Graduate School of Food and Nutritional Science, Toyo University, 1-1-1, Izumino, Itakura-machi, Ora-gun, Gunma, 374-0193, Japan; Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.
Tokyo Metropolitan Institute of Gerontology, 35-2, Sakae-cho, Itabashi-ku, Tokyo, 173-0015, Japan.
Clin Nutr ESPEN. 2021 Feb;41:391-397. doi: 10.1016/j.clnesp.2020.10.013. Epub 2020 Dec 9.
BACKGROUND & AIMS: Improving sleep quality is important for an aging society. However, no study has been conducted on the association between dietary variety and sleep efficiency among older Japanese adults using longitudinal data. The current study is a longitudinal study that aimed to elucidate the association between dietary variety and sleep efficiency in older Japanese adults.
This study was conducted among older adults (≥70 years old in 2016) in the metropolitan area of Tokyo, Japan, between 2016 and 2018. Dietary variety score (DVS) and sleep efficiency were determined for all participants and were used to assess dietary habits and sleep quality, respectively. A cross-lagged panel analysis was used to consider the prospective associations between DVS and sleep efficiency, and this analysis was performed before and after adjusting for possible covariates in the 2016 data.
After adjusting for age, sex, body mass index, current drinking status, current smoking status, exercise habits, living (alone or with others) style, the Japanese version of the Geriatric Depression Scale (Short Form), sleep duration and retiring time the DVS in 2016 predicted the sleep efficiency in 2018 (β = 0.130, p < 0.05).
The possibility that DVS can predict sleep efficiency 2 years later was demonstrated.
改善睡眠质量对于老龄化社会至关重要。然而，目前还没有使用纵向数据研究日本老年人饮食多样性与睡眠效率之间的关系的研究。本研究是一项旨在阐明日本老年人饮食多样性与睡眠效率之间关系的纵向研究。
本研究于 2016 年至 2018 年在日本东京大都市区对年龄在 70 岁及以上的老年人进行。所有参与者均进行了饮食多样性评分（DVS）和睡眠效率的测定，分别用于评估饮食习惯和睡眠质量。采用交叉滞后面板分析考虑 DVS 和睡眠效率之间的前瞻性关联，并在 2016 年数据中调整了可能的协变量后进行了该分析。
调整年龄、性别、体重指数、当前饮酒状态、当前吸烟状态、运动习惯、生活（单独或与他人）方式、日本版老年抑郁量表（短表）、睡眠时间和退休时间后，2016 年的 DVS 预测了 2018 年的睡眠效率（β=0.130，p<0.05）。
证明了 DVS 可以预测两年后的睡眠效率的可能性。
