The Shengli Clinical Medical College of Fujian Medical University, Fuzhou, People's Republic of China.
Department of Nursing, Fujian Provincial Hospital, Fuzhou, People's Republic of China.
Sci Rep. 2024 Aug 13;14(1):18786. doi: 10.1038/s41598-024-69114-y.
Rest-activity behavior clusters within individuals to form patterns are of significant importance to their intrinsic capacity (IC), yet they have rarely been studied. A total of 1253 community-dwelling older adults were recruited between July and December 2021 based on the baseline survey database of the Fujian Prospective Cohort Study on Aging. Latent profile analysis was used to identify profiles of participants based on rest-activity behaviors, whereas logistic regression analysis was carried out to investigate the relationship between profiles and IC. We identified three latent profiles including: (1) Profile 1-labeled "Gorillas": High physical activity (PA), moderate sedentary behaviors (SB), screen time (ST) and sleep (n = 154, 12%), (2) Profile 2-labeled as "Zebras": Moderate PA, low SB, ST and high sleep (n = 779, 62%), and (3) Profile 3-labeled as"Koalas": High SB, ST, low PA and sleep (n = 320, 26%). Logistic regression revealed a negative correlation between low IC and the "Gorillas" profile (β = - 0.945, P < 0.001) as well as the "Zebras" profile (β = - 0.693, P < 0.001) among community-dwelling older adults, with the "Koalas" profile showing the weakest IC compared to the other profiles. The demographic traits i.e., female, older age, living alone, and low educational level also correlated with low IC. Identifying trends of rest-activity behaviors may help in drawing focus on older adults at risk of decreasing IC, and develop personalized improvement plans for IC.
个体的静息-活动行为聚类形成模式对其固有能力(IC)具有重要意义,但这些模式很少被研究。总共招募了 1253 名居住在社区的老年人,他们是根据福建老龄化纵向队列研究的基线调查数据库在 2021 年 7 月至 12 月期间招募的。使用潜在剖面分析根据静息-活动行为对参与者进行分组,而逻辑回归分析则用于研究分组与 IC 之间的关系。我们确定了三种潜在的分组,包括:(1)“大猩猩”分组:高体力活动(PA),中度久坐行为(SB),屏幕时间(ST)和睡眠(n = 154,12%),(2)“斑马”分组:中度 PA,低 SB,ST 和高睡眠(n = 779,62%),和(3)“考拉”分组:高 SB,ST,低 PA 和睡眠(n = 320,26%)。逻辑回归显示,低 IC 与“大猩猩”分组(β = -0.945,P < 0.001)和“斑马”分组(β = -0.693,P < 0.001)之间存在负相关,而“考拉”分组的 IC 比其他分组的 IC 最低。人口统计学特征,如女性、年龄较大、独居和低教育水平也与低 IC 相关。确定静息-活动行为的趋势可能有助于关注 IC 下降风险较高的老年人,并为 IC 制定个性化的改善计划。