JMIR Publications

ABSTRACT

Background:

Wearable physiological sensing and mobile self-report methods offer new opportunities for monitoring occupational well-being in real-world settings. However, interpreting continuous physiological signals in relation to psychological states such as job burnout and work engagement remains challenging, particularly when using deployable systems that rely on proprietary signal processing.

Objective:

This study evaluated electrodermal activity (EDA) as a wearable digital biomarker of autonomic arousal for real-world monitoring of occupational well-being. We examined associations between job burnout, work engagement, and sympathetic activation indexed by a device-derived EDA metric, and assessed whether combining physiological data with mobile-based ecological momentary assessment (mEMA) of valence provides complementary information.

Methods:

A total of 75 knowledge workers participated, with sufficient paired physiological and mEMA data obtained from 57 participants in wave 1 and 51 in wave 2 (total observations = 108). Participants were monitored continuously over 14 consecutive days at two time points six months apart. Autonomic arousal was operationalized using the device-derived double normalized electrodermal (DNE) index obtained from wearable EDA recordings. Valence was assessed using mEMA. Associations between physiological activation, valence, and occupational well-being outcomes were analyzed using regression models for correlated data.

Results:

The wearable-derived EDA index exhibited expected diurnal variation consistent with patterns of sympathetic activation. Both EDA-derived indices and valence were associated with job burnout and work engagement, with significant interaction effects indicating that the relationship between physiological activation and outcomes depended on valence. Model comparisons showed that including physiological and EMA-based variables improved explanatory power.

Conclusions:

These findings support the feasibility of combining continuous EDA monitoring with mEMA for assessing occupational well-being in real-world settings. While wearable-derived EDA reflects general autonomic arousal rather than construct-specific states, the results demonstrate that meaningful insights can be obtained when such signals are interpreted alongside contextual self-report data. More broadly, this study illustrates how wearable-derived measures can be evaluated as candidate digital biomarkers in naturalistic settings through convergent validation against established assessments, even when the underlying signal-processing and algorithmic procedures are not fully accessible. Clinical Trial: ClinicalTrials.gov ID, NCT05317793, https://clinicaltrials.gov/study/NCT05317793