|Kheirkhahan, Matin - University Of Florida|
|Tudor-locke, Catrine - University Of Massachusetts|
|Axtell, Robert - Southern Connecticut State University|
|Buman, Matthew - Arizona State University|
|Fielding, Roger - Jean Mayer Human Nutrition Research Center On Aging At Tufts University|
|Glynn, Nancy - University Of Pittsburgh|
|Guralnik, Jack - University Of Maryland|
|King, Abby - Stanford University|
|White, Daniel - Boston University|
|Miller, Michael - Wake Forest University|
|Siddique, Juned - Northwestern University|
|Brubaker, Peter - Wake Forest University|
|Rejeski, W. Jack - Wake Forest University|
|Ranshous, Stephen - University Of Florida|
|Pahor, Marco - University Of Florida|
|Ranka, Sanjay - University Of Florida|
|Manini, Todd - University Of Florida|
Submitted to: Physiological Measurement
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/13/2016
Publication Date: 9/21/2016
Citation: Kheirkhahan, M., Tudor-Locke, C., Axtell, R., Buman, M., Fielding, R.A., Glynn, N.W., Guralnik, J.M., King, A.C., White, D.K., Miller, M.E., Siddique, J., Brubaker, P.H., Rejeski, W., Ranshous, S., Pahor, M., Ranka, S., Manini, T. 2016. Actigraphy features for predicting mobility disability in older adults. Physiological Measurement. 37(10):1813-1833.
Interpretive Summary: The study examined the feasibility of using a small accelerometer (worn on the waist) to measure physical activity and predict mobility function and major mobility disability (MMD). Men (N = 357) and women (N = 778) aged 70-89 years wore an accelerometer (Actigraph GT3X) on the right hip during free-living conditions for approximately 8 days. We identified a set of features from the accelerometry data which were related to movement pace and amount of activity participation, accumulation patterns of activity, and movement variability. These features significantly improved the prediction of MMD beyond that found with other known predictors of this syndrome (age, diseases, anthropometry, etc.) This study identified a subset of actigraphy features that are accurate in identifying persons with clinically-assessed mobility impairment and significantly improve the prediction of MMD.
Technical 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.