Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health.
Open accessAbstract
OBJECTIVE: Optimal mental health care is dependent upon sensitive and early detection of mental health problems. We have introduced a state-of-the-art method for the current study for remote behavioral monitoring that transports assessment out of the clinic and into the environments in which individuals negotiate their daily lives. The objective of this study was to examine whether the information captured with multimodal smartphone sensors can serve as behavioral markers for one's mental health. We hypothesized that (a) unobtrusively collected smartphone sensor data would be associated with individuals' daily levels of stress, and (b) sensor data would be associated with changes in depression, stress, and subjective loneliness over time. METHOD: A total of 47 young adults (age range: 19-30 years) were recruited for the study. Individuals were enrolled as a single cohort and participated in the study over a 10-week period. Participants were provided with smartphones embedded with a range of sensors and software that enabled continuous tracking of their geospatial activity (using the Global Positioning System and wireless fidelity), kinesthetic activity (using multiaxial accelerometers), sleep duration (modeled using device-usage data, accelerometer inferences, ambient sound features, and ambient light levels), and time spent proximal to human speech (i.e., speech duration using microphone and speech detection algorithms). Participants completed daily ratings of stress, as well as pre- and postmeasures of depression (Patient Health Questionnaire-9; Spitzer, Kroenke, & Williams, 1999), stress (Perceived Stress Scale; Cohen et al., 1983), and loneliness (Revised UCLA Loneliness Scale; Russell, Peplau, & Cutrona, 1980). RESULTS: Mixed-effects linear modeling showed that sensor-derived geospatial activity (p < .05), sleep duration (p < .05), and variability in geospatial activity (p < .05), were associated with daily stress levels. Penalized functional regression showed associations between changes in depression and sensor-derived speech duration (p < .05), geospatial activity (p < .05), and sleep duration (p < .05). Changes in loneliness were associated with sensor-derived kinesthetic activity (p < .01). CONCLUSIONS AND IMPLICATIONS FOR PRACTICE: Smartphones can be harnessed as instruments for unobtrusive monitoring of several behavioral indicators of mental health. Creative leveraging of smartphone sensing could provide novel opportunities for close-to-invisible psychiatric assessment at a scale and efficiency that far exceeds what is currently feasible with existing assessment technologies.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Audio sensing has been applied to mental health conditions including depression, bipolar, schizophrenia, autism, stress, and mood.
“Depression, as one of the leading mental disorders, has been studied using audio sounds [76]. Other mental disorders have also been investigated using audio sensing, including bipolar [66], schizophrenia [77], autism [78]. Stress and mood which are highly correlated to mental health have also been studied using audio sounds [67,76].”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [76]
Changes in daily stress levels in young adults aged 19 to 30 years were associated with changes in geospatial activity, sleep duration, and sensor-derived speech duration.
“The same observations were reproduced in younger adults (aged 19-30 years) [10], showing that changes in daily stress levels in young adults aged 19 to 30 years were associated with changes in geospatial activity, sleep duration (obtained by proxy from phone sensors), and sensor-derived speech duration.”
No significant relationship was found between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.
“However, contrary to previous findings (19, 67–69), we did not find a significant relationship between the other smartphone features (total distance, location entropy, normalized location entropy and time spent at home) and mental health measures.”
From Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data (2021) · cites this paper as [69]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Diagnostic and Statistical Manual of Mental Disorders
Williams, J. B. W., & First, M. B. (2013) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199975839.013.104 · Cited by 109,841 · see citing works
The PHQ-9: Validity of a brief depression severity measure
Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001) · Journal of General Internal Medicine · doi:10.1046/j.1525-1497.2001.016009606.x · Cited by 44,910 · see citing works
A Global Measure of Perceived Stress
Cohen, S., Kamarck, T., & Mermelstein, R. (1983) · Journal of Health and Social Behavior · doi:10.2307/2136404 · Cited by 32,807 · see citing works
Spitzer, R. L. (1999) · JAMA · doi:10.1001/jama.282.18.1737 · Cited by 9,802 · see citing works
Ecological momentary assessment
Shiffman, S., Stone, A. A., & Hufford, M. R. (2008) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.3.022806.091415 · Cited by 6,454 · see citing works
The Revised UCLA Loneliness Scale: concurrent and discriminant validity evidence
Russell, D., Peplau, L. A., & Cutrona, C. E. (1980) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.39.3.472 · Cited by 5,173 · see citing works
Loneliness matters: a theoretical and empirical review of consequences and mechanisms
Hawkley, L. C., & Cacioppo, J. T. (2010) · Annals of Behavioral Medicine · doi:10.1007/s12160-010-9210-8 · Cited by 4,782 · see citing works
Physical Activity and the Prevention of Depression
Mammen, G., & Faulkner, G. (2013) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2013.08.001 · Cited by 1,122 · see citing works
Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014) · Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2632048.2632054 · Cited by 1,116 · see citing works
Self-citationmHealth for mental health: Integrating smartphone technology in behavioral healthcare.
Luxton, D. D., McCann, R. A., Bush, N. E., Mishkind, M. C., & Reger, G. M. (2011) · Professional Psychology: Research and Practice · doi:10.1037/a0024485 · Cited by 809 · see citing works
Depression as a disease of modernity: explanations for increasing prevalence
Hidaka, B. H. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.12.036 · Cited by 769 · see citing works
Trull, T. J., & Ebner-Priemer, U. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185510 · Cited by 761 · see citing works
Swan, M. (2009) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph6020492 · Cited by 730 · see citing works
Harnessing context sensing to develop a mobile intervention for depression
Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1838 · Cited by 646 · see citing works
Sleep disturbances as the hallmark of PTSD: where are we now?
Germain, A. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2012.12040432 · Cited by 634 · see citing works
The Jigsaw continuous sensing engine for mobile phone applications
Lu, H., Yang, J., Liu, Z., Lane, N. D., Choudhury, T., & Campbell, A. T. (2010) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/1869983.1869992 · Cited by 561 · see citing works
Self-citationFeasibility, Acceptability, and Preliminary Efficacy of a Smartphone Intervention for Schizophrenia
Ben-Zeev, D., Brenner, C. J., Begale, M., Duffecy, J., Mohr, D. C., & Mueser, K. T. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu033 · Cited by 493 · see citing works
Self-citationEarly interventions to prevent psychosis: systematic review and meta-analysis
Stafford, M. R., Jackson, H., Mayo-Wilson, E., Morrison, A. P., & Kendall, T. (2013) · BMJ · doi:10.1136/bmj.f185 · Cited by 456 · see citing works
BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing
Lane, N., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., & Campbell, A. (2011) · Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2011.246161 · Cited by 409 · see citing works
Self-citationSystematic review of school-based prevention and early intervention programs for depression
Calear, A. L., & Christensen, H. (2010) · Journal of Adolescence · doi:10.1016/j.adolescence.2009.07.004 · Cited by 365 · see citing works
Asmundson, G. J. G., Fetzner, M. G., DeBoer, L. B., Powers, M. B., Otto, M. W., & Smits, J. A. J. (2013) · Depression and Anxiety · doi:10.1002/da.22043 · Cited by 361 · see citing works
aan het Rot, M., Hogenelst, K., & Schoevers, R. A. (2012) · Clinical Psychology Review · doi:10.1016/j.cpr.2012.05.007 · Cited by 325 · see citing works
EARLY INTERVENTIONS FOR PTSD: A REVIEW: Review: Early Intervention
Kearns, M. C., Ressler, K. J., Zatzick, D., & Rothbaum, B. O. (2012) · Depression and Anxiety · doi:10.1002/da.21997 · Cited by 310 · see citing works
Penalized Functional Regression
Goldsmith, J., Bobb, J. F., Crainiceanu, C. M., Caffo, B. S., & Reich, D. S. (2011) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2010.10007 · Cited by 289 · see citing works
Proudfoot, J., Clarke, J., Birch, M.-R., Whitton, A. E., Parker, G., Manicavasagar, V., Harrison, V., Christensen, H., & Hadzi-Pavlovic, D. (2013) · BMC Psychiatry · doi:10.1186/1471-244x-13-312 · Cited by 264 · see citing works
Mobile technologies among people with serious mental illness: Opportunities for future services
Ben-Zeev, D., Davis, K. E., Kaiser, S., Krzsos, I., & Drake, R. E. (2013) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-012-0424-x · Cited by 249 · see citing works
Torous, J., Chan, S. R., Yee-Marie Tan, S., Behrens, J., Mathew, I., Conrad, E. J., Hinton, L., Yellowlees, P., & Keshavan, M. (2014) · JMIR Mental Health · doi:10.2196/mental.4004 · Cited by 228 · see citing works
Periods of Recovery in Deficit Syndrome Schizophrenia: A 20-Year Multi-follow-up Longitudinal Study
Strauss, G. P., Harrow, M., Grossman, L. S., & Rosen, C. (2008) · Schizophrenia Bulletin · doi:10.1093/schbul/sbn167 · Cited by 206 · see citing works
Strategies for mHealth Research: Lessons from 3 Mobile Intervention Studies
Ben‐Zeev, D., Schueller, S. M., Begale, M. J., Duffecy, J. L., Kane, J. M., & Mohr, D. C. (2014) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-014-0556-2 · Cited by 167 · see citing works
Self-citationInterventions for helping people recognise early signs of recurrence in bipolar disorder
Morriss, R., Faizal, M. A., Jones, A. P., Williamson, P. R., Bolton, C. A., & McCarthy, J. P. (2007) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd004854.pub2 · Cited by 166 · see citing works
Proudfoot, J. (2013) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867412471441 · Cited by 126 · see citing works
Mobile health (mHealth) for mental health in Asia: Objectives, strategies, and limitations
Brian, R. M., & Ben-Zeev, D. (2014) · Asian Journal of Psychiatry · doi:10.1016/j.ajp.2014.04.006 · Cited by 121 · see citing works
Self-citationAccuracy of Hospitalized Depressed Patients' and Healthy Controls' Retrospective Symptom Reports
Ben‐Zeev, D., & Young, M. A. (2010) · The Journal of Nervous and Mental Disease · doi:10.1097/nmd.0b013e3181d6141f · Cited by 109 · see citing works
Self-citationSleep in Psychiatric Disorders: Where are We Now?
Lee, E. K., & Douglass, A. B. (2010) · The Canadian Journal of Psychiatry · doi:10.1177/070674371005500703 · Cited by 66 · see citing works
Objective Measurement of Sociability and Activity: Mobile Sensing in the Community
Berke, E. M., Choudhury, T., Ali, S., & Rabbi, M. (2011) · The Annals of Family Medicine · doi:10.1370/afm.1266 · Cited by 66 · see citing works
Goldberg, J. F., & Harrow, M. (2011) · Bipolar Disorders · doi:10.1111/j.1399-5618.2011.00903.x · Cited by 65 · see citing works
Self-monitoring of panic attacks and retrospective estimates of panic: discordant findings
de Beurs, E., Lange, A., & van Dyck, R. (1992) · Behaviour Research and Therapy · doi:10.1016/0005-7967(92)90054-k · Cited by 60 · see citing works
Gloster, A. T., Richard, D. C. S., Himle, J., Koch, E., Anson, H., Lokers, L., & Thornton, J. (2008) · Behaviour Research and Therapy · doi:10.1016/j.brat.2008.02.010 · Cited by 58 · see citing works
Carras, M. C., Mojtabai, R., Furr-Holden, D., Eaton, W. W., & Cullen, B. (2014) · Journal of Psychiatric Practice · doi:10.1097/01.pra.0000445244.08307.84 · Cited by 45 · see citing works
Detecting improvements in acute psychotic symptoms using experience sampling methodology
So, S. H., PETERS, E. R., Swendsen, J. D., Garety, P. A., & Kapur, S. (2013) · Psychiatry Research · doi:10.1016/j.psychres.2013.05.010 · Cited by 37 · see citing works
Komatsu, H., Sekine, Y., Okamura, N., Kanahara, N., Okita, K., Matsubara, S., Hirata, T., Komiyama, T., Watanabe, H., Minabe, Y., et al. (2013) · Schizophrenia Research · doi:10.1016/j.schres.2013.08.007 · Cited by 37 · see citing works
funreg: Functional Regression for Irregularly Timed Data
Dziak, J. J., & Shiyko, M. P. (2014) · doi:10.32614/cran.package.funreg · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Chen Z Lin M Chen F Lane MD Cardone G Wang R Li T Chen Y Choudhury T Campbell AT 2013 Unobtrusive sleep monitoring using Smartphones 7th International ICST Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health) Venice
- Hedeker D Gibbons R 2006 Longitudinal data analysis NJ Wiley-Interscience
Cited by
Sorted by citations
Precision medicine, AI, and the future of personalized health care
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021) · Clinical and Translational Science · doi:10.1111/cts.12884 · Cited by 1,854 · see citing works
Huckins, J. F., daSilva, A. W., Wang, W., Hedlund, E., Rogers, C., Nepal, S. K., Wu, J., Obuchi, M., Murphy, E. I., Meyer, M. L., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/20185 · Cited by 759 · see citing works
Self-citationSmartphone Sensors for Health Monitoring and Diagnosis
Majumder, S., & Deen, M. J. (2019) · Sensors · doi:10.3390/s19092164 · Cited by 440 · see citing works
The Experience Sampling Method on Mobile Devices
van Berkel, N., Ferreira, D., & Kostakos, V. (2018) · ACM Computing Surveys · doi:10.1145/3123988 · Cited by 432 · see citing works
Lee, E., Torous, J. B., Choudhury, M. D., Depp, C. A., Graham, S. A., Kim, H., Paulus, M. P., Krystal, J. H., & Jeste, D. V. (2021) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.001 · Cited by 477 · see citing works
Systematic review of smartphone-based passive sensing for health and wellbeing
Cornet, V. P., & Holden, R. J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.12.008 · Cited by 406 · see citing works
Virtual reality exposure therapy for social anxiety disorder: A randomized controlled trial.
Anderson, P. L., Price, M., Edwards, S. M., Obasaju, M. A., Schmertz, S. K., Zimand, E., & Calamaras, M. R. (2013) · Journal of Consulting and Clinical Psychology · doi:10.1037/a0033559 · Cited by 371 · see citing works
A Systematic Review of Cognitive Behavioral Therapy and Behavioral Activation Apps for Depression
Huguet, A., Rao, S., McGrath, P. J., Wozney, L., Wheaton, M., Conrod, J., & Rozario, S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154248 · Cited by 328 · see citing works
Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data
Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., & Pulkki-Råback, L. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.625247 · Cited by 261 · see citing works
Anikwe, C. V., Nweke, H. F., Ikegwu, A. C., Egwuonwu, C. A., Onu, F. U., Alo, U. R., & Teh, Y. W. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117362 · Cited by 250 · see citing works
Digital Health Tools for the Passive Monitoring of Depression: A Systematic Review of Methods
de Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., Mohr, D. C., et al. (2021) · doi:10.1101/2021.07.19.21260786 · Cited by 227 · see citing works
Rohani, D. A., Faurholt-Jepsen, M., Kessing, L. V., & Bardram, J. E. (2017) · doi:10.2196/preprints.9691 · Cited by 195 · see citing works
Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring
Sheikh, M., Qassem, M., & Kyriacou, P. A. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.662811 · Cited by 197 · see citing works
Ben‐Zeev, D., Brian, R., Wang, R., Wang, W., Campbell, A. T., Aung, M. H., Merrill, M., Tseng, V. W.-S., Choudhury, T., Hauser, M., et al. (2017) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000243 · Cited by 185 · see citing works
Self-citationBaron, K. G., Duffecy, J., Berendsen, M. A., Cheung Mason, I., Lattie, E. G., & Manalo, N. C. (2018) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2017.12.002 · Cited by 186 · see citing works
Pocket Skills: A Conversational Mobile Web App To Support Dialectical Behavioral Therapy
Schroeder, J., Wilkes, C., Rowan, K., Toledo, A., Paradiso, A., Czerwinski, M., Mark, G., & Linehan, M. M. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173972 · Cited by 175 · see citing works
Pinaya, W. H. L., Vieira, S., Garcia‐Dias, R., & Mechelli, A. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815739-8.00011-0 · Cited by 185 · see citing works
Reichert, M., Giurgiu, M., Koch, E. D., Wieland, L. M., Lautenbach, S., Neubauer, A., von Haaren‐Mack, B., Schilling, R., Timm, I., Notthoff, N., et al. (2020) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2020.101742 · Cited by 179 · see citing works
DeepMood: Forecasting Depressed Mood Based on Self-Reported Histories via Recurrent Neural Networks
Suhara, Y., Xu, Y., & Pentland, A. '. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052676 · Cited by 163 · see citing works
Doryab, A., Villalba, D. K., Chikersal, P., Dutcher, J. M., Tumminia, M. J., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J. C., Creswell, J. D., et al. (2019) · JMIR mhealth and uhealth · doi:10.2196/13209 · Cited by 162 · see citing works
Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., Berdun, J., et al. (2019) · JMIR Mental Health · doi:10.2196/mental.9819 · Cited by 148 · see citing works
Trifan, A., Oliveira, M., & Oliveira, J. L. (2019) · JMIR mhealth and uhealth · doi:10.2196/12649 · Cited by 163 · see citing works
Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139 · see citing works
Bagot, K. S., Matthews, S. A., Mason, M., Squeglia, L. M., Fowler, J., Gray, K., Herting, M., May, A., Colrain, I., Godino, J., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.03.008 · Cited by 145 · see citing works
Use of machine learning approach to predict depression in the elderly in China: A longitudinal study
Su, D., Zhang, X., He, K., & Chen, Y. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.12.160 · Cited by 143 · see citing works
Reinertsen, E., & Clifford, G. D. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aabf64 · Cited by 136 · see citing works
Chikersal, P., Doryab, A., Tumminia, M. J., Villalba, D. K., Dutcher, J. M., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J. C., Creswell, J. D., et al. (2021) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3422821 · Cited by 144 · see citing works
Bush, N. E., Smolenski, D. J., Denneson, L. M., Williams, H. B., Thomas, E. K., & Dobscha, S. K. (2017) · Psychiatric Services · doi:10.1176/appi.ps.201600283 · Cited by 124 · see citing works
Kim, H., Lee, S., Lee, S., Hong, S., Kang, H., & Kim, N. (2019) · JMIR mHealth and uHealth · doi:10.2196/14149 · Cited by 131 · see citing works
Xu, X., Chikersal, P., Doryab, A., Villalba, D. K., Dutcher, J. M., Tumminia, M. J., Althoff, T., Cohen, S., Creswell, K. G., Creswell, J. D., et al. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3351274 · Cited by 125 · see citing works
Mobile Behavioral Sensing for Outpatients and Inpatients With Schizophrenia
Ben‐Zeev, D., Wang, R., Abdullah, S., Brian, R., Scherer, E. A., Mistler, L. A., Hauser, M., Kane, J. M., Campbell, A. T., & Choudhury, T. (2015) · Psychiatric Services · doi:10.1176/appi.ps.201500130 · Cited by 125 · see citing works
Self-citationMasoud, M. Z., Jaradat, Y. M., Manasrah, A. A., & Jannoud, I. (2019) · Journal of Sensors · doi:10.1155/2019/6514520 · Cited by 125 · see citing works
Place, S., Blanch-Hartigan, D., Rubin, C., Gorrostieta, C., Mead, C., Kane, J., Marx, B. P., Feast, J., Deckersbach, T., Pentland, A. “., et al. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6678 · Cited by 125 · see citing works
Digital phenotyping for mental health of college students: a clinical review
Melcher, J., Hays, R., & Torous, J. (2020) · Evidence Based Mental Health · doi:10.1136/ebmental-2020-300180 · Cited by 121 · see citing works
Big data in psychology: A framework for research advancement.
Adjerid, I., & Kelley, K. (2018) · American Psychologist · doi:10.1037/amp0000190 · Cited by 122 · see citing works
Dubad, M., Winsper, C., Meyer, C., Livanou, M., & Marwaha, S. (2017) · Psychological Medicine · doi:10.1017/s0033291717001659 · Cited by 120 · see citing works
A Social Media Based Index of Mental Well-Being in College Campuses
Bagroy, S., Kumaraguru, P., & De Choudhury, M. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025909 · Cited by 115 · see citing works
Superior humidity sensor and photodetector of mesoporous ZnO nanosheets at room temperature
Gupta, S. P., Pawbake, A. S., Sathe, B. R., Late, D. J., & Walke, P. S. (2019) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2019.04.086 · Cited by 110 · see citing works
Birnbaum, M., Ernala, S. K., Rizvi, A., Arenare, E., Meter, A. V., de Choudhury, M., & Kane, J. M. (2019) · npj Schizophrenia · doi:10.1038/s41537-019-0085-9 · Cited by 96 · see citing works
Sequeira, L., Perrotta, S., LaGrassa, J., Merikangas, K. R., Kreindler, D. M., Kundur, D., Courtney, D., Szatmári, P., Battaglia, M., & Strauss, J. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2019.11.156 · Cited by 109 · see citing works
Mobile technology for mental health assessment
Areán, P. A., Ly, K. H., & Andersson, G. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.2/parean · Cited by 111 · see citing works
Boonstra, T. W., Nicholas, J., Wong, Q. J. J., Shaw, F., Townsend, S., & Christensen, H. M. (2018) · Journal of Medical Internet Research · doi:10.2196/10131 · Cited by 108 · see citing works
Gooding, P., & Kariotis, T. (2021) · JMIR Mental Health · doi:10.2196/24668 · Cited by 100 · see citing works
Relationships between smartphone social behavior and relapse in schizophrenia: A preliminary report
Buck, B., Scherer, E. A., Brian, R., Wang, R., Wang, W., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., & Ben‐Zeev, D. (2019) · Schizophrenia Research · doi:10.1016/j.schres.2019.03.014 · Cited by 96 · see citing works
Self-citationGLOBEM: Cross-Dataset Generalization of Longitudinal Human Behavior Modeling
Xu, X., Liu, X., Zhang, H., Wang, W., Nepal, S., Sefidgar, Y., Seo, W., Kuehn, K. S., Huckins, J. F., Morris, M. E., et al. (2022) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3569485 · Cited by 96 · see citing works
Self-citationBentley, K. H., Kleiman, E. M., Elliott, G., Huffman, J. C., & Nock, M. K. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2018.11.017 · Cited by 88 · see citing works
Immediate Mood Scaler: Tracking Symptoms of Depression and Anxiety Using a Novel Mobile Mood Scale
Nahum, M., Vleet, T. V., Sohal, V. S., Mirzabekov, J. J., Rao, V. R., Wallace, D. L., Lee, M. B., Dawes, H. E., Stark‐Inbar, A., Jordan, J. T., et al. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6544 · Cited by 91 · see citing works
Smartphone-Based Tracking of Sleep in Depression, Anxiety, and Psychotic Disorders
Aledavood, T., Torous, J. B., Triana, A. M., Naslund, J. A., Onnela, J.-P. “., & Keshavan, M. S. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1043-y · Cited by 90 · see citing works
Smartphones and the Neuroscience of Mental Health
Gillan, C. M., & Rutledge, R. B. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-101220-014053 · Cited by 84 · see citing works
Rooksby, J., Morrison, A., & Murray-Rust, D. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300655 · Cited by 87 · see citing works
The utility of smartphone-based, ecological momentary assessment for depressive symptoms
Yim, S. J., Lui, L. M. W., Lee, Y. L., Rosenblat, J. D., Ragguett, R., Park, C., Subramaniapillai, M., Cao, B., Zhou, A. J., Rong, C., et al. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.05.116 · Cited by 82 · see citing works
Predicting depressive symptoms using smartphone data
Ware, S., Yue, C., Morillo, R., Lü, J., Shang, C., Bi, J., Kamath, J., Russell, A. C., Bamis, A., & Wang, B. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100093 · Cited by 86 · see citing works
Joint modeling of heterogeneous sensing data for depression assessment via multi-task learning
Lu, J., Shang, C., Yue, C., Morillo, R., Ware, S., Kamath, J., Bamis, A., Russell, A., Wang, B., & Bi, J. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3191753 · Cited by 85 · see citing works
A Survey of Computational Methods for Online Mental State Assessment on Social Media
Ríssola, E. A., Losada, D. E., & Crestani, F. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3437259 · Cited by 84 · see citing works
Cao, J., Truong, A. L., Banu, S., Shah, A. A., Sabharwal, A., & Moukaddam, N. (2019) · doi:10.2196/preprints.14045 · Cited by 81 · see citing works
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 · see citing works
Sinha Deb, K., Tuli, A., Sood, M., Chadda, R., Verma, R., Kumar, S., Ganesh, R., & Singh, P. (2018) · PLOS ONE · doi:10.1371/journal.pone.0203353 · Cited by 79 · see citing works
Kanarachos, S. A., Christopoulos, S. G., & Chroneos, A. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.03.023 · Cited by 79 · see citing works
Zarate, D., Stavropoulos, V., Ball, M., Collier, G. D. S., & Jacobson, N. C. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04013-y · Cited by 79 · see citing works
Wearable devices: Cross benefits from healthcare to construction
Abuwarda, Z., Mostafa, K., Oetomo, A., Hegazy, T., & Morita, P. P. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104501 · Cited by 79 · see citing works
Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being
Woodward, K., Kanjo, E., Brown, D. J., McGinnity, T. M., Inkster, B., Macintyre, D. J., & Tsanas, A. (2022) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.3015018 · Cited by 18 · see citing works
Zhang, R., Ringland, K. E., Paan, M., Mohr, D. C., & Reddy, M. C. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445771 · Cited by 75 · see citing works
Luther, L., Gearhart, T., Fukui, S., Morse, G. A., Rollins, A. L., & Salyers, M. P. (2016) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000234 · Cited by 74 · see citing works
Masud, M. T., Mamun, M. A., Thapa, K., Lee, D. H., Griffiths, M. D., & Yang, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103371 · Cited by 73 · see citing works
Anomalies Detection Through Smartphone Sensors: A Review
Krichen, M. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3051931 · Cited by 73 · see citing works
Kargarandehkordi, A., Li, S., Lin, K., Phillips, K. T., Benzo, R. M., & Washington, P. Y. (2025) · Biosensors · doi:10.3390/bios15040202 · Cited by 72 · see citing works
Matteo, D. D., Fine, A., Fotinos, K., Rose, J., & Katzman, M. A. (2018) · JMIR Mental Health · doi:10.2196/mental.9539 · Cited by 68 · see citing works
Hilty, D. M., Armstrong, C. M., Luxton, D. D., Gentry, M. T., & Krupinski, E. A. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00199-2 · Cited by 68 · see citing works
Meaningless comparisons lead to false optimism in medical machine learning
DeMasi, O., Kording, K., & Recht, B. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184604 · Cited by 63 · see citing works
Kornfield, R., Zhang, R., Nicholas, J., Schueller, S. M., Cambo, S. A., Mohr, D. C., & Reddy, M. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376309 · Cited by 64 · see citing works
Leveraging Collaborative-Filtering for Personalized Behavior Modeling
Xu, X., Chikersal, P., Dutcher, J. M., Sefidgar, Y. S., Seo, W., Tumminia, M. J., Villalba, D. K., Cohen, S., Creswell, K. G., Creswell, J. D., et al. (2021) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3448107 · Cited by 65 · see citing works
Towards relatable explainable AI with the perceptual process
Zhang, W., & Lim, B. Y. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501826 · Cited by 67 · see citing works
The promise of digital mood tracking technologies: are we heading on the right track?
Malhi, G. S., Hamilton, A., Morris, G., Mannie, Z., Das, P., & Outhred, T. (2017) · Evidence Based Mental Health · doi:10.1136/eb-2017-102757 · Cited by 62 · see citing works
Müller, S. R., Peters, H., Matz, S., Wang, W., & Harari, G. M. (2020) · European Journal of Personality · doi:10.1002/per.2262 · Cited by 63 · see citing works
Huang, Z., Epps, J., & Joachim, D. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054323 · Cited by 61 · see citing works
Analyzing mHealth Engagement: Joint Models for Intensively Collected User Engagement Data
Scherer, E. A., Ben‐Zeev, D., Li, Z., & Kane, J. M. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6474 · Cited by 61 · see citing works
Self-citationPrabhakar, A. S., Guerra‐Reyes, L., Kleinschmidt, V. M., Jelen, B., MacLeod, H., Connelly, K., & Siek, K. A. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025546 · Cited by 60 · see citing works
Dogrucu, A., Perucic, A., Isaro, A., Ball, D., Toto, E., Rundensteiner, E. A., Agu, E., Davis-Martin, R. E., & Boudreaux, E. D. (2020) · Smart Health · doi:10.1016/j.smhl.2020.100118 · Cited by 62 · see citing works
Bidargaddi, N., Musiat, P., Makinen, V.-P., Ermes, M., Schrader, G., & Licinio, J. (2017) · Molecular Psychiatry · doi:10.1038/mp.2016.224 · Cited by 60 · see citing works
Smartphone apps for psychological health: A brief state of the science review.
Bush, N. E., Armstrong, C. M., & Hoyt, T. (2018) · Psychological Services · doi:10.1037/ser0000286 · Cited by 61 · see citing works
Investigation of Speech Landmark Patterns for Depression Detection
Huang, Z., Epps, J., & Joachim, D. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2944380 · Cited by 57 · see citing works
Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V. (2019) · JMIR mHealth and uHealth · doi:10.2196/11041 · Cited by 56 · see citing works
Rout, A., Nitoslawski, S. A., Ladle, A., & Galpern, P. (2021) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101705 · Cited by 54 · see citing works
Application of Machine Learning to Predict Dietary Lapses During Weight Loss
Goldstein, S. P., Zhang, F., Thomas, J. G., Butryn, M. L., Herbert, J. D., & Forman, E. M. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818775757 · Cited by 56 · see citing works
Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review
Qirtas, M. M., Zafeiridi, E., Pesch, D., & White, E. B. (2022) · JMIR mhealth and uhealth · doi:10.2196/34638 · Cited by 51 · see citing works
Using global positioning systems to study health-related mobility and participation
Brusilovskiy, E., Klein, L. A., & Salzer, M. S. (2016) · Social Science & Medicine · doi:10.1016/j.socscimed.2016.06.001 · Cited by 54 · see citing works
Huang, Z., Epps, J., Joachim, D., & Sethu, V. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2949419 · Cited by 54 · see citing works
Fusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
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. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51 · see citing works
Self-citationPersuasive E-Health Design for Behavior Change
Baumeister, H., Kraft, R., Baumel, A., Pryss, R., & Messner, E.-M. (2019) · Studies in Neuroscience, Psychology and Behavioral Economics · doi:10.1007/978-3-030-31620-4_17 · Cited by 53 · see citing works
Maharjan, S. M., Poudyal, A., van Heerden, A. C., Byanjankar, P., Thapa, A., Islam, C., Kohrt, B. A., & Hagaman, A. K. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01473-2 · Cited by 51 · see citing works
From Classification to Clinical Insights
Englhardt, Z., Ma, C., Morris, M. E., Chang, C.-C., Xu, X., Qin, L., McDuff, D., Liu, X., Patel, S., & Iyer, V. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3659604 · Cited by 66 · see citing works
Müller, S. R., Chen, X., Peters, H., Chaintreau, A., & Matz, S. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93087-x · Cited by 51 · see citing works
Huang, Z., Epps, J., Joachim, D., & Chen, M. (2018) · Interspeech 2018 · doi:10.21437/interspeech.2018-1743 · Cited by 52 · see citing works
Unlocking stress and forecasting its consequences with digital technology
Goodday, S., & Friend, S. H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0151-8 · Cited by 50 · see citing works
Using digital phenotyping to understand health-related outcomes: A scoping review
Lee, K., Lee, T. C., Yefimova, M., Kumar, S., Puga, F., Azuero, A., Kamal, A. H., Bakitas, M. A., Wright, A. A., Demiris, G., et al. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105061 · Cited by 52 · see citing works
Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study
Hung, G. C., Yang, P.-C., Chang, C.-C., Chiang, J.-H., & Chen, Y. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5551 · Cited by 49 · see citing works
Hallgren, K. A., Bauer, A. M., & Atkins, D. C. (2017) · Depression and Anxiety · doi:10.1002/da.22640 · Cited by 45 · see citing works
Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study
DeMasi, O., Feygin, S., Dembo, A., Aguilera, A., & Recht, B. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7820 · Cited by 53 · see citing works
Barac, M., Scaletty, S. J., Hassett, L. C., Stillwell, A., Croarkin, P. E., Chauhan, M., Chesak, S. S., Bobo, W. V., Athreya, A. P., & Dyrbye, L. N. (2024) · Journal of Medical Internet Research · doi:10.2196/50253 · Cited by 51 · see citing works
Behavioral Health Information Technology: From Chaos To Clarity
Ranallo, P. A., Kilbourne, A. M., Whatley, A. S., & Pincus, H. A. (2016) · Health Affairs · doi:10.1377/hlthaff.2016.0013 · Cited by 49 · see citing works
- Authors
- Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T.
- Affiliations
- Dartmouth College (US)
- Venue
- Psychiatric Rehabilitation Journal
- Published
- 2015-09
- Volume
- 38
- Issue
- 3
- Pages
- 218-226
- ISSN
- 1095-158X (ISSN-L)
- 1559-3126
- PMC
- PMC4564327
- OpenAlex
- W2093973716
- Cited by
- 429
- References
- 42
- Citations by year
- 2026: 35 · 2025: 33 · 2024: 36 · 2023: 24 · 2022: 54 · 2021: 55 · 2020: 51 · 2019: 50 · 2018: 43 · 2017: 32 · 2016: 13 · 2015: 2 · 2013: 1
- FWCI
- 45.18
- Type
- journal article
- Language
- en
- Update notices
- correction (2015-12-21)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Psychiatric Rehabilitation Journal (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (R01MH103148)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- smartphone sensors, mental health assessment, non-intrusive monitoring, daily stress, depression, loneliness, sleep duration, kinesthetic activities, speech duration, young adults, wearable devices, early detection of mental health problems, GPS tracking
- MeSH terms
- Smartphone, Adult, Mental Disorders, Female, Humans, Male, Telemedicine, Monitoring, Ambulatory, Young Adult
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
