Publications

The rising levels of obesity have been declared a global epidemic by the World Health Organization, with obesity rates surpassing 50% in many countries. Between the late 1970s and the early 2000s in the U.S., the prevalence of obesity doubled while the prevalence of severe obesity more than tripled. One of the factors underlying the obesity epidemic is secular changes in activity patterns due to an increasingly sedentary lifestyle. A better understanding is needed of how daily activity patterns relate to obesity. In this study we use wrist-worn accelerometry from the National Health And Nutrition Examination Survey (NHANES) data set to develop a number of features that characterize daily activity profiles, as well as fluctuations in those profiles over time, and determine how those features correlate with body mass index (BMI). Using a data set of 2,882 subjects split evenly between a training and test fold, we constructed regression models that estimate BMI based on activity profiles and fluctuations. We found a correlation of r=0.47 between estimated and true BMI, resulting in detection of overweight, obese, and severely obese subjects with area under the ROC curve (AUC) of 0.69, 0.73 and 0.85. These results indicate how patterns of activity levels across daily sleep/wake cycles are associated with higher risk for obesity.

Breathing rate was estimated from chest-worn accelerometry collected from 1,522 servicemembers during training by a wearable physiological monitor. A total of 29,189 hours of training and sleep data were analyzed. The primary purpose of the monitor was to assess thermal-work strain and avoid heat injuries. The monitor design was thus not optimized to estimate breathing rate. Since breathing rate cannot be accurately estimated during periods of high activity, a qualifier was applied to identify sedentary time periods, totaling 8,867 hours. Breathing rate was estimated for a total of 4,179 hours, or 14% of the total collection and 47% of the sedentary total, primarily during periods of sleep. The breathing rate estimation method was compared to an FDA 510(K)-cleared criterion breathing rate sensor (Zephyr, Annapolis MD, USA) in a controlled laboratory experiment, which showed good agreement between the two techniques. Contributions of this paper are to: 1) provide the first analysis of accelerometry-derived breathing rate on free-living data including periods of high activity as well as sleep, along with a qualifier that effectively identifies sedentary periods appropriate for estimating breathing rate; 2) test breathing rate estimation on a data set with a total duration that is more than 60 times longer than that of the largest previously reported study, 3) test breathing rate estimation on data from a physiological monitor that has not been expressly designed for that purpose.