Key result
A composite physical activity score combining self-reported and accelerometer-derived data yielded higher R2 values for explaining variability in cardiovascular health indicators than single methods.
Why the study?
Does a composite physical activity score better explain variability in cardiovascular health indicators compared to single-method assessments?
Cross-Sectional (n=3,549)
Randomly assigned to derivation or validation data sets
Does a composite physical activity score better explain variability in cardiovascular health indicators compared to single-method assessments?
Combining self-reported and accelerometer-derived physical activity data provides a more robust estimate of physical activity behavior in relation to cardiovascular health.
Composite PA metrics may strengthen observational CV analyses; leaves open clinical adoption or intervention trials.
Background : Single-method assessment of physical activity (PA) has limitations. The utility and cross-validation of a composite PA score that includes reported and accelerometer-derived PA data has not been evaluated. Methods : Participants attending the Year 20 exam were randomly assigned to the derivation (two-thirds) or validation (one-third) data set. Principal components analysis was used to create a composite score reflecting Year 20 combined reported and accelerometer PA data. Generalized linear regression models were constructed to estimate the variability explained ( R 2 ) by each PA assessment strategy (self-report only, accelerometer only, composite score, or self-report plus accelerometer) with cardiovascular health indicators. This process was repeated in the validation set to determine cross-validation. Results : At Year 20, 3549 participants (45.2 [3.6] y, 56.7% female, and 53.5% black) attended the clinic exam and 2540 agreed to wear the accelerometer. Higher R 2 values were obtained when combined assessment strategies were used; however, the approach yielding the highest R 2 value varied by cardiovascular health outcome. Findings from the cross-validation also supported internal study validity. Conclusions : Findings support continued refinement of methodological approaches to combine data from multiple sources to create a more robust estimate that reflects the complexities of PA behavior.
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Gabriel et al. (2018) conducted a cross-sectional in Cardiovascular health (n=3,549). Composite physical activity score (self-report plus accelerometer) vs. Self-report only or accelerometer only was evaluated on Variability explained (R2) in cardiovascular health indicators. A composite physical activity score combining self-reported and accelerometer-derived data yielded higher R2 values for explaining variability in cardiovascular health indicators than single methods.
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