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Datasets of Smartphone Modalities for Depression Assessment: A Scoping Review.

M L Tlachac1, Michael V Heinz2, Anastasia C Bryan3

  • 1Department of Information Systems and Analytics, Bryant University, Smithfield, RI 02911 USA.

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Summary

This review of 80 depression assessment datasets highlights common mobile sensing data, like location and communication logs. It identifies limitations and patterns to guide future mobile health research.

Keywords:
Data CollectionDigital BiomarkerDigital HealthDigital PhenotypeMobile Health

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Area of Science:

  • Digital Health
  • Mental Health Research
  • Mobile Sensing

Background:

  • Mobile sensing for depression assessment is growing rapidly.
  • Existing research often uses the same datasets, necessitating an understanding of their patterns and limitations.
  • A comprehensive review of available datasets is crucial for advancing the field.

Purpose of the Study:

  • To conduct a scoping review of datasets used in mobile sensing for depression assessment.
  • To identify common data modalities, depression screening tools, and participant demographics.
  • To uncover fundamental patterns and limitations within these datasets.

Main Methods:

  • Systematic identification of 80 datasets containing smartphone modalities and depression labels up to early 2024.
  • Analysis of data from 72 manuscripts and approximately 60 research groups.
  • Categorization of collected smartphone modalities, screening instruments, and participant recruitment strategies.

Main Results:

  • Location/activity (68.75%), communication logs (47.5%), and phone use (41.25%) were the most frequent modalities.
  • Patient Health Questionnaire (PHQ-8 and PHQ-9) were the most common screening tools (53.75%).
  • Datasets predominantly featured student (31.25%) or patient (22.5%) populations, with 73% reporting a majority of female participants.

Conclusions:

  • This scoping review provides an essential overview of current datasets for mobile sensing in depression research.
  • Understanding dataset characteristics, including common modalities and demographics, is vital for interpreting findings and guiding future studies.
  • The findings will inform the state of science and direct future mobile health research efforts.