Establishing cut-points for physical activity classification using triaxial accelerometer in middle-aged recreational marathoners

Abstract

The purpose of this study was to establish GENEA (Gravity Estimator of Normal Everyday Activity) cut-points for discriminating between six relative-intensity activity levels in middle-aged recreational marathoners. Nighty-eight (83 males and 15 females) recreational marathoners, aged 30–45 years, completed a cardiopulmonary exercise test running on a treadmill while wearing a GENEA accelerometer on their non-dominant wrist. The breath-by-breath V̇O2 data was also collected for criterion measure of physical activity categories (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous). GENEA cut-points for physical activity classification was performed via Receiver Operating Characteristic (ROC) analysis. Spearman’s correlation test was applied to determine the relationship between estimated and measured intensity classifications. Statistical analysis were done for all individuals, and separating samples by sex. The GENEA cut-points established were able to distinguish between all six-relative intensity levels with an excellent classification accuracy (area under the ROC curve (AUC) values between 0.886 and 0.973) for all samples. When samples were separated by sex, AUC values were 0.881–0.973 and 0.924–0.968 for males and females, respectively. The total variance in energy expenditure explained by GENEA accelerometer data was 78.50% for all samples, 78.14% for males, and 83.17% for females. In conclusion, the wrist-worn GENEA accelerometer presents a high capacity of classifying the intensity of physical activity in middle-aged recreational marathoners when examining all samples together, as well as when sample set was separated by sex. This study suggests that the triaxial GENEA accelerometers (worn on the non-dominant wrist) can be used to predict energy expenditure for running activities.

Estimation of energy consumed by middle-aged recreational marathoners during a marathon using accelerometry-based devices

“…For all individuals, we estimated the energy cost of running a marathon, presenting the caloric consumption for each one of the 9 marathon sections as well as for the full marathon distance (Tables 3 and 4). The calories consumed by each runner were calculated based on both accelerometer data (Table 3), as previously described by our research group 25 , and running speed (Table 4), following the methodology proposed by Ainsworth and cols 12 . The aim of applying also the speed-based method 12 in the estimation of energy consumption was to compare the results obtained with accelerometer devices 25 .…”

“…The calories consumed by each runner were calculated based on both accelerometer data (Table 3), as previously described by our research group 25 , and running speed (Table 4), following the methodology proposed by Ainsworth and cols 12 . The aim of applying also the speed-based method 12 in the estimation of energy consumption was to compare the results obtained with accelerometer devices 25 . Note that a gold standard method for energy quantification in long distance races has not been defined yet.…”

“…Accelerometer output data allowed us to know the physical effort distribution of runners during the marathon, in terms of physical activity intensity. That is, we were able to identify and quantify when a runner is racing at each one of the six relative-intensity activity levels (sedentary, light, moderate, vigorous, very vigorous and extremely vigorous) 25 . Therefore, following the values established in Table 2, the percentage of VO 2 max  produced per each runner was estimated, and this allowed then to calculate the energy of cost running above standing (Cr net ) 28 (Table 5).…”

“…In this regard, our research group aimed to monitor middle-aged recreational marathoners during a marathon using accelerometry-based devices. For this purpose, we previously established the GENEActiv cut-points that dsicriminate the six relative-intensity activity levels in recreational marathoners 25 . This lab-based study was essential in order to delineate specific GENEActiv cut-points for a specific population who presents higher relative level of fitness than the standard adult population.…”

Optimization and Validation of an Adjustable Activity Classification Algorithm for Assessment of Physical Behavior in Elderly

“…Technically, when activity trackers are not transparently described, every new physical activity classification application would require validation of the activity tracker's algorithm performance and, when not accurate enough, development of a new algorithm [12][13][14][15]. This is a time-and resource-consuming process.…”