Sedentary time is viewed as an independent risk factor for adverse cardiometabolic health (CMH). No systematic review and meta-analysis on the cross-sectional associations between objectively measured sedentary time and CMH markers has been conducted. PubMed, Scopus and Web of Science Core Collection were searched for papers that examined the cross-sectional association between objectively measured sedentary time and CMH markers in adults. Forty-six papers met the inclusion criteria. The included papers had a combined sample size of 70,576 and an age range of 18-87 years. To examine the effect of increased levels of sedentary time on CMH markers, data on effect sizes and moderators were extracted, where possible. By pooling the unadjusted data from the included papers, increased sedentary time was shown to have a significant detrimental association with fasting glucose (Δ = 0.12, 95% confidence interval [CI]: 0.02, 0.23), fasting insulin (Δ = 0.19, 95% CI: 0.06, 0.32), triglycerides (Δ = 0.25, 95% CI: 0.14, 0.37), high-density lipoprotein cholesterol (Δ = -0.20, 95% CI: -0.28, -0.13) and waist circumference (Δ = 0.25, 95% CI: 0.15, 0.35). How sedentary time was quantified and the device used to measure sedentary time significantly influence the size of the effect reported. Future interventions focused on both decreasing sedentary time and increasing physical activity may be the most effective strategy to improve CMH.

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Numerous cut-points exist to measure physical activity by accelerometry. The ability to compare accelerometer findings from different devices from different locations may be advantageous to researchers. This study aimed to develop and validate cut-points for 1.5, 3, and 6 METs in five activity monitors simultaneously. Fifty-six participants (mean age=39.9 [±11.5] years) performed six activities while wearing a CosMED K4b and five activity monitors: activPAL3 Micro, activPAL, ActiGraph GT1M, ActiGraph wGT3X-BT, and GENEActiv. Receiver operating characteristic curves and analysis were used to develop and validate cut-points for the vertical axis counts (all activity monitors) and sum of the vector magnitude (ActiGraph wGT3X-BT and GENEActiv) for 15 second (all devices) and 60 second (ActiGraph devices) epochs. A random coefficients statistical model was used to derive MET predictive equations for all activity monitors. Bland-Altman plots examined the variability in device error. No 1.5 MET cut-points were developed for the activPAL devices. All developed cut-points had high levels of sensitivity and specificity. When cross-validated in an independent group, high levels of sensitivity and specificity remained (≥77.4%, monitor and intensity dependent). The mean bias based on the Bland-Altman plots ranged from -0.03 METs to 0.35 METs (monitor dependent). This is the first study to develop and validate cut-points for five activity monitors simultaneously with high levels of sensitivity and specificity (≥77.4%). This is potentially a step toward cross-comparison/harmonization of data; however, further validation in a free-living environment is warranted.

Internationally, insufficient physical activity (PA) is a major health concern. Children in Northern Ireland (NI) are recorded as having the lowest levels of PA in the United Kingdom (UK). To date, validated and representative data on the PA levels of NI school children are limited. The aim of this study was to provide surveillance data on self-reported PA, sport and physical education (PE) participation of school children in NI. Differences between genders and factors associated with PA were also examined. A representative sample of primary (n = 446) and post-primary (n = 1508) children was surveyed in school using validated self-report measures. Findings suggest that PA levels are low, with a minority of children (13%) meeting the PA guidelines (primary pupils 20%, post-primary pupils 11%). NI school children have lower levels of PA, PE and sports participation than UK and European peers. A trend of age-related decline across all the domains of PA was apparent. The data presented highlighted that females are less likely to achieve PA guidelines, children from lower socio-economic background participate in school and community sport less often, and that enjoyment and social support are important variables in PA adherence. Policy solutions that would support implementation e.g., mandatory minimum PE time, whole school approaches to PA promotion and targeted investment in schools, particularly in areas of deprivation and for females, are suggested.

Background: The current study was the largest physical activity (PA) surveillance assessment of youth undertaken in Ireland in recent years. The purpose of this research was to assess the impact of social support, while controlling for age and screen time, on PA and sport participation, across a representative sample of Irish female youth. Methods: A total of 3503 children (mean age: 13.54 [2.05] y) across the island of Ireland participated. Participants completed a previously validated electronic questionnaire while supervised in a classroom setting, which investigated their (1) levels of PA; (2) screen time; (3) community sport participation; and (4) social support (friend, family, and teacher) to be physically active/partake in sport. Results: There were significant differences, with medium and large effect sizes, for social support from friends and family across types of sports participation. Specifically, girls who participated in the most popular team sports, when compared with the most popular individual sports, reported higher social support scores for friends and family structures. Conclusions: Findings from this study confirm the contributing influence of friends and family as sport and PA support networks for girls. Interventions should consider the importance of culturally relevant team sports for PA engagement in female youth.

Activity monitors such as the SenseWear Pro3 (SWP3) and the activPAL3 Micro (aPM) are regularly used by researchers and practitioners to provide estimates of the metabolic cost (METs) of activities in free-living settings. The purpose of this study is to examine the accuracy of the MET predictions from the SWP3 and the aPM compared to the criterion standard MET values from indirect calorimetry. Fifty-six participants (mean age: 39.9 (±11.5), 25M/31F) performed eight activities (four daily living, three ambulatory and one cycling), while simultaneously wearing a SWP3, aPM and the Cosmed K4B (K4B) mobile metabolic unit. Paired samples T-tests were used to examine differences between device predicted METs and criterion METs. Bland-Altman plots were constructed to examine the mean bias and limits of agreement for predicted METs compared to criterion METs. SWP3 predicted MET values were significantly different from the K4B for each activity (p  ⩽  0.004), excluding sweeping (p  =  0.122). aPM predicted MET values were significantly different (p < 0.001) from the K4B for each activity. When examining the activities collectively, both devices underestimated activity intensity (0.20 METs (SWP3), 0.95 METs (aPM)). The greatest mean bias for the SWP3 was for cycling (-3.25 METs), with jogging (-5.16 METs) producing the greatest mean bias for the aPM. All of the activities (excluding SWP3 sweeping) were significantly different from the criterion measure. Although the SWP3 predicted METs are more accurate than their aPM equivalent, the predicted MET values from both devices are significantly different from the criterion measure for the majority of activities.

This study holistically examined the effects of long-haul transmeridian travel (LHTT) on physiological, perceptual, sleep and performance markers in nine international level swimmers preparing for the 2019 FINA World Long Course Championships in Gwangju, South Korea. Baseline (BL) measurements were taken over two days during the week before a long-haul eastward flight across eight time-zones. Following the flight, measurements were taken over a six-day holding camp in Japan (C1-C6), and over four days at the competition venue in Gwangju before the Championships commenced (PR1-PR4). Salivary cortisol (sCort), immunoglobulin A (sIgA), alpha-amylase (sAA) concentrations and perceptual measures via the Liverpool John Moore's University Jetlag Questionnaire were assessed. Sleep was monitored using wrist activity monitors and self-report sleep diaries. Performance was assessed via squat jump (SJ), countermovement jump (CMJ) and a 4 × 100 m swim test. Participants perceived themselves to be significantly more fatigued and jet lagged than BL for five- and nine-days post-travel, respectively. Morning sCort decreased by 70% on C1 and remained significantly lower than BL until C6 ( p < 0.05). Sleep ratings improved significantly in comparison to BL from C5 onwards ( p < 0.05). Compared with BL, there was no significant change in swim performance or SJ height following travel; however, there was a 3.8 cm improvement ( p < 0.001) in CMJ height on C5. It took ten days for elite swimmers to perceive themselves recovered from jet lag following LHTT in an eastward direction across eight time-zones. LHTT did not negatively affect sleep or physical performance in the swimmers in comparison to BL.