de Zambotti Massimiliano, Goldstone Aimee, Claudatos Stephanie, Colrain Ian M, Baker Fiona C
a Center for Health Sciences , SRI International , Menlo Park , CA , USA.
Chronobiol Int. 2018 Apr;35(4):465-476. doi: 10.1080/07420528.2017.1413578. Epub 2017 Dec 13.
We evaluated the performance of a consumer multi-sensory wristband (Fitbit Charge 2™), against polysomnography (PSG) in measuring sleep/wake state and sleep stage composition in healthy adults. In-lab PSG and Fitbit Charge 2™ data were obtained from a single overnight recording at the SRI Human Sleep Research Laboratory in 44 adults (19-61 years; 26 women; 25 Caucasian). Participants were screened to be free from mental and medical conditions. Presence of sleep disorders was evaluated with clinical PSG. PSG findings indicated periodic limb movement of sleep (PLMS, > 15/h) in nine participants, who were analyzed separately from the main group (n = 35). PSG and Fitbit Charge 2™ sleep data were compared using paired t-tests, Bland-Altman plots, and epoch-by-epoch (EBE) analysis. In the main group, Fitbit Charge 2™ showed 0.96 sensitivity (accuracy to detect sleep), 0.61 specificity (accuracy to detect wake), 0.81 accuracy in detecting N1+N2 sleep ("light sleep"), 0.49 accuracy in detecting N3 sleep ("deep sleep"), and 0.74 accuracy in detecting rapid-eye-movement (REM) sleep. Fitbit Charge 2™ significantly (p < 0.05) overestimated PSG TST by 9 min, N1+N2 sleep by 34 min, and underestimated PSG SOL by 4 min and N3 sleep by 24 min. PSG and Fitbit Charge 2™ outcomes did not differ for WASO and time spent in REM sleep. No more than two participants fell outside the Bland-Altman agreement limits for all sleep measures. Fitbit Charge 2™ correctly identified 82% of PSG-defined non-REM-REM sleep cycles across the night. Similar outcomes were found for the PLMS group. Fitbit Charge 2™ shows promise in detecting sleep-wake states and sleep stage composition relative to gold standard PSG, particularly in the estimation of REM sleep, but with limitations in N3 detection. Fitbit Charge 2™ accuracy and reliability need to be further investigated in different settings (at-home, multiple nights) and in different populations in which sleep composition is known to vary (adolescents, elderly, patients with sleep disorders).
我们评估了一款消费者多感官腕带（Fitbit Charge 2™）与多导睡眠图（PSG）在测量健康成年人睡眠/觉醒状态及睡眠阶段构成方面的性能。实验室PSG和Fitbit Charge 2™数据来自于在SRI人类睡眠研究实验室对44名成年人（19 - 61岁；26名女性；25名白种人）进行的单次夜间记录。参与者经筛查无精神和医学疾病。通过临床PSG评估睡眠障碍的存在情况。PSG结果显示9名参与者存在睡眠期周期性肢体运动（PLMS，> 15次/小时），这些参与者与主要组（n = 35）分开分析。使用配对t检验、Bland - Altman图和逐段（EBE）分析对PSG和Fitbit Charge 2™的睡眠数据进行比较。在主要组中，Fitbit Charge 2™显示出0.96的敏感性（检测睡眠的准确性）、0.61的特异性（检测觉醒的准确性）、检测N1 + N2睡眠（“浅睡眠”）的准确性为0.81、检测N3睡眠（“深睡眠”）的准确性为0.49以及检测快速眼动（REM）睡眠的准确性为0.74。Fitbit Charge 2™显著（p < 0.05）高估了PSG的总睡眠时间（TST）9分钟、N1 + N2睡眠34分钟，低估了PSG的入睡潜伏期（SOL）4分钟和N3睡眠24分钟。PSG和Fitbit Charge 2™在觉醒时间（WASO）和REM睡眠时间方面的结果没有差异。对于所有睡眠测量指标，不超过两名参与者超出了Bland - Altman一致性界限。Fitbit Charge 2™正确识别了整晚PSG定义非快速眼动 - 快速眼动睡眠周期的82%。在PLMS组中也发现了类似结果。相对于金标准PSG，Fitbit Charge 2™在检测睡眠 - 觉醒状态和睡眠阶段构成方面显示出前景，特别是在REM睡眠估计方面，但在N3检测方面存在局限性。Fitbit Charge 2™的准确性和可靠性需要在不同环境（在家中、多晚）以及已知睡眠构成存在差异的不同人群（青少年、老年人、睡眠障碍患者）中进一步研究。
