Harmonising data on the correlates of physical activity and sedentary behaviour in young people: Methods and lessons learnt from the international Children's Accelerometry database (ICAD)

Evaluating the harmonization potential of oral health-related questionnaires in national longitudinal birth and child cohort surveys

BACKGROUND/OBJECTIVES

Effective use of longitudinal study data is challenging because of divergences in the construct definitions and measurement approaches over time, between studies and across disciplines. One approach to overcome these challenges is data harmonization. Data harmonization is a practice used to improve variable comparability and reduce heterogeneity across studies. This study describes the process used to evaluate the harmonization potential of oral health-related variables across each survey wave.

METHODS

National child cohort surveys with similar themes/objectives conducted in the last two decades were selected. The Maelstrom Research Guidelines were followed for harmonization potential evaluation.

RESULTS

Seven nationally representative child cohort surveys were included and questionnaires examined from 50 survey waves. Questionnaires were classified into three domains and fifteen constructs and summarized by age groups. A DataSchema (a list of core variables representing the suitable version of the oral health outcomes and risk factors) was compiled comprising 42 variables. For each study wave, the potential (or not) to generate each DataSchema variable was evaluated. Of the 2100 harmonization status assessments, 543 (26%) were complete. Approximately 50% of the DataSchema variables can be generated across at least four cohort surveys while only 10% (n = 4) variables can be generated across all surveys. For each survey, the DataSchema variables that can be generated ranged between 26% and 76%.

CONCLUSION

Data harmonization can improve the comparability of variables both within and across surveys. For future cohort surveys, the authors advocate more consistency and standardization in survey questionnaires within and between surveys.

Genetic confounding in the association of early motor development with childhood and adolescent exercise behavior

INTRODUCTION

Early motor development has been found to be a predictor of exercise behavior in children and adolescents, but whether this reflects a causal effect or confounding by genetic or shared environmental factors remains to be established.

METHODS

For 20,911 complete twin pairs from the Netherlands Twin Register a motor development score was obtained from maternal reports on the timing of five motor milestones. During a 12-year follow-up, subsamples of the mothers reported on the twins' ability to perform seven gross motor skills ability (N = 17,189 pairs), and weekly minutes of total metabolic equivalents of task (MET) spent on sports and exercise activities at age 7 (N = 3632 pairs), age 10 (N = 3735 pairs), age 12 (N = 7043 pairs), and age 14 (N = 3990 pairs). Multivariate phenotypic and genetic regression analyses were used to establish the predictive strength of the two motor development traits for future exercise behavior, the contribution of genetic and shared environmental factors to the variance in all traits, and the contribution of familial confounding to the phenotypic prediction.

RESULTS

Significant heritability (h2) and shared environmental (c2) effects were found for early motor development in boys and girls (h2 = 43-65%; c2 = 16-48%). For exercise behavior, genetic influences increased with age (boys: h2age7 = 22% to h2age14 = 51%; girls: h2age7 = 3% to h2age14 = 18%) paired to a parallel decrease in the influence of the shared environment (boys: c2age7 = 68% to c2age14 = 19%; girls: c2age7 = 80% to c2age14 = 48%). Early motor development explained 4.3% (p < 0.001) of the variance in future exercise behavior in boys but only 1.9% (p < 0.001) in girls. If the effect in boys was due to a causal effect of motor development on exercise behavior, all of the factors influencing motor development would, through the causal chain, also influence future exercise behavior. Instead, only the genetic parts of the regression of exercise behavior on motor development were significant. Shared and unique environmental parts of the regression were largely non-significant, which is at odds with the causal hypothesis.

CONCLUSION

No support was found for a direct causal effect in the association between rapid early motor development on future exercise behavior. In boys, early motor development appears to be an expression of the same genetic factors that underlie the heritability of childhood and early adolescent exercise behavior.

Accelerometer-Measured Physical Activity Data Sets (Global Physical Activity Data Set Catalogue) That Include Markers of Cardiometabolic Health: Systematic Scoping Review

BACKGROUND

Cardiovascular disease accounts for 17.9 million deaths globally each year. Many research study data sets have been collected to answer questions regarding the relationship between cardiometabolic health and accelerometer-measured physical activity. This scoping review aimed to map the available data sets that have collected accelerometer-measured physical activity and cardiometabolic health markers. These data were then used to inform the development of a publicly available resource, the Global Physical Activity Data set (GPAD) catalogue.

OBJECTIVE

This review aimed to systematically identify data sets that have measured physical activity using accelerometers and cardiometabolic health markers using either an observational or interventional study design.

METHODS

Databases, trial registries, and gray literature (inception until February 2021; updated search from February 2021 to September 2022) were systematically searched to identify studies that analyzed data sets of physical activity and cardiometabolic health outcomes. To be eligible for inclusion, data sets must have measured physical activity using an accelerometric device in adults aged ≥18 years; a sample size >400 participants (unless recruited participants in a low- and middle-income country where a sample size threshold was reduced to 100); used an observational, longitudinal, or trial-based study design; and collected at least 1 cardiometabolic health marker (unless only body mass was measured). Two reviewers screened the search results to identify eligible studies, and from these, the unique names of each data set were recorded, and characteristics about each data set were extracted from several sources.

RESULTS

A total of 17,391 study reports were identified, and after screening, 319 were eligible, with 122 unique data sets in these study reports meeting the review inclusion criteria. Data sets were found in 49 countries across 5 continents, with the most developed in Europe (n=53) and the least in Africa and Oceania (n=4 and n=3, respectively). The most common accelerometric brand and device wear location was Actigraph and the waist, respectively. Height and body mass were the most frequently measured cardiometabolic health markers in the data sets (119/122, 97.5% data sets), followed by blood pressure (82/122, 67.2% data sets). The number of participants in the included data sets ranged from 103,712 to 120. Once the review processes had been completed, the GPAD catalogue was developed to house all the identified data sets.

CONCLUSIONS

This review identified and mapped the contents of data sets from around the world that have collected potentially harmonizable accelerometer-measured physical activity and cardiometabolic health markers. The GPAD catalogue is a web-based open-source resource developed from the results of this review, which aims to facilitate the harmonization of data sets to produce evidence that will reduce the burden of disease from physical inactivity.

Influence of Guideline Operationalization on Youth Activity Prevalence in the International Children's Accelerometry Database

INTRODUCTION

The United Kingdom and World Health Organization recently changed their youth physical activity (PA) guidelines from 60 min of moderate- to vigorous-intensity PA (MVPA) every day, to an average of 60 min of MVPA per day, over a week. The changes are based on expert opinion due to insufficient evidence comparing health outcomes associated with different guideline definitions. This study used the International Children's Accelerometry Database to compare approaches to calculating youth PA compliance and associations with health indicators.

METHODS

Cross-sectional accelerometer data (n = 21,612, 5-18 yr) were used to examine compliance with four guideline definitions: daily method (DM; ≥60 min MVPA every day), average method (AM; average of ≥60 min MVPA per day), AM5 (AM compliance and ≥5 min of vigorous PA [VPA] on ≥3 d), and AM15 (AM compliance and ≥15 min VPA on ≥3 d). Associations between compliance and health indicators were examined for all definitions.

RESULTS

Compliance varied from 5.3% (DM) to 29.9% (AM). Associations between compliance and health indicators were similar for AM, AM5, and AM15. For example, compliance with AM, AM5, and AM15 was associated with a lower BMI z-score (statistics are coefficient [95% CI]): AM (-0.28 [-0.33 to -0.23]), AM5 (-0.28 [-0.33 to -0.23], and AM15 (-0.30 [-0.35 to -0.25]). Associations between compliance and health indicators for DM were similar/weaker, possibly reflecting fewer DM-compliant participants with health data and lower variability in exposure/outcome data.

CONCLUSIONS

Youth completing 60 min of MVPA every day do not experience superior health benefits to youth completing an average of 60 min of MVPA per day. Guidelines should encourage youth to achieve an average of 60 min of MVPA per day. Different guideline definitions affect inactivity prevalence estimates; this must be considered when analyzing data and comparing studies.

Cross-sectional and longitudinal associations of active travel, organised sport and physical education with accelerometer-assessed moderate-to-vigorous physical activity in young people: the International Children's Accelerometry Database

BACKGROUND

Physical activity (PA) declines during childhood. Important sources of PA are active travel, organised sport and physical education (PE), but it is unclear how these domain-specific PA sources contribute to (changes in) daily moderate-to-vigorous PA (MVPA) in young people. This study aimed to examine (1) the cross-sectional association between domain-specific physical activity (i.e., active travel, organised sport and PE) and daily minutes in accelerometer-assessed MVPA; and (2) the longitudinal association between domain-specific physical activity at baseline and change in daily minutes in MVPA.

METHODS

Participants (baseline age 11.3 ± .1.2 years) were drawn from three studies in the International Children's Accelerometry Database. The contribution of self-reported standardised active travel, organised sport and PE to accelerometer-measured daily minutes in MVPA was examined using linear regression. In cross-sectional analyses, MVPA was regressed on each PA domain in separate models, adjusted for study, age, sex, maternal education, season, and monitor wear time. In longitudinal analyses, change in MVPA was regressed on each of the baseline PA domains, additionally adjusting for changes in season and wear time, follow-up duration, and baseline MVPA. R-squared was used to compare variance explained by each PA domain.

RESULTS

In the cross-sectional analyses (n = 3871), organised sport (standardised β = 3.81, 95% confidence interval [95%CI] = 3.06, 4.56) and active travel (β = 3.46, 95%CI = 2.73, 4.19) contributed more to daily MVPA than PE (β = 0.82, 95%CI = -0.02, 1.66). Compared to the base model which included only covariates (R2 = 21.5%), organised sport (absolute change: + 1.9%) and active travel (+ 1.7%) models explained more of the variance than the PE model (± < 0.1%). Associations followed a similar pattern in the longitudinal analyses (n = 2302), but none of the PA domains predicted change in MVPA (organised sport: standardised β = 0.85, 95%CI = -0.03, 1.72; active travel: β = 0.68, 95%CI = -0.14, 1.50; PE: β = 0.02, 95%CI = -0.87, 0.91).

CONCLUSIONS

A multi-sectoral approach covering a wide range of PA domains should be promoted to minimise the age-related decline in MVPA during childhood.

Global prevalence of physical activity for children and adolescents; inconsistencies, research gaps, and recommendations: a narrative review

BACKGROUND

One of the strategic actions identified in the Global Action Plan on Physical Activity (PA) 2018-2030 is the enhancement of data systems and capabilities at national levels to support regular population surveillance of PA. Although national and international standardized surveillance of PA among children and adolescents has increased in recent years, challenges for the global surveillance of PA persist. The aims of this paper were to: (i) review, compare, and discuss the methodological inconsistencies in children and adolescents' physical activity prevalence estimates from intercontinental physical activity surveillance initiatives; (ii) identify methodological limitations, surveillance and research gaps.

METHODS

Intercontinental physical activity surveillance initiatives for children and adolescents were identified by experts and through non-systematic literature searches. Prevalence of meeting PA guidelines by country, gender, and age were extracted when available. A tool was created to assess the quality of the included initiatives. Methods and PA prevalence were compared across data/studies and against the methodological/validity/translation differences.

RESULTS

Eight intercontinental initiatives were identified as meeting the selection criteria. Methods and PA definition inconsistencies across and within included initiatives were observed, resulting in different estimated national prevalence of PA, and initiatives contradicting each other's cross-country comparisons. Three findings were consistent across all eight initiatives: insufficient level of PA of children and adolescents across the world; lower levels of PA among girls; and attenuation of PA levels with age. Resource-limited countries, younger children, children and adolescents not attending school, with disability or chronic conditions, and from rural areas were generally under/not represented.

CONCLUSIONS

There are substantial inconsistencies across/within included initiatives, resulting in varying estimates of the PA situation of children and adolescents at the global, regional and national levels. The development of a new PA measurement instrument that would be globally accepted and harmonized is a global health priority to help improve the accuracy and reliability of global surveillance.

Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: a proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Background

Pooling data from thigh-worn accelerometers across multiple studies has great potential to advance evidence on the health benefits of physical activity. This requires harmonization of information on body postures, physical activity types, volumes and time patterns across different brands of devices. The aim of this study is to compare the physical behavior estimates provided by three different brands of thigh-worn accelerometers.

Methods

Twenty participants volunteered for a 7-day free-living measurement. Three accelerometers - ActiGraph GT3X+, Axivity AX3 and ActivPAL Micro4 - were randomly placed in a vertical line on the midsection of the right thigh. Raw data from each accelerometer was processed and classified into 8 physical activities and postures using the Acti4 software. Absolute differences between estimates and the respective coefficient of variation (CV) were calculated.

Results

We observed very minor differences between physical behavior estimates from three different accelerometer brands. When averaged over 24 h (1,440 min), the absolute difference (CV) between accelerometers were: 1.2 mins (0.001) for lying/sitting, 3.4 mins (0.02) for standing, 3.5 mins (0.06) for moving, 1.9 mins (0.03) for walking, 0.1 mins (0.19) for running, 1.2 mins (0.19) for stair climbing, 1.9 mins (0.07) for cycling. Moreover, there was an average absolute difference of 282 steps (0.03) per 24 h.

Conclusions

Physical behaviors were classified with negligible difference between the accelerometer brands. These results support harmonization of data from different thigh-worn accelerometers across multiple cohorts when analyzed in an identical manner.

Electronic supplementary material

The online version of this article (10.1186/s12966-019-0835-0) contains supplementary material, which is available to authorized users.

The Finnish psychiatric birth cohort consortium (PSYCOHORTS) - content, plans and perspectives

Background: Psychiatric disorders tend to be developmental, and longitudinal settings are required to examine predictors of psychiatric phenomena. Replicating and combining data and results from different birth cohorts, which are a source of reliable data, can make research even more valuable. The Finnish Psychiatric Birth Cohort Consortium (PSYCOHORTS) project combines birth cohorts in Finland. Aim: The aim of this paper is to introduce content, plans and perspectives of the PSYCOHORTS project that brings together researchers from Finland. In addition, we illustrate an example of data harmonization using available data on causes of death. Content: PSYCOHORTS includes eight Finnish birth cohorts. The project has several plans: to harmonize different data from birth cohorts, to incorporate biobanks into psychiatric birth cohort research, to apply multigenerational perspectives, to integrate longitudinal patterns of marginalization and inequality in mental health, and to utilize data in health economics research. Data on causes of death, originally obtained from Finnish Cause of Death register, were harmonized across the six birth cohorts using SAS macro facility. Results: Harmonization of the cause of death data resulted in a total of 21,993 observations from 1965 to 2015. For example, the percentage of deaths due to suicide and the sequelae of intentional self-harm was 14% and alcohol-related diseases, including accidental poisoning by alcohol, was 13%. Conclusions: PSYCOHORTS lays the foundation for complex examinations of psychiatric disorders that is based on compatible datasets, use of biobanks and multigenerational approach to risk factors, and extensive data on marginalization and inequality.

A closer look at the relationship among accelerometer-based physical activity metrics: ICAD pooled data

BACKGROUND

Accelerometers are widely used to assess child physical activity (PA) levels. Using the accelerometer data, several PA metrics can be estimated. Knowledge about the relationships between these different metrics can improve our understanding of children's PA behavioral patterns. It also has significant implications for comparing PA metrics across studies and fitting a statistical model to examine their health effects. The aim of this study was to examine the relationships among the metrics derived from accelerometers in children.

METHODS

Accelerometer data from 24,316 children aged 5 to 18 years were extracted from the International Children's Accelerometer Database (ICAD) 2.0. Correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC) were calculated.

RESULTS

TAC was approximately 22X103 counts higher (p < 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to < 13 h/day), while MVPA was similar across the wear time categories. MVPA was very highly correlated with TAC (r = .91; 99% CI = .91 to .91). Wear time-adjusted correlation between SB and LPA was also very high (r = -.96; 99% CI = -.96, - 95). VPA was moderately correlated with MPA (r = .58; 99% CI = .57, .59).

CONCLUSIONS

TAC is mostly explained by MVPA, while it could be more dependent on wear time, compared to MVPA. MVPA appears to be comparable across different wear durations and studies when wear time is ≥8 h/day. Due to the moderate to high correlation between some PA metrics, potential collinearity should be addressed when including multiple PA metrics together in statistical modeling.

Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than "black box" counts. Consequently, many surveys and studies worldwide now assess children's physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children's present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children's physical behaviors.