Objective Assessment of Physical Activity: Classifiers for Public Health

Abstract

Purpose: Walking for health is recommended by health agencies, partly based on epidemiological studies of self-reported behaviors. Accelerometers are now replacing survey data, but it is not clear that intensity-based cut points reflect the behaviors previously reported. New computational techniques can help classify raw accelerometer data into behaviors meaningful for public health.

Methods: Five hundred twenty days of triaxial 30-Hz accelerometer data from three studies (n = 78) were employed as training data. Study 1 included prescribed activities completed in natural settings. The other two studies included multiple days of free-living data with SenseCam-annotated ground truth. The two populations in the free-living data sets were demographically and physical different. Random forest classifiers were trained on each data set, and the classification accuracy on the training data set and that applied to the other available data sets were assessed. Accelerometer cut points were also compared with the ground truth from the three data sets.

Results: The random forest classified all behaviors with over 80% accuracy. Classifiers developed on the prescribed data performed with higher accuracy than the free-living data classifier, but these did not perform as well on the free-living data sets. Many of the observed behaviors occurred at different intensities compared with those identified by existing cut points.

Conclusions: New machine learning classifiers developed from prescribed activities (study 1) were considerably less accurate when applied to free-living populations or to a functionally different population (studies 2 and 3). These classifiers, developed on free-living data, may have value when applied to large cohort studies with existing hip accelerometer data.

Conflict of interest statement

Conflicts of Interest

None of the authors have conflicts of interested and the results of the present study do not constitute endorsement by ACSM

Figures

Figure 1

Comparison of Accelerometer Classification of Physical Activity and Sedentary Behavior to Actual Behaviors using 3 Study Designs and Samples with box and whisker plots (Study 1: Prescribed transportation modes by 2 research assistants; Study 2: Usual daily activities among 40 cyclists; Study 3: Usual daily activities among 38 overweight females). *Percentages reflect the amount of behavior falling within established accelerometer intensity thresholds. Within the box is the 25–75th percentile and the solid line is the median. The lines outside the box (whiskers) represent variability outside the quartiles