SingHealth Duke-NUS Institute of Precision Medicine, Singapore.
Cancer and Stem Biology Program, Duke-NUS Medical School, Singapore.
PLoS Biol. 2018 Feb 27;16(2):e2004285. doi: 10.1371/journal.pbio.2004285. eCollection 2018 Feb.
The use of consumer-grade wearables for purposes beyond fitness tracking has not been comprehensively explored. We generated and analyzed multidimensional data from 233 normal volunteers, integrating wearable data, lifestyle questionnaires, cardiac imaging, sphingolipid profiling, and multiple clinical-grade cardiovascular and metabolic disease markers. We show that subjects can be stratified into distinct clusters based on daily activity patterns and that these clusters are marked by distinct demographic and behavioral patterns. While resting heart rates (RHRs) performed better than step counts in being associated with cardiovascular and metabolic disease markers, step counts identified relationships between physical activity and cardiac remodeling, suggesting that wearable data may play a role in reducing overdiagnosis of cardiac hypertrophy or dilatation in active individuals. Wearable-derived activity levels can be used to identify known and novel activity-modulated sphingolipids that are in turn associated with insulin sensitivity. Our findings demonstrate the potential for wearables in biomedical research and personalized health.
消费级可穿戴设备在健身追踪以外的用途尚未得到全面探索。我们从 233 名正常志愿者中生成和分析了多维数据，整合了可穿戴设备数据、生活方式问卷、心脏成像、神经鞘脂谱分析以及多种临床级心血管和代谢疾病标志物。结果表明，可根据日常活动模式将受试者分为不同的群组，这些群组具有不同的人口统计学和行为模式特征。静息心率（RHR）与心血管和代谢疾病标志物的相关性优于步数，而步数则确定了体力活动与心脏重构之间的关系，这表明可穿戴设备数据可能在减少对活跃个体心脏肥大或扩张的过度诊断方面发挥作用。可穿戴设备生成的活动水平可用于识别已知和新的与活动相关的神经鞘脂，而这些神经鞘脂又与胰岛素敏感性相关。我们的研究结果表明，可穿戴设备在生物医学研究和个性化健康方面具有潜力。
