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

Ecological momentary assessment (EMA) increases ecological validity but can be burdensome. To reduce this burden and to better understand psychological constructs in daily life, a growing chorus of voices has called for augmenting or replacing EMA data with data passively collected from wearable devices. It is thus critical to investigate the quality of wearable data and its overlap with typical self-report measures. Here we compared results from passive sensing and EMA data from a project to build a warning system for depression in students (WARN-D) in a large sample of 781 students. For 3 months, participants wore a Garmin VivoSmart 4 watch and answered EMA surveys (up to 352 observations). We investigated whether and to what extent passive sensor metrics were concurrently associated with different self-report measures purportedly measuring the same constructs. We focused on stress, tiredness, and sleep, all of which have transdiagnostic relevance to mental health and can arguably be assessed with self-report and physiological measures. We used longitudinal mixed-effects models to estimate average momentary associations and their interindividual heterogeneity. Self-report and wearable measures of sleep-related variables showed robust associations, while associations were weaker for tiredness, and measures of stress did not overlap for most individuals. These findings suggest that wearable data and their corresponding self-report measures may not necessarily measure similar constructs. We provide several explanations for this result, including semantic differences and measurement issues, and offer insights and ways forward for mental health research combining wearable and self-report data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).