Assessment of cognitive load and stress among nursing staff in an elderly home

This work investigates cognitive load and stress among nursing staff in an elderly care home using wearable technology and digital questionnaires over a 15-month period. Due to demographic changes, the nursing sector faces increasing demands and staff shortages. To address this, we implemented continuous monitoring of heart rate (HR) and heart rate variability (HRV) via chest strap sensors, complemented by self-reported stress levels and activity documentation using a custom smartphone app. -- Data preprocessing ensured quality and reliability, with HRV serving as physiological stress indicators. Individual workload was modelled as a multidimensional Ornstein-Uhlenbeck process, integrating objective (vital signs, acceleration) and subjective (PROMs) stress measures. -- Results reveal strong correlations between subjective stress ratings and physiological metrics, particularly lower HRV during high-stress activities. Break periods consistently showed the lowest stress levels. The drift matrix indicates that vital data robustly predicts possible future work stress and well-being, with recovery times to equilibrium spanning 1-3 days. Negative correlations were observed between HRV and other stress measures, validating HRV as an inverse stress marker. Background factors (sick days, days off) influenced workload dynamics, but non-significantly. The study demonstrates that wearable-based vital sign monitoring can provide early warning of excessive cognitive and physical stress, enabling timely interventions to promote staff well-being. This approach presents a scalable, unobtrusive method for ongoing assessment and management of nursing staff stress in real-world care settings.