Motor and Nonmotor Measures and Declining Daily Physical Activity in Older Adults
Authors: Oveisgharan Shahram , Wang Tianhao , Hausdorff Jeffrey M. , Bennett David A. , Buchman Aron S. 
Published: September 2024

Importance

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.

Objective

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.

Exposure

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.

Main Outcomes and Measures

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.

Results

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.

Conclusions and Relevance

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.