Seth Creasy

Ph.D.

University of Colorado Denver

Postdoctoral Fellow

IDATA (Learn more about this study)

IDATA-29

Jan 11, 2019

Establishing a Physical Activity Regularity Index

Physical activity (PA) is associated with numerous health benefits including decreased risk of mortality, obesity, type 2 diabetes, and cardiovascular disease. To date, most research has focused on the optimal PA frequency, duration, intensity, and type for health benefits. This information has been extremely valuable in shaping PA guidelines and recommendations. However, there is emerging evidence that the pattern of PA (i.e. timing and consistency) may be important for outcomes such as weight loss and glycemic control. Most studies have focused on the timing of PA because it is easily quantified and is translatable. PA consistency is much harder to quantify. We are not aware of any studies that have previously attempted to quantify PA consistency or regularity. However, the sleep field has an established sleep regularity index (SRI) which is distinct from total sleep duration and sleep quality. Interestingly, the having a higher SRI was associated with having a higher GPA amongst college students, despite no differences in total sleep duration. The primary aim of this proposal will be to establish a PA regularity index (PARI) using both the ActivPAL and the Actigraph PA monitors. Establishing a PARI may help inform future PA research. For example, the PARI may explain biological differences in response to PA; in addition, the PARI may help to predict long-term behavioral adherence to PA recommendations.

The PARI will utilize several criteria from the ActivPAL and the Actigraph separately to quantify a composite score (0-100) for PA regularity. The goal of the PARI will be to quantify consistency in PA behavior across an observation period; this measure should provide PA behavior insight beyond traditional measures such as amounts of light, moderate and vigorous PA. Criteria that will be included in the PARI include but are not limited to variability in total PA across days, the timing of PA across days, the number, duration, and intensity of bouts of PA. Using the large dataset from the Interactive Diet and Activity Tracking in AARP (iDATA) will allow us to weight PARI scores appropriately from 0-100. In addition, we will quantify the PARI using the ActivPAL and the Actigraph separately and compare scores. We will also determine whether certain demographic characteristics are associated with different PARI scores. Finally, we will determine if PARI is correlated with physical activity energy expenditure (PAEE).