Background : Multiple activity monitors are utilized for the estimation of moderate- to vigorous-intensity physical activity in youth. Due to differing methodological approaches, results are not comparable when developing thresholds for the determination of moderate- to vigorous-intensity physical activity. This study aimed to develop and validate count-to-activity thresholds for 1.5, 3, and 6 metabolic equivalents of task in five of the most commonly used activity monitors in adolescent research. Methods : Fifty-two participants (mean age = 16.1 [0.78] years) selected and performed activities of daily living while wearing a COSMED K4b2 and five activity monitors; ActiGraph GT1M, ActiGraph wGT3X-BT, activPAL3 micro, activPAL, and GENEActiv. Receiver-operating-characteristic analysis was used to examine the area under the curve and to define count-to-activity thresholds for the vertical axis (all monitors) and the sum of the vector magnitude (ActiGraph wGT3X-BT and activPAL3 micro) for 15 s (all monitors) and 60 s (ActiGraph monitors) epochs. Results : All developed count-to-activity thresholds demonstrated high levels of sensitivity and specificity. When cross-validated in an independent group (N = 20), high levels of sensitivity and specificity generally remained (≥73.1%, intensity and monitor dependent). Conclusions : This study provides researchers with the opportunity to analyze and cross-compare data from different studies that have not employed the same motion sensors.
Search Results
Simultaneous Validation of Count-to-Activity Thresholds for Five Commonly Used Activity Monitors in Adolescent Research: A Step Toward Data Harmonization
Gráinne Hayes, Kieran Dowd, Ciaran MacDonncha, and Alan Donnely
Comparative Analysis and Conversion Between Actiwatch and ActiGraph Open-Source Counts
Paul H. Lee, Ali Neishabouri, Andy C.Y. Tse, and Christine C. Guo
Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement.
A Physical Behaviour Partnership From Heaven: The Prospective Physical Activity, Sitting, and Sleep Consortium and the International Society for the Measurement of Physical Behaviour
Emmanuel Stamatakis, Bronwyn K. Clark, Matthew N. Ahmadi, Joanna M. Blodgett, Malcolm H. Granat, Alan Donnelly, Andrew J. Atkin, Li-Tang Tsai, Gregore I. Mielke, Richard M. Pulsford, Nidhi Gupta, Patrick Crawley, Matthew Stevens, Peter Johansson, Laura Brocklebank, Lauren B. Sherar, Vegar Rangul, Andreas Holtermann, Mark Hamer, and Annemarie Koster
Calibration of the Online Youth Activity Profile Assessment for School-Based Applications
Gregory J. Welk, Pedro F. Saint-Maurice, Philip M. Dixon, Paul R. Hibbing, Yang Bai, Gabriella M. McLoughlin, and Michael Pereira da Silva
/item Question text In-school PA to school How many days did you walk or bike to school? (If you can’t remember, try to estimate) PE During physical education, how often were you running and moving as part of the planned games or activities? ( If you didn’t have PE, choose “ I didn’t have physical education
Agreement Between Different Days of activPAL and Actigraph GT3X Measurement of Sedentary Behavior and Physical Activity During the School Hours in Elementary Children
Luciana L.S. Barboza, Larissa Gandarela, Josefa Graziele S. Santana, Ellen Caroline M. Silva, Elondark S. Machado, Roberto Jerônimo S. Silva, Thayse N. Gomes, and Danilo R. Silva
not, from the same week or from different weeks, and may or may not include physical education classes. The means of each variable obtained from 4 days of use were compared with the means of 3 days of use, 2 days of use, and 1 day of use by repeated-measures analysis of variance, followed by the
Towards Automatic Modeling of Volleyball Players’ Behavior for Analysis, Feedback, and Hybrid Training
Fahim A. Salim, Fasih Haider, Dees Postma, Robby van Delden, Dennis Reidsma, Saturnino Luz, and Bert-Jan van Beijnum
(e.g., the widely used player development system of Dotcomsport.nl) and school sports and physical education ( Koekoek et al., 2018 ). Identification and classification of events of interest in sports recordings, therefore, is of interest for not only coaches and players but also for sports fans who
Implications and Recommendations for Equivalence Testing in Measures of Movement Behaviors: A Scoping Review
Myles W. O’Brien
Physical Education and Exercise Science, 24 ( 4 ), 247 – 257 . https://doi.org/10.1080/1091367X.2020.1801441 10.1080/1091367X.2020.1801441 Buchan , D.S. , & McLellan , G. ( 2019 ). Comparing physical activity estimates in children from hip-worn Actigraph GT3X+ accelerometers using raw and counts
Comparison of Child and Adolescent Physical Activity Levels From Open-Source Versus ActiGraph Counts
Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry, Karin A. Pfeiffer, Alexander H.K. Montoye, and Jan Christian Brønd
.A. ( 2018 ). Accelerometer responsiveness to change between structured and unstructured physical activity in children and adolescents . Measurement in Physical Education and Exercise Science, 22 ( 3 ), 224 – 230 . https://doi.org/10.1080/1091367X.2017.1419956 10.1080/1091367X.2017.1419956 Clevenger
Impact of ActiGraph Sampling Rate and Intermonitor Comparability on Measures of Physical Activity in Adults
Kimberly A. Clevenger, Jan Christian Brønd, Daniel Arvidsson, Alexander H.K. Montoye, Kelly A. Mackintosh, Melitta A. McNarry, and Karin A. Pfeiffer
.1080/02640414.2020.1801320 Clevenger , K.A. , Pfeiffer , K.A. , & Montoye , A.H. ( 2020b ). Cross-generational comparability of raw and count-based metrics from ActiGraph GT9X and wGT3X-BT accelerometers during free-living in youth . Measurement in Physical Education and Exercise Science, 24 ( 3 ), 194 – 204 . 10
Validating Accelerometers for the Assessment of Body Position and Sedentary Behavior
Marco Giurgiu, Johannes B.J. Bussmann, Holger Hill, Bastian Anedda, Marcel Kronenwett, Elena D. Koch, Ulrich W. Ebner-Priemer, and Markus Reichert
. Applied Ergonomics, 63, 41 – 52 . PubMed ID: 28502405 doi:10.1016/j.apergo.2017.03.012 10.1016/j.apergo.2017.03.012 Janssen , X. , & Cliff , D.P. ( 2015 ). Issues related to measuring and interpreting objectively measured sedentary behavior data . Measurement in Physical Education and Exercise
