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

Journal: Interactive journal of medical research
Published Date: Aug 7, 2025

Abstract

BACKGROUND: A popular trend in depression forecasting research is the development of machine learning models trained with various types of smartphone sensor data and periodic self-ratings to derive early indications of changes in depression severity. While most works focus on model performance, there is little concern about the universal usability and reliable operation of such systems across smartphone platforms. This review serves as foundational work for the MENTINA clinical trial, which investigates smartphone-based health self-management for depression. The usability and reliability of mobile apps for depression are commonly perceived through the lens of the approaches and interventions offered rather than the reliability of the built-in mobile phone functions to support effortless and exact delivery of intended interventions.

Authors

  • Johannes Leimhofer
    Research Centre of the German Foundation for Depression and Suicide Prevention, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital, Goethe University, Heinrich-Hoffmann-Straße 10, 60528, Frankfurt am Main, Germany.
  • Milica Petrović
    Faculty of Mechanical Engineering, University of Belgrade, 11120 Belgrade, Serbia.
  • Andreas Dominik
    Technische Hochschule Mittelhessen - University of Applied Sciences, Germany.
  • Dominik Heider
    Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
  • Ulrich Hegerl
    German Foundation for Depression and Suicide Prevention, Goerdelerring 9, 04109, Leipzig, Germany.

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