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      SenseWearMini and Actigraph GT3X Accelerometer Classification of Observed Sedentary and Light-Intensity Physical Activities in a Laboratory Setting

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          Abstract

          Purpose: To compare the ability of SenseWear Mini (SWm) and Actigraph GT3X (AG 3) accelerometers to differentiate between healthy adults' observed sedentary and light activities in a laboratory setting. Methods: The 22 participants (15 women, 7 men), ages 19 to 72 years, wore SWm and AG 3 monitors and performed five sedentary and four light activities for 5 minutes each while observed in a laboratory setting. Performance was examined through comparisons of accuracy, sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Correct identification of both types of activities was examined using area under the receiver operating characteristic curve (AUC). Results: Both monitors demonstrated excellent ability to identify sedentary activities (sensitivity>0.89). The SWm monitor was better at identifying light activities (specificity 0.61–0.71) than the AG 3 monitor (specificity 0.27–0.47) and thus also showed a greater ability to correctly identify both sedentary and light activities (SWm AUC 0.84; AG 3 AUC 0.62–0.73). Conclusions: SWm may be a more suitable monitor for detecting time spent in sedentary and light-intensity activities. This finding has clinical and research relevance for evaluation of time spent in lower intensity physical activities by sedentary adults.

          Translated abstract

          Objectif : Comparer la capacité des accéléromètres Sensewear Mini (SWm) et Actigraph GT3X (AG 3) de distinguer les activités sédentaires et d'intensité légère d'adultes en bonne santé observés en laboratoire. Méthodes : Les 22 participants (15 femmes), âgés de 19 à 72 ans, ont porté des moniteurs SWm et AG 3 et se sont livrés à cinq activités sédentaires et quatre activités d'intensité légère pendant cinq minutes dans chaque cas sous observation en laboratoire. On a analysé le rendement des appareils en comparant leur exactitude, sensibilité, spécificité et leurs valeurs prédictives positive et négative et ratios de probabilité positif et négatif. On a examiné la détermination correcte des deux types d'activités au moyen de la zone située sous les courbes des caractéristiques opérationnelles du récepteur (ZSC). Résultats : Les deux moniteurs ont démontré une excellente capacité de déterminer les activités sédentaires (sensibilité>0,89). Le moniteur SWm était meilleur pour déterminer les activités d'intensité légère (spécificité variant de 0,61 à 0,71) que le moniteur AG 3 (spécificité variant de 0,27 à 0,47) et a donc montré une plus grande capacité de déterminer correctement les activités sédentaires et les activités d'intensité légère (ZSC: SWm=0,84; AG 3: variant de 0,62 à 0,73). Conclusions : Le moniteur SWm peut convenir mieux pour détecter le temps consacré à des activités sédentaires et d'intensité légère. Cette constatation présente une pertinence clinique et de recherche pour l'évaluation du temps consacré aux activités physiques de plus faible intensité par des adultes sédentaires.

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          Most cited references25

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          Calibration of the Computer Science and Applications, Inc. accelerometer.

          We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories. Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8, 6.4, and 9.7 km x h(-1)). Activity counts and steady-state oxygen consumption were highly correlated (r = 0.88), and count ranges corresponding to light, moderate, hard, and very hard intensity levels were or = 9499 cnts x min(-1), respectively. A model to predict energy expenditure from activity counts and body mass was developed using data from a random sample of 35 subjects (r2 = 0.82, SEE = 1.40 kcal x min(-1)). Cross validation with data from the remaining 15 subjects revealed no significant differences between actual and predicted energy expenditure at any treadmill speed (SEE = 0.50-1.40 kcal x min(-1)). These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer.
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            Validation of wearable monitors for assessing sedentary behavior.

            A primary barrier to elucidating the association between sedentary behavior (SB) and health outcomes is the lack of valid monitors to assess SB in a free-living environment. The purpose of this study was to examine the validity of commercially available monitors to assess SB. Twenty overweight (mean ± SD: body mass index = 33.7 ± 5.7 kg·m(-2)) inactive, office workers age 46.5 ± 10.7 yr were directly observed for two 6-h periods while wearing an activPAL (AP) and an ActiGraph GT3X (AG). During the second observation, participants were instructed to reduce sitting time. We assessed the validity of the commonly used cut point of 100 counts per minute (AG100) and several additional AG cut points for defining SB. We used direct observation (DO) using focal sampling with duration coding to record either sedentary (sitting/lying) or nonsedentary behavior. The accuracy and precision of the monitors and the sensitivity of the monitors to detect reductions in sitting time were assessed using mixed-model repeated-measures analyses. On average, the AP and the AG100 underestimated sitting time by 2.8% and 4.9%, respectively. The correlation between the AP and DO was R2 = 0.94, and the AG100 and DO sedentary minutes was R2 = 0.39. Only the AP was able to detect reductions in sitting time. The AG 150-counts-per-minute threshold demonstrated the lowest bias (1.8%) of the AG cut points. The AP was more precise and more sensitive to reductions in sitting time than the AG, and thus, studies designed to assess SB should consider using the AP. When the AG monitor is used, 150 counts per minute may be the most appropriate cut point to define SB.
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              Detection of physical activity types using triaxial accelerometers.

              The aim of this study was to validate a triaxial accelerometer setup for identifying everyday physical activity types (ie, sitting, standing, walking, walking stairs, running, and cycling). Seventeen subjects equipped with triaxial accelerometers (ActiGraph GT3X+) at the thigh and hip carried out a standardized test procedure including walking, running, cycling, walking stairs, sitting, and standing still. A method was developed (Acti4) to discriminate between these physical activity types based on threshold values of standard deviation of acceleration and the derived inclination. Moreover, the ability of the accelerometer placed at the thigh to detect sitting posture was separately validated during free living by comparison with recordings of pressure sensors in the hip pockets. Sensitivity for discriminating between the physical activity types sitting, standing, walking, running, and cycling in the standardized trials were 99%-100% and 95% for walking stairs. Specificity was higher than 99% for all activities. During free living (140 hours of measurements), sensitivity and specificity for detection of sitting posture were 98% and 93%, respectively. The developed method for detecting physical activity types showed a high sensitivity and specificity for sitting, standing, walking, running, walking stairs, and cycling in a standardized setting and for sitting posture during free living.
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                Author and article information

                Journal
                Physiother Can
                Physiother Can
                ptc
                Physiotherapy Canada
                University of Toronto Press
                0300-0508
                1708-8313
                Spring 2016
                : 68
                : 2
                : 116-123
                Affiliations
                [* ]Arthritis Research Canada, Richmond
                []Department of Physical Therapy, University of British Columbia, Vancouver
                []Faculty of Health Sciences, Simon Fraser University, Burnaby, B.C.
                Author notes

                Contributors: All authors designed the study; collected, analyzed, and interpreted the data; drafted or critically revised the article; and approved the final draft.

                Competing interests: None declared.

                Acknowledgements: The authors thank their colleagues Cynthia MacDonald, Jenny Leese, and Erin Carruthers from Arthritis Research Canada (Richmond, B.C.) for their assistance with participant recruitment and data collection.

                Correspondence to: Lynne M. Feehan, Milan Ilich Arthritis Research Centre, 5591 No. 3 Rd., Richmond, BC V6X 2C7; lfeehan@ 123456arthritisresearch.ca .
                Article
                ptc.2015-12
                10.3138/ptc.2015-12
                5125482
                27909358
                ffe5833f-8eb1-4d71-8326-e21eddf8adad
                © Canadian Physiotherapy Association, 2016. All rights reserved.
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                Physiotherapy
                accelerometer,physical activity,sedentary behaviour,accéléromètre,actigraph,activité physique,comportement sédentaire,sensewear

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