Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois.
Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois.
JAMA Netw Open. 2024 Sep 3;7(9):e2432033. doi: 10.1001/jamanetworkopen.2024.32033.
Difficulties in identifying modifiable risk factors associated with daily physical activity may impede public health efforts to mitigate the adverse health outcomes of a sedentary lifestyle in an aging population.
To test the hypothesis that adding baseline sensor-derived mobility metrics to diverse baseline motor and nonmotor variables accounts for the unexplained variance of declining daily physical activity among older adults.
DESIGN, SETTING, AND PARTICIPANTS: This cohort study analyzed data from participants of the Rush Memory and Aging Project (MAP), an ongoing longitudinal clinical pathological study that began to enroll older adults (age range, 59.4-104.9 years) in 1997. Wrist- and waist-worn sensors were added to MAP in 2005 and 2012, respectively, to record participants' physical activity and mobility performances. Included participants were examined at baseline and annually followed up for a mean (SD) duration of 4.2 (1.6) years.
Twelve blocks of variables, including 3 blocks of mobility metrics derived from recordings of a belt-worn sensor to quantify a 32-foot walk, a Timed Up and Go (TUG) test, and a standing balance task, and 9 other blocks with 41 additional variables.
A linear mixed-effects model was used to estimate the person-specific rate of change (slope) of total daily physical activity obtained from a wrist-worn sensor. Twelve linear regression models were used to estimate the adjusted R2 to quantify the associations of the variables with the slope.
A total of 650 older adults (500 females [76.9%]; mean [SD] age at baseline, 81.4 [7.5] years; 31 Black individuals [4.8%], 17 Latino individuals [2.6%], and 602 White individuals [92.6%]) were included. During follow-up, all but 1 participant showed declining daily physical activity, which was equivalent to approximately 16.8% decrease in activity level per year. In separate models, waist sensor-derived mobility metrics (32-foot walk: adjusted R2, 23.4% [95% CI, 17.3%-30.6%]; TUG test: adjusted R2, 22.8% [95% CI, 17.7%-30.1%]) and conventional motor variables (adjusted R2, 24.1% [95% CI, 17.7%-31.4%]) had the largest percentages of variance of declining daily physical activity compared with nonmotor variables. When the significant variables from all 12 blocks were included together in a single model, only turning speed (estimate [SE], 0.018 [0.006]; P = .005) and hand dexterity (estimate [SE], 0.091 [0.034]; P = .008) showed associations with declining daily physical activity.
Findings of this study suggest that sensor-derived mobility metrics and conventional motor variables compared with nonmotor measures explained most of the variance of declining daily physical activity. Further studies are needed to ascertain whether improving specific motor abilities, such as turning speed and hand dexterity, is effective in slowing the decline of daily physical activity in older adults.
难以识别与日常体力活动相关的可改变风险因素，可能会阻碍公共卫生努力，无法减轻老龄化人口中久坐生活方式的不良健康后果。
检验假设，即将基线传感器衍生的移动性指标添加到各种基线运动和非运动变量中，可以解释老年人日常体力活动下降的未解释方差。
设计、地点和参与者：这项队列研究分析了 Rush 记忆和老龄化项目 (MAP) 参与者的数据，这是一项正在进行的纵向临床病理研究，自 1997 年开始招募老年人（年龄范围为 59.4-104.9 岁）。2005 年和 2012 年分别在 MAP 中添加了腕部和腰部佩戴的传感器，以记录参与者的体力活动和移动性能。纳入的参与者在基线接受检查，并在平均（SD）4.2（1.6）年的时间内进行年度随访。
12 个变量块，包括 3 个从佩戴在腰带上的传感器记录中得出的移动性指标块，以量化 32 英尺的步行、计时起立行走（TUG）测试和站立平衡任务，以及 9 个其他块，包含 41 个额外变量。
使用线性混合效应模型来估计从腕部佩戴的传感器获得的总日常体力活动的个体特定变化率（斜率）。使用 12 个线性回归模型来估计调整后的 R2，以量化变量与斜率的关联。
共纳入 650 名老年人（500 名女性[76.9%]；基线时的平均[SD]年龄为 81.4[7.5]岁；31 名黑人[4.8%]、17 名拉丁裔[2.6%]和 602 名白人[92.6%]）。在随访期间，除 1 名参与者外，所有参与者的日常体力活动均呈下降趋势，相当于每年活动水平下降约 16.8%。在单独的模型中，腰部传感器衍生的移动性指标（32 英尺步行：调整后的 R2，23.4%[95%CI，17.3%-30.6%]；TUG 测试：调整后的 R2，22.8%[95%CI，17.7%-30.1%]）和常规运动变量（调整后的 R2，24.1%[95%CI，17.7%-31.4%]）与非运动变量相比，具有最大的日常体力活动下降的方差百分比。当将所有 12 个块中的显著变量一起包含在单个模型中时，只有转弯速度（估计值[SE]，0.018[0.006]；P = .005）和手部灵巧度（估计值[SE]，0.091[0.034]；P = .008）与日常体力活动下降有关。
这项研究的结果表明，与非运动测量相比，传感器衍生的移动性指标和常规运动变量解释了日常体力活动下降的大部分方差。需要进一步研究以确定是否改善特定的运动能力，如转弯速度和手部灵巧度，是否可以有效减缓老年人日常体力活动的下降。
