Miller Dean J, Roach Gregory D, Lastella Michele, Capodilupo Emily R, Sargent Charli
The Appleton Institute for Behavioural Science, Central Queensland University, Wayville, SA, Australia.
WHOOP Inc., Data Science and Research, Boston, MA, United States.
Front Physiol. 2023 Jul 28;14:1231835. doi: 10.3389/fphys.2023.1231835. eCollection 2023.
Recent sleep guidelines regarding evening exercise have shifted from a conservative (i.e., do not exercise in the evening) to a more nuanced approach (i.e., exercise may not be detrimental to sleep in circumstances). With the increasing popularity of wearable technology, information regarding exercise and sleep are readily available to the general public. There is potential for these data to aid sleep recommendations within and across different population cohorts. Therefore, the aim of this study was to examine if sleep, exercise, and individual characteristics can be used to predict whether evening exercise will compromise sleep. Data regarding evening exercise and the subsequent night's sleep were obtained from 5,250 participants (1,321F, 3,929M, aged 30.1 ± 5.2 yrs) using a wearable device (WHOOP 3.0). Data for females and males were analysed separately. The female and male datasets were both randomly split into subsets of training and testing data (training:testing = 75:25). Algorithms were trained to identify compromised sleep (i.e., sleep efficiency <90%) for females and males based on factors including the intensity, duration and timing of evening exercise. When subsequently evaluated using the independent testing datasets, the algorithms had sensitivity for compromised sleep of 87% for females and 90% for males, specificity of 29% for females and 20% for males, positive predictive value of 32% for females and 36% for males, and negative predictive value of 85% for females and 79% for males. If these results generalise, applying the current algorithms would allow females to exercise on ~ 25% of evenings with ~ 15% of those sleeps being compromised and allow males to exercise on ~ 17% of evenings with ~ 21% of those sleeps being compromised. The main finding of this study was that the models were able to predict a high percentage of nights with compromised sleep based on individual characteristics, exercise characteristics and habitual sleep characteristics. If the benefits of exercising in the evening outweigh the costs of compromising sleep on some of the nights when exercise is undertaken, then the application of the current algorithms could be considered a viable alternative to generalised sleep hygiene guidelines.
最近关于晚间锻炼的睡眠指南已从保守做法（即晚上不锻炼）转变为更细致入微的方法（即在某些情况下锻炼可能不会对睡眠有害）。随着可穿戴技术越来越普及，普通大众很容易就能获取有关锻炼和睡眠的信息。这些数据有可能帮助在不同人群队列内部及之间给出睡眠建议。因此，本研究的目的是检验睡眠、锻炼和个体特征是否可用于预测晚间锻炼是否会影响睡眠。使用可穿戴设备（WHOOP 3.0）从5250名参与者（1321名女性，3929名男性，年龄30.1±5.2岁）获取了有关晚间锻炼及随后夜晚睡眠的数据。对女性和男性的数据分别进行了分析。女性和男性数据集均被随机分为训练数据子集和测试数据子集（训练：测试=75：25）。基于包括晚间锻炼的强度、持续时间和时间等因素，训练算法以识别女性和男性睡眠受影响的情况（即睡眠效率<90%）。随后使用独立测试数据集进行评估时，算法对睡眠受影响情况的敏感度，女性为87%，男性为90%；特异度，女性为29%，男性为20%；阳性预测值，女性为32%，男性为36%；阴性预测值，女性为85%，男性为79%。如果这些结果具有普遍性，应用当前算法将使女性能够在约25%的夜晚进行锻炼，其中约15%的睡眠会受到影响；使男性能够在约17%的夜晚进行锻炼，其中约21%的睡眠会受到影响。本研究的主要发现是，这些模型能够根据个体特征、锻炼特征和习惯性睡眠特征，预测出很大比例的睡眠受影响的夜晚。如果晚间锻炼的益处超过了在某些锻炼的夜晚睡眠受影响的代价，那么应用当前算法可被视为通用睡眠卫生指南的一个可行替代方案。
