Švihrová Radoslava, Marzorati Davide, Bechný Michal, Grossenbacher Max, Ilchenko Yuriy, Grossenbacher Jürg, Tzovara Athina, Faraci Francesca Dalia
Institute of Computer Science, Faculty of Science, University of Bern, Bern, Switzerland.
Department of Innovative Technologies, Institute of Digital Technologies for Personalized Healthcare, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland.
Front Digit Health. 2025 Aug 28;7:1640900. doi: 10.3389/fdgth.2025.1640900. eCollection 2025.
Wearable devices have gained significant popularity in recent years, as they provide valuable insights into behavioral patterns and enable unobtrusive continuous monitoring. This work explores how daily lifestyle choices and physiological factors contribute to coping capacities and aims at designing burnout prevention systems. Key variables examined include sleep stage proportions and nocturnal stress levels, as both play a crucial role in recovery and resilience. Longitudinal data from a 1-week study incorporating wearable-derived features and contextual information are analyzed using a mixed-effects model, accounting for both overall trends and individual differences. A Bayesian inference approach is exploited to quantify uncertainty in estimated effects, providing their probabilistic interpretation and ensuring robustness despite the low sample size. Findings indicate that alcohol consumption negatively affects rapid-eye-movement sleep, increases awake time, and elevates nocturnal stress. Excessive daily stress reduces deep sleep, while an increase in daily active hours promote it. These results align with the existing literature, demonstrating the potential of consumer-grade wearables to monitor clinically relevant relationships and guide interventions for stress reduction and burnout prevention.
近年来，可穿戴设备大受欢迎，因为它们能提供有关行为模式的宝贵见解，并能实现不引人注意的持续监测。这项工作探讨了日常生活方式选择和生理因素如何影响应对能力，并旨在设计预防倦怠系统。所研究的关键变量包括睡眠阶段比例和夜间压力水平，因为二者在恢复和恢复力方面都起着至关重要的作用。使用混合效应模型分析了来自一项为期1周的研究的纵向数据，该研究纳入了可穿戴设备得出的特征和背景信息，同时考虑了总体趋势和个体差异。采用贝叶斯推理方法来量化估计效应中的不确定性，给出其概率解释，并确保尽管样本量较小但结果具有稳健性。研究结果表明，饮酒会对快速眼动睡眠产生负面影响，增加清醒时间，并提高夜间压力。每日压力过大减少深度睡眠，而每日活动时间增加则会促进深度睡眠。这些结果与现有文献一致，证明了消费级可穿戴设备在监测临床相关关系以及指导减压和预防倦怠干预方面的潜力。
