PurposeOur recent study of three accelerometer brands in various ambulatory activities showed that the mean amplitude deviation (MAD) of the resultant acceleration signal performed best in separating different intensity levels and provided excellent agreement between the three devices. The objective of this study was to derive a regression model that estimates oxygen consumption (VO2) from MAD values and validate the MAD-based cut-points for light, moderate and vigorous locomotion against VO2 within a wide range of speeds.Methods29 participants performed a pace-conducted non-stop test on a 200 m long indoor track. The initial speed was 0.6 m/s and it was increased by 0.4 m/s every 2.5 minutes until volitional exhaustion. The participants could freely decide whether they preferred to walk or run. During the test they carried a hip-mounted tri-axial accelerometer and mobile metabolic analyzer. The MAD was calculated from the raw acceleration data and compared to directly measured incident VO2. Cut-point between light and moderate activity was set to 3.0 metabolic equivalent (MET, 1 MET = 3.5 ml · kg-1 · min-1) and between moderate and vigorous activity to 6.0 MET as per standard use.ResultsThe MAD and VO2 showed a very strong association. Within individuals, the range of r values was from 0.927 to 0.991 providing the mean r = 0.969. The optimal MAD cut-point for 3.0 MET was 91 mg (milligravity) and 414 mg for 6.0 MET.ConclusionThe present study showed that the MAD is a valid method in terms of the VO2 within a wide range of ambulatory activities from slow walking to fast running. Being a device-independent trait, the MAD facilitates directly comparable, accurate results on the intensity of physical activity with all accelerometers providing tri-axial raw data.

Irrespective of the accelerometer brand, a simply calculable MAD with universal cut-off limits provides a universal method to evaluate physical activity and sedentary behaviour using raw accelerometer data. A broader application of the present approach is expected to render different accelerometer studies directly comparable with each other.

Hip-worn accelerometers are widely used to estimate physical activity (PA), but the accuracy of acceleration threshold-based analysis is compromised when it comes to identifying stationary and sedentary behaviors, let alone classifying body postures into lying, sitting, or standing. The purpose of this study was to devise a novel method for accurate classification of body posture using triaxial data from hip-worn accelerometer and to evaluate its performance in free-living conditions against a thigh-worn accelerometer. The posture classification rested on 2 facts: constant Earth's gravity vector and upright walking posture. Thirty healthy adults wore a hip-mounted accelerometer and underwent an array of lying, sitting, standing, and walking tasks.Task type, their order, and length were randomly assigned to each participant. During walking, the accelerometer orientation in terms of gravity vector was taken as reference, and the angle for posture estimation (APE) was determined from the incident accelerometer orientation in relation to the reference vector. Receiver operating characteristic (ROC) curve yielded an optimal cut-point APE of 64.9° (sensitivity 100% and specificity 100%) for lying and sitting and 11.6° (94.2%; 94.5%) for sitting and standing. In free-living conditions, high agreement (89.2% for original results and 90.4% for median-filtered results) in identifying sedentary periods (sitting and lying) was observed between the results from hip-and thigh-worn accelerometers. Walking provides a valid reference activity to determine the body posture. The proposed APE analysis of the raw data from hip-worn triaxial accelerometer gives accurate and specific information about daily times spent lying, sitting, and standing. K E Y W O R D Sbody posture, objective measurement, physical activity, sedentary behavior, walking

The 6MWT performed along a 15-m track is a valid field test for predicting VO max of healthy adults with accuracy of about 1MET.

BackgroundUsing raw acceleration data to assess the intensity of physical activity enables direct comparisons between studies using different accelerometer brands. Mean amplitude deviation (MAD in mg) calculated from resultant tri-axial raw acceleration signal was recently shown to perform best in classifying the intensity of physical activity in adults irrespective of the accelerometer brand. This study compared MAD values and cut-points between two different accelerometers in adolescents.MethodsTwenty voluntary participants (10 girls and 10 boys) of average age of 14 wore two accelerometers (Actigraph GTX3, Pensacola FL, USA and Hookie AM13, Espoo, Finland) and heart rate monitors (M61, Polar Electro Oy, Kempele, Finland) while completing ten 2-min patterns of typical activities ranging from sedentary behaviour to light, moderate and vigorous-intensity locomotion. Bland-Altman method examined the agreement of MAD values between the accelerometers. Correlation coefficient between individual heart rates and MAD values indicated the validity of pattern-based intensity classification. Generalized ordinal logistic regression determined the intensity-specific MAD cut-points for both accelerometers.ResultsMAD values varied from 3 mg (lying supine) to 1609 mg (running). Hookie gave higher values than Actigraph in accelerations exceeding 700 mg. The correlation coefficient between MAD values and heart rates was 0.96 for Hookie and 0.97 for Actigraph. Respectively, the MAD cut-points were 29 and 27 (light), 338 and 330 (moderate), and 604 and 558 (vigorous).ConclusionsMAD values and cut-points of Hookie and Actigraph showed excellent agreement. Analysing raw accelerometer data with MAD values may enable the comparison of accelerometer results between different studies also in adolescents.