ArticleJMIR public health and surveillance2022
Estimating Cardiorespiratory Fitness Without Exercise Testing or Physical Activity Status in Healthy Adults: Regression Model Development and Validation.
Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Divergent associations of estimated cardiorespiratory fitness with cognitive trajectories in Chinese and U.S. middle-aged and older adults: A descriptive cross-national comparison from CHARLS and HRS.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Observational
- [Association between cardiorespiratory fitness and mortality risk in the chilean population].Revista espanola de salud publica · 2025Article
- Estimated Cardiorespiratory Fitness and Metabolic Risks.International journal of environmental research and public health · 2024Review
- Association between Estimated Cardiorespiratory Fitness and Abnormal Glucose Risk: A Cohort Study.Journal of clinical medicine · 2023Article
- Resting heart rate is a population-level biomarker of cardiorespiratory fitness: The Fenland Study.PloS one · 2023Article
- Cardiorespiratory Fitness and Its Place in Medicine.Reviews in cardiovascular medicine · 2023Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow cardiorespiratory fitness (CRF) is an independent predictor of morbidity and mortality. Most health care settings use some type of electronic health record (EHR) system. However, many EHRs do not have CRF or physical activity data collected, thereby limiting the types of investigations and analyses that can be done.
objectiveThis study aims to develop a nonexercise equation to estimate and classify CRF (in metabolic equivalent tasks) using variables commonly available in EHRs.
methodsParticipants were 42,676 healthy adults (female participants: n=9146, 21.4%) from the Aerobics Center Longitudinal Study examined from 1974 to 2005. The nonexercise estimated CRF was based on sex, age, measured BMI, measured resting heart rate, measured resting blood pressure, and smoking status. A maximal treadmill test measured CRF.
resultsAfter conducting nonlinear feature augmentation, separate linear regression models were used for male and female participants to calculate correlation and regression coefficients. Cross-classification of actual and estimated CRF was performed using low CRF categories (lowest quintile, lowest quartile, and lowest tertile). The multiple correlation coefficient (R) was 0.70 (mean deviation 1.33) for male participants and 0.65 (mean deviation 1.23) for female participants. The models explained 48.4% (SE estimate 1.70) and 41.9% (SE estimate 1.56) of the variance in CRF for male and female participants, respectively. Correct category classification for low CRF (lowest tertile) was found in 77.2% (n=25,885) of male participants and 74.9% (n=6,850) of female participants.
conclusionsThe regression models developed in this study provided useful estimation and classification of CRF in a large population of male and female participants. The models may provide a practical method for estimating CRF derived from EHRs for population health research.
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