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Key Features of Digital Phenotyping for Monitoring Mental Disorders: Systematic Review.

Hyun Woo Jung1,2, Do Yeon Kim3, Ilju Lee2

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|November 5, 2025
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Summary

A core set of smart device features, including accelerometer, steps, heart rate (HR), and sleep, are essential for predicting depression and anxiety. Tailoring feature selection to specific devices like smart bands and smartwatches enhances mental health monitoring accuracy.

Keywords:
PRISMAPreferred Reporting Items for Systematic Reviews and Meta-Analysesanxietydepressiondigital phenotypingmobile sensingwearable devices

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Area of Science:

  • Digital Health
  • Mental Health Technology
  • Wearable Sensors

Background:

  • The COVID-19 pandemic exacerbated global mental health challenges, increasing the demand for remote monitoring solutions.
  • Digital phenotyping via smart devices offers a promising avenue for mental health assessment, yet optimal feature selection remains undetermined.

Purpose of the Study:

  • To identify key features collected by integrated smartphone and wearable systems (Actiwatches, smart bands, smartwatches).
  • To determine essential features for mental health monitoring, considering device-specific characteristics.

Main Methods:

  • A systematic review of quantitative studies (N=22) published between February 5, 2025, across major databases.
  • Inclusion criteria: adults using smart devices for passive depression/anxiety prediction; exclusion: smartphone-only or qualitative studies.
  • Data synthesis involved descriptive analysis and calculating feature coverage and importance, visualized in quadrant plots.

Main Results:

  • A core feature package (accelerometer, steps, heart rate (HR), sleep) emerged as crucial across devices.
  • Device-specific findings: Actiwatch (accelerometer, activity); Smart bands (HR, steps, sleep, phone usage, with GPS, EDA, skin temp showing promise); Smartwatches (sleep, HR most reliable; steps, accelerometer widely used but less impactful).
  • Significant underutilization of sleep features in Actiwatch studies and limited effectiveness of steps/accelerometer in smartwatches were noted.

Conclusions:

  • A universal set of features (accelerometer, steps, HR, sleep) aids mood disorder prediction, but optimal selection varies by device type.
  • Tailoring feature selection to device capabilities (e.g., smart bands, smartwatches) is key for effective digital phenotyping.
  • Enhancing data accessibility, particularly for smartwatches, and standardizing reporting are critical for future research and meta-analysis.