Physical activity and sedentary activity: population epidemiology and concordance in Australian children aged 11–12 years and their parents

Fraysse, François; Grobler, Anneke; Muller, Josh; Wake, Melissa; Olds, Tim

doi:10.1136/bmjopen-2018-023194

Cited by 31 publications

(34 citation statements)

References 61 publications

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“…Although estimates of mean values can vary a lot, they are typically highly correlated, and therefore have little effect on correlations with outcomes. The MVPA estimates in the current study are actually lower than Australian adult population norms collected using GENEActiv accelerometers using Esliger’s cut offs [40]. These findings point to a need for further evidence regarding the presence or absence of associations between objectively measured activity and subjective wellbeing.…”

Section: Discussionmentioning

confidence: 55%

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

et al. 2019

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IntroductionThis study evaluated the psychometric properties of the PERMA Profiler, a 15-item self-report measurement tool designed to measure Seligman’s five pillars of wellbeing: Positive emotions, Relationships, Engagement, Meaning, and Accomplishment.MethodsAustralian adults (N = 439) completed the PERMA Profiler and measures of physical and mental health (SF-12), depression, anxiety, stress (DASS 21), subjective physical activity (Active Australia Survey), and objective activity and sleep (GENEActiv accelerometer). Internal consistency was examined using Cronbach’s alpha and associations between theoretically related constructs examined using Pearson’s correlation. Model fit in comparison with theorised models was examined via Confirmatory Factor Analysis.ResultsResults indicated acceptable internal consistency for overall PERMA Profiler scores and all subscales (α range = 0.80–0.93) except Engagement (α = 0.66). Moderate associations were found between PERMA Profiler wellbeing scores with subjective constructs (e.g. depression, anxiety, stress; r = -0.374 - -0.645, p = <0.001) but not objective physical activity or sleep. Data failed to meet model fit criteria for neither the theorised five-factor nor an alternative single-factor structure.ConclusionsFindings were mixed, providing strong support for the scale’s internal consistency and moderate support for congervent and divergent validity, albeit not in comparison to objectively captured activity outcomes. We could not replicate the theorised data structure nor an alternative, single factor structure. Results indicate insufficient psychometric properties of the PERMA Profiler.

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Section: Discussionmentioning

confidence: 55%

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

et al. 2019

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“…Data were processed using Cobra custom software developed by coauthor Francois Fraysse. Further details of raw accelerometry data processing have been reported elsewhere 43. The sleep algorithm of van Hees and colleagues44 was used to detect sleep and wake between self-reported bedtime and get-up time.…”

Section: Methodsmentioning

confidence: 99%

Sleep: population epidemiology and concordance in Australian children aged 11–12 years and their parents

et al. 2019

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ObjectivesTo describe objectively measured sleep characteristics in children aged 11–12 years and in parents and to examine intergenerational concordance of sleep characteristics.DesignPopulation-based cross-sectional study (the Child Health CheckPoint), nested within the Longitudinal Study of Australian Children.SettingData were collected between February 2015 and March 2016 across assessment centres in Australian major cities and selected regional towns.ParticipantsOf the participating CheckPoint families (n=1874), sleep data were available for 1261 children (mean age 12 years, 50% girls), 1358 parents (mean age 43.8 years; 88% mothers) and 1077 biological parent–child pairs. Survey weights were applied and statistical methods accounted for the complex sample design, stratification and clustering within postcodes.Outcome measuresParents and children were asked to wear a GENEActive wrist-worn accelerometer for 8 days to collect objective sleep data. Primary outcomes were average sleep duration, onset, offset, day-to-day variability and efficiency. All sleep characteristics were weighted 5:2 to account for weekdays versus weekends. Biological parent–child concordance was quantified using Pearson’s correlation coefficients in unadjusted models and regression coefficients in adjusted models.ResultsThe mean sleep duration of parents and children was 501 min (SD 56) and 565 min (SD 44), respectively; the mean sleep onset was 22:42 and 22:02, the mean sleep offset was 07:07 and 07:27, efficiency was 85.4% and 84.1%, and day-to-day variability was 9.9% and 7.4%, respectively. Parent–child correlation for sleep duration was 0.22 (95% CI 0.10 to 0.28), sleep onset was 0.42 (0.19 to 0.46), sleep offset was 0.58 (0.49 to 0.64), day-to-day variability was 0.25 (0.09 to 0.34) and sleep efficiency was 0.23 (0.10 to 0.27).ConclusionsThese normative values for objective sleep characteristics suggest that, while most parents and children show adequate sleep duration, poor-quality (low efficiency) sleep is common. Parent–child concordance was strongest for sleep onset/offset, most likely reflecting shared environments, and modest for duration, variability and efficiency.

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“…While most parents and children show adequate sleep duration, poor-quality (low-efficiency) sleep is common 12More parents than children met physical activity guidelines, despite parents having lower mean activity counts, indicating a need to coordinate meaningful definitions across age groups 13Small parent–child concordance during a standardised 15-min ‘ snack break ’ could account for substantial annualised population gradients in energy, fat and sodium intake for 11–12 year olds 14…”

Section: Prefacementioning

confidence: 99%

“…More parents than children met physical activity guidelines, despite parents having lower mean activity counts, indicating a need to coordinate meaningful definitions across age groups 13…”

Section: Prefacementioning

confidence: 99%

Population health bio-phenotypes in 11–12 year old children and their midlife parents: Growing Up in Australia’s Child Health CheckPoint

2019

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In an ambitious undertaking, Growing Up in Australia’s Child Health CheckPoint streamlined and implemented wide-ranging population phenotypes and biosamples relevant to non-communicable diseases in nearly 1900 parent–child dyads throughout Australia at child aged 11–12 years. This BMJ Open Special Issue describes the methodology, epidemiology and parent–child concordance of 14 of these phenotypes, spanning cardiovascular, respiratory, bone, kidney, hearing and language, body composition, metabolic profiles, telomere length, sleep, physical activity, snack choice and health-related quality of life. The Special Issue also includes a cohort summary and study methodology paper.

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Physical activity and sedentary activity: population epidemiology and concordance in Australian children aged 11–12 years and their parents

Cited by 31 publications

References 61 publications

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

Psychometric properties of the PERMA Profiler for measuring wellbeing in Australian adults

Sleep: population epidemiology and concordance in Australian children aged 11–12 years and their parents

Population health bio-phenotypes in 11–12 year old children and their midlife parents: Growing Up in Australia’s Child Health CheckPoint

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