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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1070) Mobile Health (mhealth) (2018) e-Mental Health and Cyberpsychology (999) mHealth for Data Collection and Research (703) mHealth for Wellness, Behavior Change and Prevention (2347) Depression and Mood Disorders; Suicide Prevention (1071) mHealth for Treatment Adherence (335)

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

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

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

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

Authors of this article:

Fabian Wahle 1, 2 Author Orcid Image ;   Tobias Kowatsch 2 Author Orcid Image ;   Elgar Fleisch 1, 2 Author Orcid Image ;   Michael Rufer 3 Author Orcid Image ;   Steffi Weidt 3 Author Orcid Image
Article Authors Cited by (169) Tweetations (14) Metrics

Journals

  1. Potier R. The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11 View
  2. Guo Y, Hong Y, Qiao J, Xu Z, Zhang H, Zeng C, Cai W, Li L, Liu C, Li Y, Zhu M, Harris N, 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) View
  3. Hardy J, Veinot T, Yan X, Berrocal V, Clarke P, Goodspeed R, Gomez-Lopez I, Romero D, Vydiswaran V. User acceptance of location-tracking technologies in health research: Implications for study design and data quality. Journal of Biomedical Informatics 2018;79:7 View
  4. khan Z, Alotaibi S. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. Journal of Healthcare Engineering 2020;2020:1 View
  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 View
  6. Trifan A, Oliveira M, Oliveira J. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649 View
  7. Goodspeed R, Yan X, Hardy J, Vydiswaran V, Berrocal V, Clarke P, Romero D, Gomez-Lopez I, 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 View
  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 View
  9. Burger F, Neerincx M, 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 View
  10. Rohani D, Tuxen N, Quemada Lopategui A, Kessing L, Bardram J. Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study. JMIR Mental Health 2018;5(2):e10122 View
  11. Chan S, Godwin H, Gonzalez A, Yellowlees P, Hilty D. Review of Use and Integration of Mobile Apps Into Psychiatric Treatments. Current Psychiatry Reports 2017;19(12) View
  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) View
  13. Aledavood T, Triana Hoyos A, Alakörkkö T, Kaski K, Saramäki J, Isometsä E, Darst R. Data Collection for Mental Health Studies Through Digital Platforms: Requirements and Design of a Prototype. JMIR Research Protocols 2017;6(6):e110 View
  14. Boukhechba M, Chow P, Fua K, Teachman B, Barnes L. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101 View
  15. Di Matteo D, Fotinos K, Lokuge S, Yu J, Sternat T, Katzman M, Rose J. The Relationship Between Smartphone-Recorded Environmental Audio and Symptomatology of Anxiety and Depression: Exploratory Study. JMIR Formative Research 2020;4(8):e18751 View
  16. Severe J, Greden J, Reddy P. Consequences of Recurrence of Major Depressive Disorder: Is Stopping Effective Antidepressant Medications Ever Safe?. FOCUS 2020;18(2):120 View
  17. Lind M, Byrne M, Wicks G, Smidt A, Allen N. The Effortless Assessment of Risk States (EARS) Tool: An Interpersonal Approach to Mobile Sensing. JMIR Mental Health 2018;5(3):e10334 View
  18. Felix I, Castro L, 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 View
  19. . Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics. Journal of Psychiatry and Brain Science 2020 View
  20. Litvin S, Saunders R, Maier M, Lüttke S, Matsuoka Y. 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 View
  21. Majumder S, Deen M. Smartphone Sensors for Health Monitoring and Diagnosis. Sensors 2019;19(9):2164 View
  22. Sabharwal A, Veeraraghavan A. Bio-Behavioral Sensing. GetMobile: Mobile Computing and Communications 2017;21(3):11 View
  23. Xu X, Chikersal P, Doryab A, Villalba D, Dutcher J, Tumminia M, Althoff T, Cohen S, Creswell K, Creswell J, Mankoff J, Dey A. 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 View
  24. Graham S, Depp C, Lee E, Nebeker C, Tu X, Kim H, Jeste D. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Current Psychiatry Reports 2019;21(11) View
  25. Saeb S, Lattie E, Kording K, Mohr D. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety. JMIR mHealth and uHealth 2017;5(8):e112 View
  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 View
  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 View
  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 View
  29. Cornet V, Holden R. Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics 2018;77:120 View
  30. Matz S, Appel R, Kosinski M. Privacy in the age of psychological targeting. Current Opinion in Psychology 2020;31:116 View
  31. Shatte A, Hutchinson D, Teague S. Machine learning in mental health: a scoping review of methods and applications. Psychological Medicine 2019;49(09):1426 View
  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 View
  33. Obuchi M, Huckins J, 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 View
  34. Jacobson N, Chung Y. 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 View
  35. Hwang W, Jo H. 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 View
  36. Acikmese Y, Alptekin S. Prediction of stress levels with LSTM and passive mobile sensors. Procedia Computer Science 2019;159:658 View
  37. Dias L, Barbosa J, Feijó L, Vianna H. 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 View
  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 View
  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) View
  40. Jang J, Cho S. Mobile Health (m-health) on Mental Health. Korean Journal of Stress Research 2016;24(4):231 View
  41. Price J. What Can Big Data Offer the Pharmacovigilance of Orphan Drugs?. Clinical Therapeutics 2016;38(12):2533 View
  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 View
  43. Renn B, Pratap A, Atkins D, Mooney S, Areán P. Smartphone-based passive assessment of mobility in depression: Challenges and opportunities. Mental Health and Physical Activity 2018;14:136 View
  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 View
  45. Wang R, Wang W, daSilva A, Huckins J, Kelley W, Heatherton T, Campbell A. 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 View
  46. Chung K, Jeon M, Park J, Lee S, Kim C, Park J, Guloksuz S. Development and evaluation of a mobile-optimized daily self-rating depression screening app: A preliminary study. PLOS ONE 2018;13(6):e0199118 View
  47. Dias L, Barbosa J, Vianna H. Gamification and serious games in depression care: A systematic mapping study. Telematics and Informatics 2018;35(1):213 View
  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 View
  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 View
  50. Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing L, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow P. Smartphones in mental health: a critical review of background issues, current status and future concerns. International Journal of Bipolar Disorders 2020;8(1) View
  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 View
  52. Pham Q, Graham G, Carrion C, Morita P, Seto E, Stinson J, Cafazzo J. 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 View
  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 View
  54. Pratap A, Atkins D, Renn B, Tanana M, Mooney S, Anguera J, Areán P. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72 View
  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 View
  56. Yim S, Lui L, Lee Y, Rosenblat J, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho R, Coles A, Majeed A, Wong E, Phan L, Nasri F, McIntyre R. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602 View
  57. Faurholt-Jepsen M, Busk J, Þórarinsdóttir H, Frost M, Bardram J, Vinberg M, Kessing L. Objective smartphone data as a potential diagnostic marker of bipolar disorder. Australian & New Zealand Journal of Psychiatry 2019;53(2):119 View
  58. Cao J, Truong A, Banu S, Shah A, 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 View
  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 View
  60. Radhakrishnan K, Kim M, Burgermaster M, Brown R, Xie B, Bray M, Fournier C. The potential of digital phenotyping to advance the contributions of mobile health to self-management science. Nursing Outlook 2020;68(5):548 View
  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 View
  62. Hilty D, Armstrong C, Edwards-Stewart A, Gentry M, Luxton D, Krupinski E. Sensor, Wearable, and Remote Patient Monitoring Competencies for Clinical Care and Training: Scoping Review. Journal of Technology in Behavioral Science 2021;6(2):252 View
  63. Maharjan S, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt B, 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) View
  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 View
  65. Mercier H, Hamner J, Torous J, Onnela J, Taylor J. Digital Phenotyping to Quantify Psychosocial Well-Being Trajectories After Spinal Cord Injury. American Journal of Physical Medicine & Rehabilitation 2020;99(12):1138 View
  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 View
  67. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. 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 View
  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 View
  69. de Moura I, Teles A, Endler M, Coutinho L, da Silva e Silva F. Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals. Sensors 2020;21(1):86 View
  70. Krichen M. Anomalies Detection Through Smartphone Sensors: A Review. IEEE Sensors Journal 2021;21(6):7207 View
  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) View
  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 View
  73. Sheikh M, Qassem M, Kyriacou P. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3 View
  74. Fernandes A, Van Lenthe F, 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 View
  75. Balaskas A, Schueller S, Cox A, Doherty G, Myers B. Ecological momentary interventions for mental health: A scoping review. PLOS ONE 2021;16(3):e0248152 View
  76. Wu A, Scult M, Barnes E, Betancourt J, Falk A, Gunning F. Smartphone apps for depression and anxiety: a systematic review and meta-analysis of techniques to increase engagement. npj Digital Medicine 2021;4(1) View
  77. Khan N, Ghani M. A Survey of Deep Learning Based Models for Human Activity Recognition. Wireless Personal Communications 2021;120(2):1593 View
  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 View
  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 View
  80. Müller S, Chen X, Peters H, Chaintreau A, Matz S. Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples. Scientific Reports 2021;11(1) View
  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 View
  82. Teepe G, Da Fonseca A, Kleim B, Jacobson N, 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 View
  83. Nickels S, Edwards M, Poole S, Winter D, Gronsbell J, Rozenkrants B, Miller D, Fleck M, McLean A, Peterson B, Chen Y, Hwang A, Rust-Smith D, Brant A, Campbell A, Chen C, Walter C, Arean P, Hsin H, Myers L, Marks Jr W, Mega J, Schlosser D, Conrad A, Califf R, 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 View
  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 View
  85. Adler D, Wang F, Mohr D, 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 View
  86. Tlachac M, Flores R, Reisch M, Kayastha R, Taurich N, Melican V, Bruneau C, Caouette H, Lovering J, Toto E, Rundensteiner E. StudentSADD. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  87. Tlachac M, Flores R, Reisch M, Houskeeper K, Rundensteiner E. DepreST-CAT. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  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 View
  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 View
  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 View
  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 View
  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 View
  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 View
  94. de Angel V, Lewis S, White K, 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 View
  95. Chia A, Zhang M. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331 View
  96. Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729 View
  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 View
  98. Jacobson N, 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) View
  99. Meegahapola L, Droz W, Kun P, de Götzen A, Nutakki C, Diwakar S, Correa S, Song D, Xu H, Bidoglia M, Gaskell G, Chagnaa A, Ganbold A, Zundui T, Caprini C, Miorandi D, Hume A, Zarza J, Cernuzzi L, Bison I, Britez M, 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 View
  100. De Angel V, Lewis S, White K, Oetzmann C, Leightley D, Oprea E, Lavelle G, Matcham F, Pace A, Mohr D, Dobson R, Hotopf M. Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine 2022;5(1) View
  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 View
  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 View
  103. Griffiths C, da Silva K, Leathlean C, Jiang H, Ang C, 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 View
  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 View
  105. Mendes J, Moura I, Van de Ven P, Viana D, Silva F, Coutinho L, Teixeira S, Rodrigues J, Teles A. Sensing Apps and Public Data Sets for Digital Phenotyping of Mental Health: Systematic Review. Journal of Medical Internet Research 2022;24(2):e28735 View
  106. Neumayr C, Voderholzer U, Schlegl S. Psych-APP-Therapie: Smartphonebasierte Interventionen in der Psychotherapie – Eine systematische Übersichtsarbeit. Verhaltenstherapie 2021;31(3):182 View
  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 View
  108. Leong Q, 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 View
  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 View
  110. Meyer A, Wisniewski H, Torous J. Coaching to Support Mental Health Apps: Exploratory Narrative Review. JMIR Human Factors 2022;9(1):e28301 View
  111. Lim J, Jeong C, Lim J, Chung S, Kim G, Noh K, Jeong H. Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges. IEEE Access 2022;10:2063 View
  112. Khan N, Ghani M, Anjum G. ADAM-sense: Anxiety-displaying activities recognition by motion sensors. Pervasive and Mobile Computing 2021;78:101485 View
  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 View
  114. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  115. Laiou P, Kaliukhovich D, Folarin A, Ranjan Y, Rashid Z, Conde P, Stewart C, Sun S, Zhang Y, Matcham F, Ivan A, Lavelle G, Siddi S, Lamers F, Penninx B, Haro J, Annas P, Cummins N, Vairavan S, Manyakov N, Narayan V, Dobson R, 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 View
  116. Zhang H, Ibrahim A, Parsia B, Poliakoff E, Harper S. Passive social sensing with smartphones: a systematic review. Computing 2023;105(1):29 View
  117. Lee K, Ham B. Machine Learning on Early Diagnosis of Depression. Psychiatry Investigation 2022;19(8):597 View
  118. Balaskas A, Schueller S, Cox A, 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 View
  119. Timmons A, Duong J, Simo Fiallo N, Lee T, Vo H, Ahle M, Comer J, Brewer L, Frazier S, Chaspari T. A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspectives on Psychological Science 2022:174569162211344 View
  120. Liu Y, Kang K, Doe M. HADD: High-Accuracy Detection of Depressed Mood. Technologies 2022;10(6):123 View
  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 View
  122. Wang Z, Xiong H, Zhang J, Yang S, Boukhechba M, Zhang D, Barnes L, Dou D. From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques. IEEE Internet of Things Journal 2022;9(17):15413 View
  123. Rohani D, Springer A, Hollis V, Bardram J, 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 View
  124. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  125. Mandryk R, Birk M, 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 View
  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 View
  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 View
  128. King S, Lebert J, Karpisek L, 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 View
  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 View
  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 View

Books/Policy Documents

  1. Hegerl U, Dogan E, Oehler C, Sander C, Stöber F. Gesundheit digital. View
  2. Senders J, Maher N, Hulsbergen A, Lamba N, Bredenoord A, Broekman M. Ethics of Innovation in Neurosurgery. View
  3. Martinez-Martin N. Ethical Dimensions of Commercial and DIY Neurotechnologies. View
  4. Kowatsch T, Fischer-Taeschler D, Putzing F, Bürki P, Stettler C, Chiesa-Tanner G, Fleisch E. Digitale Transformation von Dienstleistungen im Gesundheitswesen VI. View
  5. von Wangenheim F, Ventouris J. Perspektiven des Dienstleistungsmanagements. View
  6. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  7. Sengupta S, Adragna M. Psychiatric Nonadherence. View
  8. Castro L, Rodríguez M, Martínez F, Rodríguez L, Andrade Á, Cornejo R. Intelligent Data Sensing and Processing for Health and Well-Being Applications. View
  9. Rebolledo M, Eiben A, Bartz-Beielstein T. Applications of Evolutionary Computation. View
  10. Bhatia S, Kesarwani Y, Basantani A, Jain S. Proceedings of the International Conference on Paradigms of Computing, Communication and Data Sciences. View
  11. Lamba D, Hsu W, Alsadhan M. Machine Learning, Big Data, and IoT for Medical Informatics. View
  12. Appel R, Matz S. Measuring and Modeling Persons and Situations. View
  13. Mao S, Khalifa Y, Zhang Z, Shu K, Suri A, Bouzid Z, Sejdic E. Digital Health. View
  14. Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  15. Pramanik H, Pal A, Kirtania M, Chakravarty T, Ghose A. Smartphone-Based Detection Devices. View
  16. Ingram W, Khanna R, Weston C. Mental Health Informatics. View
  17. Ghosh A, Dey S. Efficient Data Handling for Massive Internet of Medical Things. View
  18. Garatva P, Terhorst Y, Messner E, Karlen W, Pryss R, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  19. Kolenik T. Integrating Artificial Intelligence and IoT for Advanced Health Informatics. View
  20. Marchionatti L, Mastella N, Bouvier V, Passos I. Digital Mental Health. View
  21. Tlachac M, Flores R, Toto E, Rundensteiner E. Deep Learning Applications, Volume 4. View
  22. Harrer M, Terhorst Y, Baumeister H, Ebert D. Digitale Gesundheitsinterventionen. View
  23. Hilty D, Peled A, Luxton D. Tasman’s Psychiatry. View