Our 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. 29 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. The 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. The 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.

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‐pointAPEof 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 proposedAPEanalysis of the raw data from hip‐worn triaxial accelerometer gives accurate and specific information about daily times spent lying, sitting, and standing.

Using 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. Twenty 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. MAD 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). MAD 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.

Regular physical activity (PA) confers many positive effects on health and well-being. Sedentary behavior (SB), in turn, is a risk factor for health, regardless of the level of moderate to vigorous PA. The present study describes the levels of objectively measured SB, breaks in SB, standing still and PA among Finnish adults. This cross-sectional analysis is based on the sub-sample of the population-based Health 2011 Study of Finnish adults. The study population consisted of 18-to-85-year old men and women who wore a waist-worn triaxial accelerometer (Hookie AM 20) for at least 4 days, for at least 10 h per day (n = 1587) during a week. PA and SB were objectively assessed from the raw accelerometric data using novel processing and analysis algorithms with mean amplitude deviation as the processing method. The data was statistically analyzed using cross-tabulations, analysis of variance and analysis of covariance. The participants were on average 52 years old, 57 % being women. Participants were sedentary 59 % of their waking wear time, mainly sitting. They spent 17 % of the time standing still, 15 % in light intensity PA, 9 % in moderate PA and less than 1 % in vigorous PA. Participants aged 30–39 years had the highest number of breaks in SB per day. Younger participants (<30 years of age) had more moderate and vigorous PA than older ones (≥60 years of age), and 30–60-year-olds had the greatest amount of light PA. Participants spent nearly 60 % of their waking time sedentary, and the majority of their daily PA was light. From a public health perspective it is important to find effective ways to decrease SB as well as to increase the level of PA. Our analysis method of raw accelerometer data may allow more precise assessment of dose-response relationships between objectively measured PA and SB and various indicators of health and well-being.

Regular physical activity (PA) promotes and excessive sedentary behavior (SB) deteriorates health. Yet the Finnish working-aged population spends most of the day sitting. A 1-year Moving To Business (MTB) -intervention supported small and medium-size workplaces to combat sedentariness. This paper reports the changes in employees’ PA and SB from before MTB (baseline) to 1 year after baseline (follow-up). Twelve workplaces with a total of 396 employees participated. Each workplace nominated a team to promote PA and reduce SB at organizational, working unit and employee level. The teams were mentored regionally through meetings, workshop and tools. Changes in PA and SB were assessed with a questionnaire and an accelerometer. Wald Confidence Interval (Cl) for a difference of proportions with matched pairs was used in the questionnaire data (%-points with 95% CI) and linear mixed model in the accelerometer data (minutes and % of wear-time with 95% CI). The mean age of the respondents to the questionnaire (N = 296; 75%) was 42.6 (SD 10.9), 64% were women, 95% had some education after high school, 74% worked in the day shift, 71% did sedentary work and 51% were overweight. The mean number of actions implemented in the workplaces was 6.8 and the multilevel approach was fully applied in 6 workplaces. Based on the questionnaire the time spent in SB decreased from baseline to follow-up 16% (95% CI −29 to −3) in total and 22% (−41 to −3) at work. The accelerometer showed daily increases of 33.7 min (15.3 to 52.1) and 6.8% (3.1 to 10.4) in total PA, 30.9 min (15.3 to 46.5) and 6.1% (2.9 to 9.2) in light PA and 673 (209 to 1139) more steps at work. Daily SB at work decreased 44.9 min (−68.0 to −21.8) and 7.6% (−11.9 to −3.2). Daily leisure PA declined 11.0 min (−24.9 to 2.9) and 3.2% (−6.2 to −0.2). Number of levels or actions had no effect on changes. Employees’ PA increased and SB reduced at work during the intervention. At the same time leisure PA decreased slightly. Workplaces can achieve meaningful changes in employees’ PA and SB if assisted systematically. Controlled studies are needed to confirm the present findings. NCT01999205 , registration date 11/01/2013.