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

Reviews (291) Mobile Health (mhealth) (5135) Digital Health, Telehealth and e-Innovation in Clinical Settings (717) Depression and Mood Disorders; Suicide Prevention (2444) Digital Biomarkers and Digital Phenotyping (430)

Published on in Vol 14 (2025)

This is a member publication of University of Münster

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69686, first published .
Man in casual clothes sitting on a bed, holding a smartphone and looking away

Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review

Cross-Platform Availability of Smartphone Sensors for Depression Indication Systems: Mixed-Methods Umbrella Review

Authors of this article:

Johannes Leimhofer1, 2 Author Orcid Image ;   Milica Petrovic1 Author Orcid Image ;   Andreas Dominik3 Author Orcid Image ;   Dominik Heider4 Author Orcid Image ;   Ulrich Hegerl1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Busshart L, Petrovic M, Amin R, Hegerl U. Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review. JMIR mHealth and uHealth 2026;14:e70840 View
  2. Wang M, Peng F, Mu W, Tao Y, Zhao H, Yin Q, Yan Z, Shi C. Prospective observational study on behavioral monitoring, disease progression assessment, and screening model development for patients with depression using wearable devices and mobile phones: protocol. Frontiers in Psychiatry 2026;17 View
  3. Schreynemackers S, Leimhofer J, Petrovic M, Reich H, Ludwig S, Dominik A, Heider D, Hegerl U. Individual-Level Modeling of Depressive Symptom Severity Using Smartphone and Wearable Data: A Time-Aware 1-Year Study with Feature-Group Contributions (Preprint). JMIR Formative Research 2026 View