Sakal Collin, Chen Tong, Xu Wenxin, Zhang Wei, Yang Yu, Li Xinyue
Department of Data Science, College of Computing, City University of Hong Kong, Hong Kong SAR, China.
Sleep. 2025 Aug 14;48(8). doi: 10.1093/sleep/zsaf113.
Wearable devices with sleep-tracking functionalities can prompt behavioral changes to promote sleep, but proactively preventing poor sleep when it is likely to occur remains a challenge due to a lack of prediction models that can forecast sleep parameters prior to sleep onset. We developed models that forecast low sleep efficiency 4 and 8 hours prior to sleep onset using gradient boosting (CatBoost) and deep learning (Convolutional Neural Network Long Short-Term Memory, CNN-LSTM) algorithms trained exclusively on accelerometer data from 80,811 adults in the UK Biobank. Associations of various sleep and activity parameters with sleep efficiency were further examined. During repeated cross-validation, both CatBoost and CNN-LSTM exhibited excellent predictive performance (median AUCs > 0.90, median AUPRCs > 0.79). U-shaped relationships were observed between total activity within 4 and 8 hours of sleep onset and low sleep efficiency. Functional data analyses revealed higher activity 6-8 hours prior to sleep onset had negligible associations with sleep efficiency. Higher activity 4-6 hours prior had moderate beneficial associations, while higher activity within 4 hours had detrimental associations. Additional analyses showed that increased variability in sleep duration, efficiency, onset timing, and offset timing over the preceding 4 days was associated with lower sleep efficiency. Our study represents a first step towards wearable-based machine learning systems that proactively prevent poor sleep by demonstrating that sleep efficiency can be accurately forecasted prior to bedtime and by identifying pre-bed activity targets for subsequent intervention.
具有睡眠追踪功能的可穿戴设备可以促使行为改变以促进睡眠，但由于缺乏能够在睡眠开始前预测睡眠参数的预测模型，在可能出现睡眠不佳时主动预防睡眠问题仍然是一项挑战。我们开发了一些模型，这些模型使用梯度提升（CatBoost）和深度学习（卷积神经网络长短期记忆，CNN-LSTM）算法，根据英国生物银行中80,811名成年人的加速度计数据进行专门训练，来预测睡眠开始前4小时和8小时的低睡眠效率。我们进一步研究了各种睡眠和活动参数与睡眠效率之间的关联。在重复交叉验证过程中，CatBoost和CNN-LSTM均表现出出色的预测性能（中位数AUCs>0.90，中位数AUPRCs>0.79）。我们观察到睡眠开始前4小时和8小时内的总活动与低睡眠效率之间呈U形关系。功能数据分析显示，睡眠开始前6 - 8小时的较高活动与睡眠效率的关联可忽略不计。睡眠开始前4 - 6小时的较高活动有适度的有益关联，而睡眠开始前4小时内的较高活动有有害关联。额外的分析表明，在之前4天内睡眠时间、效率、开始时间和结束时间的变异性增加与较低的睡眠效率相关。我们的研究代表了朝着基于可穿戴设备的机器学习系统迈出的第一步，该系统通过证明可以在就寝前准确预测睡眠效率，并通过识别后续干预的睡前活动目标，来主动预防睡眠不佳。
