Associations between ecological momentary assessment and passive sensor data in a large student sample.

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.