When Feeling and Physiology Diverge: Understanding Dual-Indicator Stress Sensemaking and Micro-Interventions in an Emotional-Labor Workplace

Sensing-based stress management systems integrate self-report, physiological, and contextual data to assess users' perceived stress and physiological indicators related to stress responses and deliver just-in-time interventions. Prior work has largely focused on improving detection accuracy or evaluating intervention effectiveness, with relatively limited attention to how users interpret and make sense of these data in everyday work contexts. Our study addresses this gap by examining how emotional labor workers construct meaning around alignment and divergence between perceived stress and physiological responses to stress. We present a four-week in-the-wild study with 19 call center workers, combining mobile and wearable sensing, a reflection dashboard, and in-depth interviews. Our findings show that participants interpreted stress indicators through work context, bodily conditions, and prior experiences, and treated divergence as an informative cue rather than a simple error. They also recognized that perceived and physiological responses to stress could change differently before and after micro-interventions. Based on these findings, we discuss design implications for sensing-based stress management systems that support stress data literacy and flexible, context-grounded stress sensemaking in practice.

CCS Concepts: • Human-centered computing → Empirical studies in HCI.