ABSTRACT
BACKGROUND: Average daily steps (ADS) are a low-technology measurement of activity that is useful for exercise prescription. However, research demonstrates poor validity for ADS as a measure of exercise capability. We present a superior low-technology measure of exercise capability, which is easily applied by practitioners in clinical or nonclinical settings. METHODS: Based on analysis of baseline data from an intervention study to test a sustainable approach to long-term physical activity improvement for employed African American women, between 2005 and 2008, we examined exercise tolerance metabolic equivalents (METs) and ADS of 158 participants and generated an alternative measure of exercise capacity. We conducted regression analysis to determine the impact of key health indicators on exercise capacity and examined associations between our predictive model and true (MET) exercise performance. RESULTS: Using our predictive equation, 79.33% of participants were correctly categorized (very high, high, medium) based on our tool, with 10 women (6.67%) mischaracterized by one level higher than actual MET achievement and 21(14.00%) mischaracterized as one category lower than actual MET achievement. In contrast, using ADS alone resulted in 22.15% correctly categorized participants. CONCLUSIONS: The proposed tool is superior to existing low-technology measures of exercise capacity while retaining strong utility in nonclinical and low-resource settings.
