Abstract
Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide. In the USA, less than 10% of the adult population were able to meet the PA recommendations when accelerometers were used to assess PA habituation. Accelerometers significantly differ from each other in step recognition and do not reveal raw data. The aim of our study was to compare a novel accelerometer, Sartorio Xelometer, which enables to gather raw data, with existing accelerometers ActiGraph GT3X+ and activPAL in terms of step detection and energy expenditure estimation accuracy. 53 healthy subjects were divided into 2 cohorts (cohort 1 optimization; cohort 2 validation) and wore 3 accelerometers and performed an exercise routine consisting of the following speeds: 1.5, 3, 4.5, 9 and 10.5 km/h (6 km/h for 2nd cohort included). Data from optimization cohort was used to optimize Sartorio step detection algorithm. Actual taken steps were recorded with a video camera and energy expenditure (EE) was measured. To observe the similarity between video and accelerometer step counts, paired samples t test and intraclass correlation were used separately for step counts in different speeds and for total counts as well as EE estimations. In speeds of 1.5, 3, 4.5, 6, 9 and 10.5 km/h mean absolute percentage error (MAPE) % were 8.1, 3.5, 4.3, 4.2, 3.1 and 7.8 for the Xelometer, respectively (after optimization). For ActiGraph GT3X+ the MAPE-% were 96.93 (87.4), 34.69 (23.1), 2.13 (2.3), 1.96 (2.6) and 2.99 (3.8), respectively and for activPAL 6.55 (5.6), 1.59 (0.6), 0.81 (1.1), 10.60 (10.3) and 15.76 (13.8), respectively. Significant intraclass correlations were observed with Xelometer estimates and actual steps in all speeds. Xelometer estimated the EE with a MAPE-% of 30.3, activPAL and ActiGraph GT3X+ with MAPE percentages of 20.5 and 24.3, respectively. The Xelometer is a valid device for assessing step counts at different gait speeds. MAPE is different at different speeds, which is of importance when assessing the PA in obese subjects and elderly. EE estimates of all three devices were found to be inaccurate when compared with indirect calorimetry.
Highlights
Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide
Mean absolute percentage error (MAPE) percentages, paired sample t test statistics and intraclass correlation (ICC) statistics with 95% CI presented for each device in each speed and for total sum of steps. *Shows statistical significance
Three accelerometers were used to estimate the number of steps taken and each of them were individually compared with the counted steps in the video camera—recordings in the 5 different speeds (6 in cohort 2) to determine the accuracy of different methods (Table 1, 2)
Summary
Physical activity (PA) is one of the most efficient ways to prevent obesity and its associated diseases worldwide. The aim of our study was to compare a novel accelerometer, Sartorio Xelometer, which enables to gather raw data, with existing accelerometers ActiGraph GT3X+ and activPAL in terms of step detection and energy expenditure estimation accuracy. WHO recommends 150 min of moderateintensity PA in a week or a combination of moderate and vigorous-intensity PA with additional health benefits arising with increased exercise a mounts[3] This responds approximately 7000–10,000 daily s teps[4]. The use of accelerometers enables to objectively assess the subjects’ PA behavior (volume and intensity), but the devices are not equal in their step-detection thresholds, sampling frequency and data p rocessing[5,6]. Mean absolute percentage error (MAPE) percentages, paired sample t test statistics and intraclass correlation (ICC) statistics with 95% CI presented for each device in each speed and for total sum of steps. Because of these severe limitations of the available accelerometers as black boxes, a waist-worn triaxial accelerometer was manufactured registering accelerations from slow walking to running and jumping
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