Evidence map›Paper›PMID 40386883›Full record

Observational studyThe Journal of school health2025

Data Missingness and Equity Implications in the Nation's Largest Student Fitness Surveillance System: The New York City School Based Physical Fitness Testing Programs, 2006-2020.

Hannah R Thompson, Joni Ladawn Ricks-Oddie, Margaret Schneider, Sophia Day, Kira Argenio, Kevin Konty, Shlomit Radom-Aizik, Yawen Guo, Dan M Cooper

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in The Journal of school health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Observational
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

9 authors.

Hannah R ThompsonDepartment of Community Health Science, School of Public Health, University of California Berkeley, California, USA.ORCID 0000-0003-0214-5003
Joni Ladawn Ricks-OddieCenter for Statistical Consulting, Department of Statistics, University of California Irvine, Irvine, California, USA.
Margaret SchneiderJoe C. Wen School of Population & Public Health, University of California Irvine, Irvine, California, USA.
Sophia DayOffice of School Health, Data Science and Research, New York City Department of Health and Mental Hygiene, New York, New York, USA.ORCID 0000-0003-1885-5907
Kira ArgenioOffice of School Health, Data Science and Research, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Kevin KontyOffice of School Health, Data Science and Research, New York City Department of Health and Mental Hygiene, New York, New York, USA.
Shlomit Radom-AizikPediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California Irvine, Irvine, California, USA.
Yawen GuoDepartment of Informatics, University of California, Irvine, Irvine, California, USA.ORCID 0009-0006-2048-1798
Dan M CooperPediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California Irvine, Irvine, California, USA.

Funding

Institute for Clinical and Translational ScienceUM1TR004927 · NCATS · UNIVERSITY OF CALIFORNIA-IRVINE · PI DAN M COOPER, Eric J. Vilain · 2024 to 2026
$12.2M
Increasing compliance with physical education laws to reduce health inequitiesK01HL151805 · NHLBI · UNIV OF CALIF/DIV/AGRICULTURE/NAT/RESOUR · PI Hannah R Thompson · 2021 to 2026
$1.0M
NCATS NIH HHS UM1 TR004927NHLBI NIH HHS K01 HL151805
6 · The paper itself

Abstract

backgroundData missingness can bias interpretation and outcomes resulting from data use. We describe data missingness in the longest-standing US-based youth fitness surveillance system (2006/07-2019/20).

methodsThis observational study uses the New York City FITNESSGRAM (NYCFG) database from 1,983,629 unique 4th-12th grade students (9,147,873 student-year observations) from 1756 schools. NYCFG tests for aerobic capacity, muscular strength, and endurance were administered annually. Mixed effects models determined the prevalence of missingness by demographics, and associations between demographics and missingness.

resultsAcross years, 20.1% of students were missing data from all three tests (11.7% for elementary students, 15.6% middle, and 36.3% high). Missingness did not differ by sex, but differed significantly by race/ethnicity and student home neighborhood socioeconomic status.

conclusionThe nation's largest youth fitness surveillance system demonstrates the highest fitness data missingness among high school students, with more than 1/3 of students missing data. Non-Hispanic Black students and those with very poor home neighborhood SES, across all grade levels, have the highest odds of missing data. IMPLICATIONS FOR SCHOOL HEALTH: Strategies to better understand and ameliorate the causes of school-based fitness testing data missingness will increase overall data quality and begin to address health inequities in this critical metric of youth health.

Indexed as

Exercise TestPhysical FitnessSchool Health ServicesAdolescentChildFemaleHumansMaleNew York CitySchoolsStudentschild and adolescent healthdata‐driven decision‐making in school healthorganization and administration of school health programsphysical activityphysical fitness and sport

Identifiers

PMID40386883
PMCPMC12172602

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.