%0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e63095 %T Digital Translation Platform (Translatly) to Overcome Communication Barriers in Clinical Care: Pilot Study %A Olsavszky,Victor %A Bazari,Mutaz %A Dai,Taieb Ben %A Olsavszky,Ana %A Finkelmeier,Fabian %A Friedrich-Rust,Mireen %A Zeuzem,Stefan %A Herrmann,Eva %A Leipe,Jan %A Michael,Florian Alexander %A Westernhagen,Hans von %A Ballo,Olivier %+ Department of Dermatology, Venereology and Allergy, University Medical Center and Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, Mannheim, 68167, Germany, 49 621 383 2280, victor.olsavszky@medma.uni-heidelberg.de %K language barriers %K health care communication %K medical app %K real-time translation %K medical translation %D 2025 %7 14.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Language barriers in health care can lead to misdiagnosis, inappropriate treatment, and increased medical errors. Efforts to mitigate these include using interpreters and translation tools, but these measures often fall short, particularly when cultural nuances are overlooked. Consequently, medical professionals may have to rely on their staff or patients’ relatives for interpretation, compromising the quality of care. Objective: This formative pilot study aims to assess the feasibility of Translatly, a digital translation platform, in clinical practice. Specifically, the study focuses on evaluating (1) how health care professionals overcome language barriers and their acceptance of an on-demand video telephony platform, (2) the feasibility of the platform during medical consultations, and (3) identifying potential challenges for future development. Methods: The study included ethnographic interviews with health care professionals and an observational pilot to assess the use of the Translatly platform in clinical practice. Translatly was developed to make real-time translation easy and accessible on both Android and iOS devices. The system’s backend architecture uses Java-based services hosted on DigitalOcean. The app securely exchanges data between mobile devices and servers, with user information and call records stored in a MySQL database. An admin panel helps manage the system, and Firebase integration enables fast push notifications to ensure that health care professionals can connect with translators whenever they need to. The platform was piloted in a German university hospital with 170 volunteer nonprofessional translators, mainly medical students, supporting translation in over 20 languages, including Farsi, Dari, and Arabic. Results: Ethnographic research conducted by interviewing health care professionals in Frankfurt am Main and other German cities revealed that current practices for overcoming language barriers often rely on family members or digital tools such as Google Translate, raising concerns about accuracy and emotional distress. Respondents preferred an on-demand translation service staffed by medically experienced translators, such as medical students, who understand medical terminology and can empathize with patients. The observational pilot study recorded 39 requests for translation services, 16 (41%) of which were successfully completed. The translations covered 6 different languages and were carried out by a team of 10 translators. Most requests came from departments such as infectious diseases (5/16, 31%) and emergency (4/16, 25%). Challenges were identified around translator availability, with 23 (59%) total requests (N=39) going unanswered, which was further evidenced by user feedback. Conclusions: This pilot study demonstrates the feasibility of the Translatly platform in real-world health care settings. It shows the potential to improve communication and patient outcomes by addressing language barriers. Despite its potential, challenges such as translator availability highlight the need for further development. %M 39451122 %R 10.2196/63095 %U https://formative.jmir.org/2025/1/e63095 %U https://doi.org/10.2196/63095 %U http://www.ncbi.nlm.nih.gov/pubmed/39451122 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e63166 %T Smartphone Application–Based Voice and Speech Training Program for Parkinson Disease: Feasibility and Satisfaction Study With a Preliminary Rater-Blinded Single-Arm Pretest and Posttest Design %A Lee,Sol-Hee %A Kim,Jiae %A Kim,Han-Joon %+ Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 10 7279 7883, movement@snu.ac.kr %K Parkinson disease %K speech therapy %K mHealth %K home-based training %K self-delivered %K digital health care %K app %K feasibility %K voice therapy %K mobile phone %K satisfaction %K effectiveness %K smartphone %K apps %K single-arm study %K mobility %K mobile health %K acoustic analysis %K self-training %D 2025 %7 13.2.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Up to 75% of patients with Parkinson disease (PD) experience voice and speech impairments, such as breathy phonation and low speech volume, which worsen over time and negatively impact the quality of life. However, given their increasingly limited mobility, face-to-face speech therapy is often inaccessible. Mobile health (mHealth) apps offer accessible and cost-effective alternatives; yet, their application in PD-specific, self-delivered voice therapy remains underexplored. Objective: This study aimed to evaluate the feasibility, adherence, and satisfaction of a self-delivered smartphone app for voice therapy in patients with PD, designed to minimize speech-language pathologist involvement while promoting patient independence. In addition, it seeks to assess the preliminary therapeutic effectiveness of the app in addressing voice and speech problems in this population. Methods: A single-arm, rater-blinded, and pretest and posttest study was conducted between September to November 2023. Patients with PD with voice and speech problems who have no problem with using Android (Google) smartphones were recruited. Participants downloaded the researcher-developed mHealth app on their smartphone and participated in a patient-tailored 5-week home-based speech training program. Each session included 5 stages: breathing, oral motor exercises, loudness, prosody, and functional speaking. The training program consisted of 20 sessions, with participants completing 1 session per day, 4 days per week. Each session lasted approximately 20-30 minutes. Adherence was monitored through app logs, satisfaction was assessed through a phone survey, and therapeutic effectiveness was evaluated using acoustic analysis and auditory-perceptual assessments. Results: Out of 30 patients were initially recruited, but 2 of them withdrew. Out of 25 participants completed all the training sessions while 3 dropped out. The adherence was above 90% in 20 participants (80%, 20/25), 70% to 90% in 4 (16%, 4/25), and below 70% in 4 (16%, 4/25). Satisfaction was 75% (18/24) among the 24 people who participated in the survey. Significant improvements were observed in all acoustic measures: the maximum phonation time increased from 11.15 (SD 5.38) seconds to 14.01 (SD 5.64) seconds (P=.003), and vocal intensity increased from 71.59 (SD 4.39) dB to 73.81 (SD 3.48) dB (P<.001) across both sustained phonation and reading tasks. Voice quality scores on the GRBAS (grade, roughness, breathiness, asthenia, and strain) scale improved significantly (all components P<.001). Furthermore, 58.3% (14/24) of participants reported subjective improvements in their voice. Conclusions: This study demonstrates that home-based, self-training speech therapy delivered through a mHealth app is a feasible solution for patients with PD, suggesting that mHealth apps can serve as a convenient and effective alternative to face-to-face therapy by enhancing accessibility and empowering patients to actively manage their condition. %M 39946689 %R 10.2196/63166 %U https://www.jmir.org/2025/1/e63166 %U https://doi.org/10.2196/63166 %U http://www.ncbi.nlm.nih.gov/pubmed/39946689 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e53064 %T Exploring the Feasibility of Digital Voice Assistants for Delivery of a Home-Based Exercise Intervention in Older Adults With Obesity and Type 2 Diabetes Mellitus: Randomized Controlled Trial %A Glavas,Costas %A Scott,David %A Sood,Surbhi %A George,Elena S %A Daly,Robin M %A Gvozdenko,Eugene %A de Courten,Barbora %A Jansons,Paul %+ Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood, 3125, Australia, 61 448776661, cglavas@deakin.edu.au %K older adults %K type 2 diabetes mellitus %K voice activation %K digital health %K exercise %D 2024 %7 13.9.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Current clinical guidelines for the management of type 2 diabetes mellitus (T2DM) in older adults recommend the use of antihyperglycemic medications, monitoring of blood glucose levels, regular exercise, and a healthy diet to improve glycemic control and reduce associated comorbidities. However, adherence to traditional exercise programs is poor (<35%). Common barriers to adherence include fear of hypoglycemia and the need for blood glucose level monitoring before exercise. Digital health strategies offer great promise for managing T2DM as they facilitate patient-practitioner communication, support self-management, and improve access to health care services for underserved populations. We have developed a novel web-based software program allowing practitioners to create tailored interventions and deliver them to patients via digital voice assistants (DVAs) in their own homes. Objective: We aim to evaluate the feasibility of a 12-week, home-based, personalized lifestyle intervention delivered and monitored by DVAs for older adults with obesity and T2DM. Methods: In total, 50 older adults with obesity aged 50-75 years with oral hypoglycemic agent–treated T2DM were randomized to the intervention (DVA, n=25) or a control group (n=25). Participants allocated to the DVA group were prescribed a home-based muscle strengthening exercise program (~20- to 30-min sessions) and healthy eating intervention, delivered via DVAs (Alexa Echo Show 8; Amazon) using newly developed software (“Buddy Link”; Great Australian Pty Ltd). Control group participants received generalized physical activity information via email. Outcomes were feasibility, DVA usability (System Usability Scale), and objectively assessed physical activity and sedentary time (wrist-worn accelerometers). Results: In total, 45 (90%) out of 50 participants completed this study. Mean adherence to prescribed exercise was 85% (SD 43%) with no intervention-related adverse events. System usability was rated above average (70.4, SD 16.9 out of 100). Compared with controls, the DVA group significantly decreased sedentary time (mean difference –67, SD 23; 95% CI –113 to –21 min/d), which was represented by a medium to large effect size (d=–0.6). Conclusions: A home-based lifestyle intervention delivered and monitored by health professionals using DVAs was feasible for reducing sedentary behavior and increasing moderate-intensity activity in older adults with obesity and T2DM. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000307808; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381364&isReview=true %M 39270212 %R 10.2196/53064 %U https://aging.jmir.org/2024/1/e53064 %U https://doi.org/10.2196/53064 %U http://www.ncbi.nlm.nih.gov/pubmed/39270212 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e54800 %T Assessing the Feasibility and Acceptability of Smart Speakers in Behavioral Intervention Research With Older Adults: Mixed Methods Study %A Quinn,Kelly %A Leiser Ransom,Sarah %A O'Connell,Carrie %A Muramatsu,Naoko %A Marquez,David X %A Chin,Jessie %+ Department of Communication, University of Illinois Chicago, 1007 W Harrison St, 1140 BSB, MC 132, Chicago, IL, 60607, United States, 1 312 996 3187, kquinn8@uic.edu %K smart speakers %K physical activity %K older adults %K behavioral health %K intervention %K smart device %K smart devices %K conversational agent %K physical activities %K behavioral intervention %K intervention research %D 2024 %7 30.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Smart speakers, such as Amazon’s Echo and Google’s Nest Home, combine natural language processing with a conversational interface to carry out everyday tasks, like playing music and finding information. Easy to use, they are embraced by older adults, including those with limited physical function, vision, or computer literacy. While smart speakers are increasingly used for research purposes (eg, implementing interventions and automatically recording selected research data), information on the advantages and disadvantages of using these devices for studies related to health promotion programs is limited. Objective: This study evaluates the feasibility and acceptability of using smart speakers to deliver a physical activity (PA) program designed to help older adults enhance their physical well-being. Methods: Community-dwelling older adults (n=18) were asked to use a custom smart speaker app to participate in an evidence-based, low-impact PA program for 10 weeks. Collected data, including measures of technology acceptance, interviews, field notes, and device logs, were analyzed using a concurrent mixed analysis approach. Technology acceptance measures were evaluated using time series ANOVAs to examine acceptability, appropriateness, feasibility, and intention to adopt smart speaker technology. Device logs provided evidence of interaction with and adoption of the device and the intervention. Interviews and field notes were thematically coded to triangulate the quantitative measures and further expand on factors relating to intervention fidelity. Results: Smart speakers were found to be acceptable for administering a PA program, as participants reported that the devices were highly usable (mean 5.02, SE 0.38) and had strong intentions to continue their use (mean 5.90, SE 0.39). Factors such as the voice-user interface and engagement with the device on everyday tasks were identified as meaningful to acceptability. The feasibility of the devices for research activity, however, was mixed. Despite the participants rating the smart speakers as easy to use (mean 5.55, SE 1.16), functional and technical factors, such as Wi-Fi connectivity and appropriate command phrasing, required the provision of additional support resources to participants and potentially impaired intervention fidelity. Conclusions: Smart speakers present an acceptable and appropriate behavioral intervention technology for PA programs directed at older adults but entail additional requirements for resource planning, technical support, and troubleshooting to ensure their feasibility for the research context and for fidelity of the intervention. %R 10.2196/54800 %U https://www.jmir.org/2024/1/e54800 %U https://doi.org/10.2196/54800 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e56055 %T Optimizing Technology-Based Prompts for Supporting People Living With Dementia in Completing Activities of Daily Living at Home: Experimental Approach to Prompt Modality, Task Breakdown, and Attentional Support %A Cannings,Madeleine %A Brookman,Ruth %A Parker,Simon %A Hoon,Leonard %A Ono,Asuka %A Kawata,Hiroaki %A Matsukawa,Hisashi %A Harris,Celia B %+ The MARCS Institute for Brain, Behaviour, and Development, Western Sydney University, Locked Bag 1797, Penrith, 2571, Australia, 61 297726570, celia.harris@westernsydney.edu.au %K assistive technology %K accessible technology %K accessibility technology %K assistive technologies %K accessible technologies %K assistive device %K assistive devices %K dementia %K people living with dementia %K dementia care %K person-centered technology %K patient-centered technology %K person-centered technologies %K patient-centered technologies %K memory support %K prompting %K user-computer interface %K user interface %K UI %K app %K apps %K digital health %K digital technology %K digital intervention %K digital interventions %K mobile phone %D 2024 %7 23.8.2024 %9 Original Paper %J JMIR Aging %G English %X Background: Assistive technology is becoming increasingly accessible and affordable for supporting people with dementia and their care partners living at home, with strong potential for technology-based prompting to assist with initiation and tracking of complex, multistep activities of daily living. However, there is limited direct comparison of different prompt features to guide optimal technology design. Objective: Across 3 experiments, we investigated the features of tablet-based prompts that best support people with dementia to complete activities of daily living at home, measuring prompt effectiveness and gaining feedback from people with dementia and their care partners about their experiences. Methods: Across experiments, we developed a specialized iPad app to enable data collection with people with dementia at home over an extended experimental period. In experiment 1, we varied the prompts in a 3 (visual type: text instruction, iconic image, and photographic image) × 3 (audio type: no sound, symbolic sound, and verbal instruction) experimental design using repeated measures across multiple testing sessions involving single-step activities. In experiment 2, we tested the most effective prompt breakdown for complex multistep tasks comparing 3 conditions (1-prompt, 3-prompt, and 7-prompt conditions). In experiment 3, we compared initiation and maintenance alerts that involved either an auditory tone or an auditory tone combined with a verbal instruction. Throughout, we asked people with dementia and their care partners to reflect on the usefulness of prompting technology in their everyday lives and what could be developed to better meet their needs. Results: First, our results showed that audible verbal instructions were more useful for task completion than either tone-based or visual prompts. Second, a more granular breakdown of tasks was generally more useful and increased independent use, but this varied across individuals. Third, while a voice or text maintenance alert enabled people with dementia to persist with a multistep task for longer when it was more frequent, task initiation still frequently required support from a care partner. Conclusions: These findings can help inform developers of assistive technology about the design features that promote the usefulness of home prompting systems for people with dementia as well as the preferences and insights of people with dementia and their care partners regarding assistive technology design. %M 39178405 %R 10.2196/56055 %U https://aging.jmir.org/2024/1/e56055 %U https://doi.org/10.2196/56055 %U http://www.ncbi.nlm.nih.gov/pubmed/39178405 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e54388 %T Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania %A Isangula,Kahabi Ganka %A Haule,Rogers John %+ School of Nursing and Midwifery, Aga Khan University, Salama House, 344 Urambo St, PO Box 125, Dar Es Salaam, 255, United Republic of Tanzania, 255 754030726, kahabi.isangula@aku.edu %K artificial intelligence %K machine learning %K respiratory diseases %K cough classifiers %K Tanzania %K Africa %K mobile phone %K user-friendly %K cough %K detecting respiratory disease %K diagnostic study %K tuberculosis %K asthma %K chronic obstructive pulmonary disease %K treatment %K management %K noninvasive %K rural %K cross-sectional research %K analysis %K cough sound %D 2024 %7 23.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. Objective: This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. Methods: This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. Results: This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Conclusions: Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. International Registered Report Identifier (IRRID): PRR1-10.2196/54388 %M 38652526 %R 10.2196/54388 %U https://www.researchprotocols.org/2024/1/e54388 %U https://doi.org/10.2196/54388 %U http://www.ncbi.nlm.nih.gov/pubmed/38652526 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e46967 %T Multimodal In-Vehicle Hypoglycemia Warning for Drivers With Type 1 Diabetes: Design and Evaluation in Simulated and Real-World Driving %A Bérubé,Caterina %A Maritsch,Martin %A Lehmann,Vera Franziska %A Kraus,Mathias %A Feuerriegel,Stefan %A Züger,Thomas %A Wortmann,Felix %A Stettler,Christoph %A Fleisch,Elgar %A Kocaballi,A Baki %A Kowatsch,Tobias %+ Institute of Technology Management, University of St.Gallen, Office 62-314, St.Jakob-Strasse 21, St Gallen, 9000, Switzerland, 41 71 224 7244, tobias.kowatsch@unisg.ch %K digital health %K voice assistant %K ambient lighting %K in-vehicle technology %K health state %K diabetes %K hypoglycemia %K warning %K emotional reaction %K technology acceptance %K mobile phone %K diabetes %K implementation %D 2024 %7 18.4.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs. Objective: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes. Methods: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland. Results: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F2,68=4.3; P<.05 and F2,76=4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F2,68=3.9; P<.05), while blood glucose phase affected it in real-world driving (F2,76=9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F2,68=2.46; P=.09, blood glucose phase: F2,68=0.3; P=.74), nor in the real-world driving study (modality: F2,76=0.8; P=.47, blood glucose phase: F2,76=0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84). Conclusions: Despite the mixed results, this paper highlights the potential of implementing voice assistant–based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving. Trial Registration: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095 %M 38635313 %R 10.2196/46967 %U https://humanfactors.jmir.org/2024/1/e46967 %U https://doi.org/10.2196/46967 %U http://www.ncbi.nlm.nih.gov/pubmed/38635313 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50534 %T Reducing Loneliness and Social Isolation of Older Adults Through Voice Assistants: Literature Review and Bibliometric Analysis %A Marziali,Rachele Alessandra %A Franceschetti,Claudia %A Dinculescu,Adrian %A Nistorescu,Alexandru %A Kristály,Dominic Mircea %A Moșoi,Adrian Alexandru %A Broekx,Ronny %A Marin,Mihaela %A Vizitiu,Cristian %A Moraru,Sorin-Aurel %A Rossi,Lorena %A Di Rosa,Mirko %+ Centre for Innovative Models for Aging Care and Technology, IRCCS INRCA-National Institute of Health and Science on Aging, Via Santa Margherita 5, Ancona, 60124, Italy, 39 0718004788, c.franceschetti@inrca.it %K voice assistant %K loneliness %K social isolation %K older adults %K literature review %K bibliometric analysis %K mobile phone %D 2024 %7 18.3.2024 %9 Review %J J Med Internet Res %G English %X Background: Loneliness and social isolation are major public health concerns for older adults, with severe mental and physical health consequences. New technologies may have a great impact in providing support to the daily lives of older adults and addressing the many challenges they face. In this scenario, technologies based on voice assistants (VAs) are of great interest and potential benefit in reducing loneliness and social isolation in this population, because they could overcome existing barriers with other digital technologies through easier and more natural human-computer interaction. Objective: This study aims to investigate the use of VAs to reduce loneliness and social isolation of older adults by performing a systematic literature review and a bibliometric cluster mapping analysis. Methods: We searched PubMed, Embase, and Scopus databases for articles that were published in the last 6 years, related to the following main topics: voice interface, VA, older adults, isolation, and loneliness. A total of 40 articles were found, of which 16 (40%) were included in this review. The included articles were then assessed through a qualitative scoring method and summarized. Finally, a bibliometric analysis was conducted using VOSviewer software (Leiden University’s Centre for Science and Technology Studies). Results: Of the 16 articles included in the review, only 2 (13%) were considered of poor methodological quality, whereas 9 (56%) were of medium quality and 5 (31%) were of high quality. Finally, through bibliometric analysis, 221 keywords were extracted, of which 36 (16%) were selected. The most important keywords, by number of occurrences and by total link strength; results of the analysis with the Association Strength normalization method; and default values were then presented. The final bibliometric network consisted of 36 selected keywords, which were grouped into 3 clusters related to 3 main topics (ie, VA use for social isolation among older adults, the significance of age in the context of loneliness, and the impact of sex factors on well-being). For most of the selected articles, the effect of VA on social isolation and loneliness of older adults was a minor theme. However, more investigations were done on user experience, obtaining preliminary positive results. Conclusions: Most articles on the use of VAs by older adults to reduce social isolation and loneliness focus on usability, acceptability, or user experience. Nevertheless, studies directly addressing the impact that using a VA has on the social isolation and loneliness of older adults find positive and promising results and provide important information for future research, interventions, and policy development in the field of geriatric care and technology. %M 38498039 %R 10.2196/50534 %U https://www.jmir.org/2024/1/e50534 %U https://doi.org/10.2196/50534 %U http://www.ncbi.nlm.nih.gov/pubmed/38498039 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 12 %N %P e47472 %T Investigating the Integration and the Long-Term Use of Smart Speakers in Older Adults’ Daily Practices: Qualitative Study %A Chang,Fangyuan %A Sheng,Lin %A Gu,Zhenyu %+ Interaction Design Lab, School of Design, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China, 86 13167232872, zygu@sjtu.edu.cn %K smart speaker %K private home %K older adults %K long-term use %K daily practices %K smart speakers %D 2024 %7 12.2.2024 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: As smart speakers become more popular, there have been an increasing number of studies on how they may benefit older adults or how older adults perceive them. Despite the increasing ownership rates of smart speakers among older adults, studies that examine their integration and the long-term use in older adults’ daily practices are scarce. Objective: This study aims to uncover the integration of smart speakers into the daily practices of older adults over the long term, contributing to an in-depth understanding of maintained technology use among this demographic. Methods: To achieve these objectives, the study interviewed 20 older adults who had been using smart speakers for over 6 months. These semistructured interviews enabled participants to share their insights and experiences regarding the maintained use of smart speakers in the long term. Results: We identified 4 dimensions of the long-term use of smart speakers among older adults, including functional integration, spatial integration, cognitive integration, and semantic integration. For the functional integration of smart speakers, the study reported different types of use, including entertainment, information collection, medication reminders, companionship, environment modification, and emergency calls. For the spatial integration of smart speakers, the study showed older adults’ agency in defining, changing, and reshaping daily practices through the spatial organization of smart speakers. For the cognitive integration of smart speakers, the findings showed the cognitive processes involved in adapting to and incorporating smart speakers into daily habits and routines. For the semantic integration of smart speakers, the findings revealed that older adults’ enjoyable user experience and strong bonds with the device contributed to their acceptance of occasional functional errors. Finally, the study proposed several suggestions for designers and developers to better design smart speakers that promote maintainable use behaviors among older adults. Conclusions: On the basis of the findings, this study highlighted the importance of understanding how older adults use smart speakers and the practices through which they integrate them into their daily routines. The findings suggest that smart speakers can provide significant benefits for older adults, including increased convenience and improved quality of life. However, to promote maintainable use behaviors, designers and developers should consider more about the technology use contexts and the specific needs and preferences of older adults when designing these devices. %M 38345844 %R 10.2196/47472 %U https://mhealth.jmir.org/2024/1/e47472 %U https://doi.org/10.2196/47472 %U http://www.ncbi.nlm.nih.gov/pubmed/38345844 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e42823 %T Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial %A Bérubé,Caterina %A Lehmann,Vera Franziska %A Maritsch,Martin %A Kraus,Mathias %A Feuerriegel,Stefan %A Wortmann,Felix %A Züger,Thomas %A Stettler,Christoph %A Fleisch,Elgar %A Kocaballi,A Baki %A Kowatsch,Tobias %+ Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, WEV G 216, Weinbergstrasse 56/58, Zurich, 8092, Switzerland, 41 44 632 94 88, tkowatsch@ethz.ch %K hypoglycemia %K type-1 diabetes mellitus %K in-vehicle voice assistant %K voice interface %K voice warning %K digital health intervention %K mobile phone %D 2024 %7 9.1.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Hypoglycemia is a frequent and acute complication in type 1 diabetes mellitus (T1DM) and is associated with a higher risk of car mishaps. Currently, hypoglycemia can be detected and signaled through flash glucose monitoring or continuous glucose monitoring devices, which require manual and visual interaction, thereby removing the focus of attention from the driving task. Hypoglycemia causes a decrease in attention, thereby challenging the safety of using such devices behind the wheel. Here, we present an investigation of a hands-free technology—a voice warning that can potentially be delivered via an in-vehicle voice assistant. Objective: This study aims to investigate the feasibility of an in-vehicle voice warning for hypoglycemia, evaluating both its effectiveness and user perception. Methods: We designed a voice warning and evaluated it in 3 studies. In all studies, participants received a voice warning while driving. Study 0 (n=10) assessed the feasibility of using a voice warning with healthy participants driving in a simulator. Study 1 (n=18) assessed the voice warning in participants with T1DM. Study 2 (n=20) assessed the voice warning in participants with T1DM undergoing hypoglycemia while driving in a real car. We measured participants’ self-reported perception of the voice warning (with a user experience scale in study 0 and with acceptance, alliance, and trust scales in studies 1 and 2) and compliance behavior (whether they stopped the car and reaction time). In addition, we assessed technology affinity and collected the participants’ verbal feedback. Results: Technology affinity was similar across studies and approximately 70% of the maximal value. Perception measure of the voice warning was approximately 62% to 78% in the simulated driving and 34% to 56% in real-world driving. Perception correlated with technology affinity on specific constructs (eg, Affinity for Technology Interaction score and intention to use, optimism and performance expectancy, behavioral intention, Session Alliance Inventory score, innovativeness and hedonic motivation, and negative correlations between discomfort and behavioral intention and discomfort and competence trust; all P<.05). Compliance was 100% in all studies, whereas reaction time was higher in study 1 (mean 23, SD 5.2 seconds) than in study 0 (mean 12.6, SD 5.7 seconds) and study 2 (mean 14.6, SD 4.3 seconds). Finally, verbal feedback showed that the participants preferred the voice warning to be less verbose and interactive. Conclusions: This is the first study to investigate the feasibility of an in-vehicle voice warning for hypoglycemia. Drivers find such an implementation useful and effective in a simulated environment, but improvements are needed in the real-world driving context. This study is a kickoff for the use of in-vehicle voice assistants for digital health interventions. %M 38194257 %R 10.2196/42823 %U https://humanfactors.jmir.org/2024/1/e42823 %U https://doi.org/10.2196/42823 %U http://www.ncbi.nlm.nih.gov/pubmed/38194257 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e44773 %T Standardized Comparison of Voice-Based Information and Documentation Systems to Established Systems in Intensive Care: Crossover Study %A Peine,Arne %A Gronholz,Maike %A Seidl-Rathkopf,Katharina %A Wolfram,Thomas %A Hallawa,Ahmed %A Reitz,Annika %A Celi,Leo Anthony %A Marx,Gernot %A Martin,Lukas %+ Department of Intensive Care Medicine and Intermediate Care, University Hospital RWTH Aachen, Pauwelsstr. 30, Aachen, 52070, Germany, 49 241 800, apeine@ukaachen.de %K artificial intelligence %K documentation %K ICU %K intensive care medicine %K speech-recognition %K user perception %K workload %D 2023 %7 28.11.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: The medical teams in intensive care units (ICUs) spend increasing amounts of time at computer systems for data processing, input, and interpretation purposes. As each patient creates about 1000 data points per hour, the available information is abundant, making the interpretation difficult and time-consuming. This data flood leads to a decrease in time for evidence-based, patient-centered care. Information systems, such as patient data management systems (PDMSs), are increasingly used at ICUs. However, they often create new challenges arising from the increasing documentation burden. Objective: New concepts, such as artificial intelligence (AI)–based assistant systems, are hence introduced to the workflow to cope with these challenges. However, there is a lack of standardized, published metrics in order to compare the various data input and management systems in the ICU setting. The objective of this study is to compare established documentation and retrieval processes with newer methods, such as PDMSs and voice information and documentation systems (VIDSs). Methods: In this crossover study, we compare traditional, paper-based documentation systems with PDMSs and newer AI-based VIDSs in terms of performance (required time), accuracy, mental workload, and user experience in an intensive care setting. Performance is assessed on a set of 6 standardized, typical ICU tasks, ranging from documentation to medical interpretation. Results: A total of 60 ICU-experienced medical professionals participated in the study. The VIDS showed a statistically significant advantage compared to the other 2 systems. The tasks were completed significantly faster with the VIDS than with the PDMS (1-tailed t59=12.48; Cohen d=1.61; P<.001) or paper documentation (t59=20.41; Cohen d=2.63; P<.001). Significantly fewer errors were made with VIDS than with the PDMS (t59=3.45; Cohen d=0.45; P=.03) and paper-based documentation (t59=11.2; Cohen d=1.45; P<.001). The analysis of the mental workload of VIDS and PDMS showed no statistically significant difference (P=.06). However, the analysis of subjective user perception showed a statistically significant perceived benefit of the VIDS compared to the PDMS (P<.001) and paper documentation (P<.001). Conclusions: The results of this study show that the VIDS reduced error rate, documentation time, and mental workload regarding the set of 6 standardized typical ICU tasks. In conclusion, this indicates that AI-based systems such as the VIDS tested in this study have the potential to reduce this workload and improve evidence-based and safe patient care. %M 38015593 %R 10.2196/44773 %U https://medinform.jmir.org/2023/1/e44773 %U https://doi.org/10.2196/44773 %U http://www.ncbi.nlm.nih.gov/pubmed/38015593 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 11 %P e39185 %T Lessons Learned From the SoBeezy Program for Older Adults During the COVID-19 Pandemic: Experimentation and Evaluation %A Pech,Marion %A Gbessemehlan,Antoine %A Dupuy,Lucile %A Sauzéon,Hélène %A Lafitte,Stéphane %A Bachelet,Philippe %A Amieva,Hélène %A Pérès,Karine %+ Bordeaux Population Health Research Center, Inserm, UMR 1219, University of Bordeaux, 146 rue Léo Saignat, Bordeaux, 33076, France, 33 667455145, marion.pech@u-bordeaux.fr %K voice assistance %K social isolation %K healthy aging %K living in place %K acceptability %K technologies %K digital divide %K older adults %K aging %K elderly population %K voice assistant %K COVID-19 %D 2022 %7 24.11.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: The SoBeezy program is an innovative intervention aimed at promoting and fostering healthy aging and aging in place by proposing to older adults concrete solutions to face daily life, tackle loneliness, promote social participation, and reduce the digital divide, thanks to a specific, easy-to-use voice assistant (the BeeVA smart display). Objective: This study aims to assess the acceptability of the SoBeezy program and its voice assistant and to identify potential areas of improvement. Methods: A 12-month experimentation of the program was deployed in real-life conditions among older adults living in the community in 4 pilot cities of France. Launched during the first lockdown of the COVID-19 crisis, this multisite study aimed to assess acceptability using questionnaires and interviews conducted at baseline and at the end of the experimentation. In addition, a series of meetings were conducted with SoBeezy staff members to obtain direct feedback from the ground. Results: In total, 109 older individuals were equipped with BeeVA to use the SoBeezy program; of these, 32 (29.4%) left the experimentation before its end and 69 (63.3%) completed the final questionnaires. In total, 335 interventions were conducted and 27 (39%) of the participants requested services, mainly for supportive calls and visits and assistance with shopping, transportation, and crafting-gardening. Of the whole sample, 52 (75%) considered BeeVA as a reassuring presence, and few persons (15/69, 22%) reported a negative opinion about the program. Among the participants, the voice assistant appeared easy to use (n=57, 82%) and useful (n=53, 77%). They also were positive about the BeeVA smart display and the SoBeezy intervention. Conclusions: This multisite study conducted in real-life conditions among more than 100 older adults living in the community provides enlightening results of the reality from the ground of digital tools designed for the aging population. The COVID-19 context appeared both as an opportunity, given the massive needs of the older adults during this crisis, and as limiting due to sanitary constraints. Nevertheless, the experimentation showed overall good acceptability of the voice assistant and a high level of satisfaction of the participants among those who really used the system and could be a way of improving the autonomy and well-being of older adults and their families. However, the findings also highlighted resistance to change and difficulties for the users to ask for help. The experimentation also emphasized levers for next deployments and future research. The next step will be the experimentation of the activity-sharing component that could not be tested due to the COVID-19 context. %M 36355629 %R 10.2196/39185 %U https://formative.jmir.org/2022/11/e39185 %U https://doi.org/10.2196/39185 %U http://www.ncbi.nlm.nih.gov/pubmed/36355629 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e36610 %T Ensuring Interrater Reliability When Evaluating Voice Assistants. Comment on “Evaluating Voice Assistants’ Responses to COVID-19 Vaccination in Portuguese: Quality Assessment” %A Mungmunpuntipantip,Rujittika %A Wiwanitkit,Viroj %+ Private Academic Consultant, 26 Bangkok 111, Bangkok, 1-101132, Thailand, 66 2388282822, rujittika@gmail.com %K voice assistant %K natural user interface %K Portuguese language %K COVID-19 %K vaccine %D 2022 %7 21.3.2022 %9 Letter to the Editor %J JMIR Hum Factors %G English %X %M 35312626 %R 10.2196/36610 %U https://humanfactors.jmir.org/2022/1/e36610 %U https://doi.org/10.2196/36610 %U http://www.ncbi.nlm.nih.gov/pubmed/35312626 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e34674 %T Evaluating Voice Assistants' Responses to COVID-19 Vaccination in Portuguese: Quality Assessment %A Seródio Figueiredo,Carlos Maurício %A de Melo,Tiago %A Goes,Raphaela %+ Escola Superior de Tecnologia, Universidade do Estado do Amazonas, Av. Darcy Vargas, 1.200 - Parque Dez de Novembro, Manaus, 69050-020, Brazil, 55 92988120877, cfigueiredo@uea.edu.br %K voice assistant %K natural user interface %K Portuguese language %K health information %K COVID-19 %K vaccine %K immunization %K health device %K digital health %D 2022 %7 21.3.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Voice assistants (VAs) are devices that respond to human voices and can be commanded to do a variety of tasks. Nowadays, VAs are being used to obtain health information, which has become a critical point of analysis for researchers in terms of question understanding and quality of response. Particularly, the COVID-19 pandemic has and still is severely affecting people worldwide, which demands studies on how VAs can be used as a tool to provide useful information. Objective: This work aimed to perform a quality analysis of different VAs’ responses regarding the actual and important subject of COVID-19 vaccines. We focused on this important subject since vaccines are now available and society has urged for the population to be rapidly immunized. Methods: The proposed study was based on questions that were collected from the official World Health Organization website. These questions were submitted to the 5 dominant VAs (Alexa, Bixby, Cortana, Google Assistant, and Siri), and responses were evaluated according to a rubric based on the literature. We focused this study on the Portuguese language as an additional contribution, since previous works are mainly focused on the English language, and we believe that VAs cannot be optimized to foreign languages. Results: Results showed that Google Assistant has a better overall performance, and only this VA and Samsung Bixby achieved high scores on question understanding in the Portuguese language. Regarding the obtained answers, the study also showed the best Google Assistant overall performance. Conclusions: Under the urgent context of COVID-19 vaccination, this work can help to understand how VAs must be improved to be more useful to the society and how careful people must be when considering VAs as a source of health information. VAs have been demonstrated to perform well regarding comprehension and user-friendliness. However, this work has found that they must be better integrated to their information sources to be useful as health information tools. %M 35041617 %R 10.2196/34674 %U https://humanfactors.jmir.org/2022/1/e34674 %U https://doi.org/10.2196/34674 %U http://www.ncbi.nlm.nih.gov/pubmed/35041617 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 9 %N 1 %P e29249 %T Speech and Language Practitioners’ Experiences of Commercially Available Voice-Assisted Technology: Web-Based Survey Study %A Kulkarni,Pranav %A Duffy,Orla %A Synnott,Jonathan %A Kernohan,W George %A McNaney,Roisin %+ Action Lab, Department of Human Centred Computing, Monash University, 7 Innovation Walk, Clayton, 3168, Australia, 61 0444511615, pranav.kulkarni1@monash.edu %K speech and language therapy %K voice-assisted technology %K professional practice %K rehabilitation %K speech therapy %K health technology %K mobile phone %D 2022 %7 5.1.2022 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Speech and language therapy involves the identification, assessment, and treatment of children and adults who have difficulties with communication, eating, drinking, and swallowing. Globally, pressing needs outstrip the availability of qualified practitioners who, of necessity, focus on individuals with advanced needs. The potential of voice-assisted technology (VAT) to assist people with speech impairments is an emerging area of research but empirical work exploring its professional adoption is limited. Objective: This study aims to explore the professional experiences of speech and language therapists (SaLTs) using VAT with their clients to identify the potential applications and barriers to VAT adoption and thereby inform future directions of research. Methods: A 23-question survey was distributed to the SaLTs from the United Kingdom using a web-based platform, eliciting both checkbox and free-text responses, to questions on perceptions and any use experiences of VAT. Data were analyzed descriptively with content analysis of free text, providing context to their specific experiences of using VAT in practice, including barriers and opportunities for future use. Results: A total of 230 UK-based professionals fully completed the survey; most were technologically competent and were aware of commercial VATs (such as Alexa and Google Assistant). However, only 49 (21.3%) SaLTs had used VAT with their clients and described 57 use cases. They reported using VAT with 10 different client groups, such as people with dysarthria and users of augmentative and alternative communication technologies. Of these, almost half (28/57, 49%) used the technology to assist their clients with day-to-day tasks, such as web browsing, setting up reminders, sending messages, and playing music. Many respondents (21/57, 37%) also reported using the technology to improve client speech, to facilitate speech practice at home, and to enhance articulation and volume. Most reported a positive impact of VAT use, stating improved independence (22/57, 39%), accessibility (6/57, 10%), and confidence (5/57, 8%). Some respondents reported increased client communication (5/57, 9%) and sociability (3/57, 5%). Reasons given for not using VAT in practice included lack of opportunity (131/181, 72.4%) and training (63/181, 34.8%). Most respondents (154/181, 85.1%) indicated that they would like to try VAT in the future, stating that it could have a positive impact on their clients’ speech, independence, and confidence. Conclusions: VAT is used by some UK-based SaLTs to enable communication tasks at home with their clients. However, its wider adoption may be limited by a lack of professional opportunity. Looking forward, additional benefits are promised, as the data show a level of engagement, empowerment, and the possibility of achieving therapeutic outcomes in communication impairment. The disparate responses suggest that this area is ripe for the development of evidence-based clinical practice, starting with a clear definition, outcome measurement, and professional standardization. %M 34989694 %R 10.2196/29249 %U https://rehab.jmir.org/2022/1/e29249 %U https://doi.org/10.2196/29249 %U http://www.ncbi.nlm.nih.gov/pubmed/34989694 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e31737 %T Improving User Experience of Virtual Health Assistants: Scoping Review %A Curtis,Rachel G %A Bartel,Bethany %A Ferguson,Ty %A Blake,Henry T %A Northcott,Celine %A Virgara,Rosa %A Maher,Carol A %+ UniSA Allied Health and Human Performance, Alliance for Research in Exercise, Nutrition and Activity, University of South Australia, GPO Box 2471, Adelaide, 5001, Australia, 61 8 8302 2455, Rachel.Curtis@unisa.edu.au %K virtual assistant %K conversational agent %K chatbot %K eHealth %K digital health %K design %K user experience %K mobile phone %D 2021 %7 21.12.2021 %9 Review %J J Med Internet Res %G English %X Background: Virtual assistants can be used to deliver innovative health programs that provide appealing, personalized, and convenient health advice and support at scale and low cost. Design characteristics that influence the look and feel of the virtual assistant, such as visual appearance or language features, may significantly influence users’ experience and engagement with the assistant. Objective: This scoping review aims to provide an overview of the experimental research examining how design characteristics of virtual health assistants affect user experience, summarize research findings of experimental research examining how design characteristics of virtual health assistants affect user experience, and provide recommendations for the design of virtual health assistants if sufficient evidence exists. Methods: We searched 5 electronic databases (Web of Science, MEDLINE, Embase, PsycINFO, and ACM Digital Library) to identify the studies that used an experimental design to compare the effects of design characteristics between 2 or more versions of an interactive virtual health assistant on user experience among adults. Data were synthesized descriptively. Health domains, design characteristics, and outcomes were categorized, and descriptive statistics were used to summarize the body of research. Results for each study were categorized as positive, negative, or no effect, and a matrix of the design characteristics and outcome categories was constructed to summarize the findings. Results: The database searches identified 6879 articles after the removal of duplicates. We included 48 articles representing 45 unique studies in the review. The most common health domains were mental health and physical activity. Studies most commonly examined design characteristics in the categories of visual design or conversational style and relational behavior and assessed outcomes in the categories of personality, satisfaction, relationship, or use intention. Over half of the design characteristics were examined by only 1 study. Results suggest that empathy and relational behavior and self-disclosure are related to more positive user experience. Results also suggest that if a human-like avatar is used, realistic rendering and medical attire may potentially be related to more positive user experience; however, more research is needed to confirm this. Conclusions: There is a growing body of scientific evidence examining the impact of virtual health assistants’ design characteristics on user experience. Taken together, data suggest that the look and feel of a virtual health assistant does affect user experience. Virtual health assistants that show empathy, display nonverbal relational behaviors, and disclose personal information about themselves achieve better user experience. At present, the evidence base is broad, and the studies are typically small in scale and highly heterogeneous. Further research, particularly using longitudinal research designs with repeated user interactions, is needed to inform the optimal design of virtual health assistants. %M 34931997 %R 10.2196/31737 %U https://www.jmir.org/2021/12/e31737 %U https://doi.org/10.2196/31737 %U http://www.ncbi.nlm.nih.gov/pubmed/34931997 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e32161 %T Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information %A Bérubé,Caterina %A Kovacs,Zsolt Ferenc %A Fleisch,Elgar %A Kowatsch,Tobias %+ Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, WEV G 214, Weinbergstrasse 56/58, Zurich, 8092, Switzerland, 41 44 633 8419, berubec@ethz.ch %K voice assistants %K conversational agents %K health literacy %K noncommunicable diseases %K mobile phone %K smart speaker %K smart display %K evaluation %K protocol %K assistant %K agent %K literacy %K audio %K health information %K management %K factorial %K information source %D 2021 %7 20.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients’ access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients’ ability to make health-related decisions and manage their chronic conditions. Objective: This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD. Methods: We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer’s disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3×3×6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated). Results: Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM. Conclusions: Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases. %M 34932003 %R 10.2196/32161 %U https://www.jmir.org/2021/12/e32161 %U https://doi.org/10.2196/32161 %U http://www.ncbi.nlm.nih.gov/pubmed/34932003 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e26767 %T The Use of Smart Speakers in Care Home Residents: Implementation Study %A Edwards,Katie J %A Jones,Ray B %A Shenton,Deborah %A Page,Toni %A Maramba,Inocencio %A Warren,Alison %A Fraser,Fiona %A Križaj,Tanja %A Coombe,Tristan %A Cowls,Hazel %A Chatterjee,Arunangsu %+ Centre for Health Technology, University of Plymouth, Desk 6, Formation Zone, Health and Wellbeing Innovation Centre, The Knowledge Spa, Plymouth, TR1 3HD, United Kingdom, 44 07432155243, katie.edwards@plymouth.ac.uk %K voice-activated technology %K smart speaker %K care home %K technology-enabled care %K older people %K learning disability %K digital technology %K consumer device %K smart device %D 2021 %7 20.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of smart speakers to improve well-being had been trialed in social care by others; however, we were not aware of their implementation in most care homes across a region in the Southwest of the United Kingdom. For the widespread adoption of new technology, it must be locally demonstrable and become normalized. Objective: The aim of this study was to install smart speakers in care homes in a rural and coastal region and to explore if and how the devices were being used, the barriers to their implementation, and their potential benefits. Methods: Email, workshops, drop-in sessions, phone, and cold calling was used to contact all 230 care homes, offering a free smart speaker and some advisory support. Care homes accepting the devices were asked to complete a feedback diary. Nonresponse rate for diary completion was high and was thus supplemented with a telephone survey. Results: Over the course of 7 months, we installed 156 devices in 92 care homes for older people, 50 devices for people with physical or mental health needs, and 8 for others. The devices were used mainly for music but also for poetry, recipes, light controls, jokes, and video calls. Care home managers reported the benefits for the residents, including enhanced engagement with home activities, enjoyment, calming effects, and the acquisition of new skills. Implementation problems included internet connectivity, staff capacity, and skills. Conclusions: Affordable consumer devices such as smart speakers should be installed in all care homes to benefit residents. Voice-activated technologies are easy to use and promote interaction. This study indicates that implementation in care homes was possible and that smart speakers had multifaceted benefits for residents and staff. Most care homes in this region now use smart speakers for their residents, thereby normalizing this practice. %M 34932010 %R 10.2196/26767 %U https://www.jmir.org/2021/12/e26767 %U https://doi.org/10.2196/26767 %U http://www.ncbi.nlm.nih.gov/pubmed/34932010 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 11 %P e33572 %T Empowering Dementia Carers With an iSupport Virtual Assistant (e-DiVA) in Asia-Pacific Regional Countries: Protocol for a Pilot Multisite Randomized Controlled Trial %A Nguyen,Tuan Anh %A Tran,Kham %A Esterman,Adrian %A Brijnath,Bianca %A Xiao,Lily Dongxia %A Schofield,Penelope %A Bhar,Sunil %A Wickramasinghe,Nilmini %A Sinclair,Ronald %A Dang,Thu Ha %A Cullum,Sarah %A Turana,Yuda %A Hinton,Ladson %A Seeher,Katrin %A Andrade,Andre Q %A Crotty,Maria %A Kurrle,Susan %A Freel,Stefanie %A Pham,Thang %A Nguyen,Thanh Binh %A Brodaty,Henry %+ Social Gerontology Division, National Ageing Research Institute, 34-54 Poplar Road, Gate 4, Building 9, Melbourne, 3050, Australia, 61 3 8387 2305, t.nguyen@nari.edu.au %K Dementia %K informal carer %K iSupport %K virtual assistant %K digital health %D 2021 %7 16.11.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Dementia is a global public health priority with an estimated prevalence of 150 million by 2050, nearly two-thirds of whom will live in the Asia-Pacific region. Dementia creates significant care needs for people with the disease, their families, and carers. iSupport is a self-help platform developed by the World Health Organization (WHO) to provide education, skills training, and support to dementia carers. It has been adapted in some contexts (Australia, India, the Netherlands, and Portugal). Carers using the existing adapted versions have identified the need to have a more user-friendly version that enables them to identify solutions for immediate problems quickly in real time. The iSupport virtual assistant (iSupport VA) is being developed to address this gap and will be evaluated in a randomized controlled trial (RCT). Objective: This paper reports the protocol of a pilot RCT evaluating the iSupport VA. Methods: Seven versions of iSupport VA will be evaluated in Australia, Indonesia, New Zealand, and Vietnam in a pilot RCT. Feasibility, acceptability, intention to use, and preliminary impact on carer-perceived stress of the iSupport VA intervention will be assessed. Results: This study was funded by the e-ASIA Joint Research Program in November 2020. From January to July 2023, we will enroll 140 dementia carers (20 carers per iSupport VA version) for the pilot RCT. The study has been approved by the Human Research Committee, University of South Australia, Australia (203455). Conclusions: This protocol outlines how a technologically enhanced version of the WHO iSupport program—the iSupport VA—will be evaluated. The findings from this intervention study will provide evidence on the feasibility and acceptability of the iSupport VA intervention, which will be the basis for conducting a full RCT to assess the effectiveness of the iSupport VA. The study will be an important reference for countries planning to adapt and enhance the WHO iSupport program using digital health solutions. Trial Registration: Australian New Zealand Clinical Trials Registry ACTRN12621001452886; https://tinyurl.com/afum5tjz International Registered Report Identifier (IRRID): PRR1-10.2196/33572 %M 34783660 %R 10.2196/33572 %U https://www.researchprotocols.org/2021/11/e33572 %U https://doi.org/10.2196/33572 %U http://www.ncbi.nlm.nih.gov/pubmed/34783660 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e30704 %T Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment %A Bickmore,Timothy W %A Ólafsson,Stefán %A O'Leary,Teresa K %+ Khoury College of Computer Sciences, Northeastern University, 360 Huntington Ave, 524 ISEC, Boston, MA, 02115, United States, 1 6173735477, t.bickmore@northeastern.edu %K conversational assistant %K conversational interface %K dialogue system %K medical error %K patient safety %K risk mitigation %K warnings %K disclaimers %K grounding %K explainability %K mobile phone %D 2021 %7 9.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. Objective: The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. Methods: Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant’s response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message—or not—telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction. Results: A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (χ21=3.1; P=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (χ21=43.5; P=.001). Conclusions: Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice. %M 34751661 %R 10.2196/30704 %U https://www.jmir.org/2021/11/e30704 %U https://doi.org/10.2196/30704 %U http://www.ncbi.nlm.nih.gov/pubmed/34751661 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 9 %P e26361 %T Voice Assistant Reminders and the Latency of Scheduled Medication Use in Older Adults With Pain: Descriptive Feasibility Study %A Shade,Marcia %A Rector,Kyle %A Kupzyk,Kevin %+ College of Nursing, University of Nebraska Medical Center, 985330 Nebraska Medical Center, Omaha, NE, 68198, United States, 1 4025596641, marcia.shade@unmc.edu %K adherence %K pain medications %K older adults %K reminders %K mHealth %K voice assistants %D 2021 %7 28.9.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Pain is difficult to manage in older adults. It has been recommended that pain management in older adults should include both nonpharmacologic and pharmacologic strategies. Unfortunately, nonadherence to pain medication is more prevalent than nonadherence to any other chronic disease treatment. Technology-based reminders have some benefit for medication adherence, but adherence behavior outcomes have mostly been verified by self-reports. Objective: We aimed to describe objective medication adherence and the latency of medication use after a voice assistant reminder prompted participants to take pain medications for chronic pain. Methods: A total of 15 older adults created a voice assistant reminder for taking scheduled pain medications. A subsample of 5 participants were randomly selected to participate in a feasibility study, in which a medication event monitoring system for pain medications was used to validate medication adherence as a health outcome. Data on the subsample’s self-assessed pain intensity, pain interference, concerns and necessity beliefs about pain medications, self-confidence in managing pain, and medication implementation adherence were analyzed. Results: In the 5 participants who used the medication event monitoring system, the overall latency between voice assistant reminder deployment and the medication event (ie, medication bottle cap opening) was 55 minutes. The absolute latency (before or after the reminder) varied among the participants. The shortest average time taken to open the cap after the reminder was 17 minutes, and the longest was 4.5 hours. Of the 168 voice assistant reminders for scheduled pain medications, 25 (14.6%) resulted in the opening of MEMS caps within 5 minutes of the reminder, and 107 (63.7%) resulted in the opening of MEMS caps within 30 minutes of the reminder. Conclusions: Voice assistant reminders may help cue patients to take scheduled medications, but the timing of medication use may vary. The timing of medication use may influence treatment effectiveness. Tracking the absolute latency time of medication use may be a helpful method for assessing medication adherence. Medication event monitoring may provide additional insight into medication implementation adherence during the implementation of mobile health interventions. %M 34581677 %R 10.2196/26361 %U https://formative.jmir.org/2021/9/e26361 %U https://doi.org/10.2196/26361 %U http://www.ncbi.nlm.nih.gov/pubmed/34581677 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 9 %P e24352 %T Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review %A Flanagan,Olivia %A Chan,Amy %A Roop,Partha %A Sundram,Frederick %+ Department of Psychological Medicine, Faculty of Medical and Health Sciences, University of Auckland, Building 507, Level 3, 28 Park Avenue, Grafton, Auckland, 1023, New Zealand, 64 9 923 7521, f.sundram@auckland.ac.nz %K smartphone %K data science %K speech patterns %K mood disorders %K diagnosis %K monitoring %D 2021 %7 17.9.2021 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: Mood disorders are commonly underrecognized and undertreated, as diagnosis is reliant on self-reporting and clinical assessments that are often not timely. Speech characteristics of those with mood disorders differs from healthy individuals. With the wide use of smartphones, and the emergence of machine learning approaches, smartphones can be used to monitor speech patterns to help the diagnosis and monitoring of mood disorders. Objective: The aim of this review is to synthesize research on using speech patterns from smartphones to diagnose and monitor mood disorders. Methods: Literature searches of major databases, Medline, PsycInfo, EMBASE, and CINAHL, initially identified 832 relevant articles using the search terms “mood disorders”, “smartphone”, “voice analysis”, and their variants. Only 13 studies met inclusion criteria: use of a smartphone for capturing voice data, focus on diagnosing or monitoring a mood disorder(s), clinical populations recruited prospectively, and in the English language only. Articles were assessed by 2 reviewers, and data extracted included data type, classifiers used, methods of capture, and study results. Studies were analyzed using a narrative synthesis approach. Results: Studies showed that voice data alone had reasonable accuracy in predicting mood states and mood fluctuations based on objectively monitored speech patterns. While a fusion of different sensor modalities revealed the highest accuracy (97.4%), nearly 80% of included studies were pilot trials or feasibility studies without control groups and had small sample sizes ranging from 1 to 73 participants. Studies were also carried out over short or varying timeframes and had significant heterogeneity of methods in terms of the types of audio data captured, environmental contexts, classifiers, and measures to control for privacy and ambient noise. Conclusions: Approaches that allow smartphone-based monitoring of speech patterns in mood disorders are rapidly growing. The current body of evidence supports the value of speech patterns to monitor, classify, and predict mood states in real time. However, many challenges remain around the robustness, cost-effectiveness, and acceptability of such an approach and further work is required to build on current research and reduce heterogeneity of methodologies as well as clinical evaluation of the benefits and risks of such approaches. %M 34533465 %R 10.2196/24352 %U https://mhealth.jmir.org/2021/9/e24352 %U https://doi.org/10.2196/24352 %U http://www.ncbi.nlm.nih.gov/pubmed/34533465 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 7 %P e27327 %T Medication Adherence Reminder System for Virtual Home Assistants: Mixed Methods Evaluation Study %A Corbett,Cynthia F %A Combs,Elizabeth M %A Chandarana,Peyton S %A Stringfellow,Isabel %A Worthy,Karen %A Nguyen,Thien %A Wright,Pamela J %A O'Kane,Jason M %+ College of Nursing, University of South Carolina, 1601 Greene St, Columbia, SC, 29208, United States, 1 8035766275, corbett@sc.edu %K medication adherence %K medication %K virtual home assistants %K virtual assistant %K public health %K health care costs %K Echo device %K device usability %K digital health %K mobile phone %D 2021 %7 13.7.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Medication nonadherence is a global public health challenge that results in suboptimal health outcomes and increases health care costs. Forgetting to take medicines is one of the most common reasons for unintentional medication nonadherence. Research findings indicate that voice-activated virtual home assistants, such as Amazon Echo and Google Home devices, may be useful in promoting medication adherence. Objective: This study aims to create a medication adherence app (skill), MedBuddy, for Amazon Echo devices and measure the use, usability, and usefulness of this medication-taking reminder skill. Methods: A single-group, mixed methods, cohort feasibility study was conducted with women who took oral contraceptives (N=25). Participants were undergraduate students (age: mean 21.8 years, SD 6.2) at an urban university in the Southeast United States. Participants were given an Amazon Echo Dot with MedBuddy—a new medication reminder skill for Echo devices created by our team—attached to their study account, which they used for 60 days. Participants self-reported their baseline and poststudy medication adherence. MedBuddy use was objectively evaluated by tracking participants’ interactions with MedBuddy through Amazon Alexa. The usability and usefulness of MedBuddy were evaluated through a poststudy interview in which participants responded to both quantitative and qualitative questions. Results: Participants’ interactions with MedBuddy, as tracked through Amazon Alexa, only occurred on half of the study days (mean 50.97, SD 29.5). At study end, participants reported missing their medication less in the past 1 and 6 months compared with baseline (χ21=0.9 and χ21=0.4, respectively; McNemar test: P<.001 for both). However, there was no significant difference in participants’ reported adherence to consistently taking medication within the same 2-hour time frame every day in the past 1 or 6 months at the end of the study compared with baseline (χ21=3.5 and χ21=0.4, respectively; McNemar test: P=.63 and P=.07, respectively). Overall feedback about usability was positive, and participants provided constructive feedback about the skill’s features that could be improved. Participants’ evaluation of MedBuddy’s usefulness was overwhelmingly positive—most (15/23, 65%) said that they would continue using MedBuddy as a medication reminder if provided with the opportunity and that they would recommend it to others. MedBuddy features that participants enjoyed were an external prompt separate from their phone, the ability to hear the reminder prompt from a separate room, multiple reminders, and verbal responses to prompts. Conclusions: The findings of this feasibility study indicate that the MedBuddy medication reminder skill may be useful in promoting medication adherence. However, the skill could benefit from further usability enhancements. %M 34255669 %R 10.2196/27327 %U https://formative.jmir.org/2021/7/e27327 %U https://doi.org/10.2196/27327 %U http://www.ncbi.nlm.nih.gov/pubmed/34255669 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e22959 %T Artificial Intelligence Can Improve Patient Management at the Time of a Pandemic: The Role of Voice Technology %A Jadczyk,Tomasz %A Wojakowski,Wojciech %A Tendera,Michal %A Henry,Timothy D %A Egnaczyk,Gregory %A Shreenivas,Satya %+ Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Ziolowa 45-47, Katowice, 40-635, Poland, 48 512 099 211, tomasz.jadczyk@gmail.com %K artificial intelligence %K conversational agent %K COVID-19 %K virtual care %K voice assistant %K voice chatbot %D 2021 %7 25.5.2021 %9 Viewpoint %J J Med Internet Res %G English %X Artificial intelligence–driven voice technology deployed on mobile phones and smart speakers has the potential to improve patient management and organizational workflow. Voice chatbots have been already implemented in health care–leveraging innovative telehealth solutions during the COVID-19 pandemic. They allow for automatic acute care triaging and chronic disease management, including remote monitoring, preventive care, patient intake, and referral assistance. This paper focuses on the current clinical needs and applications of artificial intelligence–driven voice chatbots to drive operational effectiveness and improve patient experience and outcomes. %M 33999834 %R 10.2196/22959 %U https://www.jmir.org/2021/5/e22959 %U https://doi.org/10.2196/22959 %U http://www.ncbi.nlm.nih.gov/pubmed/33999834 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e25312 %T Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study %A Ermolina,Alena %A Tiberius,Victor %+ Faculty of Economics and Social Sciences, University of Potsdam, August-Bebel-Str 89, Potsdam, 14882, Germany, 49 331 977 ext 3593, tiberius@uni-potsdam.de %K Delphi study %K medical informatics %K voice-controlled intelligent personal assistants %K internet of things %K smart devices %D 2021 %7 9.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Voice-controlled intelligent personal assistants (VIPAs), such as Amazon Echo and Google Home, involve artificial intelligence–powered algorithms designed to simulate humans. Their hands-free interface and growing capabilities have a wide range of applications in health care, covering off-clinic education, health monitoring, and communication. However, conflicting factors, such as patient safety and privacy concerns, make it difficult to foresee the further development of VIPAs in health care. Objective: This study aimed to develop a plausible scenario for the further development of VIPAs in health care to support decision making regarding the procurement of VIPAs in health care organizations. Methods: We conducted a two-stage Delphi study with an internationally recruited panel consisting of voice assistant experts, medical professionals, and representatives of academia, governmental health authorities, and nonprofit health associations having expertise with voice technology. Twenty projections were formulated and evaluated by the panelists. Descriptive statistics were used to derive the desired scenario. Results: The panelists expect VIPAs to be able to provide solid medical advice based on patients’ personal health information and to have human-like conversations. However, in the short term, voice assistants might neither provide frustration-free user experience nor outperform or replace humans in health care. With a high level of consensus, the experts agreed with the potential of VIPAs to support elderly people and be widely used as anamnesis, informational, self-therapy, and communication tools by patients and health care professionals. Although users’ and governments’ privacy concerns are not expected to decrease in the near future, the panelists believe that strict regulations capable of preventing VIPAs from providing medical help services will not be imposed. Conclusions: According to the surveyed experts, VIPAs will show notable technological development and gain more user trust in the near future, resulting in widespread application in health care. However, voice assistants are expected to solely support health care professionals in their daily operations and will not be able to outperform or replace medical staff. %M 33835032 %R 10.2196/25312 %U https://www.jmir.org/2021/4/e25312 %U https://doi.org/10.2196/25312 %U http://www.ncbi.nlm.nih.gov/pubmed/33835032 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e27667 %T Using Speech Data From Interactions With a Voice Assistant to Predict the Risk of Future Accidents for Older Drivers: Prospective Cohort Study %A Yamada,Yasunori %A Shinkawa,Kaoru %A Kobayashi,Masatomo %A Takagi,Hironobu %A Nemoto,Miyuki %A Nemoto,Kiyotaka %A Arai,Tetsuaki %+ IBM Research, Nihonbashi, Hakozaki-cho, Chuo-ku, Tokyo, 103-8510, Japan, 81 80 6706 9381, ysnr@jp.ibm.com %K cognitive impairment %K smart speaker %K speech analysis %K accident %K prevention %K older adults %K prediction %K risk %K assistant %D 2021 %7 8.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With the rapid growth of the older adult population worldwide, car accidents involving this population group have become an increasingly serious problem. Cognitive impairment, which is assessed using neuropsychological tests, has been reported as a risk factor for being involved in car accidents; however, it remains unclear whether this risk can be predicted using daily behavior data. Objective: The objective of this study was to investigate whether speech data that can be collected in everyday life can be used to predict the risk of an older driver being involved in a car accident. Methods: At baseline, we collected (1) speech data during interactions with a voice assistant and (2) cognitive assessment data—neuropsychological tests (Mini-Mental State Examination, revised Wechsler immediate and delayed logical memory, Frontal Assessment Battery, trail making test-parts A and B, and Clock Drawing Test), Geriatric Depression Scale, magnetic resonance imaging, and demographics (age, sex, education)—from older adults. Approximately one-and-a-half years later, we followed up to collect information about their driving experiences (with respect to car accidents) using a questionnaire. We investigated the association between speech data and future accident risk using statistical analysis and machine learning models. Results: We found that older drivers (n=60) with accident or near-accident experiences had statistically discernible differences in speech features that suggest cognitive impairment such as reduced speech rate (P=.048) and increased response time (P=.040). Moreover, the model that used speech features could predict future accident or near-accident experiences with 81.7% accuracy, which was 6.7% higher than that using cognitive assessment data, and could achieve up to 88.3% accuracy when the model used both types of data. Conclusions: Our study provides the first empirical results that suggest analysis of speech data recorded during interactions with voice assistants could help predict future accident risk for older drivers by capturing subtle impairments in cognitive function. %M 33830066 %R 10.2196/27667 %U https://www.jmir.org/2021/4/e27667 %U https://doi.org/10.2196/27667 %U http://www.ncbi.nlm.nih.gov/pubmed/33830066 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e24646 %T Voice Interface Technology Adoption by Patients With Heart Failure: Pilot Comparison Study %A Apergi,Lida Anna %A Bjarnadottir,Margret V %A Baras,John S %A Golden,Bruce L %A Anderson,Kelley M %A Chou,Jiling %A Shara,Nawar %+ Robert H. Smith School of Business, University of Maryland, 7621 Mowatt Ln, College Park, MD, 20742, United States, 1 301 405 3374, lapergi@umd.edu %K heart failure %K telehealth %K voice interface %K conversational agent %K artificial intelligence %K wireless technology %K social determinants of health %K mobile phone %D 2021 %7 1.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Heart failure (HF) is associated with high mortality rates and high costs, and self-care is crucial in the management of the condition. Telehealth can promote patients’ self-care while providing frequent feedback to their health care providers about the patient’s compliance and symptoms. A number of technologies have been considered in the literature to facilitate telehealth in patients with HF. An important factor in the adoption of these technologies is their ease of use. Conversational agent technologies using a voice interface can be a good option because they use speech recognition to communicate with patients. Objective: The aim of this paper is to study the engagement of patients with HF with voice interface technology. In particular, we investigate which patient characteristics are linked to increased technology use. Methods: We used data from two separate HF patient groups that used different telehealth technologies over a 90-day period. Each group used a different type of voice interface; however, the scripts followed by the two technologies were identical. One technology was based on Amazon’s Alexa (Alexa+), and in the other technology, patients used a tablet to interact with a visually animated and voice-enabled avatar (Avatar). Patient engagement was measured as the number of days on which the patients used the technology during the study period. We used multiple linear regression to model engagement with the technology based on patients’ demographic and clinical characteristics and past technology use. Results: In both populations, the patients were predominantly male and Black, had an average age of 55 years, and had HF for an average of 7 years. The only patient characteristic that was statistically different (P=.008) between the two populations was the number of medications they took to manage HF, with a mean of 8.7 (SD 4.0) for Alexa+ and 5.8 (SD 3.4) for Avatar patients. The regression model on the combined population shows that older patients used the technology more frequently (an additional 1.19 days of use for each additional year of age; P=.004). The number of medications to manage HF was negatively associated with use (−5.49; P=.005), and Black patients used the technology less frequently than other patients with similar characteristics (−15.96; P=.08). Conclusions: Older patients’ higher engagement with telehealth is consistent with findings from previous studies, confirming the acceptability of technology in this subset of patients with HF. However, we also found that a higher number of HF medications, which may be correlated with a higher disease burden, is negatively associated with telehealth use. Finally, the lower engagement of Black patients highlights the need for further study to identify the reasons behind this lower engagement, including the possible role of social determinants of health, and potentially create technologies that are better tailored for this population. %M 33792556 %R 10.2196/24646 %U https://mhealth.jmir.org/2021/4/e24646 %U https://doi.org/10.2196/24646 %U http://www.ncbi.nlm.nih.gov/pubmed/33792556 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e25933 %T Voice-Based Conversational Agents for the Prevention and Management of Chronic and Mental Health Conditions: Systematic Literature Review %A Bérubé,Caterina %A Schachner,Theresa %A Keller,Roman %A Fleisch,Elgar %A v Wangenheim,Florian %A Barata,Filipe %A Kowatsch,Tobias %+ Center for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, WEV G 214, Weinbergstrasse 56/58, Zurich, 8092, Switzerland, 41 44 633 8419, berubec@ethz.ch %K voice %K speech %K delivery of health care %K noncommunicable diseases %K conversational agents %K mobile phone %K smart speaker %K monitoring %K support %K chronic disease %K mental health %K systematic literature review %D 2021 %7 29.3.2021 %9 Review %J J Med Internet Res %G English %X Background: Chronic and mental health conditions are increasingly prevalent worldwide. As devices in our everyday lives offer more and more voice-based self-service, voice-based conversational agents (VCAs) have the potential to support the prevention and management of these conditions in a scalable manner. However, evidence on VCAs dedicated to the prevention and management of chronic and mental health conditions is unclear. Objective: This study provides a better understanding of the current methods used in the evaluation of health interventions for the prevention and management of chronic and mental health conditions delivered through VCAs. Methods: We conducted a systematic literature review using PubMed MEDLINE, Embase, PsycINFO, Scopus, and Web of Science databases. We included primary research involving the prevention or management of chronic or mental health conditions through a VCA and reporting an empirical evaluation of the system either in terms of system accuracy, technology acceptance, or both. A total of 2 independent reviewers conducted the screening and data extraction, and agreement between them was measured using Cohen kappa. A narrative approach was used to synthesize the selected records. Results: Of 7170 prescreened papers, 12 met the inclusion criteria. All studies were nonexperimental. The VCAs provided behavioral support (n=5), health monitoring services (n=3), or both (n=4). The interventions were delivered via smartphones (n=5), tablets (n=2), or smart speakers (n=3). In 2 cases, no device was specified. A total of 3 VCAs targeted cancer, whereas 2 VCAs targeted diabetes and heart failure. The other VCAs targeted hearing impairment, asthma, Parkinson disease, dementia, autism, intellectual disability, and depression. The majority of the studies (n=7) assessed technology acceptance, but only few studies (n=3) used validated instruments. Half of the studies (n=6) reported either performance measures on speech recognition or on the ability of VCAs to respond to health-related queries. Only a minority of the studies (n=2) reported behavioral measures or a measure of attitudes toward intervention-targeted health behavior. Moreover, only a minority of studies (n=4) reported controlling for participants’ previous experience with technology. Finally, risk bias varied markedly. Conclusions: The heterogeneity in the methods, the limited number of studies identified, and the high risk of bias show that research on VCAs for chronic and mental health conditions is still in its infancy. Although the results of system accuracy and technology acceptance are encouraging, there is still a need to establish more conclusive evidence on the efficacy of VCAs for the prevention and management of chronic and mental health conditions, both in absolute terms and in comparison with standard health care. %M 33658174 %R 10.2196/25933 %U https://www.jmir.org/2021/3/e25933 %U https://doi.org/10.2196/25933 %U http://www.ncbi.nlm.nih.gov/pubmed/33658174 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 8 %N 1 %P e23006 %T Attitudes Toward the Use of Voice-Assisted Technologies Among People With Parkinson Disease: Findings From a Web-Based Survey %A Duffy,Orla %A Synnott,Jonathan %A McNaney,Roisin %A Brito Zambrano,Paola %A Kernohan,W George %+ School of Health Sciences, Faculty of Life and Health Sciences, Ulster University, Jordanstown, Shore Road, Newtownabbey, BT37 0QB, United Kingdom, 44 2890366925, od.duffy@ulster.ac.uk %K Parkinson disease %K mobile phone %K telerehabilitation %K eHealth %D 2021 %7 11.3.2021 %9 Original Paper %J JMIR Rehabil Assist Technol %G English %X Background: Speech problems are common in people living with Parkinson disease (PD), limiting communication and ultimately affecting their quality of life. Voice-assisted technology in health and care settings has shown some potential in small-scale studies to address such problems, with a retrospective analysis of user reviews reporting anecdotal communication effects and promising usability features when using this technology for people with a range of disabilities. However, there is a need for research to establish users’ perspectives on the potential contribution of voice-assisted technology for people with PD. Objective: This study aims to explore the attitudes toward the use of voice-assisted technology for people with PD. Methods: A survey was approved for dissemination by a national charity, Parkinson’s UK, to be completed on the web by people living with the condition. The survey elicited respondent demographics, PD features, voice difficulties, digital skill capability, smart technology use, voice-assisted technology ownership and use, confidentiality, and privacy concerns. Data were analyzed using descriptive statistics and summative content analysis of free-text responses. Results: Of 290 participants, 79.0% (n=229) indicated that they or others had noticed changes in their speech or voice because of the symptoms of their condition. Digital skills and awareness were reported on 11 digital skills such as the ability to find a website you have visited before. Most participants (n=209, 72.1%) reported being able to perform at least 10 of these 11 tasks. Similarly, of 70.7% (n=205) participants who owned a voice-assisted device, most of them (166/205, 80.9%) used it regularly, with 31.3% (52/166) reporting that they used the technology specifically to address the needs associated with their PD. Of these 166 users, 54.8% (n=91) sometimes, rarely, or never had to repeat themselves when using the technology. When asked about speech changes since they started using it, 25% (27/108) of participants noticed having to repeat themselves less and 14.8% (16/108) perceived their speech to be clearer. Of the 290 respondents, 90.7% (n=263) were not concerned, or only slightly concerned, about privacy and confidentiality. Conclusions: Having been added to the homes of Western society, domestic voice assist devices are now available to assist those with communication problems. People with PD reported a high digital capability, albeit those who responded to a web-based survey. Most people have embraced voice-assisted technology, find it helpful and usable, and some have found benefit to their speech. Speech and language therapists may have a virtual ally that is already in the patient’s home to support future therapy provision. %M 33704072 %R 10.2196/23006 %U https://rehab.jmir.org/2021/1/e23006 %U https://doi.org/10.2196/23006 %U http://www.ncbi.nlm.nih.gov/pubmed/33704072 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e22229 %T An Interactive Voice Response Software to Improve the Quality of Life of People Living With HIV in Uganda: Randomized Controlled Trial %A Byonanebye,Dathan Mirembe %A Nabaggala,Maria S %A Naggirinya,Agnes Bwanika %A Lamorde,Mohammed %A Oseku,Elizabeth %A King,Rachel %A Owarwo,Noela %A Laker,Eva %A Orama,Richard %A Castelnuovo,Barbara %A Kiragga,Agnes %A Parkes-Ratanshi,Rosalind %+ Cambridge Institute of Public Health, School of Clinical Medicine, University of Cambridge, Forvie Site, Cambridge Biomedical Campus, Cambridge, CB2 0SR, United Kingdom, 44 7817739450, rp549@medschl.cam.ac.uk %K mHealth %K HIV %K quality of life %K interactive voice response %K mobile health %K digital health %D 2021 %7 11.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Following the successful scale-up of antiretroviral therapy (ART), the focus is now on ensuring good quality of life (QoL) and sustained viral suppression in people living with HIV. The access to mobile technology in the most burdened countries is increasing rapidly, and therefore, mobile health (mHealth) technologies could be leveraged to improve QoL in people living with HIV. However, data on the impact of mHealth tools on the QoL in people living with HIV are limited to the evaluation of SMS text messaging; these are infeasible in high-illiteracy settings. Objective: The primary and secondary outcomes were to determine the impact of interactive voice response (IVR) technology on Medical Outcomes Study HIV QoL scores and viral suppression at 12 months, respectively. Methods: Within the Call for Life study, ART-experienced and ART-naïve people living with HIV commencing ART were randomized (1:1 ratio) to the control (no IVR support) or intervention arm (daily adherence and pre-appointment reminders, health information tips, and option to report symptoms). The software evaluated was Call for Life Uganda, an IVR technology that is based on the Mobile Technology for Community Health open-source software. Eligibility criteria for participation included access to a phone, fluency in local languages, and provision of consent. The differences in differences (DIDs) were computed, adjusting for baseline HIV RNA and CD4. Results: Overall, 600 participants (413 female, 68.8%) were enrolled and followed-up for 12 months. In the intervention arm of 300 participants, 298 (99.3%) opted for IVR and 2 (0.7%) chose SMS text messaging as the mode of receiving reminders and health tips. At 12 months, there was no overall difference in the QoL between the intervention and control arms (DID=0.0; P=.99) or HIV RNA (DID=0.01; P=.94). At 12 months, 124 of the 256 (48.4%) active participants had picked up at least 50% of the calls. In the active intervention participants, high users (received >75% of reminders) had overall higher QoL compared to low users (received <25% of reminders) (92.2 versus 87.8, P=.02). Similarly, high users also had higher QoL scores in the mental health domain (93.1 versus 86.8, P=.008) and better appointment keeping. Similarly, participants with moderate use (51%-75%) had better viral suppression at 12 months (80/94, 85% versus 11/19, 58%, P=.006). Conclusions: Overall, there was high uptake and acceptability of the IVR tool. While we found no overall difference in the QoL and viral suppression between study arms, people living with HIV with higher usage of the tool showed greater improvements in QoL, viral suppression, and appointment keeping. With the declining resources available to HIV programs and the increasing number of people living with HIV accessing ART, IVR technology could be used to support patient care. The tool may be helpful in situations where physical consultations are infeasible, including the current COVID epidemic. Trial Registration: ClinicalTrials.gov NCT02953080; https://clinicaltrials.gov/ct2/show/NCT02953080 %M 33570497 %R 10.2196/22229 %U https://mhealth.jmir.org/2021/2/e22229 %U https://doi.org/10.2196/22229 %U http://www.ncbi.nlm.nih.gov/pubmed/33570497 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e22061 %T Implementation of an Interactive Voice Response System for Cancer Awareness in Uganda: Mixed Methods Study %A Kabukye,Johnblack K %A Ilozumba,Onaedo %A Broerse,Jacqueline E W %A de Keizer,Nicolette %A Cornet,Ronald %+ Uganda Cancer Institute, Upper Mulago Hill Road, Kampala, PO Box 3935, Kampa, Uganda, 256 700447351, jkabukye@gmail.com %K telemedicine %K medical oncology %K health promotion %K low-and-middle-income countries %K participatory research %K mobile phone %D 2021 %7 26.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Cancer awareness is crucial for cancer care and prevention. However, cancer awareness in Uganda is low, and access to cancer information is limited. Objective: This study aims to (1) understand the cancer awareness situation in Uganda (perceptions, beliefs, information needs, and challenges to accessing cancer information) and opinions about interactive voice response (IVR) systems; (2) develop cancer awareness messages and implement them in an IVR system; and (3) evaluate user acceptance and use of the IVR system. Methods: A participatory design approach was adopted. To understand cancer awareness needs and challenges, 3 interviews and 7 focus group discussions (FGDs) were conducted with cancer health care providers, patients with cancer, caregivers and survivors, administrators, and lay citizens (n=73). On the basis of the resulting qualitative data, audio messages addressing cancer information needs were developed and implemented in an IVR system. The system and messages were tested with users (n=12) during 2 co-design workshops before final rollout. Finally, the system was evaluated over 6 months after going live, using call records and user feedback from telephone interviews with callers (n=40). Results: The cancer information needs included general topics such as what cancer is, what causes it, cancer screening and diagnosis, cancer treatment, and practical information on what to expect during cancer care. There were also myths and misconceptions that need to be addressed, such as that cancer is due to witchcraft and has no treatment. Information on COVID-19 was also sought after following the outbreak. We developed 20 audio cancer messages (approximately 2 minutes each) in English and Luganda, along with 14 IVR navigation instructions. These were implemented in an IVR system with 24/7 availability from all over Uganda via a toll-free multi-channel telephone number. The total number of calls made to the IVR system 6 months after going live was 3820. Of these, 2437 (63.8%) lasted at least 30 seconds and were made from 1230 unique telephone numbers. There were 191 voice messages and 760 calls to live agents, most of which (681/951, 71.6%) were in Luganda. Call volumes peaked following advertisement of the system and lockdowns due to COVID-19. Participants were generally familiar with IVR technology, and caller feedback was largely positive. Cited benefits included convenience, toll-free access, and detailed information. Recommendations for improvement of the system included adding live agents and marketing of the system to target users. Conclusions: IVR technology provides an acceptable and accessible method for providing cancer information to patients and the general public in Uganda. However, a need remains for health system reforms to provide additional cancer information sources and improve cancer care services in general. %M 33496672 %R 10.2196/22061 %U http://mhealth.jmir.org/2021/1/e22061/ %U https://doi.org/10.2196/22061 %U http://www.ncbi.nlm.nih.gov/pubmed/33496672 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 1 %P e19088 %T Physical Activity Evaluation Using a Voice Recognition App: Development and Validation Study %A Namba,Hideyuki %+ Physical Education Lab., College of Science and Technology, Nihon University, 7-24-1 Narashinodai-Funabashi, Chiba, 274-8501, Japan, 81 47 469 5518, nanba.hideyuki@nihon-u.ac.jp %K voice recognition %K smartphone %K physical activity %K accelerometer %K application %D 2021 %7 21.1.2021 %9 Original Paper %J JMIR Biomed Eng %G English %X Background: Historically, the evaluation of physical activity has involved a variety of methods such as the use of questionnaires, accelerometers, behavior records, and global positioning systems, each according to the purpose of the evaluation. The use of web-based physical activity evaluation systems has been proposed as an easy method for collecting physical activity data. Voice recognition technology not only eliminates the need for questionnaires during physical activity evaluation but also enables users to record their behavior without physically touching electronic devices. The use of a web-based voice recognition system might be an effective way to record physical activity and behavior. Objective: The purpose of this study was to develop a physical activity evaluation app to record behavior using voice recognition technology and to examine the app’s validity by comparing data obtained using both the app and an accelerometer simultaneously. Methods: A total of 20 participants (14 men, 6 women; mean age 19.1 years, SD 0.9) wore a 3-axis accelerometer and inputted behavioral data into their smartphones for a period of 7 days. We developed a behavior-recording system with a voice recognition function using a voice recognition application programming interface. The exercise intensity was determined from the text data obtained by the voice recognition program. The measure of intensity was metabolic equivalents (METs). Results: From the voice input data of the participants, 601 text-converted data could be confirmed, of which 471 (78.4%) could be automatically converted into behavioral words. In the time-matched analysis, the mean daily METs values measured by the app and the accelerometer were 1.64 (SD 0.20) and 1.63 (SD 0.20), respectively, between which there was no significant difference (P=.57). There was a significant correlation between the average METs obtained from the voice recognition app and the accelerometer in the time-matched analysis (r=0.830, P<.001). In the Bland-Altman plot for METs measured by the voice recognition app as compared with METs measured by accelerometer, the mean difference between the two methods was very small (0.02 METs), with 95% limits of agreement from –0.26 to 0.22 METs between the two methods. Conclusions: The average METs value measured by the voice recognition app was consistent with that measured by the 3-axis accelerometer and, thus, the data gathered by the two measurement methods showed a high correlation. The voice recognition method also demonstrated the ability of the system to measure the physical activity of a large number of people at the same time with less burden on the participants. Although there were still issues regarding the improvement of automatic text data classification technology and user input compliance, this research proposes a new method for evaluating physical activity using voice recognition technology. %M 38907383 %R 10.2196/19088 %U http://biomedeng.jmir.org/2021/1/e19088/ %U https://doi.org/10.2196/19088 %U http://www.ncbi.nlm.nih.gov/pubmed/38907383 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e20427 %T Exploring How Older Adults Use a Smart Speaker–Based Voice Assistant in Their First Interactions: Qualitative Study %A Kim,Sunyoung %+ School of Communication and Information, Rutgers University, 4 Huntington St, New Brunswick, NJ, 08901-1071, United States, 1 848 932 7585, sunyoung.kim@rutgers.edu %K older adults %K voice assistant %K smart speaker %K technology acceptance %K quality of life %D 2021 %7 13.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Smart speaker–based voice assistants promise support for the aging population, with the advantages of hands-free and eyes-free interaction modalities to handle requests. However, little is known about how older adults perceive the benefits of this type of device. Objective: This study investigates how older adults experience and respond to a voice assistant when they first interact with it. Because first impressions act as strong predictors of the overall attitude and acceptability of new technologies, it is important to understand the user experiences of first exposure. Methods: We conducted semistructured interviews with 18 people 74 years and older who had never used a smart speaker before, investigating the patterns of use, usability issues, and perspectives that older adults have when using a voice assistant for the first time. Results: The overall first response to a voice assistant was positive, thanks to the simplicity of a speech-based interaction. In particular, a positive and polite response to complete the interaction with a voice assistant was prevalent, such as expressing gratitude or giving feedback about the quality of answers. Two predominant topics of commands made in the first interaction include asking health care–related questions and streaming music. However, most of the follow-up reactions were unfavorable because of the difficulty in constructing a structured sentence for a command; misperceptions about how a voice assistant operates; and concerns about privacy, security, and financial burdens. Overall, a speech-based interaction was perceived to be beneficial owing to its efficiency and convenience, but no other benefits were perceived. Conclusions: On the basis of the findings, we discuss design implications that can positively influence older adults' first experiences with a voice assistant, including helping better understand how a voice assistant works, incorporating mistakes and common interaction patterns into its design, and providing features tailored to the needs of older adults. %M 33439130 %R 10.2196/20427 %U http://mhealth.jmir.org/2021/1/e20427/ %U https://doi.org/10.2196/20427 %U http://www.ncbi.nlm.nih.gov/pubmed/33439130 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 1 %P e24045 %T Clinical Advice by Voice Assistants on Postpartum Depression: Cross-Sectional Investigation Using Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana %A Yang,Samuel %A Lee,Jennifer %A Sezgin,Emre %A Bridge,Jeffrey %A Lin,Simon %+ The Ohio State University University Wexner Medical Center, 410 W 10th Ave, Columbus, OH, United States, 1 614 355 3703, samuel.yang@nationwidechildrens.org %K voice assistant %K virtual assistant %K conversational agent %K postpartum depression %K mobile health %K mental health %D 2021 %7 11.1.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: A voice assistant (VA) is inanimate audio-interfaced software augmented with artificial intelligence, capable of 2-way dialogue, and increasingly used to access health care advice. Postpartum depression (PPD) is a common perinatal mood disorder with an annual estimated cost of $14.2 billion. Only a small percentage of PPD patients seek care due to lack of screening and insufficient knowledge of the disease, and this is, therefore, a prime candidate for a VA-based digital health intervention. Objective: In order to understand the capability of VAs, our aim was to assess VA responses to PPD questions in terms of accuracy, verbal response, and clinically appropriate advice given. Methods: This cross-sectional study examined four VAs (Apple Siri, Amazon Alexa, Google Assistant, and Microsoft Cortana) installed on two mobile devices in early 2020. We posed 14 questions to each VA that were retrieved from the American College of Obstetricians and Gynecologists (ACOG) patient-focused Frequently Asked Questions (FAQ) on PPD. We scored the VA responses according to accuracy of speech recognition, presence of a verbal response, and clinically appropriate advice in accordance with ACOG FAQ, which were assessed by two board-certified physicians. Results: Accurate recognition of the query ranged from 79% to 100%. Verbal response ranged from 36% to 79%. If no verbal response was given, queries were treated like a web search between 33% and 89% of the time. Clinically appropriate advice given by VA ranged from 14% to 29%. We compared the category proportions using the Fisher exact test. No single VA statistically outperformed other VAs in the three performance categories. Additional observations showed that two VAs (Google Assistant and Microsoft Cortana) included advertisements in their responses. Conclusions: While the best performing VA gave clinically appropriate advice to 29% of the PPD questions, all four VAs taken together achieved 64% clinically appropriate advice. All four VAs performed well in accurately recognizing a PPD query, but no VA achieved even a 30% threshold for providing clinically appropriate PPD information. Technology companies and clinical organizations should partner to improve guidance, screen patients for mental health disorders, and educate patients on potential treatment. %M 33427680 %R 10.2196/24045 %U http://mhealth.jmir.org/2021/1/e24045/ %U https://doi.org/10.2196/24045 %U http://www.ncbi.nlm.nih.gov/pubmed/33427680 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 12 %P e20456 %T Readiness for Voice Technology in Patients With Cardiovascular Diseases: Cross-Sectional Study %A Kowalska,Małgorzata %A Gładyś,Aleksandra %A Kalańska-Łukasik,Barbara %A Gruz-Kwapisz,Monika %A Wojakowski,Wojciech %A Jadczyk,Tomasz %+ Department of Cardiology and Structural Heart Diseases, Medical University of Silesia, Ziolowa 45-47, Katowice, 40-635, Poland, 48 322523930, tomasz.jadczyk@gmail.com %K voice technology %K smart speaker %K acceptance %K telehealth %K cardiovascular diseases %K chatbot %D 2020 %7 17.12.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The clinical application of voice technology provides novel opportunities in the field of telehealth. However, patients’ readiness for this solution has not been investigated among patients with cardiovascular diseases (CVD). Objective: This paper aims to evaluate patients’ anticipated experiences regarding telemedicine, including voice conversational agents combined with provider-driven support delivered by phone. Methods: A cross-sectional study enrolled patients with chronic CVD who were surveyed using a validated investigator-designed questionnaire combining 19 questions (eg, demographic data, medical history, preferences for using telehealth services). Prior to the survey, respondents were educated on the telemedicine services presented in the questionnaire while being assisted by a medical doctor. Responses were then collected and analyzed, and multivariate logistic regression was used to identify predictors of willingness to use voice technology. Results: In total, 249 patients (mean age 65.3, SD 13.8 years; 158 [63.5%] men) completed the questionnaire, which showed good repeatability in the validation procedure. Of the 249 total participants, 209 (83.9%) reported high readiness to receive services allowing for remote contact with a cardiologist (176/249, 70.7%) and telemonitoring of vital signs (168/249, 67.5%). The voice conversational agents combined with provider-driven support delivered by phone were shown to be highly anticipated by patients with CVD. The readiness to use telehealth was statistically higher in people with previous difficulties accessing health care (OR 2.920, 95% CI 1.377-6.192) and was most frequent in city residents and individuals reporting a higher education level. The age and sex of the respondents did not impact the intention to use voice technology (P=.20 and P=.50, respectively). Conclusions: Patients with cardiovascular diseases, including both younger and older individuals, declared high readiness for voice technology. %M 33331824 %R 10.2196/20456 %U http://www.jmir.org/2020/12/e20456/ %U https://doi.org/10.2196/20456 %U http://www.ncbi.nlm.nih.gov/pubmed/33331824 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e20317 %T Design and Usability Evaluation of Mobile Voice-Added Food Reporting for Elderly People: Randomized Controlled Trial %A Liu,Ying-Chieh %A Chen,Chien-Hung %A Lin,Yu-Sheng %A Chen,Hsin-Yun %A Irianti,Denisa %A Jen,Ting-Ni %A Yeh,Jou-Yin %A Chiu,Sherry Yueh-Hsia %+ Department of Health Care Management and Healthy Aging Research Center, College of Management, Chang Gung University, No. 259, Wen-Hwa 1st Road, 333 Kwei-Shan, Taoyuan, Taiwan, 886 3 2118800 ext 5250, sherrychiu@mail.cgu.edu.tw %K voice-added design %K food report %K elderly %K usability evaluation %K automatic speech recognition %K mHealth %K randomized trial %D 2020 %7 28.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Advances in voice technology have raised new possibilities for apps related to daily health maintenance. However, the usability of such technologies for older users remains unclear and requires further investigation. Objective: We designed and evaluated two innovative mobile voice-added apps for food intake reporting, namely voice-only reporting (VOR) and voice-button reporting (VBR). Each app features a unique interactive procedure for reporting food intake. With VOR, users verbally report the main contents of each dish, while VBR provides both voice and existing touch screen inputs for food intake reporting. The relative usability of the two apps was assessed through the metrics of accuracy, efficiency, and user perception. Methods: The two mobile apps were compared in a head-to-head parallel randomized trial evaluation. A group of 57 adults aged 60-90 years (12 male and 45 female participants) was recruited from a retirement community and randomized into two experimental groups, that is, VOR (n=30) and VBR (n=27) groups. Both groups were tested using the same set of 17 food items including dishes and beverages selected and allocated to present distinct breakfast, lunch, and dinner meals. All participants used a 7-inch tablet computer for the test. The resulting data were analyzed to evaluate reporting accuracy and time efficiency, and the system usability scale (SUS) was used to measure user perception. Results: For eight error types identified in the experiment, the VBR group participants were significantly (P<.001) more error prone owing to the required use of button-tapping actions. The highest error rates in the VOR group were related to incomprehensible reporting speech (28/420, 6.7%), while the highest error rates in the VBR group were related to failure to make required button taps (39/378, 10.3%). The VOR group required significantly (P<.001) less time to complete food reporting. The overall subjective reactions of the two groups based on the SUS surpassed the benchmark and were not significantly different (P=.20). Conclusions: Experimental results showed that VOR outperformed VBR, suggesting that voice-only food input reporting is preferable for elderly users. Voice-added apps offer a potential mechanism for the self-management of dietary intake by elderly users. Our study contributes an evidence-based evaluation of prototype design and selection under a user-centered design model. The results provide a useful reference for selecting optimal user interaction design. Trial Registration: International Standard Randomized Controlled Trial Registry ISRCTN17335889; http://www.isrctn.com/ISRCTN17335889. %M 32985999 %R 10.2196/20317 %U http://mhealth.jmir.org/2020/9/e20317/ %U https://doi.org/10.2196/20317 %U http://www.ncbi.nlm.nih.gov/pubmed/32985999 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e18431 %T Investigating the Accessibility of Voice Assistants With Impaired Users: Mixed Methods Study %A Masina,Fabio %A Orso,Valeria %A Pluchino,Patrik %A Dainese,Giulia %A Volpato,Stefania %A Nelini,Cristian %A Mapelli,Daniela %A Spagnolli,Anna %A Gamberini,Luciano %+ Human Inspired Technologies Research Center, University of Padova, Via Luzzatti, 4, Padova, Italy, 39 049 827 5796, luciano.gamberini@unipd.it %K voice assistants %K accessibility %K cognitive functions %K disability %K ambient assisted living %D 2020 %7 25.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective: The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods: A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results: For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions: Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities. %M 32975525 %R 10.2196/18431 %U http://www.jmir.org/2020/9/e18431/ %U https://doi.org/10.2196/18431 %U http://www.ncbi.nlm.nih.gov/pubmed/32975525 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e16373 %T Electronic Health Record Portal Messages and Interactive Voice Response Calls to Improve Rates of Early Season Influenza Vaccination: Randomized Controlled Trial %A Wijesundara,Jessica G %A Ito Fukunaga,Mayuko %A Ogarek,Jessica %A Barton,Bruce %A Fisher,Lloyd %A Preusse,Peggy %A Sundaresan,Devi %A Garber,Lawrence %A Mazor,Kathleen M %A Cutrona,Sarah L %+ Health Services Research & Development, Center of Innovation, Edith Nourse Rogers Memorial Hospital, Veterans Health Administration, 200 Springs St, Building 70, Bedford, MA, 01730, United States, 1 508 856 4046, Sarah.Cutrona@umassmed.edu %K electronic health records %K influenza vaccination %K patient care %K patient engagement %D 2020 %7 25.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient reminders for influenza vaccination, delivered via an electronic health record patient portal and interactive voice response calls, offer an innovative approach to engaging patients and improving patient care. Objective: The goal of this study was to test the effectiveness of portal and interactive voice response outreach in improving rates of influenza vaccination by targeting patients in early September, shortly after vaccinations became available. Methods: Using electronic health record portal messages and interactive voice response calls promoting influenza vaccination, outreach was conducted in September 2015. Participants included adult patients within a large multispecialty group practice in central Massachusetts. Our main outcome was electronic health record–documented early influenza vaccination during the 2015-2016 influenza season, measured in November 2015. We randomly assigned all active portal users to 1 of 2 groups: (1) receiving a portal message promoting influenza vaccinations, listing upcoming clinics, and offering online scheduling of vaccination appointments (n=19,506) or (2) receiving usual care (n=19,505). We randomly assigned all portal nonusers to 1 of 2 groups: (1) receiving interactive voice response call (n=15,000) or (2) receiving usual care (n=43,596). The intervention also solicited patient self-reports on influenza vaccinations completed outside the clinic. Self-reported influenza vaccination data were uploaded into the electronic health records to increase the accuracy of existing provider-directed electronic health record clinical decision support (vaccination alerts) but were excluded from main analyses. Results: Among portal users, 28.4% (5549/19,506) of those randomized to receive messages and 27.1% (5294/19,505) of the usual care group had influenza vaccinations documented by November 2015 (P=.004). In multivariate analysis of portal users, message recipients were slightly more likely to have documented vaccinations when compared to the usual care group (OR 1.07, 95% CI 1.02-1.12). Among portal nonusers, 8.4% (1262/15,000) of those randomized to receive calls and 8.2% (3586/43,596) of usual care had documented vaccinations (P=.47), and multivariate analysis showed nonsignificant differences. Over half of portal messages sent were opened (10,112/19,479; 51.9%), and over half of interactive voice response calls placed (7599/14,984; 50.7%) reached their intended target, thus we attained similar levels of exposure to the messaging for both interventions. Among portal message recipients, 25.4% of message openers (2570/10,112) responded to a subsequent question on receipt of influenza vaccination; among interactive voice response recipients, 72.5% of those reached (5513/7599) responded to a similar question. Conclusions: Portal message outreach to a general primary care population achieved a small but statistically significant improvement in rates of influenza vaccination (OR 1.07, 95% CI 1.02-1.12). Interactive voice response calls did not significantly improve vaccination rates among portal nonusers (OR 1.03, 95% CI 0.96-1.10). Rates of patient engagement with both modalities were favorable. Trial Registration: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 %M 32975529 %R 10.2196/16373 %U http://www.jmir.org/2020/9/e16373/ %U https://doi.org/10.2196/16373 %U http://www.ncbi.nlm.nih.gov/pubmed/32975529 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 9 %P e19897 %T A Personalized Voice-Based Diet Assistant for Caregivers of Alzheimer Disease and Related Dementias: System Development and Validation %A Li,Juan %A Maharjan,Bikesh %A Xie,Bo %A Tao,Cui %+ University of Texas Health Science Center at Houston, 7000 Fannin Street Suite 600, Houston, TX, 77030, United States, 1 7135003981, cui.tao@uth.tmc.edu %K Alzheimer disease %K dementia %K diet %K knowledge %K ontology %K voice assistant %D 2020 %7 21.9.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The world’s aging population is increasing, with an expected increase in the prevalence of Alzheimer disease and related dementias (ADRD). Proper nutrition and good eating behavior show promise for preventing and slowing the progression of ADRD and consequently improving patients with ADRD’s health status and quality of life. Most ADRD care is provided by informal caregivers, so assisting caregivers to manage patients with ADRD’s diet is important. Objective: This study aims to design, develop, and test an artificial intelligence–powered voice assistant to help informal caregivers manage the daily diet of patients with ADRD and learn food and nutrition-related knowledge. Methods: The voice assistant is being implemented in several steps: construction of a comprehensive knowledge base with ontologies that define ADRD diet care and user profiles, and is extended with external knowledge graphs; management of conversation between users and the voice assistant; personalized ADRD diet services provided through a semantics-based knowledge graph search and reasoning engine; and system evaluation in use cases with additional qualitative evaluations. Results: A prototype voice assistant was evaluated in the lab using various use cases. Preliminary qualitative test results demonstrate reasonable rates of dialogue success and recommendation correctness. Conclusions: The voice assistant provides a natural, interactive interface for users, and it does not require the user to have a technical background, which may facilitate senior caregivers’ use in their daily care tasks. This study suggests the feasibility of using the intelligent voice assistant to help caregivers manage patients with ADRD’s diet. %M 32955452 %R 10.2196/19897 %U http://www.jmir.org/2020/9/e19897/ %U https://doi.org/10.2196/19897 %U http://www.ncbi.nlm.nih.gov/pubmed/32955452 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 8 %P e19018 %T Evaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis Study %A Ferrand,John %A Hockensmith,Ryli %A Houghton,Rebecca Fagen %A Walsh-Buhi,Eric R %+ School of Public Health-Bloomington, Indiana University, 1025 E. 7th Street, Room 116, Department of Applied Health Science, Bloomington, IN, 47405, United States, 1 8128554867, erwals@iu.edu %K digital health %K human papillomavirus %K smart assistants %K chatbots %K conversational agents %K misinformation %K infodemiology %K vaccination %D 2020 %7 3.8.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Almost half (46%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation. Objective: This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination–related questions. Methods: We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses (κ=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine. Results: A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, χ22=7.807, P=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54%). Most response sentiments across smart assistants were positive (n=65, 64%) or neutral (n=18, 18%), but Cortana’s responses yielded the largest proportion of negative sentiment (n=7, 12%). Conclusions: Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants. %M 32744508 %R 10.2196/19018 %U https://www.jmir.org/2020/8/e19018 %U https://doi.org/10.2196/19018 %U http://www.ncbi.nlm.nih.gov/pubmed/32744508 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 6 %N 1 %P e15859 %T Assessing Breast Cancer Survivors’ Perceptions of Using Voice-Activated Technology to Address Insomnia: Feasibility Study Featuring Focus Groups and In-Depth Interviews %A Arem,Hannah %A Scott,Remle %A Greenberg,Daniel %A Kaltman,Rebecca %A Lieberman,Daniel %A Lewin,Daniel %+ Department of Epidemiology, Milken Institute School of Public Health, George Washington University, 950 New Hampshire Ave NW, Rm 514, Washington, DC, 20052, United States, 1 2029944676, hannaharem@gwu.edu %K artificial intelligence %K breast neoplasms %K survivors %K insomnia %K cognitive behavioral therapy %K mobile phones %D 2020 %7 26.5.2020 %9 Original Paper %J JMIR Cancer %G English %X Background: Breast cancer survivors (BCSs) are a growing population with a higher prevalence of insomnia than women of the same age without a history of cancer. Cognitive behavioral therapy for insomnia (CBT-I) has been shown to be effective in this population, but it is not widely available to those who need it. Objective: This study aimed to better understand BCSs’ experiences with insomnia and to explore the feasibility and acceptability of delivering CBT-I using a virtual assistant (Amazon Alexa). Methods: We first conducted a formative phase with 2 focus groups and 3 in-depth interviews to understand BCSs’ perceptions of insomnia as well as their interest in and comfort with using a virtual assistant to learn about CBT-I. We then developed a prototype incorporating participant preferences and CBT-I components and demonstrated it in group and individual settings to BCSs to evaluate acceptability, interest, perceived feasibility, educational potential, and usability of the prototype. We also collected open-ended feedback on the content and used frequencies to describe the quantitative data. Results: We recruited 11 BCSs with insomnia in the formative phase and 14 BCSs in the prototype demonstration. In formative work, anxiety, fear, and hot flashes were identified as causes of insomnia. After prototype demonstration, nearly 79% (11/14) of participants reported an interest in and perceived feasibility of using the virtual assistant to record sleep patterns. Approximately two-thirds of the participants thought lifestyle modification (9/14, 64%) and sleep restriction (9/14, 64%) would be feasible and were interested in this feature of the program (10/14, 71% and 9/14, 64%, respectively). Relaxation exercises were rated as interesting and feasible using the virtual assistant by 71% (10/14) of the participants. Usability was rated as better than average, and all women reported that they would recommend the program to friends and family. Conclusions: This virtual assistant prototype delivering CBT-I components by using a smart speaker was rated as feasible and acceptable, suggesting that this prototype should be fully developed and tested for efficacy in the BCS population. If efficacy is shown in this population, the prototype should also be adapted for other high-risk populations. %M 32348274 %R 10.2196/15859 %U http://cancer.jmir.org/2020/1/e15859/ %U https://doi.org/10.2196/15859 %U http://www.ncbi.nlm.nih.gov/pubmed/32348274 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 2 %P e15823 %T Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures %A Kocaballi,Ahmet Baki %A Quiroz,Juan C %A Rezazadegan,Dana %A Berkovsky,Shlomo %A Magrabi,Farah %A Coiera,Enrico %A Laranjo,Liliana %+ Australian Institute of Health Innovation
, Macquarie University, Level 6, 75 Talavera Road, Sydney, New South Wales, 2109, Australia, 61 0466431900, abakik@gmail.com %K conversational agents %K chatbots %K patient safety %K health literacy %K public health %K design principles %K evaluation %D 2020 %7 10.2.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. Objective: This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. Methods: We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. Results: The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. Conclusions: Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types. %R 10.2196/15823 %U https://www.jmir.org/2020/2/e15823 %U https://doi.org/10.2196/15823 %0 Journal Article %@ 2369-6893 %I JMIR Publications %V 5 %N 1 %P e15231 %T Look to the Future and SMILE: Feasibility of Interactive Voice Assistant Technology to Support Maternal Infant Health %A Sezgin,Emre %A Militello,Lisa %A Huang,Yungui %A Lin,Simon %+ The Abigail Wexner Research Institute, Nationwide Children's Hospital, 700 Children's Drive, Columbus, OH, United States, 6143556814, emre.sezgin@nationwidechildrens.org %K behavioral health %K interactive voice response %K mobile health %K mothers %K pregnancy %K self care %K self-management %D 2019 %7 2.10.2019 %9 Abstract %J iproc %G English %X Background: Both maternal and infant mortality rates serve as indicators of population health and are unacceptably high worldwide. Voice assistant (VA) technologies present a potential new modality to support maternal child health. We developed an interactive VA intervention app (SMILE) to deliver brief, maternal-infant education and management skills (eg, perinatal care, stress management, breast feeding, infant-care) using evidence-based content. Objective: The objective was to understand the feasibility and usability of an interactive VA intervention to support maternal and infant health among a group of pregnant women. Methods: We employed a mixed methods study design. Pregnant women were recruited via email and word of mouth. Participants completed a baseline demographic and technology-use survey and were asked to use the intervention over the course of two weeks. Postintervention, participants were invited to participate in an individual or group interview. Interviews were conducted to elicit feedback regarding thoughts and attitudes towards VA technology to support the health of mothers and infants. Descriptive analysis was used to summarize quantitative data (ie, survey responses, app logs) and thematic analysis was used for qualitative data (ie, transcriptions of voice recordings collected from SMILE, transcriptions of follow-up interviews). Results: Out of 46 respondents, 19 participants were consented, completed baseline surveys and used SMILE. Approximately 63% (n=12) of participants participated in exit interviews. The sample was predominantly 25-34 years old (n=16, 84%), part of a two-parent household (n=19, 100%), white (n=15, 79%), and pregnant with their first child (n=12, 63%). Nine participants (47.4%) reported that they practice stress management, and favorable stress-management activities were mainly comprised of exercise activities, yoga, and outdoor activities without technology involvement. Over half of the participants reported using technology to support pregnancy self-management (n=10, 53%). However, participants preferred mobile apps for education and self-management support during pregnancy and relied on the Internet to access health-related information. More than half of participants reported using default VAs on their phone (n=11, 58%) and on smart speakers (n=10, 53%). Yet, VA technology was mainly reported as being used for basic tasks, such as setting a timer or reminder, checking the weather, turning on/off the lights, or playing music. Postintervention, participants verbalized that VA technology was a potential medium for receiving health information, pregnancy-related information, and could be a strategy to engage other family members in the process. Major concerns revolved around security, privacy, trust, and concerns regarding interacting via voice when in public. Conclusions: Although this research is limited by the small and predominantly white sample size, this research represents one of the first studies to explore perceptions and attitudes towards VA to promote maternal-infant health. As VA technology increases in popularity, adoption and utility to support health and well-being among pregnant women is nascent. While VA technology offers some benefits (eg, reduce literacy barriers, hands-free), familiarity and trust of nonvoice digital health tools (eg, mobile apps, Web-based content) remain important in supporting maternal-child health. Digital health solutions that incorporate multiple platforms (eg, mobile apps, Internet, voice) warrant further exploration to optimize support for maternal child health. %R 10.2196/15231 %U https://www.iproc.org/2019/1/e15231 %U https://doi.org/10.2196/15231 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 9 %P e174 %T Health and Fitness Apps for Hands-Free Voice-Activated Assistants: Content Analysis %A Chung,Arlene E %A Griffin,Ashley C %A Selezneva,Dasha %A Gotz,David %+ Division of General Medicine & Clinical Epidemiology, Department of Medicine, University of North Carolina School of Medicine, 5034 Old Clinic Building, CB 7110, Chapel Hill, NC, 27599-7110, United States, 1 9199662276, arlene_chung@med.unc.edu %K voice-activated assistant %K intelligent personal assistant %K virtual personal assistant %K Amazon Alexa %K Google Assistant %K artificial intelligence %K voice-activated technology %K voice assistant %D 2018 %7 24.9.2018 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Hands-free voice-activated assistants and their associated devices have recently gained popularity with the release of commercial products, including Amazon Alexa and Google Assistant. Voice-activated assistants have many potential use cases in healthcare including education, health tracking and monitoring, and assistance with locating health providers. However, little is known about the types of health and fitness apps available for voice-activated assistants as it is an emerging market. Objective: This review aimed to examine the characteristics of health and fitness apps for commercially available, hands-free voice-activated assistants, including Amazon Alexa and Google Assistant. Methods: Amazon Alexa Skills Store and Google Assistant app were searched to find voice-activated assistant apps designated by vendors as health and fitness apps. Information was extracted for each app including name, description, vendor, vendor rating, user reviews and ratings, cost, developer and security policies, and the ability to pair with a smartphone app and website and device. Using a codebook, two reviewers independently coded each app using the vendor’s descriptions and the app name into one or more health and fitness, intended age group, and target audience categories. A third reviewer adjudicated coding disagreements until consensus was reached. Descriptive statistics were used to summarize app characteristics. Results: Overall, 309 apps were reviewed; health education apps (87) were the most commonly occurring, followed by fitness and training (72), nutrition (33), brain training and games (31), and health monitoring (25). Diet and calorie tracking apps were infrequent. Apps were mostly targeted towards adults and general audiences with few specifically geared towards patients, caregivers, or medical professionals. Most apps were free to enable or use and 18.1% (56/309) could be paired with a smartphone app and website and device; 30.7% (95/309) of vendors provided privacy policies; and 22.3% (69/309) provided terms of use. The majority (36/42, 85.7%) of Amazon Alexa apps were rated by the vendor as mature or guidance suggested, which were geared towards adults only. When there was a user rating available, apps had a wide range of ratings from 1 to 5 stars with a mean of 2.97. Google Assistant apps did not have user reviews available, whereas most of Amazon Alexa apps had at least 1-9 reviews available. Conclusions: The emerging market of health and fitness apps for voice-activated assistants is still nascent and mainly focused on health education and fitness. Voice-activated assistant apps had a wide range of content areas but many published in the health and fitness categories did not actually have a clear health or fitness focus. This may, in part, be due to Amazon and Google policies, which place restrictions on the delivery of care or direct recording of health data. As in the mobile app market, the content and functionalities may evolve to meet growing demands for self-monitoring and disease management. %M 30249581 %R 10.2196/mhealth.9705 %U http://mhealth.jmir.org/2018/9/e174/ %U https://doi.org/10.2196/mhealth.9705 %U http://www.ncbi.nlm.nih.gov/pubmed/30249581 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 9 %P e11510 %T Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant %A Bickmore,Timothy W %A Trinh,Ha %A Olafsson,Stefan %A O'Leary,Teresa K %A Asadi,Reza %A Rickles,Nathaniel M %A Cruz,Ricardo %+ College of Computer and Information Science, Northeastern University, 910-177, 360 Huntington Avenue, Boston, MA, 02115, United States, 1 6173735477, bickmore@ccs.neu.edu %K conversational assistant %K conversational interface %K dialogue system %K medical error %K patient safety %D 2018 %7 04.09.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Conversational assistants, such as Siri, Alexa, and Google Assistant, are ubiquitous and are beginning to be used as portals for medical services. However, the potential safety issues of using conversational assistants for medical information by patients and consumers are not understood. Objective: To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information. Methods: Participants were given medical problems to pose to Siri, Alexa, or Google Assistant, and asked to determine an action to take based on information from the system. Assignment of tasks and systems were randomized across participants, and participants queried the conversational assistants in their own words, making as many attempts as needed until they either reported an action to take or gave up. Participant-reported actions for each medical task were rated for patient harm using an Agency for Healthcare Research and Quality harm scale. Results: Fifty-four subjects completed the study with a mean age of 42 years (SD 18). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) were college educated. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.“ Forty-four (82%) used computers regularly. Subjects were only able to complete 168 (43%) of their 394 tasks. Of these, 49 (29%) reported actions that could have resulted in some degree of patient harm, including 27 (16%) that could have resulted in death. Conclusions: Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider. %M 30181110 %R 10.2196/11510 %U http://www.jmir.org/2018/9/e11510/ %U https://doi.org/10.2196/11510 %U http://www.ncbi.nlm.nih.gov/pubmed/30181110 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 2 %P e27 %T Increasing Physical Activity Amongst Overweight and Obese Cancer Survivors Using an Alexa-Based Intelligent Agent for Patient Coaching: Protocol for the Physical Activity by Technology Help (PATH) Trial %A Hassoon,Ahmed %A Schrack,Jennifer %A Naiman,Daniel %A Lansey,Dina %A Baig,Yasmin %A Stearns,Vered %A Celentano,David %A Martin,Seth %A Appel,Lawrence %+ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe Street, E6035, Baltimore, MD, 21205, United States, 1 443 287 2775, ahassoo1@jhu.edu %D 2018 %7 12.02.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Physical activity has established health benefits, but motivation and adherence remain challenging. Objective: We designed and launched a three-arm randomized trial to test artificial intelligence technology solutions to increase daily physical activity in cancer survivors. Methods: A single-center, three-arm randomized clinical trial with an allocation ration of 1:1:1: (A) control, in which participants are provided written materials about the benefits of physical activity; (B) text intervention, where participants receive daily motivation from a fully automated, data-driven algorithmic text message via mobile phone (Coachtext); and (C) Voice Assist intervention, where participants are provided with an in-home on demand autonomous Intelligent Agent using data driven Interactive Digital Voice Assist on the Amazon Alexa/Echo (MyCoach). Results: The study runs for 5 weeks: a one-week run-in to establish baseline, followed by 4 weeks of intervention. Data for study outcomes is collected automatically through a wearable sensor, and data are transferred in real-time to the study server. The recruitment goal is 42 participants, 14 in each arm. Electronic health records are used to prescreen candidates, with 39 participants recruited to date. Discussion: This study aims to investigate the effects of different types of intelligent technology solutions on promoting physical activity in cancer survivors. This innovative approach can easily be expanded and customized to other interventions. Early lessons from our initial participants are helping us develop additional advanced solutions to improve health outcomes. Trial Registration: Retrospectively registered on July 10, 2017 at ClinicalTrials.gov: NCT03212079; https://clinicaltrials.gov/ct2/show/NCT03212079 (Archived by WebCite at http://www.webcitation.org/6wgvqjTji) %M 29434016 %R 10.2196/resprot.9096 %U https://www.researchprotocols.org/2018/2/e27/ %U https://doi.org/10.2196/resprot.9096 %U http://www.ncbi.nlm.nih.gov/pubmed/29434016