从可穿戴设备数据到个性化且可操作的健康洞察
From Wearable Data to Personalized and Actionable Health Insights
- Harvard University（哈佛大学）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出结合用户标注与可穿戴数据的网络框架，通过可视化辅助压力管理，经四周试点证实社交互动、刻意休息、正念活动可显著改善压力相关生理指标。
AI中文摘要：
商用可穿戴设备持续捕获丰富的生理数据（如心率、呼吸），为监测健康状况，尤其是压力相关状况，开辟了新可能。尽管前景广阔，但将原始可穿戴生理数据流转化为能在日常活动中呈现压力相关洞察、最终促进反思、提升意识并改善压力管理的可视化内容，仍是一项重大挑战。这些数据存在噪声且依赖于上下文：同一心率峰值可能来自冲刺、紧张的演示或与朋友欢笑。为应对这些挑战，我们提出一种结合用户标注与可穿戴数据以支持更好压力管理的框架。我们推出一款网络框架，提供交互式可视化，将日常活动、压力事件和干预措施分层叠加在原始生理流上，使用户能够反思并识别趋势。在一项为期四周的试点研究中，七名大学研究生和本科生参与者记录了269个事件，我们的工具揭示了不同类型干预措施与压力之间的模式：社交互动使平均心率降低4.35至5.0次/分钟，刻意休息使平均Garmin压力评分降低10.03至13.83分，正念活动使平均HRV降低6.61至13.22毫秒。
英文摘要：
Commercial wearable devices continuously capture rich physiological data (e.g., heart rate, respiration), opening new possibilities for monitoring health conditions, notably around stress. Despite their promise, turning raw wearable physiological data streams into visualizations that surface stress-related insights in daily activities, and that ultimately foster reflection, awareness, and better stress management, remains a significant challenge. The data are noisy and context-dependent: the same spike in heart rate can come from sprinting, a tense presentation, or laughing with friends. To address these challenges, we propose a framework that combines user annotations with wearable data to support better stress management. We introduce a web framework offering interactive visualizations that layer daily activities, stress events, and interventions onto raw physiological streams, enabling users to reflect and identify trends. In a four-week pilot with seven university graduate and undergraduate student participants who logged 269 events, our tool revealed patterns between different types of interventions and stress: social interaction reduced average heart rate by 4.35 to 5.0 beats per minute, deliberate rest reduced average Garmin stress scores by 10.03 to 13.83 points, and mindfulness activities decreased average HRV by 6.61 to 13.22 milliseconds.
