Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.

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Physical Activity and the Prevention of Depression

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StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones

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mHealth for mental health: Integrating smartphone technology in behavioral healthcare.

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Predicting Next Call Duration: A Future Direction to Promote Mental Health in the Age of Lockdown

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Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study

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Emotional change in its “natural habitat”: Measuring everyday emotion regulation with passive and active ambulatory assessment methods.

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Role of AI/ML in the study of mental health problems of the students: a bibliometric study

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Neuropsychological detection and prediction using machine learning algorithms: a comprehensive review

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Mobile RDoC: Using Smartphones to Understand the Relationship Between Auditory Verbal Hallucinations and Need for Care

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Do you have your smartphone with you? Behavioral barriers for measuring everyday activities with smartphone sensors

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A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals

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"Money Doesn't Buy You Happiness": Negative Consequences of Using the Freemium Model for Mental Health Apps

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Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals

Wang, Z., Larrazabal, M. A., Rucker, M., Toner, E. R., Daniel, K. E., Kumar, S., Boukhechba, M., Teachman, B. A., & Barnes, L. E. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3610916 · Cited by 15

Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach

Akbarova, S., Im, M., Kim, S. H., Toshnazarov, K. E., Chung, K., Chun, J., Noh, Y., & Kim, Y.-A. (2023) · Sensors · doi:10.3390/s23218866 · Cited by 19

Utility of Digital Phenotyping Based on Wrist Wearables and Smartphones in Psychosis: Observational Study

Yang, Z., Heaukulani, C., Sim, A., Buddhika, T., Rashid, N. A. A., Wang, X., Zheng, S., Quek, Y. F., Basu, S., Lee, K. W., et al. (2025) · JMIR mhealth and uhealth · doi:10.2196/56185 · Cited by 15

Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians

de Angel, V., Lewis, S., White, K. M., Matcham, F., & Hotopf, M. H. (2022) · JMIR Mental Health · doi:10.2196/38934 · Cited by 15

Workshop on Emerging Technology and Data Analytics for Behavioral Health

Kotz, D., Lord, S. E., O'Malley, A. J., Stark, L., & Marsch, L. A. (2018) · JMIR Research Protocols · doi:10.2196/resprot.9589 · Cited by 14

Persuasive e-Health Design for Behavior Change

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Automatic depression screening using social interaction data on smartphones

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Passive sensing data predicts stress in university students: a supervised machine learning method for digital phenotyping

Shvetcov, A., Kupper, J. F., Zheng, W. Y., Slade, A., Han, J., Whitton, A. E., Spoelma, M. J., Hoon, L., Mouzakis, K., Vasa, R., et al. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1422027 · Cited by 16

Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study

Nejadshamsi, S., Karami, V., Ghourchian, N., Armanfard, N., Bergman, H., Grad, R. M., Wilchesky, M., Khanassov, V., Vedel, I., & Rahimi, S. A. (2025) · JMIR Aging · doi:10.2196/67715 · Cited by 14

Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists

Dziak, J. J., Coffman, D. L., Reimherr, M. L., Petrovich, J., Li, R., Shiffman, S. S., & Shiyko, M. P. (2019) · Statistics Surveys · doi:10.1214/19-ss126 · Cited by 13

Reporting health data in waiting rooms with mobile technology: Patient expectation and confirmation

Reychav, I., Arora, A., Sabherwal, R., Polyak, K., Sun, J., & Azuri, J. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104376 · Cited by 13

A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

Lee, H., Park, J., & Lee, U. (2022) · ACM Computing Surveys · doi:10.1145/3530814 · Cited by 17

E-Health in Diagnostik und Therapie psychischer Störungen

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Tracking and Modeling Subjective Well-Being Using Smartphone-Based Digital Phenotype

Rhim, S., Uichin, L., & Han, K. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3394855 · Cited by 14

Cancer rehabilitation at home : the potential of telehealthcare to support functional recovery of lung cancer survivors

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Using Wearable Devices and Speech Data for Personalized Machine Learning in Early Detection of Mental Disorders: Protocol for a Participatory Research Study

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A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity

Zhuparris, A., Maleki, G., van Londen, L., Koopmans, I. W., Aalten, V., Yocarini, I. E., Exadaktylos, V., van Hemert, A. M., Cohen, A. F., Gal, P., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46075-2 · Cited by 15

Talk Around Town: A Mobile Phone Application to Support Parent–Child Talk in the Community

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WatchOver

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Extending the usefulness of the verbal memory test: The promise of machine learning

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Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study

Nguyen, V. C., Lu, N. Y., Kane, J. M., Birnbaum, M. L., & Choudhury, M. D. (2022) · JMIR Mental Health · doi:10.2196/39747 · Cited by 14

Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study

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Technology in the Assessment, Treatment, and Management of Depression

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Passive Mobile Self-tracking of Mental Health by Veterans With Serious Mental Illness: Protocol for a User-Centered Design and Prospective Cohort Study

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Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective

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Life within a limited radius: Investigating activity space in women with a history of child abuse using global positioning system tracking

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Exploring the Strategies People with Parkinson's Disease Use to Self-track Symptoms and Medications

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Use of voice features from smartphones for monitoring depressive disorders: Scoping review

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Digital Health Sensing for Personalized Dermatology

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Leveraging Mobile Sensing to Understand and Develop Intervention Strategies to Improve Medication Adherence

Baglione, A. N., Gong, J., Boukhechba, M., Wells, K. J., & Barnes, L. E. (2020) · IEEE Pervasive Computing · doi:10.1109/mprv.2020.2993993 · Cited by 11

Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study

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Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test

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A Tool for Capturing Smartphone Screen Text

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EVAL Cane: Nonintrusive Monitoring Platform With a Novel Gait-Based User-Identification Scheme

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Anxiety Detection Leveraging Mobile Passive Sensing

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What Does Social Support Sound Like? Challenges and Opportunities for Using Passive Episodic Audio Collection to Assess the Social Environment

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Conversational agents on smartphones and the web

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Social Integration

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Predicting Symptom Improvement During Depression Treatment Using Sleep Sensory Data

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Emotion Recognition in the Real World: Passively Collecting and Estimating Emotions From Natural Speech Data of Individuals With Bipolar Disorder

Provost, E. M., Sperry, S. H., Tavernor, J., Anderau, S., Yocum, A. K., & McInnis, M. G. (2024) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2024.3407683 · Cited by 10

Automatic Elicitation Compliance for Short-Duration Speech Based Depression Detection

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Social-Ecological Measurement of Daily Life: How Relationally Focused Ambulatory Assessment Can Advance Clinical Intervention Science

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Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study

Rohani, D. A., Tuxen, N., Lopategui, A. Q., Kessing, L. V., & Bardram, J. E. (2018) · JMIR Mental Health · doi:10.2196/10122 · Cited by 8

Attitudes and barriers to mobile mental health interventions among first-year college students: a mixed-methods study

McCarthy, K., & Horwitz, A. G. (2025) · Journal of American College Health · doi:10.1080/07448481.2025.2458085 · Cited by 7

Mobile medical systems for equitable healthcare

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Digital phenotyping for mental health conditions: a systematic review of implementation and application

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Identity Health

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Predicting Stress in Older Adults with RNN and LSTM from Time Series Sensor Data and Cortisol

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Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances

Alamoudi, D., Nabney, I. T., & Crawley, E. M. (2024) · Sensors · doi:10.3390/s24030722 · Cited by 7

Empathy and Empathic Disconnection in Difficult and Uneasy Situations: Facing the Suicidal Individual

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HealthSense

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Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

Kumar, S., Tran, J. L., Ramirez, E. R., Lee, W.-N., Foschini, L., & Juusola, J. L. (2020) · JMIR Mental Health · doi:10.2196/17075 · Cited by 7

Developing personalized algorithms for sensing mental health symptoms in daily life

Timmons, A. C., Tutul, A. A., Avramidis, K., Duong, J. B., Carta, K. E., Walters, S. N., Jumonville, G. A., Carrasco, A. S., Freitag, G. F., Romero, D. N., et al. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00147-5 · Cited by 7

Smartphone-Based Digital Phenotyping Across Health Conditions: Scoping Review

Dumas, A., Hokayem, J., Goodman, G. R., Venkatasubramanian, K., & Chai, P. R. (2026) · Journal of Medical Internet Research · doi:10.2196/84146 · Cited by 8

AI-Based Mental Health Assessment for Adolescents Using Their Daily Digital Activities

Kim, D., Lee, J., Lee, T., Baek, S., Jin, S., Yoo, H., Cho, Y., Park, S., Cho, K., & Lee, C.-G. (2024) · International Conference on Data Science and Advanced Analytics · doi:10.1109/dsaa61799.2024.10722823 · Cited by 9

Towards Clustering Human Behavioral Patterns based on Digital Phenotyping

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Readiness of Smartphones for Data Collection and Data Mining with an Example Application in Mental Health

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Expanding the Reach of Research: Quantitative Evaluation of a Web-Based Approach for Remote Recruitment of People Who Hear Voices

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Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life

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Predicting maternal social loneliness by passive sensing with wearable devicesPreprint

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A Topic-Guided Self-Attention Network for Daily Mental Wellbeing Prediction Using Mobile Devices

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Personal Digital Life Coach for Physical Therapy

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Assessing Mobile Usage, Physical Activity and Sleep Through Smartphone Sensing: A Digital Phenotype Study

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The psychology of mobile technology and daily mobility.

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Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious Activities

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Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement

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Wearable Sensor-Based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom

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Analyzing Long-Duration and High-Frequency Data Using the Time-Varying Effect Model

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Personal productivity monitoring through smartphones

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Comparing speech recognition services for HCI applications in behavioral health

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Inflexitext: A program assessing psychological inflexibility in unstructured verbal data

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Depression and Functional Impairment: A Pernicious Pairing in Older Adults

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Messenger Use and Video Calls as Correlates of Depressive and Anxiety Symptoms: Results From the Corona Health App Study of German Adults During the COVID-19 Pandemic

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Wearable ANS monitoring in real life: A critical review of context-sensitive interpretation and implications for psychophysiology

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The Effectiveness of Technology‐Based Interventions for Mental Health and Well‐Being: A Systematic Review

Alqahtani, F., & Orji, R. (2025) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/8111089 · Cited by 14

Detecting Depression, Anxiety and Mental Stress in One Sequential Model with Multi-task Learning

Zhang, S., Tu, M., Yan, Y., Zhuang, Y., Ge, L., & Wei, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17902-0_14 · Cited by 6

Smartphone-based activity tracking for spine patients: Current technology and future opportunities

Leibold, A. T., Ali, D. M., Harrop, J. S., Sharan, A. D., Vaccaro, A. R., & Sivaganesan, A. (2023) · World Neurosurgery X · doi:10.1016/j.wnsx.2023.100238 · Cited by 6

The Ethics of Passive Data and Digital Phenotyping in Neurosurgery

Senders, J. T., Maher, N., Hulsbergen, A. F. C., Lamba, N., Bredenoord, A. L., & Broekman, M. L. D. (2019) · Springer eBooks · doi:10.1007/978-3-030-05502-8_14 · Cited by 4

Designing for hourly activity sampling in behavioral activation

Rohani, D. A., Tuxen, N., Kessing, L. V., & Bardram, J. E. (2017) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3154862.3154919 · Cited by 4

Atención psicológica y Tecnologías: oportunidades y conflictos

Soto‐Pérez, F., & Franco, M. (2019) · Revista Iberoamericana de Psicología · doi:10.33881/2027-1786.rip.11310 · Cited by 5

Psychological targeting in the age of Big Data

Appel, R. E., & Matz, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-819200-9.00015-6 · Cited by 4

Psychiatry is the flagship of personalized and precision medicine: proposing an epistemic horizon to biological psychiatry

Joober, R. (2022) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.220222 · Cited by 5

Predicting Depression Treatment Outcome Using Daily Step Count Sensory Data

Hossain, M. Z., Sahoo, S., Shende, C., Patel, P., Morillo, R., Pan, Z., Wang, X., Bi, J., Kamath, J., Russell, A. C., et al. (2025) · ACM Transactions on Computing for Healthcare · doi:10.1145/3777480 · Cited by 3

Uncovering the potential of smartphones for behavior monitoring during migraine follow-up

Stojchevska, M., Donckt, J. V. D., Vandenbussche, N., Brouwer, M. D., Paemeleire, K., Ongenae, F., & Hoecke, S. V. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02916-w · Cited by 3

Pandemic as a Service (PNDaaS) – A Cloud-Based Approach for Early Detection of Pandemic Diseases (like COVID-19) Through Smart Phone Sensors

Biswas, H., Sarddar, D., & Sarkar, V. (2022) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/110176 · Cited by 3

Sensor technology to monitor health, well-being and movement among healthcare personnel at workplace: a systematic scoping review protocol

Valimaki, M. A., Hipp, K., Chen, J., Huang, X., Guo, J., & Wong, M. S. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-054408 · Cited by 3

Bio-Behavioral Sensing

Sabharwal, A., & Veeraraghavan, A. (2017) · GetMobile Mobile Computing and Communications · doi:10.1145/3161587.3161591 · Cited by 3

Persönlichkeitsdiagnostik in der Online-Psychotherapieforschung

Tibubos, A. N., Zwerenz, R., Brähler, E., & Beutel, M. E. (2018) · Zeitschrift für Psychiatrie Psychologie und Psychotherapie · doi:10.1024/1661-4747/a000353 · Cited by 3

Mining relationships between mental health, academic performance and human behaviour

Yang, Z., Mo, X., Shi, D., & Wang, R. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397544 · Cited by 4

Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College StudentsPreprint

Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/276568 · Cited by 3

Remote measurement technologies for depression in young people: A realist review with meaningful lived experience involvement and recommendations for future research and practicePreprint

Walsh, A. E. L., Naughton, G., Sharpe, T., Zajkowska, Z., Malys, M. K., van Heerden, A. C., & Mondelli, V. (2022) · medRxiv · doi:10.1101/2022.06.16.22276510 · Cited by 3

Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study

Meyerhoff, J., Liu, T., Kording, K. P., Ungar, L. H., Kaiser, S. M., Karr, C. J., & Mohr, D. C. (2021) · Journal of Medical Internet Research · doi:10.2196/22844 · Cited by 82

Digital Assessment of Depression, Acute Stress, and Socioeconomic Disparities Using Wearable and Smartphone Devices Across the Lifespan

Nelson, B. W., Lockwood, K. G., Vega, J., & Harvie, H. M. K. (2022) · TMS Proceedings 2021 · doi:10.1037/tms0000049

Monitoring mental health disorder symptoms and behavioral problems of undergraduates in Sri Lanka using smartphone sensors

Gunarathne, K. A. A. B., & Jayasena, K. P. N. (2021) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa53625.2021.9682277 · Cited by 1

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study

Choudhary, S., Thomas, N., Ellenberger, J., Srinivasan, G., & Cohen, R. (2022) · JMIR Formative Research · doi:10.2196/37736 · Cited by 39

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling

Nickels, S., Edwards, M. D., Poole, S. F., Winter, D., Gronsbell, J. L., Rozenkrants, B., Miller, D. P., Fleck, M. S., McLean, A., Peterson, B. E., et al. (2021) · JMIR Mental Health · doi:10.2196/27589 · Cited by 50