Background
To date, no studies have examined adherence to the 2018 Physical Activity Guidelines for Americans (PAGA) in real-world longitudinal settings using objectively measured activity monitoring data. This study addresses this gap by utilizing commercial activity monitoring (Fitbit) data from the All of Us (AoU) dataset. The primary objectives were to describe the prevalence of adherence to the 2018 PAGA and identify associated socio-demographic determinants. Additionally, we compared three distinct methods of processing physical activity data to estimate adherence to the 2008 PAGA.

Methods
We used the National Institutes of Health (NIH) AoU dataset, which contains minute-level Fitbit data for 13,947 US adults over a seven-year time span (2015 to 2022), to estimate adherence to PAGA. We used a published step-based method to estimate metabolic equivalents (METs) and assess adherence to 2018 PAGA (i.e., ≥ 150 minutes of moderate to vigorous intensity physical activity per week). We compared the step-based method, a heart rate (HR) based method and the proprietary Fitbit-developed algorithm to estimate adherence to the 2008 PAGA.

Results
The average overall adherence to 2018 PAGA was 21.6% (SE=±0.4%). Factors associated with lower adherence included female sex, higher BMI, being aged 30 – 49 or aged ≥ 70 (relative to being 18 – 29 years old), and non-Hispanic Black race/ethnicity (relative to non-Hispanic White race/ethnicity). The Fitbit algorithm estimated that a larger percentage of the sample (73.9% CI: 71.2–76.6) adhered to the 2008 PAGA compared to the HR method estimate (34.0% CI: 32.8–35.2) and the step-based method (10.0% CI: 9.4–10.6).

Conclusion
Our results show significant sociodemographic differences in PAGA adherence, and notably different estimates of adherence depending on the algorithm used. These findings warrant the need to account for these disparities when implementing physical activity interventions, and the need to establish an accurate and reliable method of using commercial accelerometers to examine physical activity, particularly as healthcare systems begin integrating wearable device data into patient health records.