Actigraphy features for predicting mobility disability in older adults. (21st September 2016)

• Record Type: Journal Article
• Authors: 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; Rejeski, W Jack; Ranshous, Stephen; Pahor, Marco; Ranka, Sanjay; Manini, Todd M
• Is Part Of: Physiological measurement. Volume 37:Number 10(2016:Oct.)
• Page Start: 1813
• Page End: 1833
• Publication Date: 2016-09-21
• Subjects: aging -- disability -- physical activity -- sedentary -- machine learning -- data mining; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/0967-3334/37/10/1813
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: 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 ~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.