Dataset Information

Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

ABSTRACT: PURPOSE:Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the reference method. METHODS:Data from 62 adults (mean age, 78.4 yr) of the Aging Research Evaluating Accelerometry study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 d in a free-living environment. Using the activPAL as the reference criteria, we compared classification of sedentary time to hip-worn and wrist-worn ActiGraph accelerometers over a range of cutoff points for both 60-s and 15-s epochs. RESULTS:The optimal cutoff point for the hip vertical axis was <22 counts per minute with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cutoff point was <174 counts per minute with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cutoff point to define sedentary time was <2303 counts per minute (AUC, 0.86) and for the nondominant wrist <1853 counts per minute (AUC, 0.86). The optimal 15-s cutoff points resulted in lower agreements compared with activPAL. CONCLUSIONS:Hip- and wrist-worn ActiGraph data may be used to define sedentary time with a moderate to high accuracy when compared with activPAL. The observed optimal cutoff point for hip vertical axis <22 counts per minute is substantially lower than the standard <100 counts per minute. It is unknown how these optimal cutoff points perform in different populations. Results on an individual basis should therefore be interpreted with caution.

SUBMITTER: Koster A

PROVIDER: S-EPMC4993533 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

Koster Annemarie A Shiroma Eric J EJ Caserotti Paolo P Matthews Charles E CE Chen Kong Y KY Glynn Nancy W NW Harris Tamara B TB

Medicine and science in sports and exercise 20160801 8

<h4>Purpose</h4>Sedentary behavior is an emerging independent health risk factor. The accuracy of measuring sedentary time using accelerometers may depend on the wear location. This study in older adults evaluated the accuracy of various hip- and wrist-worn ActiGraph accelerometer cutoff points to define sedentary time using the activPAL as the reference method.<h4>Methods</h4>Data from 62 adults (mean age, 78.4 yr) of the Aging Research Evaluating Accelerometry study were used. Participants sim ...[more]

PMID: 27031744

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Project description:Objectives: Our count-scaled algorithm automatically scores sleep across 24 hours to process sleep timing, quantity, and quality. The aim of this study was to validate the algorithm against overnight PSG in children to determine the best site placement for sleep. Methods: 28 children (5-8 years) with no history of sleep disturbance wore two types of accelerometers (ActiGraph GT3X+ and Actical) at two sites (left hip, non-dominant wrist) for 24-h. Data were processed using the count-scaled algorithm. PSG data were collected using an in-home Type 2 device. PSG-actigraphy epoch sensitivity (sleep agreement) and specificity (wake agreement) were determined and sleep outcomes compared for timing (onset and offset), quantity [sleep period time (SPT) and total sleep time (TST)], and quality metrics [sleep efficiency and waking after sleep onset (WASO)]. Results: Overall, sensitivities were high (89.1% to 99.5%) and specificities low (21.1% to 45.7%). Sleep offset was accurately measured by actigraphy, regardless of brand or placement site. By contrast, sleep onset agreed with PSG using hip-positioned but not wrist-positioned devices (difference ActiGraph : PSG 21 min, P < .001; Actical : PSG 14 min, P < .001). The ActiGraph at the wrist accurately detected WASO and sleep efficiency, but under (-34 min, P < .001) and overestimated (5.8%, P < .001) these at the hip. The Actical under- and over-estimated these variables respectively at both sites. Results for TST varied ranging from significant differences to PSG of -26 to 21 min (ActiGraph wrist and hip respectively) and 9 min (ns) to 59 min for Actical (wrist and hip respectively). Conclusion: Overall the count-scaled algorithm produced high sensitivity at the expense of low specificity in comparison with PSG. A best site placement for estimates of all sleep variables could not be determined, but overall the results suggested ActiGraph GT3X+ at the hip may be superior for sleep timing and quantity metrics, whereas the wrist may be superior for sleep quality metrics. Both devices placed at the hip performed well for sleep timing but not for sleep quality. Differences are likely linked to freedom of movement of the wrist vs the trunk (hip) during overnight sleep.

Project description:Although accelerometry data are widely utilized to estimate physical activity and sedentary behavior among children age 3 years or older, for toddlers age 1 and 2 year(s), accelerometry data recorded during such behaviors have been far less examined. In particular, toddler's unique behaviors, such as riding in a stroller or being carried by an adult, have not yet been examined. The objective of this study was to describe accelerometry signal outputs recorded during participation in nine types of behaviors (i.e., running, walking, climbing up/down, crawling, riding a ride-on toy, standing, sitting, riding in a stroller/wagon, and being carried by an adult) among toddlers. Twenty-four toddlers aged 13 to 35 months (50% girls) performed various prescribed behaviors during free play in a commercial indoor playroom while wearing ActiGraph wGT3X-BT accelerometers on a hip and a wrist. Participants' performances were video-recorded. Based on the video data, accelerometer data were annotated with behavior labels to examine accelerometry signal outputs while performing the nine types of behaviors. Accelerometer data collected during 664 behavior assessments from the 21 participants were used for analysis. Hip vertical axis counts for walking were low (median = 49 counts/5 s). They were significantly lower than those recorded while a toddler was "carried" by an adult (median = 144 counts/5 s; p < 0.01). While standing, sitting, and riding in a stroller, very low hip vertical axis counts were registered (median ≤ 5 counts/5 s). Although wrist vertical axis and vector magnitude counts for "carried" were not higher than those for walking, they were higher than the cut-points for sedentary behaviors. Using various accelerometry signal features, machine learning techniques showed 89% accuracy to differentiate the "carried" behavior from ambulatory movements such as running, walking, crawling, and climbing. In conclusion, hip vertical axis counts alone may be unable to capture walking as physical activity and "carried" as sedentary behavior among toddlers. Machine learning techniques that utilize additional accelerometry signal features could help to recognize behavior types, especially to differentiate being "carried" from ambulatory movements.

Dataset Information

Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

Publications

Comparison of Sedentary Estimates between activPAL and Hip- and Wrist-Worn ActiGraph.

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