Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices
Estimating circadian phase in elementary school children: leveraging advances in physiologically informed models of circadian entrainment and wearable devices
复制标题
估计小学生的昼夜节律阶段：利用昼夜节律夹带和可穿戴设备的生理学模型的进步
DOI：
10.1093/sleep/zsac061
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发表时间：
2022
期刊：
影响因子：
5.6
通讯作者：
Park, Rebekah Julie
中科院分区：
文献类型：
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作者：
Moreno, Jennette P.;Hannay, Kevin M.;Walch, Olivia;Dadabhoy, Hafza;Christian, Jessica;Puyau, Maurice;El-Mubasher, Abeer;Bacha, Fida;Grant, Sarah R.;Park, Rebekah Julie
研究目的：检测基于生理学的人体昼夜节律数学模型预测儿童昼夜节律相位的能力，通过唾液昏暗光褪黑激素起始（DLMO）测量，与其他昼夜节律相位的替代测量进行比较（就寝时间、睡眠中点和醒来时间）。方法作为正在进行的临床试验的一部分，对29名小学生进行了抽样（平均年龄：7.4 ± .97岁）在实验室访视前完成7天腕关节活动记录，以评估DLMO。在昏暗的光照条件下（<5lx）每小时收集唾液褪黑激素样品。来自体动记录仪的数据用于使用基于生理学的昼夜节律极限循环振荡器数学模型（Hannay模型）和利用平均睡眠开始、中点和偏移来预测DLMO的公布回归方程来生成昼夜节律相位的预测。代理预测与测量DLMO的协议进行了评估和compared.ResultsDLMO预测使用Hannay模型优于DLMO预测的基础上儿童的睡眠/觉醒参数林的一致性相关系数（LinCCC）为0.79相比，0.41-0.59睡眠/觉醒参数。平均绝对误差为31分钟的Hannay模型相比，35-38分钟的sleep/wake variables.ConclusionOur研究结果表明，睡眠/觉醒行为是弱代理DLMO阶段的儿童，但数学模型使用的可穿戴数据收集的数据可以用来提高这些预测的准确性。需要更多的研究来更好地适应这些成人模型用于儿童。临床试验i心脏节律项目：健康的睡眠和行为节律预防肥胖https://clinicaltrials.gov/ct2/show/NCT04445740。
Study ObjectivesExamine the ability of a physiologically based mathematical model of human circadian rhythms to predict circadian phase, as measured by salivary dim light melatonin onset (DLMO), in children compared to other proxy measurements of circadian phase (bedtime, sleep midpoint, and wake time).MethodsAs part of an ongoing clinical trial, a sample of 29 elementary school children (mean age: 7.4 ± .97 years) completed 7 days of wrist actigraphy before a lab visit to assess DLMO. Hourly salivary melatonin samples were collected under dim light conditions (<5 lx). Data from actigraphy were used to generate predictions of circadian phase using both a physiologically based circadian limit cycle oscillator mathematical model (Hannay model), and published regression equations that utilize average sleep onset, midpoint, and offset to predict DLMO. Agreement of proxy predictions with measured DLMO were assessed and compared.ResultsDLMO predictions using the Hannay model outperformed DLMO predictions based on children’s sleep/wake parameters with a Lin’s Concordance Correlation Coefficient (LinCCC) of 0.79 compared to 0.41–0.59 for sleep/wake parameters. The mean absolute error was 31 min for the Hannay model compared to 35–38 min for the sleep/wake variables.ConclusionOur findings suggest that sleep/wake behaviors were weak proxies of DLMO phase in children, but mathematical models using data collected from wearable data can be used to improve the accuracy of those predictions. Additional research is needed to better adapt these adult models for use in children.Clinical TrialThe i Heart Rhythm Project: Healthy Sleep and Behavioral Rhythms for Obesity Prevention https://clinicaltrials.gov/ct2/show/NCT04445740.