Background: Close relationships in older adulthood are characterized by heightened interdependence, which has implications for health and well-being as partners age together. Purpose: We describe a novel method that uses partners’ spatial proximity to examine the dynamics of interpersonal relationships. Research Design: In a sample of 10 older adult couples over a 14-day study period, we linked a continuous measure of partners’ spatial proximity with partners’ heart rates—a physiological marker of arousal. Results: Cross-correlations showed that proximity was consistently associated with each partner’s heart rate, but the magnitude and sequence of the correlation varied from day-to-day, suggesting that the coupling of proximity and heart rate is a dynamic of the interaction, rather than the couple. Additionally, our predictive model showed that all three time-series were necessary for optimal prediction, demonstrating that proximity and partners’ heart rates are dynamically intertwined. Conclusion: Together, these results demonstrate meaningful and predictable variation in couple dynamics at the momentary level that consists of a complex association between physiological and spatial proximity.

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Health care professionals (HCPs) are frequently exposed to Human Factors/Ergonomics (HFE) issues that result in stress, adversely affecting their health and negatively impacting the quality of care. Chronic stress can result in burnout, with negative implications for individuals, health care organizations, and patients. Current approaches to monitor burnout are reactive and require additional work (e.g., survey completion). In this study, we pilot a methodology using unobtrusive sensors and advanced statistics to bridge this important gap. We collected two types of physiological data - heart rate variability (HRV) and electrodermal activity (EDA) - and measures of perceived workload and burnout from three HCPs in a COVID-19 Testing Laboratory. We identified meaningful relationships between physiological data, workload, and burnout, demonstrating that burnout can be identified proactively using real-time sensor data. Future work will expand the timeframe of data collection and include a larger sample with different types of HCPs.