Imamura Keigo, Kamide Naoto, Sakamoto Miki, Sato Haruhiko, Shiba Yoshitaka, Matsunaga Atsuhiko
Kitasato University, Graduate School of Medical Sciences.
Kitasato University, Graduate School of Medical Sciences, Kitasato University, School of Allied Health Sciences.
Phys Ther Res. 2020 Jul 22;23(2):153-159. doi: 10.1298/ptr.E10028. eCollection 2020.
A poor social network and the decline of physical function are known to be critical risk factors for functional decline in older adults. The aim of this study was to investigate the relationships between social network and physical function in Japanese community-dwelling older adults.
Participants were 339 adults aged 65 years or older (mean age : 73.0 years, women :70.2%), living independently in their communities. A self-reported questionnaire was used to assess social network on two different scales-the 6-item Lubben Social Network Scale (6LSNS) and frequency of contact with other people. Handgrip strength, knee extension strength, gait speed, Timed Up and Go Test (TUG) results, and 5-repetition chair stand test (CST) scores were used to determine physical function. A multiple regression analysis that adjusted for confounding factors was used to analyze the relationship between the social network scales and each physical function test.
According to the results of a multiple regression analysis, a high 6LSNS score was significantly associated with greater handgrip strength (B = 0.63, p = 0.03), faster CST (B = -0.23, p = 0.01), and faster TUG (B = -0.12, p = 0.03), and high frequency of contact was significantly associated with greater handgrip strength (B = 1.08, p = 0.01).
Social network was associated with muscle strength and physical performance. Consequently, older adults with poor social networks require an assessment of physical function, since their physical functions have possibly deteriorated.
社会网络不佳和身体功能衰退是老年人功能下降的关键风险因素。本研究旨在调查日本社区居住老年人的社会网络与身体功能之间的关系。
参与者为339名65岁及以上的成年人(平均年龄:73.0岁,女性:70.2%),独立生活在社区中。使用一份自我报告问卷,通过两个不同量表评估社会网络——6项鲁本社会网络量表(6LSNS)和与他人的接触频率。握力、膝关节伸展力量、步速、定时起立行走测试(TUG)结果以及5次重复坐立测试(CST)分数用于确定身体功能。采用调整混杂因素的多元回归分析来分析社会网络量表与各项身体功能测试之间的关系。
根据多元回归分析结果,6LSNS得分高与握力更强显著相关(B = 0.63,p = 0.03)、CST更快(B = -0.23,p = 0.01)以及TUG更快(B = -0.12,p = 0.03),接触频率高与握力更强显著相关(B = 1.08,p = 0.01)。
社会网络与肌肉力量和身体表现相关。因此,社会网络不佳的老年人需要评估身体功能,因为他们的身体功能可能已经恶化。