Background: Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to whether people move differently, rather than how they move differently. Objective: This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (n = 45) and healthy controls (n = 30). Methods: Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent t tests determined differences between the groups. Results: No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (t = –4.24, p = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different. Conclusion: This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.

中文翻译：

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使用活动记录数据对患有关节炎和健康对照的成年人的早晨活动模式进行彻底检查

背景：可穿戴传感器允许研究人员远程捕获数字健康数据，包括身体活动，这可以识别数字生物标志物以区分健康人群和临床人群。迄今为止，研究主要集中在高级数据（例如，总步数）上，这可能会限制我们对人们是否以不同方式移动的洞察力，而不是他们如何以不同方式移动。目的：因此，本研究旨在使用活动记录数据来彻底检查关节炎患者（n = 45）和健康对照组（n = 30）醒来后最初几个小时的活动模式。方法：参与者佩戴 Actigraph GT9X Link 28 天。从早上醒来开始，分析和比较不同时期的活动计数，范围从 15 分钟到 4 小时。总和，并计算每个参与者、每一天的每个时间段醒来后立即累积活动的变化率（曲线下面积 [AUC]），并计算个人和团体平均值。双尾独立 t 检验确定组之间的差异。结果：在所研究的任何时间段内，总活动计数均未见差异。然而，在相对活动的离散测量的 AUC 分析中发现了差异。具体而言，在醒来后的前 15、30、45 和 60 分钟内，关节炎患者的活动计数 AUC 显着高于对照组，特别是在 30 分钟期间 (t = –4.24, p = 0.0002)。因此，虽然两个队列的移动量相同，但它们移动的方式却不同。结论：这项研究首次表明，对活动记录变量的详细分析可以识别与关节炎相关的活动模式变化，而高级每日总结却没有。结果表明，源自原始数据的离散变量可能有助于识别临床队列，应进一步探索以确定它们是否可能是有效的临床生物标志物。