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Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study.

Robyn E Kilshaw1, Abigail Boggins1, Olivia Everett1

  • 1Department of Psychology, University of Utah, Salt Lake City, UT, United States.

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PubMed
Summary
This summary is machine-generated.

This study is creating a new, privacy-focused dataset for computational psychiatry research in the general population, using smartphone data to improve mental health risk assessment.

Keywords:
audio datacomputational psychiatrydata repositorydigital phenotypingmachine learningpassive sensor data

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Area of Science:

  • Computational psychiatry
  • Digital phenotyping
  • Mental health research

Background:

  • Computational psychiatry offers potential for advancing mental health diagnosis and treatment.
  • Existing methods are limited in scalability for general population research.
  • Digital phenotyping using smartphone sensors can extend computational psychiatry to the general public.

Purpose of the Study:

  • To develop the first computational psychiatry dataset for general population mental health risk assessment.
  • To incorporate multimodal, sensor-based behavioral features.
  • To ensure wide data sharing with robust privacy and integrity.

Main Methods:

  • Recruited 400 community-dwelling adults using stratified sampling based on emotion regulation and life stress.
  • Collected self-report questionnaires, daily mood/event logs, smartphone sensor data, and audio recordings over 7 days.
  • Administered follow-up questionnaires at 6 and 12 months.

Main Results:

  • Data collection ongoing (June 2022 - July 2024); 310 participants consented.
  • 149 participants completed initial data collection; follow-ups are in progress.
  • The dataset will be made available with a privacy-preserving approach.

Conclusions:

  • This dataset complements existing research, aiming to enhance general population mental health risk assessment.
  • It supports a shift towards interdisciplinary collaboration and open data sharing in computational psychiatry.
  • The goal is to foster collaborations integrating clinical, technical, and quantitative expertise.