The minimal physical activity intensity that would confer health benefits among adolescents is unknown. The purpose of this study was to examine the associations of accelerometer-derived ...light-intensity (split into low and high) physical activity, and moderate- to vigorous-intensity physical activity with cardiometabolic biomarkers in a large population-based sample.
The study is based on 1,731 adolescents, aged 12-19 years from the 2003/04 and 2005/06 National Health and Nutrition Examination Survey. Low light-intensity activity (100-799 counts/min), high light-intensity activity (800 counts/min to <4 METs) and moderate- to vigorous-intensity activity (≥ 4 METs, Freedson age-specific equation) were accelerometer-derived. Cardiometabolic biomarkers, including waist circumference, systolic blood pressure, diastolic blood pressure, HDL-cholesterol, and C-reactive protein were measured. Triglycerides, LDL- cholesterol, insulin, glucose, and homeostatic model assessments of β-cell function (HOMA-%B) and insulin sensitivity (HOMA-%S) were also measured in a fasting sub-sample (n=807).
Adjusted for confounders, each additional hour/day of low light-intensity activity was associated with 0.59 (95% CI: 1.18-0.01) mmHG lower diastolic blood pressure. Each additional hour/day of high light-intensity activity was associated with 1.67 (2.94-0.39) mmHG lower diastolic blood pressure and 0.04 (0.001-0.07) mmol/L higher HDL-cholesterol. Each additional hour/day of moderate- to vigorous-intensity activity was associated with 3.54 (5.73-1.35) mmHG lower systolic blood pressure, 5.49 (1.11-9.77)% lower waist circumference, 25.87 (6.08-49.34)% lower insulin, and 16.18 (4.92-28.53)% higher HOMA-%S.
Time spent in low light-intensity physical activity and high light-intensity physical activity had some favorable associations with biomarkers. Consistent with current physical activity recommendations for adolescents, moderate- to vigorous-intensity activity had favorable associations with many cardiometabolic biomarkers. While increasing MVPA should still be a public health priority, further studies are needed to identify dose-response relationships for light-intensity activity thresholds to inform future recommendations and interventions for adolescents.
Total volumes of physical activity and sedentary behaviour have been associated with cardio-metabolic risk profiles; however, little research has examined whether patterns of activity (e.g., ...prolonged bouts, frequency of breaks in sitting) impact cardio-metabolic risk. The aim of this review was to synthesise the evidence concerning associations between activity patterns and cardio-metabolic risk factors in children and adolescents aged 5-19 years.
A systematic search of seven databases was completed in October 2017. Included studies were required to report associations between objectively-measured activity patterns and cardio-metabolic risk factors in children and/or adolescents, and be published between 1980 and 2017. At least two researchers independently screened each study, extracted data, and undertook risk of bias assessments.
From the 15,947 articles identified, 29 were included in this review. Twenty-four studies were observational (cross-sectional and/or longitudinal); five were experimental. Ten studies examined physical activity patterns, whilst 19 studies examined sedentary patterns. Only one study examined both physical activity and sedentary time patterns. Considerable variation in definitions of activity patterns made it impossible to identify which activity patterns were most beneficial to children's and adolescents' cardio-metabolic health. However, potential insights and current research gaps were identified.
A consensus on how to define activity patterns is needed in order to determine which activity patterns are associated with children's and adolescents' cardio-metabolic risk. This will inform future research on the impact of activity patterns on children's and adolescents' short- and longer-term health.
Background
Gross motor competence confers health benefits, but levels in children and adolescents are low. While interventions can improve gross motor competence, it remains unclear which correlates ...should be targeted to ensure interventions are most effective, and for whom targeted and tailored interventions should be developed.
Objective
The aim of this systematic review was to identify the potential correlates of gross motor competence in typically developing children and adolescents (aged 3–18 years) using an ecological approach.
Methods
Motor competence was defined as gross motor skill competency, encompassing fundamental movement skills and motor coordination, but excluding motor fitness. Studies needed to assess a summary score of at least one aspect of motor competence (i.e., object control, locomotor, stability, or motor coordination). A structured electronic literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Six electronic databases (CINAHL Complete, ERIC, MEDLINE Complete, PsycINFO
®
, Scopus and SPORTDiscus with Full Text) were searched from 1994 to 5 August 2014. Meta-analyses were conducted to determine the relationship between potential correlates and motor competency if at least three individual studies investigated the same correlate and also reported standardized regression coefficients.
Results
A total of 59 studies were identified from 22 different countries, published between 1995 and 2014. Studies reflected the full range of age groups. The most examined correlates were biological and demographic factors. Age (increasing) was a correlate of children’s motor competence. Weight status (healthy), sex (male) and socioeconomic background (higher) were consistent correlates for certain aspects of motor competence only. Physical activity and sport participation constituted the majority of investigations in the behavioral attributes and skills category. Whilst we found physical activity to be a positive correlate of skill composite and motor coordination, we also found indeterminate evidence for physical activity being a correlate of object control or locomotor skill competence. Few studies investigated cognitive, emotional and psychological factors, cultural and social factors or physical environment factors as correlates of motor competence.
Conclusion
This systematic review is the first that has investigated correlates of gross motor competence in children and adolescents. A strength is that we categorized correlates according to the specific ways motor competence has been defined and operationalized (object control, motor coordination, etc.), which enables us to have an understanding of what correlates assist what types of motor competence. Indeed our findings do suggest that evidence for some correlates differs according to how motor competence is operationalized.
Variability exists in children's activity patterns due to the association with environmental, social, demographic, and inter-individual factors. This study described accelerometer assessed physical ...activity patterns of high and low active children during segmented school week days whilst controlling for potential correlates.
Two hundred and twenty-three children (mean age: 10.7 ± 0.3 yrs, 55.6% girls, 18.9% overweight/obese) from 8 north-west England primary schools wore ActiGraph GT1M accelerometers for 7 consecutive days during autumn of 2009. ActiGraph counts were converted to minutes of moderate (MPA), vigorous (VPA) and moderate-to-vigorous (MVPA) physical activity. Children were classified as high active (HIGH) or low active (LOW) depending on the percentage of week days they accumulated at least 60 minutes of MVPA. Minutes spent in MPA and VPA were calculated for school time and non-school time and for five discrete school day segments (before-school, class time, recess, lunchtime, and after-school). Data were analysed using multi-level modelling.
The HIGH group spent significantly longer in MPA and/or VPA before-school, during class time, lunchtime, and after-school (P < .05), independent of child and school level factors. The greatest differences occurred after-school (MPA = 5.5 minutes, VPA = 3.8 minutes, P < 0.001). MPA and VPA were also associated with gender, BMI z-score, number of enrolled children, playground area per student, and temperature, depending on the segment analysed.The additive effect of the segment differences was that the HIGH group accumulated 12.5 minutes per day more MVPA than the LOW group.
HIGH active children achieved significantly more MPA and VPA than LOW active during four of the five segments of the school day when analyses were adjusted for potential correlates. Physical activity promotion strategies targeting low active children during discretionary physical activity segments of the day, and particularly via structured afterschool physical activity programs may be beneficial.
Government responses to managing the COVID-19 pandemic may have impacted the way individuals were able to engage in physical activity. Digital platforms are a promising way to support physical ...activity levels and may have provided an alternative for people to maintain their activity while at home.
This study aimed to examine associations between the use of digital platforms and adherence to the physical activity guidelines among Australian adults and adolescents during the COVID-19 stay-at-home restrictions in April and May 2020.
A national online survey was distributed in May 2020. Participants included 1188 adults (mean age 37.4 years, SD 15.1; 980/1188, 82.5% female) and 963 adolescents (mean age 16.2 years, SD 1.2; 685/963, 71.1% female). Participants reported demographic characteristics, use of digital platforms for physical activity over the previous month, and adherence to moderate- to vigorous-intensity physical activity (MVPA) and muscle-strengthening exercise (MSE) guidelines. Multilevel logistic regression models examined differences in guideline adherence between those who used digital platforms (ie, users) to support their physical activity compared to those who did not (ie, nonusers).
Digital platforms include streaming services for exercise (eg, YouTube, Instagram, and Facebook); subscriber fitness programs, via an app or online (eg, Centr and MyFitnessPal); facilitated online live or recorded classes, via platforms such as Zoom (eg, dance, sport training, and fitness class); sport- or activity-specific apps designed by sporting organizations for participants to keep up their skills (eg, TeamBuildr); active electronic games (eg, Xbox Kinect); and/or online or digital training or racing platforms (eg, Zwift, FullGaz, and Rouvy). Overall, 39.5% (469/1188) of adults and 26.5% (255/963) of adolescents reported using digital platforms for physical activity. Among adults, MVPA (odds ratio OR 2.0, 95% CI 1.5-2.7), MSE (OR 3.3, 95% CI 2.5-4.5), and combined (OR 2.7, 95% CI 2.0-3.8) guideline adherence were higher among digital platform users relative to nonusers. Adolescents' MVPA (OR 2.4, 95% CI 1.3-4.3), MSE (OR 3.1, 95% CI 2.1-4.4), and combined (OR 4.3, 95% CI 2.1-9.0) guideline adherence were also higher among users of digital platforms relative to nonusers.
Digital platform users were more likely than nonusers to meet MVPA and MSE guidelines during the COVID-19 stay-at-home restrictions in April and May 2020. Digital platforms may play a critical role in helping to support physical activity engagement when access to facilities or opportunities for physical activity outside the home are restricted.
In developed and developing countries, social, economic, and environmental transitions have led to physical inactivity and large amounts of time spent sitting. Research is now unraveling the adverse ...public health consequences of too much sitting. We describe improvements in device-based measurement that are providing new insights into sedentary behavior and health. We consider the implications of research linking evidence from epidemiology and behavioral science with mechanistic insights into the underlying biology of sitting time. Such evidence has led to new sedentary behavior guidelines and initiatives. We highlight ways that this emerging knowledge base can inform public health strategy: First, we consider epidemiologic and experimental evidence on the health consequences of sedentary behavior; second, we describe solutions-focused research from initiatives in workplaces and schools. To inform a broad public health strategy, researchers need to pursue evidence-informed collaborations with occupational health, education, and other sectors.
Accelerometers have been used to determine the amount of time that children spend sedentary. However, as time spent sitting may be detrimental to health, research is needed to examine whether ...accelerometer sedentary cut-points reflect the amount of time children spend sitting. The aim of this study was to: a) examine agreement between ActiGraph (AG) cut-points for sedentary time and objectively-assessed periods of free-living sitting and sitting plus standing time using the activPAL (aP); and b) identify cut-points to determine time spent sitting and sitting plus standing.
Forty-eight children (54% boys) aged 8-12 years wore a waist-mounted AG and thigh-mounted aP for two consecutive school days (9-3:30 pm). AG data were analyzed using 17 cut-points between 50-850 counts·min-1 in 50 counts·min-1 increments to determine sedentary time during class-time, break time and school hours. Sitting and sitting plus standing time were obtained from the aP for these periods. Limits of agreement were computed to evaluate bias between AG50 to AG850 sedentary time and sitting and sitting plus standing time. Receiver Operator Characteristic (ROC) analyses identified AG cut-points that maximized sensitivity and specificity for sitting and sitting plus standing time.
The smallest mean bias between aP sitting time and AG sedentary time was AG150 for class time (3.8 minutes), AG50 for break time (-0.8 minutes), and AG100 for school hours (-5.2 minutes). For sitting plus standing time, the smallest bias was observed for AG850. ROC analyses revealed an optimal cut-point of 96 counts·min-1 (AUC = 0.75) for sitting time, which had acceptable sensitivity (71.7%) and specificity (67.8%). No optimal cut-point was obtained for sitting plus standing (AUC = 0.51).
Estimates of free-living sitting time in children during school hours can be obtained using an AG cut-point of 100 counts·min-1. Higher sedentary cut-points may capture both sitting and standing time.
Abstract Objectives The relationship between actual and perceived object control competence (ball skills) and the contribution to young children's physical activity is not known. Design Cross ...sectional study. Methods The Test Gross Motor Development-2 assessed actual object control competence and a modified version of the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children assessed perceived object control competence. Moderate- to vigorous-intensity physical activity was measured via accelerometry. Three mixed regression models were performed: (i) object control competence as the predictor and the outcome as perceived object control, (ii) perceived object control competence as the predictor and the outcome moderate to vigorous physical activity and (iii) actual object control as the predictor and the outcome moderate to vigorous physical activity. Models adjusted for school clustering, monitor wear time, sex and age. Interactions between respective predictor variables and sex were performed if warranted. A total of 102 children (56% boys, 44% girls) aged 4–8 years ( M 6.3, SD 0.92) completed assessments. Results Girls had lower perceived and actual object control competence and were less active than boys. Actual object control competence was positively associated with perceived object control competence ( B = 0.11, t (96) = 2.25, p < 0.001, p = 0.027) and this relationship did not differ by sex ( p = 0.449); however, neither actual ( p = 0.092) nor perceived object control competence ( p = 0.827) were associated with moderate to vigorous physical activity. Discussion Young children's perceived ball skill abilities appear to relate to actual competence; however, these measures were not associated with physical activity. In older children, object control skill is associated with physical activity so targeting young children's object control skills is an intervention priority.
Despite significant interest in assessing activity patterns in different populations, there has been no consensus concerning the definition and operationalisation of this term. This has limited the ...comparability, interpretability, and synthesis of study findings to date. The aim of this study was to establish a consensus regarding the way in which activity patterns and activity pattern components are defined and reported.
The activity patterns literature was searched to identify experts to be invited to participate and to develop a proposed definition of activity patterns and activity pattern components. A three-round modified Delphi survey was conducted online (November 2021 to May 2022). In Round 1, participants were asked to rate their agreement with a proposed activity patterns definition, which also included six activity pattern components (e.g., activity intensity, activity bout, transitions), six examples of activity patterns (e.g., frequency of postural transitions in discrete time periods) and eight items for reporting activity patterns in future research (n = 21 items). Open-ended questions enabled participants to provide further comments and suggestions for additional items. Consensus was defined a priori as ≥ 80% participants rating their agreement with an item. In Round 2, participants were asked to rate their agreement with 25 items (13 original items, eight amended, and four new). In Round 3, participants rated their agreement with 10 items (five original items, four amended, and one new).
Twenty experts in activity patterns research participated in Round 1, with response rates of 80% and 60% in Rounds 2 and 3, respectively. The proposed activity pattern definition, all activity pattern components definitions, four of the six activity pattern examples, and 10 items in the activity patterns reporting framework achieved consensus. The removal of one activity component item between Rounds 1 and 2 achieved consensus.
This modified Delphi study achieved consensus for defining and reporting activity patterns for the first time. This consensus definition enables standardisation of activity patterns terminology, which is important given the significant interest in quantifying how individuals accumulate their physical activity and sedentary behaviour across the lifespan to inform the development of future public health guidelines and interventions efforts.
BACKGROUND: Seasonal variations may influence children’s physical activity patterns. The aim of this study was to examine how children’s objectively-measured physical activity differed across ...seasons, and whether different seasonal patterns were observed for boys and girls. METHODS: Three hundred and twenty-six children aged 8–11 years from nine primary schools in Melbourne, Australia, participated in the study. Physical activity was measured every 15-s using hip-mounted GT3X+ ActiGraph accelerometers for seven consecutive days in the Winter (n = 249), Spring (n = 221), Summer (n = 174) and Autumn (n = 152) school terms. Time spent in moderate (MPA), vigorous (VPA) and moderate- to vigorous-intensity physical activity (MVPA) at each time point was derived using age-specific cut-points. Meteorological data (maximum temperature, precipitation, daylight hours) were obtained daily during each season. Longitudinal data were analysed using multilevel analyses, adjusted for age, sex, accelerometer wear time, number of valid days, and meteorological variables. RESULTS: Compared to Winter, children engaged in significantly less MPA (−5.0 min) and MVPA (−7.8 min) in Summer. Girls engaged in less MVPA in Spring (−18 min) and Summer (−9.2 min) and more MVPA in Autumn (9.9 min) compared to Winter. Significant changes in MPA and VPA bout frequency and duration were also observed. Significant decreases in VPA bout frequency (3.4 bouts) and duration (2.6 min) were observed for girls in Spring compared to Winter. No significant seasonal changes were observed for boys for all intensities and physical activity accumulation. CONCLUSIONS: Physical activity decreased in Summer compared to Winter, contrasting previous research that typically reports that children are most active in summer. Greater fluctuations were observed for girls’ activity levels. In addition, girls’ activity duration and bouts appeared to be more susceptible to seasonal changes compared to boys. The results suggest that strategies to promote physical activity may be needed in Australia during the hot summer months, particularly for girls.
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