Translational journal of the American College of Sports Medicine最新文献

{"title":"Call to Action for Promoting Exercise Is Medicine in Pregnancy—Collaboration Is Key","authors":"M. Sánchez-Polán, Taniya S. Nagpal, R. Barakat","doi":"10.1249/tjx.0000000000000222","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000222","url":null,"abstract":"","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66085427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Sectional and Individual Relationships between Physical Activity and Glycemic Variability.","authors":"Joshua R Sparks, Mark A Sarzynski, J Mark Davis, Peter W Grandjean, Xuewen Wang","doi":"10.1249/tjx.0000000000000207","DOIUrl":"10.1249/tjx.0000000000000207","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>Overweight or obese adults spend more time sedentary and less time performing physical activity (PA) and are at an increased risk for developing impaired glycemic health. Free-living environments may provide insight into glycemic health in addition to clinical assessments. The purpose of this study was to examine the relationship between PA and glycemic health assessed by continuous glucose monitoring (CGM).</p><p><strong>Methods: </strong>Twenty-eight overweight or obese adults each wore an accelerometer and CGM over the same 7 consecutive days. Average daily time (minutes and metabolic-equivalent minutes (MET-minutes)) and associated energy expenditure performing light (LPA), moderate-to-vigorous (MVPA), total PA, and standard deviation (SD) across days were calculated. Average daily 24-h and waking glycemia, mean glucose concentration, glycemic variability measured as the continuous overlapping net glycemic action, mean amplitude of glycemic excursions, and mean of daily difference were assessed.</p><p><strong>Results: </strong>LPA MET-minutes per day was positively associated with 24-h and waking glycemia time-in-range and negatively associated with 24-h and waking time in hyperglycemia. Total PA time and the SD of MVPA and total PA time were negatively associated with 24-h mean glucose concentration. Individual-level analysis identified that most participants (50%-71%) expressed negative associations between LPA and MVPA time with 24-h mean glucose concentration, mean amplitude of glycemic excursion, and 4-h continuous overlapping net glycemic action.</p><p><strong>Conclusions: </strong>Expectedly, greater total PA time and intensity-specific PA time were associated with lower 24-h and waking mean glucose concentration, greater glycemia time-in-range, and less time in hyperglycemia. The relationship between glucose concentrations and PA time SD was unexpected, whereas most participants expressed hypothesized relationships, which necessitates further exploration.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 4","pages":"1-12"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460942/pdf/nihms-1834220.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9396922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Weight Goals on Sitting and Moving During a Worksite Sedentary Time Reduction Intervention.","authors":"Krista S Leonard, Junia N de Brito, Miranda L Larouche, Sarah A Rydell, Nathan R Mitchell, Mark A Pereira, Matthew P Buman","doi":"10.1249/tjx.0000000000000210","DOIUrl":"10.1249/tjx.0000000000000210","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>Although many US adults report trying to lose weight, little research has examined weight loss goals as a motivator for reducing workplace sitting and increasing physical activity. This exploratory analysis examined weight goals and the association with changes in workplace sitting, physical activity, and weight.</p><p><strong>Methods: </strong>Employees (<i>N</i> = 605) were drawn from worksites participating in Stand and Move at Work. Worksites (<i>N</i> = 24) were randomized to a multilevel behavioral intervention with (STAND+) or without (MOVE+) sit-stand workstations for 12 months; MOVE+ worksites received sit-stand workstations from 12 to 24 months. At each assessment (baseline and 3, 12, and 24 months), participants were weighed and wore activPAL monitors. Participants self-reported baseline weight goals and were categorized into the \"Lose Weight Goal\" (LWG) group if they reported trying to lose weight or into the \"Other Weight Goal\" (OWG) group if they did not.</p><p><strong>Results: </strong>Generalized linear mixed models revealed that within STAND+, LWG and OWG had similar sitting time through 12 months. However, LWG sat significantly more than OWG at 24 months. Within MOVE+, sitting time decreased after introduction of sit-stand workstations for LWG and OWG, although LWG sat more than OWG. Change in physical activity was minimal and weight remained stable in all groups.</p><p><strong>Conclusions: </strong>Patterns of change in workplace sitting were more favorable in OWG relative to LWG, even in the absence of notable weight change. Expectations of weight loss might be detrimental for reductions in workplace sitting. Interventionists may want to emphasize non-weight health benefits of reducing workplace sitting.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 4","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9534174/pdf/nihms-1816661.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10626324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic Review of Fitbit Charge 2 Validation Studies for Exercise Tracking.","authors":"Crista Irwin, Rebecca Gary","doi":"10.1249/tjx.0000000000000215","DOIUrl":"10.1249/tjx.0000000000000215","url":null,"abstract":"<p><strong>Context: </strong>There are research-grade devices that have been validated to measure either heart rate (HR) by electrocardiography (ECG) with a Polar chest strap, or step count with ACTiGraph accelerometer. However, wearable activity trackers that measure HR and steps concurrently have been tested against research-grade accelerometers and HR monitors with conflicting results. This review examines validation studies of the Fitbit Charge 2 (FBC2) for accuracy in measuring HR and step count and evaluates the device's reliability for use by researchers and clinicians.</p><p><strong>Design: </strong>This registered review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The robvis (risk-of-bias visualization) tool was used to assess the strength of each considered article.</p><p><strong>Eligibility criteria: </strong>Eligible articles published between 2018 and 2019 were identified using PubMed, CINHAL, Embase, Cochran, and World of Science databases and hand-searches. All articles were HR and/or step count validation studies for the FBC2 in adult ambulatory populations.</p><p><strong>Study selection: </strong>Eight articles were examined in accordance with the eligibility criteria alignment and agreement among the authors and research librarian.</p><p><strong>Main outcome measures: </strong>Concordance correlation coefficients (CCC) were used to measure agreement between the tracker and criterion devices. Mean absolute percent error (MAPE) was used to average the individual absolute percent errors.</p><p><strong>Results: </strong>Studies that measured CCC found agreement between the FBC2 and criterion devices ranged between 26% and 92% for HR monitoring, decreasing in accuracy as exercise intensity increased. Inversely, CCC increased from 38% to 99% for step count when exercise intensity increased. HR error between MAPE was 9.21% to 68% and showed more error as exercise intensity increased. Step measurement error MAPE was 12% for healthy persons aged 24-72 years but was reported at 46% in an older population with heart failure.</p><p><strong>Conclusions: </strong>Relative agreement with criterion and low-to-moderate MAPE were consistent in most studies reviewed and support validation of the FBC2 to accurately measure HR at low or moderate exercise intensities. However, more investigation controlling testing and measurement congruency is needed to validate step capabilities. The literature supports the validity of the FBC2 to accurately monitor HR, but for step count is inconclusive so the device may not be suitable for recommended use in all populations.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 4","pages":"1-7"},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881599/pdf/nihms-1826450.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9142918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Program Barriers and Facilitators in Virtual Cancer Exercise Implementation: A Qualitative Analysis.","authors":"Jessica S Gorzelitz, Nour Bouji, Nicole L Stout","doi":"10.1249/tjx.0000000000000199","DOIUrl":"10.1249/tjx.0000000000000199","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>Due to the coronavirus disease 2019 (COVID-19) pandemic, many in-person cancer exercise and rehabilitation programs necessarily transitioned to virtual formats to meet the needs of individuals living with and beyond cancer. The purpose of this study was to qualitatively assess program-level facilitators and barriers to virtual exercise program implementation and to identify preferred strategies to overcome implementation barriers.</p><p><strong>Methods: </strong>US-based virtual cancer exercise and rehabilitation programs were recruited from professional networks via an emailed screening questionnaire. Eligible programs identified a point of contact for a 1:1 semi-structured interview to discuss program-level barriers and facilitators to implementing virtual exercise programs. Interview transcript analysis was conducted via inductive coding techniques using NVivo software. Barriers were categorized according to the Consolidated Framework for Implementation Research and a prioritized list of strategies to support implementation was created by mapping barriers to a list of Expert Recommendations for Implementing Change.</p><p><strong>Results: </strong>Of the 41 unique responses received, 24 program representatives completed semi-structured interviews. Interviewees represented individual programs, community-based programs, and hospital-based cancer exercise/rehabilitation programs. Analysis showed high correlation between facilitators and barriers by program type, with both program- and individual-level strategies used to implement exercise programs virtually. Strategies that ranked highest to support implementation include promoting program adaptability, building a coalition of stakeholders and identifying program champions, developing an implementation blueprint, altering organizational incentives and allowances, providing education across stakeholder groups, and accessing funding.</p><p><strong>Conclusions: </strong>Learning from the transition of cancer exercise and rehabilitation programs to virtual formats due to the COVID-19 pandemic, we identify program-level barriers and facilitators encountered in the implementation of virtual programs and highlight implementation strategies that are most relevant to overcome common barriers. We present a roadmap for programs to use these strategies for future work in virtual exercise and rehabilitation program implementation.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119660/pdf/nihms-1787100.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10107690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The IMAGINE Intervention: Impacting Physical Activity, Body Fat, Body Mass Index, and Dietary Inflammatory Index.","authors":"John A Bernhart,&nbsp;Gabrielle M Turner-McGrievy,&nbsp;Michael D Wirth,&nbsp;Nitin Shivappa,&nbsp;James R Hébert","doi":"10.1249/tjx.0000000000000181","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000181","url":null,"abstract":"<p><strong>Background: </strong>Many behavior-change interventions focused on nutrition and physical activity (PA) have been implemented to prevent disease and promote optimal health.</p><p><strong>Purpose: </strong>This study examined changes in PA with Energy-adjusted Dietary Inflammatory Index (E-DII™) and chronic disease risk factors in participants of a multicomponent intervention.</p><p><strong>Methods: </strong>Data from the Inflammation Management Intervention (IMAGINE) were used. Participants self-selected into the intervention or control group. At baseline and 12 weeks (post-intervention), participants completed three unannounced 24-hour dietary recalls (24HR), anthropometric measures (height, weight), and a dual x-ray absorptiometry scan. PA was measured using Sensewear^® armbands. E-DII scores were calculated from the 24HR. Descriptive statistics and t-tests summarized variables and multiple regression assessed relationships between PA and body mass index (BMI), total body fat percent, and E-DII scores.</p><p><strong>Results: </strong>Intervention participants increased moderate-to-vigorous PA (MVPA) and lowered BMI, total body fat, and E-DII scores compared to controls. Every 10-minute increase in post-intervention MVPA was associated with 1.6 kg/m² lower BMI (p<0.01) and 2.4% lower body fat percent (p<0.01) among control participants, after adjusting for covariates. Every 10-minute increase in post-intervention MVPA was associated with 0.3 lower (i.e., less inflammatory) post-intervention E-DII (p=0.01) scores among intervention participants, after adjusting for covariates.</p><p><strong>Conclusion: </strong>Participants who changed dietary intake changed PA. While changes were in expected directions, this intervention's emphasis on dietary behaviors compared to PA may have attenuated the relationship between PA and study outcomes.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9272997/pdf/nihms-1729178.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10807828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change in Objectively Measured Activity Levels Resulting from the EMPOWER Study Lifestyle Intervention.","authors":"B Rockette-Wagner,&nbsp;J Cheng,&nbsp;Z Bizhanova,&nbsp;A M Kriska,&nbsp;S M Sereika,&nbsp;C E Kline,&nbsp;C C Imes,&nbsp;J K Kariuki,&nbsp;D D Mendez,&nbsp;L E Burke","doi":"10.1249/tjx.0000000000000184","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000184","url":null,"abstract":"<p><strong>Purpose: </strong>To examine changes in physical activity (PA) during a behavioral weight-loss intervention and determine baseline factors associated with PA goal achievement.</p><p><strong>Methods: </strong>Overweight/obese community-dwelling adults with valid PA accelerometer data (N=116; mean age 51.7 years; 89% female; 83% non-Hispanic White) were recruited into a single-arm prospective cohort study examining the effects of a 12-month intervention that included 24 in-person group sessions, weight-loss, calorie, fat gram, and PA goals, self-monitoring, and feedback. Minutes of moderate-to-vigorous (MV) PA and steps were measured using a waist-worn accelerometer (ActiGraph GT3x) at baseline, 6 months, and 12 months. Achievement of the 150 minute/week MVPA goal was examined using total minutes and bout minutes (i.e., counting only PA occurring in bouts ≥10 minutes in length). Change in PA was analyzed using non-parametric tests for multiple comparisons. Associations of factors with meeting the PA goal were modeled using binary logistic regression.</p><p><strong>Results: </strong>At 6 months, there were increases from baseline in MVPA (median [p25, p75]: 5.3 [-0.9, 17.6] minutes/day) and steps (863 [-145, 2790] steps/day), both p<0.001. At 12 months, improvements were attenuated (MVPA: 2.4 [-2.0, 11.4] minutes/day, p=0.047; steps: 374[-570, 1804] p=0.14). At 6 months, 33.6% of individuals met the PA goal (using total or bout minutes). At 12 months, the percent meeting the goal using total MVPA [31%] differed from bout MVPA [22.4%]. Male gender (OR=4.14, p=0.027) and an autumn program start (versus winter; OR=3.39, p=0.011) were associated with greater odds of goal achievement at 6 months.</p><p><strong>Conclusions: </strong>The intervention increased PA goal achievement at 6 and 12 months with many making clinically meaningful improvements. Our results suggest female participants may require extra support toward improving PA levels.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982931/pdf/nihms-1739960.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10459044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Step Monitor Accuracy During PostStroke Physical Therapy and Simulated Activities.","authors":"Christopher E Henderson,&nbsp;Lindsay Toth,&nbsp;Andrew Kaplan,&nbsp;T George Hornby","doi":"10.1249/tjx.0000000000000186","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000186","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>The amount of stepping activity during rehabilitation post-stroke can predict walking outcomes, although the most accurate methods to evaluate stepping activity are uncertain with conflicting findings on available stepping monitors during walking assessments. Rehabilitation sessions also include non-stepping activities and the ability of activity monitors to differentiate these activities from stepping is unclear. The objective of this study was to examine the accuracy of different activity monitors worn by individuals post-stroke with variable walking speeds during clinical physical therapy (PT) and research interventions focused on walking.</p><p><strong>Methods: </strong>In Part I, 28 participants post-stroke wore a StepWatch, ActiGraph with and without a Low Frequency Extension (LFE) filter, and Fitbit on paretic and non-paretic distal shanks at or above the ankle during clinical PT or research interventions with steps simultaneously hand counted. Mean absolute percent errors were compared between limbs and tasks performed. In Part II, 12 healthy adults completed 8 walking and 9 non-walking tasks observed during clinical PT or research. Data were descriptively analyzed and used to assist interpretation of Part I results.</p><p><strong>Results: </strong>Part I results indicate most devices did not demonstrate an optimal limb configuration during research sessions focused on walking, with larger errors during clinical PT on the non-paretic limb. Using the limb that minimized errors for each device, the StepWatch had smaller errors than the ActiGraph and Fitbit (p<0.01), particularly in those who walked < 0.8 m/s. Conversely, errors from the ActiGraph-LFE demonstrated inconsistent differences in step counts between Fitbit and ActiGraph. Part II results indicate that errors observed during different stepping and non-stepping activities were often device-specific, with non-stepping tasks frequently detected as stepping.</p><p><strong>Conclusions: </strong>The StepWatch and ActiGraph-LFE had smaller errors than the Fitbit or ActiGraph, with greater errors in those walking at slower speeds. Inclusion of non-stepping activities affected step counts and should be considered when measuring stepping activity in individuals post-stroke to predict locomotor outcomes following rehabilitation.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9004549/pdf/nihms-1745420.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10450494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regular and App-enhanced Maintenance of Physical Activity among Latinas: A Feasibility Study.","authors":"Andrea S Mendoza-Vasconez, Esther Solis Becerra, Nathaniel Badii, Noe Crespo, Samantha Hurst, Britta Larsen, Bess H Marcus, Elva M Arredondo","doi":"10.1249/tjx.0000000000000188","DOIUrl":"10.1249/tjx.0000000000000188","url":null,"abstract":"<p><strong>Introduction: </strong>Few studies have focused on physical activity (PA) maintenance, particularly among minority populations; smartphone apps could provide valuable tools. This study aimed to 1) assess and understand PA maintenance among Latinas who completed a PA intervention; and 2) evaluate the feasibility, acceptability and preliminary efficacy of commercial smartphone apps as tools for PA maintenance.</p><p><strong>Methods: </strong>For this feasibility study, 27 Latinas who completed a PA intervention and increased their PA were randomly assigned to enhanced maintenance (i.e., taught to use commercial smartphone apps, N=14), or regular maintenance (i.e., no additional treatment, N=13). After 3-months, the feasibility and acceptability of using apps for PA maintenance was assessed via survey questions, analyzed using descriptive statistics. PA was reassessed via the 7-day PA Recall and analyzed using longitudinal mixed effects regression models. Qualitative data were collected via open-ended interview questions and analyzed using thematic analysis.</p><p><strong>Results: </strong>43% of enhanced maintenance participants reported using study apps at least \"a little\" and 21% using them \"a lot.\" Although not statistically significant, enhanced maintenance participants reported a smaller drop in PA from post-intervention to post-maintenance, compared to regular maintenance participants. Several participants expressed approval of the apps, while others reported on factors that kept them from using the apps for PA maintenance.</p><p><strong>Conclusion: </strong>Incorporating lessons learned from this study, larger randomized trials are warranted to evaluate the efficacy of using smartphone apps to support PA maintenance. The widespread use of apps could make them ideal tools to support PA maintenance after interventions in different settings.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9094163/pdf/nihms-1747173.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10457861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between Amount of Recess, Physical Activity, and Cardiometabolic Traits in U.S. Children.","authors":"Kimberly A Clevenger, Britni R Belcher, David Berrigan","doi":"10.1249/tjx.0000000000000202","DOIUrl":"10.1249/tjx.0000000000000202","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>In the United States, it is recommended that schools provide at least 20 minutes of daily recess, but the optimal amount for health benefits is unknown. We examined associations between amount of recess and health indicators using National Health and Nutrition Examination Survey data (NHANES; 2013-2016).</p><p><strong>Methods: </strong>For this cross-sectional analysis, parents/guardians of 6-11 year olds (n=738) reported recess provision which was classified as low (22.8%; approximately 10-15 min, 5 days per week), medium (54.9%; approximately 16-30 min, 5 days per week), or high (22.3%; approximately >30 min, 5 days per week). Outcomes measured included parent/guardian-reported and accelerometer-measured physical activity (PA), blood pressure, cholesterol, grip strength, bone mineral content, weight status, percent body fat, vitamin D level, and C-reactive protein level. Linear and logistic regression compared outcomes by level of recess provision accounting for the NHANES complex survey design.</p><p><strong>Results: </strong>The odds of meeting PA guidelines according to parent/guardian reports were 1.70 and 2.05 times higher in those with medium and high (respectively) versus low recess provision. Accelerometer-measured weekday activity was highest in those with high recess provision while weekend activity was highest in those with low recess provision (Cohen's <i>d</i> = 0.40-0.45). There were no other significant associations.</p><p><strong>Conclusion: </strong>At least 30 minutes of daily recess is associated with two-fold greater odds of achieving recommended PA levels according to parent/guardian reports; accelerometer data suggest this is through increased weekday activity. This finding suggests current national recess recommendations are insufficient for PA promotion. More detailed data on the frequency and duration of recess are needed to quantify optimal provision more precisely.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"7 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9531844/pdf/nihms-1798041.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9722787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}