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Martinez MF, Weatherwax C, Piercy K, Whitley MA, Bartsch SM, Heneghan J, Fox M, Bowers MT, Chin KL, Velmurugan K, Dibbs A, Smith AL, Pfeiffer KA, Farrey T, Tsintsifas A, Scannell SA, Lee BY. Benefits of Meeting the Healthy People 2030 Youth Sports Participation Target. Am J Prev Med 2024; 66:760-769. [PMID: 38416089 PMCID: PMC11034834 DOI: 10.1016/j.amepre.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 12/18/2023] [Accepted: 12/18/2023] [Indexed: 02/29/2024]
Abstract
INTRODUCTION Healthy People 2030, a U.S. government health initiative, has indicated that increasing youth sports participation to 63.3% is a priority in the U.S. This study quantified the health and economic value of achieving this target. METHODS An agent-based model developed in 2023 represents each person aged 6-17 years in the U.S. On each simulated day, agents can participate in sports that affect their metabolic and mental health in the model. Each agent can develop different physical and mental health outcomes, associated with direct and indirect costs. RESULTS Increasing the proportion of youth participating in sports from the most recent participation levels (50.7%) to the Healthy People 2030 target (63.3%) could reduce overweight/obesity prevalence by 3.37% (95% CI=3.35%, 3.39%), resulting in 1.71 million fewer cases of overweight/obesity (95% CI=1.64, 1.77 million). This could avert 352,000 (95% CI=336,200, 367,500) cases of weight-related diseases and gain 1.86 million (95% CI=1.86, 1.87 million) quality-adjusted life years, saving $22.55 billion (95% CI=$22.46, $22.63 billion) in direct medical costs and $25.43 billion (95% CI= $25.25, $25.61 billion) in productivity losses. This would also reduce depression/anxiety symptoms, saving $3.61 billion (95% CI=$3.58, $3.63 billion) in direct medical costs and $28.38 billion (95% CI=$28.20, $28.56 billion) in productivity losses. CONCLUSIONS This study shows that achieving the Healthy People 2030 objective could save third-party payers, businesses, and society billions of dollars for each cohort of persons aged 6-17 years, savings that would continue to repeat with each new cohort. This suggests that even if a substantial amount is invested toward this objective, such investments could pay for themselves.
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Affiliation(s)
- Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Katrina Piercy
- Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services (HHS), Rockville, Maryland
| | - Meredith A Whitley
- Ruth S. Ammon College of Education and Health Sciences, Adelphi University, Garden City, New York; Stellenbosch University, Maties Sport, Centre for Sport Leadership, Stellenbosch, South Africa
| | - Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Jessie Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Martin Fox
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Matthew T Bowers
- Department of Kinesiology and Health Education, College of Education, The University of Texas at Austin, Austin, Texas
| | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alexis Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Alan L Smith
- Emma Eccles Jones College of Education and Human Services, Utah State University, Logan, Utah
| | - Karin A Pfeiffer
- Department of Kinesiology, College of Education, Michigan State University, East Lansing, Michigan
| | - Tom Farrey
- Project Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia
| | - Alexandra Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), City University of New York (CUNY) Graduate School of Public Health and Health Policy, New York City, New York; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York.
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Bonafiglia JT, Preobrazenski N, Gurd BJ. A Systematic Review Examining the Approaches Used to Estimate Interindividual Differences in Trainability and Classify Individual Responses to Exercise Training. Front Physiol 2021; 12:665044. [PMID: 34819869 PMCID: PMC8606564 DOI: 10.3389/fphys.2021.665044] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 10/05/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Many reports describe statistical approaches for estimating interindividual differences in trainability and classifying individuals as "responders" or "non-responders." The extent to which studies in the exercise training literature have adopted these statistical approaches remains unclear. Objectives: This systematic review primarily sought to determine the extent to which studies in the exercise training literature have adopted sound statistical approaches for examining individual responses to exercise training. We also (1) investigated the existence of interindividual differences in trainability, and (2) tested the hypothesis that less conservative thresholds inflate response rates compared with thresholds that consider error and a smallest worthwhile change (SWC)/minimum clinically important difference (MCID). Methods: We searched six databases: AMED, CINAHL, EMBASE, Medline, PubMed, and SportDiscus. Our search spanned the aerobic, resistance, and clinical or rehabilitation training literature. Studies were included if they used human participants, employed standardized and supervised exercise training, and either: (1) stated that their exercise training intervention resulted in heterogenous responses, (2) statistically estimated interindividual differences in trainability, and/or (3) classified individual responses. We calculated effect sizes (ESIR) to examine the presence of interindividual differences in trainability. We also compared response rates (n = 614) across classification approaches that considered neither, one of, or both errors and an SWC or MCID. We then sorted response rates from studies that also reported mean changes and response thresholds (n = 435 response rates) into four quartiles to confirm our ancillary hypothesis that larger mean changes produce larger response rates. Results: Our search revealed 3,404 studies, and 149 were included in our systematic review. Few studies (n = 9) statistically estimated interindividual differences in trainability. The results from these few studies present a mixture of evidence for the presence of interindividual differences in trainability because several ESIR values lay above, below, or crossed zero. Zero-based thresholds and larger mean changes significantly (both p < 0.01) inflated response rates. Conclusion: Our findings provide evidence demonstrating why future studies should statistically estimate interindividual differences in trainability and consider error and an SWC or MCID when classifying individual responses to exercise training. Systematic Review Registration: [website], identifier [registration number].
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Affiliation(s)
- Jacob T Bonafiglia
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON, Canada
| | | | - Brendon J Gurd
- School of Kinesiology and Health Studies, Queen's University, Kingston, ON, Canada
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