Estimating Physical Activity in Youth Using a Wrist Accelerometer

Crouter, Scott E.; Flynn, Jennifer; Bassett, David R.

doi:10.1249/mss.0000000000000502

Cited by 120 publications

(159 citation statements)

References 19 publications

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“…Furthermore, the AREA optimal wrist cut-off points for each wrist performed similarly in these data. Several studies in children and adolescents aimed to estimate sedentary behavior from ActiGraph wrist accelerometer data (4, 10, 11). However, currently, there are no accepted cut-off points to determine sedentary behavior for the ActiGraph wrist for adult populations.…”

Section: Discussionmentioning

confidence: 99%

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

Koster

Shiroma

Caserotti

et al. 2016

Medicine &Amp; Science in Sports &Amp; Exercise

125

133

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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 years) of the Aging Research Evaluating Accelerometry (AREA) study were used. Participants simultaneously wore an activPAL accelerometer on the thigh and ActiGraph accelerometers on the hip, dominant, and nondominant wrist for 7 days 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 cut-off points for both 60-second and 15-second epochs. Results The optimal cut-off point for the hip vertical axis was <22 counts per minute (cpm) with an area under the curve (AUC) of 0.85; the optimal hip vector magnitude cut-off point was <174 cpm with an AUC of 0.89. For the dominant wrist, the optimal vector magnitude cut-off point to define sedentary time was <2303 cpm (AUC: 0.86) and for the nondominant wrist <1853 cpm (AUC: 0.86). The optimal 15s cutoff points results 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 to activPAL. The observed optimal cut-off point for hip vertical axis <22 cpm is substantially lower than the standard <100 cpm. It is unknown how these optimal cut-off points perform in different populations. Results on an individual basis should therefore be interpreted with caution.

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Section: Discussionmentioning

confidence: 99%

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

Koster

Shiroma

Caserotti

et al. 2016

Medicine &Amp; Science in Sports &Amp; Exercise

125

133

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“…At this time, there have been limited validation studies using wrist-placed Actigraph accelerometers in children, and the intensity cutpoint used in the current investigation to define MVPA has only been validated by a single study. 15 Eventual standardization of accelerometer measurements and comparisons across placement sites would be a significant advancement for this field of research.…”

Section: Discussionmentioning

confidence: 99%

“…15 These cutpoints were chosen because they yielded better classification accuracy of activity intensity in previous studies. 16, 17 …”

Section: Methodsmentioning

confidence: 99%

Accelerometer-determined physical activity and the cardiovascular response to mental stress in children

Heffernan

Dumas

Gump

2017

Journal of Science and Medicine in Sport

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Objectives Cardiovascular reactivity has been associated with future hypertension and cardiovascular mortality. Higher physical activity (PA) has been associated with lower cardiovascular reactivity in adults, but little data is available in children. The purpose of this study was to examine the relationship between PA and cardiovascular reactivity to mental stress in children. Design Cross-sectional study. Methods This study sample included children from the Oswego Lead Study (n=79, 46% female, 9-11 years old). Impedance cardiography was performed while children participated in a stress response protocol. Children were also asked to wear Actigraph accelerometers on their wrists for 3 days to measure intensity and duration of PA and sedentary time. Results In multivariable models, moderate to vigorous (MV) PA was associated with lower body mass index (BMI) percentile and lower total peripheral resistance (TPR) response to stress (beta = −0.025, p = 0.02; beta = −0.009, p = 0.05). After additional adjustment for BMI, MVPA was also associated with lower diastolic blood pressure response to stress (beta = −0.01, p = 0.03). Total PA and sedentary time were not associated with BMI or cardiovascular responses to stress. Conclusions A modest, inverse relation of PA to vascular reactivity to mental stress was observed in children. These data provide confirmatory evidence that the promotion of PA recommendations for children are important for cardiovascular health.

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“…Crouter et al (5) have developed wrist cut-points for youths. One modification to the hip accelerometer activity count linear regression modeling was developed by Crouter et al (4,6) where activity counts were directed to one of two regressions models based on the variability in the activity counts.…”

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confidence: 99%

Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements

Staudenmayer

Hickey

et al. 2015

Journal of Applied Physiology

120

117

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This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high-frequency wrist-worn accelerometer data. The models were developed and tested on 20 participants (n = 10 males, n = 10 females, mean age = 24.1, mean body mass index = 23.9), who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip while performing a variety of scripted activities. Energy expenditure was concurrently measured by a portable indirect calorimetry system. Those calibration data were then used to develop and assess both machine-learning and simpler models with fewer unknown parameters (linear regression and decision trees) to estimate metabolic equivalent scores (METs) and to classify activity intensity, sedentary time, and locomotion time. The wrist models, applied to 15-s windows, estimated METs [random forest: root mean squared error (rSME) = 1.21 METs, hip: rMSE = 1.67 METs] and activity intensity (random forest: 75% correct, hip: 60% correct) better than a previously developed model that used counts per minute measured at the hip. In a separate set of comparisons, the simpler decision trees classified activity intensity (random forest: 75% correct, tree: 74% correct), sedentary time (random forest: 96% correct, decision tree: 97% correct), and locomotion time (random forest: 99% correct, decision tree: 96% correct) nearly as well or better than the machine-learning approaches. Preliminary investigation of the models' performance on two free-living people suggests that they may work well outside of controlled conditions.

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Estimating Physical Activity in Youth Using a Wrist Accelerometer

Cited by 120 publications

References 19 publications

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

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

Accelerometer-determined physical activity and the cardiovascular response to mental stress in children

Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements

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