Prenatal and birth predictors of objectively measured physical activity and sedentary time in three population-based birth cohorts in Brazil

Socioeconomic gradients in 24-hour movement patterns across weekends and weekdays in a working-age sample: evidence from the 1970 British Cohort Study

BACKGROUND

Socioeconomic differences in movement behaviours may contribute to health inequalities. The aim of this descriptive study was to investigate socioeconomic patterns in device-measured 24-hour movement and assess whether patterns differ between weekdays and weekends.

METHODS

4894 individuals aged 46 years from the 1970 British Cohort Study were included. Participants wore thigh-worn accelerometers for 7 days. Movement behaviours were classified in two 24-hour compositions based on intensity and posture, respectively: (1) sleep, sedentary behaviour, light-intensity activity and moderate-vigorous activity; and (2) sleep, lying, sitting, standing, light movement, walking and combined exercise-like activity. Four socioeconomic measures were explored: education, occupation, income and deprivation index. Movement behaviours were considered compositional means on a 24-hour scale; isometric log ratios expressed per cent differences in daily time in each activity compared with the sample mean.

RESULTS

Associations were consistent across all socioeconomic measures. For example, those with a degree spent more time in exercise-like activities across weekdays (10.8%, 95% CI 7.3 to 14.7; ref: sample mean) and weekends (21.9%, 95% CI 17.2 to 26.9). Other patterns differed markedly by the day of the week. Those with no formal qualifications spent more time standing (5.1%, 95% CI 2.3 to 7.1), moving (10.8%, 95% CI 8.6 to 13.1) and walking(4.0%, 95% CI 2.2 to 6.1) during weekdays, with no differences on weekends. Conversely, those with no formal qualifications spent less time sitting during weekdays (-6.6%, 95% CI -7.8 to -4.8), yet more time lying on both weekends (8.8%, 95% CI 4.9 to 12.2) and weekdays (7.5%, 95% CI 4.0 to 11.5).

CONCLUSIONS

There were strong socioeconomic gradients in 24-hour movement behaviours, with notable differences between weekdays/weekends and behaviour type/posture. These findings emphasise the need to consider socioeconomic position, behaviour type/posture and the day of the week when researching or designing interventions targeting working-age adults.

Trajectories of Device-Measured Physical Activity During Early Childhood and Its Determinants: Findings From the 2015 Pelotas (Brazil) Birth Cohort Study

BACKGROUND

The objective was to describe trajectories of physical activity (PA) measured by accelerometry during early childhood and to test associations with sociodemographic, gestational, maternal, and perinatal determinants.

METHODS

Data from 1798 children from the 2015 Pelotas (Brazil) Birth Cohort were analyzed. PA was measured with wrist accelerometers at 1, 2, and 4 years. PA trajectories were estimated using group-based trajectory modeling, and associations with determinants were tested using Poisson regression with robust variance.

RESULTS

Two trajectories were identified: Moderate and high PA, both showing a linear increase in PA in the first years but differing in volume. Girls (prevalence ratio [PR]: 0.91; 95% confidence interval [CI], 0.88-0.94), highly educated mothers (PR: 0.93; 95% CI, 0.88-0.97), and high birth weight children (PR: 0.91; 95% CI, 0.85-0.97) showed less probability of high PA trajectory. Birth order ≥3 (PR: 1.06; 95% CI, 1.01-1.11) was associated with higher likelihood of high PA trajectory.

CONCLUSIONS

Children showed an increase in PA during the first years, with 2 trajectories that differ in PA levels. Female sex, high maternal schooling, and high birth weight reduced the probability of having a high PA trajectory, while higher birth order increased this probability. These characteristics should be considered when planning PA interventions for children in early childhood.

Absolute intensity thresholds for tri-axial wrist and waist accelerometer-measured movement behaviors in adults

AIM

This study was aimed to: (1) compare raw triaxial acceleration data from GENEActiv (GA) and ActiGraph GT3X+ (AG) placed on the non-dominant wrist; (2) compare AG placed on the non-dominant and dominant wrist, and waist; (3) derive brand- and placement-specific absolute intensity thresholds for inactive and sedentary time, and physical activity intensity in adults.

METHODS

Eighty-six adults (44 men; 34.6 ± 10.8 years) performed nine activities while simultaneously wearing GA and AG on wrist and waist. Acceleration (in gravitational equivalent units; mg) was compared with oxygen uptake (measured with indirect calorimetry).

RESULTS

Increases in acceleration mirrored increases in intensity of activities, regardless of device brand and placement. Differences in acceleration between GA and AG worn at the non-dominant wrist were small but tended to be high at lower intensity activities. Thresholds for differentiating inactivity (<1.5 MET) from activity (≥1.5 MET) ranged from 25 mg (AG non-dominant wrist; sensitivity 93%, specificity 95%) to 40 mg (AG waist; sensitivity 78%, specificity 100%). For moderate intensity (≥3 METs), thresholds ranged from 65 mg (AG waist; sensitivity 96%, specificity 94%) to 92 mg (GA non-dominant; sensitivity 93%, specificity 98%); vigorous intensity (≥6 METs) thresholds ranged from 190 mg (AG waist; sensitivity 82%, specificity 92%) to 283 mg (GA non-dominant; sensitivity 93%, specificity 98%).

CONCLUSION

Raw triaxial acceleration outputs from two widely used accelerometer brands may have limited comparability in low intensity activities. Thresholds derived in this study can be utilized in adults to reasonably classify movement behaviors into categories of intensity.

Socio-economic status, psychosocial factors, health behaviours and incidence of dental caries in 12-year-old children living in deprived communities in Manaus, Brazil

OBJECTIVES

This study examines the relationships between socio-economic status, psychosocial factors, health-related behaviours and the incidence of dental caries among 12-year-old schoolchildren living in deprived communities in Manaus, Brazil.

METHODS

A longitudinal study involving 312 children aged 12 years was conducted in the city of Manaus, Brazil. Baseline data including socio-economic status (number of goods, household overcrowding, parents' schooling, family income), psychosocial factors (sense of coherence [SOC-13], social support [Social Support Appraisals questionnaire]) and health-related behaviours (frequency of toothbrushing, sugar consumption, sedentary behaviour) were collected through structured questionnaires. The number of decayed teeth was clinically assessed at baseline and one-year follow-up. A hypothesised model evaluating the direct and indirect pathways between the variables was tested using confirmatory factor analysis and structural equation modelling.

RESULTS

The incidence of dental caries at the one-year follow-up was 25.6%. Sugar consumption (β = 0.103) and sedentary behaviour (β = 0.102) directly predicted the incidence of dental caries. A higher socio-economic status was directly linked with lower sugar consumption (β = -0.243) and higher sedentary behaviour (β = 0.227). Higher social support directly predicted lower sugar consumption (β = -0.114). Lower socio-economic status (β = -0.046) and lower social support (β = -0.026) indirectly predicted the incidence of dental caries via sugar consumption and sedentary behaviour.

CONCLUSIONS

In the population studied, sugar consumption and sedentary behaviour are meaningful predictors of the incidence of dental caries among schoolchildren living in deprived communities. Indirect pathways of lower socio-economic status and low social support with dental caries incidence via sugar consumption and sedentary behaviour were detected. These findings should be considered in oral interventions and oral health care policies to prevent dental caries among children living in deprivation.

CLINICAL SIGNIFICANCE

Social conditions, social support, sedentary behaviour and sugar consumption directly influence dental caries in children.

Skeletal Muscles of Sedentary and Physically Active Aged People Have Distinctive Genic Extrachromosomal Circular DNA Profiles

To bring new extrachromosomal circular DNA (eccDNA) enrichment technologies closer to the clinic, specifically for screening, early diagnosis, and monitoring of diseases or lifestyle conditions, it is paramount to identify the differential pattern of the genic eccDNA signal between two states. Current studies using short-read sequenced purified eccDNA data are based on absolute numbers of unique eccDNAs per sample or per gene, length distributions, or standard methods for RNA-seq differential analysis. Previous analyses of RNA-seq data found significant transcriptomics difference between sedentary and active life style skeletal muscle (SkM) in young people but very few in old. The first attempt using circulomics data from SkM and blood of aged lifelong sedentary and physically active males found no difference at eccDNA level. To improve the capability of finding differences between circulomics data groups, we designed a computational method to identify Differentially Produced per Gene Circles (DPpGCs) from short-read sequenced purified eccDNA data based on the circular junction, split-read signal, of the eccDNA, and implemented it into a software tool DifCir in Matlab. We employed DifCir to find to the distinctive features of the influence of the physical activity or inactivity in the aged SkM that would have remained undetected by transcriptomics methods. We mapped the data from tissue from SkM and blood from two groups of aged lifelong sedentary and physically active males using Circle_finder and subsequent merging and filtering, to find the number and length distribution of the unique eccDNA. Next, we used DifCir to find up-DPpGCs in the SkM of the sedentary and active groups. We assessed the functional enrichment of the DPpGCs using Disease Gene Network and Gene Set Enrichment Analysis. To find genes that produce eccDNA in a group without comparison with another group, we introduced a method to find Common PpGCs (CPpGCs) and used it to find CPpGCs in the SkM of the sedentary and active group. Finally, we found the eccDNA that carries whole genes. We discovered that the eccDNA in the SkM of the sedentary group is not statistically different from that of physically active aged men in terms of number and length distribution of eccDNA. In contrast, with DifCir we found distinctive gene-associated eccDNA fingerprints. We identified statistically significant up-DPpGCs in the two groups, with the top up-DPpGCs shed by the genes AGBL4, RNF213, DNAH7, MED13, and WWTR1 in the sedentary group, and ZBTB7C, TBCD, ITPR2, and DDX11-AS1 in the active group. The up-DPpGCs in both groups carry mostly gene fragments rather than whole genes. Though the subtle transcriptomics difference, we found RYR1 to be both transcriptionally up-regulated and up-DPpGCs gene in sedentary SkM. DifCir emphasizes the high sensitivity of the circulome compared to the transcriptome to detect the molecular fingerprints of exercise in aged SkM. It allows efficient identification of gene hotspots that excise more eccDNA in a health state or disease compared to a control condition.

Associations between Device-measured Physical Activity and Cardiometabolic Health in the Transition to Early Adulthood

PURPOSES

The aims of this study were to investigate the cross-sectional and prospective associations between accelerometer-measured physical activity and cardiometabolic health in the transition to adulthood.

METHODS

Data from the 1993 Pelotas (Brazil) Birth Cohort were analysed (N=2,280). Moderate-to-vigorous intensity physical activity (MVPA, measured using a triaxial accelerometer) and cardiometabolic health (total fat mass, blood glucose, non-HDL cholesterol, triglycerides and mean resting blood pressure) were examined at age 18 and 22 yr.

RESULTS

Overall, inverse dose-response associations between MVPA and cardiometabolic health at age 18 and 22 yr were observed in cross-sectional analyses of data from males and females. Prospective analyses showed that, in general, MVPA declined, and cardiometabolic health worsened in this 4-yr period in both males and females. Cardiometabolic health at age 22 reflected both MVPA at age 18 [β: -0.007 (95% CI: -0.014; 0.000)] and changes in MVPA from 18 to 22 yr [β: -0.030 (95% CI: -0.043; -0.016)] in males, but only changes in MVPA in females [β: -0.035 (95% CI: -0.058; -0.011)]. In analyses of change over time, males who improved MVPA by 20-30 min per day showed significant improvements in cardiometabolic health over 4 yr. The magnitude of association was slightly stronger for MVPA in 10-min bouts than for MVPA accumulated in bouts of 1-min, especially in females.

CONCLUSION

MVPA is an important predictor of cardiometabolic health in early adulthood. Strategies to prevent declines in MVPA at this life stage are required to prevent deteriorating cardiometabolic health profiles.

Clustering Patterns of Physical Fitness, Physical Activity, Sedentary, and Dietary Behavior among School Children

Background: Some healthy lifestyle behaviors may have a greater impact on childhood obesity in combination, compared to the independent effects of those behaviors in an isolated manner. The present study aimed to identify the different healthy lifestyle patterns of children according to their physical fitness, physical activity (PA) patterns, screen time, and adherence to the Mediterranean Diet, as well as to examine the association between anthropometric indicators and the membership to a certain cluster. Methods: A final sample of 353 Spanish school children (45.9% females) from the Region of Murcia (Spain) was included in this study (aged 6-13). First, we conducted a hierarchical cluster analysis using Ward's method; based on squared Euclidean distances. Second, we used the k-means cluster analysis to get the final cluster solution. Results: Three different clusters were established: Cluster 1 [high cardiorespiratory fitness (CRF), PA, and Mediterranean Diet], Cluster 2 (low CRF, PA, and Mediterranean Diet + high muscular strength), and Cluster 3 (low physical fitness and PA). Cluster 3 had negative values in all the health-related variables analyzed. Regarding the anthropometric parameters analyzed (BMI, tri-ponderal mass index, waist-to-height ratio, and body fat percentage), Cluster 3 presented the highest values in all anthropometric parameters than the other two clusters (p < 0.001), while Cluster 1 showed the lowest values. Conclusions: The study has identified three clusters respect to health-related variables with the higher prevalence in the cluster established as the unhealthiest lifestyle. Also, cluster classification is associated to obesity indicators such as BMI, tri-ponderal mass index, waist-to-height ratio, and body fat percentage.