Anticipating key innovations in physical activity epidemiology for the coming decades

In a previously published article in this journal, a research team from the China Kadoorie Biobank (CKB) (www.ckbiobank.org) used a novel phenome-wide association analysis to examine the prospective associations between physical activity (PA), 425 different types of diseases, and 53 specific causes of death in more than 500,000 participants.1 Throughout the duration of this study, there were 722,183 incident events and 39,320 mortality events recorded over a period of 12 years. PA was associated with a lower risk for 65 different diseases and lowered mortality risk of 19 causes. This study emphasizes that the benefits of PA go beyond those traditionally recognized, such as all-cause mortality, cardiovascular disease, certain types of cancer, and diabetes. Strengths of the study include the large sample size, a long follow-up period, and linkage to multiple health registries. Thus, this study provides comprehensive insights into the role of PA to improve long-term health.

This study also has important limitations acknowledged by the authors. One of them is that PA information was obtained by self-reports, which is a method known to include important measurement errors. Measurement error in self-reports can result, for example, from recall bias, misclassification of activity intensity, and individual and contextual interpretation differences when individuals are asked to estimate their PA levels. Research in PA epidemiology has increasingly incorporated more device-based measures as a result of recent technological advancements. Wearable devices, ranging from simple pedometers to more sophisticated tools, such as heart rate monitors and accelerometers, are being used to assess PA more accurately and have gained widespread use in large cohorts. To date, the UK Biobank (UKB; www.ukbiobank.ac.uk) is the largest cohort with device-measured PA. The UKB includes a population-based sample of approximately 100,000 adults who agreed to participate in a study of PA using wearable devices. One example of the use of the UKB wearable device data includes a phenome-wide association analysis performed recently, examining the associations of device-measured moderate to vigorous PA and the incidence of 697 diseases over a median of 6.3 years of follow-up.2 For comparative purposes, the authors also examined the associations between self-reported PA and the incidence of diseases in the total sample (n = 500,000), and, interestingly, they found similar benefits (e.g., cardiovascular and metabolic diseases) or harms (e.g., musculoskeletal conditions and injuries) of PA compared with those found in the CKB cohort. However, there were substantially stronger effect sizes, and harms were not so evident when using device-measured data as opposed to self-reported PA. These two phenome-wide association analyses in the CKB and UKB exemplify how research in PA epidemiology continues to evolve.3 The question then arises: what key technological and methodological innovations should PA epidemiologists leverage to further advance our knowledge about the health benefits of PA?