Several steps/day indicators predict changes in anthropometric outcomes: HUB City Steps

Area of Science:

• Exercise Physiology
• Public Health Interventions
• Biostatistics

Background:

• Walking is a popular, accessible form of exercise.
• Pedometers offer a simple method to measure physical activity (steps/day).
• Limited research exists on the link between pedometer data and health outcome changes.

Purpose of the Study:

• To assess how pedometer-determined steps/day predict changes in anthropometric and clinical outcomes.
• To evaluate the impact of data cleaning (handling implausible values) on prediction accuracy.
• To analyze data from a community-based walking intervention (HUB City Steps) in an African American population.

Main Methods:

• Secondary analysis of data from 269 participants in a 6-month walking intervention.
• Weekly self-reported steps/day recorded via pedometers.
• Changes in anthropometric (BMI, waist circumference, %BF, fat mass) and clinical (blood pressure, lipids, glucose) outcomes measured.
• Bivariate and multivariable regression analyses used, controlling for covariates.

Main Results:

• Significant negative associations found between steps/day and most outcome changes at the bivariate level.
• After covariate adjustment, only associations with anthropometric outcomes remained significant.
• A 1,000 steps/day increase correlated with a 0.1% decrease in body fat percentage (%BF).
• Data cleaning methods (full, truncated, excluded) showed minimal impact on results.

Conclusions:

• Descriptive indicators of steps/day effectively predict changes in anthropometric measures.
• Data manipulation for implausible step counts did not significantly alter predictive ability for anthropometric outcomes.
• Pedometers are valuable tools for monitoring physical activity's impact on body composition.