This paper is in the following e-collection/theme issue:

Mobile Health (mhealth) (2748) mHealth for Data Collection and Research (907) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) Mobile Health in Psychiatry (399) Wearable Devices and Sensors (811) Anxiety and Stress Disorders (999) Fitness Trackers and Smart Pedometers/Accelerometers (539) Sleep Monitoring, Sleep Quality, Sleep Disorders (167) Digital Biomarkers and Digital Phenotyping (177)

Published on in Vol 20, No 6 (2018): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9410, first published .
Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Authors of this article:

Akane Sano1 Author Orcid Image ;   Sara Taylor1 Author Orcid Image ;   Andrew W McHill2, 3 Author Orcid Image ;   Andrew JK Phillips2, 3 Author Orcid Image ;   Laura K Barger2, 3 Author Orcid Image ;   Elizabeth Klerman2, 3 Author Orcid Image ;   Rosalind Picard1 Author Orcid Image
Article Authors Cited by (190) Tweetations (45) Metrics

Journals

  1. Fischer D, McHill A, Sano A, Picard R, Barger L, Czeisler C, Klerman E, Phillips A. Irregular sleep and event schedules are associated with poorer self-reported well-being in US college students. Sleep 2020;43(6) View
  2. Cheong S, Bautista C, Ortiz L. Sensing Physiological Change and Mental Stress in Older Adults From Hot Weather. IEEE Access 2020;8:70171 View
  3. Mueller A, Hoefling H, Muaremi A, Praestgaard J, Walsh L, Bunte O, Huber R, Fürmetz J, Keppler A, Schieker M, Böcker W, Roubenoff R, Brachat S, Rooks D, Clay I. Continuous Digital Monitoring of Walking Speed in Frail Elderly Patients: Noninterventional Validation Study and Longitudinal Clinical Trial. JMIR mHealth and uHealth 2019;7(11):e15191 View
  4. Jim H, Hoogland A, Brownstein N, Barata A, Dicker A, Knoop H, Gonzalez B, Perkins R, Rollison D, Gilbert S, Nanda R, Berglund A, Mitchell R, Johnstone P. Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians 2020;70(3):182 View
  5. Lovejoy C. Technology and mental health: The role of artificial intelligence. European Psychiatry 2019;55:1 View
  6. Kallio J, Vildjiounaite E, Koivusaari J, Räsänen P, Similä H, Kyllönen V, Muuraiskangas S, Ronkainen J, Rehu J, Vehmas K. Assessment of perceived indoor environmental quality, stress and productivity based on environmental sensor data and personality categorization. Building and Environment 2020;175:106787 View
  7. Radhakrishnan K, Kim M, Burgermaster M, Brown R, Xie B, Bray M, Fournier C. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548 View
  8. Tuerk P, Schaeffer C, McGuire J, Adams Larsen M, Capobianco N, Piacentini J. Adapting Evidence-Based Treatments for Digital Technologies: a Critical Review of Functions, Tools, and the Use of Branded Solutions. Current Psychiatry Reports 2019;21(10) View
  9. Byrne S, Kotze B, Ramos F, Casties A, Harris A. Using a mobile health device to manage severe mental illness in the community: What is the potential and what are the challenges?. Australian & New Zealand Journal of Psychiatry 2020;54(10):964 View
  10. Šimon M, Vašát P, Daňková H, Gibas P, Poláková M. Mobilities and commons unseen: spatial mobility in homeless people explored through the analysis of GPS tracking data. GeoJournal 2020;85(5):1411 View
  11. Goodday S, Friend S. Unlocking stress and forecasting its consequences with digital technology. npj Digital Medicine 2019;2(1) View
  12. Berrocal A, Concepcion W, De Dominicis S, Wac K. Complementing Human Behavior Assessment by Leveraging Personal Ubiquitous Devices and Social Links: An Evaluation of the Peer-Ceived Momentary Assessment Method. JMIR mHealth and uHealth 2020;8(8):e15947 View
  13. Pakhomov S, Thuras P, Finzel R, Eppel J, Kotlyar M, Cabiati M. Using consumer-wearable technology for remote assessment of physiological response to stress in the naturalistic environment. PLOS ONE 2020;15(3):e0229942 View
  14. Nosakhare E, Picard R. Toward Assessing and Recommending Combinations of Behaviors for Improving Health and Well-Being. ACM Transactions on Computing for Healthcare 2020;1(1):1 View
  15. Darvariu V, Convertino L, Mehrotra A, Musolesi M. Quantifying the Relationships between Everyday Objects and Emotional States through Deep Learning Based Image Analysis Using Smartphones. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1 View
  16. Keith J, Jamieson J, Bennetto L. The Importance of Adolescent Self-Report in Autism Spectrum Disorder: Integration of Questionnaire and Autonomic Measures. Journal of Abnormal Child Psychology 2019;47(4):741 View
  17. Vildjiounaite E, Huotari V, Kallio J, Kyllönen V, Mäkelä S, Gimel’farb G. Unobtrusive assessment of stress of office workers via analysis of their motion trajectories. Pervasive and Mobile Computing 2019;58:101028 View
  18. Arenas-Castañeda P, Aroca Bisquert F, Martinez-Nicolas I, Castillo Espíndola L, Barahona I, Maya-Hernández C, Lavana Hernández M, Manrique Mirón P, Alvarado Barrera D, Treviño Aguilar E, Barrios Núñez A, De Jesus Carlos G, Vildosola Garcés A, Flores Mercado J, Barrigon M, Artes A, de Leon S, Molina-Pizarro C, Rosado Franco A, Perez-Rodriguez M, Courtet P, Martínez-Alés G, Baca-Garcia E. Universal mental health screening with a focus on suicidal behaviour using smartphones in a Mexican rural community: protocol for the SMART-SCREEN population-based survey. BMJ Open 2020;10(7):e035041 View
  19. Victory A, Letkiewicz A, Cochran A. Digital solutions for shaping mood and behavior among individuals with mood disorders. Current Opinion in Systems Biology 2020;21:25 View
  20. De Witte N, Scheveneels S, Sels R, Debard G, Hermans D, Van Daele T. Augmenting Exposure Therapy: Mobile Augmented Reality for Specific Phobia. Frontiers in Virtual Reality 2020;1 View
  21. Kajitani K, Higashijima I, Kaneko K, Matsushita T, Fukumori H, Kim D, Hashimoto K. Short-term effect of a smartphone application on the mental health of university students: A pilot study using a user-centered design self-monitoring application for mental health. PLOS ONE 2020;15(9):e0239592 View
  22. Pardamean B, Soeparno H, Budiarto A, Mahesworo B, Baurley J. Quantified Self-Using Consumer Wearable Device: Predicting Physical and Mental Health. Healthcare Informatics Research 2020;26(2):83 View
  23. McHill A, Czeisler C, Phillips A, Keating L, Barger L, Garaulet M, Scheer F, Klerman E. Caloric and Macronutrient Intake Differ with Circadian Phase and between Lean and Overweight Young Adults. Nutrients 2019;11(3):587 View
  24. Pastor N, Khalilian E, Caballeria E, Morrison D, Sanchez Luque U, Matrai S, Gual A, López-Pelayo H. Remote Monitoring Telemedicine (REMOTE) Platform for Patients With Anxiety Symptoms and Alcohol Use Disorder: Protocol for a Case-Control Study. JMIR Research Protocols 2020;9(6):e16964 View
  25. Taylor S, Ferguson C, Peng F, Schoeneich M, Picard R. Use of In-Game Rewards to Motivate Daily Self-Report Compliance: Randomized Controlled Trial. Journal of Medical Internet Research 2019;21(1):e11683 View
  26. Coffman D, Cai X, Li R, Leonard N. Challenges and Opportunities in Collecting and Modeling Ambulatory Electrodermal Activity Data. JMIR Biomedical Engineering 2020;5(1):e17106 View
  27. Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819 View
  28. Drissi N, Ouhbi S, Janati Idrissi M, Fernandez-Luque L, Ghogho M. Connected Mental Health: Systematic Mapping Study. Journal of Medical Internet Research 2020;22(8):e19950 View
  29. Li B, Sano A. Extraction and Interpretation of Deep Autoencoder-based Temporal Features from Wearables for Forecasting Personalized Mood, Health, and Stress. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(2):1 View
  30. Rashid H, Mendu S, Daniel K, Beltzer M, Teachman B, Boukhechba M, Barnes L. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1 View
  31. Thammasan N, Stuldreher I, Schreuders E, Giletta M, Brouwer A. A Usability Study of Physiological Measurement in School Using Wearable Sensors. Sensors 2020;20(18):5380 View
  32. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine 2019;2(1) View
  33. Tonacci A, Dellabate A, Dieni A, Bachi L, Sansone F, Conte R, Billeci L. Can Machine Learning Predict Stress Reduction Based on Wearable Sensors’ Data Following Relaxation at Workplace? A Pilot Study. Processes 2020;8(4):448 View
  34. Aubourg T, Demongeot J, Renard F, Provost H, Vuillerme N. Association between social asymmetry and depression in older adults: A phone Call Detail Records analysis. Scientific Reports 2019;9(1) View
  35. . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020 View
  36. McHill A, Sano A, Hilditch C, Barger L, Czeisler C, Picard R, Klerman E. Robust stability of melatonin circadian phase, sleep metrics, and chronotype across months in young adults living in real‐world settings. Journal of Pineal Research 2021;70(3) View
  37. Pedrelli P, Fedor S, Ghandeharioun A, Howe E, Ionescu D, Bhathena D, Fisher L, Cusin C, Nyer M, Yeung A, Sangermano L, Mischoulon D, Alpert J, Picard R. Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors. Frontiers in Psychiatry 2020;11 View
  38. Byrne S, Kotze B, Ramos F, Casties A, Starling J, Harris A. Integrating a Mobile Health Device Into a Community Youth Mental Health Team to Manage Severe Mental Illness: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2020;9(11):e19510 View
  39. Ueafuea K, Boonnag C, Sudhawiyangkul T, Leelaarporn P, Gulistan A, Chen W, Mukhopadhyay S, Wilaiprasitporn T, Piyayotai S. Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review. IEEE Sensors Journal 2021;21(6):7162 View
  40. Connelly M, Boorigie M. Feasibility of using “SMARTER” methodology for monitoring precipitating conditions of pediatric migraine episodes. Headache: The Journal of Head and Face Pain 2021;61(3):500 View
  41. Sükei E, Norbury A, Perez-Rodriguez M, Olmos P, Artés A. Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach. JMIR mHealth and uHealth 2021;9(3):e24465 View
  42. Debard G, De Witte N, Sels R, Mertens M, Van Daele T, Bonroy B, Riziotis C. Making Wearable Technology Available for Mental Healthcare through an Online Platform with Stress Detection Algorithms: The Carewear Project. Journal of Sensors 2020;2020:1 View
  43. DiStefano C, Sadhwani A, Wheeler A. Comprehensive Assessment of Individuals With Significant Levels of Intellectual Disability: Challenges, Strategies, and Future Directions. American Journal on Intellectual and Developmental Disabilities 2020;125(6):434 View
  44. Regalia G, Gerboni G, Migliorini M, Lai M, Pham J, Puri N, Pavlova M, Picard R, Sarkis R, Onorati F. Sleep assessment by means of a wrist actigraphy-based algorithm: agreement with polysomnography in an ambulatory study on older adults. Chronobiology International 2021;38(3):400 View
  45. Straw I. Ethical implications of emotion mining in medicine. Health Policy and Technology 2021;10(1):191 View
  46. Eken A. Assessment of flourishing levels of individuals by using resting-state fNIRS with different functional connectivity measures. Biomedical Signal Processing and Control 2021;68:102645 View
  47. Leonidis A, Korozi M, Sykianaki E, Tsolakou E, Kouroumalis V, Ioannidi D, Stavridakis A, Antona M, Stephanidis C. Improving Stress Management and Sleep Hygiene in Intelligent Homes. Sensors 2021;21(7):2398 View
  48. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278 View
  49. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  50. Kiguchi M, Sutoko S, Atsumori H, Nishimura A, Obata A, Funane T, Nakagawa H, Egi M, Kuriyama H. Proposal of layered mental healthcare for mental well‐being. Healthcare Technology Letters 2021;8(4):85 View
  51. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  52. Baumeister H, Bauereiss N, Zarski A, Braun L, Buntrock C, Hoherz C, Idrees A, Kraft R, Meyer P, Nguyen T, Pryss R, Reichert M, Sextl T, Steinhoff M, Stenzel L, Steubl L, Terhorst Y, Titzler I, Ebert D. Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders. Frontiers in Psychiatry 2021;12 View
  53. Hickey B, Chalmers T, Newton P, Lin C, Sibbritt D, McLachlan C, Clifton-Bligh R, Morley J, Lal S. Smart Devices and Wearable Technologies to Detect and Monitor Mental Health Conditions and Stress: A Systematic Review. Sensors 2021;21(10):3461 View
  54. Faust L, Feldman K, Lin S, Mattingly S, D'Mello S, Chawla N. Examining Response to Negative Life Events Through Fitness Tracker Data. Frontiers in Digital Health 2021;3 View
  55. van Baardewijk J, Agarwal S, Cornelissen A, Joosen M, Kentrop J, Varon C, Brouwer A. Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology. Sensors 2021;21(11):3616 View
  56. Hoogeboom M, Saeed A, Noordzij M, Wilderom C. Physiological arousal variability accompanying relations-oriented behaviors of effective leaders: Triangulating skin conductance, video-based behavior coding and perceived effectiveness. The Leadership Quarterly 2021;32(6):101493 View
  57. Zheng C, Ji H, Kalemaki K. Analysis of the intervention effect and self-satisfaction of sports dance exercise on the psychological stress of college students. Work 2021;69(2):637 View
  58. Brown L, St. Hilaire M, McHill A, Phillips A, Barger L, Sano A, Czeisler C, Doyle F, Klerman E. A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data. Journal of Pineal Research 2021;71(1) View
  59. Adler D, Tseng V, Qi G, Scarpa J, Sen S, Choudhury T. Identifying Mobile Sensing Indicators of Stress-Resilience. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(2):1 View
  60. Rykov Y, Thach T, Bojic I, Christopoulos G, Car J. Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling. JMIR mHealth and uHealth 2021;9(10):e24872 View
  61. Wan C, Mchill A, Klerman E, Sano A. Sensor-Based Estimation of Dim Light Melatonin Onset Using Features of Two Time Scales. ACM Transactions on Computing for Healthcare 2021;2(3):1 View
  62. Szabo E, Green S, Karunakaran K, Sieberg C, Elman I, Burstein R, Borsook D. Migraine: interactions between brain’s trait and state. CNS Spectrums 2022;27(5):561 View
  63. Aristizabal S, Byun K, Wood N, Mullan A, Porter P, Campanella C, Jamrozik A, Nenadic I, Bauer B. The Feasibility of Wearable and Self-Report Stress Detection Measures in a Semi-Controlled Lab Environment. IEEE Access 2021;9:102053 View
  64. Cosoli G, Poli A, Scalise L, Spinsante S. Measurement of multimodal physiological signals for stimulation detection by wearable devices. Measurement 2021;184:109966 View
  65. Gagnon J, Khau M, Lavoie-Hudon L, Vachon F, Drapeau V, Tremblay S. Comparing a Fitbit Wearable to an Electrocardiogram Gold Standard as a Measure of Heart Rate Under Psychological Stress: A Validation Study. JMIR Formative Research 2022;6(12):e37885 View
  66. Srinivasan K, Currim F, Ram S. A Human-in-the-Loop Segmented Mixed-Effects Modeling Method for Analyzing Wearables Data. ACM Transactions on Management Information Systems 2023;14(2):1 View
  67. Stein D, Shoptaw S, Vigo D, Lund C, Cuijpers P, Bantjes J, Sartorius N, Maj M. Psychiatric diagnosis and treatment in the 21st century: paradigm shifts versus incremental integration. World Psychiatry 2022;21(3):393 View
  68. Jyotsna C, Amudha J, Ram A, Nollo G. IntelEye: An Intelligent Tool for the Detection of Stressful State based on Eye Gaze Data While Watching Video. Procedia Computer Science 2023;218:1270 View
  69. Goodday S, Karlin E, Alfarano A, Brooks A, Chapman C, Desille R, Rangwala S, Karlin D, Emami H, Woods N, Boch A, Foschini L, Wildman M, Cormack F, Taptiklis N, Pratap A, Ghassemi M, Goldenberg A, Nagaraj S, Walsh E, Friend S. An Alternative to the Light Touch Digital Health Remote Study: The Stress and Recovery in Frontline COVID-19 Health Care Workers Study. JMIR Formative Research 2021;5(12):e32165 View
  70. Yao W, Kaminishi K, Yamamoto N, Hamatani T, Yamada Y, Kawada T, Hiyama S, Okimura T, Terasawa Y, Maeda T, Mimura M, Ota J. Passive Way of Measuring QOL/Well-Being Levels Using Smartphone Log. Frontiers in Digital Health 2022;4 View
  71. van der Mee D, Gevonden M, Westerink J, de Geus E. Validity of electrodermal activity-based measures of sympathetic nervous system activity from a wrist-worn device. International Journal of Psychophysiology 2021;168:52 View
  72. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  73. Watanabe K, Tsutsumi A. The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study. JMIR Formative Research 2022;6(11):e40339 View
  74. Khan H, Nguyen T, Shafiq G, Mirza J, Javed M. A Secure Wearable Framework for Stress Detection in Patients Affected by Communicable Diseases. IEEE Sensors Journal 2023;23(2):981 View
  75. Clay I, Cormack F, Fedor S, Foschini L, Gentile G, van Hoof C, Kumar P, Lipsmeier F, Sano A, Smarr B, Vandendriessche B, De Luca V. Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint. Journal of Medical Internet Research 2022;24(5):e35951 View
  76. Kılıç A, Karakuş A, Alptekin E. Prediction of University Students’ Subjective Well-Being with Sleep and Physical Activity Data using Classification Algorithms. Procedia Computer Science 2022;207:2648 View
  77. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  78. Dias L, Vianna H, Barbosa J. Human behaviour data analysis and noncommunicable diseases: a systematic mapping study. Behaviour & Information Technology 2023;42(14):2485 View
  79. Long N, Lei Y, Peng L, Xu P, Mao P. A scoping review on monitoring mental health using smart wearable devices. Mathematical Biosciences and Engineering 2022;19(8):7899 View
  80. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  81. Huang Y, Kabir M, Upadhyay U, Dhar E, Uddin M, Syed-Abdul S. Exploring the Potential Use of Wearable Devices as a Prognostic Tool among Patients in Hospice Care. Medicina 2022;58(12):1824 View
  82. Orr M, MacLeod L, Bagnell A, McGrath P, Wozney L, Meier S. The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. Computers in Human Behavior 2023;140:107603 View
  83. de Looff P, Duursma R, Noordzij M, Taylor S, Jaques N, Scheepers F, de Schepper K, Koldijk S. Wearables: An R Package With Accompanying Shiny Application for Signal Analysis of a Wearable Device Targeted at Clinicians and Researchers. Frontiers in Behavioral Neuroscience 2022;16 View
  84. McHill A, Brown L, Phillips A, Barger L, Garaulet M, Scheer F, Klerman E. Later energy intake relative to mathematically modeled circadian time is associated with higher percentage body fat. Obesity 2023;31(S1):50 View
  85. Makhmutova M, Kainkaryam R, Ferreira M, Min J, Jaggi M, Clay I. Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study. JMIR mHealth and uHealth 2022;10(3):e34148 View
  86. Chen M, Shen K, Wang R, Miao Y, Jiang Y, Hwang K, Hao Y, Tao G, Hu L, Liu Z. Negative Information Measurement at AI Edge: A New Perspective for Mental Health Monitoring. ACM Transactions on Internet Technology 2022;22(3):1 View
  87. Amin M, Faghih R. Robust Inference of Autonomic Nervous System Activation Using Skin Conductance Measurements: A Multi-Channel Sparse System Identification Approach. IEEE Access 2019;7:173419 View
  88. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  89. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  90. Stavropoulos V, Motti-Stefanidi F, Griffiths M. Risks and Opportunities for Youth in the Digital Era. European Psychologist 2022;27(2):86 View
  91. Langener A, Stulp G, Kas M, Bringmann L. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Mental Health 2023;10:e42646 View
  92. Vermeesch A, Coro A, Mattes K, Ostendorff D, Timko Olson E, Garrigues L. Nature-Based Feasibility Intervention to Influence Mitigation Strategies for Perceived Stress. International Journal of Environmental Research and Public Health 2022;19(19):12277 View
  93. Saito T, Suzuki H, Kishi A. Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach. Frontiers in Digital Health 2022;4 View
  94. Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022;10(7):1189 View
  95. Robinson T, Condell J, Ramsey E, Leavey G. Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices. International Journal of Environmental Research and Public Health 2023;20(3):2636 View
  96. Rahmani A, Lai J, Jafarlou S, Azimi I, Yunusova A, Rivera A, Labbaf S, Anzanpour A, Dutt N, Jain R, Borelli J. Personal mental health navigator: Harnessing the power of data, personal models, and health cybernetics to promote psychological well-being. Frontiers in Digital Health 2022;4 View
  97. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  98. ERGÜN G, GÜZEL A, UMUCU E. Associated Factors of Smartphone Addiction in the Students of the Faculty of Health Sciences. Hacettepe Üniversitesi Hemşirelik Fakültesi Dergisi 2022;9(2):192 View
  99. Moukaddam N, Sano A, Salas R, Hammal Z, Sabharwal A. Turning data into better mental health: Past, present, and future. Frontiers in Digital Health 2022;4 View
  100. Booth B, Vrzakova H, Mattingly S, Martinez G, Faust L, D’Mello S. Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers. IEEE Transactions on Affective Computing 2022;13(4):2201 View
  101. Kathan A, Harrer M, Küster L, Triantafyllopoulos A, He X, Milling M, Gerczuk M, Yan T, Rajamani S, Heber E, Grossmann I, Ebert D, Schuller B. Personalised depression forecasting using mobile sensor data and ecological momentary assessment. Frontiers in Digital Health 2022;4 View
  102. Magal N, Rab S, Goldstein P, Simon L, Jiryis T, Admon R. Predicting Chronic Stress among Healthy Females Using Daily-Life Physiological and Lifestyle Features from Wearable Sensors. Chronic Stress 2022;6:247054702211009 View
  103. Zhang D, Lim J, Zhou L, Dahl A. Breaking the Data Value-Privacy Paradox in Mobile Mental Health Systems Through User-Centered Privacy Protection: A Web-Based Survey Study. JMIR Mental Health 2021;8(12):e31633 View
  104. De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr D, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine 2022;5(1) View
  105. Otte Andersen T, Skovlund Dissing A, Rosenbek Severinsen E, Kryger Jensen A, Thanh Pham V, Varga T, Hulvej Rod N. Predicting stress and depressive symptoms using high-resolution smartphone data and sleep behavior in Danish adults. Sleep 2022;45(6) View
  106. Milne-Ives M, Selby E, Inkster B, Lam C, Meinert E, Narasimhan P. Artificial intelligence and machine learning in mobile apps for mental health: A scoping review. PLOS Digital Health 2022;1(8):e0000079 View
  107. Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica K, Kunta A, Low C. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Frontiers in Digital Health 2021;3 View
  108. Baba A, Bunji K. Prediction of Mental Health Problem Using Annual Student Health Survey: Machine Learning Approach. JMIR Mental Health 2023;10:e42420 View
  109. Mewengkang A, Liando O, Wahab I, Mardian R, Bayuseno A. Impact of Mobile Learning using social media platform on Vocational Student’s Achievement Results. E3S Web of Conferences 2021;328:04003 View
  110. Moura I, Teles A, Coutinho L, Silva F. Towards identifying context-enriched multimodal behavioral patterns for digital phenotyping of human behaviors. Future Generation Computer Systems 2022;131:227 View
  111. Vidal Bustamante C, Coombs G, Rahimi-Eichi H, Mair P, Onnela J, Baker J, Buckner R. Fluctuations in behavior and affect in college students measured using deep phenotyping. Scientific Reports 2022;12(1) View
  112. Whiston A, Igou E, Fortune D, Analog Devices Team , Semkovska M. Examining Stress and Residual Symptoms in Remitted and Partially Remitted Depression Using a Wearable Electrodermal Activity Device: A Pilot Study. IEEE Journal of Translational Engineering in Health and Medicine 2023;11:96 View
  113. Roy D, Ali A, Pal S, Adhikary A. Impedimetric Hydrogel Sensor for the Identification of Hexose Using Machine Learning. IEEE Sensors Journal 2023;23(6):6272 View
  114. Li X, Kane M, Zhang Y, Sun W, Song Y, Dong S, Lin Q, Zhu Q, Jiang F, Zhao H. Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach. Journal of Medical Internet Research 2021;23(10):e18403 View
  115. Ba S, Hu X. Measuring emotions in education using wearable devices: A systematic review. Computers & Education 2023;200:104797 View
  116. Klimek A, Mannheim I, Schouten G, Wouters E, Peeters M. Wearables measuring electrodermal activity to assess perceived stress in care: a scoping review. Acta Neuropsychiatrica 2023:1 View
  117. Hirten R, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger E, Keefer L, Charney D, Nadkarni G, Suarez-Farinas M, Fayad Z. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023;6(2) View
  118. Vásquez Navarro G, Córdova Dávila A, Cano Lengua M, Andrade Arenas L. Design of a mobile app for the learning of algorithms for university students. Advances in Mobile Learning Educational Research 2023;3(1):727 View
  119. Dalilian F, Nembhard D. Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition. International Journal of Human–Computer Interaction 2023:1 View
  120. Ekiz D, Can Y, Ersoy C. Long Short-Term Memory Network Based Unobtrusive Workload Monitoring With Consumer Grade Smartwatches. IEEE Transactions on Affective Computing 2023;14(2):895 View
  121. Haniffa S, Narain P, Hughes M, Petković A, Šušić M, Mlambo V, Chaudhury D. Chronic social stress blunts core body temperature and molecular rhythms of Rbm3 and Cirbp in mouse lateral habenula. Open Biology 2023;13(7) View
  122. Paromita P, Mundnich K, Nadarajan A, Booth B, Narayanan S, Chaspari T. Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers. Frontiers in Digital Health 2023;5 View
  123. Clay I, De Luca V, Sano A. Editorial: Multimodal digital approaches to personalized medicine. Frontiers in Big Data 2023;6 View
  124. Wang Z, Larrazabal M, Rucker M, Toner E, Daniel K, Kumar S, Boukhechba M, Teachman B, Barnes L. Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1 View
  125. Bambang Dwi Kuncoro C, Efendi A, Mahardini Sakanti M. Wearable sensor for psychological stress monitoring of pregnant woman – State of the art. Measurement 2023;221:113556 View
  126. Rajkishan S, Meitei A, Singh A. Role of AI/ML in the study of mental health problems of the students: a bibliometric study. International Journal of System Assurance Engineering and Management 2023 View
  127. Jan M, Coppin-Renz A, West R, Gallo C, Cochran J, Heumen E, Fahmy M, Reuteman-Fowler J. Safety Evaluation in Iterative Development of Wearable Patches for Aripiprazole Tablets With Sensor: Pooled Analysis of Clinical Trials. JMIR Formative Research 2023;7:e44768 View
  128. Hartson K, Huntington-Moskos L, Sears C, Genova G, Mathis C, Ford W, Rhodes R. Use of Electronic Ecological Momentary Assessment Methodologies in Physical Activity, Sedentary Behavior, and Sleep Research in Young Adults: Systematic Review. Journal of Medical Internet Research 2023;25:e46783 View
  129. Azizan A, Ahmed W, Razak A. Sensing health: a bibliometric analysis of wearable sensors in healthcare. Health and Technology 2024;14(1):15 View
  130. Abd-alrazaq A, Alajlani M, Ahmad R, AlSaad R, Aziz S, Ahmed A, Alsahli M, Damseh R, Sheikh J. The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e52622 View
  131. Nagaraj S, Goodday S, Hartvigsen T, Boch A, Garg K, Gowda S, Foschini L, Ghassemi M, Friend S, Goldenberg A. Dissecting the heterogeneity of “in the wild” stress from multimodal sensor data. npj Digital Medicine 2023;6(1) View
  132. Stamatis C, Meyerhoff J, Meng Y, Lin Z, Cho Y, Liu T, Karr C, Liu T, Curtis B, Ungar L, Mohr D. Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. npj Mental Health Research 2024;3(1) View
  133. Castro Ribeiro T, García Pagès E, Ballester L, Vilagut G, García Mieres H, Suárez Aragonès V, Amigo F, Bailón R, Mortier P, Pérez Sola V, Serrano-Blanco A, Alonso J, Aguiló J. Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study. JMIR Research Protocols 2024;13:e51298 View
  134. Kraft R, Reichert M, Pryss R. Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis. Sensors 2024;24(2):472 View
  135. Langener A, Bringmann L, Kas M, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. Administration and Policy in Mental Health and Mental Health Services Research 2024;51(4):455 View
  136. Walsh A, Naughton G, Sharpe T, Zajkowska Z, Malys M, van Heerden A, Mondelli V. A collaborative realist review of remote measurement technologies for depression in young people. Nature Human Behaviour 2024;8(3):480 View
  137. Langener A, Stulp G, Jacobson N, Costanzo A, Jagesar R, Kas M, Bringmann L. It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data. Advances in Methods and Practices in Psychological Science 2024;7(1) View
  138. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  139. Kaur I, Kamini , Kaur J, Gagandeep , Singh S, Gupta U. Enhancing explainability in predicting mental health disorders using human–machine interaction. Multimedia Tools and Applications 2024 View
  140. Tlachac M, Heinz M, Reisch M, Ogden S. Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1 View
  141. Khalid M, Klerman E, McHill A, Phillips A, Sano A. SleepNet. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1 View
  142. Kumar M, Aijaz A, Chattar O, Shukla J, Mutharaju R. Opacity, Transparency, and the Ethics of Affective Computing. IEEE Transactions on Affective Computing 2023:1 View
  143. Lu S, Stone J, Klerman E, McHill A, Barger L, Robbins R, Fischer D, Sano A, Czeisler C, Rajaratnam S, Phillips A. The organization of sleep–wake patterns around daily schedules in college students. SLEEP 2023 View
  144. Li A, Xue C, Wu R, Wu W, Zhao J, Qiang Y. Unearthing Subtle Cognitive Variations: A Digital Screening Tool for Detecting and Monitoring Mild Cognitive Impairment. International Journal of Human–Computer Interaction 2024:1 View
  145. Bloomfield L, Fudolig M, Kim J, Llorin J, Lovato J, McGinnis E, McGinnis R, Price M, Ricketts T, Dodds P, Stanton K, Danforth C, Simões de Almeida R. Predicting stress in first-year college students using sleep data from wearable devices. PLOS Digital Health 2024;3(4):e0000473 View
  146. Vidal Bustamante C, Coombs III G, Rahimi-Eichi H, Mair P, Onnela J, Baker J, Buckner R. Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study. JMIR Formative Research 2024;8:e53441 View
  147. Timm I, Giurgiu M, Ebner-Priemer U, Reichert M. The Within-Subject Association of Physical Behavior and Affective Well-Being in Everyday Life: A Systematic Literature Review. Sports Medicine 2024 View
  148. Bolpagni M, Pardini S, Dianti M, Gabrielli S. Personalized Stress Detection Using Biosignals from Wearables: A Scoping Review. Sensors 2024;24(10):3221 View
  149. Song S, Seo Y, Hwang S, Kim H, Kim J. Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study. JMIR mHealth and uHealth 2024;12:e55842 View
  150. Lee J, Kim M, Hwang S, Lee K, Park J, Shin T, Lim H, Urtnasan E, Chung M, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024;14(6):e073290 View
  151. de Looff P, Noordzij M, Nijman H, Goedhard L, Bogaerts S, Didden R. Putting the usability of wearable technology in forensic psychiatry to the test: a randomized crossover trial. Frontiers in Psychiatry 2024;15 View

Books/Policy Documents

  1. Santhanagopalan M, Chetty M, Foale C, Aryal S, Klein B. Neural Information Processing. View
  2. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  3. Savazzi P, Vasile F, Brondino N, Vercesi M, Politi P. Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. View
  4. Beutel M, Kraft-Bauersachs C, Kreß S, Leinberger B, Loew T, Olbrich D, Schonnebeck M, Zwerenz R. Praxishandbuch Psychosomatische Medizin in der Rehabilitation. View
  5. Debnath S, Basu S. Proceedings of the International Conference on Computing and Communication Systems. View
  6. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View
  7. Garatva P, Terhorst Y, Messner E, Karlen W, Pryss R, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  8. Zwerenz R, Ebert D, Baumeister H. Digitale Gesundheitsinterventionen. View
  9. Saylam B, Durmaz İncel Ö. Smart Technologies for Sustainable and Resilient Ecosystems. View
  10. Fadzil I, Ghazali A, Jasni F, Hafizalshah M. Proceedings of the 2nd Human Engineering Symposium. View