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Nicolas Aguilar-Farias, Sebastian Miranda-Marquez, Pia Martino-Fuentealba, Kabir P. Sadarangani, Damian Chandia-Poblete, Camila Mella-Garcia, Jaime Carcamo-Oyarzun, Carlos Cristi-Montero, Fernando Rodriguez-Rodriguez, Pedro Delgado-Floody, Astrid Von Oetinger, Teresa Balboa-Castillo, Sebastian Peña, Cristobal Cuadrado, Paula Bedregal, Carlos Celis-Morales, Antonio Garcia-Hermoso and Andrea Cortínez-O’Ryan

Background: The study summarizes the findings of the 2018 Chilean Report Card (RC) on Physical Activity (PA) for Children and Adolescents and compares the results with the first Chilean RC and with other countries from the Global Matrix 3.0. Methods: A Research Work Group using a standardized methodology from the Global Matrix 3.0 awarded grades for 13 PA-related indicators based on the percentage of compliance for defined benchmarks. Different public data sets, government reports, and papers informed the indicators. Results: The grades assigned were for (1) “behaviors that contribute to overall PA levels”: overall PA, D−; organized sport participation, D−; active play, INC; and active transportation, F; (2) “factors associated with cardiometabolic risk”: sedentary behavior, C−; overweight and obesity, F; fitness, D; sleep, INC; and (3) “factors that influence PA”: family and peers, F; school, D; inclusion, INC; community and built environment, B; government strategies and investments, B−. Conclusions: Chile’s grades remained low compared with the first RC. On the positive side, Chile is advancing in environmental and policy aspects. Our findings indicate that the implementation of new strategies should be developed through collaboration between different sectors to maximize effective investments for increasing PA and decreasing sedentary time among children and adolescents in Chile.

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Sarah A. Roelker, Elena J. Caruthers, Rachel K. Hall, Nicholas C. Pelz, Ajit M.W. Chaudhari and Robert A. Siston

Two optimization techniques, static optimization (SO) and computed muscle control (CMC), are often used in OpenSim to estimate the muscle activations and forces responsible for movement. Although differences between SO and CMC muscle function have been reported, the accuracy of each technique and the combined effect of optimization and model choice on simulated muscle function is unclear. The purpose of this study was to quantitatively compare the SO and CMC estimates of muscle activations and forces during gait with the experimental data in the Gait2392 and Full Body Running models. In OpenSim (version 3.1), muscle function during gait was estimated using SO and CMC in 6 subjects in each model and validated against experimental muscle activations and joint torques. Experimental and simulated activation agreement was sensitive to optimization technique for the soleus and tibialis anterior. Knee extension torque error was greater with CMC than SO. Muscle forces, activations, and co-contraction indices tended to be higher with CMC and more sensitive to model choice. CMC’s inclusion of passive muscle forces, muscle activation-contraction dynamics, and a proportional-derivative controller to track kinematics contributes to these differences. Model and optimization technique choices should be validated using experimental activations collected simultaneously with the data used to generate the simulation.

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Sathvik Namburar, William Checkley, Oscar Flores-Flores, Karina M. Romero, Katherine Tomaino Fraser, Nadia N. Hansel, Suzanne L. Pollard and GASP Study Investigators

Background: The authors sought to examine physical activity patterns among children with and without asthma in 2 peri-urban communities in Lima, Peru, to identify socioeconomic and demographic risk factors for physical inactivity and examine the relationship between asthma and physical activity. Methods: The authors measured mean steps per day in 114 children (49 with asthma and 65 without) using pedometers worn over a 1-week period. They also used the 3-day physical activity recall to determine the most common activities carried out by children. Results: The authors found that 84.2% of the children did not meet the daily international physical activity recommendations. Girls took significantly fewer mean steps per day as compared with boys (2258 fewer steps, 95% confidence interval, 1042–3474), but no other factors, including asthma status, showed significant differences in the mean daily steps. Mean daily steps were positively associated with higher socioeconomic status among girls, and current asthma had a larger inverse effect on daily steps in boys when compared with girls. Conclusion: Physical activity levels were below recommended guidelines in all children. There is a need for policy and neighborhood-level interventions to address low physical activity levels among Peruvian youth. Special focus should be given to increasing the physical activity levels in girls.

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Robert J. Gregor

Open access

Christopher C. Moore, Aston K. McCullough, Elroy J. Aguiar, Scott W. Ducharme and Catrine Tudor-Locke

Background: The authors conducted a scoping review as a first step toward establishing harmonized (ie, consistent and compatible), empirically based best practices for validating step-counting wearable technologies. Purpose: To catalog studies validating step-counting wearable technologies during treadmill ambulation. Methods: The authors searched PubMed and SPORTDiscus in August 2019 to identify treadmill-based validation studies that employed the criterion of directly observed (including video recorded) steps and cataloged study sample characteristics, protocol details, and analytical procedures. Where reported, speed- and wear location–specific mean absolute percentage error (MAPE) values were tabulated. Weighted median MAPE values were calculated by wear location and a 0.2-m/s speed increment. Results: Seventy-seven eligible studies were identified: most had samples averaging 54% (SD = 5%) female and 27 (5) years of age, treadmill protocols consisting of 3 to 5 bouts at speeds of 0.8 (0.1) to 1.6 (0.2) m/s, and reported measures of bias. Eleven studies provided MAPE values at treadmill speeds of 1.1 to 1.8 m/s; their weighted median MAPE values were 7% to 11% for wrist-worn, 1% to 4% for waist-worn, and ≤1% for thigh-worn devices. Conclusions: Despite divergent study methodologies, the authors identified common practices and summarized MAPE values representing device step-count accuracy during treadmill walking. These initial empirical findings should be further refined to ultimately establish harmonized best practices for validating wearable technologies.

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Assumpta Ensenyat, Gemma Espigares-Tribo, Leonardo Machado-Da-Silva, Xenia Sinfreu-Bergués and Alfonso Blanco

Background: The purpose of this study was to evaluate the feasibility and effectiveness of a high-intensity semisupervised exercise program alongside lifestyle counseling as an intervention for managing cardiometabolic risk in sedentary adults. Methods: A 40-week 3-arm randomized controlled clinical trial (16-wk intervention and 24-wk follow-up) was used. Seventy-five sedentary adults (34–55 y) with at least 1 cardiometabolic risk factor were randomized into one of the following arms: (1) aerobic interval training (AIT) plus lifestyle counseling (n = 25), (2) low- to moderate-intensity continuous training plus lifestyle counseling (traditional continuous training, TCT) (n = 27), or (3) lifestyle counseling alone (COU) (n = 23). Metabolic syndrome severity scores, accelerometer-based physical activity, and self-reported dietary habits were assessed at baseline, after the intervention, and at follow-up. Results: AIT was well accepted with high enjoyment scores. All groups showed similar improvements in metabolic syndrome severity scores (standardized effect size = 0.46) and dietary habits (standardized effect size = 0.30). Moderate to vigorous physical activity increased in all study groups, with the number of responders higher in AIT and TCT groups (50%) than in COU group (21%). Both AIT and TCT had a greater impact on sedentary behavior than COU (63.5% vs 30.4% responders). Conclusions: AIT appears to be a feasible and effective strategy in sedentary individuals with cardiometabolic risk factors. AIT could be included in intervention programs tackling unhealthy lifestyles.