Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study.
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Abstract
BACKGROUND: Research studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression. Smartphone technologies could play a significant role in complementing comorbid care. OBJECTIVE: This study aimed to analyze the association between smartphone-sensing parameters and symptoms of depression and to explore an approach to risk-stratify people with diabetes. METHODS: A cross-sectional observational study (Project SHADO-Analyzing Social and Health Attributes through Daily Digital Observation) was conducted on 47 participants with diabetes. The study's smartphone-sensing app passively collected data regarding activity, mobility, sleep, and communication from each participant. Self-reported symptoms of depression using a validated Patient Health Questionnaire-9 (PHQ-9) were collected once every 2 weeks from all participants. A descriptive analysis was performed to understand the representation of the participants. A univariate analysis was performed on each derived sensing variable to compare behavioral changes between depression states-those with self-reported major depression (PHQ-9>9) and those with none (PHQ-9≤9). A classification predictive modeling, using supervised machine-learning methods, was explored using derived sensing variables as input to construct and compare classifiers that could risk-stratify people with diabetes based on symptoms of depression. RESULTS: A noticeably high prevalence of self-reported depression (30 out of 47 participants, 63%) was found among the participants. Between depression states, a significant difference was found for average activity rates (daytime) between participant-day instances with symptoms of major depression (mean 16.06 [SD 14.90]) and those with none (mean 18.79 [SD 16.72]), P=.005. For average number of people called (calls made and received), a significant difference was found between participant-day instances with symptoms of major depression (mean 5.08 [SD 3.83]) and those with none (mean 8.59 [SD 7.05]), P<.001. These results suggest that participants with diabetes and symptoms of major depression exhibited lower activity through the day and maintained contact with fewer people. Using all the derived sensing variables, the extreme gradient boosting machine-learning classifier provided the best performance with an average cross-validation accuracy of 79.07% (95% CI 74%-84%) and test accuracy of 81.05% to classify symptoms of depression. CONCLUSIONS: Participants with diabetes and self-reported symptoms of major depression were observed to show lower levels of social contact and lower activity levels during the day. Although findings must be reproduced in a broader randomized controlled study, this study shows promise in the use of predictive modeling for early detection of symptoms of depression in people with diabetes using smartphone-sensing information.
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Studies could classify depressive symptoms based on sensing variables with an accuracy of 81%.
“could classify depressive symptoms based on sensing variables with an accuracy of 81% (40).”
12 of the 17 included studies had sample sizes smaller than 100.
“12 of the 17 included studies had sample sizes smaller than 100 [28–31,33–35,37–39,41,42].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
There were a large variety of study types among the 17 studies, including pilot randomised controlled trials, observational studies, model evaluations, and a qualitative study.
“There were a large variety of study types among the 17 studies, including pilot randomised controlled trials (RCTs) [28–30], observational studies [31–35], model evaluations [36,37], and a qualitative study [38].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
The most common use of artificial intelligence in the mental health apps was prediction (of mood, stress, and risk) (8/17 studies).
“The most common uses of artificial intelligence in the mental health apps were prediction (of mood, stress, and risk) (8/17 studies) [31–37,41]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
6 of the 17 studies focused on depression.
“6 of the 17 studies focused on depression [29,31,36,40,42,44]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
Random Forest classifier was used for feature selection, risk-stratification of diabetic patients based on symptoms of depression, inferring context for depression level prediction, and predicting mood in the included studies.
“It is used for feature selection [41], risk-stratification of diabetic patients based on symptoms of depression [31], inferring context for depression level prediction [44], and predicting mood [34,35].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
Only 2 out of the 17 studies reported used boosting algorithms.
“Only 2 out of the 17 studies reported used boosting algorithms [31,37].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions, provide natural language support, develop risk profiles, and deploy context-sensitive notifications or interventions.
“Many of the studies successfully modelled data collected by the app or associated inputs to identify behaviours or responses associated with mental health conditions [44], provide natural language support [29,30,38,40,42,43], develop risk profiles [31–37,41], and deploy context-sensitive notifications [28] or interventions [44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
All of the studies reviewed used data collected via mobile devices to inform AI models-for natural language analysis and output, risk stratification and prediction or for personalising the user's app experience.
“All of the studies reviewed used data collected via mobile devices to inform AI models—for natural language analysis and output [29,30,38,40,42,43], risk stratification and prediction [31,41] or for personalising the user’s app experience [28,44].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [31]
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Cites
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
The Prevalence of Comorbid Depression in Adults With Diabetes
Anderson, R. J., Freedland, K. E., Clouse, R. E., & Lustman, P. J. (2001) · Diabetes Care · doi:10.2337/diacare.24.6.1069 · Cited by 3,926 · see citing works
Ruxton, G. D. (2006) · Behavioral Ecology · doi:10.1093/beheco/ark016 · Cited by 1,988 · see citing works
Manea, L., Gilbody, S., & McMillan, D. (2012) · Canadian Medical Association Journal · doi:10.1503/cmaj.110829 · Cited by 1,966 · 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
Inkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063 · see citing works
Self-citationDepression and Diabetes Treatment Nonadherence: A Meta-Analysis
Gonzalez, J. S., Peyrot, M., McCarl, L. A., Collins, E. M., Serpa, L., Mimiaga, M. J., & Safren, S. A. (2008) · Diabetes Care · doi:10.2337/dc08-1341 · Cited by 1,060 · see citing works
The challenge of patient adherence.
Martin, L. R., Williams, S. L., Haskard, K. B., & DiMatteo, M. R. (2005) · PubMed Central · Cited by 994 · see citing works
Standards of Medical Care in Diabetes—2017 Abridged for Primary Care Providers
American Diabetes Association (2017) · Clinical Diabetes · doi:10.2337/cd16-0067 · Cited by 977 · see citing works
Depression as a risk factor for the onset of type 2 diabetes mellitus. A meta-analysis
Knol, M. J., Twisk, J. W. R., Beekman, A. T. F., Heine, R. J., Snoek, F. J., & Pouwer, F. (2006) · Diabetologia · doi:10.1007/s00125-006-0159-x · Cited by 974 · see citing works
Standards of Medical Care in Diabetes—2015 Abridged for Primary Care Providers
American Diabetes Association (2015) · Clinical Diabetes · doi:10.2337/diaclin.33.2.97 · Cited by 919 · see citing works
Examining a Bidirectional Association Between Depressive Symptoms and Diabetes
Golden, S. H. (2008) · JAMA · doi:10.1001/jama.299.23.2751 · Cited by 890 · 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
for the European Depression in Diabetes (EDID) Research Consortium, Nouwen, A., Winkley, K., Twisk, J., Lloyd, C. E., Peyrot, M., Ismail, K., & Pouwer, F. (2010) · Diabetologia · doi:10.1007/s00125-010-1874-x · Cited by 756 · see citing works
Kocalevent, R.-D., Hinz, A., & Brähler, E. (2013) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2013.04.006 · Cited by 748 · see citing works
Relationship of depression to diabetes types 1 and 2: epidemiology, biology, and treatment
Musselman, D. L., Betan, E. J., Larsen, H., & Phillips, L. S. (2003) · Biological Psychiatry · doi:10.1016/s0006-3223(03)00569-9 · Cited by 670 · see citing works
The Association of Comorbid Depression With Mortality in Patients With Type 2 Diabetes
Katon, W. J., Rutter, C., Simon, G., Lin, E. H. B., Ludman, E., Ciechanowski, P., Kinder, L., Young, B., & Von Korff, M. (2005) · Diabetes Care · doi:10.2337/diacare.28.11.2668 · Cited by 640 · see citing works
Depression in diabetic patients
Lustman, P. J., & Clouse, R. E. (2005) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2004.01.002 · Cited by 600 · see citing works
Cuijpers, P., & Smit, F. (2004) · Acta Psychiatrica Scandinavica · doi:10.1111/j.1600-0447.2004.00301.x · Cited by 595 · cited 1× · see citing works
“Mild symptoms or subclinical depression in people with diabetes has been found to be common, associated with high levels of diabetes-related distress, psychological distress, and lower quality of life and also a risk indicator of major depression [59-61].”
The vital link between chronic disease and depressive disorders.
Chapman, D. P., Perry, G. S., & Strine, T. W. (2005) · Europe PMC (PubMed Central) · Cited by 537 · see citing works
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., & Gosling, S. D. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616650285 · Cited by 515 · 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
Schram, M. T., Baan, C. A., & Pouwer, F. (2009) · Current Diabetes Reviews · doi:10.2174/157339909788166828 · Cited by 441 · 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
A review of the relationship between depression and diabetes in adults: is there a link?
Talbot, F., & Nouwen, A. (2000) · Diabetes Care · doi:10.2337/diacare.23.10.1556 · Cited by 410 · 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
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
Hermanns, N., Kulzer, B., Krichbaum, M., Kubiak, T., & Haak, T. (2006) · Diabetologia · doi:10.1007/s00125-005-0094-2 · Cited by 324 · see citing works
Mitchell, A., Yadegarfar, M., Gill, J., & Stubbs, B. (2016) · BJPsych Open · doi:10.1192/bjpo.bp.115.001685 · Cited by 322 · 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
Cost-effectiveness of Systematic Depression Treatment Among People With Diabetes Mellitus
Simon, G. E., Katon, W. J., Lin, E. H. B., Rutter, C. M., Manning, W. G., Korff, M. V., Ciechanowski, P. S., Ludman, E. J., & Young, B. A. (2007) · Archives of General Psychiatry · doi:10.1001/archpsyc.64.1.65 · Cited by 312 · see citing works
To test or not to test: Preliminary assessment of normality when comparing two independent samples
Rochon, J., Gondan, M., & Kieser, M. (2012) · BMC Medical Research Methodology · doi:10.1186/1471-2288-12-81 · Cited by 296 · see citing works
Torous, J., Friedman, R., & Keshavan, M. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.2994 · Cited by 287 · see citing works
Glynn, L. G., Hayes, P. S., Casey, M., Glynn, F., Alvarez‐Iglesias, A., Newell, J., OLaighin, G. M., Heaney, D., O'Donnell, M. J., & Murphy, A. W. (2014) · British Journal of General Practice · doi:10.3399/bjgp14x680461 · Cited by 280 · see citing works
Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Rabbi, M., Ali, S., Choudhury, T., & Berke, E. (2011) · Proceedings of the 13th international conference on Ubiquitous computing · doi:10.1145/2030112.2030164 · Cited by 243 · see citing works
Social sensing for epidemiological behavior change
Madan, A., Cebrian, M., Lazer, D., & Pentland, A. (2010) · Proceedings of the 12th ACM international conference on Ubiquitous computing · doi:10.1145/1864349.1864394 · Cited by 219 · see citing works
Katon, W. J., Simon, G. E., Russo, J. E., Korff, M. V., Lin, E. H. B., Ludman, E. J., Ciechanowski, P. S., & Bush, T. M. (2004) · Medical Care · doi:10.1097/00005650-200412000-00009 · Cited by 199 · see citing works
Mental disorders in primary care
Wittchen, H.-U., Mühlig, S., & Beesdo, K. (2003) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2003.5.2/huwittchen · Cited by 180 · see citing works
Depression Increases Diabetes Symptoms by Complicating Patients’ Self-Care Adherence
Mckellar, J. D., Humphreys, K. N., & Piette, J. D. (2004) · The Science of Diabetes Self-Management and Care · doi:10.1177/014572170403000320 · Cited by 163 · see citing works
Kochhar, P., Rajadhyaksha, S., & Suvarna, V. (2007) · Journal of Postgraduate Medicine · doi:10.4103/0022-3859.32209 · Cited by 148 · see citing works
National Rates and Patterns of Depression Screening in Primary Care: Results From 2012 and 2013
Akıncıgil, A., & Matthews, E. B. (2017) · Psychiatric Services · doi:10.1176/appi.ps.201600096 · Cited by 141 · see citing works
Katon, W. J., Lin, E. H. B., Williams, L. H., Ciechanowski, P., Heckbert, S. R., Ludman, E., Rutter, C., Crane, P. K., Oliver, M., & Von Korff, M. (2010) · Journal of General Internal Medicine · doi:10.1007/s11606-009-1248-6 · Cited by 141 · see citing works
Predicting students' happiness from physiology, phone, mobility, and behavioral data
Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. W. (2015) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2015.7344575 · Cited by 140 · 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
Hermanns, N., SCHMITT, A. J., Gahr, A., Herder, C., Nowotny, B., Roden, M., Ohmann, C., Kruse, J., Haak, T., & Kulzer, B. (2015) · Diabetes Care · doi:10.2337/dc14-1416 · Cited by 123 · see citing works
Depression in diabetes mellitus: a comprehensive review.
Andreoulakis, E., Hyphantis, T. N., Kandylis, D., & Iacovides, A. (2012) · Europe PMC (PubMed Central) · Cited by 116 · see citing works
Dirmaier, J., Watzke, B., Koch, U., Schulz, H., Lehnert, H., Pieper, L., & Wïttchen, H. (2010) · Psychotherapy and Psychosomatics · doi:10.1159/000296135 · Cited by 114 · see citing works
Recognizing and treating depression in patients with diabetes
Rubin, R. R., Ciechanowski, P. S., Egede, L. E., Lin, E. H. B., & Lustman, P. J. (2004) · Current Diabetes Reports · doi:10.1007/s11892-004-0067-8 · Cited by 108 · 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
Potentially Modifiable Factors Associated With Disability Among People With Diabetes
Korff, M. V., Katon, W. J., Lin, E. H. B., Simon, G. E., Ludman, E. J., Oliver, M. M., Ciechanowski, P. S., Rutter, C. M., & Bush, T. M. (2005) · Psychosomatic Medicine · doi:10.1097/01.psy.0000155662.82621.50 · Cited by 107 · see citing works
(2014) · Diabetes Care · doi:10.2337/dc15-s001 · Cited by 98 · 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
Egede, L. E., Bishu, K. G., Walker, R. J., & Dismuke, C. E. (2016) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.02.011 · 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
Prevalence and treatment coverage for depression: a population-based survey in Vidarbha, India
Shidhaye, R. R., Gangale, S., & Patel, V. (2016) · Social Psychiatry and Psychiatric Epidemiology · doi:10.1007/s00127-016-1220-9 · Cited by 81 · see citing works
Egede, L. E., Walker, R. J., Bishu, K., & Dismuke, C. E. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3650-1 · Cited by 70 · see citing works
Koopmans, B., Pouwer, F., de Bie, R. A., van Rooij, E. S., Leusink, G. L., & Pop, V. J. (2009) · Family Practice · doi:10.1093/fampra/cmp016 · Cited by 66 · see citing works
Depression Symptoms and Antidepressant Medicine Use in Diabetes Prevention Program Participants
The Diabetes Prevention Program Research Group (2005) · Diabetes Care · doi:10.2337/diacare.28.4.830 · Cited by 60 · see citing works
Cherrington, A., Ayala, G. X., Sleath, B. L., & Corbie-Smith, G. M. (2006) · The Science of Diabetes Self-Management and Care · doi:10.1177/0145721706290836 · Cited by 57 · 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
Jin, H., Wu, S., & Capua, P. D. (2015) · Preventing Chronic Disease · doi:10.5888/pcd12.150047 · Cited by 51 · 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
Predicting Depression among Patients with Diabetes Using Longitudinal Data
Jin, H., Vidyanti, I., Capua, P. D., Wu, B. W., & Wu, S. (2015) · Methods of Information in Medicine · doi:10.3414/me14-02-0009 · Cited by 32 · see citing works
Diabetes and Depression in the Hispanic/Latino Community
Colón, E. A., Giachello, A. L. M., McIver, L., Pacheco, G., & Vela, L. (2013) · Clinical Diabetes · doi:10.2337/diaclin.31.1.43 · Cited by 18 · see citing works
Frequency and risk factors of depression in type 1 diabetes in a developing country
Khater, D., & Omar, M. A. (2017) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2016-0414 · Cited by 14 · see citing works
Beneficial effects of yogasanas and pranayama in limiting the cognitive decline in Type 2 diabetes
Nagothu, R., Archana, R., Indla, Y. R., & Paluru, R. (2016) · National Journal of Physiology Pharmacy and Pharmacology · doi:10.5455/njppp.2017.7.0825508092016 · Cited by 13 · see citing works
Aguilera, A., & Lyles, C. R. (2017) · JMIR Diabetes · doi:10.2196/diabetes.6916 · Cited by 7 · see citing works
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- [30]Doryab A Min J Wiese J Zimmerman J Hong J Association for the Advancement of Artificial Intelligence 2014 06 2018-12-10 North America AAAI Workshops Detection of behavior change in people with depression https://www.aaai.org/ocs/index.php/WS/AAAIW14/paper/view/8850/8303
Cited by
Sorted by citations
Trifan, A., Oliveira, M., & Oliveira, J. L. (2019) · JMIR mhealth and uhealth · doi:10.2196/12649 · Cited by 163 · see citing works
Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review
Rodriguez-León, C., Villalonga, C., Munoz-Torres, M., Ruiz, J. R., & Banos, O. (2021) · JMIR mHealth and uHealth · doi:10.2196/25138 · Cited by 132 · see citing works
“Faccioli et al [45] studied the relation between physiological information on physical exercise and glucose models, mainly from ACC and GL data sensed via an activity tracker and CGM. Similarly, Merickel et al [32] found a relation between the driver state and the health and physiological state of patients with T1D us…”
BackgroundMethodInfluentialGooding, P., & Kariotis, T. (2021) · JMIR Mental Health · doi:10.2196/24668 · Cited by 100 · 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
Adler, D. A., Wang, F., Mohr, D. C., & Choudhury, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266516 · Cited by 79 · see citing works
Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
Milne-Ives, M., Selby, E., Inkster, B., Lam, C., & Meinert, E. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000079 · Cited by 68 · see citing works
Kaonga, N., & Morgan, J. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112594 · Cited by 67 · see citing works
Artificial intelligence-assisted psychosis risk screening in adolescents: Practices and challenges
Cao, X., & Liu, X. (2022) · World Journal of Psychiatry · doi:10.5498/wjp.v12.i10.1287 · Cited by 65 · 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
Artificial intelligence and diabetes technology: A review
Gautier, T., Ziegler, L. B., Gerber, M. S., Campos-Náñez, E., & Patek, S. D. (2021) · Metabolism · doi:10.1016/j.metabol.2021.154872 · Cited by 52 · 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
Technology and Mental Health: State of the Art for Assessment and Treatment
Harvey, P. D., Depp, C. A., Rizzo, A., Strauss, G. P., Spelber, D., Carpenter, L. L., Kalin, N. H., Krystal, J. H., McDonald, W. M., Nemeroff, C. B., et al. (2022) · American Journal of Psychiatry · doi:10.1176/appi.ajp.21121254 · Cited by 46 · see citing works
A 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
Roca, S., del Hoyo, M. L. L., García, J., & Alesanco, Á. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182212056 · Cited by 40 · see citing works
Shah, A., Hussain‐Shamsy, N., Strudwick, G., Sockalingam, S., Nolan, R. P., & Seto, E. (2022) · Journal of Medical Internet Research · doi:10.2196/38030 · Cited by 38 · see citing works
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Kulkarni, P., Kirkham, R., & Mcnaney, R. (2022) · Italian National Conference on Sensors · doi:10.3390/s22103893 · Cited by 33 · see citing works
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Howard, D. M., Folkersen, L., Coleman, J. R. I., Adams, M. J., Glanville, K. P., Werge, T. M., Hagenaars, S. P., Han, B., Porteous, D. J., Campbell, A. I., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0848-0 · Cited by 30 · see citing works
Choudhary, S., Thomas, N., Ellenberger, J., Srinivasan, G., & Cohen, R. (2022) · JMIR Formative Research · doi:10.2196/37736 · Cited by 36 · 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
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
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
Mental health ubiquitous monitoring supported by social situation awareness: A systematic review
de Moura, I. R., Teles, A. S., Silva, F., Viana, D., Coutinho, L., Barros, F., & Endler, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103454 · Cited by 23 · see citing works
Radhakrishnan, K., Kim, M. T., Burgermaster, M., Brown, R. A., Xie, B., Bray, M. S., & Fournier, C. A. (2020) · Nursing Outlook · doi:10.1016/j.outlook.2020.03.007 · Cited by 22 · 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
Vlisides‐Henry, R. D., Gao, M. M., Thomas, L. R., Kaliush, P. R., Conradt, E. D., & Crowell, S. E. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.618442 · Cited by 18 · see citing works
Gopalakrishnan, A., Venkataraman, R., Gururajan, R., Zhou, X., & Genrich, R. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1042 · Cited by 19 · see citing works
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Orr, M., MacLeod, L., Bagnell, A. L., McGrath, P., Wozney, L., & Meier, S. M. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107603 · Cited by 13 · see citing works
Yu, J., Chiu, C., Wang, Y., Dzubur, E., Lu, W., & Hoffman, J. E. (2021) · Journal of Medical Internet Research · doi:10.2196/27709 · Cited by 11 · see citing works
Howard, D. M., Folkersen, L., Coleman, J. R. I., Adams, M. J., Glanville, K. P., Werge, T. M., Hagenaars, S. P., Han, B., Porteous, D. J., Campbell, A. I., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/134601 · Cited by 9 · see citing works
de Moura, I. R., Teles, A. S., Endler, M., Coutinho, L., & Silva, F. (2020) · Sensors · doi:10.3390/s21010086 · Cited by 9 · see citing works
Knights, J., Shen, J., Mysliwiec, V., & DuBois, H. (2023) · SLEEP Advances · doi:10.1093/sleepadvances/zpad027 · Cited by 9 · see citing works
Liu, H., Zhang, W., Goh, C., Dai, F., Sadiq, S., & Tse, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00024-1 · Cited by 7 · see citing works
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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
Chen, E., Jiang, J., Zhou, J., Wang, H., Sun, G., Zhou, R. M., Su, R., Zhu, S., & Huo, Y. (2021) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2021.0347 · Cited by 6 · see citing works
Exploring Unique App Signature of the Depressed and Non-depressed Through Their Fingerprints on Apps
Ahmed, M. S., & Ahmed, N. (2022) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-99194-4_15 · Cited by 6 · see citing works
Peng, F., Zhang, D., & Yan, Z. (2024) · Journal of Management Information Systems · doi:10.1080/07421222.2024.2415770 · Cited by 5 · see citing works
Forecasting mental states in schizophrenia using digital phenotyping data
Jean, T., Hottin, R. G., & Orban, P. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000734 · Cited by 5 · see citing works
Reich, H., Schreynemackers, S., Amin, R., Ludwig, S., Zippelius, J., Leimhofer, J., Dunker, T., Schriewer, E., Carell, A., Weber, Y. G., et al. (2025) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2025.101492 · Cited by 4 · see citing works
Knauer, J., Baumeister, H., Schmitt, A., & Terhorst, Y. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1352762 · Cited by 2 · see citing works
DeepStressScan: A Dual-Forest Intelligence Model for Anomaly Detection and Stress Prediction
Johnson, F., Abayomi-Alli, A. A., Olugbenga, O., Elugbadebo, O., & Aborisade, D. O. (2025) · Cureus Journal of Computer Science. · doi:10.7759/s44389-025-09820-4 · Cited by 2 · see citing works
Bourkhime, H., Qarmiche, N., Bahra, N., Omari, M., Chakri, I., Berraho, M., Tachfouti, N., Fakir, S. E., & Otmani, N. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-48465-0_35 · Cited by 2 · see citing works
Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities
Badier, N., Lafitte, A., Difernand, A., Aguayo, G., Fagherazzi, G., Onnela, J.-P. “., Vittrant, B., Lechat, B., Larochelambert, Q. D., Toussaint, J., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/91747 · Cited by 1 · see citing works
Jean, T., Hottin, R. G., & Orban, P. (2023) · doi:10.2196/preprints.54741
Harnessing Artificial Intelligence in Mobile Health for Diabetes Management: A Systematic Review
Bourkhime, H., Qarmiche, N., Omari, M., Charef, N., Ghazi, S. E., Tachfouti, N., Fakir, S. E., Berraho, M., & Otmani, N. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-88304-0_23
Diagnosis of depressive symptoms using mobile devices: a systematic review
Damasceno, M., Abrantes, P., Takahata, A. K., & Netto, A. V. (2025) · Revista de Investigação & Inovação em Saúde · doi:10.37914/riis.v8i2.439
Wang, N., Liu, Z., Yang, H., Chang, F., & Shen, B. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791812
Lui, E. H. Y., & Mobbs, R. J. (2026) · Journal of Neurology · doi:10.1007/s00415-026-14131-2
Hou, W., Sun, W., Dai, H., He, Y., Xu, G., Xie, X., Bai, F., & Xie, D. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.2005262
- Authors
- Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V.
- Corresponding author
- Sarda, Shubhankar
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2019-01-29
- Volume
- 7
- Issue
- 1
- Pages
- e11041
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC6371066
- OpenAlex
- W2901375199
- Cited by
- 56
- References
- 67
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 6 · 2023: 3 · 2022: 16 · 2021: 11 · 2020: 4 · 2019: 2 · 2017: 1
- FWCI
- 5.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Public Health Foundation of India
- University of Canberra
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- depression, diabetes mellitus, smartphone sensing, passive sensing, risk stratification, comorbidities, physical activity, sleep, social communication, PHQ-9, machine learning classification, digital phenotyping, glycemic control, self-management, social isolation, social support, extreme gradient boosting
- MeSH terms
- Smartphone, Adult, Cross-Sectional Studies, Depression, Diabetes Mellitus, Female, Humans, Male, Middle Aged, Surveys and Questionnaires, Risk Assessment, Mobile Applications
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