Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Department of International Health, Center for Human Nutrition, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Sleep. 2023 Nov 8;46(11). doi: 10.1093/sleep/zsad160.
To longitudinally compare sleep/wake identification and sleep parameter estimation from sleep diaries to accelerometers using different algorithms and epoch lengths in infants.
Mothers and other caregivers from the Nurture study (southeastern United States, 2013-2018) reported infants' 24-hour sleep in sleep diaries for 4 continuous days, while infants concurrently wore accelerometers on the left ankle at 3, 6, 9, and 12 months of age. We applied the Sadeh, Sadeh Infant, Cole, and Count-scaled algorithm to accelerometer data at 15 and 60 seconds epochs. For sleep/wake identification, we assessed agreement by calculating epoch-by-epoch percent agreement and kappas. We derived sleep parameters from sleep diaries and accelerometers separately and evaluated agreement using Bland-Altman plots. We estimated longitudinal trajectories of sleep parameters using marginal linear and Poisson regressions with generalized estimation equation estimation.
Among the 477 infants, 66.2% were black and 49.5% were female. Agreement for sleep/wake identification varied by epoch length and algorithm. Relative to sleep diaries, we observed similar nighttime sleep offset, onset, and total nighttime sleep duration from accelerometers regardless of algorithm and epoch length. However, accelerometers consistently estimated about 1 less nap per day using the 15 seconds epoch, 70 and 50 minutes' shorter nap duration per day using the 15 and 60 seconds epoch, respectively; but accelerometers estimated over 3 times more wake after nighttime sleep onset (WASO) per night. Some consistent sleep parameter trajectories from 3 to 12 months from accelerometers and sleep diaries included fewer naps and WASOs, shorter total daytime sleep, longer total nighttime sleep, and higher nighttime sleep efficiency.
Although there is no perfect measure of sleep in infancy, our findings suggest that a combination of accelerometer and diary may be needed to adequately measure infant sleep.
通过使用不同算法和时间窗长度，纵向比较睡眠日记和加速度计在婴儿中对睡眠/觉醒识别和睡眠参数估计的结果。
2013 年至 2018 年，来自美国东南部 Nurture 研究的母亲和其他照顾者连续 4 天在睡眠日记中报告婴儿 24 小时睡眠，同时婴儿在 3、6、9 和 12 个月时将加速度计佩戴在左踝上。我们将 Sadeh、Sadeh Infant、Cole 和 Count-scaled 算法应用于 15 秒和 60 秒时间窗的加速度计数据。对于睡眠/觉醒识别，我们通过计算逐时窗的百分比一致性和 Kappa 值来评估一致性。我们分别从睡眠日记和加速度计中得出睡眠参数，并使用 Bland-Altman 图评估一致性。我们使用边际线性和泊松回归以及广义估计方程估计来估计睡眠参数的纵向轨迹。
在 477 名婴儿中，66.2%为黑人，49.5%为女性。睡眠/觉醒识别的一致性因时间窗长度和算法而异。相对于睡眠日记，我们发现使用不同算法和时间窗长度，从加速度计中估计的夜间睡眠潜伏期、起始时间和总夜间睡眠时间相似。然而，使用 15 秒时间窗，加速度计每天估计的小睡减少 1 次，每天小睡持续时间减少 70 分钟和 50 分钟；但是，加速度计估计的夜间睡眠后觉醒时间（WASO）每晚增加 3 倍以上。从 3 个月到 12 个月，加速度计和睡眠日记中一些一致的睡眠参数轨迹包括小睡和 WASO 减少、白天总睡眠时间延长、夜间总睡眠时间延长和夜间睡眠效率提高。
尽管婴儿睡眠没有完美的测量方法，但我们的研究结果表明，可能需要结合使用加速度计和日记来充分测量婴儿睡眠。
