Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods

Buchan, Duncan S.; McLellan, Gillian

doi:10.1080/02640414.2018.1527198

Cited by 14 publications

(16 citation statements)

References 44 publications

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“…However, AG rather seems to overestimate some epochs. These results are in line with previous research where less MVPA has been captured using an unfiltered acceleration metric compared to AG [33]. Although less MVPA was captured, the amount captured seems to reflect the actual PA better.…”

Section: Discussionsupporting

confidence: 91%

Effects of Frequency Filtering on Intensity and Noise in Accelerometer-Based Physical Activity Measurements

Fridolfsson

Börjesson

Buck

et al. 2019

Sensors

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In objective physical activity (PA) measurements, applying wider frequency filters than the most commonly used ActiGraph (AG) filter may be beneficial when processing accelerometry data. However, the vulnerability of wider filters to noise has not been investigated previously. This study explored the effect of wider frequency filters on measurements of PA, sedentary behavior (SED), and capturing of noise. Apart from the standard AG band-pass filter (0.29–1.63 Hz), modified filters with low-pass component cutoffs at 4 Hz, 10 Hz, or removed were analyzed. Calibrations against energy expenditure were performed with lab data from children and adults to generate filter-specific intensity cut-points. Free-living accelerometer data from children and adults were processed using the different filters and intensity cut-points. There was a contribution of acceleration related to PA at frequencies up to 10 Hz. The contribution was more pronounced at moderate and vigorous PA levels, although additional acceleration also occurred at SED. The classification discrepancy between AG and the wider filters was small at SED (1–2%) but very large at the highest intensities (>90%). The present study suggests an optimal low-pass frequency filter with a cutoff at 10 Hz to include all acceleration relevant to PA with minimal effect of noise.

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Section: Discussionsupporting

confidence: 91%

Effects of Frequency Filtering on Intensity and Noise in Accelerometer-Based Physical Activity Measurements

Fridolfsson

Börjesson

Buck

et al. 2019

Sensors

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“…There are reviews that may guide researchers towards the best methodology available , but the choice of method should also be based on the measure of health to be related to. The outcome is highly dependent on what methodology is used, although it might be presented the same way . Therefore, the results of studies with different methodology should be compared with caution.…”

Section: Section 1: Backgroundmentioning

confidence: 99%

Measurement of physical activity in clinical practice using accelerometers

2019

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Accelerometers are commonly used in clinical and epidemiological research for more detailed measures of physical activity and to target the limitations of self‐report methods. Sensors are attached at the hip, wrist and thigh, and the acceleration data are processed and calibrated in different ways to determine activity intensity, body position and/or activity type. Simple linear modelling can be used to assess activity intensity from hip and thigh data, whilst more advanced machine‐learning modelling is to prefer for the wrist. The thigh position is most optimal to assess body position and activity type using machine‐learning modelling. Frequency filtering and measurement resolution needs to be considered for correct assessment of activity intensity. Simple physical activity measures and statistical methods are mostly used to investigate relationship with health, but do not take advantage of all information provided by accelerometers and do not consider all components of the physical activity behaviour and their interrelationships. More advanced statistical methods are suggested that analyse patterns of multiple measures of physical activity to demonstrate stronger and more specific relationships with health. However, evaluations of accelerometer methods show considerable measurement errors, especially at individual level, which interferes with their use in clinical research and practice. Therefore, better objective methods are needed with improved data processing and calibration techniques, exploring both simple linear and machine‐learning alternatives. Development and implementation of accelerometer methods into clinical research and practice requires interdisciplinary collaboration to cover all aspects contributing to useful and accurate measures of physical activity behaviours related to health.

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“…The cut‐points by Butte et al 10 provide PA intensities based on the VMCounts measurements in preschool‐aged children. The more recently developed ENMO measurements offer an open‐source method increasing comparability between studies, and the cut‐points by Hildebrand et al 13,14 have been previously used in children and provide the most reference data 6,19,22 . A previous study in preschoolers 7 reported that cut‐points by Butte et al 10 led to less sedentary time (SED) and more light PA and MVPA compared to estimates using cut‐points by Janssen et al 23 based on VACounts.…”

Section: Introductionmentioning

confidence: 99%

Comparing estimates of physical activity in children across different cut‐points and the associations with weight status

Leppänen

Migueles

Abdollahi

et al. 2022

Scandinavian Med Sci Sports

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This study aimed to compare sedentary time (SED) and intensity-specific physical activity (PA) estimates and the associations of SED and PA with body mass index (BMI) and waist circumference (WC) using three different sets of cut-points in preschool-aged children. A total of 751 children (4.7 ± 0.9 years, boys 52.7%) wore an ActiGraph GT3X+BT accelerometer on their hip for 7 days (24 h). Euclidean norm −1 G with negative values rounded to zero (ENMO) and activity counts from vertical axis (VACounts) and vector magnitude (VMCounts) were derived. Estimates of SED and light, moderate, vigorous, and moderate-to-vigorous PA (MVPA) were calculated for commonly used cut-points by Hildebrand et al., Butte et al., and Evenson et al. Furthermore, the prevalence of meeting the PA recommendation, 180 min/day of which at least 60 min/day being MVPA, were assessed for the cut-points. Multilevel mixed analysis was used to examine associations of SED and PA with BMI and WC. In accordance with the results, SED and PA intensity estimates differed largely across cut-points (i.e., SED = 22-341 min/day; light PA = 52-257 min/day; moderate PA = 5-18 min/day; vigorous PA = 7-17 min/day; MVPA = 13-35 min/day), and the prevalence of children meeting the PA recommendation varied from 4% to 70%. Associations of SED and PA with BMI or WC varied between the cut-points. Our results indicate that SED and PA estimates in preschool-aged children between studies using these This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts based processing methods

Cited by 14 publications

References 44 publications

Effects of Frequency Filtering on Intensity and Noise in Accelerometer-Based Physical Activity Measurements

Effects of Frequency Filtering on Intensity and Noise in Accelerometer-Based Physical Activity Measurements

Measurement of physical activity in clinical practice using accelerometers

Comparing estimates of physical activity in children across different cut‐points and the associations with weight status

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