TY - JOUR AU - Haghayegh, Shahab AU - Khoshnevis, Sepideh AU - Smolensky, Michael H AU - Diller, Kenneth R AU - Castriotta, Richard J PY - 2019 DA - 2019/11/28 TI - Fitbit腕带模型在评估睡眠中的准确性:系统回顾和荟萃分析乔- J地中海互联网Res SP - e16273六世- 21 - 11 KW - Fitbit千瓦多导睡眠图KW -睡眠跟踪KW -可穿戴KW -活动检测仪KW -睡眠日记KW -睡眠阶段KW -准确性KW -验证KW -性能比较AB -背景:可穿戴睡眠监测感兴趣的消费者和研究人员,因为他们能够提供估计睡眠模式在独立生存的条件下成本最低的方法。目的:我们对报告Fitbit腕带模型在评估睡眠参数和阶段方面表现的出版物进行了系统回顾。方法:遵循系统评价和荟萃分析(PRISMA)首选报告项目声明，我们使用Fitbit关键字全面检索了护理和相关健康文献累积索引(CINAHL)、Cochrane、Embase、MEDLINE、PubMed、PsycINFO和Web of Science数据库，以确定符合预定义纳入和排除标准的相关出版物。结果:筛选出3085篇候选文章。在剔除重复文献并符合纳入和排除标准后，22篇文献符合系统综述条件，其中8篇文献为meta分析提供了定量数据。在多导睡眠描记术(PSG)方面，非睡眠阶段Fitbit模型倾向于高估总睡眠时间(TST;范围约7至67分钟;效应量=-0.51,P<.001;异质性:I2=8.8%， P=.36)和睡眠效率(SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy. Conclusions: Sleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG. SN - 1438-8871 UR - //www.mybigtv.com/2019/11/e16273/ UR - https://doi.org/10.2196/16273 UR - http://www.ncbi.nlm.nih.gov/pubmed/31778122 DO - 10.2196/16273 ID - info:doi/10.2196/16273 ER -