Department of Medical Sciences, Occupational and Environmental Medicine, Uppsala University, Uppsala University Hospital, Uppsala, Sweden.
The National Research Centre for the Working Environment, Copenhagen, Denmark.
J Sleep Res. 2023 Apr;32(2):e13725. doi: 10.1111/jsr.13725. Epub 2022 Sep 27.
Accelerometers placed on the thigh provide accurate measures of daily physical activity types, postures and sedentary behaviours, over 24 h and across consecutive days. However, the ability to estimate sleep duration or quality from thigh-worn accelerometers is uncertain and has not been evaluated in comparison with the 'gold-standard' measurement of sleep polysomnography. This study aimed to develop an algorithm for sleep estimation using the raw data from a thigh-worn accelerometer and to evaluate it in comparison with polysomnography. The algorithm was developed and optimised on a dataset consisting of 23 single-night polysomnography recordings, collected in a laboratory, from 15 asymptomatic adults. This optimised algorithm was then applied to a separate evaluation dataset, in which, 71 adult males (mean [SD] age 57 [11] years, height 181 [6] cm, weight 82 [13] kg) wore ambulatory polysomnography equipment and a thigh-worn accelerometer, simultaneously, whilst sleeping at home. Compared with polysomnography, the algorithm had a sensitivity of 0.84 and a specificity of 0.55 when estimating sleep periods. Sleep intervals were underestimated by 21 min (130 min, Limits of Agreement Range [LoAR]). Total sleep time was underestimated by 32 min (233 min LoAR). Our results evaluate the performance of a new algorithm for estimating sleep and outline the limitations. Based on these results, we conclude that a single device can provide estimates of the sleep interval and total sleep time with sufficient accuracy for the measurement of daily physical activity, sedentary behaviour, and sleep, on a group level in free-living settings.
放置在大腿上的加速度计可以在 24 小时内和连续几天内准确测量日常身体活动类型、姿势和久坐行为。然而,从大腿佩戴的加速度计估计睡眠持续时间或质量的能力尚不确定,并且尚未与睡眠多导睡眠图的“金标准”测量进行比较评估。本研究旨在开发一种使用大腿佩戴的加速度计原始数据进行睡眠估计的算法,并将其与多导睡眠图进行比较评估。该算法是在一个由 23 名无症状成年人进行的实验室单次多导睡眠图记录的数据集上开发和优化的。然后,将经过优化的算法应用于另一个评估数据集,其中 71 名成年男性(平均[标准差]年龄 57[11]岁,身高 181[6]厘米,体重 82[13]公斤)在家中睡觉时同时佩戴动态多导睡眠图设备和大腿佩戴的加速度计。与多导睡眠图相比,该算法在估计睡眠时间时的敏感性为 0.84,特异性为 0.55。算法低估了睡眠期 21 分钟(130 分钟,界限协议范围 [LoAR])。总睡眠时间低估了 32 分钟(233 分钟 LoAR)。我们的结果评估了一种新的估计睡眠的算法的性能,并概述了其局限性。基于这些结果,我们得出结论,单个设备可以提供足够准确的睡眠间隔和总睡眠时间估计值,可用于在自由生活环境中测量日常身体活动、久坐行为和睡眠,适用于群体水平。