Background

The health-related fitness model (HRFM) was created to integrate five components of physical fitness (cardiorespiratory fitness, muscular strength, muscular endurance, flexibility, and body composition) known to support positive health. Research has evaluated individual health-related fitness markers relative to various biometric and perception measures; however, the HRFM has not been used to establish an overall fitness score. The purpose of this research was to create and examine an overall fitness score relative to individual health-related fitness markers, self-reported quality of life (QOL), biometric markers, and disease risk factors.

Methods

Health risk appraisals (HRA) were performed at three midwest manufacturing and processing companies. The HRA collected participant demographic and medical information, biometric measures, physical fitness, and QOL. The physical fitness assessments consisted of easy to administer protocols for the five markers of health-related fitness. Individual health-related fitness markers were used to create an overall fitness score (0-5 points) with a pass/fail system based on normative categorization tables. Descriptive statistics, Spearman correlations, and non-parametric tests were used to examine individual fitness markers, overall pass/fail fitness and QOL. Analysis of Variance (ANOVA) with Tukey's post hoc was used to detect differences in biometric measures by overall pass/fail fitness score and individual fitness markers. Likelihood modeling explored the predictive significance of overall/pass fail fitness score and individual fitness marker categorizations relative to coronary heart disease (CHD) and metabolic syndrome risk factors.

Results

A total of 176 participants between the ages of 20 to 76 years participated in the HRA. Gender distribution was essentially equal with 48.3% male (n=85) and 51.7% female (n=91). The majority of participants were of white ethnicity (94.3%) and reported education status of at least a high school degree (96.7%). Participants demonstrated low overall pass/fail fitness with 81.8% of participants passing fewer than two of the individual physical fitness assessments. Overall pass/fail fitness reflected each of the five health-related fitness markers through significant positive correlations (r=.34-.52; p<0.01), significant distribution differences (p<0.01), and significantly similar gender distribution differences (p<0.05). QOL also demonstrated similar distributions between overall pass/fail fitness and various fitness markers (cardiorespiratory fitness, muscular strength, and flexibility) while exhibiting a negative correlation (r = -0.27; p<0.01) and significantly different distribution (p<0.05) with body composition. Low density lipoproteins (LDL) were significantly different (p

Conclusion

Results suggest an overall pass/fail fitness score is able to comprehensively reflect individual health-related fitness markers. An overall pass/fail fitness score may also serve as a successful outlet in understanding the relationship between health-related fitness and QOL. However, overall pass/fail fitness score was unable to distinguish chronic disease risk factors and minimal predictive associations were seen between normative categorizations and risk factors. These findings suggest the overall pass/fail fitness score reflects individual health-related fitness markers and QOL, but does not serve as a reference for understanding disease risk.