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

Mobile Health (mhealth) (3187) Depression and Mood Disorders; Suicide Prevention (1535) Wearable Devices and Sensors (1014) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1839)

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

Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis

Authors of this article:

Shaoxiong Sun1 Author Orcid Image ;   Amos A Folarin1, 2, 3, 4, 5 Author Orcid Image ;   Yuezhou Zhang1 Author Orcid Image ;   Nicholas Cummins1 Author Orcid Image ;   Rafael Garcia-Dias6 Author Orcid Image ;   Callum Stewart1 Author Orcid Image ;   Yatharth Ranjan1 Author Orcid Image ;   Zulqarnain Rashid1 Author Orcid Image ;   Pauline Conde1 Author Orcid Image ;   Petroula Laiou1 Author Orcid Image ;   Heet Sankesara1 Author Orcid Image ;   Faith Matcham7, 8 Author Orcid Image ;   Daniel Leightley7 Author Orcid Image ;   Katie M White7 Author Orcid Image ;   Carolin Oetzmann7 Author Orcid Image ;   Alina Ivan7 Author Orcid Image ;   Femke Lamers9, 10 Author Orcid Image ;   Sara Siddi11, 12, 13 Author Orcid Image ;   Sara Simblett14 Author Orcid Image ;   Raluca Nica15, 16 Author Orcid Image ;   Aki Rintala17, 18 Author Orcid Image ;   David C Mohr19 Author Orcid Image ;   Inez Myin-Germeys17 Author Orcid Image ;   Til Wykes14, 20 Author Orcid Image ;   Josep Maria Haro11, 12, 13 Author Orcid Image ;   Brenda W J H Penninx9, 10 Author Orcid Image ;   Srinivasan Vairavan21 Author Orcid Image ;   Vaibhav A Narayan22 Author Orcid Image ;   Peter Annas23 Author Orcid Image ;   Matthew Hotopf3, 7, 20 Author Orcid Image ;   Richard J B Dobson1, 2, 3, 4, 5 Author Orcid Image ;   RADAR-CNS Consortium24
Article Authors Cited by (13) Tweetations (25) Metrics

Journals

  1. 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
  2. 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
  3. 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
  4. Wang W, Chen J, Hu Y, Liu H, Chen J, Gadekallu T, Garg L, Guizani M, Hu X. Integration of Artificial Intelligence and Wearable Internet of Things for Mental Health Detection. International Journal of Cognitive Computing in Engineering 2024;5:307 View
  5. Langener A, Siepe B, Elsherif M, Niemeijer K, Andresen P, Akre S, Bringmann L, Cohen Z, Choukas N, Drexl K, Fassi L, Green J, Hoffmann T, Jagesar R, Kas M, Kurten S, Schoedel R, Stulp G, Turner G, Jacobson N. A template and tutorial for preregistering studies using passive smartphone measures. Behavior Research Methods 2024;56(8):8289 View
  6. Guo Q, Liu M, Li Y, Sun Q. Overcoming technical hurdles in mobile health: insights from the Fit2ThriveMB breast cancer study. Breast Cancer Research and Treatment 2024;208(2):467 View
  7. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View
  8. Zhang Y, Stewart C, Ranjan Y, Conde P, Sankesara H, Rashid Z, Sun S, Dobson R, Folarin A. Large-scale digital phenotyping: Identifying depression and anxiety indicators in a general UK population with over 10,000 participants. Journal of Affective Disorders 2025;375:412 View
  9. Akre S, Cohen Z, Welborn A, Zbozinek T, Balliu B, Craske M, Bui A. Comparing self reported and physiological sleep quality from consumer devices to depression and neurocognitive performance. npj Digital Medicine 2025;8(1) View
  10. Yang M, Ngai E, Hu X, Hu B, Liu J, Gelenbe E, Leung V. Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection. Proceedings of the IEEE 2024;112(12):1773 View
  11. Leaning I, Costanzo A, Jagesar R, Reus L, Visser P, Kas M, Beckmann C, Ruhé H, Marquand A. Uncovering Social States in Healthy and Clinical Populations Using Digital Phenotyping and Hidden Markov Models: Observational Study. Journal of Medical Internet Research 2025;27:e64007 View

Books/Policy Documents

  1. Perna G, Spiti A, Torti T, Daccò S, Caldirola D. Recent Advances and Challenges in the Treatment of Major Depressive Disorder. View

Conference Proceedings

  1. Hung K, Man G, Chui J, Chow D, Ling B, Pun S, Liu T. TENCON 2024 - 2024 IEEE Region 10 Conference (TENCON). Window-Based Quaternion Principal Component Analysis of Eye Gaze Dynamics for Depression Severity Prediction View