Abstract:The aim was to establish which postural and physical activity outcomes are comparable across different accelerometer brands worn on the thigh when processed using open-source methods. Twenty participants wore four accelerometers (Axivity, ActiGraph, activPAL, GENEActiv) for three free-living days. Postural and physical activity outputs (average acceleration, intensity gradient, intensity of the most active 30 min, 60 min, and 8 h) were generated. Postural outputs: Mean absolute percent errors (MAPEs) were low,… Show more
“…While these cut-points were developed using GENEActiv devices, the GENEActiv and Axivity accelerometers have shown excellent equivalence and agreement across most raw data outcome measures. 13 , 23 Furthermore, AvAcc (m g ), intensity gradient (IG), and the acceleration above which a person's most active 180, 60, 45, 30, 15, and 5 min were accumulated (MX metrics; m g ), are reported from GGIR output Part 2. The AvAcc is a direct measure of dynamic acceleration and is used as a single metric for the overall activity averaged per day.…”
Objective
: The coronavirus disease-2019 (COVID-19) pandemic and national lockdowns took away opportunities for children to be physically active. This study aimed to determine the effect of the COVID-19 lockdown on accelerometer-assessed physical activity (PA) in children in Wales.
Methods
: 800 participants (8–18 years old), stratified by sex, age, and socio-economic status, wore Axivity AX3 accelerometers for 7 days in February 2021, during the lockdown, and in May 2021, while in school. Raw accelerometer data were processed in R-package GGIR, and cut-point data, average acceleration (AvAcc), intensity gradient (IG), and MX metrics were extracted. Linear mixed models were used to assess the influence of time-point, sex, age, and SES on PA.
Results
: During lockdown, moderate-to-vigorous PA (MVPA) was 38.4 ± 24.3 min/day; sedentary time was 849.4 ± 196.6 min/day. PA levels increased significantly upon return to school (all variables
p
< 0.001). While there were no sex differences during lockdown (
p
= 0.233), girls engaged in significantly less MVPA than boys once back in school (
p
< 0.001). Furthermore, boys had more favorable intensity profiles than girls (IG:
p
< 0.001), regardless of time-point. PA levels decreased with age at both time-points; upper secondary school (USS) girls were the least active group, with an average M30 of 195.2 m
g
(while in school).
Conclusion
: The lockdown affected boys more than girls, as reflected by the disappearance of the typical sex difference in PA levels during lockdown, although these were re-established on return to school. USS (especially girls) might need specific COVID-recovery intervention.
“…While these cut-points were developed using GENEActiv devices, the GENEActiv and Axivity accelerometers have shown excellent equivalence and agreement across most raw data outcome measures. 13 , 23 Furthermore, AvAcc (m g ), intensity gradient (IG), and the acceleration above which a person's most active 180, 60, 45, 30, 15, and 5 min were accumulated (MX metrics; m g ), are reported from GGIR output Part 2. The AvAcc is a direct measure of dynamic acceleration and is used as a single metric for the overall activity averaged per day.…”
Objective
: The coronavirus disease-2019 (COVID-19) pandemic and national lockdowns took away opportunities for children to be physically active. This study aimed to determine the effect of the COVID-19 lockdown on accelerometer-assessed physical activity (PA) in children in Wales.
Methods
: 800 participants (8–18 years old), stratified by sex, age, and socio-economic status, wore Axivity AX3 accelerometers for 7 days in February 2021, during the lockdown, and in May 2021, while in school. Raw accelerometer data were processed in R-package GGIR, and cut-point data, average acceleration (AvAcc), intensity gradient (IG), and MX metrics were extracted. Linear mixed models were used to assess the influence of time-point, sex, age, and SES on PA.
Results
: During lockdown, moderate-to-vigorous PA (MVPA) was 38.4 ± 24.3 min/day; sedentary time was 849.4 ± 196.6 min/day. PA levels increased significantly upon return to school (all variables
p
< 0.001). While there were no sex differences during lockdown (
p
= 0.233), girls engaged in significantly less MVPA than boys once back in school (
p
< 0.001). Furthermore, boys had more favorable intensity profiles than girls (IG:
p
< 0.001), regardless of time-point. PA levels decreased with age at both time-points; upper secondary school (USS) girls were the least active group, with an average M30 of 195.2 m
g
(while in school).
Conclusion
: The lockdown affected boys more than girls, as reflected by the disappearance of the typical sex difference in PA levels during lockdown, although these were re-established on return to school. USS (especially girls) might need specific COVID-recovery intervention.
“…Both devices are worn on the midline of the anterior thigh between the hip and the knee joint, based on manufacturers' recommendations and previous validation studies [ 6 , 7 ]. Wearing accelerometers on the thigh has become a wear position of interest for the researchers because of its accuracy in measuring postural component of sedentary behaviours as well as active physical behaviours [ 8 ].…”
“…With recent technological advancements, tri-axial research grade accelerometers can provide users with the collected raw accelerometer data that can facilitate comparisons between devices using identical processing methods. Using open-source accelerometer processing and analyzing software such as GGIR, previous studies have examined the comparability of the same/different devices within and between body locations with promising findings for future data harmonization [18][19][20]. The widely used raw acceleration MVPA cut-point of 100 milli-gravitational units (mg) is often applied to raw acceleration accelerometer data to estimate time spent in MVPA, and to facilitate comparisons between devices [18,21].…”
Section: Introductionmentioning
confidence: 99%
“…One such approach that removes the reliance upon using proprietary algorithms from PAL Technologies Ltd. to process data collected from the activPAL device has recently been proposed [20]. As the activPAL device collects raw acceleration data across three axis, the raw data can be downloaded using PAL Technologies Ltd. freely available software and saved in raw format as .csv files, to be subsequently processed using the opensource software GGIR [20,26].…”
Section: Introductionmentioning
confidence: 99%
“…One such approach that removes the reliance upon using proprietary algorithms from PAL Technologies Ltd. to process data collected from the activPAL device has recently been proposed [20]. As the activPAL device collects raw acceleration data across three axis, the raw data can be downloaded using PAL Technologies Ltd. freely available software and saved in raw format as .csv files, to be subsequently processed using the opensource software GGIR [20,26]. Notwithstanding the obvious benefits of transparency and reproducibility for the research community when using GGIR, users also have the ability to adapt and expand the functionality of GGIR by specifying certain input arguments and/or selecting certain output variables.…”
The purpose of this study was to develop sedentary cut-points for the activPAL and evaluate their performance against a criterion measure (i.e., activPAL processed by PALbatch). Part 1: Thirty-five adults (23.4 ± 3.6 years) completed 12 laboratory activities (6 sedentary and 6 non-sedentary activities). Receiver operator characteristic (ROC) curves proposed optimal Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD) cut-points of 26.4 mg (ENMO) and 30.1 mg (MAD). Part 2: Thirty-eight adults (22.6 ± 4.1 years) wore an activPAL during free-living. Estimates from PALbatch and MAD revealed a mean percent error (MPE) of 2.2%, mean absolute percent error (MAPE) of 6.5%, limits of agreement (LoA) of 19% with absolute and relative equivalence zones of 5% and 0.3 SD. Estimates from PALbatch and ENMO revealed an MPE of −10.6%, MAPE of 14.4%, LoA of 31% and 16% and 1 SD equivalence zones. After standing was isolated from sedentary behaviours, ROC analysis proposed an optimal cut-off of 21.9 mg (herein ENMOs). Estimates from PALbatch and ENMOs revealed an MPE of 3.1%, MAPE of 7.5%, LoA of 25% and 9% and 0.5 SD equivalence zones. The MAD and ENMOs cut-points performed best in discriminating between sedentary and non-sedentary activity during free-living.
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