Facilitating ambulatory heart rate variability analysis using accelerometry-based classifications of body position and self-reported sleep.

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-05-24 DOI:10.1088/1361-6579/ad450d
Marlene Rietz, Jesper Schmidt-Persson, Martin Gillies Banke Rasmussen, Sarah Overgaard Sørensen, Sofie Rath Mortensen, Søren Brage, Peter Lund Kristensen, Anders Grøntved, Jan Christian Brønd
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Abstract

Objective.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.Approach.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.Main results.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.Significance.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.

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利用基于加速度计的体位分类和自我报告的睡眠,促进动态心率变异性分析。
目的 本研究旨在利用开源方法,研究在自由生活环境中,不同加速度计得出的位置、自我报告的睡眠以及不同的汇总测量(睡眠、24h-HRV)之间心率变异性(HRV)的差异。由于心率变异受身体行为、压力和睡眠等因素的影响很大,因此进行动态心率变异分析具有挑战性。参加 SCREENS 试验的 160 名成年人使用单导联心电图仪和佩戴在躯干和大腿上的加速度计收集了逐次心率(HR)和加速度数据。心率文件由 RHRV R 软件包处理和分析。提取身体行为的开始时间和持续时间,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中心率变异估计值的差异,该模型根据年龄和性别进行调整,并将受试者 ID 作为随机效应。然后,使用重复测量布兰-阿尔特曼分析法比较 24 小时 RMSSD 估计值和自我报告睡眠期间的心率变异。敏感性分析评估了该方法的准确性,并介绍了采用加速度计确定的发作来检查与活动无关的心率变异的方法。主要结果:对 160 名平均年龄为 41.4 岁的受试者(53.1% 为女性)的 31,289 次发作的心率变异进行了估计。不同体位下的心率和大多数心率变异指标存在显著差异[平均差异 RMSSD:坐姿(参考)-站姿(-2.63 毫秒)或躺姿(4.53 毫秒)]。此外,不同睡眠状态下的动态心率变异有显著差异,24 小时估计值与睡眠心率变异的一致性较差。敏感性分析证实,去除加速度计确定的心率事件的前 30 秒和后 30 秒是考虑正定效应的准确策略。建议的自由生活心率变异分析方法可能是一种有效的策略,可以在监测一般自律神经压力时消除体力活动的干扰。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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