Published on in Vol 25 (2023)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/45233, first published
.
![Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis](https://asset.jmir.pub/assets/6863ef42c198963bc81fbe4f6778ba9c.png 480w,https://asset.jmir.pub/assets/6863ef42c198963bc81fbe4f6778ba9c.png 960w,https://asset.jmir.pub/assets/6863ef42c198963bc81fbe4f6778ba9c.png 1920w,https://asset.jmir.pub/assets/6863ef42c198963bc81fbe4f6778ba9c.png 2500w)
Journals
- Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
- Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Stewart C, Conde P, Sankesara H, Laiou P, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Vairavan S, Myin-Germeys I, Mohr D, Wykes T, Haro J, Annas P, Penninx B, Narayan V, Hotopf M, Dobson R. Longitudinal Assessment of Seasonal Impacts and Depression Associations on Circadian Rhythm Using Multimodal Wearable Sensing: Retrospective Analysis. Journal of Medical Internet Research 2024;26:e55302 View
- 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