Validity and Reliability of the Global Physical Activity Questionnaire (GPAQ)

There is limited published validity and reliability evidence to support using the Global Physical Activity Questionnaire. Two studies were conducted to evaluate validity and reliability evidence for the Global Physical Activity Questionnaire. In Study 1: 69 adults completed the Global Physical Activity Questionnaire (three months apart; n?=?54), International Physical Activity Questionnaire, fitness and anthropometric measures. All participants wore a pedometer and 53 participants wore an accelerometer for seven days at baseline. In Study 2, 16 adults completed the Global Physical Activity Questionnaire 10 days apart. Global Physical Activity Questionnaire moderate and vigorous minutes were correlated with the accelerometer moderate (r?=?0.28) and vigorous (r?=?0.48) physical activity. The Global Physical Activity Questionnaire and International Physical Activity Questionnaire were related for sedentary behaviors (r?=?0.51), moderate-to-vigorous (r?=?0.48) and vigorous (r?=?0.63) PA. Global Physical Activity Questionnaire moderate-to-vigorous physical activity was associated with percent fat (r =??0.32), estimated VO₂ max (r?=?0.26), and step count (r?=?0.39). The Global Physical Activity Questionnaire demonstrated graded differences across categories for step count, body mass index, waist circumference, percent fat, fitness, and accelerometer measured activity. Short-term test–retest reliability (10 days) ranged from 0.83 to 0.96 while long-term reliability (three months) was 0.53 to 0.83. These data provide low-to-moderate validity and generally acceptable reliability evidence for the Global Physical Activity Questionnaire.     

Improving energy expenditure estimates from wearable devices: A machine learning approach

ABSTRACT

A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.     

加速度计应用于体育训练的探讨

主要从运动员的体能训练及运动器械制作等方面,就加速计的应用进行阐述和探讨。     

The translation of preschoolers’ physical activity guidelines into a daily step count target

This study aimed at translating the physical activity (PA) guideline (180 min of total PA per day) into a step count target in preschoolers. 535 Flemish preschoolers (mean age: 4.41 ± 0.58) wore an ActiGraph accelerometer (GT1M, GT3X and GT3X+) – with activated step count function – for four consecutive days. The step count target was calculated from the accelerometer output using a regression equation, applying four different cut-points for light-to-vigorous PA: Pate, Evenson, Reilly, and Van Cauwenberghe. The present analysis showed that 180 min of total PA per day is equivalent to the following step count targets: 5,274 steps/day using the Pate cut-point, 4,653 steps/day using the Evenson cut-point, 11,379 steps/day using the Reilly cut-point and 13,326 steps/day using the Van Cauwenberghe cut-point. Future studies should focus on achieving consensus on which cut-points to use in preschoolers before a definite step count target in preschoolers can be proposed. Until then, we propose to use a provisional step count target of 11,500 steps/day as this step count target is attainable, realistic and helpful in promoting preschoolers’ PA.     

Physical activity levels during youth sport practice: does coach training or experience have an influence?

This study examined moderate-to-vigorous physical activity (MVPA) levels in youth during flag football practice and compared youth MVPA in practices led by trained or untrained, and by experienced or inexperienced, coaches. Boys (n = 111, mean age = 7.9 ± 1.2 years) from 14 recreation-level flag football teams wore an accelerometer during two practices. Each team’s volunteer head coach reported prior training and coaching experience. Mixed-model team-adjusted means showed the proportion of practice time spent in sedentary (13 ± 1%), MVPA (34 ± 2%) and vigorous (12 ± 1%) activity. Practice contributed ~20 min of MVPA towards public health guidelines. There was no significant difference in percentage time spent in MVPA between teams with trained (mean = 33.3%, 95% CI = 29.4%, 37.2%) and untrained coaches (mean = 35.9%, 95% CI = 25.5%, 42.4%) or between experienced (mean = 34.1%, 95% CI = 30.2%, 38.0%) and inexperienced coaches (mean = 33.8, 95% CI = 27.9%, 39.7%). Although sport provides a setting for youth to accrue MVPA, two-thirds of practice was spent sedentarily or in light activity. Participation in a coach training programme was not associated with higher MVPA. Further research is needed to inform volunteer coach training programmes that provide coaches with skills necessary to increase the percentage of practice time spent in MVPA.     

Comparison of children’s free-living physical activity derived from wrist and hip raw accelerations during the segmented week

This study assessed children’s physical activity (PA) levels derived from wrist-worn GENEActiv and hip-worn ActiGraph GT3X+ accelerometers and examined the comparability of PA levels between the two devices throughout the segmented week. One hundred and twenty-nine 9–10-year-old children (79 girls) wore a GENEActiv (GAwrist) and ActiGraph GT3X+ (AGhip) accelerometer on the left wrist and right hip, respectively, for 7 days. Mean minutes of light PA (LPA) and moderate-to-vigorous PA (MVPA) per weekday (whole-day, before-school, school and after-school) and weekend day (whole-day, morning and afternoon–evening) segments were calculated, and expressed as percentage of segment time. Repeated measures analysis of variance examined differences in LPA and MVPA between GAwrist and AGhip for each time segment. Bland–Altman plots assessed between-device agreement for LPA and MVPA for whole weekday and whole weekend day segments. Correlations between GAwrist and AGhip were weak for LPA (r = 0.18–0.28), but strong for MVPA (r = 0.80–0.86). LPA and MVPA levels during all weekday and weekend day segments were significantly higher for GAwrist than AGhip (p < 0.001). The largest inter-device percent difference of 26% was observed in LPA during the school day segment. Our data suggest that correction factors are needed to improve raw PA level comparability between GAwrist and AGhip.     

Validity of SenseWear® Armband v5.2 and v2.2 for estimating energy expenditure

We compared SenseWear Armband versions (v) 2.2 and 5.2 for estimating energy expenditure in healthy adults. Thirty-four adults (26 women), 30.1 ± 8.7 years old, performed two trials that included light-, moderate- and vigorous-intensity activities: (1) structured routine: seven activities performed for 8-min each, with 4-min of rest between activities; (2) semi-structured routine: 12 activities performed for 5-min each, with no rest between activities. Energy expenditure was measured by indirect calorimetry and predicted using SenseWear v2.2 and v5.2. Compared to indirect calorimetry (297.8 ± 54.2 kcal), the total energy expenditure was overestimated (P < 0.05) by both SenseWear v2.2 (355.6 ± 64.3 kcal) and v5.2 (342.6 ± 63.8 kcal) during the structured routine. During the semi-structured routine, the total energy expenditure for SenseWear v5.2 (275.2 ± 63.0 kcal) was not different than indirect calorimetry (262.8 ± 52.9 kcal), and both were lower (P < 0.05) than v2.2 (312.2 ± 74.5 kcal). The average mean absolute per cent error was lower for the SenseWear v5.2 than for v2.2 (P < 0.001). SenseWear v5.2 improved energy expenditure estimation for some activities (sweeping, loading/unloading boxes, walking), but produced larger errors for others (cycling, rowing). Although both algorithms overestimated energy expenditure as well as time spent in moderate-intensity physical activity (P < 0.05), v5.2 offered better estimates than v2.2.     

The validity of the commercially-available,low-cost,wrist-worn Movband accelerometer during treadmill exercise and free-living physical activity

Low-cost physical activity monitors may provide a more accurate measure of physical activity than subjective methods (e.g., self-report) while being less costly than research-grade accelerometers. The present study assessed the validity of a low-cost monitor (Movband 2) to estimate physical activity behavior/intensity. Participants (N = 23, n = 16 female, BMI = 22.9 ± 4.0 kg/m², age = 21.9 ± 1.6 years) completed four, five-minute treadmill stages (2.0, 3.0, 4.0, 5.0 MPH) while wearing both the Movband and the previously-validated Actigraph monitor. Oxygen consumption (VO₂) was recorded during each stage (Laboratory assessment). A subset (n = 15, n = 10 female, BMI = 22.2 ± 3.2 kg/m², age = 21.5 ± 0.8 years) of these participants then wore the two accelerometers for three days (Free-living assessment). During the Laboratory assessment there were strong, significant (r = 0.94, p ≤ 0.001) relationships between Movband and Actigraph counts and VO₂. During Free-living assessment there was also a strong, significant (r = 0.97, p < 0.001) correlation between Movband and Actigraph counts. The low-cost, Movband accelerometer appears to provide a valid assessment of physical activity behavior/intensity.     

Effects of treadmill running and fatigue on impact acceleration in distance running

The effects of treadmill running on impact acceleration were examined together with the interaction between running surface and runner's fatigue state. Twenty recreational runners (11 men and 9 women) ran overground and on a treadmill (at 4.0 m/s) before and after a fatigue protocol consisting of a 30-minute run at 85% of individual maximal aerobic speed. Impact accelerations were analysed using two lightweight capacitive uniaxial accelerometers. A two-way repeated-measure analysis of variance showed that, in the pre-fatigue condition, the treadmill running decreased head and tibial peak impact accelerations and impact rates (the rate of change of acceleration), but no significant difference was observed between the two surfaces in shock attenuation. There was no significant difference in acceleration parameters between the two surfaces in the post-fatigue condition. There was a significant interaction between surface (treadmill and overground) and fatigue state (pre-fatigue and post-fatigue). In particular, fatigue when running overground decreased impact acceleration severity, but it had no such effect when running on the treadmill. The effects of treadmill running and the interaction need to be taken into account when interpreting the results of studies that use a treadmill in their experimental protocols, and when prescribing physical exercise.     

Seasonal variation in physical activity and sedentary time in different European regions. The HELENA study

Abstract

This report aims (1) to examine the association between seasonality and physical activity (PA) and sedentary time in European adolescents and (2) to investigate whether this association was influenced by geographical location (Central-North versus South of Europe), which implies more or less extreme weather and daylight hours. Valid data on PA, sedentary time and seasonality were obtained in 2173 adolescents (1175 females; 12.5–17.5 years) included in this study. Physical activity and sedentary time were measured by accelerometers. ANCOVA was conducted to analyse the differences in PA and sedentary time across seasons. Results showed that girls had lower levels of moderate to vigorous PA (MVPA) and average PA, and spent more time in sedentary activities in winter compared with spring (all P < 0.05). Stratified analyses showed differences in PA and sedentary time between winter and spring in European girls from Central-North of Europe (P < 0.05 for sedentary time). There were no differences between PA and sedentary time across seasonality in boys. In conclusion, winter is related with less time spent in MVPA, lower average PA and higher time spent in sedentary activities in European adolescent girls, compared with spring. These differences seem to mainly occur in Central-North Europe.