基于代谢、身体成分和身体健康变量预测老年人睡眠质量:使用传统机器学习模型的探索性分析。

IF 2.5 Q1 SPORT SCIENCES
Pedro Forte, Samuel G Encarnação, José E Teixeira, Luís Branquinho, Tiago M Barbosa, António M Monteiro, Daniel Pecos-Martín
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引用次数: 0

摘要

背景:睡眠在老年人健康中起着至关重要的作用,其质量受多种生理和功能因素的影响。然而,睡眠质量与身体健康、身体成分和代谢指标之间的关系尚不清楚。本探索性研究旨在探讨老年人睡眠质量与身体、代谢和身体成分变量之间的关系,并评估逻辑回归模型在睡眠质量分类中的初步表现。方法:共32例受试者参加本研究,平均年龄69岁。评估静息动脉压(收缩压和舒张压)、静息心率、人体测量(高腰围)、身体组成(生物阻抗)、体能(功能体能测试)和睡眠质量(匹兹堡睡眠质量指数)。采用分组比较、关联分析和逻辑回归,并结合5倍分层交叉验证,根据选择的非睡眠相关预测因素对睡眠质量进行分类。结果:睡眠质量好的个体背部伸展度(t = 2.592; p = 0.015; η2 = 0.239)、下肢强度(5TSTS; t = 2.564; p = 0.016; η2 = 0.476)、总睡眠时间较长(t = 6.882; p < 0.001; η2 = 0.675)。探索性相关性显示,睡眠质量差与下肢力量和活动能力降低中度相关。包括5TSTS和TUG在内的logistic回归模型在交叉验证折叠间的平均准确率为0.76±0.15,精密度为0.79±0.18,召回率为0.83±0.21,AUC为0.74±0.16。结论:这些初步研究结果表明,身体素质和临床变量显著影响老年人的睡眠质量。睡眠质量依赖模式表明,改善下肢力量的干预措施可能促进更好的睡眠结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting Sleep Quality Based on Metabolic, Body Composition, and Physical Fitness Variables in Aged People: Exploratory Analysis with a Conventional Machine Learning Model.

Background: Sleep plays a crucial role in the health of older adults, and its quality is influenced by multiple physiological and functional factors. However, the relationship between sleep quality and physical fitness, body composition, and metabolic markers remains unclear. This exploratory study aimed to investigate the associations between sleep quality and physical, metabolic, and body composition variables in older adults, and to evaluate the preliminary performance of a logistic regression model in classifying sleep quality. Methods: A total of 32 subjects participated in this study, with a mean age of 69. The resting arterial pressure (systolic and diastolic), resting heart rate, anthropometrics (high waist girth), body composition (by bioimpedance), and physical fitness (Functional Fitness Test) and sleep quality (Pitsburg sleep-quality index) were evaluated. Group comparisons, associative analysis and logistic regression with 5-fold stratified cross-validation was used to classify sleep quality based on selected non-sleep-related predictors. Results: Individuals with good sleep quality showed significantly better back stretch (t = 2.592; p = 0.015; η2 = 0.239), lower limb strength (5TSTS; t = 2.564; p = 0.016; η2 = 0.476), and longer total sleep time (t = 6.882; p < 0.001; η2 = 0.675). Exploratory correlations showed that poor sleep quality was moderately associated with reduced lower-limb strength and mobility. The logistic regression model including 5TSTS and TUG achieved a mean accuracy of 0.76 ± 0.15, precision of 0.79 ± 0.18, recall of 0.83 ± 0.21, and AUC of 0.74 ± 0.16 across cross-validation folds. Conclusions: These preliminary findings suggest that physical fitness and clinical variables significantly influence sleep quality in older adults. Sleep-quality-dependent patterns suggest that interventions to improve lower limb strength may promote better sleep outcomes.

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来源期刊
Journal of Functional Morphology and Kinesiology
Journal of Functional Morphology and Kinesiology Health Professions-Physical Therapy, Sports Therapy and Rehabilitation
CiteScore
4.20
自引率
0.00%
发文量
94
审稿时长
12 weeks
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