A total of 8898 records were retrieved from the search. After removing duplicates, 64.31% (5722/8898) of the records were filtered at the title and abstract screening stage. After title and abstract screening, 97.68% (5589/5722) of the records were removed. The remaining 2.32% (133/5722) of the records underwent full-text screening. A total of 46 studies were included after both screening stages and included in the final review. Figure 1 presents the PRISMA [Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]23] flow diagram.

Multimedia Appendix 3

Mixed Methods Appraisal Tool matrix.

XLS File (Microsoft Excel File), 185 KBMultimedia Appendix 3 [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9-Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58] details the results of the evaluation of included studies based on the Mixed Methods Appraisal Tool. All 46 studies were published after 2012, and 67% (n=31) of them were published between 2017 and 2023.

Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram illustrating the screening process for papers included in this study.

Study Characteristics

Of the 46 studies, 14 (30%) recruited participants with hand dysfunction, 7 (15%) included only healthy participants, and 23 (50%) recruited both types of participants (Table 1). The summarized smartphone specification is shown in Table 2. The age range was 21 to 91 years for patients with hand dysfunction and 17 to 81 years for healthy participants; the sample size varied from 1 to 1815.

Table 1. Characteristics of the studies (n=46).
CharacteristicReferences
Participants

Patients only[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36]

Healthy participants only[Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Patients and healthy participants[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42-Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61-Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]

a[Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]
Sex

Male only[Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37]

Female only[Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]

Male and female[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9-Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26-Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33-Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59-Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]

[Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]
Study design

Quantitative descriptive study[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31-García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Observation study[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64,Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]66]

Nonrandomized study[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47-Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52-Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61-Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]

Case-control study[Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]
Study duration

0-4 minutes[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

10 minutes[Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59]

1.5 hours[Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]

10 hours[Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]

1-4 weeks[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52]

6-12 weeks[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63,Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]66]

[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32-Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43-Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52-Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]
Sample size distribution

0-32[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31-Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

33-64[Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]

65-95[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]66]

96-126[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56]

127-189[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59]

190-220

221-252[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29]

253-598

599-629[Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]

630-1851[Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]

aNot applicable.

Table 2. Summary of smartphone specification.
Study, yearProcessing powerOperating systemSmartphone typeSensor sampling rateCamera resolution
Matera et al [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26], 2016aAndroidNuans Neo Reloaded and HUAWEI GR5
Miyake et al [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24], 20201.2 GHz dual-core processorAccelerometer (range +2 to –2 g, 100 Hz)
García-Magariño et al [García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42], 2016AndroidSamsung Galaxy Trend Plus
Bercht et al [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25], 2012iOS and Android 4.4.2iPhone 4S,Samsung Galaxy S4, and Google Nexus 5
Janarthanan et al [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39], 2020AndroidLG Optimus G smartphone
Pan et al [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28], 2015iOSiPhoneAccelerometer (100 Hz)
Orozco-Arroyave et al [Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61], 2020AndroidAndroid smartphoneAccelerometer (100 Hz)
Sarwat et al [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32], 2021
Kostikis et al [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10], 2015AndroidAccelerometer and gyroscope (20 Hz)
Lee et al [Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43], 2016AndroidGalaxy S3 mini and Android phone
Lipsmeier et al [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6], 2018AndroidTabletAccelerometer and gyroscope (+66.6 to –10 Hz), magnetometer (+66.6 to –7 Hz), and microphone (44.1 kHz)
Sandison et al [Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], 2020
Halic et al [Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46], 2014iOSiPhone 5
Koyama et al [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30], 2021
Chén et al [Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51], 2020iOSiPhone 4
Arroyo-Gallego et al [Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62], 2017Android 7.0Huawei P9 PlusCustom screen keyboard (1.2 GHz)
Pratap et al [Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63], 2020Huawei Mate 9 Pro smartphone
Waddell et al [Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64], 2021App touchscreen, accelerometer, and gyroscope (50 Hz)
Mousavi et al [Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56], 2020Android 4.0Mobile accelerometer software (100 Hz)
Lee et al [Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55], 2016
Hidayat et al [Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58], 2015Huawei P10 Lite
Wang et al [Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37], 2016
Lee et al [Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38], 2018Android
Iakovakis et al [Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44], 2019iOSiPhone XS Max
Modest et al [Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47], 2019iOSiPhone XS Max
Lendner et al [Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59], 2019iOSiPhone
Tian et al [Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48], 2019AndroidSamsung Galaxy S3 Mini
Ge et al [Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27], 2020Android20 million pixels
Lee et al [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9], 2016AndroidLG Optimus S smartphone
Reed et al [Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29], 2022Android 5.0Motorola Moto G II
Williams et al [Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31], 2021Android 2.2HTC Desire smartphone60 frames per second and 1920×1080–pixel resolution
Gu et al [Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49], 2022AndroidSony XperiaImage resolution: 1980×1080 pixels
Gu et al [Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60], 2023iOSiPhone 5 or a newer deviceImage resolution: 1980×1081 pixels
Prince et al [Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50], 2018Android
Arora et al [Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52], 2015
Kassavetis et al [Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33], 2015Huawei Mate 9 ProSmartphone accelerometers (50 Hz)
Ienaga et al [Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41], 2022
Espinoza et al [Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34], 2016iOSiPhone SE
Chén et al [Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51], 202020 million pixels
Surangsrirat et al [Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36], 2022iOSiPhone
Williams et al [Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53], 2020iOS and AndroidiPhone 11 Pro Max60 frames per second, 1920×1080 pixels
Williams et al [Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11], 2020Android
Prince and de Vos [Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54], 2018AndroidSmartphone app, screen, and accelerometer (100 Hz)
Santos et al [Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65], 2017IosiPhone 5
Porkodi et al [Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40], 2023Android2400×1080–pixels and 64 megapixel f/1.89
Akhbardeh et al [Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57], 2015Sony Xperia Z120.7 mega pixel

aNot applicable.

RQ 1: What Types of Hand Dysfunctions Are Studied, and What Clinical Hand Assessment Tools Are Used?

Overview

The hand dysfunctions discussed in the 46 articles were classified as an abnormal hand range of motion (ROM; n=18, 39%), hand tremor (n=15, 33%), hand bradykinesia (n=9, 20%), fine hand use decline (n=9, 20%), hypokinesia (n=4, 9%), and hand arthritis–related hand dysfunction (n=2, 4%). A total of 27 (59%) studies used clinical hand assessment tools (Table 3).

Table 3. Hand dysfunction type.
Hand dysfunctionReference
Abnormal range of motion[Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45-Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]
Tremor[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]
Bradykinesia[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]
Decline of fine motor skills[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61-Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]
Hypokinesia[Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]
Hand arthritis–related hand dysfunction[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]
Abnormal Hand ROM

ROM describes how far a joint or muscle can move [Pratt AL, Ball C. What are we measuring? A critique of range of motion methods currently in use for Dupuytren's disease and recommendations for practice. BMC Musculoskelet Disord. Jan 13, 2016;17:20. [FREE Full text] [CrossRef] [Medline]67]. The measurement of ROM can indicate joint impairments in patients or the efficacy of rehabilitation programs [Pratt AL, Ball C. What are we measuring? A critique of range of motion methods currently in use for Dupuytren's disease and recommendations for practice. BMC Musculoskelet Disord. Jan 13, 2016;17:20. [FREE Full text] [CrossRef] [Medline]67]. Of the 46 studies, 19 (41%) focused on abnormal ROM, 11 (24%) focused on wrist ROM, and 10 (21%) focused on finger ROM. Smartphones were generally placed on the flexor carpi radialis and extensor pollicis longus [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59] to measure wrist ROM and on the distal interphalangeal joint and proximal interphalangeal joint to measure finger ROM [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]. In addition, 6 related problems, namely hand injury [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65], wrist injury [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59], stroke [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], after hand surgery [Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60], flexor tendon injury [Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35], and nerve injury [Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49], were studied. Most studies (13/19, 68%) showed that the smartphone-based measurement method had the same reliability as the conventional goniometer when evaluating the ROM of healthy people and patients.

Hand Tremor

Hand tremor is a rhythmic, involuntary, and oscillatory (ie, rotating around a central plane) movement involving hand distal joints (eg, fingers and wrist) that is regularly recurrent [Lenka A, Jankovic J. Tremor syndromes: an updated review. Front Neurol. Jul 26, 2021;12:684835. [FREE Full text] [CrossRef] [Medline]68]. All studies, except for 1 study on multiple sclerosis (MS), focused on PD hand tremors. For PD hand tremor assessment, the acceleration, rotational velocity, signal shake number and intensity were collected during daily life activities [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]. The number of taps or accuracy of each tap was measured during the finger-tapping activity of the smartphone app [Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]. Smartphone-based hand dysfunction assessment shows satisfactory repeatability and validity when measured against the Movement Disorder Society of Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52].

Hand Bradykinesia

Hand bradykinesia is characterized by slowness, reduced amplitude of movement, and sequence effect [Bologna M, Paparella G, Fasano A, Hallett M, Berardelli A. Evolving concepts on bradykinesia. Brain. Mar 01, 2020;143(3):727-750. [FREE Full text] [CrossRef] [Medline]69]. Hand bradykinesia is observed in patients with PD and patients with MS. PD and MS bradykinesia were detected in touch gestures, including finger tapping [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54] and flick and pinch tactile behaviors [Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]. The number of tapping trials and finger positions were examined to assess bradykinesia in hands. Daily activities and finger-to-nose tests were performed when holding the smartphone [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33]. It was found that smartphones were comparable to conventional methods (such as MDS-UPDRS and Modified Bradykinesia Rating Scale) for assessing hand bradykinesia and may be useful in clinical practice [Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53].

Fine Hand Use Decline

Fine hand use refers to the use of small hand muscles to create movements, such as the use of a pencil to draw [West-Higgins T. Improving reading through fine motor skill development in first grade. Dominican University of California. URL: https://scholar.dominican.edu/masters-theses/343, [accessed 2024-04-29] 70]. A total of 4 diseases were mentioned: PD [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62], stroke [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39], MS [Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63], and Huntington disease [Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]. This type of hand dysfunction was assessed through smartphone screen interaction, such as playing games and typing activities [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39]. Users’ hold time, flight time, and pressure sequences during smartphone keystroke typing activity were used to quantify fine motor functions [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62-Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]. Studies show that smartphone has the potential to detect PD symptoms from the users’ typing activity, which facilitates the development of digital tools for remote pathological symptom screening [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61].

Hypokinesia

Hypokinesia is a decline in muscle strength that causes the muscle to not contract or move as it used to [Sartori R, Romanello V, Sandri M. Mechanisms of muscle atrophy and hypertrophy: implications in health and disease. Nat Commun. Jan 12, 2021;12(1):330. [FREE Full text] [CrossRef] [Medline]71]. Three diseases related to this type of hand dysfunction are stroke [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58], carpal tunnel syndrome (CTS) [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30], and hand arthritis [Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]. Patients who had a stroke were asked to perform gestures of grasping and floating [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58] with a sensor glove worn. Hand information, such as finger position and velocity, were collected from patients with CTS as they played a game [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30]. Patients with arthritis participated in power, pinch, and tripod grip tasks to capture grip measures [Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]. These new methods show high sensitivity and specificity for disease detection and self-assessment [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34].

Hand Arthritis–Related Hand Dysfunction

Arthritis is a common condition and is the most frequent cause of disability in American adults [Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]. The most common form of arthritis is osteoarthritis, followed by inflammatory arthritis [Theis KA, Steinweg A, Helmick CG, Courtney-Long E, Bolen JA, Lee R. Which one? What kind? How many? Types, causes, and prevalence of disability among U.S. adults. Disabil Health J. Jul 2019;12(3):411-421. [CrossRef] [Medline]72]. A method of analyzing hand dysfunction related to hand arthritis involved capturing photographs of each patient’s hands. The results indicated that this approach could assist in the primary care, clinical assessment, and management of patients with hand arthritis [Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29].

Hand assessment tools used in the reviewed studies included clinical scales and instruments (Table 4). Clinical hand assessment tools were used for 2 purposes in 32 (70%) of the 46 studies: task design (n=7, 15% studies) and smartphone assessment outcome validation (n=25, 54% studies). The rest of the studies (14/46, 30%) did not mention the clinical tools. MDS-UPDRS was the most used clinical scale (15/46, 33%), while a conventional goniometer was the most used instrument (10/46, 22%) [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]. Some studies used the MDS-UPDRS and the alternative finger-tapping test as reference tasks to set up experiment tasks. The effectiveness and reliability of smartphone-based assessment methods were validated by comparing the results with those of the MDS-UPDRS and manual goniometry.

Table 4. Clinical hand assessment tools used.
Clinical scale or instrumentReferences
For task design

MDS-UPDRSa[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9-Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11]

CAPSIT-PDb[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]

AFTc[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

TTTd[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]
For outcome validation

MDS-UPDRS[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

PDDSe[Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]

Neuro-QoLf[Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]

UHDRSg[Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

Disease Activity Score-28[Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]

PDQ-8h[Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36]

MBRSi[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11]

Tang criteria[Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35]

Conventional goniometer[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]

Mechanical tappers[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]

Accelerometer[Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]

Electronic digital caliper[Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]

aMDS-UPDRS: Movement Disorder Society of Unified Parkinson’s Disease Rating Scale.

bCAPSIT-PD: Core Assessment Program for Surgical Interventional Therapies in Parkinson’s Disease.

cAFT: alternating finger tapping.

dTTT: time-tapping test.

ePDDS: patient-determined disease step.

fNeuro-QoL: quality of life in neurological disorders.

gUHDRS: Unified Huntington Disease Rating Scale.

hPDQ-8: 8-question Parkinson’s Disease Questionnaire.

iMBRS: Modified Bradykinesia Rating Scale.

RQ 2: How Are Smartphone-Based Hand Assessment Tools Applied in Clinical Practice?

Smartphone-based hand assessment has been applied in 4 different ways. It has been used for the measurement of function parameters (ie, wrist and finger ROM and hand strength), the early detection of disease-related dysfunction, real-time assessment during rehabilitation, and function assessment and rating (Table 5).

Table 5. Functions of smartphone-based hand assessment tools.
Application setting and task scenarioReferences
Measurement

Finger or wrist extension or flexion[Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Finger implement squeeze and finger forward flexor tendon gliding[Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25]

A grip force–tracking task[Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]

TTTa[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]

RAMb, tremor tracker, and CITc[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]

Wrist pronation and supination[Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]
(Early) detection

Daily activity[García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]

Extended and rest versions of MDS-UPDRSd[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]

Finger-tapping test[Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

Daily motor active tests[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6]

Flick, drag, pinch, and handwriting gestures[Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]

Play a game[Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30]

Finger-to-nose test, pronation supination test, and arm-circle exercise[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28]

Photographic capture of the patient’s hands[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]

Reaction time test[Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52]
Real-time assessment during rehabilitation

Finger and wrist extension[Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39]

Wrist flexion, wrist extension, finger implement squeeze, and finger forward flexor tendon gliding[Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25]

A grip force–tracking task[Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]

Play a game[Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46]

Grasping, pinching, and waving[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32]

Hand grip and flat[Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]
Function-level rating

Hanging gestures[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33]

Finger-to-nose test[Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]

Photographic capture of the patient’s hands[Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]

Grip force–tracking task[Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30]

Extended and rest versions of MDS-UPDRS[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]

Finger-tapping test[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

Hold the phone[Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]

aTTT: time-tapping test.

bRAM: rapid alternating movement.

cCIT: Cognitive Interference Test.

dMDS-UPDRS: Movement Disorder Society of Unified Parkinson’s Disease Rating Scale.

Of the 46 studies, 18 (39%) focused on the measurement of hand function parameters such as wrist ROM [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65], finger ROM [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49], hand gesture [Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49], hand dexterity [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9], or hand grip strength [Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]. Hand grip strength measurement and hand dexterity measurement were conducted on smartphones and shown to have good constancy with traditional measurement tools [Rovini E, Galperti G, Lorenzon L, Radi L, Fiorini L, Cianchetti M, et al. Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device. Int J Interact Des Manuf. Dec 14, 2023;18(1):591-607. [CrossRef]16,Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]23,Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38].

Furthermore, 15 (33%) out of the 46 papers focused on dysfunction assessment for early disease detection. Dysfunctions, such as hand tremor (10/46, 22%), hand bradykinesia (3/46, 7%), fine hand use decline (5/46, 11%), and hypokinesia (2/46, 4%), were used as biomarkers for certain diseases, such as PD [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62], CTS [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30], and hand arthritis [Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]. The detection exhibited high sensitivity and specificity, supporting personalized treatment plan adjustments and enabling early disease diagnosis and optimized management [Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55].

Among the 46 studies, 14 (30%) concentrated on rating hand dysfunction severity, mostly in PD- or MS-induced hand tremor (8/46, 17%) and bradykinesia (4/46, 9%). The findings demonstrate that smartphones can determine the degree to which the patient is affected by the disease, rating the severity of both the disease and hand dysfunction [Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Pratt AL, Ball C. What are we measuring? A critique of range of motion methods currently in use for Dupuytren's disease and recommendations for practice. BMC Musculoskelet Disord. Jan 13, 2016;17:20. [FREE Full text] [CrossRef] [Medline]67,Lenka A, Jankovic J. Tremor syndromes: an updated review. Front Neurol. Jul 26, 2021;12:684835. [FREE Full text] [CrossRef] [Medline]68].

Furthermore, 8 (17%) of the 46 studies explored how smartphones were used for real-time hand function assessment during hand rehabilitation [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]. Smartphones provide an interactive interface with guided exercises, therapeutic games, and performance feedback [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]. The results of real-time assessment during rehabilitation can help increase patients’ motivation and interest, reduce discontinuity in the rehabilitation process, and lower treatment costs [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58].

RQ 3: How Are Smartphones Used to Assess Hand Function?

The literature showed that smartphones had been used in 4 ways for hand function assessment: data collection (38/46, 83% studies), data display (17/46, 37% studies), data transmission (15/46, 33% studies), and data processing (6/46, 13% studies).

Data Collection

Data were mainly collected via embedded smartphone sensors or smartphone apps [García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]. Accelerometers (12/46, 26%) [Kheirkhahan M, Nair S, Davoudi A, Rashidi P, Wanigatunga AA, Corbett DB, et al. A smartwatch-based framework for real-time and online assessment and mobility monitoring. J Biomed Inform. Jan 2019;89:29-40. [FREE Full text] [CrossRef] [Medline]15,Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64] were the most used built-in smartphone sensors, followed by smartphone cameras (11/46, 24%) [Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60], gyroscopes (5/46, 11%) [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64], and goniometers (2/46, 4%) [Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47] (Table 6). Some of the smartphone apps (16/46, 35%) [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64] were developed to work as a digital tapper to collect the number of trials and position of each tap during the time-tapping test, and AFT task was used to detect hand use, hand tremor, bradykinesia, or ROM. Accelerometers can collect rich information, including angles and the rotational velocity vector of the finger [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26]. The sampling rate range of accelerometers was 20 to 100 Hz. By using a smartphone’s camera, the patient’s hand picture can be captured to extract information such as wrist and finger extension and flexion, allowing the measurement of joint ROM or extension [Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]. The camera resolution range was 1920×1080 pixels to 2400×1080 pixels.

Table 6. Built-in sensors involving data collection.
Sensor and measurementApp nameReferences
Accelerometers

All angles of DIPja, PIPjb, and MPjc, including the right and left, active and passive, and extensor and flexor positionsGoogle LLC and EHMROM[Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24]

Still accelerationHTrembAPPd[García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]

The acceleration vector and the rotational velocity vectorDNMe[Kheirkhahan M, Nair S, Davoudi A, Rashidi P, Wanigatunga AA, Corbett DB, et al. A smartwatch-based framework for real-time and online assessment and mobility monitoring. J Biomed Inform. Jan 2019;89:29-40. [FREE Full text] [CrossRef] [Medline]15]

Accelerometer signalRoche PD Mobile Application (version; Roche), PD Dr, Apkinson, GEORGE, mPower, and mobile accelerometer software[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

Orientation, velocity, and motionHandRehab app[Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26]
Smartphone app

Number, time, velocity, position, consistency, amplitude, and accuracy of each tapSmTf, DNM, mPower, Apkinson, elevateMS, ReHand, GEORGE, and HLTapper[Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61-Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

150 test parametersDNM[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]

Kinetic tremor and dysmetria in movementelevateMS[Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63]

Pronation, supination, flexion, and extensionDNM and Angulus app[Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]
Camera

Movement and tremorDid not use an app[Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31]

Hand videoDid not use an app[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]

Hand pictureDNM[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Joints’ angles and key point’s distanceDid not use an app[Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49]

Extension or flexion of the jointDid not use an app[Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Movement of fingerDid not use an app[Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

Tapping frequency, amplitude, speed, or rhythmDid not use an app[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]
Gyroscope

Gyroscope data in discrete timeDNM[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]

Gyroscope signalRoche PD Mobile Application (version 1; Roche) and GEORGE[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64,Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]66]

Height, rotation, slope, and accelerationGyroscope[Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59]
Goniometer

Finger flexion at MCPj, PIPj, and DIPj and flexion angles of the fingerGoniometer[Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]38]

Wrist flexion, extension, supination, and pronation ROMgCompass app[Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47]
GPS

Orientation, velocity, and motionHandRehab app and newly created smartphone apps[Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26]
Microphone

VoiceRoche PD Mobile Application (version 1)[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6]
Pressure sensor

Pressure-based featuresCustom Android app (the name of the app was not mentioned)[Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]

Finger pressureDNM[Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46]
IMUh

IMU–based featuresCustom Android app (the name of the app was not mentioned)[Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]

aDIPj: distal interphalangeal joint.

bPIPj: proximal interphalangeal joint.

cMPj: metacarpophalangeal joint.

dHTrembAPP: Hand Trembling Detector App.

eDNM: did not mention.

fSmT: smartphone tapper.

gROM: range of motion.

hIMU: inertial measurement unit.

Data Display

Data display (17/46, 37%) included the display of raw data (12/46, 26%) [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61], visual instructions (10/46, 22%) [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64], and information notification [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61] (2/46, 4%). Data were frequently displayed in the text form [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61] and graphic form [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]. Test details, such as date and patient information [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], were usually displayed. Assessment feedback was also displayed in the form of results or scores [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]. The real-time feedback displayed included hand motion data [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], virtual 3D representation of finger posture [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26], and interactive game interfaces [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39].

Data Transmission

Data transmission describes how data are transmitted between smartphones and external devices (Table 7). Due to limited data processing capacity, smartphones generally send data to other resources through Bluetooth, USB dongles, and Wi-Fi for data processing and storage [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]. Of the 46 studies, 12 (26%) transmitted the data to a cloud server through a unidirectional transfer, meaning data only flowed in 1 direction. Among these 12 studies, 7 (58%) developed a smartphone app to receive the built-in sensor data [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61], and the other 5 (42%) designed a smartphone app to receive the training data from external devices (ie, gloves) [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]. A total of 3 (%) of the 46 papers reported that smartphones transmitted data with an external device via bidirectional communication [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58], indicating smartphones can send and receive data in both directions. Furthermore, 2 (%) of the 46 papers discussed data privacy and security and referred to Health Insurance Portability and Accountability Act regulations [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32].

Table 7. The objects involved in data transmission.
ReceiverReferences
Remote server

Computer[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56]

Google Drive[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]

Cloud storage facility[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6]

Cloud computing[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47]

Remote server[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

Physician[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46]
External device

Glove[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]

HandMATE device[Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]
Data Processing

Data processing involves the use of smartphones as terminals to analyze, manipulate, and transform raw data into useful information or machine-readable content [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39]. Among the 46 studies, 6 (13%) used a smartphone app to process data [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42], and 1 (2%) reported the smartphone’s processing power [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24]. The smartphone processed motion data collected from built-in sensors and external devices. Data collected from built-in sensors, such as ulnar and radius deviations, were converted into ROM and total active motion [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]. Data from external devices’ sensors, such as flex-sensor signals and electromyography, were transformed into flexion and extension angles (in degrees) [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32]. One of the studies extracted the features from electromyography sensors and then fed them to an ML algorithm for further gesture recognition on smartphone apps [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25].

Use of Smartphones for Multiple Functions

A total of 21 (46%) of the 46 studies designed smartphones integrating more than one of the functions mentioned earlier. The most frequent combination was using a smartphone for data transmission and data display [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25,Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46,Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61] (Table 8). A total of 8 (17%) studies combined ≥3 functions [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61]. For example, in the study by Bercht et al [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25], the smartphone was designed to integrate processing capabilities, enabling the real-time reception of game information from the glove’s flex sensor and then display of the information on the smartphone screen after local data processing.

Table 8. Use of smartphones for multiple purposes.
Study, yearData collectionData processingData transmissionData display
Matera et al [Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26], 2016
Miyake et al [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24], 2020
García-Magariño et al [García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42], 2016
Bercht et al [Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25], 2012
Janarthanan et al [Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39], 2020
Pan et al [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28], 2015
Orozco-Arroyave et al [Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61], 2020
Sarwat et al, 2021 [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32]
Kostikis et al [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10], 2015

Lee et al [Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43], 2016

Lipsmeier et al [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6], 2018

Sandison et al [Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], 2020

Halic et al [Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46], 2014

Koyama et al [Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30], 2021

Chén et al [Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51], 2020

Arroyo-Gallego et al [Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62], 2017

Pratap et al [Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]63], 2020

Waddell et al [Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64], 2021

Mousavi et al [Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56], 2020

Lee et al [Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55], 2016

Hidayat et al [Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58], 2015

RQ 4: What Statistics or ML Algorithms Are Used for Hand Function Assessment?

Overview

Among the 46 studies, 39 (85%) used statistical methods to process the hand motion data, including parameters such as tapping speed, error, and speed during smartphone screen interaction; 20 (43%) applied ML to analyze the raw data or statistical features; and 17 (37%) used both statistical and ML methods. By contrast, 4 (9%) studies used neither statistics nor ML for data analysis [Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59].

Statistical Methods

Overall, 21 types of statistical methods were used to process 6 types of hand motion raw data (Table 9). The most used method was summary statistics (23/46, 50%), followed by normalization (7/46, 15%) and Fourier transform (6/46, 13%).

Table 9. Studies classified by statistical methods.
Data processed and statistical methodReferences
Data collected during the smartphone screen interaction (ie, tapping speed, error, speed, path, pressure, and distance)

Pythagorean theorem[Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43]

Normalization[Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

Bootstrap multiple regression[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]

Summary statistics (range, mean, median, and SD)[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]50,Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

Akaike information criterion[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]

Fourier transform[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]
Accelerometer values and rotational velocity vector

ObtainDirection[García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]

ObtainAlpha[García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]

Band-pass filter[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]64]

Spectral analysis[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]

Fourier transform[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28]

Summary statistics (range, mean, median, and SD)[Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]

Mass univariate[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]

Feature-wise correlation test[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]

Regularization[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]

Butterworth high-pass filter[Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]33]

EMDa[Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56]
Smartphone video or picture

Fourier transform[Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]31]

Normalization[Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]57]

Summary statistics (minimum, maximum, mean, median, and SD)[Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]

One-hot encoding categorical and scaling numerical responses[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29]

Savitzky-Golay filter[Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11]
Initiating, terminating flexion, extension, and ROMb

RMSc error[Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]35,Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]
FSRd, IMUe, or pressure sensor signals

Ōtsu’s 11 binarization[Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41]

RMS error[Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45]

Summary statistics (range, mean, median, and SD)[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37]

SMAf filtering[Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]58]
Variables for model prediction (ie, age, sex, and occupation)

Linear mixed models[Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59]

Multiple linear regression[Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9]

aEMD: empirical mode decomposition.

bROM: range of motion.

cRMS: root mean square.

dFSR: force sensing resistor.

eIMU: inertial measurement unit.

fSMA: simple moving average.

ML Methods

In total, 16 types of ML methods were identified (Table 10). They were applied for 4 purposes: disease detection, disease severity evaluation, disease prediction, and feature aggregation. Support vector machines (SVMs) were the most used ML method [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]. The input features of SVMs were preprocessed acceleration signals, such as the sums of squared magnitudes [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10] and path- or time-based features [Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]. Tian et al [Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60] reported SVMs as a reliable ML method for early PD detection and multivariate classification with 0.89 sensitivity and 0.88 specificity. Gu et al [Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49] reported the highest gesture classification accuracy of 1, with a sensitivity of 1 and specificity of 1.

Among the 46 studies, 5 (11%) applied logistic regression for disease severity classification and prediction and hand gesture discrimination [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]. The spatiotemporal features from the pixel coordinate data during finger tapping and accelerometer waveforms were the input for this ML method. Logistic regression showed an average accuracy of 88.5% (SD 8.03%; grasp), 83% (SD 10.9%; pinch), and 86.5% (SD 12.57%; wave) [Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32] and an accuracy of 0.61 and area under the curve (AUC) of 0.59 in PD prediction [Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53].

Of the 46 studies, 3 (7%) [Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44,Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54] exploited convolutional neural networks to distinguish patients with PD from healthy controls based on hold time, flight time, and pressure sequences [Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44]. Convolutional neural networks exploited the finger-tapping rate data for PD severity identification with an AUC of 0.64 and accuracy of 0.62 [Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]. They also worked as the base layer for training 2 image preprocessing models and for discriminating PD tremors from other types of tremors with 95% agreement with the accelerometer [Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29].

Among the 46 studies, 7 (15%) [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53,Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62] compared the classification performance of different ML algorithms. For example, Kostikis et al [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10] applied decision tree (DT), Naive Bayes, C4.5 DT, and a bagged ensemble of DTs for distinguishing patients with PD from healthy participants based on PD hand tremor features. Bagged ensemble of DTs performed better than other classifiers, with an accuracy of 0.90 for the healthy group and 0.82 for the PD group and an AUC of 0.94.

Table 10. Studies classified by MLa algorithms.
ML and featureValidity and accuracyReferences
SVMb

Magαc, magωd, sdαe, and mAmpωfDistinguishing patients with PDg from healthy participants: sensitivity=0.56 and specificity=1[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]

Path-based, time-based, pressure-based, and IMUh-based features and additional features for handwriting gestures and pinch gesturesIn healthy controls: sensitivity=0.89 and specificity=0.88[Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48]

The total, peak and fraction power and average acceleration of the motion dataPD hand resting tremor detection: sensitivity=0.77 and accuracy=0.82[Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28]

Angles of the MCPji, PIPjj, DIPjk, and CMCjl of fingers; webspace; etcHighest gesture classification: accuracy=1, sensitivity=1, and specificity=1[Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49]

SFSm to select the best feature from the mean, SD, skewness, etc, from accelerometer signalsTremor activity identified with the highest accuracy of 0.91, specificity of 0.90, and sensitivity of 0.90[Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]56]

Touchscreen typing features: covariance, skewness, and kurtosis analysis of the timing informationThe typing feature aggregated with an AUCn of 0.88 (linear-SVM)[Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

Tapping frequency, amplitude, energy spectral density, and peak-to-peak variabilityPD diagnosis predicted with an accuracy of 0.63 and AUC of 0.60 (linear-SVM)[Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]

Tapping frequency, amplitude, energy spectral density, and peak-to-peak variabilityPD diagnosis predicted with an accuracy of 0.69 and AUC of 0.68 (SVM-RBFo)[Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]
Logistic regression

The mean, RMSp, SMAq, and SD for each axis of the accelerometer and gyroscopePatient performance assessed with average accuracy of 88.5% (SD 8.03%; grasp), 83% (SD 10.9%; pinch), and 86.5% (SD 12.57%; wave)[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32]

Touchscreen typing features: covariance, skewness, and kurtosis analysis of the timing informationThe typing feature aggregated with an AUC of 0.87[Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]

Tapping frequency, amplitude, energy spectral density, and peak-to-peak variabilityPD diagnosis predicted with an accuracy of 0.61 and AUC of 0.59[Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]

13 spatiotemporal features from the pixel coordinate data about speed, rhythm, accuracy, and fatigue and 28 features from 3 accelerometer waveforms, frequency, and temporal domainsPD severity classified with an AUC of 63.1 (SD 2.11) accuracy of 59.5 (SD 0.96)[Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]

Features selected according to formulas and parametersPatients with PD distinguished from healthy participants with an accuracy of 0.94, sensitivity of 0.95, and specificity of 0.94 (multivariate logistic regression)[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]
CNNr

4 statistical features from HTs, FTt, and pressure sequencesClassification of patients with PD and healthy controls: in the clinic, mean performance=0.89, sensitivity=0.79, and specificity=0.79; in the wild, mean performance=0.79, sensitivity=0.74, and specificity=0.78[Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44]

12 features, such as sex, age, and the duration of symptomDiscriminant PD tremor with 95% agreement with accelerometer[Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29]

Raw data of finger tappingPD severity identified with an AUC of 63.5 (SD 1.56) and accuracy of 62.1 (SD 0.95)[Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]
RFu

Angle of fingers’ MCPj, PIPj, DIPj, and CMCj; webspace; etcHighest gesture classification: accuracy=1, sensitivity=1, and specificity=1[Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49]

Mean, SD, and median accelerationIn discriminating participants with PD from controls, sensitivity=0.96 (SD 0.2) and specificity=0.97[Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]52]

13 spatiotemporal features from the pixel coordinate data about speed, rhythm, accuracy, and fatigue and 28 features from 3 accelerometer waveforms, frequency, and temporal domainsPD severity identified with an AUC of 64.1 (SD 1.08) and accuracy of 60.2 (SD 1.56)[Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]
Linear regression

Magα, magω, sdα, and mAmpωPatients with PD distinguished from healthy participants with a sensitivity of 0.74 and specificity of 1[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]

Angle of fingers’ MCPj, PIPj, DIPj, and CMCj; webspace; etcHighest gesture classification: accuracy=1, sensitivity=1, and specificity=1[Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49]
AdaBoost

Magα, magω, sdα, and mAmpωPatients with PD distinguished from healthy participants with a sensitivity of 0.83 and specificity of 0.85[Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34]

Touchscreen typing features: covariance, skewness, and kurtosis analysis of the data timing informationThe typing feature aggregated with an AUC of 0.82[Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]62]
KNNv

Time domain: the signal length, mean value, RMS value, number of vertices, and number of baseline crosses; frequency domain: fundamental frequency, region length, and Fourier varianceValidated with self-defined hand gesture performance classification standards with an accuracy of >95%[Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]25]
NBw

Magα, magω, sdα, and mAmpωPatients with PD distinguished from healthy participants with a sensitivity of 0.56% and specificity of 1[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]

Tapping frequency, amplitude, energy spectral density, and peak-to-peak variabilityPD diagnosis predicted with an accuracy of 0.69 and AUC of 0.70[Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]53]
XGBoostx

Features selected according to formulas and parametersPatients with PD distinguished from healthy participants with an accuracy of 0.81, a sensitivity of 0.83, and a specificity of 0.9[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]

The mean, RMS, SMA, and SD for each axis of the accelerometer and gyroscopePatient performance assessed with average accuracy of 88% (SD 9.88%; grasp), 83.5% (SD 7.74%; pinch), and 82% (SD 14.71%; wave)[Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32]
C4.5 DTy

Magα, magω, sdα, and mAmpωPatients with PD distinguished from healthy participants with a sensitivity of 0.83 and specificity of 0.75[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]
BagDTz

Magα, magω, sdα, and mAmpωPatients with PD distinguished from healthy participants with a sensitivity of 0.82 and specificity of 0.90[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]
DT

Magα, magω, sdα, and mAmpωPatients with PD (accuracy rate 82%) distinguished from healthy people (accuracy rate 90%)[Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]
HARaa

Sustained phonation: MFCC2ab; rest tremor: skewness; postural tremor: total power; finger tapping; balance: mean velocity; and gait: turn speedUnlabeled PD activity test data: PD balance activity test: 99.5%; gait activity test: 96.9%; and distinguishing between resting and gait activities: 98%[Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6]
Anomaly detection and an autoencoder

The position, time, or velocity of the thumb movementParticipants with and participants without CTSac classified with a sensitivity of 0.94, a specificity of 0.67, and an AUC of 0.86[Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]30]
Elastic net


Features selected according to formulas and parametersPatients with PD distinguished from healthy participants with an accuracy of 1, a sensitivity of 0.95, and a specificity of 1[Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51]
DNNad

13 spatiotemporal features from the pixel coordinate data, including speed, rhythm, accuracy, and fatigue, and 28 features from 3 accelerometer waveforms, frequency, and temporal domainsPD severity classified with an AUC of 65.7 (SD 1.05) and accuracy of 61.2 (SD 1.07)[Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]54]

aML: machine learning.

bSVM: support vector machine.

cmagα: the sums of squared magnitudes of the acceleration.

dmagω: the sums of squared magnitudes of the rotation rate vector.

esdα: the sums of absolute differences in the acceleration vector.

fmAmpω: the maximum sums of the 3 axial components of the rotation vector ω calculated by Fourier transform.

gPD: Parkinson disease.

hIMU: inertial measurement unit.

iMCPj: metacarpophalangeal joint.

jPIPj: proximal interphalangeal joint.

kDIPj: distal interphalangeal joint.

lCMCj: carpometacarpal joint.

mSFS: feature selection algorithm.

nAUC: area under the curve.

oRBF: radial basis function.

pRMS: root mean square.

qSMA: simple moving average.

rCNN: convolutional neural network.

sHT: hold time.

tFT: flight time.

uRF: random forest.

vKNN: K-nearest neighbor.

wNB: naive Bayes.

xXGBoost: extreme gradient boosting.

yDT: decision tree.

zBagDT: bagged ensemble of decision trees.

aaHAR: human activity recognition.

abMFCC2: mel-frequency cepstral coefficient2.

acCTS: carpal tunnel syndrome.

adDNN: deep neural network.


To the best of our knowledge, this is the first systematic review on the primary design ideas and development of smartphone-based technologies for hand function assessment.

RQ 1: What Types of Hand Dysfunctions Are Studied, and What Assessment Inventory Tools Are Used?

In the literature, smartphones only assessed 6 types of hand dysfunctions, namely abnormal ROM, tremor, bradykinesia, fine motor skill decline, hypokinesia, and hand arthritis–related hand dysfunction. The reason might be that smartphones are limited in capturing the complexity and variety of hand movements to measure all aspects of clinically relevant hand functions [Mennella C, Alloisio S, Novellino A, Viti F. Characteristics and applications of technology-aided hand functional assessment: a systematic review. Sensors (Basel). Dec 28, 2021;22(1):199. [FREE Full text] [CrossRef] [Medline]73]. Other types of hand dysfunctions such as decreased grip strength, altered sensation, and impaired coordination are important biomarkers clinically, requiring the future development of smartphones to collect related parameters [Garcia-Agundez A, Eickhoff C. Towards objective quantification of hand tremors and bradykinesia using contactless sensors: a systematic review. Front Aging Neurosci. 2021;13:716102. [FREE Full text] [CrossRef] [Medline]74].

ROM is a critical and objective measurement that can reflect various diseases, such as arthritis, trauma, and stroke [Keogh JW, Cox A, Anderson S, Liew B, Olsen A, Schram B, et al. Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: a systematic review. PLoS One. 2019;14(5):e0215806. [FREE Full text] [CrossRef] [Medline]75]. Abnormal ROM was the most studied smartphone-based hand function assessment [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45-Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]47,Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]49,Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]51,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60,Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65], indicating the advantages of smartphones in obtaining ROM parameters. Therefore, the further development of smartphones to achieve better accuracy and reliability in capturing ROM is warranted. With the advancement of built-in accelerometers and gyroscopes in smartphones, capturing and analyzing hand ROM data have become more accessible [Keogh JW, Cox A, Anderson S, Liew B, Olsen A, Schram B, et al. Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: a systematic review. PLoS One. 2019;14(5):e0215806. [FREE Full text] [CrossRef] [Medline]75,Theile H, Walsh S, Scougall P, Ryan D, Chopra S. Smartphone goniometer for reliable and convenient measurement of finger range of motion: a comparative study. Australas J Plast Surg. Sep 29, 2022;5(2):37-43. [CrossRef]76]. Furthermore, smartphones can accurately measure both dynamic ROM and static ROM, providing good potential for long-term monitoring even without the presence of professionals [Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]27].

PD is the most studied disease that causes hand dysfunction. PD can cause multiple hand dysfunctions, such as tremors [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48], bradykinesia [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]43,Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]48], abnormal ROM [Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45], and fine hand use decline [Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44]. It provides evidence that smartphones have the potential to provide a comprehensive assessment platform for multiple hand dysfunctions [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42-Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44].

In addition, chronic neurodegenerative diseases, such as PD, exhibit progressive symptoms that require continuous monitoring [Zwar N, Harris M, Griffiths R, Roland M, Dennis S, Powell Davies G, et al. A systematic review of chronic disease management. Australian Primary Health Care Research Institute. 2006. URL: https://unsworks.unsw.edu.au/entities/publication/9a36a75a-ba4b-44c0-a5d5-91ace271b0ad [accessed 2024-04-29] 7]. However, existing clinical assessment tools, such as MDS-UPDRS, tend to be subjective, time constrained, and time consuming [Serra-Añó P, Pedrero-Sánchez JF, Inglés M, Aguilar-Rodríguez M, Vargas-Villanueva I, López-Pascual J. Assessment of functional activities in individuals with Parkinson's disease using a simple and reliable smartphone-based procedure. Int J Environ Res Public Health. Jun 09, 2020;17(11):4123. [FREE Full text] [CrossRef] [Medline]77]. Smartphone apps could exploit the multiple built-in sensors in smartphones to detect changes indicative of the disease progression or treatment response [Tong HL, Maher C, Parker K, Pham TD, Neves AL, Riordan B, et al. The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: a cross-sectional survey. PLOS Digit Health. Aug 18, 2022;1(8):e0000087. [FREE Full text] [CrossRef] [Medline]78-Pronovost PJ, Cole MD, Hughes RM. Remote patient monitoring during COVID-19: an unexpected patient safety benefit. JAMA. Mar 22, 2022;327(12):1125-1126. [CrossRef] [Medline]82], indicating that smartphones can be prosperous tools for managing chronic hand dysfunction in the long run.

Above all, for a reliable clinical application of hand dysfunction assessment, the following should be achieved:

  1. Gold standards should be established and validated, specific to the smartphone as an assessment platform.
  2. Smartphone assessment should be customizable according to an individual’s condition and rehabilitation expectations [Duruöz MT. Hand Function: A Practical Guide to Assessment. Cham, Switzerland. Springer; 2014. 83].
  3. Smartphone assessment procedures and tasks should adhere to the operational specifications of the clinical assessment criteria [Moral-Munoz JA, Zhang W, Cobo MJ, Herrera-Viedma E, Kaber DB. Smartphone-based systems for physical rehabilitation applications: a systematic review. Assist Technol. Jul 04, 2021;33(4):223-236. [FREE Full text] [CrossRef] [Medline]2,Krishnan G. Telerehabilitation: an overview. Telehealth Med Today. Nov 30, 2021;6(4):1-14. [FREE Full text] [CrossRef]84].
  4. An individualized rehabilitation plan should be generated from the assessment and evaluated in real-time pace to monitor the individual’s rehabilitation progress.

RQ 2: How Are Smartphone-Based Hand Assessment Tools Applied in Clinical Practice?

Real-time assessment during hand rehabilitation is beneficial in clinical practice because it allows the modification of the rehabilitation tasks and goals according to an individual’s specific needs and ongoing recovery progress [Li C, Cheng L, Yang H, Zou Y, Huang F. An automatic rehabilitation assessment system for hand function based on leap motion and ensemble learning. Cybern Syst. Oct 06, 2020;52(1):3-25. [FREE Full text] [CrossRef]85]. In our review, studies on real-time smartphone-based assessment were primarily conducted between 2016 and 2022, indicating an emerging trend focusing on real-time hand assessment. A potential technical challenge may lie in identifying the best sensor configuration and feature extraction method for hand function assessment [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Krishnan G. Telerehabilitation: an overview. Telehealth Med Today. Nov 30, 2021;6(4):1-14. [FREE Full text] [CrossRef]84].

The early detection of a degenerative disease through hand assessment is important because it can help slow down further disease progression [Govindu A, Palwe S. Early detection of Parkinson's disease using machine learning. Procedia Comput Sci. 2023;218:249-261. [CrossRef]86]. The reviewed literature discussed conditions such as PD and CTS [Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]34,Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]36,Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37]. Future work could use smartphones for biomarker acquisition to monitor disease-relevant physiological and behavioral symptoms and provide personalized rehabilitation guidance [Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, et al. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep. May 11, 2022;12(1):7690. [FREE Full text] [CrossRef] [Medline]87-Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer's disease: the mobile/ wearable devices opportunity. NPJ Digit Med. Feb 21, 2019;2(1):9. [FREE Full text] [CrossRef] [Medline]89]. The use of smartphones for biomarker acquisition offers advantages, including portability, accessibility, affordability, noninvasiveness, and continuous monitoring, benefiting both patients and clinicians [Mohamed T. Digital biomarkers provide a way for doctors and patients to work collaboratively at a distance. URGENT Matter. 2023:10. [FREE Full text]90]. However, challenges exist in terms of data quality, reliability, and privacy concerns [Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. Wiley Interdiscip Rev Data Min Knowl Discov. Feb 19, 2023;13(3):e1492. [FREE Full text] [CrossRef] [Medline]91].

RQ 3: How Are Smartphones Used to Assess Hand Function?

Smartphones were mostly used for data collection. With more sensors embedded in smartphones, richer and more dimensional data can be collected for function measurement. For example, the resolution of smartphones’ built-in camera is between 1920×1080 and 2400×1080 pixels, which is higher than the commonly used camera resolution in clinical settings, which typically ranges from 1280×720 to 1920×1080 pixels [Zuo KJ, Guo D, Rao J. Mobile teledermatology: a promising future in clinical practice. J Cutan Med Surg. Nov 01, 2013;17(6):387-391. [CrossRef] [Medline]8]. Compared to smartwatches and ring-shaped sensors, smartphones are more indispensable in people’s daily lives, making them an easily available assessment tool and requiring no extra investment like others [Park CS. Examination of smartphone dependence: functionally and existentially dependent behavior on the smartphone. Comput Human Behav. Apr 2019;93:123-128. [CrossRef]92]. While webcams provide high resolution and frame rates, they rely on a stable internet connection and can potentially raise privacy and security concerns [Pape M, Geisler BL, Cornelsen L, Bottel L, Te Wildt BT, Dreier M, et al. A short-term manual for webcam-based telemedicine treatment of internet use disorders. Front Psychiatry. Feb 23, 2023;14:1053930. [FREE Full text] [CrossRef] [Medline]93]. In comparison, smartphones can collect data offline and protect the patient’s privacy by encrypting data, anonymizing personal information and storing data locally [Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]40]. This also shows that smartphones, as general-purpose devices, do not require excessive hardware requirements, are available at a low cost, and are easy to access. Smartwatches and wearables usually feature multiple sensors similar to those found in smartphones, allowing for the collection of hand motion and physiological data with real-time feedback. However, their functionality is confined by a fixed position of the body, resulting in the limited scope of data collection [González-Cañete FJ, Casilari E. A feasibility study of the use of smartwatches in wearable fall detection systems. Sensors (Basel). Mar 23, 2021;21(6):2254. [FREE Full text] [CrossRef] [Medline]14]. In contrast, smartphones, being portable devices, are not constrained by fixed positions, granting convenience and flexibility for hand dysfunction assessment. Ring-shaped sensors offer high precision and accuracy and provide real-time data. However, their use may be limited due to comfort and portability constraints [Rovini E, Galperti G, Lorenzon L, Radi L, Fiorini L, Cianchetti M, et al. Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device. Int J Interact Des Manuf. Dec 14, 2023;18(1):591-607. [CrossRef]16]. Smartphones are equipped with data processing modules, which can analyze and process data in real time, providing better accuracy at the same cost [Carroll A, Heiser G. An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference. 2010. Presented at: USENIXATC '10; June 23-25, 2010:21; Boston, MA. URL: https://dl.acm.org/doi/10.5555/1855840.185586194]. In terms of user experience, as a more familiar product, smartphones reduce the users’ learning cost and provide a more convenient, personalized, and friendly hand dysfunction evaluation experience, which helps improve user participation and satisfaction [Talwar Y, Karthikeyan S, Bindra N, Medhi B. Smartphone - a user-friendly device to deliver affordable healthcare - a practical paradigm. J Health Med Inform. 2016;7(3):1-7. [FREE Full text] [CrossRef]19]. However, one of the weaknesses of using a smartphone for data collection may be data errors or biases due to the smartphone user’s lack of training, supervision, and quality control [Tomlinson M, Solomon W, Singh Y, Doherty T, Chopra M, Ijumba P, et al. The use of mobile phones as a data collection tool: a report from a household survey in South Africa. BMC Med Inform Decis Mak. Dec 23, 2009;9(1):51. [FREE Full text] [CrossRef] [Medline]95].

Using smartphones for data processing was the least mentioned in the studies [Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]24-Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]26,Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]32,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42]. The benefits of smartphone data processing are manifold, including mobility, real-time processing, and interactive nature [Boulos M, Wheeler S, Tavares C, Jones R. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. BioMed Eng OnLine. 2011;10(1):24. [FREE Full text] [CrossRef]96]. This empowers users to access and process data at any time, receive real-time feedback, and seamlessly interact with their smartphones, regardless of location [Morikawa C, Kobayashi M, Satoh M, Kuroda Y, Inomata T, Matsuo H, et al. Image and video processing on mobile devices: a survey. Vis Comput. 2021;37(12):2931-2949. [FREE Full text] [CrossRef] [Medline]97]. Despite the advantages, there are also obstacles to overcome, including short battery life, limited storage capacity, and weak processing power [Trucano M. Using mobile phones in data collection: opportunities, issues and challenges. World Bank. 2014. URL: https:/​/blogs.​worldbank.org/​en/​education/​using-mobile-phones-data-collection-opportunities-issues-and-challenges [accessed 2024-04-29] 98]. Therefore, most of our reviewed studies focused on the wireless transmission of data to computers or the cloud for subsequent data processing [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]39,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46]. This approach would allow for efficient data management and processing without consuming the limited storage space available in smartphones (Figure 2) [Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10].

Figure 2. The primary design ideas for the development of smartphone-based hand function assessment technology. AI: artificial intelligence; FSR: force sensing resistor; IMU: inertial measurement unit; ML: machine learning.

In this review, among the 46 studies, 7 (15%) exclusively involved healthy participants, while 23 (50%) recruited both patients and healthy participants. Consequently, 65% (30/46) of the studies included healthy participants, marking a noteworthy finding. In smartphone-based hand dysfunction assessment, incorporating baseline data from healthy participants is important for several reasons [Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]37-Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]41,Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]59,Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]60]. First, a standard reference range is typically derived from data collected from healthy participants, which could enable a more precise evaluation of a patient’s hand dysfunction. By comparing the hand function of patients to that of healthy participants, potential abnormalities can be identified more effectively, assisting in the accurate diagnosis of issues and facilitating the implementation of appropriate treatments [Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]6,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]11,Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]29,García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]42-Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]55,Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]61-Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]65]. Second, during the rehabilitation process, the patient’s recovery progress and improvement can be quantified by comparing against data from health people [Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]45,Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]46]. The effectiveness of the treatment can be more accurately assessed, and rehabilitation protocols could be adjusted for better outcomes. Third, it’s necessary to establish a normal reference range from healthy participants, including different ages, sex, and demographic characteristics. A broader set of data is available, ensuring that assessments are not limited to a specific group and can cover a broader population, resulting in a complete and more comprehensive understanding of hand function assessment [Padilla-Magaña JF, Peña-Pitarch E, Sánchez-Suarez I, Ticó-Falguera N. Quantitative assessment of hand function in healthy subjects and post-stroke patients with the action research arm test. Sensors (Basel). May 10, 2022;22(10):3604. [FREE Full text] [CrossRef] [Medline]99]. In summary, remote assessment platforms have been developed for a wide range of users, including professionals, caregivers, and patients [Moral-Munoz JA, Zhang W, Cobo MJ, Herrera-Viedma E, Kaber DB. Smartphone-based systems for physical rehabilitation applications: a systematic review. Assist Technol. Jul 04, 2021;33(4):223-236. [FREE Full text] [CrossRef] [Medline]2,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10]. However, certain aspects need to be considered when using smartphones for hand assessment. They are as follows [Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]28,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. Smartphone-based evaluation of parkinsonian hand tremor: quantitative measurements vs clinical assessment scores. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:906-909. [CrossRef] [Medline]100-Bull TP, Dewar AR, Malvey DM, Szalma JL. Considerations for the telehealth systems of tomorrow: an analysis of student perceptions of telehealth technologies. JMIR Med Educ. Jul 08, 2016;2(2):e11. [FREE Full text] [CrossRef] [Medline]102]:

  1. Establishing standardized data formats is of utmost importance to ensure compatibility and consistency in data analysis. Inconsistent data formats can pose challenges in data analysis, making it difficult to compare and analyze data obtained from various smartphones.
  2. It is necessary to ensure the robustness of smartphone processors or network connections. The effectiveness of the smartphone processor and network can impact the frequency of data updates, which may result in delays when acquiring and displaying real-time data.
  3. It is necessary to consider privacy and security. It is important to prioritize data security and privacy by implementing app-appropriate encryption measures during data transmission to mitigate potential ethical and legal issues and ensure compliance with relevant data-protection regulations.

RQ 4: What Statistics or ML Algorithms Are Used for Hand Function Assessment?

Statistical methods (39/46, 85%) were more commonly used than ML methods (20/46, 43%). The most commonly used statistical method was summary statistics such as mean and SD. Summary statistics offer concise insights into data, facilitating comparisons and simplifying analysis [Ozdalga E, Ozdalga A, Ahuja N. The smartphone in medicine: a review of current and potential use among physicians and students. J Med Internet Res. Sep 27, 2012;14(5):e128. [FREE Full text] [CrossRef] [Medline]103]. However, they can be subjective, relying on expert experience, and may distort information [Wang X, Fu Y, Ye B, Babineau J, Ding Y, Mihailidis A. Technology-based compensation assessment and detection of upper extremity activities of stroke survivors: systematic review. J Med Internet Res. Jun 13, 2022;24(6):e34307. [FREE Full text] [CrossRef] [Medline]104]. In addition, due to the multiple independent variables present in hand function assessment [Duruöz MT. Hand Function: A Practical Guide to Assessment. Cham, Switzerland. Springer; 2014. 83], it is important to consider statistical methods that are capable of analyzing a multifactor model, such as multiple linear regression [Krzywinski M, Altman N. Multiple linear regression. Nat Methods. Dec 2015;12(12):1103-1104. [FREE Full text] [CrossRef] [Medline]105].

ML methods have been increasingly used in various health care apps [Verma VK, Verma S. Machine learning applications in healthcare sector: an overview. Mater Today Proc. 2022;57:2144-2147. [FREE Full text] [CrossRef]106]. In the studies in our review, ML methods were mainly used for detecting and classifying patient hand posture, analyzing and classifying behavior patterns (ie, tremor, bradykinesia, and ROM), and identifying disease severity and prediction. Our review found SVMs to be the most commonly used ML algorithm, particularly for disease classification. This may be attributed to the fact that SVMs are capable of effectively addressing multi-dimensional data with small sample sizes while providing a good generalization performance and the ability to work with the primary processing stage data [Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Their Appl. Jul 1998;13(4):18-28. [FREE Full text] [CrossRef]107]. The main limitation of the SVM algorithm is its inability to handle multiclass classification problems without additional modifications or extensions [Noble WS. What is a support vector machine? Nat Biotechnol. Dec 2006;24(12):1565-1567. [FREE Full text] [CrossRef] [Medline]108].

Strengths and Limitations of the Study

The strengths of this review are as follows: (1) the relevant database searches were conducted in a comprehensive and reproducible manner; (2) this was the first review that aimed to comprehensively discuss the role of smartphones and their functionalities in hand assessment from a holistic perspective; and (3) this review provides an analytical demonstration of the technical feasibility and advantages of using smartphones for hand function assessment across various domains, including sensor support, clinical practice, and application scenarios. It recommends potential directions for future studies in this field, such as multisensor fusion, gold-standard establishment, real-time assessment, and ML algorithms for data analysis exploration. This review also has some limitations. First, given that smartphone-based hand function assessment is at its nascent stage, the number of relevant studies is limited. This may contribute to a lack of sufficient evidence, completeness, and comprehensiveness in research materials, posing challenges in supporting viewpoints, drawing conclusions, and gaining a comprehensive understanding of the field. Second, this review encompassed only studies in the English language. Third, due to the exploratory and developmental nature of this topic, the literature quality varied, with potential limitations, such as inconsistency and a lack of high-quality reference studies and as well as meta-analyses.

Conclusions and Future Research

This systematic review focused on how smartphones are used for hand function assessment. It covered the evaluation and measurement of hand dysfunction caused by various diseases, different embedded smartphone sensors, and statistical and artificial intelligence methods for hand function assessment. The evidence demonstrated that smartphones could facilitate a convenient, inexpensive, and reliable hand-functional assessment [Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]9,Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]10,Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]44]. Future research could (1) explore how to develop a gold standard for smartphone-based hand function assessment; (2) take advantage of smartphones’ integrated systems with multiple sensors to collect patients’ data in various dimensions to assess hand function holistically; and (3) develop ML methods that are more suitable for processing data collected by smartphones. On the basis of the growing capabilities of smartphones for data collection and analysis, digital technology holds promise for bringing revolutionary changes to hand function assessment.

Acknowledgments

This work was performed with close collaboration among researchers affiliated with the University of Toronto and Huazhong University of Science and Technology Collaborative Center for Robotics and Eldercare. The authors thank the rehabilitation professionals at the Hubei Provincial Hospital of Traditional Chinese Medicine. This study was supported by the National Natural Science Foundation of China (71771098).

Data Availability

The data sets generated during and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

All authors contributed to the conception, design, and methodology of the study and approved the protocol. JB was responsible for overseeing the search of databases and literature. YZ and YF handled the management of database and deduplication of records. YZ, YF, and BY were involved in the screening of citations and data extraction. YZ was responsible for software use, formal analysis, investigation, writing the original draft, reviewing, editing, and visualization. YF and BY were responsible for writing the original draft, supervision, and project administration. YZ, ZG, and AM were responsible for conceptualization, writing, reviewing, and editing. All authors provided support in revising and formatting the manuscript. All authors have provided final approval of the version of the manuscript submitted for publication, and all authors agree to be accountable for all aspects of the work.

Conflicts of Interest

None declared.

Multimedia Appendix 1

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

DOC File , 92 KB

Multimedia Appendix 2

Search strategy.

DOCX File , 23 KB

Multimedia Appendix 3

Mixed Methods Appraisal Tool matrix.

XLS File (Microsoft Excel File), 185 KB

  1. Hathaliya JJ, Modi H, Gupta R, Tanwar S, Sharma P, Sharma R. Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity. Comput Electr Eng. Jul 2022;101:107946. [FREE Full text] [CrossRef]
  2. Moral-Munoz JA, Zhang W, Cobo MJ, Herrera-Viedma E, Kaber DB. Smartphone-based systems for physical rehabilitation applications: a systematic review. Assist Technol. Jul 04, 2021;33(4):223-236. [FREE Full text] [CrossRef] [Medline]
  3. Fowler NK, Nicol AC. Functional and biomechanical assessment of the normal and rheumatoid hand. Clin Biomech (Bristol, Avon). Oct 2001;16(8):660-666. [FREE Full text] [CrossRef] [Medline]
  4. Fiems CL, Miller SA, Buchanan N, Knowles E, Larson E, Snow R, et al. Does a sway-based mobile application predict future falls in people with Parkinson disease? Arch Phys Med Rehabil. Mar 2020;101(3):472-478. [FREE Full text] [CrossRef] [Medline]
  5. Yang K, Xiong WX, Liu FT, Sun YM, Luo S, Ding ZT, et al. Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. Ann Transl Med. Mar 2016;4(5):90. [FREE Full text] [CrossRef] [Medline]
  6. Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 27, 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]
  7. Zwar N, Harris M, Griffiths R, Roland M, Dennis S, Powell Davies G, et al. A systematic review of chronic disease management. Australian Primary Health Care Research Institute. 2006. URL: https://unsworks.unsw.edu.au/entities/publication/9a36a75a-ba4b-44c0-a5d5-91ace271b0ad [accessed 2024-04-29]
  8. Zuo KJ, Guo D, Rao J. Mobile teledermatology: a promising future in clinical practice. J Cutan Med Surg. Nov 01, 2013;17(6):387-391. [CrossRef] [Medline]
  9. Lee W, Evans A, Williams DR. Validation of a smartphone application measuring motor function in Parkinson's disease. J Parkinsons Dis. Apr 02, 2016;6(2):371-382. [CrossRef] [Medline]
  10. Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. A smartphone-based tool for assessing Parkinsonian hand tremor. IEEE J Biomed Health Inform. Nov 2015;19(6):1835-1842. [CrossRef] [Medline]
  11. Williams S, Zhao Z, Hafeez A, Wong DC, Relton SD, Fang H, et al. The discerning eye of computer vision: can it measure Parkinson's finger tap bradykinesia? J Neurol Sci. Sep 15, 2020;416:117003. [FREE Full text] [CrossRef] [Medline]
  12. Gopal A, Hsu WY, Allen DD, Bove R. Remote assessments of hand function in neurological disorders: systematic review. JMIR Rehabil Assist Technol. Mar 09, 2022;9(1):e33157. [FREE Full text] [CrossRef] [Medline]
  13. Mourcou Q, Fleury A, Diot B, Franco C, Vuillerme N. Mobile phone-based joint angle measurement for functional assessment and rehabilitation of proprioception. Biomed Res Int. 2015;2015:328142. [FREE Full text] [CrossRef] [Medline]
  14. González-Cañete FJ, Casilari E. A feasibility study of the use of smartwatches in wearable fall detection systems. Sensors (Basel). Mar 23, 2021;21(6):2254. [FREE Full text] [CrossRef] [Medline]
  15. Kheirkhahan M, Nair S, Davoudi A, Rashidi P, Wanigatunga AA, Corbett DB, et al. A smartwatch-based framework for real-time and online assessment and mobility monitoring. J Biomed Inform. Jan 2019;89:29-40. [FREE Full text] [CrossRef] [Medline]
  16. Rovini E, Galperti G, Lorenzon L, Radi L, Fiorini L, Cianchetti M, et al. Design of a novel wearable system for healthcare applications: applying the user-centred design approach to SensHand device. Int J Interact Des Manuf. Dec 14, 2023;18(1):591-607. [CrossRef]
  17. Creagh AP, Simillion C, Scotland A, Lipsmeier F, Bernasconi C, Belachew S, et al. Smartphone-based remote assessment of upper extremity function for multiple sclerosis using the Draw a Shape test. Physiol Meas. Jun 19, 2020;41(5):054002. [CrossRef] [Medline]
  18. Park YM, Kim CH, Lee SJ, Lee MK. Multifunctional hand-held sensor using electronic components embedded in smartphones for quick PCR screening. Biosens Bioelectron. Sep 15, 2019;141:111415. [CrossRef] [Medline]
  19. Talwar Y, Karthikeyan S, Bindra N, Medhi B. Smartphone - a user-friendly device to deliver affordable healthcare - a practical paradigm. J Health Med Inform. 2016;7(3):1-7. [FREE Full text] [CrossRef]
  20. Weisel KK, Fuhrmann LM, Berking M, Baumeister H, Cuijpers P, Ebert DD. Standalone smartphone apps for mental health-a systematic review and meta-analysis. NPJ Digit Med. 2019;2:118. [FREE Full text] [CrossRef] [Medline]
  21. Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, et al. Movement Disorder Society UPDRS Revision Task Force. Movement disorder society-sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov Disord. Nov 15, 2008;23(15):2129-2170. [CrossRef] [Medline]
  22. Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The mixed methods appraisal tool (MMAT) version 2018 for information professionals and researchers. Educ Inf. Dec 18, 2018;34(4):285-291. [CrossRef]
  23. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]
  24. Miyake K, Mori H, Matsuma S, Kimura C, Izumoto M, Nakaoka H, et al. A new method measurement for finger range of motion using a smartphone. J Plast Surg Hand Surg. Apr 24, 2020;54(4):207-214. [FREE Full text] [CrossRef]
  25. Bercht D, Boisvert T, Lowe J, Stearns K, Ganz A. ARhT: a portable hand therapy system. Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:264-267. [CrossRef] [Medline]
  26. Matera G, Boonyasirikool C, Saggini R, Pozzi A, Pegoli L. The new smartphone application for wrist rehabilitation. J Hand Surg Asian-Pac Vol. Feb 16, 2016;21(01):2-7. [FREE Full text] [CrossRef]
  27. Ge M, Chen J, Zhu ZJ, Shi P, Yin LR, Xia L. Wrist ROM measurements using smartphone photography: reliability and validity. Hand Surg Rehabil. Sep 2020;39(4):261-264. [FREE Full text] [CrossRef] [Medline]
  28. Pan D, Dhall R, Lieberman A, Petitti DB. A mobile cloud-based Parkinson's disease assessment system for home-based monitoring. JMIR Mhealth Uhealth. Mar 26, 2015;3(1):e29. [FREE Full text] [CrossRef] [Medline]
  29. Reed M, Rampono B, Turner W, Harsanyi A, Lim A, Paramalingam S, et al. A multicentre validation study of a smartphone application to screen hand arthritis. BMC Musculoskelet Disord. May 09, 2022;23(1):433. [FREE Full text] [CrossRef] [Medline]
  30. Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, et al. A screening method using anomaly detection on a smartphone for patients with carpal tunnel syndrome: diagnostic case-control study. JMIR Mhealth Uhealth. Mar 14, 2021;9(3):e26320. [FREE Full text] [CrossRef] [Medline]
  31. Williams S, Fang H, Relton SD, Wong DC, Alam T, Alty JE. Accuracy of smartphone video for contactless measurement of hand tremor frequency. Mov Disord Clin Pract. Jan 2021;8(1):69-75. [FREE Full text] [CrossRef] [Medline]
  32. Sarwat H, Sarwat H, Maged SA, Emara TH, Elbokl AM, Awad MI. Design of a data glove for assessment of hand performance using supervised machine learning. Sensors (Basel). Oct 20, 2021;21(21):6948. [FREE Full text] [CrossRef] [Medline]
  33. Kassavetis P, Saifee TA, Roussos G, Drougkas L, Kojovic M, Rothwell JC, et al. Developing a tool for remote digital assessment of Parkinson's disease. Mov Disord Clin Pract. 2015;3(1):59-64. [FREE Full text] [CrossRef] [Medline]
  34. Espinoza F, Le Blay P, Coulon D, Lieu S, Munro J, Jorgensen C, et al. Handgrip strength measured by a dynamometer connected to a smartphone: a new applied health technology solution for the self-assessment of rheumatoid arthritis disease activity. Rheumatology (Oxford). May 2016;55(5):897-901. [FREE Full text] [CrossRef] [Medline]
  35. Chen J, Xian Zhang AI, Jia Qian SI, Jing Wang YU. Measurement of finger joint motion after flexor tendon repair: smartphone photography compared with traditional goniometry. J Hand Surg Eur Vol. Oct 2021;46(8):825-829. [FREE Full text] [CrossRef] [Medline]
  36. Surangsrirat D, Sri-Iesaranusorn P, Chaiyaroj A, Vateekul P, Bhidayasiri R. Parkinson's disease severity clustering based on tapping activity on mobile device. Sci Rep. Feb 24, 2022;12(1):3142. [FREE Full text] [CrossRef] [Medline]
  37. Wang HP, Guo AW, Bi ZY, Zhou YX, Wang ZG, Lu XY. A novel distributed functional electrical stimulation and assessment system for hand movements using wearable technology. In: Proceedings of the 2016 IEEE Biomedical Circuits and Systems Conference. 2016. Presented at: BioCAS '16; October 17-19, 2016:74-77; Shanghai, Chaina. URL: https://ieeexplore.ieee.org/document/7833728 [CrossRef]
  38. Lee H, St Louis K, Fowler JR. Accuracy and reliability of visual inspection and smartphone applications for measuring finger range of motion. Orthopedics. Mar 01, 2018;41(2):e217-e221. [FREE Full text] [CrossRef] [Medline]
  39. Janarthanan V, Assad-Uz-Zaman MD, Rahman MH, McGonigle E, Wang I. Design and development of a sensored glove for home-based rehabilitation. J Hand Ther. 2020;33(2):209-219. [FREE Full text] [CrossRef] [Medline]
  40. Porkodi J, Karthik V, Mathunny JJ, Ashokkumar D. Reliability and validity of Angulus- smartphone application for measuring wrist flexion and extension. In: Proceedings of the 3rd International conference on Artificial Intelligence and Signal Processing. 2023. Presented at: AISP '23; March 18-20, 2023:1-4; Vijaywada, India. URL: https://ieeexplore.ieee.org/document/10135006 [CrossRef]
  41. Ienaga N, Fujita K, Koyama T, Sasaki T, Sugiura Y, Saito H. Development and user evaluation of a smartphone-based system to assess range of motion of wrist joint. J Hand Surg Glob Online. 2022;2(6):339-342. [FREE Full text] [CrossRef] [Medline]
  42. García-Magariño I, Medrano C, Plaza I, Oliván B. A smartphone-based system for detecting hand tremors in unconstrained environments. Pers Ubiquit Comput. Sep 8, 2016;20(6):959-971. [FREE Full text] [CrossRef]
  43. Lee CY, Kang SJ, Hong SK, Ma HI, Lee U, Kim YJ. A validation study of a smartphone-based finger tapping application for quantitative assessment of bradykinesia in Parkinson's disease. PLoS One. 2016;11(7):e0158852. [FREE Full text] [CrossRef] [Medline]
  44. Iakovakis D, Diniz JA, Trivedi D, Chaudhuri RK, Hadjileontiadis LJ, Hadjidimitriou S, et al. Early Parkinson's disease detection via touchscreen typing analysis using convolutional neural networks. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2019;2019:3535-3538. [CrossRef] [Medline]
  45. Sandison M, Phan K, Casas R, Nguyen L, Lum M, Pergami-Peries M, et al. HandMATE: wearable robotic hand exoskeleton and integrated android app for at home stroke rehabilitation. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2020;2020:4867-4872. [FREE Full text] [CrossRef] [Medline]
  46. Halic T, Kockara S, Demirel D, Willey M, Eichelberger K. MoMiReS: mobile mixed reality system for physical and occupational therapies for hand and wrist ailments. In: Proceedings of the 2014 IEEE Innovations in Technology Conference. 2014. Presented at: InnoTek '14; May 16, 2014:1-6; Warwick, RI. URL: https://ieeexplore.ieee.org/document/6877376 [CrossRef]
  47. Modest J, Clair B, DeMasi R, Meulenaere S, Howley A, Aubin M, et al. Self-measured wrist range of motion by wrist-injured and wrist-healthy study participants using a built-in iPhone feature as compared with a universal goniometer. J Hand Ther. 2019;32(4):507-514. [FREE Full text] [CrossRef] [Medline]
  48. Tian F, Fan X, Fan J, Zhu Y, Gao J, Wang D, et al. What can gestures tell?: detecting motor impairment in early Parkinson's from common touch gestural interactions. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 2019. Presented at: CHI '19; May 4-9, 2019:1-14; Glasgow, UK. URL: https://dl.acm.org/doi/10.1145/3290605.3300313 [CrossRef]
  49. Gu F, Fan J, Cai C, Wang Z, Liu X, Yang J, et al. Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation. Front Neurol. 2022;13:1052505. [FREE Full text] [CrossRef] [Medline]
  50. Prince J, Arora S, de Vos M. Big data in Parkinson's disease: using smartphones to remotely detect longitudinal disease phenotypes. Physiol Meas. Apr 26, 2018;39(4):044005. [FREE Full text] [CrossRef] [Medline]
  51. Chén OY, Lipsmeier F, Phan H, Prince J, Taylor KI, Gossens C, et al. Building a machine-learning framework to remotely assess Parkinson's disease using smartphones. IEEE Trans Biomed Eng. Dec 2020;67(12):3491-3500. [FREE Full text] [CrossRef]
  52. Arora S, Venkataraman V, Zhan A, Donohue S, Biglan KM, Dorsey ER, et al. Detecting and monitoring the symptoms of Parkinson's disease using smartphones: a pilot study. Parkinsonism Relat Disord. Jun 2015;21(6):650-653. [FREE Full text] [CrossRef] [Medline]
  53. Williams S, Relton SD, Fang H, Alty J, Qahwaji R, Graham CD, et al. Supervised classification of bradykinesia in Parkinson's disease from smartphone videos. Artif Intell Med. Nov 2020;110:101966. [FREE Full text] [CrossRef] [Medline]
  54. Prince J, de Vos M. A deep learning framework for the remote detection of Parkinson'S disease using smart-phone sensor data. Annu Int Conf IEEE Eng Med Biol Soc. Jul 2018;2018:3144-3147. [CrossRef] [Medline]
  55. Lee U, Kang SJ, Choi JH, Kim YJ, Ma HI. Mobile application of finger tapping task assessment for early diagnosis of Parkinson's disease. Electron Lett. Nov 2016;52(24):1976-1978. [FREE Full text] [CrossRef]
  56. Mousavi SA, Abdulrazzaq MH, Hasan MA, Naghavizadeh M. Diagnosis of hand tremor using a smart phone accelerometer and SVM. In: Proceedings of the 4th International Symposium on Multidisciplinary Studies and Innovative Technologies. 2020. Presented at: ISMSIT '20; October 22-24, 2020:1-4; Istanbul, Turkey. URL: https://ieeexplore.ieee.org/document/9254969 [CrossRef]
  57. Akhbardeh F, Vasefi F, Tavakolian K, Bradley D, Fazel-Rezai R. Toward development of mobile application for hand arthritis screening. Annu Int Conf IEEE Eng Med Biol Soc. 2015;2015:7075-7078. [CrossRef] [Medline]
  58. Hidayat AA, Arief Z, Happyanto DC. Mobile application with simple moving average filtering for monitoring finger muscles therapy of post-stroke people. In: Proceedings of the 2015 Conference on International Electronics Symposium. 2015. Presented at: ELECSYM '15; September 29-30, 2015:1-6; Surabaya, Indonesia. URL: https://ieeexplore.ieee.org/abstract/document/7380803 [CrossRef]
  59. Lendner N, Wells E, Lavi I, Kwok YY, Ho PC, Wollstein R. Utility of the iPhone 4 Gyroscope application in the measurement of wrist motion. Hand (N Y). May 2019;14(3):352-356. [FREE Full text] [CrossRef] [Medline]
  60. Gu F, Fan J, Wang Z, Liu X, Yang J, Zhu Q. Automatic range of motion measurement via smartphone images for telemedicine examination of the hand. Sci Prog. 2023;106(1):368504231152740. [FREE Full text] [CrossRef] [Medline]
  61. Orozco-Arroyave JR, Vásquez-Correa JC, Klumpp P, Pérez-Toro PA, Escobar-Grisales D, Roth N, et al. Apkinson: the smartphone application for telemonitoring Parkinson's patients through speech, gait and hands movement. Neurodegener Dis Manag. Jun 2020;10(3):137-157. [FREE Full text] [CrossRef] [Medline]
  62. Arroyo-Gallego T, Ledesma-Carbayo MJ, Sanchez-Ferro A, Butterworth I, Mendoza CS, Matarazzo M, et al. Detection of motor impairment in Parkinson's disease via mobile touchscreen typing. IEEE Trans Biomed Eng. Sep 2017;64(9):1994-2002. [FREE Full text] [CrossRef]
  63. Pratap A, Grant D, Vegesna A, Tummalacherla M, Cohan S, Deshpande C, et al. Evaluating the utility of smartphone-based sensor assessments in persons with multiple sclerosis in the real-world using an app (elevateMS): observational, prospective pilot digital health study. JMIR Mhealth Uhealth. Oct 27, 2020;8(10):e22108. [FREE Full text] [CrossRef] [Medline]
  64. Waddell EM, Dinesh K, Spear K, Elson MJ, Wagner E, Curtis MJ, et al. GEORGE®: a pilot study of a smartphone application for Huntington’s disease. J Huntingt Dis. Jun 09, 2021;10(2):293-301. [FREE Full text] [CrossRef]
  65. Santos C, Pauchard N, Guilloteau A. Reliability assessment of measuring active wrist pronation and supination range of motion with a smartphone. Hand Surg Rehabil. Oct 2017;36(5):338-345. [FREE Full text] [CrossRef] [Medline]
  66. Lipsmeier F, Taylor KI, Kilchenmann T, Wolf D, Scotland A, Schjodt-Eriksen J, et al. Evaluation of smartphone-based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial. Mov Disord. Aug 2018;33(8):1287-1297. [FREE Full text] [CrossRef] [Medline]
  67. Pratt AL, Ball C. What are we measuring? A critique of range of motion methods currently in use for Dupuytren's disease and recommendations for practice. BMC Musculoskelet Disord. Jan 13, 2016;17:20. [FREE Full text] [CrossRef] [Medline]
  68. Lenka A, Jankovic J. Tremor syndromes: an updated review. Front Neurol. Jul 26, 2021;12:684835. [FREE Full text] [CrossRef] [Medline]
  69. Bologna M, Paparella G, Fasano A, Hallett M, Berardelli A. Evolving concepts on bradykinesia. Brain. Mar 01, 2020;143(3):727-750. [FREE Full text] [CrossRef] [Medline]
  70. West-Higgins T. Improving reading through fine motor skill development in first grade. Dominican University of California. URL: https://scholar.dominican.edu/masters-theses/343, [accessed 2024-04-29]
  71. Sartori R, Romanello V, Sandri M. Mechanisms of muscle atrophy and hypertrophy: implications in health and disease. Nat Commun. Jan 12, 2021;12(1):330. [FREE Full text] [CrossRef] [Medline]
  72. Theis KA, Steinweg A, Helmick CG, Courtney-Long E, Bolen JA, Lee R. Which one? What kind? How many? Types, causes, and prevalence of disability among U.S. adults. Disabil Health J. Jul 2019;12(3):411-421. [CrossRef] [Medline]
  73. Mennella C, Alloisio S, Novellino A, Viti F. Characteristics and applications of technology-aided hand functional assessment: a systematic review. Sensors (Basel). Dec 28, 2021;22(1):199. [FREE Full text] [CrossRef] [Medline]
  74. Garcia-Agundez A, Eickhoff C. Towards objective quantification of hand tremors and bradykinesia using contactless sensors: a systematic review. Front Aging Neurosci. 2021;13:716102. [FREE Full text] [CrossRef] [Medline]
  75. Keogh JW, Cox A, Anderson S, Liew B, Olsen A, Schram B, et al. Reliability and validity of clinically accessible smartphone applications to measure joint range of motion: a systematic review. PLoS One. 2019;14(5):e0215806. [FREE Full text] [CrossRef] [Medline]
  76. Theile H, Walsh S, Scougall P, Ryan D, Chopra S. Smartphone goniometer for reliable and convenient measurement of finger range of motion: a comparative study. Australas J Plast Surg. Sep 29, 2022;5(2):37-43. [CrossRef]
  77. Serra-Añó P, Pedrero-Sánchez JF, Inglés M, Aguilar-Rodríguez M, Vargas-Villanueva I, López-Pascual J. Assessment of functional activities in individuals with Parkinson's disease using a simple and reliable smartphone-based procedure. Int J Environ Res Public Health. Jun 09, 2020;17(11):4123. [FREE Full text] [CrossRef] [Medline]
  78. Tong HL, Maher C, Parker K, Pham TD, Neves AL, Riordan B, et al. The use of mobile apps and fitness trackers to promote healthy behaviors during COVID-19: a cross-sectional survey. PLOS Digit Health. Aug 18, 2022;1(8):e0000087. [FREE Full text] [CrossRef] [Medline]
  79. Ernsting C, Dombrowski SU, Oedekoven M, O Sullivan JL, Kanzler M, Kuhlmey A, et al. Using smartphones and health apps to change and manage health behaviors: a population-based survey. J Med Internet Res. Apr 05, 2017;19(4):e101. [FREE Full text] [CrossRef] [Medline]
  80. Johnson SA, Karas M, Burke KM, Straczkiewicz M, Scheier ZA, Clark AP, et al. Wearable device and smartphone data quantify ALS progression and may provide novel outcome measures. NPJ Digit Med. Mar 06, 2023;6(1):34. [FREE Full text] [CrossRef] [Medline]
  81. Weizenbaum EL, Fulford D, Torous J, Pinsky E, Kolachalama VB, Cronin-Golomb A. Smartphone-based neuropsychological assessment in Parkinson’s disease: feasibility, validity, and contextually driven variability in cognition. J Int Neuropsychol Soc. May 17, 2021;28(4):401-413. [CrossRef]
  82. Pronovost PJ, Cole MD, Hughes RM. Remote patient monitoring during COVID-19: an unexpected patient safety benefit. JAMA. Mar 22, 2022;327(12):1125-1126. [CrossRef] [Medline]
  83. Duruöz MT. Hand Function: A Practical Guide to Assessment. Cham, Switzerland. Springer; 2014.
  84. Krishnan G. Telerehabilitation: an overview. Telehealth Med Today. Nov 30, 2021;6(4):1-14. [FREE Full text] [CrossRef]
  85. Li C, Cheng L, Yang H, Zou Y, Huang F. An automatic rehabilitation assessment system for hand function based on leap motion and ensemble learning. Cybern Syst. Oct 06, 2020;52(1):3-25. [FREE Full text] [CrossRef]
  86. Govindu A, Palwe S. Early detection of Parkinson's disease using machine learning. Procedia Comput Sci. 2023;218:249-261. [CrossRef]
  87. Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, et al. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep. May 11, 2022;12(1):7690. [FREE Full text] [CrossRef] [Medline]
  88. Goni M, Eickhoff SB, Far MS, Patil KR, Dukart J. Smartphone-based digital biomarkers for Parkinson’s disease in a remotely-administered setting. IEEE Access. 2022;10:28361-28384. [CrossRef]
  89. Kourtis LC, Regele OB, Wright JM, Jones GB. Digital biomarkers for Alzheimer's disease: the mobile/ wearable devices opportunity. NPJ Digit Med. Feb 21, 2019;2(1):9. [FREE Full text] [CrossRef] [Medline]
  90. Mohamed T. Digital biomarkers provide a way for doctors and patients to work collaboratively at a distance. URGENT Matter. 2023:10. [FREE Full text]
  91. Ford E, Milne R, Curlewis K. Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia. Wiley Interdiscip Rev Data Min Knowl Discov. Feb 19, 2023;13(3):e1492. [FREE Full text] [CrossRef] [Medline]
  92. Park CS. Examination of smartphone dependence: functionally and existentially dependent behavior on the smartphone. Comput Human Behav. Apr 2019;93:123-128. [CrossRef]
  93. Pape M, Geisler BL, Cornelsen L, Bottel L, Te Wildt BT, Dreier M, et al. A short-term manual for webcam-based telemedicine treatment of internet use disorders. Front Psychiatry. Feb 23, 2023;14:1053930. [FREE Full text] [CrossRef] [Medline]
  94. Carroll A, Heiser G. An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Conference on USENIX Annual Technical Conference. 2010. Presented at: USENIXATC '10; June 23-25, 2010:21; Boston, MA. URL: https://dl.acm.org/doi/10.5555/1855840.1855861
  95. Tomlinson M, Solomon W, Singh Y, Doherty T, Chopra M, Ijumba P, et al. The use of mobile phones as a data collection tool: a report from a household survey in South Africa. BMC Med Inform Decis Mak. Dec 23, 2009;9(1):51. [FREE Full text] [CrossRef] [Medline]
  96. Boulos M, Wheeler S, Tavares C, Jones R. How smartphones are changing the face of mobile and participatory healthcare: an overview, with example from eCAALYX. BioMed Eng OnLine. 2011;10(1):24. [FREE Full text] [CrossRef]
  97. Morikawa C, Kobayashi M, Satoh M, Kuroda Y, Inomata T, Matsuo H, et al. Image and video processing on mobile devices: a survey. Vis Comput. 2021;37(12):2931-2949. [FREE Full text] [CrossRef] [Medline]
  98. Trucano M. Using mobile phones in data collection: opportunities, issues and challenges. World Bank. 2014. URL: https:/​/blogs.​worldbank.org/​en/​education/​using-mobile-phones-data-collection-opportunities-issues-and-challenges [accessed 2024-04-29]
  99. Padilla-Magaña JF, Peña-Pitarch E, Sánchez-Suarez I, Ticó-Falguera N. Quantitative assessment of hand function in healthy subjects and post-stroke patients with the action research arm test. Sensors (Basel). May 10, 2022;22(10):3604. [FREE Full text] [CrossRef] [Medline]
  100. Kostikis N, Hristu-Varsakelis D, Arnaoutoglou M, Kotsavasiloglou C. Smartphone-based evaluation of parkinsonian hand tremor: quantitative measurements vs clinical assessment scores. Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:906-909. [CrossRef] [Medline]
  101. Harris B, Regan T, Schueler J, Fields SA. Problematic mobile phone and smartphone use scales: a systematic review. Front Psychol. May 5, 2020;11:672. [FREE Full text] [CrossRef] [Medline]
  102. Bull TP, Dewar AR, Malvey DM, Szalma JL. Considerations for the telehealth systems of tomorrow: an analysis of student perceptions of telehealth technologies. JMIR Med Educ. Jul 08, 2016;2(2):e11. [FREE Full text] [CrossRef] [Medline]
  103. Ozdalga E, Ozdalga A, Ahuja N. The smartphone in medicine: a review of current and potential use among physicians and students. J Med Internet Res. Sep 27, 2012;14(5):e128. [FREE Full text] [CrossRef] [Medline]
  104. Wang X, Fu Y, Ye B, Babineau J, Ding Y, Mihailidis A. Technology-based compensation assessment and detection of upper extremity activities of stroke survivors: systematic review. J Med Internet Res. Jun 13, 2022;24(6):e34307. [FREE Full text] [CrossRef] [Medline]
  105. Krzywinski M, Altman N. Multiple linear regression. Nat Methods. Dec 2015;12(12):1103-1104. [FREE Full text] [CrossRef] [Medline]
  106. Verma VK, Verma S. Machine learning applications in healthcare sector: an overview. Mater Today Proc. 2022;57:2144-2147. [FREE Full text] [CrossRef]
  107. Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. Support vector machines. IEEE Intell Syst Their Appl. Jul 1998;13(4):18-28. [FREE Full text] [CrossRef]
  108. Noble WS. What is a support vector machine? Nat Biotechnol. Dec 2006;24(12):1565-1567. [FREE Full text] [CrossRef] [Medline]


CTS: carpal tunnel syndrome
DT: decision tree
MDS-UPDRS: Movement Disorder Society of Unified Parkinson’s Disease Rating Scale
ML: machine learning
MS: multiple sclerosis
PD: Parkinson disease
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
ROM: range of motion
RQ: research question
SVM: support vector machine


Edited by A Mavragani; submitted 03.08.23; peer-reviewed by S Okita, KY Hsieh; comments to author 11.01.24; revised version received 05.03.24; accepted 24.07.24; published 16.09.24.

Copyright

©Yan Fu, Yuxin Zhang, Bing Ye, Jessica Babineau, Yan Zhao, Zhengke Gao, Alex Mihailidis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.09.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.