-2015-095947 National Health and Nutrition Examination Survey (U.S.) . ( 2011 ). Physical Activity Monitor (PAM) Procedures Manual . National Centers for Disease Control and Prevention; pp. 1 – 36 . Nettlefold , L. , Naylor , P.J. , Warburton , D.E. , Bredin , S.S. , Race , D. , & McKay , H
Search Results
The Use of Accelerometers in Young Children: A Methodological Scoping Review
Becky Breau, Hannah J. Coyle-Asbil, and Lori Ann Vallis
Simulation-Based Evaluation of Methods for Handling Nonwear Time in Accelerometer Studies of Physical Activity
Kristopher I. Kapphahn, Jorge A. Banda, K. Farish Haydel, Thomas N. Robinson, and Manisha Desai
-sponsored Childhood Obesity Prevention and Treatment Research Consortium ( Pratt et al., 2013 ; Robinson et al., 2013 ). Preparation of the Accelerometer Data Sampling Pool For simplicity and without loss of generality to other univariable measures such as the triaxial-based vector of magnitude, we generated data
Association of Individual Motor Abilities and Accelerometer-Derived Physical Activity Measures in Preschool-Aged Children
Becky Breau, Berit Brandes, Marvin N. Wright, Christoph Buck, Lori Ann Vallis, and Mirko Brandes
This study explored the relationship between motor abilities and accelerometer-derived measures of physical activity (PA) within preschool-aged children. A total of 193 children (101 girls, 4.2 ± 0.7 years) completed five tests to assess motor abilities, shuttle run (SR), standing long jump, lateral jumping, one-leg stand, and sit and reach. Four PA variables derived from 7-day wrist-worn GENEActiv accelerometers were analyzed including moderate to vigorous PA (in minutes), total PA (in minutes), percentage of total PA time in moderate to vigorous PA, and whether or not children met World Health Organization guidelines for PA. Linear regressions were conducted to explore associations between each PA variable (predictor) and motor ability (outcome). Models were adjusted for age, sex, height, parental education, time spent at sports clubs, and wear time. Models with percentage of total PA time in moderate to vigorous PA were adjusted for percentage of total PA time. Regression analyses indicated that no PA variables were associated with any of the motor abilities, but demographic factors such as age (e.g., SR: ß = −0.45; 95% confidence interval [−1.64, −0.66]), parental education (e.g., SR: ß = 0.25; 95% confidence interval [0.11, 1.87]), or sports club time (e.g., SR: ß = −0.08; 95% confidence interval [−0.98, 0.26]) showed substantial associations with motor abilities. Model strength varied depending on the PA variable and motor ability entered. Results demonstrate that total PA and meeting current PA guidelines may be of importance for motor ability development and should be investigated further. Other covariates showed stronger associations with motor abilities such as time spent at sports clubs and should be investigated in longitudinal settings to assess the associations with individual motor abilities.
ActiGraph Cutpoints Impact Physical Activity and Sedentary Behavior Outcomes in Young Children
Becky Breau, Hannah J. Coyle-Asbil, Jess Haines, David W.L. Ma, Lori Ann Vallis, and on behalf of the Guelph Family Health Study
Purpose: Examine the effect of cutpoint selection on physical activity (PA) metrics calculated from young children’s accelerometer data and on the proportion of children meeting PA guidelines. Methods: A total of 262 children (3.6 ± 1.4 years, 126 males) wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days, 24 hr/day. Ten cutpoint sets were applied to the sample categorized by age, based on populations of the original cutpoint calibration studies using ActiLife software. Resulting sedentary behavior, light PA, moderate to vigorous PA, and total PA were compared using repeated-measures analysis of variance. Proportion of children meeting age-appropriate PA guidelines based on each cutpoint set was assessed using Cochran’s q tests. Results: Children wore the accelerometer for an average of 7.6 ± 1.2 days for an average of 11.9 ± 1.2 hr/day. Significant differences in time spent in each intensity were found across all cutpoints except for sedentary, and total PA for three comparisons (Trost vs. Butte Vertical Axis [VA], Pate vs. Puyau, and Costa VA vs. Evenson) and moderate to vigorous PA for four comparisons (Trost vs. Pate, Trost vs. Pate and Pfeiffer, Pate vs. Pate and Pfeiffer, and van Cauwenberghe vs. Evenson). When examined within age-appropriate groups, all sets of cutpoints resulted in significant differences across all intensities and in the number of children meeting PA guidelines. Conclusion: Choice of cutpoints applied to data from young children significantly affects times calculated for different movement intensities, which in turn impacts the proportion of children meeting guidelines. Thus, comparisons of movement intensities should not be made across studies using different sets of cutpoints.
Shaking Up Activity Counts: Assessing the Comparability of Accelerometers and Activity Count Computation
Hannah J. Coyle-Asbil, Bernadette Murphy, and Lori Ann Vallis
Accelerometers have been at the forefront of free-living activity capture for decades, and accordingly ActiGraph the largest distributor. Historically, limitations in data storage and battery power led to the use of summary metrics, which have been termed activity counts. Recently, ActiGraph publicly released their count-based algorithm, marking a notable development in the field. This study aimed to assess and compare activity counts generated through different processing techniques (ActiLife and open-source), filters that are available through ActiGraph count generation (normal- and low-frequency extension), and data from various ActiGraph models and GENEActiv devices. We evaluated ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and secondary sensors), OPAL (n = 6), and GENEActiv (n = 5), subjected to oscillations across their full dynamic range (0.005–8 G) using a multiaxis shaker table. Results indicated that the low-frequency extension produced significantly higher counts compared to the normal frequency across the devices and processing techniques. Notably, open-source counts (R and Python) were statistically equivalent to ActiLife-generated counts (p < .05) for the GT9X, wGT3X-BT, and the GT3X+. Overall, many of the counts generated by different ActiGraph models were statistically equivalent or had mean differences <5.03 counts. Conversely, the GENEActiv, OPAL, and GT9X secondary monitor exhibited significantly higher responses than the other ActiGraph models at higher frequencies with mean differences ranging from 55.50 to 104.91 counts. This study provides insights into accelerometer data processing methods and highlights the comparability of counts across different devices and techniques.
Maximizing the Utility and Comparability of Accelerometer Data From Large-Scale Epidemiologic Studies
I-Min Lee, Christopher C. Moore, and Kelly R. Evenson
the prevention of cancer and CVD among 39,876 women aged ≥45 years, enrolled from throughout the United States ( Cook et al., 2005 ; Lee et al., 2005 ; Ridker et al., 2005 ). After an average follow-up of 10 years, the trial ended as scheduled. At trial completion, women were invited to participate
Comparison of activPAL and Actiwatch for Estimations of Time in Bed in Free-Living Adults
Mary C. Hidde, Kate Lyden, Josiane L. Broussard, Kim L. Henry, Julia L. Sharp, Elizabeth A. Thomas, Corey A. Rynders, and Heather J. Leach
Cardiovascular disease (CVD), obesity, and diabetes contribute to an estimated $3.5 trillion in annual health care costs in the United States ( National Center for Chronic Disease Prevention and Health Promotion, 2019 ). Physical activity (PA), reduced sedentary time, and adequate sleep duration
A Walkthrough of ActiGraph Counts
Ali Neishabouri, Joe Nguyen, Matthew R. Patterson, Rakesh Pilkar, and Christine C. Guo
counts on data from subject 62161 of the National Health and Nutrition Examination Survey (NHANES) study ( Centers for Disease Control and Prevention [CDC], National Center for Health Statistics [NCHS], n.d. ). Figure 2 Differences between counts calculated on the magnitude of the accelerometer signal
Comparing Counts of Park Users With a Wearable Video Device and an Unmanned Aerial System
Richard R. Suminski, Gregory M. Dominick, and Matthew Saponaro
, & Schiller, 2015 ). Community-level interventions are highly recommended for promoting PA, and a key component of such interventions involves creating built environments (i.e., man-made structures and features) that facilitate PA ( Centers for Disease Control and Prevention, 2010 ; Healthy People 2020, 2017
Interchangeability of Research and Commercial Wearable Device Data for Assessing Associations With Cardiometabolic Risk Markers
Andrew P. Kingsnorth, Elena Moltchanova, Jonah J.C. Thomas, Maxine E. Whelan, Mark W. Orme, Dale W. Esliger, and Matthew Hobbs
, linking metrics provided by commercial wearable devices into existing health care pathways such as patient medical records could provide the necessary platform for prevention-focused activities and offer an opportunity for important conversations about health and well-being within routine interactions
