Identifying bedrest using 24-h waist or wrist accelerometry in adults

RESEARCH ARTICLE Identifying bedrest using 24-h waist or wrist accelerometry in adults J. Dustin Tracy1¤, Sari Acra2, Kong Y. Chen3, Maciej S. Buchowski1* 1 Energy Balance Laboratory, Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 2 Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America, 3 National Institute of Diabetes and Digestive and Kidney Diseases, Diabetes, Endocrinology, and Obesity Branch, National Institutes of Health, Bethesda, Maryland, United States of America a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 ¤ Current address: Economic Science Institute, Chapman University, Orange, California, United States of America * Abstract Objectives OPEN ACCESS Citation: Tracy JD, Acra S, Chen KY, Buchowski MS (2018) Identifying bedrest using 24-h waist or wrist accelerometry in adults. PLoS ONE 13(3): e0194461. https://doi.org/10.1371/journal. pone.0194461 Editor: Rod K Dishman, University of Georgia, UNITED STATES Received: October 19, 2017 To adapt and refine a previously-developed youth-specific algorithm to identify bedrest for use in adults. The algorithm is based on using an automated decision tree (DT) analysis of accelerometry data. Design Healthy adults (n = 141, 85 females, 19–69 years-old) wore accelerometers on the waist, with a subset also wearing accelerometers on the dominant wrist (n = 45). Participants spent 24-h in a whole-room indirect calorimeter equipped with a force-platform floor to detect movement. Accepted: March 2, 2018 Published: March 23, 2018 Methods Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Minute-by-minute data from recordings of waist-worn or wrist-worn accelerometers were used to identify bedrest and wake periods. Participants were randomly allocated to development (n = 69 and 23) and validation (n = 72 and 22) groups for waist-worn and wrist-worn accelerometers, respectively. The optimized DT algorithm parameters were block length, threshold, bedrest-start trigger, and bedrest-end trigger. Differences between DT classification and synchronized objective classification by the room calorimeter to bedrest or wake were assessed for sensitivity, specificity, and accuracy using a Receiver Operating Characteristic (ROC) procedure applied to 1-min epochs (n = 92,543 waist; n = 30,653 wrist). Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Raw data set from accelerometry recordings is available at https://doi.org/10.6084/m9.figshare. c.3952507.v1. Funding: This work was supported by: DK69465, National Institutes of Health, https://www.nih.gov/ (MSB); RR024975, National Institutes of Health, https://www.nih.gov/; DK20593, National Institutes of Health, https://www.nih.gov/; and DK058404, National Institutes of Health, https://www.nih.gov/. Results The optimal algorithm parameter values for block length were 60 and 45 min, thresholds 12.5 and 400 counts/min, bedrest-start trigger 120 and 400 counts/min, and bedrest-end trigger 1,200 and 1,500 counts/min, for the waist and wrist-worn accelerometers, respectively. Bedrest was identified correctly in the validation group with sensitivities of 0.819 and PLOS ONE | https://doi.org/10.1371/journal.pone.0194461 March 23, 2018 1 / 16 Bedrest assessment using accelerometry Competing interests: The authors have declared that no competing interests exist. 0.912, specificities of 0.966 and 0.923, and accuracies of 0.755 and 0.859 by the waist and wrist-worn accelerometer, respectively. The DT algorithm identified bedrest/sleep with greater accuracy than a commonly used automated algorithm (Cole-Kripke) for wrist-worn accelerometers (p<0.001). Conclusions The adapted DT accurately identifies bedrest in data from accelerometers worn by adults on either the wrist or waist. The automated bedrest/sleep detection DT algorithm for both youth and adults is openly accessible as a package “PhysActBedRest” for the R-computer language. Introduction Accelerometry-based technology for health and wellness tracking is expanding rapidly, outpacing the ability to validate the data generated and creating a barrier to employing these devices in clinical and research settings, which might otherwise benefit from the rich data provided[1,2]. Wearable accelerometers have become a major tool for the measurement of physical activity (PA), the prediction of PA-induced energy expenditure, and sleep assessment[3,4]. Although the detailed analysis of human sleep requires polysomnography (PSG) measures, accelerometry is considered a reasonably reliable and valid alternative method to estimate sleep-wake patterns[3,5] Technological advances such as watch-like waterproof devices with large data storage capacity allow assessing PA for extended monitoring periods (e.g., 24 hours/day for seven days)[6]. This “24/7” approach has gained gradual acceptance in research because it improves the ability to examine associations between physical activity, sedentary behaviors, sleep, and health in the natural or free-living environment[6]. Accelerometers for PA assessment have been commonly worn on the waist or hip, but a moderate compliance rate in participants for wearing these devices demonstrated by free-living studies has led to the use of wrist-worn accelerometers, especially for assessing sleep patterns in cross-sectional and epidemiological studies[6,7]. Analysis of the 24-h per day and multiple-day accelerometer recordings from free-living requires a comprehensive approach. This includes assessing adherence to the monitor-wearing protocol using a wearing/nonwearing algorithm or other methodologies[8,9]. The next step is to discriminate periods of sleep or bedtime rest periods from wake periods encompassing sedentary behaviors as well as more active periods commonly categorized as light, moderate, and vigorous intensity PA. Especially challenging is distinguishing nighttime sleep and daytime naps from sedentary behaviors[10]. Traditionally, sleep periods under free-living conditions have been assessed using selfreports, or more objectively, recordings from accelerometers equipped with a light sensor, an inclinometer, or an event button[11]. An alternative approach is to use automated algorithms that classify accelerometer wear-time into the bedrest/sleep and wake periods using empirically determined cut points from the accelerometer output (i.e., counts) such as those developed for wrist-worn accelerometers in children and adults by Sadeh or Cole-Kripke, respectively[12,13]. Although these algorithms were specifically developed to identify wake (...truncated)