PASSIVE SMARTPHONE SENSING FOR MENTAL HEALTH MONITORING: A STRUCTURED REVIEW OF BEHAVIORAL BIOMARKERS, PREDICTIVE VALIDITY, AND ETHICAL RISKS
DOI:
https://doi.org/10.31435/ijitss.4(52).2026.6268Keywords:
Digital Phenotyping, Behavioral Biomarkers, Passive Smartphone Sensing, Mental Health Monitoring, Predictive Validity, Data EthicsAbstract
Methods: This structured review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. Systematic searches were carried out in PubMed/MEDLINE and Google Scholar to identify peer-reviewed articles published between January 2018 and June 2026. Studies met inclusion criteria if they examined passive sensing frameworks (e.g., AWARE, Beiwe, RADAR-Base) collecting GPS, accelerometry, or communication metadata for psychiatric monitoring, resulting in a final sample of 15 core empirical studies.
Findings: The evidence shows that changes in spatial mobility, sleep architecture, and communication latency display strong longitudinal correlations with symptom severity scores for major depression (PHQ-9) and generalized anxiety (GAD-7). Machine learning models incorporating multi-modal data streams reached high predictive accuracy (AUC 0.72–0.88) when forecasting depressive shifts and spotting prodromal relapse signatures in bipolar disorder. Even with these advancements, notable translation barriers persist, especially concerning algorithmic generalizability across diverse socioeconomic groups and the ethical threat of "surveillance creep."
Conclusion: Passive smartphone sensing offers incredible potential for proactive, continuous psychiatric care. However, actual clinical translation will require strict cross-cultural validation of digital biomarkers alongside the creation of solid data governance frameworks to protect patient autonomy and privacy.
References
Akbarova, S., Im, M., Kim, S., Toshnazarov, K., Chung, K. M., Chun, J., Noh, Y., & Kim, Y. A. (2023). Improving depression severity prediction from passive sensing: Symptom-profiling approach. Sensors, 23(21), 8866 https://doi.org/10.3390/s23218866
Amin, R., Schreynemackers, S., Oppenheimer, H., Petrovic, M., Hegerl, U., & Reich, H. (2025). Use of mobile sensing data for longitudinal monitoring and prediction of depression severity: Systematic review. Journal of Medical Internet Research. https://doi.org/10.2196/57418
Barnett, I., Torous, J., Staples, P., Sandoval, L., Keshavan, M. S., & Onnela, J. P. (2018). Relapse prediction in schizophrenia through digital phenotyping: A pilot study. Neuropsychopharmacology, 43(8), 1660–1666 https://doi.org/10.1038/s41386-018-0030-z
Bennett, C. C., Ross, M. K., Baek, E. G., Kim, D., & Leow, A. D. (2022). Smartphone accelerometer data as a proxy for clinical data in modeling of bipolar disorder symptom trajectory. npj Digital Medicine, 5(1), 181 https://doi.org/10.1038/s41746-022-00741-3
Bufano, P., Laurino, M., Said, S., & Tognetti, A. (2023). Digital phenotyping for monitoring mental disorders: A systematic review. Journal of Medical Internet Research, 25, e46778 https://doi.org/10.2196/46778
Büscher, R., Winkler, T., Mocellin, J., Homan, S., Čihařová, M., van Breda, W., Kwon, S., Larsen, M., Torous, J., Firth, J., & Sander, L. B. (2024). A systematic review on passive sensing for the prediction of suicidal thoughts and behaviors. npj Mental Health Research, 3, 89 https://doi.org/10.1038/s44184-024-00089-4
Carr, O., Andreotti, F., Saunders, K. E. A., Palmius, N., Goodwin, G. M., & De Vos, M. (2022). Monitoring depression in bipolar disorder using circadian measures from smartphone accelerometers. Journal of Medical Internet Research, 24(4), e30206 https://doi.org/10.2196/30206
Chen, K., Torous, J., & Cheong, J. (2026). The current state and trends in digital phenotyping for mental health research and care. Psychiatric Clinics of North America, 49(1), 273–287 https://doi.org/10.1016/j.psc.2025.08.019
Di Matteo, D., Fotinos, K., Lokuge, S., Mason, G., Sternat, T., Katzman, M. A., & Rose, J. (2021). Automated screening for social anxiety, generalized anxiety, and depression from objective smartphone-collected data: Cross-sectional study. Journal of Medical Internet Research, 23(8), e28918 https://doi.org/10.2196/28918
Guth, M. A. S., Bittendorf, C. A., Krug, C. K., Ludwig, V. M., Muehlbauer, E., Hartnagel, L. M., Severus, W. E., Bauer, M., Ritter, P., & Ebner-Priemer, U. (2026). Predicting affective episodes in bipolar disorder using statistical process control analysis of GPS-based mobility patterns: Quantitative study. JMIR mHealth and uHealth, 14, e77272 https://doi.org/10.2196/77272
Hsu, J. H., Wu, C. H., Lin, E. C. L., & Chen, P. S. (2024). MoodSensing: A smartphone app for digital phenotyping and assessment of bipolar disorder. Psychiatry Research, 332, 115790 https://doi.org/10.1016/j.psychres.2024.115790
Huckvale, K., Venkatesh, S., & Christensen, H. (2019). Digital phenotyping for mental health: A review of the science and the ethical, legal and social implications. The Lancet Digital Health, 1(4), e180–e187 https://doi.org/10.1016/S2589-7500(19)30121-1
Jacobson, N. C., Summers, B., & Wilhelm, S. (2019). Digital biomarkers of anxiety and depression: Using smartphone-based passive sensing to predict clinical symptom severity. Frontiers in Psychiatry, 10, 656 https://doi.org/10.3389/fpsyt.2019.00656
Kutcher, T. P., Chakraborty, I., Kostick, K. M., Sano, A., Moukaddam, N., Herron, J. A., Goodman, W. K., Sheth, S. A., Sabharwal, A., & Provenza, N. R. (2025). Digital biomarkers for passive remote monitoring of bipolar disorder: A systematic review. Journal of Medical Internet Research, 27, e84675 https://doi.org/10.2196/84675
MacLeod, L., Suruliraj, B., Gall, D., Bessenyei, K., Hamm, S., Romkey, I., Bagnell, A., Mattheisen, M., Muthukumaraswamy, V., & Orji, R. (2021). A mobile sensing app to monitor youth mental health: Observational pilot study. JMIR mHealth and uHealth, 9(10), e20638 https://doi.org/10.2196/20638
Matcham, F., Barattieri di San Pietro, C., Bulgari, V., de Girolamo, G., Dobson, R., Eriksson, H., Folarin, A. M., Haro, J. M., Kerz, M., & Penninx, B. (2019). Remote assessment of disease and relapse in major depressive disorder (RADAR-MDD): A multi-centre prospective cohort study protocol. BMC Psychiatry, 19(1), 72 https://doi.org/10.1186/s12888-019-2049-z
Mohr, D. C., Shilton, K., & Hotopf, M. (2020). Digital phenotyping, behavioral sensing, or personal sensing: Names and transparency in the digital age. npj Digital Medicine, 3, 45 https://doi.org/10.1038/s41746-020-0251-5
Morshed, M. B., Saha, K., Li, R., D'Mello, S. K., De Choudhury, M., Abowd, G. D., & Plötz, T. (2019). Prediction of mood instability with passive sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 1–22 https://doi.org/10.1145/3351233
Onnela, J. P. (2021). Opportunities and challenges in digital phenotyping. Neuropsychopharmacology, 46(1), 45–54 https://doi.org/10.1038/s41386-020-00826-1
Opoku Asare, K., Moshe, I., Terhorst, Y., Vega, J., Hosio, S., Baumeister, H., Pulkki-Råback, L., & Ferreira, D. (2022). Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis. Pervasive and Mobile Computing, 87, 101621 https://doi.org/10.1016/j.pmcj.2022.101621
Rashid, Z., Folarin, A. A., Zhang, Y., Ranjan, Y., Conde, P., Sankesara, H., Sun, S., Stewart, C., Laiou, P., & Dobson, R. J. B. (2024). Digital phenotyping of mental and physical conditions: Remote monitoring of patients through RADAR-Base platform. JMIR Mental Health, 11, e51259 https://doi.org/10.2196/51259
Reinertsen, E., & Clifford, G. D. (2018). A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses. Physiological Measurement, 39(5), 05TR01 https://doi.org/10.1088/1361-6579/aabf64
Xu, X., Chikersal, P., Doryab, A., Villalba, D. K., Dutcher, J. M., Tumminia, M. J., Creswell, K. G., Mankoff, J., Creswell, J. D., & Dey, A. K. (2019). Leveraging mobile sensing and machine learning for personalized depression detection in college students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(3), 102 https://doi.org/10.1145/3351260
Stamatis, C. A., Meyerhoff, J., Meng, Y., Lin, Z. C. C., Cho, Y. M., Liu, T., Karr, C. J., Curtis, B. L., Ungar, L. H., & Mohr, D. C. (2024). Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: A longitudinal cohort study. npj Mental Health Research, 3(1), 10 https://doi.org/10.1038/s44184-023-00041-y
Terhorst, Y., Messner, E., Opoku Asare, K., Montag, C., Kannen, K., & Baumeister, H. (2025). Investigating smartphone-based sensing features for depression severity prediction: Observation study. Journal of Medical Internet Research, 27, e55308 https://doi.org/10.2196/55308
Torous, J., Wisniewski, H., Liu, G., & Keshavan, M. (2018a). Mental health mobile phone app usage, discovery, and evaluation. JMIR mHealth and uHealth, 6(5), e115 https://doi.org/10.2196/mhealth.9700
Torous, J., Larsen, M. E., Depp, C. A., Cosco, T. D., Barnett, I., Nock, M. K., & Firth, J. (2018b). Smartphones, sensors, and machine learning to advance real-time prediction and interventions for suicide prevention: A review of current progress and next steps. Current Psychiatry Reports, 20(7), 51 https://doi.org/10.1007/s11920-018-0914-y
Torous, J., Onnela, J. P., & Keshavan, M. (2021). The remote assessment of disease and relapse (RADAR-MDD) consortium: State of the art in remote monitoring. World Psychiatry, 20(2), 291–292 https://doi.org/10.1002/wps.20857
Vander Zwalmen, Y., Maerevoet, M., Coenen, T. J. M., Hoorelbeke, K., Chen, S., De Brouwer, M., Vanderhasselt, M. A., Van Hoecke, S., Bombeke, K., & De Raedt, R. (2026). Mobile technology for just-in-time prediction of depression: A scoping review. Nature Mental Health, 4(5), 851–869 https://doi.org/10.1038/s44220-026-00624-6
Vlisides-Henry, R. D., Gao, M., Thomas, L., Kaliush, P. R., Conradt, E., & Crowell, S. E. (2021). Digital phenotyping of emotion dysregulation across lifespan transitions to better understand psychopathology risk. Frontiers in Psychiatry, 12, 618442 https://doi.org/10.3389/fpsyt.2021.618442
Wang, R., Wang, W., Dasilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T. (2018). Tracking depression dynamics in college students using mobile phone and wearable sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), 1–26 https://doi.org/10.1145/3191763
Webb, C. A., Ren, B., Rahimi-Eichi, H., Gillis, B., Chung, Y., & Baker, J. T. (2025). Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping. Translational Psychiatry, 15, 142 https://doi.org/10.1038/s41398-025-03200-x
Zhang, H., Ibrahim, A., Parsia, B., Poliakoff, E., & Harper, S. (2023). Passive social sensing with smartphones: A systematic review. Computing, 105(1), 29–51 https://doi.org/10.1007/s00607-022-01112-2
Zhong, M., van Zoest, V., Bilal, A., Papadopoulos, F. C., & Castellano, G. (2022). Unimodal vs. multimodal prediction of antenatal depression from smartphone-based survey data in a longitudinal study. In Proceedings of the 2022 International Conference on Multimodal Interaction (ICMI '22, pp. 368–376). Association for Computing Machinery https://doi.org/10.1145/3536221.3556605
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Eliza Zofia Kabat, Krzysztof Konopka, Maciej Kamil Majchrzak, Faustyna Magdalena Kołodziej, Alicja Kwiatkowska, Jakub Juskowiak, Mateusz Lis, Zuzanna Julia Komin, Maryan Liasota, Michalina Justyna Kozłowska
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles are published in open-access and licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Hence, authors retain copyright to the content of the articles.
CC BY 4.0 License allows content to be copied, adapted, displayed, distributed, re-published or otherwise re-used for any purpose including for adaptation and commercial use provided the content is attributed.
