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

{"title":"Hormone Fluctuations, Dietary Intake, and Daily Activity in Premenopausal Women: An 8-Week Observational Study.","authors":"Ariel J Aguiar Bonfim Cruz, Alexa J Chandler, Gena L Irwin, Malayna Schwartz, Ann Frost","doi":"10.1249/TJX.0000000000000353","DOIUrl":"https://doi.org/10.1249/TJX.0000000000000353","url":null,"abstract":"<p><strong>Introduction: </strong>Female reproductive hormonal fluctuations may be influenced by lifestyle behaviors such as diet, physical activity (PA), and sleep. However, little is known about these interactions over time or how they may differ between naturally menstruating (NM) and hormonal contraceptive (HC) women. This 8-wk observational study examined relationships between hormone levels and dietary intake, PA, exercise, and sleep quality, and explored differences by menstrual cycle phase and contraceptive status.</p><p><strong>Methods: </strong>Sixteen premenopausal women (NM = 9, HC = 7; age: 21.2 ± 2.8) completed weekly serum blood draws, 24-h dietary recalls, Simple PA Questionnaire, and Pittsburgh Sleep Quality Questionnaire. Body composition was measured at baseline.</p><p><strong>Results: </strong>In HC participants, leptin was positively associated with dietary fat (<i>r</i> = 0.348, <i>P</i> = 0.019). In NM participants, estradiol positively predicted daily calorie intake (β = 0.134, <i>P</i> = 0.019), added sugar intake (β = 0.0028, <i>P</i> = 0.0013), and was positively associated with total fat intake (<i>r</i> = 0.427, <i>P</i> = 0.003). Progesterone positively predicted added sugar intake (β = 0.12, <i>P</i> = 0.0024). Luteinizing hormone was negatively associated with calories (<i>r</i> = -0.327, <i>P</i> = 0.029), carbohydrate (<i>r</i> = -0.406, <i>P</i> = 0.006), and added sugar (<i>r</i> = -0.380, <i>P</i> = 0.010). Sex hormone binding globulin was positively associated with added sugar (<i>r</i> = 0.427, <i>P</i> = 0.003). In NM participants, leptin was positively associated with sedentary time (β = 0.072, <i>P</i> = 0.048). No other significant associations existed between hormones or menstrual cycle phase and PA, exercise, or sleep.</p><p><strong>Conclusion: </strong>Dietary intake was more strongly linked to hormone levels in NM than HC participants. Self-reported activity and sleep quality appeared consistent across the study, suggesting limited behavior fluctuations across the menstrual cycle.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"11 2","pages":"e000353"},"PeriodicalIF":2.2,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13107348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791605","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":"Barriers and Predictors of Long-Term Physical Activity Maintenance: The STRRIDE I Reunion Cohort.","authors":"Katherine A Collins-Bennett, Leanna M Ross, Johanna L Johnson, Cris A Slentz, Kim M Huffman, William E Kraus","doi":"10.1249/TJX.0000000000000276","DOIUrl":"10.1249/TJX.0000000000000276","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to identify barriers and predictors of self-reported physical activity (PA) maintenance 10 yr following the Studies of a Targeted Risk Reduction Intervention through Defined Exercise (STRRIDE I) randomized trial among young older adults.</p><p><strong>Methods: </strong>Participants completed a PA recall questionnaire that assessed barriers to PA maintenance. Chi-square tests assessed differences in barriers by PA maintenance tertile. Demographic and clinical measures collected at baseline and post-intervention from the STRRIDE I parent trial were included in model development to identify predictors of PA maintenance. Three models were generated using a backward bootstrap variable selection algorithm followed by multiple linear regression.</p><p><strong>Results: </strong>Participants who returned for the STRRIDE I Reunion study (<i>n</i> = 104; 63.0 ± 6.2 yr old) reported mean PA participation of 77.9 ± 76.5 min·wk^-1. Commonly reported barriers to PA maintenance included lack of self-motivation (41%), time constraints (33%), illness or injury (29%), and family obligations (23%). There was a significant association between the percentage of individuals who reported one or more barriers versus no barriers by PA maintenance tertile (frequency of PA: <i>χ</i> ² ratio = 26.1, <i>P</i> < 0.0001; amount of PA: <i>χ</i> ² ratio = 15.1, <i>P</i> = 0.0005). The baseline predictive model had an adjusted <i>R</i> ² value of 0.05, the post-intervention predictive model had an adjusted <i>R</i> ² of 0.12, and the change score (post-intervention minus pre-intervention) predictive model had an adjusted <i>R</i> ² of 0.17.</p><p><strong>Conclusions: </strong>Maintaining PA beyond a structured exercise intervention setting continues to be challenging for older adults. Compared to those who reported no barriers, young older adults who reported one or more barriers to PA maintenance were less active 10 yr following STRRIDE I. Additionally, how participants respond to a structured exercise intervention in certain clinical variables may be the most indicative of future PA maintenance.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"10 1","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12532306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330826","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":"Call to Action: Contribute to the Development of the Third Edition of the Physical Activity Guidelines for Americans.","authors":"Katrina L Piercy, Alison Vaux-Bjerke, Malorie Polster, Janet E Fulton, Stephanie George, Kenneth M Rose, Geoffrey P Whitfield, Dana L Wolff-Hughes, Elizabeth Y Barnett","doi":"10.1249/tjx.0000000000000275","DOIUrl":"10.1249/tjx.0000000000000275","url":null,"abstract":"","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"10 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694757","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 of physical and social workplace characteristics with movement behaviors at work.","authors":"Anthony J Holmes, Tyler D Quinn, Molly B Conroy, Joshua L Paley, Kimberly A Huber, Bethany Barone Gibbs","doi":"10.1249/tjx.0000000000000225","DOIUrl":"10.1249/tjx.0000000000000225","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>Sedentary behavior (SB) is common in desk-based work and prolonged periods of SB are associated with negative health outcomes. This study assessed associations between workplace characteristics and setting and movement patterns during working hours.</p><p><strong>Methods: </strong>This secondary analysis used baseline data from the Reducing Sedentary Behavior to Decrease Blood Pressure (RESET BP) clinical trial which enrolled inactive, desk-based workers with elevated blood pressure (<i>n</i>=271; mean age: 45.3±11.6 years; body mass index (BMI): 30.66±7.1 kg/m²; 59.4% women). Physical and social workplace characteristics were assessed by a study-developed questionnaire and the Office Environment and Sitting Scale (OFFESS). Participants also wore an activPAL activity monitor for 7 days and reported working hours in a diary to measure SB and physical activity (PA) specifically while working. Linear regression was used to analyze cross-sectional associations between workplace characteristics and SB and PA. A stratified analysis was also conducted to assess associations among home-based and in-office desk workers separately. Analyses were adjusted for age, gender, BMI, and work wear time.</p><p><strong>Results: </strong>Participants spent 77% of working hours in SB. Public vs. private offices, working in-office vs. at home, higher local connectivity, and greater overall connectedness were associated with lower SB and/or greater PA (all p<0.05). Higher frequency of face-to-face interactions, and greater visibility and proximity to co-workers was associated with less SB and more PA (all p<0.05). For example, home-based workers had more total SB (+17.2±8.4 mins/day), more SB bouts ≥30 mins (+39.1±12.8 mins/day), and less steps (695±201 steps/day) than in-office employees. Stratification by office setting revealed differences in associations between SB and PA and workplace characteristics.</p><p><strong>Conclusions: </strong>More public, open spaces with more social interactions and physical walkways could improve SB and PA patterns during work. Home-based workers had more SB, less PA, and unique associations of these activities with workplace characteristics, suggesting a need for tailored interventions.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10856919","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":"Framework to Classify Physical Activity Intervention Studies for Older Adults","authors":"J. Baldwin, L. Hassett, C. Sherrington","doi":"10.1249/tjx.0000000000000230","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000230","url":null,"abstract":"","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66085465","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":"The Biological Basis of Sex Differences in Athletic Performance: Consensus Statement for the American College of Sports Medicine","authors":"Sandra K. Hunter, Siddhartha S. Angadi, Aditi Bhargava, Joanna Harper, Angelica Lindén Hirschberg, Benjamin D. Levine, Kerrie L. Moreau, Natalie J. Nokoff, Nina S. Stachenfeld, Stéphane Bermon","doi":"10.1249/tjx.0000000000000236","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000236","url":null,"abstract":"ABSTRACT Biological sex is a primary determinant of athletic performance because of fundamental sex differences in anatomy and physiology dictated by sex chromosomes and sex hormones. Adult men are typically stronger, more powerful, and faster than women of similar age and training status. Thus, for athletic events and sports relying on endurance, muscle strength, speed, and power, males typically outperform females by 10%–30% depending on the requirements of the event. These sex differences in performance emerge with the onset of puberty and coincide with the increase in endogenous sex steroid hormones, in particular testosterone in males, which increases 30-fold by adulthood, but remains low in females. The primary goal of this consensus statement is to provide the latest scientific knowledge and mechanisms for the sex differences in athletic performance. This review highlights the differences in anatomy and physiology between males and females that are primary determinants of the sex differences in athletic performance and in response to exercise training, and the role of sex steroid hormones (particularly testosterone and estradiol). We also identify historical and nonphysiological factors that influence the sex differences in performance. Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial opportunities for high-impact studies. A major step toward closing the knowledge gap is to include more and equitable numbers of women to that of men in mechanistic studies that determine any of the sex differences in response to an acute bout of exercise, exercise training, and athletic performance.","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135784073","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":"Demographic, Clinical, and Psychosocial Predictors of Exercise Adherence: The STRRIDE Trials.","authors":"Katherine A Collins, Kim M Huffman, Ruth Q Wolever, Patrick J Smith, Leanna M Ross, Ilene C Siegler, John M Jakicic, Paul T Costa, William E Kraus","doi":"10.1249/tjx.0000000000000229","DOIUrl":"10.1249/tjx.0000000000000229","url":null,"abstract":"<p><strong>Purpose: </strong>To identify baseline demographic, clinical, and psychosocial predictors of exercise intervention adherence in the Studies of a Targeted Risk Reduction Intervention through Defined Exercise (STRRIDE) trials.</p><p><strong>Methods: </strong>A total of 947 adults with dyslipidemia or prediabetes were enrolled into an inactive control group or one of ten exercise interventions with doses of 10-23 kcal/kg/week, intensities of 40-80% of peak oxygen consumption, and training for 6-8-months. Two groups included resistance training. Mean percent aerobic and resistance adherence were calculated as the amount completed divided by the prescribed weekly minutes or total sets of exercise times 100, respectively. Thirty-eight clinical, demographic, and psychosocial measures were considered for three separate models: 1) clinical + demographic factors, 2) psychosocial factors, and 3) all measures. A backward bootstrapped variable selection algorithm and multiple regressions were performed for each model.</p><p><strong>Results: </strong>In the clinical and demographic measures model (<i>n</i>=947), variables explained 16.7% of the variance in adherence (p<0.001); lesser fasting glucose explained the greatest amount of variance (partial R² = 3.2%). In the psychosocial factors model (<i>n</i>=561), variables explained 19.3% of the variance in adherence (p<0.001); greater 36-Item Short Form Health Survey (SF-36) physical component score explained the greatest amount of variance (partial R² = 8.7%). In the model with all clinical, demographic, and psychosocial measures (<i>n</i>=561), variables explained 22.1% of the variance (p<0.001); greater SF-36 physical component score explained the greatest amount of variance (partial R² = 8.9%). SF-36 physical component score was the only variable to account for >5% of the variance in adherence in any of the models.</p><p><strong>Conclusions: </strong>Baseline demographic, clinical, and psychosocial variables explain approximately 22% of the variance in exercise adherence. The limited variance explained suggests future research should investigate additional measures to better identify participants who are at risk for poor exercise intervention adherence.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"8 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41159710","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":"Translational Journal of the American College of Sports Medicine: 2022 Paper of the Year","authors":"Kristin L. Campbell","doi":"10.1249/tjx.0000000000000238","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000238","url":null,"abstract":"The journal publishes original research, clinical trials, systematic review articles, and meta-analysis and policy research that discuss the translational implications of basic, clinical, and policy science to everyday real-world practice. Specifically, studies that apply basic and clinical research findings that move discovery and knowledge into clinical practice and community settings will be published.","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66085477","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":"Healthcare Professionals’ Insights on the Integration of Kinesiologists into Ontario’s Health System","authors":"Leslie E. Auger, Scott G. Thomas, Steve Fischer, Leanne Smith, John Armstrong, Raheel M. Dar, John Srbely","doi":"10.1249/tjx.0000000000000237","DOIUrl":"https://doi.org/10.1249/tjx.0000000000000237","url":null,"abstract":"ABSTRACT Introduction/Purpose Kinesiologists are well suited to work collaboratively or independently within the health system to improve patient/client care and well-being. This cross-sectional survey explored perceptions of the integration of registered kinesiologists (RKins) into the health system in Ontario. Methods RKins ( n = 202) and other health professionals (OHP; n = 337), including physicians, physiotherapists, nurse practitioners, etc., participated in an online survey. Results RKins reported working in diverse practice environments, and more than half reported receiving patients/clients through referrals. Of the OHP, 37.7% had ongoing professional interactions with RKins and 86.7% reported high satisfaction with these interactions; 32.6% of OHP reported referring patients/clients to RKins, primarily for exercise prescription (86.0%), treatment of clinical conditions (48.8%), and patient education (46.5%). Perceived barriers to referral included lack of awareness of the RKins’ scope of practice (81.0%), inadequate funding for services (67.1%), and low confidence in the clinical competency of RKins (61.8%). Conclusions RKins are experts in exercise-based interventions to prevent, treat, and manage many chronic lifestyle-related diseases. Initiatives to increase awareness of the RKins’ scope of practice, clinical competency, and standards of practice and to increase funding for RKin services are important next steps.","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135494613","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":"Lifestyle strategies after intentional weight loss: results from the MAINTAIN-pc randomized trial.","authors":"Tanya M Halliday, Molly McFadden, Maribel Cedillo, Bethany Barone Gibbs, Rachel Hess, Cindy Bryce, Gary S Fischer, Kimberly Huber, Kathleen M McTigue, Molly B Conroy","doi":"10.1249/tjx.0000000000000220","DOIUrl":"10.1249/tjx.0000000000000220","url":null,"abstract":"<p><strong>Introduction/purpose: </strong>Weight maintenance following intentional weight loss is challenging and often unsuccessful. Physical activity and self-monitoring are strategies associated with successful weight loss maintenance. However, less is known about the type and number of lifestyle strategies used following intentional weight loss. The purpose of this study was to determine the types and amounts of strategies associated with successful long-term weight loss maintenance.</p><p><strong>Methods: </strong>Data from the 24-month Maintaining Activity and Nutrition Through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) trial were analyzed. MAINTAIN-pc recruited adults (<i>n</i>=194; 53.4±12.2 years of age, body mass index (BMI): 30.4±5.9 kg/m², 74% female) with recent intentional weight loss of ≥5%, randomized to tracking tools plus coaching (i.e., coaching group) or tracking tools without coaching (i.e., tracking-only group). At baseline, 6, 12, and 24 months, participants reported lifestyle strategies used in the past 6 months, including self-monitoring, group support, behavioral skills, and professional support. General linear models evaluated changes in the number of strategies over time between groups and the consistency of strategies used over the 24-month intervention.</p><p><strong>Results: </strong>At baseline, 100% used behavioral skills, 73% used group support, 69% used self-monitoring, and 68% used professional support in the past 6 months; at 24 months, these rates were 98%, 60%, 75%, and 61%, respectively. While the number of participants utilizing individual strategies did not change significantly over time, the overall number of strategies participants reported decreased. More strategies were used at baseline and 6 months compared to 12- and 24-month follow-ups. The coaching group used more strategies at months 6 and 12 than the tracking-only group. Consistent use of professional support strategies over the 24-month study period was associated with less weight regain.</p><p><strong>Conclusion: </strong>Weight loss maintenance interventions that incorporate continued follow-up and support from healthcare professionals are likely to prevent weight regain after intentional weight loss.</p>","PeriodicalId":75243,"journal":{"name":"Translational journal of the American College of Sports Medicine","volume":"8 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10348773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9830913","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}