Published in

Human Kinetics, Journal of Physical Activity and Health, 11(15), p. 847-856, 2018

DOI: 10.1123/jpah.2017-0692

Links

Tools

Export citation

Search in Google Scholar

The Utility and Cross-Validation of a Composite Physical Activity Score in Relation to Cardiovascular Health Indicators: Coronary Artery Risk Development in Young Adults

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Red circle
Preprint: archiving forbidden
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

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 (R2) 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 R2 values were obtained when combined assessment strategies were used; however, the approach yielding the highest R2 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.