Accelerometer data reduction in adolescents: effects on sample retention and bias

BackgroundAccelerometry is increasingly being recognized as an accurate and reliable method to assess free-living physical activity (PA) in children and adolescents. However, accelerometer data reduction criteria remain inconsistent, and the consequences of excluding participants in for example intervention studies are not well described. In this study, we investigated how different data reduction criteria changed the composition of the adolescent population retained in accelerometer data analysis.MethodsAccelerometer data (Actigraph GT3X), anthropometric measures and survey data were obtained from 1348 adolescents aged 11–14 years enrolled in the Danish SPACE for physical activity study. Accelerometer data were analysed using different settings for each of the three key data reduction criteria: (1) number of valid days; (2) daily wear time; and (3) non-wear time. The effects of the selected setting on sample retention and PA counts were investigated and compared. Ordinal logistic regression and multilevel mixed-effect linear regression models were used to analyse the impact of differing non-wear time definitions in different subgroups defined by body mass index, age, sex, and self-reported PA and sedentary levels.ResultsIncreasing the minimum requirements for daily wear time and the number of valid days and applying shorter non-wear definitions, resulted in fewer adolescents retained in the dataset. Moreover, the different settings for non-wear time significantly influenced which participants would be retained in the accelerometer data analyses. Adolescents with a higher BMI (OR:0.93, CI:0.87-0.98, p=0.015) and older adolescents (OR:0.68, CI:0.49-0.95, p=0.025) were more likely to be excluded from analysis using 10 minutes of non-wear compared to longer non-wear time periods. Overweight and older adolescents accumulated more daily non-wear time if the non-wear time setting was short, and the relative difference between groups changed depending on the non-wear setting. Overweight and older adolescents did also accumulate more sedentary time, but this was not significant correlated to the non-wear setting used.ConclusionsEven small differences in accelerometer data reduction criteria can have substantial impact on sample size and PA and sedentary outcomes. This study highlighted the risk of introducing bias with more overweight and older adolescents excluded from the analysis when using short non-wear time definitions.

[1]  G. Cardon,et al.  Measuring physical activity using accelerometry in 13–15-year-old adolescents: the importance of including non-wear activities , 2011, Public Health Nutrition.

[2]  Soyang Kwon,et al.  Tracking of accelerometry-measured physical activity during childhood: ICAD pooled analysis , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[3]  Mark S Tremblay,et al.  Quality control and data reduction procedures for accelerometry-derived measures of physical activity. , 2010, Health reports.

[4]  C. Pedersen,et al.  The Danish Civil Registration System , 2011, Scandinavian journal of public health.

[5]  Melody Oliver,et al.  Identification of Accelerometer Nonwear Time and Sedentary Behavior , 2011, Research quarterly for exercise and sport.

[6]  N J Wareham,et al.  Does physical activity equally predict gain in fat mass among obese and nonobese young adults? , 2007, International Journal of Obesity.

[7]  Stewart G Trost,et al.  Conducting accelerometer-based activity assessments in field-based research. , 2005, Medicine and science in sports and exercise.

[8]  S. Belle,et al.  Determining activity monitor wear time: an influential decision rule. , 2011, Journal of physical activity & health.

[9]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[10]  Richard P Troiano,et al.  Large-scale applications of accelerometers: new frontiers and new questions. , 2007, Medicine and science in sports and exercise.

[11]  J. Sallis,et al.  Using accelerometers in youth physical activity studies: a review of methods. , 2013, Journal of physical activity & health.

[12]  Dale W. Esliger,et al.  Standardizing and Optimizing the Use of Accelerometer Data for Free-Living Physical Activity Monitoring , 2005 .

[13]  Gregory J Welk,et al.  Everything you wanted to know about selecting the "right" Actigraph accelerometer cut-points for youth, but…: a systematic review. , 2012, Journal of science and medicine in sport.

[14]  Stewart G Trost,et al.  Comparison of accelerometer cut points for predicting activity intensity in youth. , 2011, Medicine and science in sports and exercise.

[15]  U. Ekelund,et al.  International children's accelerometry database (ICAD): Design and methods , 2011, BMC public health.

[16]  Ulf Ekelund,et al.  Assessment of physical activity in youth. , 2008, Journal of applied physiology.

[17]  Lars Bo Andersen,et al.  Mechanical and free living comparisons of four generations of the Actigraph activity monitor , 2012, International Journal of Behavioral Nutrition and Physical Activity.

[18]  J. Fulton,et al.  Feasibility of using accelerometers to measure physical activity in young adolescents. , 2005, Medicine and science in sports and exercise.

[19]  U. Ekelund,et al.  Assessing Physical Activity Among Children With Accelerometers Using Different Time Sampling Intervals and Placements , 2002 .

[20]  P. Freedson,et al.  Using objective physical activity measures with youth: how many days of monitoring are needed? , 2000, Medicine and science in sports and exercise.

[21]  T. Cole,et al.  Establishing a standard definition for child overweight and obesity worldwide: international survey , 2000, BMJ : British Medical Journal.

[22]  Stewart G Trost,et al.  Comparison of three generations of ActiGraph™ activity monitors in children and adolescents , 2012, Journal of sports sciences.

[23]  Nicole Probst-Hensch,et al.  Effects of filter choice in GT3X accelerometer assessments of free-living activity. , 2013, Medicine and science in sports and exercise.

[24]  N. Wareham,et al.  Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. , 2008, Journal of physical activity & health.

[25]  S. Going,et al.  A Longitudinal Study of Sedentary Behavior and Overweight in Adolescent Girls , 2009, Obesity.

[26]  Yan Liu,et al.  Fit for Life Boy Scout badge: outcome evaluation of a troop and Internet intervention. , 2006, Preventive medicine.

[27]  U. Ekelund,et al.  Physical activity levels and patterns of 9- and 15-yr-old European children. , 2004, Medicine and science in sports and exercise.

[28]  W. van Mechelen,et al.  Validity and reproducibility of motion sensors in youth: a systematic update. , 2009, Medicine and science in sports and exercise.

[29]  S. Grant,et al.  Objective measurement of physical activity and sedentary behaviour: review with new data , 2008, Archives of Disease in Childhood.

[30]  R. Mcmurray,et al.  Calibration of two objective measures of physical activity for children , 2008, Journal of sports sciences.

[31]  D. Bassett,et al.  Effect of BMI on pedometers in early adolescents under free-living conditions. , 2013, Medicine and science in sports and exercise.

[32]  A. Rowlands Accelerometer assessment of physical activity in children: an update. , 2007, Pediatric exercise science.

[33]  Bernard F Fuemmeler,et al.  Accelerometer data reduction: a comparison of four reduction algorithms on select outcome variables. , 2005, Medicine and science in sports and exercise.

[34]  James A. Levine,et al.  Pedometer Accuracy for Children: Can We Recommend Them for Our Obese Population? , 2009, Pediatrics.

[35]  Wendy J Brown,et al.  Is the pain of activity log-books worth the gain in precision when distinguishing wear and non-wear time for tri-axial accelerometers? , 2013, Journal of science and medicine in sport.

[36]  Mikkel Baadsgaard,et al.  Danish registers on personal income and transfer payments , 2011, Scandinavian journal of public health.