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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

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Title: 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
Language: English
Authors: Hannah R. Thompson (ORCID 0000-0003-0214-5003), Joni Ladawn Ricks-Oddie, Margaret Schneider, Sophia Day (ORCID 0000-0003-1885-5907), Kira Argenio, Kevin Konty, Shlomit Radom-Aizik, Yawen Guo (ORCID 0009-0006-2048-1798), Dan M. Cooper
Source: Journal of School Health. 2025 95(7):498-509.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 12
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Secondary Education
Descriptors: Physical Fitness, Data Interpretation, Statistical Bias, Youth, Elementary School Students, Secondary School Students, Tests, Exercise, Muscular Strength, Demography, Gender Differences, Racial Differences, Socioeconomic Status
Geographic Terms: New York (New York)
DOI: 10.1111/josh.70021
ISSN: 0022-4391
1746-1561
Abstract: Background: Data 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). Methods: This 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. Results: Across 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. Conclusion: The 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1474360
Database: ERIC
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  Data: 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
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  Data: <searchLink fieldCode="AR" term="%22Hannah+R%2E+Thompson%22">Hannah R. Thompson</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0214-5003">0000-0003-0214-5003</externalLink>)<br /><searchLink fieldCode="AR" term="%22Joni+Ladawn+Ricks-Oddie%22">Joni Ladawn Ricks-Oddie</searchLink><br /><searchLink fieldCode="AR" term="%22Margaret+Schneider%22">Margaret Schneider</searchLink><br /><searchLink fieldCode="AR" term="%22Sophia+Day%22">Sophia Day</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1885-5907">0000-0003-1885-5907</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kira+Argenio%22">Kira Argenio</searchLink><br /><searchLink fieldCode="AR" term="%22Kevin+Konty%22">Kevin Konty</searchLink><br /><searchLink fieldCode="AR" term="%22Shlomit+Radom-Aizik%22">Shlomit Radom-Aizik</searchLink><br /><searchLink fieldCode="AR" term="%22Yawen+Guo%22">Yawen Guo</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0006-2048-1798">0009-0006-2048-1798</externalLink>)<br /><searchLink fieldCode="AR" term="%22Dan+M%2E+Cooper%22">Dan M. Cooper</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+School+Health%22"><i>Journal of School Health</i></searchLink>. 2025 95(7):498-509.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Physical+Fitness%22">Physical Fitness</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Interpretation%22">Data Interpretation</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+Bias%22">Statistical Bias</searchLink><br /><searchLink fieldCode="DE" term="%22Youth%22">Youth</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Tests%22">Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Exercise%22">Exercise</searchLink><br /><searchLink fieldCode="DE" term="%22Muscular+Strength%22">Muscular Strength</searchLink><br /><searchLink fieldCode="DE" term="%22Demography%22">Demography</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Socioeconomic+Status%22">Socioeconomic Status</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22New+York+%28New+York%29%22">New York (New York)</searchLink>
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  Data: 10.1111/josh.70021
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  Data: 0022-4391<br />1746-1561
– Name: Abstract
  Label: Abstract
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  Data: Background: Data 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). Methods: This 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. Results: Across 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. Conclusion: The 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.
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        Value: 10.1111/josh.70021
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      – SubjectFull: Physical Fitness
        Type: general
      – SubjectFull: Data Interpretation
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      – SubjectFull: Statistical Bias
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