Evidence map›Paper›PMID 38416089›Full record

ArticleAmerican journal of preventive medicine2024

Benefits of Meeting the Healthy People 2030 Youth Sports Participation Target.

Marie F Martinez, Colleen Weatherwax, Katrina Piercy, Meredith A Whitley, Sarah M Bartsch, Jessie Heneghan, Martin Fox, Matthew T Bowers, Kevin L Chin, Kavya Velmurugan and 7 more

Abstract read
In one paragraph

Article in American journal of preventive medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  4. Article
  5. Article
  6. General and sport-specific nutrition knowledge and behaviors of adolescent athletes.Journal of the International Society of Sports Nutrition · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Marie F MartinezPublic 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 WeatherwaxPublic 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 PiercyOffice of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services (HHS), Rockville, Maryland.
Meredith A WhitleyRuth 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 BartschPublic 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 HeneghanPublic 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 FoxProject Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia.
Matthew T BowersDepartment of Kinesiology and Health Education, College of Education, The University of Texas at Austin, Austin, Texas.
Kevin L ChinPublic 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 VelmuruganPublic 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 DibbsPublic 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 SmithEmma Eccles Jones College of Education and Human Services, Utah State University, Logan, Utah.
Karin A PfeifferDepartment of Kinesiology, College of Education, Michigan State University, East Lansing, Michigan.
Tom FarreyProject Play, Sports & Society Program, The Aspen Institute, Washington, District of Columbia.
Alexandra TsintsifasPublic 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 ScannellPublic 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 LeePublic 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. Electronic address: bruceleemdmba@gmail.com.

Funding

Project 4: Virtual Public Health Precision Nutrition LaboratoryU54TR004279 · NCATS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI Bruce Y Lee · 2022 to 2026
$5.4M
Simulating the Spread and Control of Multiple MDROs Across a Network of Different Nursing HomesP01AI172725 · NIAID · UNIVERSITY OF CALIFORNIA-IRVINE · PI HUANG, SUSAN S. · 2023 to 2023
$2.9M
Regional Healthcare Ecosystem Analyst (RHEA) Modeling the Environment (MODE): SARS-CoV-2R01GM127512 · NIGMS · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2020 to 2023
$2.1M
MOdeling Nursing homes to Affect Response to COVID-19 (MONARC)R01HS028165 · AHRQ · GRADUATE SCHOOL OF PUBLIC HEALTH AND HEALTH POLICY · PI LEE, BRUCE Y · 2021 to 2022
$993k
AHRQ HHS R01 HS028165NCATS NIH HHS U54 TR004279NIAID NIH HHS P01 AI172725NIGMS NIH HHS R01 GM127512
6 · The paper itself

Abstract

introductionHealthy 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.

methodsAn 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.

resultsIncreasing 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.

conclusionsThis 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.

Indexed as

Healthy People ProgramsYouth SportsAdolescentChildFemaleHumansMaleMental HealthOverweightUnited States

Identifiers

PMID38416089
PMCPMC11034834

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.