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Wearable devices and self-report surveys show robust sleep associations but weaker links for tiredness and stress. This highlights potential differences in constructs measured by passive sensing and ecological momentary assessment (EMA) in mental health research.

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Area of Science:

  • Digital mental health
  • Wearable technology
  • Psychological assessment

Background:

  • Ecological momentary assessment (EMA) enhances ecological validity but can be burdensome.
  • Passive sensing from wearable devices offers a potential solution to reduce EMA burden.
  • Investigating the quality and overlap of wearable data with self-report measures is critical for mental health research.

Purpose of the Study:

  • To compare passive sensing data from wearable devices with EMA self-report data.
  • To assess the concurrent associations between wearable metrics and self-report measures of stress, tiredness, and sleep.
  • To evaluate the utility of wearable data for mental health monitoring in students.

Main Methods:

  • A 3-month study involving 781 students using Garmin VivoSmart 4 watches and EMA surveys.
  • Longitudinal mixed-effects models were employed to analyze momentary associations.
  • Focus on concurrent validity of passive sensor metrics against self-report measures for stress, tiredness, and sleep.

Main Results:

  • Robust associations were found between wearable and self-report measures for sleep-related variables.
  • Weaker associations were observed for tiredness.
  • Measures of stress showed minimal overlap between wearable and self-report data for most individuals.

Conclusions:

  • Wearable data and self-report measures may not always capture the same psychological constructs.
  • Discrepancies may arise from semantic differences and measurement issues.
  • Insights are provided for integrating wearable and self-report data in future mental health research.