Published on 03.03.17 in Vol 19, No 3 (2017): March
Works citing "Using Mobile Sensing to Test Clinical Models of Depression, Social Anxiety, State Affect, and Social Isolation Among College Students"
According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.6820):
(note that this is only a small subset of citations)
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Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454
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Jacobson NC, Chung YJ. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572
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Ware S, Yue C, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Predicting depressive symptoms using smartphone data. Smart Health 2020;15:100093
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Paolillo EW, Tang B, Depp CA, Rooney AS, Vaida F, Kaufmann CN, Mausbach BT, Moore DJ, Moore RC. Temporal Associations Between Social Activity and Mood, Fatigue, and Pain in Older Adults With HIV: An Ecological Momentary Assessment Study. JMIR Mental Health 2018;5(2):e38
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. Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020;
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Wang R, Wang W, daSilva A, Huckins JF, Kelley WM, Heatherton TF, Campbell AT. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(1):1
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Ike KG, de Boer SF, Buwalda B, Kas MJ. Social withdrawal: An initially adaptive behavior that becomes maladaptive when expressed excessively. Neuroscience & Biobehavioral Reviews 2020;116:251
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Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Automatic depression prediction using Internet traffic characteristics on smartphones. Smart Health 2020;18:100137
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Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819
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Calvo RA, Dinakar K, Picard R, Christensen H, Torous J. Toward Impactful Collaborations on Computing and Mental Health. Journal of Medical Internet Research 2018;20(2):e49
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Friedmann F, Santangelo P, Ebner-Priemer U, Hill H, Neubauer AB, Rausch S, Steil R, Müller-Engelmann M, Kleindienst N, Bohus M, Fydrich T, Priebe K, Matsumura K. Life within a limited radius: Investigating activity space in women with a history of child abuse using global positioning system tracking. PLOS ONE 2020;15(5):e0232666
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Rohani DA, Faurholt-Jepsen M, Kessing LV, Bardram JE. Correlations Between Objective Behavioral Features Collected From Mobile and Wearable Devices and Depressive Mood Symptoms in Patients With Affective Disorders: Systematic Review. JMIR mHealth and uHealth 2018;6(8):e165
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Chow PI, Drago F, Kennedy EM, Cohn WF. A Novel Mobile Phone App Intervention With Phone Coaching to Reduce Symptoms of Depression in Survivors of Women’s Cancer: Pre-Post Pilot Study. JMIR Cancer 2020;6(1):e15750
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Agarwal R, Dugas M, Gao G, Kannan PK. Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science 2020;48(1):9
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Dejonckheere E, Mestdagh M, Houben M, Rutten I, Sels L, Kuppens P, Tuerlinckx F. Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour 2019;3(5):478
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Cai L, Boukhechba M, Gerber MS, Barnes LE, Showalter SL, Cohn WF, Chow PI. An integrated framework for using mobile sensing to understand response to mobile interventions among breast cancer patients. Smart Health 2020;15:100086
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Xu X, Chikersal P, Doryab A, Villalba DK, Dutcher JM, Tumminia MJ, Althoff T, Cohen S, Creswell KG, Creswell JD, Mankoff J, Dey AK. Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(3):1
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Jacobson NC, Summers B, Wilhelm S. Digital Biomarkers of Social Anxiety Severity: Digital Phenotyping Using Passive Smartphone Sensors. Journal of Medical Internet Research 2020;22(5):e16875
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Armstrong CM, Ciulla RP, Williams SA, Micheel LJ. An Applied Test of Knowledge Translation Methods Using a Mobile Health Solution. Military Medicine 2020;185(Supplement_1):526
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Chow PI, Showalter SL, Gerber MS, Kennedy E, Brenin DR, Schroen AT, Mohr DC, Lattie EG, Cohn WF. Use of Mental Health Apps by Breast Cancer Patients and Their Caregivers in the United States: Protocol for a Pilot Pre-Post Study. JMIR Research Protocols 2019;8(1):e11452
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Rashid H, Mendu S, Daniel KE, Beltzer ML, Teachman BA, Boukhechba M, Barnes LE. Predicting Subjective Measures of Social Anxiety from Sparsely Collected Mobile Sensor Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1
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Chan S, Godwin H, Gonzalez A, Yellowlees PM, Hilty DM. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12)
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Nicholas J, Shilton K, Schueller SM, Gray EL, Kwasny MJ, Mohr DC. The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study. JMIR mHealth and uHealth 2019;7(4):e12578
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Montag C, Baumeister H, Kannen C, Sariyska R, Meßner E, Brand M. Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology. J 2019;2(2):102
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Peleh T, Ike KG, Frentz I, Buwalda B, de Boer SF, Hengerer B, Kas MJ. Cross-site Reproducibility of Social Deficits in Group-housed BTBR Mice Using Automated Longitudinal Behavioural Monitoring. Neuroscience 2020;445:95
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Harari GM, Müller SR, Aung MS, Rentfrow PJ. Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences 2017;18:83
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Watson RJ, Christensen JL. Big data and student engagement among vulnerable youth: A review. Current Opinion in Behavioral Sciences 2017;18:23
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Ware S, Yue C, Morillo R, Lu J, Shang C, Kamath J, Bamis A, Bi J, Russell A, Wang B. Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018;2(4):1
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McQuoid J, Thrul J, Ling P. A geographically explicit ecological momentary assessment (GEMA) mixed method for understanding substance use. Social Science & Medicine 2018;202:89
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Sano A, Taylor S, McHill AW, Phillips AJ, Barger LK, Klerman E, Picard R. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study. Journal of Medical Internet Research 2018;20(6):e210
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Chan S, Li L, Torous J, Gratzer D, Yellowlees PM. Review of Use of Asynchronous Technologies Incorporated in Mental Health Care. Current Psychiatry Reports 2018;20(10)
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Poudyal A, van Heerden A, Hagaman A, Maharjan SM, Byanjankar P, Subba P, Kohrt BA. Wearable Digital Sensors to Identify Risks of Postpartum Depression and Personalize Psychological Treatment for Adolescent Mothers: Protocol for a Mixed Methods Exploratory Study in Rural Nepal. JMIR Research Protocols 2019;8(8):e14734
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Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman MA, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751
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Hur J, DeYoung KA, Islam S, Anderson AS, Barstead MG, Shackman AJ. Social context and the real-world consequences of social anxiety. Psychological Medicine 2020;50(12):1989
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Weingarden H, Matic A, Calleja RG, Greenberg JL, Harrison O, Wilhelm S. Optimizing Smartphone-Delivered Cognitive Behavioral Therapy for Body Dysmorphic Disorder Using Passive Smartphone Data: Initial Insights From an Open Pilot Trial. JMIR mHealth and uHealth 2020;8(6):e16350
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Garcia-Ceja E, Riegler M, Nordgreen T, Jakobsen P, Oedegaard KJ, Tørresen J. Mental health monitoring with multimodal sensing and machine learning: A survey. Pervasive and Mobile Computing 2018;51:1
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de Moura IR, Teles AS, Endler M, Coutinho LR, da Silva e Silva FJ. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. Sensors 2020;21(1):86
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Chikersal P, Doryab A, Tumminia M, Villalba DK, Dutcher JM, Liu X, Cohen S, Creswell KG, Mankoff J, Creswell JD, Goel M, Dey AK. Detecting Depression and Predicting its Onset Using Longitudinal Symptoms Captured by Passive Sensing. ACM Transactions on Computer-Human Interaction 2021;28(1):1
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Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161
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dos Santos Paula L, Barbosa JLV, Dias LPS. A model for assisting in the treatment of anxiety disorder. Universal Access in the Information Society 2022;21(2):533
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Page-Reeves J, Murray-Krezan C, Regino L, Perez J, Bleecker M, Perez D, Wagner B, Tigert S, Bearer EL, Willging CE. A randomized control trial to test a peer support group approach for reducing social isolation and depression among female Mexican immigrants. BMC Public Health 2021;21(1)
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Wu C, Barczyk AN, Craddock RC, Harari GM, Thomaz E, Shumake JD, Beevers CG, Gosling SD, Schnyer DM. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health 2021;20:100180
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Mei S, Hu Y, Sun M, Fei J, Li C, Liang L, Hu Y. Association between Bullying Victimization and Symptoms of Depression among Adolescents: A Moderated Mediation Analysis. International Journal of Environmental Research and Public Health 2021;18(6):3316
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Hilty DM, Armstrong CM, Luxton DD, Gentry MT, Krupinski EA. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278
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Xu X, Chikersal P, Dutcher JM, Sefidgar YS, Seo W, Tumminia MJ, Villalba DK, Cohen S, Creswell KG, Creswell JD, Doryab A, Nurius PS, Riskin E, Dey AK, Mankoff J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1
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Maharjan SM, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt BA, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1)
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Melcher J, Lavoie J, Hays R, D'Mello R, Rauseo-Ricupero N, Camacho E, Rodriguez-Villa E, Wisniewski H, Lagan S, Vaidyam A, Torous J. Digital phenotyping of student mental health during COVID-19: an observational study of 100 college students. Journal of American College Health 2023;71(3):736
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Yue C, Ware S, Morillo R, Lu J, Shang C, Bi J, Kamath J, Russell A, Bamis A, Wang B. Fusing Location Data for Depression Prediction. IEEE Transactions on Big Data 2021;7(2):355
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Zhang Y, Folarin AA, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BWJH, Narayan VA, Annas P, Hotopf M, Dobson RJB. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840
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Müller SR, Chen X, Peters H, Chaintreau A, Matz SC. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports 2021;11(1)
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Daniel KE, Mendu S, Baglione A, Cai L, Teachman BA, Barnes LE, Boukhechba M. Cognitive bias modification for threat interpretations: using passive Mobile Sensing to detect intervention effects in daily life. Anxiety, Stress, & Coping 2022;35(3):298
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Di Matteo D, Fotinos K, Lokuge S, Mason G, Sternat T, Katzman MA, Rose J. Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study. Journal of Medical Internet Research 2021;23(8):e28918
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Burr C, Morley J, Taddeo M, Floridi L. Digital Psychiatry: Risks and Opportunities for Public Health and Wellbeing. IEEE Transactions on Technology and Society 2020;1(1):21
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Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296
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Meyerhoff J, Liu T, Kording KP, Ungar LH, Kaiser SM, Karr CJ, Mohr DC. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(9):e22844
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Birtwistle E, Schoedel R, Bemmann F, Wirth A, Sürig C, Stachl C, Bühner M, Niklas F. Mobile sensing in psychological and educational research: Examples from two application fields. International Journal of Testing 2022;22(3-4):264
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Keusch F, Conrad FG. Using Smartphones to Capture and Combine Self-Reports and Passively Measured Behavior in Social Research. Journal of Survey Statistics and Methodology 2022;10(4):863
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Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893
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Chikersal P, Venkatesh S, Masown K, Walker E, Quraishi D, Dey A, Goel M, Xia Z. Predicting Multiple Sclerosis Outcomes During the COVID-19 Stay-at-home Period: Observational Study Using Passively Sensed Behaviors and Digital Phenotyping. JMIR Mental Health 2022;9(8):e38495
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. Henry Clerval Scolding Victor Frankenstein: An autoethnographic poem about graduate students and their daemons. McGill Journal of Education 2021;55(3):685
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Jacobson NC, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013
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MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638
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LeBaron V, Boukhechba M, Edwards J, Flickinger T, Ling D, Barnes LE. Exploring the Use of Wearable Sensors and Natural Language Processing Technology to Improve Patient-Clinician Communication: Protocol for a Feasibility Study. JMIR Research Protocols 2022;11(5):e37975
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Flechsenhar A, Kanske P, Krach S, Korn C, Bertsch K. The (un)learning of social functions and its significance for mental health. Clinical Psychology Review 2022;98:102204
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Chia AZ, Zhang MW. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331
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McLeish AC, Walker KL, Hart JL. Changes in Internalizing Symptoms and Anxiety Sensitivity Among College Students During the COVID-19 Pandemic. Journal of Psychopathology and Behavioral Assessment 2022;44(4):1021
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. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78
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Laiou P, Kaliukhovich DA, Folarin AA, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F, Ivan A, Lavelle G, Siddi S, Lamers F, Penninx BW, Haro JM, Annas P, Cummins N, Vairavan S, Manyakov NV, Narayan VA, Dobson RJ, Hotopf M. The Association Between Home Stay and Symptom Severity in Major Depressive Disorder: Preliminary Findings From a Multicenter Observational Study Using Geolocation Data From Smartphones. JMIR mHealth and uHealth 2022;10(1):e28095
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Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes LE, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413
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Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1)
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Brogly C, Shoemaker JK, Lizotte DJ, Kueper JK, Bauer M. A Mobile App to Identify Lifestyle Indicators Related to Undergraduate Mental Health (Smart Healthy Campus): Observational App-Based Ecological Momentary Assessment. JMIR Formative Research 2021;5(10):e29160
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Abdullah S, Choudhury T. Sensing Technologies for Monitoring Serious Mental Illnesses. IEEE MultiMedia 2018;25(1):61
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Zhang Y, Folarin AA, Sun S, Cummins N, Vairavan S, Bendayan R, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Sankesara H, Matcham F, White KM, Oetzmann C, Ivan A, Lamers F, Siddi S, Vilella E, Simblett S, Rintala A, Bruce S, Mohr DC, Myin-Germeys I, Wykes T, Haro JM, Penninx BW, Narayan VA, Annas P, Hotopf M, Dobson RJ. Longitudinal Relationships Between Depressive Symptom Severity and Phone-Measured Mobility: Dynamic Structural Equation Modeling Study. JMIR Mental Health 2022;9(3):e34898
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Ware S, Yue C, Morillo R, Shang C, Bi J, Kamath J, Russell A, Song D, Bamis A, Wang B. Automatic depression screening using social interaction data on smartphones. Smart Health 2022;26:100356
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Wang Z, Larrazabal MA, Rucker M, Toner ER, Daniel KE, Kumar S, Boukhechba M, Teachman BA, Barnes LE. Detecting Social Contexts from Mobile Sensing Indicators in Virtual Interactions with Socially Anxious Individuals. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1
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Fernández-Álvarez J, Colombo D, Gómez Penedo JM, Pierantonelli M, Baños RM, Botella C. Studies of Social Anxiety Using Ambulatory Assessment: Systematic Review. JMIR Mental Health 2024;11:e46593
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.6820):
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Lee J, Lam M, Chiu C. Pervasive Computing Paradigms for Mental Health. 2019. Chapter 2:12
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Hur J, Stockbridge MD, Fox AS, Shackman AJ. Emotion and Cognition. 2019. :375
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Lee H, Cho A, Jo Y, Whang M. Advances in Computer Science and Ubiquitous Computing. 2018. Chapter 212:1332
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. The Geographies of COVID-19. 2022. Chapter 5:49
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Chemagosi MJ, Barongo SM. Student Stress in Higher Education. 2023. chapter 2:19
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