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

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Date: Mar. 23, 2018
From: PLoS ONE(Vol. 13, Issue 3)
Publisher: Public Library of Science
Document Type: Report
Length: 6,362 words
Lexile Measure: 1500L

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Author(s): J. Dustin Tracy 1, Sari Acra 2, Kong Y. Chen 3, Maciej S. Buchowski 1,*

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 self-reports, 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 periods during a time in bed or sleep, they are commonly used as automated algorithms to detect sleep in 24-h accelerometer data[14]. Similar algorithms based on accelerometry recordings or body posture classification to identify sleep in young adults and children have been developed[4,15-18]. The major concern about the validity of sleep-wake scoring algorithms is their relatively low specificity defined as an ability to identify wake intervals correctly during sleep period [19].

We previously developed a decision tree (DT) to identify the time in bedrest within 24-h data collected using Actigraph accelerometers worn by healthy youth ages 10-18 years on either their waist or wrist[20]. Although the algorithm showed good accuracy to separate bedrest from wake in the youth population, its validity cannot be assumed for adults with different personal characteristics and irregular bedtime habits....

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Gale Document Number: GALE|A532055241