STUDY OBJECTIVES Actigraphy is a useful tool for estimating sleep, but less accurately distinguishes sleep and wakefulness in patients with insomnia disorder (ID) than in good sleepers. Specific algorithm parameter settings have been suggested to improve the accuracy of actigraphic estimates of sleep onset or nocturnal sleep and wakefulness in ID. However, a direct comparison of how different algorithm parameter settings affect actigraphic estimates of sleep features has been lacking. This study aimed to define the optimal algorithm parameter settings for actigraphic estimates of polysomnographic sleep features in people suffering from ID and matched good sleepers. METHODS We simultaneously recorded actigraphy and polysomnography without sleep diaries during 210 laboratory nights of people with ID (n = 58) and matched controls (CTRL) without sleep complaints (n = 56). We analyzed cross-validation errors using 150 algorithm parameter configurations and Bland-Altman plots of sleep features using the optimal settings. RESULTS Optimal sleep onset latency and total sleep time (TST) errors were lower in CTRL (8.9 ± 2.1 and 16.5 ± 2.1 min, respectively) than in ID (11.7 ± 0.8 and 29.1 ± 3.4 min). The sleep-wake algorithm, a period duration of 5 min, and a wake sensitivity threshold of 40 achieved optimal results in ID and near-optimal results in CTRL. Bland-Altman plots were nearly identical for ID and controls for all common all-night sleep features except for TST. CONCLUSION This systematic evaluation shows that actigraphic sleep feature estimation can be improved by using uncommon parameter settings. One specific parameter setting provides (near-)optimal estimation of sleep onset and nocturnal sleep across ID and controls.

中文翻译：

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优化失眠障碍中多导睡眠图睡眠特征的活动图估计

研究目标 活动描记法是一种有用的睡眠评估工具，但在区分失眠障碍 (ID) 患者的睡眠和觉醒时不如睡眠良好者准确。已经建议特定的算法参数设置来提高 ID 中睡眠开始或夜间睡眠和觉醒的活动图估计的准确性。然而，缺乏对不同算法参数设置如何影响睡眠特征的活动图估计的直接比较。本研究旨在为患有 ID 和匹配良好睡眠者的多导睡眠图睡眠特征的活动图估计定义最佳算法参数设置。方法 我们在 210 个实验室之夜同时记录没有睡眠日记的活动记录和多导睡眠图，其中有 ID（n = 58）和没有睡眠投诉的匹配对照（CTRL）（n = 56）。我们使用 150 个算法参数配置和使用最佳设置的睡眠特征的 Bland-Altman 图分析了交叉验证错误。结果 CTRL（分别为 8.9 ± 2.1 和 16.5 ± 2.1 分钟）的最佳睡眠开始潜伏期和总睡眠时间 (TST) 误差低于 ID（11.7 ± 0.8 和 29.1 ± 3.4 分钟）。睡眠-唤醒算法、5 分钟的周期持续时间和 40 的唤醒敏感度阈值在 ID 中获得了最佳结果，在 CTRL 中获得了接近最佳的结果。除了 TST 之外，所有常见的通宵睡眠特征的 ID 和对照的 Bland-Altman 图几乎相同。结论 该系统评估表明，可以通过使用不常见的参数设置来改进活动记录睡眠特征估计。一种特定的参数设置提供了对 ID 和对照的睡眠开始和夜间睡眠的（接近）最佳估计。