Sport and Exercise Science, School of Allied Health, Human Services and Sport, La Trobe University, Melbourne, Victoria, Australia.
National Youth Sports Institute, Singapore.
Sports Health. 2022 Jan-Feb;14(1):77-83. doi: 10.1177/19417381211056078. Epub 2021 Nov 9.
Identifying key variables that predict sleep quality in youth athletes allows practitioners to monitor the most parsimonious set of variables that can improve athlete buy-in and compliance for athlete self-report measurement. Translating these findings into a decision-making tool could facilitate practitioner willingness to monitor sleep in athletes.
Key predictor variables, identified by feature reduction techniques, will lead to higher predictive accuracy in determining youth athletes with poor sleep quality.
Cross-sectional study.
Level 3.
A group (N = 115) of elite youth athletes completed questionnaires consisting of the Pittsburgh Sleep Quality Index and questions on sport participation, training, sleep environment, and sleep hygiene habits. A least absolute shrinkage and selection operator regression model was used for feature reduction and to select factors to train a feature-reduced sleep quality classification model. These were compared with a classification model utilizing the full feature set.
Sport type, training before 8 am, training hours per week, presleep computer usage, presleep texting or calling, prebedtime reading, and during-sleep time checks on digital devices were identified as variables of greatest influence on sleep quality and used for the reduced feature set modeling. The reduced feature set model performed better (area under the curve, 0.80; sensitivity, 0.57; specificity, 0.80) than the full feature set models in classifying youth athlete sleep quality.
The findings of our study highlight that sleep quality of elite youth athletes is best predicted by specific sport participation, training, and sleep hygiene habits.
Education and interventions around the training and sleep hygiene factors that were identified to most influence the sleep quality of youth athletes could be prioritized to optimize their sleep characteristics. The developed sleep quality nomogram may be useful as a decision-making tool to improve sleep monitoring practice among practitioners.
识别出预测青年运动员睡眠质量的关键变量，可使从业者监测到最精简的变量集合，从而提高运动员对自我报告测量的接受度和依从性。将这些发现转化为决策工具，可以促进从业者监测运动员睡眠的意愿。
通过特征降维技术确定的关键预测变量，将提高确定睡眠质量差的青年运动员的预测准确性。
横断面研究。
3 级。
一组（N=115）精英青年运动员完成了调查问卷，内容包括匹兹堡睡眠质量指数以及有关运动参与、训练、睡眠环境和睡眠卫生习惯的问题。使用最小绝对收缩和选择算子回归模型进行特征降维和选择因素来训练特征降维的睡眠质量分类模型。并将其与使用全特征集的分类模型进行比较。
运动类型、8 点前进行训练、每周训练时间、睡前使用计算机、睡前发短信或打电话、睡前阅读以及在睡眠中查看数字设备，被确定为对睡眠质量影响最大的变量，并用于简化特征集建模。简化特征集模型在分类青年运动员睡眠质量方面表现优于全特征集模型（曲线下面积为 0.80；敏感性为 0.57；特异性为 0.80）。
我们的研究结果强调，精英青年运动员的睡眠质量最好由特定的运动参与、训练和睡眠卫生习惯来预测。
围绕对青年运动员睡眠质量影响最大的训练和睡眠卫生因素进行教育和干预，可以优先考虑优化他们的睡眠特征。开发的睡眠质量列线图可作为决策工具，提高从业者的睡眠监测实践。
