Adults with ASD who completed PACT demonstrated improvements in self-report PA and a positive trend in steps/day. Although, a small cohort, study results support use of a Fitbit device and peer-supported telehealth for this population who face multiple barriers to participate in community-based PA programs and have greater propensity towards obesity and high-risk abdominal adiposity.

We examined the association between indicators of the school environment with sedentary behavior and different intensities of physical activity in children. The study that included 515 children (265 boys) aged 9–11 years old from public and private schools in the city of São Caetano do Sul. Sedentary behavior and different intensities of physical activity were evaluated with an accelerometer. Inside school environment (policies, supervision committee, extracurricular activities, breaks, and access to school facilities) was evaluated using a questionnaire. Policies and practice (β: 8.49; 95% CI: 3.62–13.36), supervision committee (5.42; 0.64–10.19), inter-school competitions (2.40, 2.25–2.55), breaks of 15–29 min/day (6.87; 2.20–10.75), and outdoor sports field (5.40; 0.37–10.44), were positively associated with moderate-to-vigorous-intensity physical activity. Furthermore, crossing guards (7.65; 3.00–12.30) were positively associated with moderate-to-vigorous-intensity physical activity. We concluded that an association was found between school environment indicators with higher levels of physical activity and greater odds of meeting physical activity guidelines.

RESULTS:Twenty-seven studies (635 participants) were included. A significant difference was found for same-day PPG control, which favored accumulated exercise over one bout of energy-matched continuous exercise (SMD −0.36 [95%CI: (−0.56, -0.17)], P=0.0002, I 2 =1%), specifically in accumulated exercise with PA breaks (SMD −0.36 [95%CI: (-0.64, -0.08)], P=0.01, I 2 =30%), low-moderate intensity exercise (SMD −0.38 [(95%CI: (-0.59, -0.17)], P=0.0005, I 2 =0%), and in non-diabetic populations (SMD -0.36 [95%CI: (-0.62, -0.10)], P=0.007, I 2 =16%). No differences were found for same-day postprandial insulin and triglycerides, or second-morning effects for all previously mentioned markers between different exercise patterns. CONCLUSIONS:Compared with one session of continuous exercise, accumulated exercise-specifically in subgroups of PA breaks, low-moderate intensity exercises-produced greater acute effects on same-day PPG control for non-diabetic adults. There were no differences between continuous and accumulated patterns of exercise in terms of same-day postprandial insulin and triglycerides and second-morning effects.

Objective: To describe and compare physical fitness variables according to compliance with the recommendations of physical activity, measured by accelerometry. Methods: The sample gathered 120 students, 57 boys and 63 girls aged 9 to 11 years. The variables analyzed were: weight, stature, BMI, skinfolds, waist circumference, agility, flexibility, speed and strength of upper and lower limbs, and abdominal strength. Physical activity was measured objectively using an accelerometer. The students were divided into two groups: “complies with recommendations” (≥60min/day) and “does not comply with recommendations” (<60min/day). To verify the normality of the data, the Kolmogorov-Smirnov test was used. The mean values of students who do or do not comply with the physical activity recommendation were compared using Student's t and U-Mann Whitney tests. The level of significance was set at p<0.05. Results: The students who followed the recommendation showed significantly lower values compared to those who did not for adiposity (sum of 7 skinfolds); body weight; body mass index (BMI) and abdominal strength. No significant differences were found in the variables of speed and agility, and the upper limbs’ strength was greater in subjects who did not comply with recommendations. Conclusions: Students who complied with physical activity recommendations had better body composition and more abdominal strength than those who did not.

Background: Sedentary behavior is considered a health risk independent of physical activity. We evaluated the relationship between sedentary behavior, bone mass, and bone geometry among young male basketball and volleyball players.Methods: Fifty-five athletes (basketball n=21; volleyball n=34) aged 14 to 17 years old were included. Body composition and bone mass were measured by dual-energy X-ray absorptiometry, comprising bone mineral density, bone mineral content at the lumbar spine (L1-L4), and femoral neck. Bone quality was evaluated by bone geometry considering the femur strength index, section modulus, cross-sectional moment of inertia, and cross-sectional area. Information on all foods and beverages were obtained by a nutritionist through a 24-hour food recall and a semi-quantitative food frequency questionnaire. The sedentary behavior was assessed using the Adolescent Sedentary Activity Questionnaire. A series of multilevel linear regression models were fitted to explore whether there was variation for players' body composition, bone parameters, diary nutrient intake and sedentary behavior by sport. All models were fitted using Bayesian methods.Results: Body composition and bone mass values were high for both basketball and volleyball players. However, there was no substantial variation between players by sport for body composition. Adjusting for age, there was no association of sedentary behavior on both bone mass and geometry among the athletes. Except for femoral strength index, age had a substantial moderate to large association with all bone mass and geometry indicators. Lastly, there was no substantial influence of sport (level-2 unit) on the estimates of the association between sedentary behavior and age with bone mass and geometry, as uncertainty estimates for group-level effects were high. Conclusions: In conclusion, there is no association between sedentary behaviour and bone mass and bone geometry, showing that accumulated training loads (15+ h/week) among young basketball and volleyball players are critical; they produce a positive stimulus on bone mass and bone geometry development.

RESULTS:Average energy consumption decreased from pre-surgery to post-surgery (1797.3 ± 588.9 vs. 1587.8 ± 604.9, respectively; p=0.029). Comparisons pre-and post-surgery for weight (74.9 kg ±18.0 vs. 74.5 ± 18.5), kcal/kg (24.8 ± 7.5 vs 22.3 ± 8.8), and protein g/kg (0.99 ± 0.35 vs. 0.97 ± 0.44) did not show differences. HEI-2015 was significantly lower in teenagers pre-and post-surgery compared to the US average of 53 for Americans age 2-19 (42.4 ± 8.5; p <.001; 42.7 ± 9.8; p <.001). Adult participants' HEI was significantly lower than the US average of 59 only after ACL reconstruction (53.2 ± 13.3; p= 0.073; 50.5 ± 13.2; p= 0.011). Children had significantly lower HEI-2015 than adults before reconstruction (42.4 ± 8.5 vs 53.2 ± 13.3; p=0.007) but not after (42.7 ± 9.8 vs 50.5 ± 13.2; p=0.057). CONCLUSION: On average, energy intake decreases following ACL surgery in young participants. Future research should address links between diet quality, reduced energy intake and recovery following ACL surgery.

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