Cardiovascular disease (CVD) is among the leading causes of death in the US adult population (Go et al., 2014; Heron and Anderson, 2016; Kochanek et al., 2019; Kochanek et al., 2020). Physical activity (PA) plays an important role in decreasing the risk of CVD (Joseph et al., 2017; Li and Siegrist, 2012; Martinez-Gomez et al., 2019). There is extensive research linking the association between self-reported PA and the risk of developing CVD (Fang et al., 2019; McGuire et al., 2009; Georgousopoulou et al., 2016; Young et al., 2014), CVD mortality (Evenson et al., 2016; Kraus et al., 2019), as well as all-cause mortality (Evenson et al., 2016; Jeong et al., 2019). An increase in PA that corresponds to 500 metabolic equivalent (MET) min/week reduced the risk of mortality by 14% for those who had a history of CVD and 7% for those without a history of CVD (Jeong et al., 2019). According to Evenson et al. (2016) (Evenson et al., 2016), adults over 40 years old who self-reported meeting the PA guidelines for Americans (Piercy et al., 2018) had a significant reduction of CVD mortality risk. However, these results rely on self-reported PA, which has been shown to have recall and social desirability bias (Luke et al., 2011; Wanner et al., 2017). To address this limitation, methods that measure a person's fitness level can be used to predict CVD mortality risk (Wickramasinghe et al., 2014). However, the test requires the use of a treadmill and speed/incline modifications every minute and indirect calorimetry for reliable oxygen uptake (VO2) peak testing (Keteyian et al., 2008), which makes it difficult to perform for persons without access to the equipment and is less reliable due to self-administration.

Accelerometers provide objective measures of PA and provide a viable, objective alternative to self-reported PA. A major advantage of accelerometers is easy self-administration, which leads to reliable use in both personal and large populations. Recent studies explored the association between CVD and accelerometer-based measures of PA (Luke et al., 2011; Dempsey et al., 2020; Andersson et al., 2015), death attributed to CVD (Evenson et al., 2016) and to all causes (Saint-Maurice et al., 2018). Evenson et al. (2016) (Evenson et al., 2016) and Luke et al. (2011) (Luke et al., 2011) based their studies on the National Health and Nutrition Examination Study (NHANES), which collects data on the US population. Dempsey et al. (2020) (Dempsey et al., 2020) analyzed the European Prospective Investigation into Cancer and Nutrition-Norfolk study (EPICNN), a population-based data set of adults in Norfolk, UK. Andersson et al. (2015) (Andersson et al., 2015) analyzed the Framingham Heart Study, a US multigenerational data set spanning from 1948 to the present. They demonstrated that individuals with higher levels of PA had a lower risk of incident CVD (Dempsey et al., 2020), a lower risk of mortality attributed to CVD (Evenson et al., 2016), and a reduced risk of all-cause mortality (Saint-Maurice et al., 2018). These studies concentrated on the association between CVD mortality and a small number of common accelerometer-derived PA measures, such as total activity count (TAC), moderate to vigorous physical activity (MVPA), light intensity physical activity (LIPA), sedentary/sleep time (ST), and wear time (WT). However, these summaries may not be sufficient to capture the full complexity of daily minute level PA (Di et al., 2017). To the best of our knowledge, there have not been any studies which focused on quantification of individual and combined CVD mortality risk prediction using a large number of objective PA summaries and traditional risk factors.

To address this gap in the literature, we explored the prediction performance of 12 accelerometer-derived PA and 16 traditional measures for CVD linked mortality prediction in NHANES 2003–2006. We hypothesized that PA measures will be highly predictive of short to medium term CVD mortality and adding PA to the model with traditional predictors will increase predictive ability. The main goals of the current study were: (Go et al., 2014) to compare and rank the performance of individual PA and traditional measures to predict CVD mortality using Cox proportional hazards; (Heron and Anderson, 2016) to identify the best combination of traditional and PA predictors for quantifying the CVD mortality risk; and (Kochanek et al., 2019) to quantify the improvement in CVD mortality prediction by adding the PA measures to the best traditional CVD mortality measures. A major advantage of our study is the NHANES data used a large and representative sample of the US non-institutionalized civilian population. Thus, this study is crucial for CVD risk assessment in free-living conditions and generalizable to the US population.