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Citing this Article

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Published on 21.09.16 in Vol 4, No 3 (2016): Jul-Sept

This paper is in the following e-collection/theme issue:

Works citing "Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild"

Fabian Wahle, Tobias Kowatsch, Elgar Fleisch, Michael Rufer, Steffi Weidt

JMIR Mhealth Uhealth 2016 (Sep 21); 4(3):e111 

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.5960):

(note that this is only a small subset of citations)

  1. . The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11
    CrossRef
  2. Guo Y, Hong YA, Qiao J, Xu Z, Zhang H, Zeng C, Cai W, Li L, Liu C, Li Y, Zhu M, Harris NA, Yang C. Run4Love, a mHealth (WeChat-based) intervention to improve mental health of people living with HIV: a randomized controlled trial protocol. BMC Public Health 2018;18(1)
    CrossRef
  3. Hardy J, Veinot TC, Yan X, Berrocal VJ, Clarke P, Goodspeed R, Gomez-Lopez IN, Romero D, Vydiswaran VV. User acceptance of location-tracking technologies in health research: Implications for study design and data quality. Journal of Biomedical Informatics 2018;79:7
    CrossRef
  4. khan ZF, Alotaibi SR. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. Journal of Healthcare Engineering 2020;2020:1
    CrossRef
  5. 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
    CrossRef
  6. Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649
    CrossRef
  7. Goodspeed R, Yan X, Hardy J, Vydiswaran VV, Berrocal VJ, Clarke P, Romero DM, Gomez-Lopez IN, Veinot T. Comparing the Data Quality of Global Positioning System Devices and Mobile Phones for Assessing Relationships Between Place, Mobility, and Health: Field Study. JMIR mHealth and uHealth 2018;6(8):e168
    CrossRef
  8. Lüscher J, Kowatsch T, Boateng G, Santhanam P, Bodenmann G, Scholz U. Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory Assessment Application. JMIR Research Protocols 2019;8(10):e13685
    CrossRef
  9. Burger F, Neerincx MA, Brinkman W. Technological State of the Art of Electronic Mental Health Interventions for Major Depressive Disorder: Systematic Literature Review. Journal of Medical Internet Research 2020;22(1):e12599
    CrossRef
  10. Rohani DA, Tuxen N, Quemada Lopategui A, Kessing LV, Bardram JE. Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study. JMIR Mental Health 2018;5(2):e10122
    CrossRef
  11. 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)
    CrossRef
  12. Stieger M, Nißen M, Rüegger D, Kowatsch T, Flückiger C, Allemand M. PEACH, a smartphone- and conversational agent-based coaching intervention for intentional personality change: study protocol of a randomized, wait-list controlled trial. BMC Psychology 2018;6(1)
    CrossRef
  13. Aledavood T, Triana Hoyos AM, Alakörkkö T, Kaski K, Saramäki J, Isometsä E, Darst RK. Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype. JMIR Research Protocols 2017;6(6):e110
    CrossRef
  14. Boukhechba M, Chow P, Fua K, Teachman BA, Barnes LE. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101
    CrossRef
  15. 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
    CrossRef
  16. Severe J, Greden JF, Reddy P. Consequences of Recurrence of Major Depressive Disorder: Is Stopping Effective Antidepressant Medications Ever Safe?. FOCUS 2020;18(2):120
    CrossRef
  17. Lind MN, Byrne ML, Wicks G, Smidt AM, Allen NB. The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health 2018;5(3):e10334
    CrossRef
  18. Felix IR, Castro LA, Rodriguez L, Banos O. Mobile sensing for behavioral research: A component-based approach for rapid deployment of sensing campaigns. International Journal of Distributed Sensor Networks 2019;15(9):155014771987418
    CrossRef
  19. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020;
    CrossRef
  20. Litvin S, Saunders R, Maier MA, Lüttke S, Matsuoka YJ. Gamification as an approach to improve resilience and reduce attrition in mobile mental health interventions: A randomized controlled trial. PLOS ONE 2020;15(9):e0237220
    CrossRef
  21. Majumder S, Deen MJ. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164
    CrossRef
  22. Sabharwal A, Veeraraghavan A. Bio-Behavioral Sensing. GetMobile: Mobile Computing and Communications 2017;21(3):11
    CrossRef
  23. 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
    CrossRef
  24. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 2019;21(11)
    CrossRef
  25. Saeb S, Lattie EG, Kording KP, Mohr DC. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112
    CrossRef
  26. Dogan E, Sander C, Wagner X, Hegerl U, Kohls E. Smartphone-Based Monitoring of Objective and Subjective Data in Affective Disorders: Where Are We and Where Are We Going? Systematic Review. Journal of Medical Internet Research 2017;19(7):e262
    CrossRef
  27. Kerst A, Zielasek J, Gaebel W. Smartphone applications for depression: a systematic literature review and a survey of health care professionals’ attitudes towards their use in clinical practice. European Archives of Psychiatry and Clinical Neuroscience 2020;270(2):139
    CrossRef
  28. Wahle F, Bollhalder L, Kowatsch T, Fleisch E. Toward the Design of Evidence-Based Mental Health Information Systems for People With Depression: A Systematic Literature Review and Meta-Analysis. Journal of Medical Internet Research 2017;19(5):e191
    CrossRef
  29. Cornet VP, Holden RJ. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120
    CrossRef
  30. Matz SC, Appel RE, Kosinski M. Privacy in the age of psychological targeting. Current Opinion in Psychology 2020;31:116
    CrossRef
  31. Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine 2019;49(09):1426
    CrossRef
  32. Tsanas A, Saunders K, Bilderbeck A, Palmius N, Goodwin G, De Vos M. Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment. JMIR Mental Health 2017;4(2):e15
    CrossRef
  33. Obuchi M, Huckins JF, Wang W, daSilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1
    CrossRef
  34. 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
    CrossRef
  35. Hwang WJ, Jo HH. Evaluation of the Effectiveness of Mobile App-Based Stress-Management Program: A Randomized Controlled Trial. International Journal of Environmental Research and Public Health 2019;16(21):4270
    CrossRef
  36. Acikmese Y, Alptekin SE. Prediction of stress levels with LSTM and passive mobile sensors. Procedia Computer Science 2019;159:658
    CrossRef
  37. Dias LPS, Barbosa JLV, Feijó LP, Vianna HD. Development and testing of iAware model for ubiquitous care of patients with symptoms of stress, anxiety and depression. Computer Methods and Programs in Biomedicine 2020;187:105113
    CrossRef
  38. 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
    CrossRef
  39. Huckvale K, Venkatesh S, Christensen H. Toward clinical digital phenotyping: a timely opportunity to consider purpose, quality, and safety. npj Digital Medicine 2019;2(1)
    CrossRef
  40. Jang JS, Cho SH. Mobile Health (m-health) on Mental Health. Korean Journal of Stress Research 2016;24(4):231
    CrossRef
  41. . What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?. Clinical Therapeutics 2016;38(12):2533
    CrossRef
  42. Barnett S, Huckvale K, Christensen H, Venkatesh S, Mouzakis K, Vasa R. Intelligent Sensing to Inform and Learn (InSTIL): A Scalable and Governance-Aware Platform for Universal, Smartphone-Based Digital Phenotyping for Research and Clinical Applications. Journal of Medical Internet Research 2019;21(11):e16399
    CrossRef
  43. Renn BN, Pratap A, Atkins DC, Mooney SD, Areán PA. Smartphone-based passive assessment of mobility in depression: Challenges and opportunities. Mental Health and Physical Activity 2018;14:136
    CrossRef
  44. Aafjes-van Doorn K, Kamsteeg C, Bate J, Aafjes M. A scoping review of machine learning in psychotherapy research. Psychotherapy Research 2021;31(1):92
    CrossRef
  45. 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
    CrossRef
  46. Chung K, Jeon M, Park J, Lee S, Kim CO, Park JY, Guloksuz S. Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study. PLOS ONE 2018;13(6):e0199118
    CrossRef
  47. Dias LPS, Barbosa JLV, Vianna HD. Gamification and serious games in depression care: A systematic mapping study. Telematics and Informatics 2018;35(1):213
    CrossRef
  48. Sarda A, Munuswamy S, Sarda S, Subramanian V. Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study. JMIR mHealth and uHealth 2019;7(1):e11041
    CrossRef
  49. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149
    CrossRef
  50. Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing LV, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow PC. Smartphones in mental health: a critical review of background issues, current status and future concerns. International Journal of Bipolar Disorders 2020;8(1)
    CrossRef
  51. Bourla A, Ferreri F, Ogorzelec L, Guinchard C, Mouchabac S. Évaluation des troubles thymiques par l’étude des données passives : le concept de phénotype digital à l’épreuve de la culture de métier de psychiatre. L'Encéphale 2018;44(2):168
    CrossRef
  52. Pham Q, Graham G, Carrion C, Morita PP, Seto E, Stinson JN, Cafazzo JA. A Library of Analytic Indicators to Evaluate Effective Engagement with Consumer mHealth Apps for Chronic Conditions: Scoping Review. JMIR mHealth and uHealth 2019;7(1):e11941
    CrossRef
  53. Miralles I, Granell C, Díaz-Sanahuja L, Van Woensel W, Bretón-López J, Mira A, Castilla D, Casteleyn S. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR mHealth and uHealth 2020;8(4):e14897
    CrossRef
  54. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Areán PA. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72
    CrossRef
  55. Miralles I, Granell C. Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions. International Journal of Environmental Research and Public Health 2019;16(7):1197
    CrossRef
  56. Yim SJ, Lui LM, Lee Y, Rosenblat JD, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho RC, Coles AS, Majeed A, Wong ER, Phan L, Nasri F, McIntyre RS. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602
    CrossRef
  57. Faurholt-Jepsen M, Busk J, Þórarinsdóttir H, Frost M, Bardram JE, Vinberg M, Kessing LV. Objective smartphone data as a potential diagnostic marker of bipolar disorder. Australian & New Zealand Journal of Psychiatry 2019;53(2):119
    CrossRef
  58. Cao J, Truong AL, Banu S, Shah AA, Sabharwal A, Moukaddam N. Tracking and Predicting Depressive Symptoms of Adolescents Using Smartphone-Based Self-Reports, Parental Evaluations, and Passive Phone Sensor Data: Development and Usability Study. JMIR Mental Health 2020;7(1):e14045
    CrossRef
  59. Van Ameringen M, Turna J, Khalesi Z, Pullia K, Patterson B. There is an app for that! The current state of mobile applications (apps) for DSM-5 obsessive-compulsive disorder, posttraumatic stress disorder, anxiety and mood disorders. Depression and Anxiety 2017;34(6):526
    CrossRef
  60. Radhakrishnan K, Kim MT, Burgermaster M, Brown RA, Xie B, Bray MS, Fournier CA. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548
    CrossRef
  61. Taeger J, Bischoff S, Hagen R, Rak K. Utilization of Smartphone Depth Mapping Cameras for App-Based Grading of Facial Movement Disorders: Development and Feasibility Study. JMIR mHealth and uHealth 2021;9(1):e19346
    CrossRef
  62. Hilty DM, Armstrong CM, Edwards-Stewart A, Gentry MT, Luxton DD, Krupinski EA. Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review. Journal of Technology in Behavioral Science 2021;6(2):252
    CrossRef
  63. 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)
    CrossRef
  64. Basantani A, Kesarwani Y, Bhatia S, Jain S. EmoCure: Utilising Social Media Data and Smartphones to Predict and Cure Depression. IOP Conference Series: Materials Science and Engineering 2021;1110(1):012010
    CrossRef
  65. Mercier HW, Hamner JW, Torous J, Onnela JP, Taylor JA. Digital Phenotyping to Quantify Psychosocial Well-Being Trajectories After Spinal Cord Injury. American Journal of Physical Medicine & Rehabilitation 2020;99(12):1138
    CrossRef
  66. Zhang H, Yu Q, Li Z, Xiu X, Lv F, Han M, Wang L. Efficacy of Psychological Interventions Towards the Reduction of High-Risk Sexual Behaviors Among People Living with HIV: A Systematic Review and Meta-analysis, 2010–2020. AIDS and Behavior 2021;25(10):3355
    CrossRef
  67. 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
    CrossRef
  68. Roux de Bézieux H, Bullard J, Kolterman O, Souza M, Perraudeau F. Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study. JMIR Formative Research 2021;5(3):e20175
    CrossRef
  69. 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
    CrossRef
  70. . Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sensors Journal 2021;21(6):7207
    CrossRef
  71. Patoz M, Hidalgo-Mazzei D, Blanc O, Verdolini N, Pacchiarotti I, Murru A, Zukerwar L, Vieta E, Llorca P, Samalin L. Patient and physician perspectives of a smartphone application for depression: a qualitative study. BMC Psychiatry 2021;21(1)
    CrossRef
  72. Tokgöz P, Hrynyschyn R, Hafner J, Schönfeld S, Dockweiler C. Digital Health Interventions in Prevention, Relapse, and Therapy of Mild and Moderate Depression: Scoping Review. JMIR Mental Health 2021;8(4):e26268
    CrossRef
  73. Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3
    CrossRef
  74. Fernandes A, Van Lenthe FJ, Vallée J, Sueur C, Chaix B. Linking physical and social environments with mental health in old age: a multisensor approach for continuous real-life ecological and emotional assessment. Journal of Epidemiology and Community Health 2021;75(5):477
    CrossRef
  75. Balaskas A, Schueller SM, Cox AL, Doherty G, Myers B. Ecological momentary interventions for mental health: A scoping review. PLOS ONE 2021;16(3):e0248152
    CrossRef
  76. Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. npj Digital Medicine 2021;4(1)
    CrossRef
  77. Khan NS, Ghani MS. A Survey of Deep Learning Based Models for Human Activity Recognition. Wireless Personal Communications 2021;120(2):1593
    CrossRef
  78. Gooding P, Kariotis T. Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review. JMIR Mental Health 2021;8(6):e24668
    CrossRef
  79. Sels L, Homan S, Ries A, Santhanam P, Scheerer H, Colla M, Vetter S, Seifritz E, Galatzer-Levy I, Kowatsch T, Scholz U, Kleim B. SIMON: A Digital Protocol to Monitor and Predict Suicidal Ideation. Frontiers in Psychiatry 2021;12
    CrossRef
  80. 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)
    CrossRef
  81. Emden D, Goltermann J, Dannlowski U, Hahn T, Opel N. Technical feasibility and adherence of the Remote Monitoring Application in Psychiatry (ReMAP) for the assessment of affective symptoms. Journal of Affective Disorders 2021;294:652
    CrossRef
  82. Teepe GW, Da Fonseca A, Kleim B, Jacobson NC, Salamanca Sanabria A, Tudor Car L, Fleisch E, Kowatsch T. Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review. Journal of Medical Internet Research 2021;23(9):e29412
    CrossRef
  83. Nickels S, Edwards MD, Poole SF, Winter D, Gronsbell J, Rozenkrants B, Miller DP, Fleck M, McLean A, Peterson B, Chen Y, Hwang A, Rust-Smith D, Brant A, Campbell A, Chen C, Walter C, Arean PA, Hsin H, Myers LJ, Marks Jr WJ, Mega JL, Schlosser DA, Conrad AJ, Califf RM, Fromer M. Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling. JMIR Mental Health 2021;8(8):e27589
    CrossRef
  84. Choudhary S, Thomas N, Ellenberger J, Srinivasan G, Cohen R. A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study. JMIR Formative Research 2022;6(5):e37736
    CrossRef
  85. Adler DA, Wang F, Mohr DC, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516
    CrossRef
  86. Tlachac M, Flores R, Reisch M, Kayastha R, Taurich N, Melican V, Bruneau C, Caouette H, Lovering J, Toto E, Rundensteiner EA. StudentSADD. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1
    CrossRef
  87. Tlachac M, Flores R, Reisch M, Houskeeper K, Rundensteiner EA. DepreST-CAT. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1
    CrossRef
  88. Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F. Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning. Information 2022;13(9):406
    CrossRef
  89. Oyebode O, Fowles J, Steeves D, Orji R. Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness. International Journal of Human–Computer Interaction 2023;39(9):1938
    CrossRef
  90. Orr M, MacLeod L, Bagnell A, McGrath P, Wozney L, Meier S. The comfort of adolescent patients and their parents with mobile sensing and digital phenotyping. Computers in Human Behavior 2023;140:107603
    CrossRef
  91. Baumeister H, Garatva P, Pryss R, Ropinski T, Montag C. Digitale Phänotypisierung in der Psychologie – ein Quantensprung in der psychologischen Forschung?. Psychologische Rundschau 2023;74(2):89
    CrossRef
  92. Ortet C, Vale Costa L. “Listen to Your Immune System When It’s Calling for You”: Monitoring Autoimmune Diseases Using the iShU App. Sensors 2022;22(10):3834
    CrossRef
  93. Moura I, Teles A, Coutinho L, Silva F. Towards identifying context-enriched multimodal behavioral patterns for digital phenotyping of human behaviors. Future Generation Computer Systems 2022;131:227
    CrossRef
  94. de Angel V, Lewis S, White KM, Matcham F, Hotopf M. Clinical Targets and Attitudes Toward Implementing Digital Health Tools for Remote Measurement in Treatment for Depression: Focus Groups With Patients and Clinicians. JMIR Mental Health 2022;9(8):e38934
    CrossRef
  95. Chia AZ, Zhang MW. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331
    CrossRef
  96. . Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729
    CrossRef
  97. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042
    CrossRef
  98. Jacobson NC, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1)
    CrossRef
  99. Meegahapola L, Droz W, Kun P, de Götzen A, Nutakki C, Diwakar S, Correa SR, Song D, Xu H, Bidoglia M, Gaskell G, Chagnaa A, Ganbold A, Zundui T, Caprini C, Miorandi D, Hume A, Zarza JL, Cernuzzi L, Bison I, Britez MR, Busso M, Chenu-Abente R, Günel C, Giunchiglia F, Schelenz L, Gatica-Perez D. Generalization and Personalization of Mobile Sensing-Based Mood Inference Models. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(4):1
    CrossRef
  100. De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr DC, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine 2022;5(1)
    CrossRef
  101. Bremer W, Sarker A. Recruitment and retention in mobile application-based intervention studies: a critical synopsis of challenges and opportunities. Informatics for Health and Social Care 2023;48(2):139
    CrossRef
  102. Cousins A, Nakano L, Schofield E, Kabaila R. A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Computing and Applications 2023;35(16):11497
    CrossRef
  103. Griffiths C, da Silva KM, Leathlean C, Jiang H, Ang CS, Searle R. Investigation of physical activity, sleep, and mental health recovery in treatment resistant depression (TRD) patients receiving repetitive transcranial magnetic stimulation (rTMS) treatment. Journal of Affective Disorders Reports 2022;8:100337
    CrossRef
  104. König A, Müller P, Tröger J, Lindsay H, Alexandersson J, Hinze J, Riemenschneider M, Postin D, Ettore E, Lecomte A, Musiol M, Amblard M, Bremond F, Balazia M, Hurlemann R. Multimodal phenotyping of psychiatric disorders from social interaction: Protocol of a clinical multicenter prospective study. Personalized Medicine in Psychiatry 2022;33-34:100094
    CrossRef
  105. Mendes JPM, Moura IR, Van de Ven P, Viana D, Silva FJS, Coutinho LR, Teixeira S, Rodrigues JJPC, Teles AS. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735
    CrossRef
  106. Neumayr C, Voderholzer U, Schlegl S. Psych-APP-Therapie: Smartphonebasierte Interventionen in der Psychotherapie – Eine systematische Übersichtsarbeit. Verhaltenstherapie 2021;31(3):182
    CrossRef
  107. Gual-Montolio P, Jaén I, Martínez-Borba V, Castilla D, Suso-Ribera C. Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review. International Journal of Environmental Research and Public Health 2022;19(13):7737
    CrossRef
  108. Leong QY, Sridhar S, Blasiak A, Tadeo X, Yeo G, Remus A, Ho D. Characteristics of Mobile Health Platforms for Depression and Anxiety: Content Analysis Through a Systematic Review of the Literature and Systematic Search of Two App Stores. Journal of Medical Internet Research 2022;24(2):e27388
    CrossRef
  109. Rout A, Nitoslawski S, Ladle A, Galpern P. Using smartphone-GPS data to understand pedestrian-scale behavior in urban settings: A review of themes and approaches. Computers, Environment and Urban Systems 2021;90:101705
    CrossRef
  110. Meyer A, Wisniewski H, Torous J. Coaching to Support Mental Health Apps: Exploratory Narrative Review. JMIR Human Factors 2022;9(1):e28301
    CrossRef
  111. Lim J, Jeong CY, Lim JM, Chung S, Kim G, Noh KJ, Jeong H. Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges. IEEE Access 2022;10:2063
    CrossRef
  112. Khan NS, Ghani MS, Anjum G. ADAM-sense: Anxiety-displaying activities recognition by motion sensors. Pervasive and Mobile Computing 2021;78:101485
    CrossRef
  113. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278
    CrossRef
  114. . Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78
    CrossRef
  115. 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
    CrossRef
  116. Zhang H, Ibrahim A, Parsia B, Poliakoff E, Harper S. Passive social sensing with smartphones: a systematic review. Computing 2023;105(1):29
    CrossRef
  117. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597
    CrossRef
  118. Balaskas A, Schueller SM, Cox AL, Doherty G. The Functionality of Mobile Apps for Anxiety: Systematic Search and Analysis of Engagement and Tailoring Features. JMIR mHealth and uHealth 2021;9(10):e26712
    CrossRef
  119. Timmons AC, Duong JB, Simo Fiallo N, Lee T, Vo HPQ, Ahle MW, Comer JS, Brewer LC, Frazier SL, Chaspari T. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspectives on Psychological Science 2023;18(5):1062
    CrossRef
  120. Liu Y, Kang K, Doe MJ. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123
    CrossRef
  121. Watanabe K, Tsutsumi A. The Passive Monitoring of Depression and Anxiety Among Workers Using Digital Biomarkers Based on Their Physical Activity and Working Conditions: 2-Week Longitudinal Study. JMIR Formative Research 2022;6(11):e40339
    CrossRef
  122. 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
    CrossRef
  123. Rohani DA, Springer A, Hollis V, Bardram JE, Whittaker S. Recommending Activities for Mental Health and Well-Being: Insights From Two User Studies. IEEE Transactions on Emerging Topics in Computing 2021;9(3):1183
    CrossRef
  124. 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)
    CrossRef
  125. Mandryk RL, Birk MV, Vedress S, Wiley K, Reid E, Berger P, Frommel J. Remote Assessment of Depression Using Digital Biomarkers From Cognitive Tasks. Frontiers in Psychology 2021;12
    CrossRef
  126. Yao L, Wang Z, Gu H, Zhao X, Chen Y, Liu L. Prediction of Chinese clients’ satisfaction with psychotherapy by machine learning. Frontiers in Psychiatry 2023;14
    CrossRef
  127. Samy Helmy B, Samy Helmy A. Role of Artificial Intelligence in Mental Wellbeing: Opportunities and Challenges. Journal of Artificial Intelligence 2022;15(1):1
    CrossRef
  128. King SL, Lebert J, Karpisek LA, Phillips A, Neal T, Kosyluk K. Characterizing User Experiences With an SMS Text Messaging–Based mHealth Intervention: Mixed Methods Study. JMIR Formative Research 2022;6(5):e35699
    CrossRef
  129. 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
    CrossRef
  130. Zou B, Zhang X, Xiao L, Bai R, Li X, Liang H, Ma H, Wang G. Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1786
    CrossRef
  131. Vize CE, G. C. Wright A. Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances. Assessment 2024;31(1):199
    CrossRef
  132. Lamichhane B, Zhou J, Sano A. Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model. IEEE Journal of Biomedical and Health Informatics 2023;27(7):3246
    CrossRef
  133. Lim J, Lee S, Noh J, Lee W, Su P, Yoon Y. Effectiveness of Mental Health Care by Using Machine Learning on Manufacturing Worker. International Journal of Precision Engineering and Manufacturing-Smart Technology 2023;1(2):227
    CrossRef
  134. Hornstein S, Zantvoort K, Lueken U, Funk B, Hilbert K. Personalization strategies in digital mental health interventions: a systematic review and conceptual framework for depressive symptoms. Frontiers in Digital Health 2023;5
    CrossRef
  135. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502
    CrossRef
  136. Zhang H, Parsia B, Poliakoff E, Harper S. Tracking social behaviour with smartphones in people with Parkinson's: a longitudinal study. Behaviour & Information Technology 2023;:1
    CrossRef
  137. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778
    CrossRef
  138. Leibold A, Mansoor Ali D, Harrop J, Sharan A, Vaccaro AR, Sivaganesan A. Smartphone-based activity tracking for spine patients: Current technology and future opportunities. World Neurosurgery: X 2024;21:100238
    CrossRef
  139. Wang W, Xu W, Chander A, Nepal S, Buck B, Pakhomov S, Cohen T, Ben-Zeev D, Campbell A. The Power of Speech in the Wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2023;7(3):1
    CrossRef
  140. . Supporting patients’ workload through wearable devices and mobile health applications, a systematic literature review. Ergonomics 2023;:1
    CrossRef
  141. Zon M, Ganesh G, Deen MJ, Fang Q. Context-Aware Medical Systems within Healthcare Environments: A Systematic Scoping Review to Identify Subdomains and Significant Medical Contexts. International Journal of Environmental Research and Public Health 2023;20(14):6399
    CrossRef
  142. Kadirvelu B, Bellido Bel T, Wu X, Burmester V, Ananth S, Cabral C C Branco B, Girela-Serrano B, Gledhill J, Di Simplicio M, Nicholls D, Faisal AA. Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study. JMIR Formative Research 2023;7:e44877
    CrossRef
  143. Althobiani MA, Ranjan Y, Jacob J, Orini M, Dobson RJB, Porter JC, Hurst JR, Folarin AA. Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study. JMIR Formative Research 2023;7:e51507
    CrossRef
  144. Xu Z, Smit E. Using a complexity science approach to evaluate the effectiveness of just-in-time adaptive interventions: A meta-analysis. DIGITAL HEALTH 2023;9
    CrossRef
  145. Shin J, Bae SM. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984
    CrossRef
  146. Breitinger S, Gardea-Resendez M, Langholm C, Xiong A, Laivell J, Stoppel C, Harper L, Volety R, Walker A, D'Mello R, Byun AJS, Zandi P, Goes FS, Frye M, Torous J. Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study. Journal of Medical Internet Research 2023;25:e47006
    CrossRef
  147. Ferrario A, Demiray B. Understanding reminiscence and its negative functions in the everyday conversations of young adults: A machine learning approach. Heliyon 2024;10(1):e23825
    CrossRef
  148. Bibbo D, De Marchis C, Schmid M, Ranaldi S. Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review. Physiological Measurement 2023;44(12):12TR01
    CrossRef
  149. Xu X, Liu X, Zhang H, Wang W, Nepal S, Sefidgar Y, Seo W, Kuehn KS, Huckins JF, Morris ME, Nurius PS, Riskin EA, Patel S, Althoff T, Campbell A, Dey AK, Mankoff J. GLOBEM. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(4):1
    CrossRef
  150. Bryan AC, Heinz MV, Salzhauer AJ, Price GD, Tlachac ML, Jacobson NC. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;
    CrossRef
  151. Tlachac M, Heinz M, Reisch M, Ogden SS. Symptom Detection with Text Message Log Distributions for Holistic Depression and Anxiety Screening. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(1):1
    CrossRef
  152. Saddaf Khan N, Qadir S, Anjum G, Uddin N. StresSense: Real-Time detection of stress-displaying behaviors. International Journal of Medical Informatics 2024;185:105401
    CrossRef

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  1. Hegerl U, Dogan E, Oehler C, Sander C, Stöber F. Gesundheit digital. 2019. Chapter 4:47
    CrossRef
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    CrossRef
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