Prof Demosthenes B Panagiotakos, Harokopio University, 70 El. Venizelou St., 176 71 Athens, Greece, Tel. +30 210-9549332, Email:
J Frailty Aging. 2023;12(1):16-23. doi: 10.14283/jfa.2022.37.
The aging of global population has increased the scientific interest in the concept of healthy aging and its determinants.
The aim of this study was to investigate the association of sleep characteristics with trajectories of healthy aging.
Prospective observational study conducted in two cities, Maroussi and Larissa.
A total of 1226 older adults (≥65 years, 704 women) were selected through random sampling.
Sleep quality was assessed with the Sleep Index II, and sleep duration was self-reported. A healthy aging metric was introduced using an Item Response Theory approach based on validated questionnaires that assessed functionality. Four healthy aging trajectories were developed based on whether the healthy aging status of the participants was above (High) or below (Low) the median at baseline and follow-up, i.e., High-High, High-Low, Low-High, and Low-Low. The association of sleep characteristics with the trajectories was investigated using a multinomial logistic regression with the Low-Low group as reference, adjusting for potential confounders.
34.3% participants classified to the High-High group, 15.7% to the High-Low, 18.6% to the Low-High, and 31.4% to the Low-Low group. Better sleep quality was associated with the probability of belonging to the High-High group (p-value<0.001); while, long sleep duration was inversely associated with likelihood of being classified in the High-High group (p-value < 0.05).
Poor sleep quality and long sleep duration seem to have a significant negative association with healthy aging. Public health policies are needed to raise awareness about the importance of sleep characteristics on human health.
全球人口老龄化增加了人们对健康老龄化及其决定因素这一概念的科学兴趣。
本研究旨在探讨睡眠特征与健康老龄化轨迹的关系。
在两个城市（马里乌斯和拉里萨）进行的前瞻性观察研究。
通过随机抽样，共选择了 1226 名老年人（≥65 岁，704 名女性）。
使用睡眠指数 II 评估睡眠质量，睡眠时间由自我报告。采用基于验证问卷的项目反应理论方法引入健康老龄化指标，该问卷评估了功能。根据参与者在基线和随访时的健康老龄化状况是否高于（高）或低于（低）中位数，制定了 4 种健康老龄化轨迹，即高-高、高-低、低-高和低-低。使用多变量逻辑回归，以低-低组为参照，调整潜在混杂因素，探讨睡眠特征与轨迹的关系。
34.3%的参与者归入高-高组，15.7%归入高-低组，18.6%归入低-高组，31.4%归入低-低组。睡眠质量较好与归入高-高组的概率相关（p 值<0.001）；而较长的睡眠时间与归入高-高组的可能性呈负相关（p 值<0.05）。
较差的睡眠质量和较长的睡眠时间似乎与健康老龄化呈显著负相关。需要制定公共卫生政策，提高人们对睡眠特征对人类健康重要性的认识。
