Center for Research in Childhood Health, Research Unit for Exercise Epidemiology, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense M, Denmark.
Division of Clinical Physiology, Department for Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden.
Physiol Meas. 2024 May 24;45(5). doi: 10.1088/1361-6579/ad450d.
This study aimed to examine differences in heart rate variability (HRV) across accelerometer-derived position, self-reported sleep, and different summary measures (sleep, 24 h HRV) in free-living settings using open-source methodology.HRV is a biomarker of autonomic activity. As it is strongly affected by factors such as physical behaviour, stress, and sleep, ambulatory HRV analysis is challenging. Beat-to-beat heart rate (HR) and accelerometry data were collected using single-lead electrocardiography and trunk- and thigh-worn accelerometers among 160 adults participating in the SCREENS trial. HR files were processed and analysed in the RHRV R package. Start time and duration spent in physical behaviours were extracted, and time and frequency analysis for each episode was performed. Differences in HRV estimates across activities were compared using linear mixed models adjusted for age and sex with subject ID as random effect. Next, repeated-measures Bland-Altman analysis was used to compare 24 h RMSSD estimates to HRV during self-reported sleep. Sensitivity analyses evaluated the accuracy of the methodology, and the approach of employing accelerometer-determined episodes to examine activity-independent HRV was described.HRV was estimated for 31 289 episodes in 160 individuals (53.1% female) at a mean age of 41.4 years. Significant differences in HR and most markers of HRV were found across positions [Mean differences RMSSD: Sitting (Reference) - Standing (-2.63 ms) or Lying (4.53 ms)]. Moreover, ambulatory HRV differed significantly across sleep status, and poor agreement between 24 h estimates compared to sleep HRV was detected. Sensitivity analyses confirmed that removing the first and last 30 s of accelerometry-determined HR episodes was an accurate strategy to account for orthostatic effects.Ambulatory HRV differed significantly across accelerometry-assigned positions and sleep. The proposed approach for free-living HRV analysis may be an effective strategy to remove confounding by physical activity when the aim is to monitor general autonomic stress.
这项研究旨在使用开源方法，在自由生活环境中，检查加速度计衍生位置、自我报告睡眠和不同汇总测量（睡眠、24 小时 HRV）之间的心率变异性（HRV）差异。HRV 是自主活动的生物标志物。由于它受到身体行为、压力和睡眠等因素的强烈影响，因此动态 HRV 分析具有挑战性。在参与 SCREENS 试验的 160 名成年人中，使用单导联心电图和躯干和大腿佩戴的加速度计收集心率（HR）和加速度计数据。在 RHRV R 包中处理和分析 HR 文件。提取开始时间和物理行为持续时间，并对每个事件进行时间和频率分析。使用线性混合模型比较不同活动的 HRV 估计值，模型调整了年龄和性别，使用个体 ID 作为随机效应。接下来，使用重复测量 Bland-Altman 分析比较 24 小时 RMSSD 估计值与自我报告睡眠期间的 HRV。敏感性分析评估了该方法的准确性，并描述了使用加速度计确定的事件来检查与活动无关的 HRV 的方法。在 160 名个体（53.1%为女性）中，对 31289 个事件进行了 HRV 估计，平均年龄为 41.4 岁。在位置方面，HR 和 HRV 的大多数标志物都存在显著差异[平均差异 RMSSD：坐姿（参考）-站立（-2.63 毫秒）或仰卧（4.53 毫秒）]。此外，在睡眠状态下，动态 HRV 存在显著差异，并且检测到与睡眠 HRV 相比，24 小时估计值的一致性较差。敏感性分析证实，去除加速度计确定的 HR 事件的前 30 秒和最后 30 秒是一种准确的策略，可以解释体位效应。动态 HRV 在加速度计分配位置和睡眠方面存在显著差异。当目的是监测一般自主压力时，用于自由生活 HRV 分析的建议方法可能是一种有效的策略，可以消除身体活动的混杂影响。
