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

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1301) Mobile Health (mhealth) (2748) e-Mental Health and Cyberpsychology (1386) mHealth for Data Collection and Research (907) mHealth for Wellness, Behavior Change and Prevention (2728) Depression and Mood Disorders; Suicide Prevention (1271) mHealth for Treatment Adherence (383)

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 Wahle1, 2 Author Orcid Image ;   Tobias Kowatsch2 Author Orcid Image ;   Elgar Fleisch1, 2 Author Orcid Image ;   Michael Rufer3 Author Orcid Image ;   Steffi Weidt3 Author Orcid Image
Article Authors Cited by (199) Tweetations (14) Metrics

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  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 2023;18(5):1062 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
  131. Vize C, G. C. Wright A. Translating the Transdiagnostic: Aligning Assessment Practices With Research Advances. Assessment 2024;31(1):199 View
  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 View
  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 View
  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 View
  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 View
  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 View
  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 View
  138. Leibold A, Mansoor Ali D, Harrop J, Sharan A, Vaccaro A, Sivaganesan A. Smartphone-based activity tracking for spine patients: Current technology and future opportunities. World Neurosurgery: X 2024;21:100238 View
  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 View
  140. Elkefi S. Supporting patients’ workload through wearable devices and mobile health applications, a systematic literature review. Ergonomics 2023:1 View
  141. Zon M, Ganesh G, Deen M, 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 View
  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 A. Mindcraft, a Mobile Mental Health Monitoring Platform for Children and Young People: Development and Acceptability Pilot Study. JMIR Formative Research 2023;7:e44877 View
  143. Althobiani M, Ranjan Y, Jacob J, Orini M, Dobson R, Porter J, Hurst J, Folarin A. Evaluating a Remote Monitoring Program for Respiratory Diseases: Prospective Observational Study. JMIR Formative Research 2023;7:e51507 View
  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 View
  145. Shin J, Bae S. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984 View
  146. Breitinger S, Gardea-Resendez M, Langholm C, Xiong A, Laivell J, Stoppel C, Harper L, Volety R, Walker A, D'Mello R, Byun A, Zandi P, Goes F, Frye M, Torous J. Digital Phenotyping for Mood Disorders: Methodology-Oriented Pilot Feasibility Study. Journal of Medical Internet Research 2023;25:e47006 View
  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 View
  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 View
  149. Xu X, Liu X, Zhang H, Wang W, Nepal S, Sefidgar Y, Seo W, Kuehn K, Huckins J, Morris M, Nurius P, Riskin E, Patel S, Althoff T, Campbell A, Dey A, Mankoff J. GLOBEM. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(4):1 View
  150. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  151. Tlachac M, Heinz M, Reisch M, Ogden S. 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 View
  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 View
  153. Hossain M, Yang S. A decision integration strategy algorithm to detect the depression severity level using wearable and profile data. Iran Journal of Computer Science 2024 View
  154. Englhardt Z, Ma C, Morris M, Chang C, Xu X, Qin L, McDuff D, Liu X, Patel S, Iyer V. From Classification to Clinical Insights. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024;8(2):1 View
  155. Mullick T, Shaaban S, Radovic A, Doryab A. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling. JMIR AI 2024;3:e47805 View
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  157. Postma M, Vrancken S, Daemen M, Meulen I, Volbragt N, Delespaul P, Haan L, Pluijm M, Breedvelt J, Gaag M, Lindauer R, Berg D, Bockting C, Amelsvoort T, Schwannauer M, Doi L, Reininghaus U. Working mechanisms of the use and acceptability of ecological momentary interventions: a realist evaluation of a guided self-help ecological momentary intervention targeting self-esteem. BMC Public Health 2024;24(1) 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
  24. Nag A, Das A, Sil R, Kar A, Mandal D, Das B. Intelligent Systems Design and Applications. View
  25. Bhasin H, Chirag , Kumar N, Thakur H. Advanced Computing. View