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Background: An important decision with accelerometry is the threshold in counts per minute (CPM) used to define moderate to vigorous physical activity (MVPA). We explore the ability of different thresholds to track changes in MVPA due to a physical activity (PA) intervention among older adults with compromised function: 760 CPM, 1,041 CPM, and an individualized threshold. We also evaluate the ability of change in accelerometry and self-reported PA to attenuate treatment effects on major mobility disability (MMD). Methods: Data from a week of hip worn accelerometers and self-reported PA data (30-day recall) were examined from baseline, 6-, 12-, and 24-months of follow-up on 1,528 older adults. Participants were randomized to either PA or Health Education (HE). MMD was objectively defined by loss of ability to walk 400 m during the follow-up. Results: The three thresholds yielded similar and higher levels of MVPA for PA than HE (p ¡ .001), however, this difference was significantly attenuated in participants with lower levels of physical function. Self-reported PA that captured both walking and strength training totally attenuated the intervention effect for MMD, an 18% reduction to a 3% increase. Accelerometer CPMs showed less attenuation of the intervention effect. Conclusions: Accelerometry assessment within the LIFE study was not sensitive to change in level in physical activity for older adults with very low levels of physical function. A combination of self-report and objective measures are recommended for use in physical activity intervention studies of the elderly; limitations of accelerometry deserve closer attention.

The Lifestyle Interventions and Independence for Elders (LIFE) Study is a Phase III randomized controlled clinical trial (Clinicaltrials.gov identifier: NCT01072500) that will provide definitive evidence regarding the effect of physical activity (PA) on major mobility disability in older adults (70-89 years old) who have compromised physical function. This paper describes the methods employed in the delivery of the LIFE Study PA intervention, providing insight into how we promoted adherence and monitored the fidelity of treatment. Data are presented on participants' motives and self-perceptions at the onset of the trial along with accelerometry data on patterns of PA during exercise training. Prior to the onset of training, 31.4% of participants noted slight conflict with being able to meet the demands of the program and 6.4% indicated that the degree of conflict would be moderate. Accelerometry data collected during PA training revealed that the average intensity - 1,555 counts/minute for men and 1,237 counts/minute for women - was well below the cutoff point used to classify exercise as being of moderate intensity or higher for adults. Also, a sizable subgroup required one or more rest stops. These data illustrate that it is not feasible to have a single exercise prescription for older adults with compromised function. Moreover, the concept of what constitutes “moderate” exercise or an appropriate volume of work is dictated by the physical capacities of each individual and the level of comfort/stability in actually executing a specific prescription.

Actigraphy has attracted much attention for assessing physical activity in the past decade. Many algorithms have been developed to automate the analysis process, but none has targeted a general model to discover related features for detecting or predicting mobility function, or more specifically, mobility impairment and major mobility disability (MMD). Men (N = 357) and women (N = 778) aged 70-89 years wore a tri-axial accelerometer (Actigraph GT3X) on the right hip during free-living conditions for 8.4 +/- 3.0 d. One-second epoch data were summarized into 67 features. Several machine learning techniques were used to select features from the free-living condition to predict mobility impairment, defined as 400 m walking speed ¡0.80 m s(-1). Selected features were also included in a model to predict the first occurrence of MMD-defined as the loss in the ability to walk 400 m. Each method yielded a similar estimate of 400 m walking speed with a root mean square error of similar to 0.07 m s(-1) and an R-squared values ranging from 0.37-0.41. Sensitivity and specificity of identifying slow walkers was approximately 70% and 80% for all methods, respectively. The top five features, which were related to movement pace and amount (activity counts and steps), length in activity engagement (bout length), accumulation patterns of activity, and movement variability significantly improved the prediction of MMD beyond that found with common covariates (age, diseases, anthropometry, etc). This study identified a subset of actigraphy features collected in free-living conditions that are moderately accurate in identifying persons with clinically-assessed mobility impaired and significantly improve the prediction of MMD. These findings suggest that the combination of features as opposed to a specific feature is important to consider when choosing features and/or combinations of features for prediction of mobility phenotypes in older adults.

OBJECTIVES: To evaluate the effect of hospitalizations on patterns of sedentary and physical activity time in mobility-limited older adults randomized to structured physical activity or health education. DESIGN: Secondary analysis of investigator-blinded, parallel-group, randomized trial conducted at 8 U.S. centers between February 2010 and December 2013. PARTICIPANTS: Sedentary men and women aged 70 to 89 at baseline who wore a hip-fitted accelerometer 7 consecutive days at baseline and 6, 12, and 24 months after randomization (N=1,341). MEASUREMENTS: Participants were randomized to a physical activity (PA; n = 669) intervention that included aerobic, resistance, and flexibility training or to a health education (HE; n = 672) intervention that consisted of workshops on older adult health and light upper-extremity stretching. Accelerometer patterns were characterized as bouts of sedentary (¡100 counts/min; ¿= 1, ¿= 10, ¿= 30, ¿= 60 minute lengths) and activity (¿= 100 counts/min; ¿= 1, ¿= 2, ¿= 5, ¿= 10 minute lengths) time. Each participant was categorized as having 0, 1 to 3, or 4 or more cumulative hospital days before each accelerometer assessment. RESULTS: Hospitalization increased sedentary time similarly in both intervention groups (8 min/d for 1-3 cumulative hospital days and 16 min/d for ¿= 4 cumulative hospital days). Hospitalization was also associated with less physical activity time across all bouts of less than 10 minutes (¿= 1: -7 min/d for 1-3 cumulative hospital days, -16 min/d for ¿= 4 cumulative hospital days; ¿= 2: -5 min/d for 1-3 cumulative hospital days, -11 min/d for ¿= 4 cumulative hospital days; ¿= 5: -3 min/d for 1-3 cumulative hospital days, -4 min/d for ¿= 4 cumulative hospital days). There was no evidence of recovery to prehospitalization levels (time effect p ¿ .41). PA participants had less sedentary time in bouts of less than 30 minutes than HE participants (-8 to -10 min/d) and more total activity (+3 to +6 min/d), although hospital-related changes were similar between the intervention groups (interaction effect p ¿ .26). CONCLUSION: Participating in a PA intervention before hospitalization had expected benefits, but participants remained susceptible to hospitalization's detrimental effects on their daily activity levels. There was no evidence of better activity recovery after hospitalization.