Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones
Open accessAbstract
Prior research has recently shown that passively collected sensor data collected within the contexts of persons daily lives via smartphones and wearable sensors can distinguish those with major depressive disorder (MDD) from controls, predict MDD severity, and predict changes in MDD severity across days and weeks. Nevertheless, very little research has examined predicting depressed mood within a day, which is essential given the large amount of variation occurring within days. The current study utilized passively collected sensor data collected from a smartphone application to future depressed mood from hour-to-hour in an ecological momentary assessment study in a sample reporting clinical levels of depression (N = 31). Using a combination of nomothetic and idiographically-weighted machine learning models, the results suggest that depressed mood can be accurately predicted from hour to hour with an average correlation between out of sample predicted depressed mood levels and observed depressed mood of 0.587, CI [0.552, 0.621]. This suggests that passively collected smartphone data can accurately predict future depressed mood among a sample reporting clinical levels of depression. If replicated in other samples, this modeling framework may allow just-in-time adaptive interventions to treat depression as it changes in the context of daily life.
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?
Multimodal and intensive sampling data streams might be particularly promising inputs for machine learning models because these models can often handle many correlated predictors.
“Multimodal and intensive sampling data streams (for example, experience sampling and passive measures gathered through smartphones and other electronic devices) used in some MBI studies133 and mental health research broadly134,135 might be particularly promising inputs for machine learning models, given that these models can often handle many correlated predictors136.”
From A common factors perspective on mindfulness-based interventions (2022) · cites this paper as [136]
Passive sensor data can be used to predict various symptoms of mental disorders or mental health problems.
“This type of research has established that passive sensor data can be used to predict various symptoms of mental disorders or mental health problems (23, 33–38), as well as their future development [e.g., (39)].”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [39]
The inherent clinical heterogeneity associated with mental disorders may necessitate a greater focus on inter-individual differences to ensure generalisability to unseen cases.
“However, it has been recognised that the inherent clinical heterogeneity associated with mental disorders may necessitate a greater focus on inter-individual differences to ensure generalisability to unseen cases (39, 43, 44).”
From Personalised depression forecasting using mobile sensor data and ecological momentary assessment (2022) · cites this paper as [39]
Only two studies have combined physiological and smartphone data streams in models to monitor changing depressive symptoms.
“only two studies have combined these data streams in models to monitor changing depressive symptoms (26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
Prior investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy.
“This has resulted in possible reliability problems, since these investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy (14–18, 23–27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
Studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.
“Those studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) and HRV.
“Previous models estimating mood states with moderate accuracy have included measures of heart rate (HR) (27, 44) and HRV (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
Previous evaluations of models to estimate depression passively have primarily relied on examining correlations between estimated and observed symptoms.
“Previous evaluations of models to estimate depression passively have primarily relied on examining correlations between estimated and observed symptoms (18, 26, 27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
The time-split mobile phone model's correlation (r = 0.7) was higher than a previous model combining features from the fitbit and from smartphones (best r2 = 0.44 or r = 0.66) and a model aggregating mobile-based and physiological features (r = 0.58).
“was higher than the one of a previous model combining features from the fitbit and from smartphones (the best model yielded an r2 = 0.44 or r = 0.66) (27) and the one of a model aggregating mobile-based and physiological features (r = 0.58) (26).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [26]
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
Geurts, P., Ernst, D., & Wehenkel, L. (2006) · Machine Learning · doi:10.1007/s10994-006-6226-1 · Cited by 9,300 · see citing works
ranger : A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Wright, M. N., & Ziegler, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v077.i01 · Cited by 3,814 · see citing works
Depression Anxiety Stress Scales
Lovibond, S. H., & Lovibond, P. F. (2011) · PsycTESTS Dataset · doi:10.1037/t01004-000 · Cited by 2,416 · see citing works
Cacioppo, J. T., Hughes, M. E., Waite, L. J., Hawkley, L. C., & Thisted, R. A. (2006) · Psychology and Aging · doi:10.1037/0882-7974.21.1.140 · Cited by 2,380 · see citing works
Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9830-8 · Cited by 2,171 · see citing works
Psychometric properties of the Depression Anxiety Stress Scales (DASS) in clinical samples
Brown, T. A., Chorpita, B. F., Korotitsch, W., & Barlow, D. H. (1997) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(96)00068-x · Cited by 1,860 · see citing works
Blanco, C., Okuda, M., Wright, C., Hasin, D. S., Grant, B. F., Liu, S.-M., & Olfson, M. (2008) · Archives of General Psychiatry · doi:10.1001/archpsyc.65.12.1429 · Cited by 1,582 · see citing works
Lack of group-to-individual generalizability is a threat to human subjects research
Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711978115 · Cited by 1,099 · 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 · cited 6× · see citing works
“Of these studies, two of three studies found strong agreement between predicted and observed daily depression outcomes (although this depended upon the analytic approach) [30,36], whereas one study found that they were unable to significantly predict depressed mood within the day in a small sample [31].”
BackgroundMethodResultInfluentialJacobson, N. C., & Newman, M. G. (2017) · Psychological Bulletin · doi:10.1037/bul0000111 · Cited by 576 · see citing works
Self-citationTwenge, J. M., Gentile, B., DeWall, C. N., Ma, D., Lacefield, K., & Schurtz, D. R. (2009) · Clinical Psychology Review · doi:10.1016/j.cpr.2009.10.005 · Cited by 559 · see citing works
Canzian, L., & Musolesi, M. (2015) · Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2750858.2805845 · Cited by 477 · see citing works
Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., & Campbell, A. T. (2015) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000130 · Cited by 429 · see citing works
Exploring the idiographic dynamics of mood and anxiety via network analysis
Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000311 · Cited by 385 · see citing works
Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384 · see citing works
Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild
Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.5960 · Cited by 333 · see citing works
Depressive symptoms in late life: a 10-year follow-up
Heikkinen, R.-L., & Kauppinen, M. (2004) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2003.10.004 · Cited by 319 · see citing works
The relationship between mobile phone location sensor data and depressive symptom severity
Saeb, S., Lattie, E. G., Schueller, S. M., Kording, K. P., & Mohr, D. C. (2016) · PeerJ · doi:10.7717/peerj.2537 · Cited by 313 · see citing works
Diurnal mood variation in major depressive disorder
Peeters, F., Berkhof, J., Delespaul, P., Rottenberg, J., & Nicolson, N. A. (2006) · Emotion · doi:10.1037/1528-3542.6.3.383 · Cited by 306 · see citing works
Psychometric properties of the Depression Anxiety Stress Scales (DASS) in depressed clinical samples
Page, A. C., Hooke, G. R., & Morrison, D. L. (2007) · British Journal of Clinical Psychology · doi:10.1348/014466506x158996 · Cited by 301 · see citing works
Toward a dynamic model of psychological assessment: Implications for personalized care.
Fisher, A. J. (2015) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000026 · Cited by 263 · see citing works
Depression and Cardiovascular Disorders
Whooley, M. A., & Wong, J. M. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185526 · Cited by 241 · see citing works
Zulueta, J., Piscitello, A., Rasic, M., Easter, R., Babu, P., Langenecker, S. A., McInnis, M., Ajilore, O., Nelson, P. C., Ryan, K., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9775 · Cited by 233 · see citing works
Automatic detection of social rhythms in bipolar disorder
Abdullah, S., Matthews, M. L., Frank, E., Doherty, G. J., Gay, G. K., & Choudhury, T. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv200 · Cited by 225 · see citing works
Jacobson, N. C., Lekkas, D., Price, G. D., Heinz, M. V., Song, M., O’Malley, A., & Barr, P. J. (2020) · JMIR Mental Health · doi:10.2196/19347 · Cited by 195 · see citing works
Self-citationUsing Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study
Beiwinkel, T., Kindermann, S., Maier, A., Kerl, C., Moock, J., Barbian, G., & Rössler, W. (2016) · JMIR Mental Health · doi:10.2196/mental.4560 · Cited by 191 · see citing works
Smartphone data as objective measures of bipolar disorder symptoms
Faurholt-Jepsen, M., Frost, M., Vinberg, M., Christensen, E. M., Bardram, J. E., & Kessing, L. V. (2014) · Psychiatry Research · doi:10.1016/j.psychres.2014.03.009 · Cited by 185 · see citing works
Detecting Bipolar Depression From Geographic Location Data
Palmius, N., Tsanas, A., Saunders, K. E. A., Bilderbeck, A. C., Geddes, J. R., Goodwin, G. M., & De Vos, M. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2611862 · Cited by 180 · see citing works
Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6820 · Cited by 172 · see citing works
Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors
Jacobson, N. C., Summers, B., & Wilhelm, S. (2020) · Journal of Medical Internet Research · doi:10.2196/16875 · Cited by 162 · see citing works
Self-citationDigital biomarkers of mood disorders and symptom change
Jacobson, N. C., Weingarden, H., & Wilhelm, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0078-0 · Cited by 160 · see citing works
Self-citationOpen trial of a personalized modular treatment for mood and anxiety
Fisher, A. J., Bosley, H. G., Fernandez, K. C., Reeves, J. W., Soyster, P. D., Diamond, A. E., & Barkin, J. (2019) · Behaviour Research and Therapy · doi:10.1016/j.brat.2019.01.010 · Cited by 156 · see citing works
Avoidance mediates the relationship between anxiety and depression over a decade later
Jacobson, N. C., & Newman, M. G. (2014) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2014.03.007 · Cited by 149 · see citing works
Self-citationFaurholt-Jepsen, M., Vinberg, M., Frost, M., Debel, S., Margrethe Christensen, E., Bardram, J. E., & Kessing, L. V. (2016) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1502 · Cited by 146 · see citing works
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works
Cognitive-Behavioral Therapy in the Digital Age: Presidential Address
Wilhelm, S., Weingarden, H. M., Ladis, I., Braddick, V., Shin, J. Y., & Jacobson, N. C. (2019) · Behavior Therapy · doi:10.1016/j.beth.2019.08.001 · Cited by 122 · see citing works
Self-citationCurrent research and trends in the use of smartphone applications for mood disorders
Torous, J., & Powell, A. C. (2015) · Internet Interventions · doi:10.1016/j.invent.2015.03.002 · Cited by 121 · see citing works
The accuracy of passive phone sensors in predicting daily mood
Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A. (2019) · Depression and Anxiety · doi:10.1002/da.22822 · Cited by 119 · see citing works
Diurnal mood variation in depression: A signal of disturbed circadian function?
Murray, G. (2007) · Journal of Affective Disorders · doi:10.1016/j.jad.2006.12.001 · Cited by 118 · see citing works
Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety
Saeb, S., Lattie, E. G., Kording, K. P., & Mohr, D. C. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7297 · Cited by 116 · see citing works
Jacobson, N. C., Lord, K. A., & Newman, M. G. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.01.011 · Cited by 99 · see citing works
Self-citationUsing digital phenotyping to accurately detect depression severity
Jacobson, N. C., Weingarden, H., & Wilhelm, S. (2019) · Journal of Nervous & Mental Disease · doi:10.1097/nmd.0000000000001042 · Cited by 94 · see citing works
Self-citationJoint 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
Thirty Years of the National Survey of Counseling Center Directors: A Personal Account
Gallagher, R. P. (2012) · Journal of College Student Psychotherapy · doi:10.1080/87568225.2012.685852 · Cited by 83 · see citing works
Smartphones in Mental Health: Detecting Depressive and Manic Episodes
Osmani, V. (2015) · IEEE Pervasive Computing · doi:10.1109/mprv.2015.54 · Cited by 82 · see citing works
Mehrotra, A., Hendley, R., & Musolesi, M. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968299 · Cited by 77 · see citing works
The Idiographic / Nomothetic Dichotomy: Tracing Historical Origins of Contemporary Confusions
Robinson, O. C. (2011) · History & Philosophy of Psychology · doi:10.53841/bpshpp.2011.13.2.32 · Cited by 71 · see citing works
“Being all alone makes me sad”: loneliness in older adults with depressive symptoms
van Beljouw, I. M. J., van Exel, E. R., Gierveld, J. D. J., Comijs, H. C., Heerings, M., Stek, M. L., & van Marwijk, H. (2014) · International Psychogeriatrics · doi:10.1017/s1041610214000581 · Cited by 69 · see citing works
Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls
Muaremi, A., Gravenhorst, F., Grünerbl, A., Bert Arnrich, A., & Tröster, G. (2014) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-11564-1_11 · Cited by 62 · see citing works
Arving, C., Glimelius, B. L. G., & Brandberg, Y. (2007) · Quality of Life Research · doi:10.1007/s11136-007-9275-4 · Cited by 62 · see citing works
Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression
Farhan, A. A., Lu, J., Bi, J., Russell, A., Wang, B., & Bamis, A. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.27 · Cited by 61 · see citing works
Bolkhovsky, J., Scully, C. G., & Chon, K. H. (2012) · doi:10.1109/embc.2012.6346253 · Cited by 58 · see citing works
Identification and characterization of greater mood variance in depression
Hall, D. P., Sing, H. C., & Romanoski, A. J. (1991) · American Journal of Psychiatry · doi:10.1176/ajp.148.10.1341 · Cited by 57 · see citing works
Elections Have Consequences for Student Mental Health: An Accidental Daily Diary Study
Roche, M. J., & Jacobson, N. C. (2018) · Psychological Reports · doi:10.1177/0033294118767365 · Cited by 53 · see citing works
Self-citationCollege Student Adjustment and Coping
Cousins, C. L., Servaty‐Seib, H. L., & Lockman, J. D. (2016) · OMEGA - Journal of Death and Dying · doi:10.1177/0030222815598911 · Cited by 50 · see citing works
Jacobson, N. C., & Newman, M. (2015) · Depression and Anxiety · doi:10.1002/da.22402 · Cited by 49 · see citing works
Self-citationLoneliness in Relation to Depression and Self-Esteem
Ouellet, R., & Joshi, P. (1986) · Psychological Reports · doi:10.2466/pr0.1986.58.3.821 · Cited by 49 · see citing works
Detection of behavior change in people with depression
Doryab, A., Min, J. K., Wiese, J., et al. (2018) · KiltHub Repository · doi:10.1184/r1/6469988.v1 · Cited by 46 · see citing works
Detection of behavior change in people with depression
Doryab, A., Min, J. K., Wiese, J., Zimmerman, J., & Hong, J. I. (2018) · KiltHub Repository · doi:10.1184/r1/6469988 · Cited by 45 · see citing works
Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure
Ware, S., Yue, C., Morillo, R., Lü, J., Shang, C., Kamath, J., Bamis, A., Bi, J., Russell, A. C., & Wang, B. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3287073 · Cited by 41 · see citing works
Affective models of depression and anxiety: Extension to within-person processes in daily life
Naragon-Gainey, K. (2019) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.09.061 · Cited by 37 · see citing works
Wardenaar, K. J., Wanders, R. B. K., Jeronimus, B. F., & de Jonge, P. (2017) · Journal of Psychopathology and Behavioral Assessment · doi:10.1007/s10862-017-9626-6 · Cited by 34 · see citing works
Variability of activity patterns across mood disorders and time of day
Krane‐Gartiser, K., Vaaler, A. E., Fasmer, O. B., Sørensen, K., Morken, G., & Scott, J. L. (2017) · BMC Psychiatry · doi:10.1186/s12888-017-1574-x · Cited by 33 · see citing works
Individualizing Risk Assessment: Balancing Idiographic and Nomothetic Data
DeMatteo, D. S., Batastini, A. B., Foster, E. E., & Hunt, E. D. (2010) · Journal of Forensic Psychology Practice · doi:10.1080/15228932.2010.481244 · Cited by 23 · see citing works
Jacobson, N. C., & O’Cleirigh, C. M. (2019) · The British Journal of Psychiatry · doi:10.1192/bjp.2019.168 · Cited by 20 · see citing works
Self-citationThe Structure of Depression in Community Based Young Adolescent, Older Adolescent, and Adult Mothers
Reis, J. (1989) · Family Relations · doi:10.2307/583670 · Cited by 15 · see citing works
Bradley, K. L., Santor, D. A., & Oram, R. (2016) · Canadian Journal of Community Mental Health · doi:10.7870/cjcmh-2015-025 · Cited by 5 · see citing works
College Student Adjustment and Coping for Bereaved and Nonbereaved College Students
Cousins, C. L., Servaty‐Seib, H. L., & Lockman, J. D. (2014) · PsycEXTRA Dataset · doi:10.1037/e563502014-001 · Cited by 3 · see citing works
Recognition and Treatment of Depression
Swartz, H. A. (2005) · The AMA Journal of Ethic · doi:10.1001/virtualmentor.2005.7.6.cprl1-0506 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [30]Canzian L. Musolesi M. Trajectories of depression Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers—UbiComp ’15 Association for Computing Machinery (ACM) New York, NY, USA 2015 1293 1304
- [36]Krane-Gartiser K. Vaaler A.E. Fasmer O.B. Sørensen K. Morken G. Scott J. Variability of activity patterns across mood disorders and time of day BMC Psychiat. 2017 17 404 10.1186/s12888-017-1574-x PMC5735510 29258468 · DOI
Cited by
Sorted by citations
Ecological momentary interventions for mental health: A scoping review
Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. (2021) · PLOS ONE · doi:10.1371/journal.pone.0248152 · Cited by 235 · 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
Digital Phenotyping for Monitoring Mental Disorders: Systematic Review
Bufano, P., Laurino, M., Said, S., Tognetti, A., & Menicucci, D. (2023) · Journal of Medical Internet Research · doi:10.2196/46778 · Cited by 194 · see citing works
Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors
Pedrelli, P., Fedor, S., Ghandeharioun, A., Howe, E., Ionescu, D. F., Bhathena, D., Fisher, L. B., Cusin, C., Nyer, M., Yeung, A., et al. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.584711 · Cited by 158 · see citing works
Personalized machine learning of depressed mood using wearables
Shah, R. V., Grennan, G., Zafar-Khan, M., Alim, F., Dey, S., Ramanathan, D., & Mishra, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01445-0 · Cited by 123 · see citing works
Deisenhofer, A., Barkham, M., Beierl, E. T., Schwartz, B., Doorn, K. A., Beevers, C. G., Berwian, I. M., Blackwell, S. E., Bockting, C., Brakemeier, E., et al. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104443 · Cited by 115 · see citing works
Self-citationThe dynamics, processes, mechanisms, and functioning of personality: An overview of the field
Kuper, N., Modersitzki, N., Phan, L. V., & Rauthmann, J. F. (2021) · British Journal of Psychology · doi:10.1111/bjop.12486 · Cited by 107 · see citing works
Jacobson, N. C., & Bhattacharya, S. (2022) · Behaviour Research and Therapy · doi:10.1016/j.brat.2021.104013 · Cited by 92 · see citing works
Self-citationUnderstanding the construction of 'behavior' in smartphone addiction: A scoping review.
James, R., Dixon, G., Dragomir, M.-G., Thirlwell, E., & Hitcham, L. (2022) · Addictive Behaviours · doi:10.1016/j.addbeh.2022.107503 · Cited by 83 · see citing works
de Moura, I. R., Teles, A. S., Viana, D., Marques, J., Coutinho, L., & Silva, F. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104278 · Cited by 91 · see citing works
Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review
Mendes, J. P. M., de Moura, I. R., van de Ven, P. W. J., Viana, D., Silva, F., Coutinho, L., Teixeira, S. S., Rodrigues, J. J. P. C., & Teles, A. S. (2021) · Journal of Medical Internet Research · doi:10.2196/28735 · Cited by 85 · see citing works
Asare, K. O., Moshe, I., Terhorst, Y., Vega, J., Hosio, S. J., Baumeister, H., Pulkki-Råbäck, L., & Ferreira, D. (2022) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2022.101621 · Cited by 82 · see citing works
Some Recommendations on the Use of Daily Life Methods in Affective Science
Kuppens, P., Dejonckheere, E., Kalokerinos, E. K., & Koval, P. (2022) · Affective Science · doi:10.1007/s42761-022-00101-0 · Cited by 78 · 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
Self-citationA common factors perspective on mindfulness-based interventions
Goldberg, S. B. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00090-8 · Cited by 75 · see citing works
Jacobson, N. C., Lekkas, D., Huang, R., & Thomas, N. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.12.086 · Cited by 70 · see citing works
Self-citationShen, S., Qi, W., Zeng, J., Shujie, L. I., Liu, X., Zhu, X., Dong, C., Wang, B., Shi, Y., Yao, J., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/77066 · Cited by 81 · see citing works
Teepe, G. W., Fonseca, A. D., Kleim, B., Jacobson, N. C., Salamanca-Sanabria, A., Car, L. T., Fleisch, E., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/29412 · Cited by 60 · see citing works
Self-citationFrom 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
Understanding behaviours in context using mobile sensing
Harari, G. M., & Gosling, S. D. (2023) · Nature Reviews Psychology · doi:10.1038/s44159-023-00235-3 · Cited by 56 · see citing works
Personalised depression forecasting using mobile sensor data and ecological momentary assessment
Kathan, A., Harrer, M., Küster, L., Triantafyllopoulos, A., He, X., Milling, M., Gerczuk, M., Yan, T., Rajamani, S. T., Heber, E., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.964582 · Cited by 47 · see citing works
Stamatis, C. A., Meyerhoff, J., Meng, Y., Lin, Z. C. C., Cho, Y. M., Liu, T., Karr, C. J., Liu, T., Curtis, B. L., Ungar, L., et al. (2024) · npj Mental Health Research · doi:10.1038/s44184-023-00041-y · Cited by 50 · see citing works
Digital phenotype of mood disorders: A conceptual and critical review
Maatoug, R., Oudin, A., Adrien, V., Saudreau, B., Bonnot, O., Millet, B., Ferreri, F., Mouchabac, S., & Bourla, A. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.895860 · Cited by 43 · see citing works
Ren, B., Balkind, E. G., Pastro, B., Israel, E. S., Pizzagalli, D. A., Rahimi-Eichi, H., Baker, J. T., & Webb, C. A. (2022) · Psychological Medicine · doi:10.1017/s0033291722002161 · Cited by 42 · see citing works
Digital Phenotyping in Livestock Farming
Neethirajan, S. R., & Kemp, B. (2021) · Animals · doi:10.3390/ani11072009 · Cited by 39 · see citing works
Lekkas, D., & Jacobson, N. C. (2021) · Scientific Reports · doi:10.1038/s41598-021-89768-2 · Cited by 38 · see citing works
Self-citationHehlmann, M. I., Schwartz, B., Lutz, T., Penedo, J. M. G., Rubel, J. A., & Lutz, W. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.613085 · Cited by 38 · see citing works
Kolenik, T. (2022) · Internet of things · doi:10.1007/978-3-030-91181-2_7 · Cited by 34 · see citing works
Ahmed, A., Ramesh, J., Ganguly, S., Aburukba, R., Sagahyroon, A. A., & Aloul, F. (2022) · Information · doi:10.3390/info13090406 · Cited by 33 · see citing works
Lekkas, D., Price, G. D., & Jacobson, N. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107029 · Cited by 26 · see citing works
Self-citationLangener, A. M., Stulp, G., Jacobson, N. C., Costanzo, A., Jagesar, R. R., Kas, M. J., & Bringmann, L. F. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459231202677 · Cited by 29 · see citing works
Self-citationAssociation between Daily Pattern of Physical Activity and Depression: A Systematic Review
Gianfredi, V., Ferrara, P., Pennisi, F., Casu, G., Amerio, A., Odone, A., Nucci, D., & Dinu, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19116505 · Cited by 30 · see citing works
Zierer, C., Behrendt, C., & Lepach-Engelhardt, A. C. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.03.163 · Cited by 30 · see citing works
Beames, J. R., Han, J., Shvetcov, A., Zheng, W. Y., Slade, A., Dabash, O., Rosenberg, J., O’Dea, B., Kasthurirathne, S. N., Hoon, L., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e35472 · Cited by 34 · see citing works
A Systematic Review of Location Data for Depression Prediction
Shin, J., & Bae, S. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20115984 · Cited by 29 · see citing works
Linardon, J., Chen, K., Gajjar, S., Eadara, A., Wang, S., Flathers, M., Burns, J., & Torous, J. B. (2025) · Psychiatry Research · doi:10.1016/j.psychres.2025.116483 · Cited by 32 · see citing works
Lazarus, G., & Rafaeli, E. (2023) · Journal of Psychopathology and Clinical Science · doi:10.1037/abn0000699 · Cited by 27 · see citing works
Digital phenotyping in psychiatry: A scoping review
Chia, A. Z. R., & Zhang, M. W. (2022) · Technology and Health Care · doi:10.3233/thc-213648 · Cited by 26 · see citing works
Machine learning applied to digital phenotyping: A systematic literature review and taxonomy
dos Santos, M. P., Heckler, W. F., Bavaresco, R. S., & Barbosa, J. L. V. (2024) · Computers in Human Behavior · doi:10.1016/j.chb.2024.108422 · Cited by 25 · see citing works
Zilcha-Mano, S., & Webb, C. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.609585 · Cited by 25 · see citing works
Hart, A., Reis, D., Prestele, E., & Jacobson, N. C. (2022) · Journal of Medical Internet Research · doi:10.2196/34015 · Cited by 22 · see citing works
Self-citationPark, Y., Park, S., & Lee, M. (2024) · Journal of Affective Disorders · doi:10.1016/j.jad.2024.06.035 · Cited by 24 · see citing works
Lekkas, D., Gyorda, J. A., Price, G. D., & Jacobson, N. C. (2023) · Behaviour Research and Therapy · doi:10.1016/j.brat.2023.104382 · Cited by 23 · see citing works
Self-citationNelson, B. W., Flannery, J., Flournoy, J. C., Duell, N., Prinstein, M. J., & Telzer, E. H. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13471 · Cited by 19 · see citing works
Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals
Nan, J., Herbert, M. S., Purpura, S., Henneken, A. N., Ramanathan, D., & Mishra, J. (2024) · Sensors · doi:10.3390/s24082640 · Cited by 22 · see citing works
Kadirvelu, B., Bel, T. B., Freccero, A., Simplicio, M. C. D., Nicholls, D., & Faisal, A. A. (2025) · Journal of Medical Internet Research · doi:10.2196/72501 · Cited by 30 · see citing works
Transforming mental health research and care through artificial intelligence
Opel, N., & Breakspear, M. J. (2026) · Science · doi:10.1126/science.adz9193 · Cited by 25 · see citing works
Zou, B., Zhang, X., Xiao, L., Bai, R., Li, X., Liang, H., Ma, H., & Wang, G. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3260301 · Cited by 23 · see citing works
Rahmani, A. M., Lai, J., Jafarlou, S., Azimi, I., Yunusova, A., Rivera, A. P., Labbaf, S., Anzanpour, A., Dutt, N. D., Jain, R., et al. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.933587 · Cited by 23 · see citing works
A collaborative realist review of remote measurement technologies for depression in young people
Walsh, A. E. L., Naughton, G., Sharpe, T., Zajkowska, Z., Malys, M. K., van Heerden, A. C., & Mondelli, V. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01793-5 · Cited by 26 · see citing works
Amin, R., Schreynemackers, S., Oppenheimer, H., Petrovic, M., Hegerl, U., & Reich, H. (2025) · Journal of Medical Internet Research · doi:10.2196/57418 · Cited by 24 · see citing works
Leveraging the Power of Nondisruptive Technologies to Optimize Mental Health Treatment: Case Study
Sadeh‐Sharvit, S., & Hollon, S. D. (2020) · JMIR Mental Health · doi:10.2196/20646 · Cited by 17 · see citing works
Yunusova, A., Lai, J., Rivera, A. P., Hu, S., Labbaf, S., Rahmani, A. M., Dutt, N. D., Jain, R., & Borelli, J. L. (2021) · JMIR Research Protocols · doi:10.2196/25775 · Cited by 15 · see citing works
Towards Personalised Mood Prediction and Explanation for Depression from Biophysical Data
Chatterjee, S., Mishra, J., Sundram, F., & Roop, P. (2023) · Sensors · doi:10.3390/s24010164 · Cited by 15 · see citing works
Slade, C. R., Sun, Y., Chen, W., Chen, C.-C., Benzo, R. M., & Washington, P. Y. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf025 · Cited by 17 · see citing works
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 17 · see citing works
Brügger, V., Kowatsch, T., & Jovanova, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-08003-4 · Cited by 17 · see citing works
Langener, A. M., Bringmann, L. F., Kas, M. J., & Stulp, G. (2024) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-023-01328-0 · Cited by 12 · see citing works
Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study
Wyant, K., Moshontz, H., Ward, S. B., Fronk, G. E., & Curtin, J. J. (2023) · JMIR mhealth and uhealth · doi:10.2196/41833 · Cited by 14 · see citing works
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 14 · see citing works
Schmitter‐Edgecombe, M., Luna, C. M., Dai, S., & Cook, D. J. (2024) · The Clinical Neuropsychologist · doi:10.1080/13854046.2024.2330143 · Cited by 12 · see citing works
"What Does Your Robot Do?" A Tabletop Role-Playing Game to Support Robot Design
Collins, S., & Šabanović, S. (2021) · IEEE RO-MAN · doi:10.1109/ro-man50785.2021.9515554 · Cited by 12 · see citing works
Kadirvelu, B., Bel, T. B., Wu, X., Burmester, V., Ananth, S., Branco, B. C. C. C., Serrano, B. G., Gledhill, J. A., Simplicio, M. C. D., Nicholls, D., et al. (2023) · JMIR Formative Research · doi:10.2196/44877 · Cited by 11 · see citing works
Webb, C. A., Ren, B., Rahimi-Eichi, H., Gillis, B. W., Chung, Y., & Baker, J. T. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03394-4 · Cited by 15 · see citing works
Self-citationCollins, A. C., Price, G. D., Woodworth, R. J., & Jacobson, N. C. (2023) · The Journal of Positive Psychology · doi:10.1080/17439760.2023.2254743 · Cited by 11 · see citing works
Self-citationPlaitano, E. G., McNeish, D., Bartels, S. M., Bell, K. R., Dallery, J., Grabinski, M. J., Kiernan, M., Lavoie, H. A., Lemley, S. M., Lowe, M. R., et al. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1467772 · Cited by 9 · see citing works
Leimhofer, J., Petrovic, M., Dominik, A., Heider, D., & Hegerl, U. (2025) · Interactive Journal of Medical Research · doi:10.2196/69686 · Cited by 7 · see citing works
Receptivity to mobile health interventions
Keller, R., von Wangenheim, F., Mair, J. L., & Kowatsch, T. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00006-x · Cited by 8 · see citing works
Blending digital therapeutics within the healthcare system
Keller, O. C., Budney, A. J., Struble, C. A., & Teepe, G. W. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90045-4.00016-2 · Cited by 8 · see citing works
HADD: High-Accuracy Detection of Depressed Mood
Liu, Y., Kang, K., & Doe, M. J. (2022) · Technologies · doi:10.3390/technologies10060123 · Cited by 8 · see citing works
Adolescents’ daily social media use and mood during the COVID-19 lockdown period
Dreier, M. J., Boyd, S. I., Jorgensen, S. L., Merai, R., Fedor, J., Durica, K. C., Low, C. A., & Hamilton, J. L. (2024) · Current Research in Ecological and Social Psychology · doi:10.1016/j.cresp.2024.100196 · Cited by 8 · see citing works
Garzón‐Partida, A. P., Padilla-Gómez, C. B., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1595243 · Cited by 8 · see citing works
Webb, C. A., & Fisher, H. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.19054 · Cited by 7 · see citing works
Vincent, P. M. D. R., Mahendran, N., Nebhen, J., Deepa, N., Srinivasan, K., & Hu, Y. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/9950332 · Cited by 7 · see citing works
Lee, J., Solomonov, N., Banerjee, S., Alexopoulos, G. S., & Sirey, J. A. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.732773 · Cited by 9 · see citing works
Lee, T., Lee, H.-J., Lee, J.-B., & Kim, J.-D. (2023) · Italian National Conference on Sensors · doi:10.3390/s23208544 · Cited by 8 · see citing works
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 6 · see citing works
Vega, J., Bell, B. T., Taylor, C. S., Xie, J., Ng, H., Honary, M., & McNaney, R. (2022) · JMIR Mental Health · doi:10.2196/32146 · Cited by 6 · see citing works
Predicting ADHD Symptoms Using Smartphone Sensing Data
Ware, S., Knouse, L. E., Draz, I., & Enikeeva, A. (2022) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3544793.3563430 · Cited by 7 · see citing works
Neural Mixed Effects for Nonlinear Personalized Predictions
Wörtwein, T., Allen, N. B., Sheeber, L., Auerbach, R. P., Cohn, J. F., & Morency, L. (2023) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3577190.3614115 · Cited by 6 · see citing works
Brown, R. D., Bondy, E., Prim, J., Dichter, G. S., & Schiller, C. E. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1448914 · Cited by 6 · see citing works
Weingarden, H. M., Meng, X., Armey, M. F., Onnela, J.-P. “., Jaroszewski, A. C., Armstrong, C. H., & Wilhelm, S. (2025) · Internet Interventions · doi:10.1016/j.invent.2025.100833 · Cited by 5 · see citing works
Fisher, H., Jaffe, N. M., Rahimi-Eichi, H., Forbes, E. E., Pizzagalli, D. A., Baker, J. T., & Webb, C. A. (2025) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-025-00045-w · Cited by 5 · see citing works
Müller-Bardorff, M., Schulz, A., Paersch, C., Recher, D., Schlup, B., Seifritz, E., Kolassa, I., Kowatsch, T., Fisher, A. J., Galatzer‐Levy, I. R., et al. (2022) · JMIR Research Protocols · doi:10.2196/42547 · Cited by 8 · see citing works
Using Mobile Daily Mood and Anxiety Self-ratings to Predict Depression Symptom Improvement
Sahoo, S., Shende, C., Hossain, M. Z., Patel, P. A., Wang, X., Mahmud, M. I., Bi, J., Kamath, J., Russell, A. C., Song, D., et al. (2024) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase60773.2024.00012 · Cited by 5 · see citing works
Technological Advances in Clinical Assessment
Heinz, M. V., Thomas, N., Nguyen, N. D., Griffin, T. Z., & Jacobson, N. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-818697-8.00171-0 · Cited by 5 · see citing works
Self-citationEdler, J.-S., Terhorst, Y., Pryss, R. C., Baumeister, H., & Cohrdes, C. (2024) · Journal of Medical Internet Research · doi:10.2196/45530 · Cited by 4 · see citing works
Garzón‐Partida, A. P., Magaña-Plascencia, K., Martínez-Fernández, D. E., Garcı́a‐Estrada, J., Luquı́n, S., & Fernández-Quezada, D. (2025) · JMIR Research Protocols · doi:10.2196/71374 · Cited by 5 · see citing works
Care for the Mind amid Chronic Diseases: An Interpretable AI Approach Using IoT
Xie, J., Zhao, X., Liu, X., & Fang, X. (2025) · Management Science · doi:10.1287/mnsc.2023.04183 · Cited by 4 · see citing works
Thapa, S., Tay, L., & Hou, D. (2021) · Research in occupational stress and well being · doi:10.1108/s1479-355520210000019007 · Cited by 5 · see citing works
Social aspects of the COVID-19 pandemic in the education system
Mukhametzyanov, I. S. (2021) · SHS Web of Conferences · doi:10.1051/shsconf/202110103006 · Cited by 4 · see citing works
Ghorbankhani, M., & Safara, M. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103320 · Cited by 5 · see citing works
Orgaz, G. B., Menéndez, H., Quintana-Quiroga, F., & Díaz-Álvarez, A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109683 · Cited by 3 · see citing works
Gyorda, J. A., Lekkas, D., & Jacobson, N. C. (2024) · Digital Biomarkers · doi:10.1159/000540546 · Cited by 4 · see citing works
Self-citationKilshaw, R. E., Boggins, A., Everett, O., Butner, E., Leifker, F. R., & Baucom, B. (2024) · JMIR Research Protocols · doi:10.2196/53857 · Cited by 3 · see citing works
Jacob, J., & Kannan, K. S. (2024) · Informatics and Automation · doi:10.15622/ia.23.6.7 · Cited by 3 · see citing works
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 · see citing works
Kadirvelu, B., Bel, T. B., Freccero, A., Simplicio, M. C. D., Nicholls, D., & Faisal, A. A. (2025) · doi:10.2196/preprints.72501 · Cited by 3 · see citing works
Comparing training window selection methods for prediction in non‐stationary time series
Petersen, F., Haslbeck, J. M. B., Tendeiro, J. N., Langener, A. M., Kas, M. J., Rizopoulos, D., & Bringmann, L. F. (2026) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/bmsp.70018 · Cited by 2 · see citing works
Jacobucci, R., Shao, W., Kobrinsky, V., & Ammerman, B. A. (2026) · JMIR Mental Health · doi:10.2196/90581 · Cited by 4 · see citing works
- Authors
- Jacobson, N. C., & Chung, Y. J.
- Affiliations
- Dartmouth College (US)
- Corresponding author
- Jacobson, Nicholas C.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-06-24
- Volume
- 20
- Issue
- 12
- Pages
- 3572
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7349045
- OpenAlex
- W3037651932
- Cited by
- 139
- References
- 69
- Citations by year
- 2026: 23 · 2025: 28 · 2024: 23 · 2023: 18 · 2022: 23 · 2021: 21 · 2020: 3
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute on Drug Abuse (P30DA029926)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- passive sensing, major depressive disorder, smartphone sensor data, ecological momentary assessment, just-in-time adaptive interventions
- MeSH terms
- Smartphone, Affect, Depressive Disorder, Major, Major Depressive Disorder, Humans, Students
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
