Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study
Open accessPublished version of a preprint
Abstract
Background: The assessment of behaviors related to mental health typically relies on self-report data. Networked sensors embedded in smartphones can measure some behaviors objectively and continuously, with no ongoing effort. Objective: This study aims to evaluate whether changes in phone sensor–derived behavioral features were associated with subsequent changes in mental health symptoms. Methods: This longitudinal cohort study examined continuously collected phone sensor data and symptom severity data, collected every 3 weeks, over 16 weeks. The participants were recruited through national research registries. Primary outcomes included depression (8-item Patient Health Questionnaire), generalized anxiety (Generalized Anxiety Disorder 7-item scale), and social anxiety (Social Phobia Inventory) severity. Participants were adults who owned Android smartphones. Participants clustered into 4 groups: multiple comorbidities, depression and generalized anxiety, depression and social anxiety, and minimal symptoms. Results: A total of 282 participants were aged 19-69 years (mean 38.9, SD 11.9 years), and the majority were female (223/282, 79.1%) and White participants (226/282, 80.1%). Among the multiple comorbidities group, depression changes were preceded by changes in GPS features (Time: r=−0.23, P=.02; Locations: r=−0.36, P<.001), exercise duration (r=0.39; P=.03) and use of active apps (r=−0.31; P<.001). Among the depression and anxiety groups, changes in depression were preceded by changes in GPS features for Locations (r=−0.20; P=.03) and Transitions (r=−0.21; P=.03). Depression changes were not related to subsequent sensor-derived features. The minimal symptoms group showed no significant relationships. There were no associations between sensor-based features and anxiety and minimal associations between sensor-based features and social anxiety. Conclusions: Changes in sensor-derived behavioral features are associated with subsequent depression changes, but not vice versa, suggesting a directional relationship in which changes in sensed behaviors are associated with subsequent changes in symptoms.
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?
Telepsychiatry has been adopted in direct patient care, telepsychiatry-enhanced referral, and collaborative or integrated care models.
“Telepsychiatry, the use of electronic information and telecommunication technologies to provide mental health services, has been adopted in direct patient care, telepsychiatry-enhanced referral, and collaborative or integrated care models.16”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [16]
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
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482 · see citing works
Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2008.08.010 · Cited by 51,384 · see citing works
A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7
Spitzer, R. L., Kroenke, K., Williams, J. B. W., & Löwe, B. (2006) · Archives of Internal Medicine · doi:10.1001/archinte.166.10.1092 · Cited by 31,765 · see citing works
Availability: A heuristic for judging frequency and probability
Tversky, A., & Kahneman, D. (1973) · Cognitive Psychology · doi:10.1016/0010-0285(73)90033-9 · Cited by 9,984 · cited 1× · see citing works
“Retrospective evaluations of these behaviors through self-report measures or interviews rely heavily on retrospective recall, which is subject to systematic biases [5,6].”
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 PHQ-8 as a measure of current depression in the general population
Kroenke, K., Strine, T. W., Spitzer, R. L., Williams, J. B. W., Berry, J. T., & Mokdad, A. H. (2009) · Journal of Affective Disorders · doi:10.1016/j.jad.2008.06.026 · Cited by 5,121 · see citing works
Reliability of the PEDro Scale for Rating Quality of Randomized Controlled Trials
Maher, C. G., Sherrington, C., Herbert, R. D., Moseley, A. M., & Elkins, M. (2003) · Physical Therapy · doi:10.1093/ptj/83.8.713 · Cited by 4,808 · see citing works
A wandering mind is an unhappy mind
Killingsworth, M. A., & Gilbert, D. T. (2010) · Science · doi:10.1126/science.1192439 · Cited by 2,575 · 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
Bakdash, J. Z., & Marusich, L. R. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00456 · Cited by 2,067 · see citing works
Cacioppo, J. T., Hawkley, L. C., & Thisted, R. A. (2010) · Psychology and Aging · doi:10.1037/a0017216 · Cited by 1,757 · see citing works
Psychometric properties of the Social Phobia Inventory (SPIN): New self-rating scale
Connor, K. M., Davidson, J. R. T., Churchill, L. E., Sherwood, A., Weisler, R. H., & Foa, E. (2000) · British Journal of Psychiatry · doi:10.1192/bjp.176.4.379 · Cited by 1,624 · see citing works
Digital Phenotyping: Technology for a New Science of Behavior
Insel, T. R. (2017) · JAMA · doi:10.1001/jama.2017.11295 · Cited by 833 · see citing works
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802 · see citing works
Self-citationSaeb, S., Zhang, M., Karr, C. J., Schueller, S. M., Corden, M. E., Kording, K. P., & Mohr, D. C. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4273 · Cited by 782 · see citing works
Self-citationMicrorandomized trials: An experimental design for developing just-in-time adaptive interventions.
Klasnja, P., Hekler, E. B., Shiffman, S., Boruvka, A., Almirall, D., Tewari, A., & Murphy, S. A. (2015) · Health Psychology · doi:10.1037/hea0000305 · Cited by 671 · see citing works
(2019) · Cited by 633 · 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
Neurocognitive Mechanisms in Depression: Implications for Treatment
Clark, L., Chamberlain, S. R., & Sahakian, B. J. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.31.060407.125618 · Cited by 446 · see citing works
Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety
Huckvale, K., Venkatesh, S., & Christensen, H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0166-1 · Cited by 433 · 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
Passive and Active Social Media Use and Depressive Symptoms Among United States Adults
Escobar-Viera, C. G., Shensa, A., Bowman, N. D., Sidani, J. E., Knight, J., James, A. E., & Primack, B. A. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2017.0668 · Cited by 351 · see citing works
Azevedo Da Silva, M., Singh-Manoux, A., Brunner, E. J., Kaffashian, S., Shipley, M. J., Kivimäki, M., & Nabi, H. (2012) · European Journal of Epidemiology · doi:10.1007/s10654-012-9692-8 · Cited by 324 · 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
Self-citationChekroud, A. M., Gueorguieva, R., Krumholz, H. M., Trivedi, M. H., Krystal, J. H., & McCarthy, G. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0025 · Cited by 276 · see citing works
Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
Taylor, S., Jaques, N., Nosakhare, E., Sano, A., & Picard, R. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2784832 · Cited by 251 · 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
Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry
Torous, J. B., Staples, P. C., & Onnela, J.-P. “. (2015) · Current Psychiatry Reports · doi:10.1007/s11920-015-0602-0 · Cited by 173 · 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
Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., Rentfrow, P. J., Campbell, A. T., & Gosling, S. D. (2019) · Journal of Personality and Social Psychology · doi:10.1037/pspp0000245 · Cited by 168 · see citing works
Liao, P., Greenewald, K. H., Klasnja, P., & Murphy, S. A. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3381007 · Cited by 152 · 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
Depressive Symptoms and Physical Activity During 3 Decades in Adult Life
Pereira, S. M. P., Geoffroy, M., & Power, C. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.1240 · Cited by 147 · see citing works
Hiles, S. A., Lamers, F., Milaneschi, Y., & Penninx, B. W. J. H. (2017) · Psychological Medicine · doi:10.1017/s0033291716003548 · Cited by 142 · see citing works
The Experience of Psychopathology: Investigating Mental Disorders in Their Natural Settings
Plutchik, R. (1993) · The Journal of Nervous and Mental Disease · doi:10.1097/00005053-199303000-00015 · Cited by 126 · 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
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
Self-citationBehavior vs. introspection: refining prediction of clinical depression via smartphone sensing data
Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., & Wang, B. (2016) · 2016 IEEE Wireless Health (WH) · doi:10.1109/wh.2016.7764553 · Cited by 115 · see citing works
How smartphone usage correlates with social anxiety and loneliness
Gao, Y., Li, A., Zhu, T., Liu, X., & Liu, X. (2016) · PeerJ · doi:10.7717/peerj.2197 · Cited by 111 · see citing works
The Relationship between Clinical, Momentary, and Sensor-based Assessment of Depression
Saeb, S., Zhang, M., Kwasny, M., Karr, C., Kording, K., & Mohr, D. (2015) · Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare · doi:10.4108/icst.pervasivehealth.2015.259034 · Cited by 89 · see citing works
Self-citationPredicting 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
Cruz, B. D. P., Alfonso‐Rosa, R. M., McGregor, D., CHASTIN, S. F. M., Palarea‐Albaladejo, J., & Cruz, J. D. P. (2020) · Journal of Affective Disorders · doi:10.1016/j.jad.2020.01.023 · Cited by 84 · see citing works
Depression screening using mobile phone usage metadata: a machine learning approach
Razavi, R., Gharipour, A., & Gharipour, M. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz221 · Cited by 74 · see citing works
Let your fingers do the talking: Passive typing instability predicts future mood outcomes
Stange, J. P., Zulueta, J., Langenecker, S. A., Ryan, K. A., Piscitello, A., Duffecy, J., McInnis, M. G., Nelson, P., Ajilore, O., & Leow, A. (2018) · Bipolar Disorders · doi:10.1111/bdi.12637 · Cited by 49 · see citing works
Automatic depression prediction using Internet traffic characteristics on smartphones
Yue, C., Ware, S., Morillo, R., Lü, J., Shang, C., Bi, J., Kamath, J., Russell, A. C., Bamis, A., & Wang, B. (2020) · Smart Health · doi:10.1016/j.smhl.2020.100137 · Cited by 38 · see citing works
Montague, E., & Xu, J. (2011) · Applied Ergonomics · doi:10.1016/j.apergo.2011.11.002 · Cited by 37 · see citing works
Geography and neuroscience: Critical engagements with geography's “neural turn”
Pykett, J. (2017) · Transactions of the Institute of British Geographers · doi:10.1111/tran.12213 · Cited by 37 · see citing works
A Look at Wearable Abandonment
Lee, H., & Lee, Y. (2017) · Proceedings/IEEE International Conference on Mobile Data Management · doi:10.1109/mdm.2017.70 · Cited by 22 · see citing works
Vittengl, J., Clark, L., Smits, J., Thase, M., & Jarrett, R. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.08.053 · Cited by 16 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Digital health technologies and major depressive disorder
McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
Kornfield, R., Stamatis, C. A., Bhattacharjee, A., Pang, B., Nguyen, T., Williams, J. J., Kumar, H., Popowski, S., Beltzer, M., Karr, C. J., et al. (2023) · Internet Interventions · doi:10.1016/j.invent.2023.100667 · Cited by 33 · see citing works
“Sensor-based measurements (e.g., location, movement) offer another potential avenue to understand users' contexts (Meyerhoff et al., 2021).”
BackgroundSelf-citationA call for open data to develop mental health digital biomarkers
Adler, D. A., Wang, F., Mohr, D. C., Estrin, D., Livesey, C., & Choudhury, T. (2022) · BJPsych Open · doi:10.1192/bjo.2022.28 · Cited by 23 · see citing works
Self-citation
- Authors
- Meyerhoff, J., Liu, T., Kording, K. P., Ungar, L. H., Kaiser, S. M., Karr, C. J., & Mohr, D. C.
- Affiliations
- Northwestern University (US)
- University of Pennsylvania (US)
- Centers for New Horizons (US)
- Corresponding author
- Mohr, David C
- ORCID
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2021-09-03
- Volume
- 23
- Issue
- 9
- Pages
- e22844
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8449302
- OpenAlex
- W3198144000
- Cited by
- 82
- References
- 49
- Citations by year
- 2026: 10 · 2025: 22 · 2024: 18 · 2023: 16 · 2022: 15 · 2021: 1
- FWCI
- 12.06
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001422)
- National Institute of Mental Health (R01MH111610, P50MH119029, T32MH115882)
- National Center for Advancing Translational Sciences
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- smartphone sensors, depression, generalized anxiety disorder, social anxiety, mental health monitoring, GPS location data, physical activity, behavioral characteristics, symptom severity, comorbidities
- MeSH terms
- Smartphone, Adult, Anxiety, Anxiety Disorders, Depression, Female, Humans, Longitudinal Studies, Male
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
