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

mHealth for Data Collection and Research (955) Depression and Mood Disorders; Suicide Prevention (1363) Evaluation and Research Methodology for mHealth (208) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) mHealth for Wellness, Behavior Change and Prevention (2871)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34148, first published .
Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study

Authors of this article:

Mariko Makhmutova1 Author Orcid Image ;   Raghu Kainkaryam2 Author Orcid Image ;   Marta Ferreira2 Author Orcid Image ;   Jae Min2 Author Orcid Image ;   Martin Jaggi1 Author Orcid Image ;   Ieuan Clay2, 3 Author Orcid Image
Article Authors Cited by (11) Tweetations (7) Metrics

Journals

  1. Makhmutova M, Kainkaryam R, Ferreira M, Min J, Jaggi M, Clay I. Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study. JMIR mHealth and uHealth 2022;10(3):e34148 View
  2. Abd-alrazaq A, AlSaad R, Aziz S, Ahmed A, Denecke K, Househ M, Farooq F, Sheikh J. Wearable Artificial Intelligence for Anxiety and Depression: Scoping Review. Journal of Medical Internet Research 2023;25:e42672 View
  3. Abd-Alrazaq A, AlSaad R, Shuweihdi F, Ahmed A, Aziz S, Sheikh J. Systematic review and meta-analysis of performance of wearable artificial intelligence in detecting and predicting depression. npj Digital Medicine 2023;6(1) View
  4. Sun S, Folarin A, Zhang Y, Cummins N, Garcia-Dias R, Stewart C, Ranjan Y, Rashid Z, Conde P, Laiou P, Sankesara H, Matcham F, Leightley D, White K, Oetzmann C, Ivan A, Lamers F, Siddi S, Simblett S, Nica R, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Vairavan S, Narayan V, Annas P, Hotopf M, Dobson R. Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis. Journal of Medical Internet Research 2023;25:e45233 View
  5. Clay I, De Luca V, Sano A. Editorial: Multimodal digital approaches to personalized medicine. Frontiers in Big Data 2023;6 View
  6. Peerenboom N, Aryal S, Blankenship J, Swibas T, Zhai Y, Clay I, Lyden K. The Case for the Patient-Centric Development of Novel Digital Sleep Assessment Tools in Major Depressive Disorder. Digital Biomarkers 2023;7(1):124 View
  7. Price G, Heinz M, Song S, Nemesure M, Jacobson N. Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data. Translational Psychiatry 2023;13(1) View
  8. Clay I, Peerenboom N, Connors D, Bourke S, Keogh A, Wac K, Gur-Arie T, Baker J, Bull C, Cereatti A, Cormack F, Eggenspieler D, Foschini L, Ganea R, Groenen P, Gusset N, Izmailova E, Kanzler C, Leyens L, Lyden K, Mueller A, Nam J, Ng W, Nobbs D, Orfaniotou F, Perumal T, Piwko W, Ries A, Scotland A, Taptiklis N, Torous J, Vereijken B, Xu S, Baltzer L, Vetter T, Goldhahn J, Hoffmann S. Reverse Engineering of Digital Measures: Inviting Patients to the Conversation. Digital Biomarkers 2023:28 View
  9. Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. Journal of Affective Disorders 2024;361:445 View
  10. Walschots Q, Zarchev M, Unkel M, Kamperman A. Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder—A Scoping Review and Introductory Text for Clinical Professionals. Algorithms 2024;17(9):408 View
  11. Zhou M, Tan Y, Wang J, Song Y, Li Q, Wang Y, Quan W, Tian J, Yin L, Dong W, Liu B. Construction and evaluation of two nomograms for screening major depressive disorder and subthreshold depression individuals based on anxiety, depression, and sleep items. Journal of Affective Disorders 2025;369:288 View