Reliability and validity of a brief self-report measure of health-related fitness in adults: the Multidimensional Health-Related Fitness Scale
Bibliographic record
Abstract
BACKGROUND: Health-related fitness (HRF) is essential for wellbeing and daily functioning. While objective fitness assessments are preferred, self-report measures are practical for large-scale or geographically diverse studies. Existing self-report HRF measures may lack sensitivity for younger or healthy adults. Additionally, many include items with no or poorly defined reference populations, potentially limiting their validity and comparability. This study examined the reliability and validity of single-item self-reported HRF measures of aerobic fitness, muscular strength and endurance, flexibility, coordination, agility, and body composition. METHODS: Between April and July 2023, University of Calgary students and staff (N.=129; mean age 28±9 years) completed the first questionnaire, with subsets completing a second questionnaire and validated fitness assessment. Nine items captured participants' self-rated HRF relative to those of the same age and gender. The nine self-reported HRF items were aggregated to obtain an estimate of overall HRF (Multidimensional Health-Related Fitness Scale, MHFS). We used intraclass correlations (ICC) to estimate test-retest reliability of the individual self-reported HRF items and MHFS. We assessed convergent validity with self-reported leisure physical activity (LPA) and concurrent validity with objective fitness measures using age- and sex-adjusted partial correlations. RESULTS: The single-item self-reported HRF measures (ICC=0.60-0.85) and MHFS (ICC=0.87) had acceptable test-retest reliability. The MHFS also had high internal consistency (Cronbach's α=0.87). Evidence of validity was observed with partial correlations ≥0.30 between self-reported HRF and LPA, and objective fitness measures. CONCLUSIONS: The MHFS provides a reliable and valid HRF indicator among younger adult populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".