Reference values for accelerometer metrics and associations with cardiorespiratory fitness: a prospective cohort study of healthy adults and patients with heart failure
Accelerometry has gained increasing popularity and yields numerous physical activity (PA) outcomes (Rowlands et al., 2019). These include traditional cut-point-based (i.e. light, moderate, and vigorous PA) and cut-point-free metrics (i.e. intensity gradient [IG] and average acceleration [AvAcc]). IG reflects the intensity distribution of PA across the day (Rowlands et al., 2018; Fairclough et al., 2019). AvAcc is a proxy for the daily volume of PA ( Rowlands et al., 2018; Fairclough et al., 2019). Cut-point-based metrics are commonly expressed in minutes per day, making their interpretation simple (Troiano et al., 2014). Yet, the measured acceleration needs to be categorised by setting population- and device-dependent cut-points to obtain these metrics (Troiano et al., 2014). Cut-point-free metrics, on the other hand, are comparable across studies, accelerometer brands (Migueles et al., 2022), and diverse populations (Rowlands et al., 2018). However, their interpretation is not easy. Besides, it is unknown how cut-point-free metrics are associated with cardiorespiratory fitness (CRF), an important health indicator in healthy individuals and patient populations with impaired CRF (Kodama et al., 2009). We thus aimed to 1) compare the association of CRF with cut-point-free metrics to that with cut-point-based metrics in a prospective cohort of healthy adults aged 20 to 89 years and patients with heart failure, and 2) provide age-, sex-, and CRF-related reference values for healthy adults.
The COmPLETE study was cross-sectional. Healthy individuals were recruited via unaddressed letters sent to randomly selected postal districts in the Basel area (Wagner et al., 2019). Patients with heart failure were approached as described elsewhere (Wagner et al., 2019). Subjects were asked to wear GENEActiv accelerometers on their non-dominant wrist for up to 14 days and undergo cardiopulmonary exercise testing on a cycle ergometer to determine CRF. Raw accelerometer data were processed using the R-package GGIR (Migueles et al., 2019; van Hees et al., 2013). Associations between CRF and accelerometer metrics were examined using multiple linear regression models adjusted for sex, age, and body mass index. Percentile curves were generated with Generalised Additive Models for Location, Scale, and Shape (Stasinopoulos & Rigby, 2008).
Four hundred and sixty-three healthy adults and 67 patients with heart failure were included in the analyses. IG and AvAcc provide complementary information on PA. Both metrics were independently associated with CRF in healthy individuals. The best cut-point-free regression model (AvAcc+IG) performed similar to the best cut-point-based model (vigorous activity) and explained 73.9% and 74.2% of the variance in CRF, respectively. In patients with heart failure, IG was associated with CRF, independent of AvAcc. Cut-point-free models (IG+AvAcc, IG alone) had comparable predictive value for CRF as the best cut-point-based metric (moderate-to-vigorous activity). We produced age-, sex-, and CRF-related reference values for IG, AvAcc, moderate-to-vigorous, and vigorous activity for healthy adults. Moreover, we developed a web-based application (rawacceleration) facilitating the interpretation of cut-point-free metrics.
Cut-point-free metrics are not only more robust than cut-point-based metrics, but also have similar predictive value for CRF and, in turn, indirectly for the risk of mortality and longevity (Kodama et al., 2009; Mok et al., 2019). This may be the case in both healthy individuals and patients with heart failure. Our findings together with those of previous studies (Rowlands et al., 2018; Fairclough et al., 2019), therefore, provide a rationale that cut-point-free metrics facilitate the capture of the volume and intensity distribution of the PA profile across populations, and thus may be a viable alternative to cut-point-based metrics in describing PA. Our reference values will enhance the utility of IG and AvAcc and facilitate their interpretation. Finally, our web-based application will simplify this process and also support the translation of cut-point-free metrics into meaningful outcomes.
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Copyright (c) 2023 Fabian Schwendinger, Jonathan Wagner, Raphael Knaier, Denis Infanger, Alex V. Rowlands, Timo Hinrichs, Arno Schmidt-Trucksäss
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