DESIGN: Cross-sectional validation study.
METHODS: We used data involving 3- and 4-year-olds from 13 middle- and high-income countries who participated in the SUNRISE study. We used Spearman's rank-order correlation, Bland-Altman plots, and Kappa statistics to validate parent-reported child habitual total physical activity against activPAL™-measured total physical activity over 3 days. Additionally, we used Receiver Operating Characteristic Area Under the Curve analysis to validate existing step-count thresholds (Gabel, Vale, and De Craemer) using step-counts derived from activPAL™.
RESULTS: Of the 352 pre-schoolers, 49.1 % were girls. There was a very weak but significant positive correlation and slight agreement between parent-reported total physical activity and accelerometer-measured total physical activity (r: 0.140; p = 0.009; Kappa: 0.030). Parents overestimated their child's total physical activity compared to accelerometry (mean bias: 69 min/day; standard deviation: 126; 95 % limits of agreement: -179, 316). Of the three step-count thresholds tested, the De Craemer threshold of 11,500 steps/day provided excellent classification of meeting the total physical activity guideline as measured by accelerometry (area under the ROC curve: 0.945; 95 % confidence interval: 0.928, 0.961; sensitivity: 100.0 %; specificity: 88.9 %).
CONCLUSIONS: Parent reports may have limited validity for assessing pre-schoolers' level of total physical activity. Step-counting is a promising alternative - low-cost global surveillance initiatives could potentially use pedometers for assessing compliance with the physical activity guideline in early childhood.
OBJECTIVE: This study aimed to investigate the validity of HR measures of a high-cost consumer-based tracker (Polar A370) and a low-cost tracker (Tempo HR) in the laboratory and free-living settings.
METHODS: Participants underwent a laboratory-based cycling protocol while wearing the two trackers and the chest-strapped Polar H10, which acted as criterion. Participants also wore the devices throughout the waking hours of the following day during which they were required to conduct at least one 10-min bout of moderate-to-vigorous physical activity (MVPA) to ensure variability in the HR signal. We extracted 10-second values from all devices and time-matched HR data from the trackers with those from the Polar H10. We calculated intraclass correlation coefficients (ICCs), mean absolute errors, and mean absolute percentage errors (MAPEs) between the criterion and the trackers. We constructed decile plots that compared HR data from Tempo HR and Polar A370 with criterion measures across intensity deciles. We investigated how many HR data points within the MVPA zone (≥64% of maximum HR) were detected by the trackers.
RESULTS: Of the 57 people screened, 55 joined the study (mean age 30.5 [SD 9.8] years). Tempo HR showed moderate agreement and large errors (laboratory: ICC 0.51 and MAPE 13.00%; free-living: ICC 0.71 and MAPE 10.20%). Polar A370 showed moderate-to-strong agreement and small errors (laboratory: ICC 0.73 and MAPE 6.40%; free-living: ICC 0.83 and MAPE 7.10%). Decile plots indicated increasing differences between Tempo HR and the criterion as HRs increased. Such trend was less pronounced when considering the Polar A370 HR data. Tempo HR identified 62.13% (1872/3013) and 54.27% (5717/10,535) of all MVPA time points in the laboratory phase and free-living phase, respectively. Polar A370 detected 81.09% (2273/2803) and 83.55% (9323/11,158) of all MVPA time points in the laboratory phase and free-living phase, respectively.
CONCLUSIONS: HR data from the examined wrist-worn trackers were reasonably accurate in both the settings, with the Polar A370 showing stronger agreement with the Polar H10 and smaller errors. Inaccuracies increased with increasing HRs; this was pronounced for Tempo HR.
METHODS: Data came from the South-East Asia Community Observatory health surveillance cohort, 2021-2022. Children aged 7-18 years within selected households in Segamat, Malaysia wore an Axivity AX6 accelerometer on their wrist for 24 hours/day over 7 days, completed the PAQ-C questionnaire, and demographic information was obtained. Accelerometer data was processed using GGIR to determine time spent asleep, inactive, in light-intensity PA (LPA) and moderate-to-vigorous PA (MVPA). Differences in accelerometer-measured PA by demographic characteristics (sex, age, ethnicity, socioeconomic group) were explored using univariate linear regression. Differences between data collected during vs outside Ramadan or during vs after COVID-19 restrictions, were investigated through univariate and multiple linear regressions, adjusted for age, sex and ethnicity.
RESULTS: The 491 participants providing accelerometer data spent 8.2 (95% confidence interval (CI) = 7.9-8.4) hours/day asleep, 12.4 (95% CI = 12.2-12.7) hours/day inactive, 2.8 (95% CI = 2.7-2.9) hours/day in LPA, and 33.0 (95% CI = 31.0-35.1) minutes/day in MVPA. Greater PA and less time inactive were observed in boys vs girls, children vs adolescents, Indian and Chinese vs Malay children and higher income vs lower income households. Data collection during Ramadan or during COVID-19 restrictions were not associated with MVPA engagement after adjustment for demographic characteristics.
CONCLUSIONS: Demographic characteristics remained the strongest correlates of accelerometer-measured 24-hour movement behaviours in Malaysian children and adolescents. Future studies should seek to understand why predominantly girls, adolescents and children from Malay ethnicities have particularly low movement behaviours within Malaysia.
PURPOSE: To examine the influence of physical activity (PA) and sedentary time on bone strength, structure, and density in older adolescents.
METHODS: We used peripheral quantitative computed tomography to estimate bone strength at the distal tibia (8% site; bone strength index, BSI) and tibial midshaft (50% site; polar strength strain index, SSIp) in adolescent boys (n = 86; 15.3 ± 0.4 years) and girls (n = 106; 15.3 ± 0.4 years). Using accelerometers (GT1M, Actigraph), we measured moderate-to-vigorous PA (MVPAAccel), vigorous PA (VPAAccel), and sedentary time in addition to self-reported MVPA (MVPAPAQ-A) and impact PA (ImpactPAPAQ-A). We examined relations between PA and sedentary time and bone outcomes, adjusting for ethnicity, maturity, tibial length, and total body lean mass.
RESULTS: At the distal tibia, MVPAAccel and VPAAccel positively predicted BSI (explained 6-7% of the variance, p
METHODS: A total of 381 children (mean age 9.7 [1.6] y, 57% girls) provided 24-hour wrist-worn GENEActiv accelerometry data which captured time spent for sleep, SB, light PA and moderate to vigorous PA (MVPA). Indicators of adiposity were derived from anthropometry and bioelectrical impedance analysis: body-mass-index-for-age, waist circumference, waist-to-height ratio, percent body fat, and body mass index. The composition of 4-part movement behaviors was expressed as isometric log-ratio coordinates which were entered into regression models. Isotemporal substitution analysis was used to assess changes in adiposity indicators when reallocating time between movement behaviors.
RESULTS: Relative to other movement behaviors, time spent on MVPA was significantly associated with waist circumference, waist-to-height ratio, percent body fat, and fat mass index. A 15-minute one-to-one reallocation from other movement behaviors to MVPA predicted lower body-mass-index-for-age (-0.03 to -0.11), smaller waist circumference (-0.67 to -1.28 cm), lower waist-to-height ratio (-0.004 to -0.008), percent body fat (-0.87% to -1.47%), and fat mass index (-0.23 to -0.42). Replacing SB and light PA with sleep or MVPA was associated with lower adiposity.
CONCLUSIONS: The overall composition of movement behavior was significantly associated with the adiposity of Malaysian schoolchildren. Promoting MVPA and sleep and reducing SB and light PA are important for prevention of childhood obesity.