Actigraphy features for predicting mobility disability in older adults

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.

Figures

Figure 1

Eligible participant flow diagram for accelerometry analyses.

Figure 2

Univariate correlation between features and walking speed (target variable). The minimal subset of features used for mobility assessment is bolded. AVG, average; STD, standard deviation; AC, activity counts; Ai, ith axis (e.g. A1= axis 1); VM, vector magnitude; NO, number of. The black bars were selected by machine learning techniques in Table 3 and the ranking algorithm described in the methods.

Figure 3

Harell’s C-statistic where a value of 0.50 equates to a random chance of prediction was derived from post-estimations of Cox proportional hazards regression for predicting incident MMD (i.e., major mobility disability or the inability to walk 400 m). The results for sequential nested models: model 1 with covariates for age, gender and intervention arm, model 2 adds comorbidity count, model 3 adds 4-meter walking speed, model 4 adds body weight and height and model 5 adds the top five actigraphy features. All models are statistically significant and each sequential model represented a significant increase in prediction of MMD (p