Physical activity and food environments in and around schools: a case study in regional North-West Tasmania

A better understanding of the physical activity (PA) infrastructure in schools, the walkability of neighborhoods close to schools, and the food environments around schools, particularly in rural, socioeconomically challenged areas such as the North-West (NW) of Tasmania, could be important in the wider effort to improve the health of school-age children. Accordingly, this research aimed to assess PA resources, walkability, and food environments in and around schools in three socioeconomically disadvantaged, regional/rural Local Government Areas (LGAs) of Tasmania, Australia. A census of schools (including assessment of the PA infrastructure quality within school grounds), a walkability assessment, and a census of food outlets surrounding schools (through geospatial mapping) were executed. Most of the schools in the study region had access to an oval, basketball/volleyball/netball court, and free-standing exercise equipment. In all instances (i.e., regardless of school type), the quality of the available infrastructure was substantially higher than the number of incivilities observed. Most schools also had good (i.e., within the first four deciles) walkability. Numerous food outlets were within the walking zones of all schools in the study region, with an abundance of food outlets that predominantly sold processed unhealthy food

Genome-wide SNP analysis reveals population structure and demographic history of the ryukyu islanders in the southern part of the Japanese archipelago.

The Ryukyu Islands are located to the southwest of the Japanese archipelago. Archaeological evidence has revealed the existence of prehistoric cultural differentiation between the northern Ryukyu islands of Amami and Okinawa, and the southern Ryukyu islands of Miyako and Yaeyama. To examine a genetic subdivision in the Ryukyu Islands, we conducted genome-wide single nucleotide polymorphism typing of inhabitants from the Okinawa Islands, the Miyako Islands, and the Yaeyama Islands. Principal component and cluster analyses revealed genetic differentiation among the island groups, especially between Okinawa and Miyako. No genetic affinity was observed between aboriginal Taiwanese and any of the Ryukyu populations. The genetic differentiation observed between the inhabitants of the Okinawa Islands and the Miyako Islands is likely to have arisen due to genetic drift rather than admixture with people from neighboring regions. Based on the observed genetic differences, the divergence time between the inhabitants of Okinawa and Miyako islands was dated to the Holocene. These findings suggest that the Pleistocene inhabitants, whose bones have been found on the southern Ryukyu Islands, did not make a major genetic contribution, if any, to the present-day inhabitants of the southern Ryukyu Islands

Accumulated exposure to rural areas of residence over the life course is associated with overweight and obesity in adulthood: a 25-year prospective cohort study

Purpose :This prospective cohort study investigated whether body mass index (BMI) and weight status in mid-adulthood were predicted by trajectories of urban-rural residence from childhood to adulthood.Methods :Participants aged 7–15 years in 1985 (n = 8498) were followed up in 2004–2006 (n = 3999, aged 26–36 years) and 2009–2011 (n = 3049, aged 31–41 years). Area of residence (AOR) was classified as urban or rural at each time point. BMI and/or weight status was calculated from self-reported weight and height (2009–2011). We tested which of three life-course models (“accumulation,” “sensitive period,” “mobility”) best explained the AOR-BMI and/or weight status association using a novel life-course modeling framework.Results :Accumulation and sensitive period models best described the effect of AOR on mid-adulthood BMI and weight status. Those with greater accumulated exposure to rural areas had a higher BMI (β = 0.29 kg/m2 per time in a rural area, P = .005) and were more likely obese (relative risk = 1.13 per time in a rural area, P = .002). Living in rural areas at ages 26–30 years was also associated with a higher BMI and obesity in mid-adulthood.Conclusions :Greater cumulative exposure to rurality and exposure during the “sensitive period” of young adulthood is associated with obesity in middle-aged adults. This study highlights the important contribution of context to the development of obesity over the life course

Cluster patterns of behavioural risk factors among children: Longitudinal associations with adult cardio-metabolic risk factors

Much of what is known about childhood clusters of cardiovascular disease behavioural risk factors (RFs) comes from cross-sectional studies, providing little insight into the long-term health impacts of different behavioural cluster profiles. This study aimed to establish the longitudinal relationship between cluster patterns of childhood behavioural RFs and adult cardio-metabolic RFs. Data were from an Australian prospective cohort study of 1265 participants measured in 1985 (ages 9-15 yrs), and in 2004-06 (ages 26-36 yrs). At baseline, children self-reported smoking status, alcohol consumption, physical activity (PA), dietary behaviour and psychological well-being. At follow-up, participants completed questionnaires and attended study clinics where the following component indicators of the metabolic syndrome (MetS) score were measured: waist circumference, blood pressure, fasting blood glucose and lipids. TwoStep cluster analyses were carried out to identify clusters in childhood. Linear regression was used to examine the longitudinal associations between cluster patterns of childhood behavioural RFs and adult cardio-metabolic RFs. Four childhood cluster patterns of behavioural RFs labelled 'most healthy', 'high PA', 'most unhealthy', and 'breakfast skippers' were identified. The unhealthier childhood clusters predicted a significantly higher adult MetS score ('most unhealthy': β = 0.10, 95%CI = 0.01, 0.19) and adult waist circumference ('most unhealthy': β = 2.29, 95%CI = 0.90, 6.67; 'breakfast skippers': β = 2.15, 95%CI = 0.30, 4.00). These associations were independent of adult behavioural RFs and socio-economic position. These findings emphasise the impact of multiple childhood behavioural RFs on important adult health outcomes and may be useful for the development of early intervention strategies, where identification of children at higher risk of poorer adult cardio-metabolic health is vital

Cluster patterns of behavioural risk factors among children: longitudinal associations with adult cardio-metabolic risk factors

Much of what is known about childhood clusters of cardiovascular disease behavioural risk factors (RFs) comesfrom cross-sectional studies, providing little insight into the long-term health impacts of different behaviouralcluster profiles. This study aimed to establish the longitudinal relationship between cluster patterns of childhoodbehavioural RFs and adult cardio-metabolic RFs.Data were from an Australian prospective cohort study of 1265 participants measured in 1985 (ages9–15 yrs), and in 2004–06 (ages 26–36 yrs). At baseline, children self-reported smoking status, alcohol consumption, physical activity (PA), dietary behaviour and psychological well-being. At follow-up, participantscompleted questionnaires and attended study clinics where the following component indicators of the metabolicsyndrome (MetS) score were measured: waist circumference, blood pressure, fasting blood glucose and lipids.TwoStep cluster analyses were carried out to identify clusters in childhood. Linear regression was used to examine the longitudinal associations between cluster patterns of childhood behavioural RFs and adult cardiometabolic RFs.Four childhood cluster patterns of behavioural RFs labelled ‘most healthy’, ‘high PA’, ‘most unhealthy’, and‘breakfast skippers’ were identified. The unhealthier childhood clusters predicted a significantly higher adultMetS score (‘most unhealthy’: β = 0.10, 95%CI = 0.01, 0.19) and adult waist circumference (‘most unhealthy’:β = 2.29, 95%CI = 0.90, 6.67; ‘breakfast skippers’: β = 2.15, 95%CI = 0.30, 4.00). These associations wereindependent of adult behavioural RFs and socio-economic position.These findings emphasise the impact of multiple childhood behavioural RFs on important adult health outcomes and may be useful for the development of early intervention strategies, where identification of children athigher risk of poorer adult cardio-metabolic health is vital