The advent of small, cheap accelerometers - such as the one found inside every mobile device these days - has profoundly changed the nature of the data used in scientific studies of the relationship between health and exercise. As in the study I'll point out here, it has been noted that self-reported exercise levels bear only a modest correlation to accelerometer data gathered from those same individuals. There are definitely questions on the interpretation of this data, however. This all starts to suggest that some of the results from older studies are based on artifacts in the data, not reality. For example, that there is a large difference in outcomes between no exercise and some exercise, but little further gain in health and reduced mortality for increased exercise past that point. Is that curve of benefits real, or does it result from the muddiness of self-reported levels of exercise?
Self-reported physical activity questionnaires remain the primary assessment method for large observational studies despite their limitations. Physical activity questionnaires rank participant physical activity levels moderately well, but are less precise assessing the absolute volume of physical activity (e.g. the total amount of time spent in moderate-to-vigorous physical activity (MVPA)) compared to objective measures. Objective measures of physical activity, such as those obtained from accelerometers, may allow for a more precise assessment of physical activity volume.
It is important to understand the relationship between accelerometer-assessment and self-report. Accelerometers, due to their decreasing cost and size, have become increasingly prevalent in both research settings and as commercial products, but are unlikely to fully replace self-report as the primary MVPA assessment method in large observational studies. Self-report questionnaires may be preferred due to fewer logistical challenges as well as to examine specific activities or domains of activity (such as leisure-time, transportation, occupational, and home-based physical activity). Finally, the majority of the existing research examining physical activity and health is based on self-reported physical activity.
Previous studies have shown a low to moderate correlation between self-report questionnaires and uniaxial accelerometer measures, as well as significant differences in absolute volume of MVPA measured. A challenge to describing accelerometer-assessed physical activity is determining the appropriate cutpoint to translate accelerometer measures into physical activity carried out at different intensities. Numerous accelerometer cutpoints for MVPA, all using data collected from the vertical axis, have been proposed based on calibration studies primarily carried out under laboratory settings. Since no 'gold standard' cutpoint for older adults exists, studies have used a variety of cutpoints to describe accelerometer-assessed time in MVPA.
Perhaps the largest challenge in comparing data collected using accelerometers or questionnaires lies in what each method truly measures. Accelerometers measure accelerations in physical motion, and do not directly measure behavior. While accelerometers offer the possibility of greater characterization of physical activity (e.g., identification of short bouts), innovative analytical methods hold promise but are still under development.
According to self-reported physical activity, 67% of women met the US federal physical activity guidelines, engaging in ≥150 minutes per week of MVPA. The percent of women who met the guidelines varied widely depending on the accelerometer MVPA definition (≥760 cpm: 50%, ≥1041 cpm: 33%, ≥1952 cpm: 13%, and ≥2690 cpm: 19%). The main strength of this study is a large sample of more than 10,000 older women, in whom we simultaneously examined assessments from self-report and accelerometer across a range of cutpoints. We show that the choice of accelerometer cutpoint impacts MVPA estimation. Among the cutpoints examined, the triaxial accelerometer MVPA cutpoint compared to self-report yields the most similar median, and lowest interquartile range of MVPA minutes per week. However, use of uniaxial and triaxial cutpoints yielded similar correlations when compared with self-reported physical activity. Although cutpoints may be a simplistic use of the rich accelerometer data, this is the only well-studied metric available today, pending further development of methods.