Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Sleep & Health Zurich, University Center of Competence, University of Zurich, Zurich, Switzerland.
Chronobiol Int. 2021 Dec;38(12):1702-1713. doi: 10.1080/07420528.2021.1941074. Epub 2021 Jul 18.
Consumer-grade, multi-sensor, rest-activity trackers may be powerful tools, to help optimize rest-activity management in shiftwork populations undergoing circadian misalignment. Nevertheless, performance testing of such devices under field conditions is scarce. We previously validated Fitbit Charge 2 against home polysomnography and now evaluated the potential of this device to document differences in rest-activity behavior, including sleep macrostructure, in first-responder shift workers in an operational setting. We continuously monitored 89 individuals (54% females; mean age: 33.9 ± 7.7 years) for 32.5 ± 9.3 days and collected 2,974 individual sleep episodes scattered around the clock. We stratified the study participants according to their self-reported circadian preference on the reduced Horne-Östberg Morningness-Evening Questionnaire (rMEQ; the scores from 4 participants were missing). Fitbit estimates of sleep duration, wakefulness after sleep onset (WASO), REM sleep percentage in the first NREM-REM sleep cycle, and REM sleep latency formed approximately sinusoidal oscillations across 24 hours. Generalized additive mixed model analyses revealed that the phase position of sleep duration minimum was delayed by 2.8 h in evening types (ET; rMEQ ≤ 11; n = 20) and by 2.6 h in intermediate types (IT; 11 < rMEQ < 18; n = 45) when compared to morning types (MT; rMEQ ≥ 18; n = 20). Similarly, the phase position of WASO was delayed by 2.7 h in ET compared to MT. While nocturnal sleep duration did not differ among the three groups, sleep episodes during the biological day decreased in duration from ET to IT to MT. Together, the findings support the notion that a consumer-grade, rest-activity tracker allows estimation of behavioral sleep/wake cycles and sleep macrostructure in shift workers under naturalistic conditions that are consistent with their self-reported chronotype.
消费级多传感器活动和休息追踪器可能是强大的工具，有助于优化昼夜节律失调的倒班人群的休息-活动管理。然而，在现场条件下对这些设备进行性能测试的情况很少。我们之前已经对 Fitbit Charge 2 进行了家庭多导睡眠图验证，现在评估了该设备在操作环境中记录一线急救人员休息-活动行为差异（包括睡眠宏观结构）的潜力。我们连续监测了 89 名个体（54%为女性；平均年龄：33.9±7.7 岁）32.5±9.3 天，并收集了 2974 个分散在全天的个体睡眠片段。我们根据自我报告的简化 Horne-Östberg 晨型-晚型问卷（rMEQ；4 名参与者的分数缺失）对研究参与者进行分层。Fitbit 估计的睡眠时间、睡眠起始后清醒时间（WASO）、第一个非快速眼动（NREM）-快速眼动（REM）睡眠周期中的 REM 睡眠百分比和 REM 睡眠潜伏期在 24 小时内形成近似正弦波振荡。广义加性混合模型分析表明，与晨型（rMEQ≥18；n=20）相比，夜间型（rMEQ≤11；n=20）的睡眠持续时间最小值相位延迟 2.8 小时，中间型（11<rMEQ<18；n=45）的相位延迟 2.6 小时。同样，与晨型相比，夜间型的 WASO 相位延迟 2.7 小时。虽然三组的夜间睡眠时间没有差异，但生物日期间的睡眠片段持续时间从夜间型到中间型再到晨型逐渐减少。总的来说，这些发现支持了这样一种观点，即消费级的活动和休息追踪器允许在自然条件下估计倒班工人的行为性睡眠/觉醒周期和睡眠宏观结构，这些结果与他们自我报告的昼夜类型一致。
