Evaluating accelerometry thresholds for detecting changes in levels of moderate physical activity and resulting major mobility disability - Rejeski, W. Jack; Walkup, Michael P.; Fielding, Roger A.; King, Abby C.; Manini, Todd M.; Marsh, Anthony P.; McDermott, Mary McGrae; Miller, Emily Y; Newman, Anne B.; Tudor-Locke, Catrine; Axtell, Robert S.; Miller, Michael E. (Journals of Gerontology Series A-biological Sciences and Medical Sciences, 2018)
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.
6 citations (Crossref) [2023-10-31] Citation Key Alias: ISI:000430696300014 tex.eissn: [object Object] tex.unique-id: [object Object]
Keywords: Accelerometry; Aged; Aged; 80 and over; Female; Geriatric Assessment; Humans; LIFE-study; Male; Mobility Disability; Mobility Limitation; Motor Activity; Older Adults; Thresholds; Walking; accelerometry; aged; clinical trial; controlled study; dept.hms; female; geriatric assessment; human; male; motor activity; multicenter study; peer-reviewed; physiology; procedures; randomized controlled trial; very elderly; walking; walking difficulty

Promoting physical activity for elders with compromised function: the Lifestyle Interventions and Independence for Elders (LIFE) Study physical activity intervention - Rejeski, W. Jack; Axtell, Robert; Fielding, Roger; Katula, Jeffrey; King, Abby C.; Manini, Todd M.; Marsh, Anthony P.; Pahor, Marco; Rego, Alvito; Tudor-Locke, Catrine; Newman, Mark; Walkup, Michael P.; et al. (Clinical Interventions in Aging, 2013)
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.
36 citations (Crossref) [2023-10-31] tex.orcid-numbers: Buford, Thomas W/0000-0002-9541-4358 Handberg, Eileen/0000-0002-7805-9577 Newman, Mark/0000-0002-0444-8246 tex.researcherid-numbers: Buford, Thomas W/B-1111-2011 Katula, Jeffrey/K-5905-2013 tex.unique-id: ISI:000324170300001
Keywords: Accelerometry; Aged; Aged; 80 and over; Aging; Compromised physical function; Exercise; Female; Health Promotion; Humans; Independent Living; Male; Mobility Limitation; Older adults; Physical disability; Qualitative Research; Risk Reduction Behavior; Sex Distribution; accelerometry; aged; aging; article; autoregulation; body mass; compromised physical function; computer interface; computer program; conceptual framework; controlled study; dept.hms; education; elderly care; ethnicity; exercise intensity; female; health education; human; human experiment; lifestyle; male; motor performance; normal human; older adults; phase 3 clinical trial; physical activity; physical capacity; physical disability; quality of life; randomized controlled trial; semi structured interview; sex difference; social behavior; social environment

Actigraphy features for predicting mobility disability in older adults - Kheirkhahan, Matin; Tudor-Locke, Catrine; Axtell, Robert; Buman, Matthew P.; Fielding, Roger A.; Glynn, Nancy W.; Guralnik, Jack M.; King, Abby C.; White, Daniel K.; Miller, Michael E.; Siddique, Juned; Brubaker, Peter; et al. (Physiological Measurement, 2016)
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.
Citation Key Alias: lens.org/144-715-377-748-547 tex.eissn: [object Object] tex.orcid-numbers: [object Object] tex.unique-id: [object Object]
Keywords: Accelerometers; Actigraphy; Analysis process; Artificial intelligence; Data mining; Disability; Feature extraction; Forecasting; Free livings; General model; Learning systems; Living conditions; Mean square error; Older adults; Patient monitoring; Physical activity; Sedentary; Walking aids; Walking speed; aging; data mining; dept.hms; disability; machine learning; physical activity; sedentary; ⚠️ Invalid DOI