Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students
Open accessAbstract
BACKGROUND: Research in psychology demonstrates a strong link between state affect (moment-to-moment experiences of positive or negative emotionality) and trait affect (eg, relatively enduring depression and social anxiety symptoms), and a tendency to withdraw (eg, spending time at home). However, existing work is based almost exclusively on static, self-reported descriptions of emotions and behavior that limit generalizability. Despite adoption of increasingly sophisticated research designs and technology (eg, mobile sensing using a global positioning system [GPS]), little research has integrated these seemingly disparate forms of data to improve understanding of how emotional experiences in everyday life are associated with time spent at home, and whether this is influenced by depression or social anxiety symptoms. OBJECTIVE: We hypothesized that more time spent at home would be associated with more negative and less positive affect. METHODS: We recruited 72 undergraduate participants from a southeast university in the United States. We assessed depression and social anxiety symptoms using self-report instruments at baseline. An app (Sensus) installed on participants' personal mobile phones repeatedly collected in situ self-reported state affect and GPS location data for up to 2 weeks. Time spent at home was a proxy for social isolation. RESULTS: We tested separate models examining the relations between state affect and time spent at home, with levels of depression and social anxiety as moderators. Models differed only in the temporal links examined. One model focused on associations between changes in affect and time spent at home within short, 4-hour time windows. The other 3 models focused on associations between mean-level affect within a day and time spent at home (1) the same day, (2) the following day, and (3) the previous day. Overall, we obtained many of the expected main effects (although there were some null effects), in which higher social anxiety was associated with more time or greater likelihood of spending time at home, and more negative or less positive affect was linked to longer homestay. Interactions indicated that, among individuals higher in social anxiety, higher negative affect and lower positive affect within a day was associated with greater likelihood of spending time at home the following day. CONCLUSIONS: Results demonstrate the feasibility and utility of modeling the relationship between affect and homestay using fine-grained GPS data. Although these findings must be replicated in a larger study and with clinical samples, they suggest that integrating repeated state affect assessments in situ with continuous GPS data can increase understanding of how actual homestay is related to affect in everyday life and to symptoms of anxiety and depression.
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The lack of significance for positive affect contradicted many other ambulatory assessment studies that yielded significant between-person associations between lower levels of positive affect and higher levels of negative affect with social anxiety.
“This lack of significance contradicted many other AA studies that yielded significant between-person associations between lower levels of PA and higher levels of NA with SA [16,20,30,54,57,58,61,69,70].”
From Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review (2024) · cites this paper as [58]
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Experimental — accuracy not yet measuredMobile phones have been used to monitor stress and mental health.
“Mobile phones have been used to monitor stress and mental health [4-10].”
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Cites
Lovibond, P. F., & Lovibond, S. H. (1995) · Behaviour Research and Therapy · doi:10.1016/0005-7967(94)00075-u · Cited by 14,523 · see citing works
Carver, C. S., & White, T. L. (1994) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.67.2.319 · Cited by 7,306 · see citing works
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., & Wang, P. (2010) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2010.09091379 · Cited by 7,067 · see citing works
What's wrong with Bonferroni adjustments
Perneger, T. V. (1998) · BMJ · doi:10.1136/bmj.316.7139.1236 · Cited by 5,900 · see citing works
Mattick, R. P., & Clarke, J. C. (1998) · Behaviour Research and Therapy · doi:10.1016/s0005-7967(97)10031-6 · Cited by 3,367 · 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
Attentional bias for threat: evidence for delayed disengagement from emotional faces
Fox, E., Russo, R., & Dutton, K. (2002) · Cognition & Emotion · doi:10.1080/02699930143000527 · Cited by 954 · see citing works
Subthreshold Depression in Adolescence and Mental Health Outcomes in Adulthood
Fergusson, D. M., Horwood, L. J., Ridder, E. M., & Beautrais, A. L. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.1.66 · Cited by 792 · see citing works
Saeb, 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
Kok, B. E., Coffey, K. A., Cohn, M. A., Catalino, L. I., Vacharkulksemsuk, T., Algoe, S. B., Brantley, M., & Fredrickson, B. L. (2013) · Psychological Science · doi:10.1177/0956797612470827 · Cited by 755 · see citing works
Heimberg, R. G., Mueller, G. P., Holt, C. S., Hope, D. A., & Liebowitz, M. R. (1992) · Behavior Therapy · doi:10.1016/s0005-7894(05)80308-9 · Cited by 740 · see citing works
A Population-Based Twin Study of the Relationship Between Neuroticism and Internalizing Disorders
Hettema, J. M., Neale, M. C., Myers, J., Prescott, C. A., & Kendler, K. S. (2006) · American Journal of Psychiatry · doi:10.1176/ajp.2006.163.5.857 · Cited by 633 · see citing works
Kashdan, T. B., & Steger, M. F. (2006) · Psychological Science · doi:10.1111/j.1467-9280.2006.01674.x · Cited by 458 · see citing works
Using Heterogeneous Choice Models to Compare Logit and Probit Coefficients Across Groups
Williams, R. A. (2009) · Sociological Methods & Research · doi:10.1177/0049124109335735 · Cited by 436 · see citing works
Nutt, D., Demyttenaere, K., Janka, Z., Aarre, T., Bourin, M., Canonico, P. L., Carrasco, J. L., & Stahl, S. (2007) · Journal of Psychopharmacology · doi:10.1177/0269881106069938 · Cited by 366 · 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
Hasan, S., Zhan, X., & Ukkusuri, S. V. (2013) · doi:10.1145/2505821.2505823 · Cited by 309 · see citing works
Extracting places from traces of locations
Kang, J. H., Welbourne, W., Stewart, B., & Borriello, G. (2004) · Wireless Mobile Applications And Services On WLAN Hotspots · doi:10.1145/1024733.1024748 · Cited by 238 · see citing works
Xiong, H., Huang, Y., Barnes, L. E., & Gerber, M. S. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2971648.2971711 · Cited by 116 · see citing works
Self-citationThe Oxford Handbook of Clinical Psychology
(2014) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199328710.001.0001 · Cited by 113 · see citing works
Assessing social anxiety using gps trajectories and point-of-interest data
Huang, Y., Xiong, H., Leach, K., Zhang, Y., Chow, P., Fua, K., Teachman, B. A., & Barnes, L. E. (2016) · Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing · doi:10.1145/2971648.2971761 · Cited by 109 · see citing works
Self-citationSocial anxiety disorder: what are we losing with the current diagnostic criteria?
Filho, A. S., Hetem, L. A. B., Ferrari, M. C. F., Trzesniak, C., Martín‐Santos, R., Borduqui, T., De Lima Osório, F., Loureiro, S. R., Busatto Filho, G., Zuardi, A. W., et al. (2010) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2009.01459.x · Cited by 87 · see citing works
Kaiser, R. H., Hubley, S. H., & Dimidjian, S. (2015) · doi:10.1002/9781119114482.ch9 · Cited by 12 · see citing works
Chow, P. I., Bonelli, W. P., Huang, Y., Fua, K., Teachman, B. A., & Barnes, L. E. (2016) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2968219.2968300 · Cited by 11 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [11]Barlow D Clinical Handbook of Psychological Disorders 2014 New York, NY Guilford Press
Cited by
Sorted by citations
Complex affect dynamics add limited information to the prediction of psychological well-being.
Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0555-0 · Cited by 431 · see citing works
Sano, A., Taylor, S., McHill, A. W., Phillips, A. J., Barger, L. K., Klerman, E., & Picard, R. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9410 · Cited by 333 · see citing works
“Mobile phones have been used to monitor stress and mental health [4-10].”
MethodMental health monitoring with multimodal sensing and machine learning: A survey
Garcia-Ceja, E., Riegler, M., Nordgreen, T., Jakobsen, P., Oedegaard, K. J., & Tørresen, J. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.09.003 · Cited by 356 · see citing works
Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing
Wang, R., Wang, W., daSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3191775 · Cited by 340 · see citing works
Smartphone sensing methods for studying behavior in everyday life
Harari, G. M., Müller, S. R., Aung, M. H., & Rentfrow, P. J. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.018 · Cited by 248 · 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
Emerging technologies and analytics for a new era of value-centered marketing in healthcare
Agarwal, R., Dugas, M., Gao, G. (., & Kannan, P. K. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00692-4 · Cited by 175 · 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
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
Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139 · see citing works
Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review
Bettis, A. H., Burke, T. A., Nesi, J. L., & Liu, R. T. (2021) · Clinical Psychological Science · doi:10.1177/21677026211011982 · Cited by 147 · 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
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
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
Mental imagery in psychiatry: conceptual & clinical implications
Ji, J. L., Kavanagh, D. J., Holmes, E. A., MacLeod, C., & Simplicio, M. C. D. (2019) · CNS Spectrums · doi:10.1017/s1092852918001487 · Cited by 121 · see citing works
Sensing Technologies for Monitoring Serious Mental Illnesses
Abdullah, S., & Choudhury, T. (2018) · IEEE Multimedia · doi:10.1109/mmul.2018.011921236 · Cited by 112 · 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
Hur, J., Stockbridge, M. D., Fox, A. S., & Shackman, A. J. (2019) · Progress in brain research · doi:10.1016/bs.pbr.2019.03.012 · Cited by 91 · see citing works
Review of Use and Integration of Mobile Apps Into Psychiatric Treatments
Chan, S. R., Godwin, H. T., González, Á. D. S., Yellowlees, P., & Hilty, D. M. (2017) · Current Psychiatry Reports · doi:10.1007/s11920-017-0848-9 · Cited by 91 · see citing works
Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health
Akhter‐Khan, S. C., & Au, R. (2020) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20200016 · Cited by 89 · see citing works
Review of Use of Asynchronous Technologies Incorporated in Mental Health Care
Chan, S. R., Li, L., Torous, J. B., Gratzer, D., & Yellowlees, P. (2018) · Current Psychiatry Reports · doi:10.1007/s11920-018-0954-3 · Cited by 90 · 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
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
Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing
Burr, C., Morley, J., Taddeo, M., & Floridi, L. (2020) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2020.2977059 · Cited by 78 · see citing works
Nicholas, J., Shilton, K., Schueller, S. M., Gray, E. L., Kwasny, M. J., & Mohr, D. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/12578 · Cited by 77 · 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
Social context and the real-world consequences of social anxiety
Hur, J., DeYoung, K. A., Islam, S., Anderson, A. S., Barstead, M. G., & Shackman, A. J. (2020) · Psychological Medicine · doi:10.1017/s0033291719002022 · Cited by 74 · see citing works
Montag, C., Baumeister, H., Kannen, C., Sariyska, R., Meßner, E.-M., & Brand, M. (2019) · J — Multidisciplinary Scientific Journal · doi:10.3390/j2020008 · Cited by 70 · see citing works
Zhang, Y., Folarin, A. A., Sun, S., Cummins, N., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Laiou, P., Matcham, F., et al. (2021) · JMIR mHealth and uHealth · doi:10.2196/29840 · 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
Rackoff, G. N., & Newman, M. G. (2020) · Journal of Abnormal Psychology · doi:10.1037/abn0000639 · 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
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
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Wang, Z., Xiong, H., Zhang, J., Yang, S., Boukhechba, M., Zhang, D., Barnes, L. E., & Dou, D. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3161046 · Cited by 62 · see citing works
Self-citationMcQuoid, J., Thrul, J., & Ling, P. M. (2018) · Social Science & Medicine · doi:10.1016/j.socscimed.2018.02.014 · Cited by 61 · see citing works
Ike, K. G. O., de Boer, S. F., Buwalda, B., & Kas, M. J. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.06.030 · Cited by 61 · 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
Балоглу, М., Kozan, H. İ. Ö., & Kesici, Ş. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8947 · Cited by 57 · 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
Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection
Yang, M., Ngai, E. C. ‐., Hu, X., Hu, B., Liu, J., Erol Gelenbe, A., & Leung, V. C. M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2025.3542324 · Cited by 54 · 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
Melcher, J., Lavoie, J., Hays, R., D’Mello, R., Rauseo-Ricupero, N., Camacho, E., Rodriguez-Villa, E., Wisniewski, H., Lagan, S., Vaidyam, A. N., et al. (2021) · Journal of American College Health · doi:10.1080/07448481.2021.1905650 · Cited by 49 · see citing works
Matteo, D. D., Fotinos, K., Lokuge, S., Mason, G. E., Sternat, T., Katzman, M. A., & Rose, J. (2021) · Journal of Medical Internet Research · doi:10.2196/28918 · Cited by 48 · see citing works
Zhang, Y., Folarin, A., Sun, S., Cummins, N., Vairavan, S., Bendayan, R., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., et al. (2022) · JMIR Mental Health · doi:10.2196/34898 · Cited by 43 · see citing works
Matteo, D. D., Fotinos, K., Lokuge, S., Yu, J., Sternat, T., Katzman, M. A., & Rose, J. (2020) · JMIR Formative Research · doi:10.2196/18751 · Cited by 43 · see citing works
Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data
Rashid, H., Mendu, S., Daniel, K. E., Beltzer, M., Teachman, B. A., Boukhechba, M., & Barnes, L. E. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3411823 · Cited by 44 · see citing works
Self-citationA Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study
MacLeod, L., Suruliraj, B., Gall, D., Bessenyei, K., Hamm, S. I., Romkey, I. D., Bagnell, A. L., Mattheisen, M., Muthukumaraswamy, V., Orji, R., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/20638 · Cited by 43 · 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
Wu, C., Barczyk, A. N., Craddock, R. C., Harari, G. M., Thomaz, E., Shumake, J., Beevers, C. G., Gosling, S. D., & Schnyer, D. M. (2021) · Smart Health · doi:10.1016/j.smhl.2021.100180 · Cited by 40 · see citing works
Mei, S., Hu, Y., Sun, M., Fei, J., Li, C., Li, C., & Hu, Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18063316 · Cited by 40 · see citing works
Laiou, P., Kaliukhovich, D. A., Folarin, A., Ranjan, Y., Rashid, Z., Conde, P., Stewart, C., Sun, S., Zhang, Y., Matcham, F., et al. (2022) · JMIR mhealth and uhealth · doi:10.2196/28095 · Cited by 39 · 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
Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review
Kulkarni, P., Kirkham, R., & Mcnaney, R. (2022) · Italian National Conference on Sensors · doi:10.3390/s22103893 · Cited by 33 · see citing works
Teachman, B. A., McKay, D., Barch, D. M., Prinstein, M. J., Hollon, S. D., & Chambless, D. L. (2018) · American Psychologist · doi:10.1037/amp0000361 · Cited by 32 · see citing works
Self-citationFusing Location Data for Depression Prediction
Yue, C., Ware, S., Morillo, R., Lü, J., Shang, C., Bi, J., Kamath, J., Russell, A. C., Bamis, A., & Wang, B. (2018) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2872569 · Cited by 32 · see citing works
Langener, 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
Keusch, F., & Conrad, F. G. (2021) · Journal of Survey Statistics and Methodology · doi:10.1093/jssam/smab035 · 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
Poudyal, A., van Heerden, A. C., Hagaman, A. K., Maharjan, S. M., Byanjankar, P., Subba, P., & Kohrt, B. A. (2019) · JMIR Research Protocols · doi:10.2196/14734 · Cited by 28 · 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
Chow, P. I., Drago, F., Kennedy, E. M., & Cohn, W. F. (2019) · JMIR Cancer · doi:10.2196/15750 · Cited by 28 · see citing works
Self-citationThe (un)learning of social functions and its significance for mental health
Flechsenhar, A., Kanske, P., Krach, S., Korn, C. W., & Bertsch, K. (2022) · Clinical Psychology Review · doi:10.1016/j.cpr.2022.102204 · Cited by 27 · see citing works
Paolillo, E. W., Tang, B., Depp, C. A., Rooney, A. S., Vaida, F., Kaufmann, C. N., Mausbach, B. T., Moore, D. J., & Moore, R. C. (2018) · JMIR Mental Health · doi:10.2196/mental.9802 · 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
González‐Pérez, A., Matey-Sanz, M., Granell, C., Díaz-Sanahuja, L., Bretón-López, J. M., & Casteleyn, S. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104359 · Cited by 30 · see citing works
Designing Dashboard for Campus Stakeholders to Support College Student Mental Health
Yoo, D. W., & Choudhury, M. D. (2019) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329200 · Cited by 24 · see citing works
Mental health ubiquitous monitoring supported by social situation awareness: A systematic review
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Jantara, R. D., Abreu, D. P. G., Santana, L. D. L., Piexak, D. R., Ribeiro, J. P., & Tomaschewski-Barlem, J. G. (2022) · Revista Enfermagem UERJ · doi:10.12957/reuerj.2022.63609 · Cited by 12 · see citing works
Friedmann, F., Santangelo, P., Ebner‐Priemer, U., Hill, H., Neubauer, A. B., Rausch, S., Steil, R., Müller‐Engelmann, M., Kleindienst, N., Bohus, M., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232666 · Cited by 13 · see citing works
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- Authors
- Chow, P. I., Fua, K., Huang, Y., Bonelli, W., Xiong, H., Barnes, L. E., & Teachman, B. A.
- Affiliations
- University of Virginia (US)
- Missouri University of Science and Technology (US)
- Corresponding author
- Barnes, Laura E
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-03-03
- Volume
- 19
- Issue
- 3
- Pages
- e62
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5357317
- OpenAlex
- W2592965535
- Cited by
- 172
- References
- 24
- Citations by year
- 2026: 6 · 2025: 17 · 2024: 22 · 2023: 12 · 2022: 23 · 2021: 29 · 2020: 22 · 2019: 16 · 2018: 18 · 2017: 6 · 2013: 1
- FWCI
- 21.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile sensing, state affect, negative affect, positive affect, social anxiety, depression, social isolation, time spent at home, GPS location data, university students, everyday emotions
- MeSH terms
- Phobia, Social, Adolescent, Adult, Depression, Female, Humans, Male, Models, Psychological, Social Isolation, Students, Universities, Internet, Young Adult, Self Report
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