Department of Evolutionary Anthropology, Duke University, Durham, NC.
Department of Anthropology, University of New Mexico, Albuquerque, NM.
Sleep Health. 2016 Dec;2(4):341-347. doi: 10.1016/j.sleh.2016.09.006. Epub 2016 Oct 29.
To compare different scoring parameter settings in actigraphy software for inferring sleep and wake bouts for validating analytical techniques outside of laboratory environments.
To identify parameter settings that best identify napping during periods of wakefulness, we analyzed 137 days on which participants reported daytime napping, as compared with a random subset of 30 days when no naps were reported. To identify settings that identify periods of wakefulness during sleep, we used data from a subsample of women who reported discrete wake bouts while nursing at night.
Equatorial Tanzania in January to February 2016.
The Hadza-a non-industrial foraging population.
Thirty-three subjects participated in the study for 393 observation days. Using the Bland-Altman technique to determine concordance, we analyzed reported events of daytime napping and nighttime wake bouts.
Only 1 parameter setting could reliably detect reported naps (15-minute nap length, ≤50 counts). Moreover, of the 6 tested parameter settings to detect wake bouts, the setting where the sleep-wake algorithm was parameterized to detect 20 consecutive minutes throughout the designated sleep period did not overestimate or underestimate wake bouts, had the lowest mean difference, and did not significantly differ from reported wake-bout events.
We propose operational definitions for multiple dimensions of segmented sleep and conclude that actigraphy is an effective method for detecting segmented sleep in future cross-site comparative research. The implications of such work are far reaching, as sleep research in preindustrial and developing societies is documenting natural sleep-wake patterns in previously inaccessible environments.
比较活动记录仪软件中不同评分参数设置，以推断睡眠和清醒状态，从而验证实验室环境以外的分析技术。
为了确定最佳参数设置以识别清醒期间的小睡，我们分析了 137 天参与者报告的日间小睡，与随机选择的 30 天无小睡报告的时间进行比较。为了确定在睡眠期间识别清醒期的设置，我们使用了报告夜间哺乳时离散清醒期的女性亚样本数据。
2016 年 1 月至 2 月期间，赤道坦桑尼亚。
Hadza-一个非工业化的觅食人群。
33 名受试者参与了 393 天的观察。我们使用 Bland-Altman 技术来确定一致性，分析报告的日间小睡和夜间清醒期事件。
只有 1 个参数设置可以可靠地检测到报告的小睡（15 分钟小睡长度，≤50 计数）。此外，在测试的 6 个用于检测清醒期的参数设置中，睡眠-觉醒算法被参数化为在指定的睡眠期间连续 20 分钟检测的设置没有高估或低估清醒期，平均差异最小，并且与报告的清醒期事件没有显著差异。
我们提出了分段睡眠多个维度的操作定义，并得出结论，活动记录仪是未来跨站点比较研究中检测分段睡眠的有效方法。这项工作的意义深远，因为在工业化前和发展中社会的睡眠研究正在记录以前无法进入的环境中的自然睡眠-觉醒模式。
