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Ítalo R. Lemes, Rômulo A. Fernandes, Bruna C. Turi-Lynch, Jamile S. Codogno, Luana C. de Morais, Kelly A.K. Koyama and Henrique L. Monteiro

Background: Metabolic syndrome (MetS) is a combination of risk factors for cardiovascular disease and type 2 diabetes mellitus. The prevalence of MetS worldwide is increasing. There is no study investigating the economic burden of MetS, especially in developing countries, on medication-related expenditure. The aim of this study was to investigate the association of medication-related expenditures with MetS and to explore how physical activity (PA) may influence this association. Methods: A total of 620 participants, 50 years or older, randomly selected in the city of Bauru, Brazil. Participants were followed from 2010 to 2014, and data on health care expenditure were collected annually. PA questionnaire was applied at baseline, 2 (2012), and 4 (2014) years later. Results: Mean age was 64.7 (95% confidence interval, 64.1–65.3). MetS was associated with higher medication expenditure related to diseases of the circulatory (P <.01) and endocrine (P <.01) systems. MetS explained 17.2% of medication-related expenditures, whereas PA slightly attenuated this association, explaining 1.1% of all health care costs. Conclusion: This study demonstrates that MetS has a significant burden on health care expenditures among adults, whereas PA seems to affect this phenomenon significantly, but in low magnitude.

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David M. Shaw, Fabrice Merien, Andrea Braakhuis, Daniel Plews, Paul Laursen and Deborah K. Dulson

This study investigated the effect of the racemic β-hydroxybutyrate (βHB) precursor, R,S-1,3-butanediol (BD), on time-trial (TT) performance and tolerability. A repeated-measures, randomized, crossover study was conducted in nine trained male cyclists (age, 26.7 ± 5.2 years; body mass, 69.6 ± 8.4 kg; height, 1.82 ± 0.09 m; body mass index, 21.2 ± 1.5 kg/m2; VO2peak,63.9 ± 2.5 ml·kg−1·min−1; W max, 389.3 ± 50.4 W). Participants ingested 0.35 g/kg of BD or placebo 30 min before and 60 min during 85 min of steady-state exercise, which preceded a ∼25- to 35-min TT (i.e., 7 kJ/kg). The ingestion of BD increased blood D-βHB concentration throughout exercise (0.44–0.79 mmol/L) compared with placebo (0.11–0.16 mmol/L; all p < .001), which peaked 1 hr following the TT (1.38 ± 0.35 vs. 0.34 ± 0.24 mmol/L; p < .001). Serum glucose and blood lactate concentrations were not different between trials (all p > .05). BD ingestion increased oxygen consumption and carbon dioxide production after 20 min of steady-state exercise (p = .002 and p = .032, respectively); however, no further effects on cardiorespiratory parameters were observed. Within the BD trial, moderate to severe gastrointestinal symptoms were reported in five participants, and low levels of dizziness, nausea, and euphoria were reported in two participants. However, this had no effect on TT duration (placebo, 28.5 ± 3.6 min; BD, 28.7 ± 3.2 min; p = .62) and average power output (placebo, 290.1 ± 53.7 W; BD, 286.4 ± 45.9 W; p = .50). These results suggest that BD has no benefit for endurance performance.

Open access

Jairo H. Migueles, Alex V. Rowlands, Florian Huber, Séverine Sabia and Vincent T. van Hees

Recent technological advances have transformed the research on physical activity initially based on questionnaire data to the most recent objective data from accelerometers. The shift to availability of raw accelerations has increased measurement accuracy, transparency, and the potential for data harmonization. However, it has also shifted the need for considerable processing expertise to the researcher. Many users do not have this expertise. The R package GGIR has been made available to all as a tool to convermulti-day high resolution raw accelerometer data from wearable movement sensors into meaningful evidence-based outcomes and insightful reports for the study of human daily physical activity and sleep. This paper aims to provide a one-stop overview of GGIR package, the papers underpinning the theory of GGIR, and how research contributes to the continued growth of the GGIR package. The package includes a range of literature-supported methods to clean the data and provide day-by-day, as well as full recording, weekly, weekend, and weekday estimates of physical activity and sleep parameters. In addition, the package also comes with a shell function that enables the user to process a set of input files and produce csv summary reports with a single function call, ideal for users less proficient in R. GGIR has been used in over 90 peer-reviewed scientific publications to date. The evolution of GGIR over time and widespread use across a range of research areas highlights the importance of open source software development for the research community and advancing methods in physical behavior research.

Open access

Salomé Aubert, Joel D. Barnes, Megan L. Forse, Evan Turner, Silvia A. González, Jakub Kalinowski, Peter T. Katzmarzyk, Eun-Young Lee, Reginald Ocansey, John J. Reilly, Natasha Schranz, Leigh M. Vanderloo and Mark S. Tremblay

Background: In response to growing concerns over high levels of physical inactivity among young people, the Active Healthy Kids Global Alliance developed a series of national Report Cards on physical activity for children and youth to advocate for the promotion of physical activity. This article provides updated evidence of the impact of the Report Cards on powering the movement to get children and youth moving globally. Methods: This assessment was performed using quantitative and qualitative sources of information, including surveys, peer-reviewed publications, e-mails, gray literature, and other sources. Results: Although it is still too early to observe a positive change in physical activity levels among children and youth, an impact on raising awareness and capacity building in the national and international scientific community, disseminating information to the general population and stakeholders, and on powering the movement to get kids moving has been observed. Conclusions: It is hoped that the Report Card activities will initiate a measurable shift in the physical activity levels of children and contribute to achieving the 4 strategic objectives of the World Health Organization Global Action Plan as follows: creating an active society, creating active environments, creating active lives, and creating active systems.

Open access

Pauline M. Genin, Frédéric Dutheil, Benjamin Larras, Yoland Esquirol, Yves Boirie, Angelo Tremblay, Bruno Pereira, Corinne Praznoczy, David Thivel and Martine Duclos

Open access

Iñigo Mujika and Ritva S. Taipale

Open access

Meera Sreedhara, Karin Valentine Goins, Christine Frisard, Milagros C. Rosal and Stephenie C. Lemon

Background: Local health departments (LHDs) are increasingly involved in Community Health Improvement Plans (CHIPs), a collaborative planning process that represents an opportunity for prioritizing physical activity. We determined the proportion of LHDs reporting active transportation strategies in CHIPs and associations between LHD characteristics and such strategies. Methods: A national probability survey of US LHDs (<500,000 residents; 30.2% response rate) was conducted in 2017 (n = 162). LHDs reported the inclusion of 8 active transportation strategies in a CHIP. We calculated the proportion of LHDs reporting each strategy. Multivariate logistic regression models determined the associations between LHD characteristics and inclusion of strategies in a CHIP. Inverse probability weights were applied for each stratum. Results: 45.6% of US LHDs reported participating in a CHIP with ≥1 active transportation strategy. Proportions for specific strategies ranged from 22.3% (Safe Routes to School) to 4.1% (Transit-Oriented Development). Achieving national accreditation (odds ratio [OR] = 3.67; 95% confidence interval [CI], 1.11–12.05), pursuing accreditation (OR = 3.40; 95% CI, 1.25–9.22), using credible resources (OR = 5.25; 95% CI, 1.77–15.56), and collaborating on a Community Health Assessment (OR = 4.48; 95% CI, 1.23–16.29) were associated with including a strategy in a CHIP after adjusting for covariates. Conclusions: CHIPs are untapped tools, but national accreditation, using credible resources, and Community Health Assessment collaboration may support strategic planning efforts to improve physical activity.

Open access

Natalie M. Golaszewski and John B. Bartholomew

Research suggests 5 forms of social support: companionship, emotional, informational, instrumental, and validation. Despite this, existing measures of social support for physical activity are limited to emotional, companionship, and instrumental support. The purpose was to develop the Physical Activity and Social Support Scale (PASSS) with subscales that reflected all 5 forms. Participants (N = 506, mean age = 34.3 yr) who were active at least twice per week completed a 235-item questionnaire assessing physical activity behaviors, social support for physical activity, general social support, and other psychosocial questions. Exploratory and confirmatory factor analyses were used to develop and validate the PASSS. Exploratory factor analysis supported a 5-factor, 20-item model, χ2(100) = 146.22, p < .05, root mean square error of approximation = .05. Confirmatory factor analysis indicated good fit, Satorra–Bentler χ2(143) = 199.57, p < .001, root mean square error of approximation = .04, comparative-fit index = .97, standardized root mean square residual = .06. Findings support the PASSS to measure all 5 forms for physical activity.