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Kelley Pettee Gabriel, Adriana Pérez, David R. Jacobs Jr, Joowon Lee, Harold W. Kohl III and Barbara Sternfeld

Background: Single-method assessment of physical activity (PA) has limitations. The utility and cross-validation of a composite PA score that includes reported and accelerometer-derived PA data has not been evaluated. Methods: Participants attending the Year 20 exam were randomly assigned to the derivation (two-thirds) or validation (one-third) data set. Principal components analysis was used to create a composite score reflecting Year 20 combined reported and accelerometer PA data. Generalized linear regression models were constructed to estimate the variability explained (R 2) by each PA assessment strategy (self-report only, accelerometer only, composite score, or self-report plus accelerometer) with cardiovascular health indicators. This process was repeated in the validation set to determine cross-validation. Results: At Year 20, 3549 participants (45.2 [3.6] y, 56.7% female, and 53.5% black) attended the clinic exam and 2540 agreed to wear the accelerometer. Higher R 2 values were obtained when combined assessment strategies were used; however, the approach yielding the highest R 2 value varied by cardiovascular health outcome. Findings from the cross-validation also supported internal study validity. Conclusions: Findings support continued refinement of methodological approaches to combine data from multiple sources to create a more robust estimate that reflects the complexities of PA behavior.

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Emily Borgundvaag, Michael McIsaac, Michael M. Borghese and Ian Janssen

Background: A limitation of accelerometer measures of moderate to vigorous physical activity (MVPA) is nonwear time. Nonwear-time data is typically deleted prior to estimating MVPA. In this study, we used an approach that used sociodemographic, health, and time data to guide the imputation of nonwear-time data. We determined whether imputing nonwear-time data influences estimates of MVPA and the association between MVPA, body mass index, and blood pressure. Methods: Seven days of accelerometer data were collected on 332 children aged 10–13 years. MVPA was estimated in a “nonimputed dataset,” wherein nonwear-time data were deleted prior to estimating MVPA, and in an “imputed dataset,” wherein nonwear-time data were imputed using sociodemographic and health characteristics of participants and time characteristics of the nonwear period prior to estimating MVPA. Results: Nonwear time represented 7% of waking hours. Average MVPA estimates did not differ in the nonimputed and imputed datasets (56.8 vs 58.4 min/d). The strength of the relationship between MVPA and the 2 health outcomes did not differ in the nonimputed and imputed datasets. Conclusions: Studies achieving high accelerometer wear-time compliance can obtain MVPA estimates without substantial bias if they use the traditional approach of deleting nonwear-time data.

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Brigid M. Lynch, Suzanne C. Dixon-Suen, Andrea Ramirez Varela, Yi Yang, Dallas R. English, Ding Ding, Paul A. Gardiner and Terry Boyle

Background: It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies. Methods: We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to minimize this. Results: Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation. Conclusions: We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.

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Ryan D. Burns, Youngwon Kim, Wonwoo Byun and Timothy A. Brusseau

Background: To examine the relationships among school day sedentary times (SED), light physical activity (LPA), and moderate to vigorous physical activity (MVPA) with gross motor skills in children using Compositional Data Analysis. Methods: Participants were 409 children (mean age = 8.4 [1.8] y) recruited across 5 low-income schools. Gross motor skills were assessed using the test for gross motor development—third edition (TGMD-3), and physical activity was assessed using accelerometers. Isometric log-ratio coordinates were calculated by quantifying the relative proportion of percentage of the school day spent in SED, LPA, and MVPA. The associations of the isometric log-ratio coordinates with the TGMD-3 scores were estimated using general linear mixed-effects models adjusted for age, body mass index, estimated aerobic capacity, and school affiliation. Results: A higher proportion of the school day spent in %MVPA relative to %SED and %LPA was significantly associated with higher TGMD-3 total scores (γ MVPA = 14.44, P = .01). This relationship was also observed for the ball skills subtest scores (γ MVPA = 16.12, P = .003). Conclusions: Replacing %SED and %LPA with %MVPA during school hours may be an effective strategy for improving gross motor skills, specifically ball skills, in low-income elementary school-aged children.

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Benjamin C. Guinhouya, Mohamed Lemdani, Géoffroy K. Apété, Alain Durocher, Christian Vilhelm and Hervé Hubert


This study was designed to model the relationship between an ActiGraph-based “in-school” physical activity (PA) and the daily one among children and to quantify how school can contribute to the daily PA recommendations.


Fifty boys and 43 girls (aged 8 to 11 years) wore ActiGraph for 2 schooldays of no structured PA. The daily moderate-to-vigorous PA (MVPAd) was regressed on the school time MVPA (MVPAs). Then, a ROC analysis was computed to define the required MVPAs.


Children spent 57% of their awaking time at school. School time PA opportunities (ie, recesses: ~18% of a child’s awaking time) accounted for >70% of the MVPAd among children. Then, MVPAd (Y) could be predicted from MVPAs (X) using the equation: Y = 2.06 X 0.88; R 2 = .889, P < .0001. Although, this model was sex-specifically determined, cross-validations showed valid estimates of MVPAd. Finally, with a sensitivity of 100% and a specificity of 90%, MVPAs, a 34 min.d−1 was required to prompt the daily recommendation.


The current study shows the contribution of MVPA at school to recommended activity levels and suggests the value of activity performed during recesses. It also calls for encouraging both home- and community-based interventions, predominantly directed toward girls.

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Robert J. Kowalsky, Sophy J. Perdomo, John M. Taormina, Christopher E. Kline, Andrea L. Hergenroeder, Jeffrey R. Balzer, John M. Jakicic and Bethany Barone Gibbs

variables . Clin Neurophysiol . 2006 ; 117 ( 7 ): 1574 – 1581 . PubMed ID: 16679057 doi:10.1016/j.clinph.2006.03.011 10.1016/j.clinph.2006.03.011 16679057 29. Kenward MG , Roger JH . The use of baseline covariates in crossover studies . Biostatistics . 2010 ; 11 ( 1 ): 1 – 17 . PubMed ID: 19915170

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Andrew D. Govus, Aaron Coutts, Rob Duffield, Andrew Murray and Hugh Fullagar

, Saugy M . Bayesian detection of abnormal values in longitudinal biomarkers with an application to T/E ratio . Biostatistics . 2007 ; 8 ( 2 ): 285 – 296 . PubMed doi:10.1093/biostatistics/kxl009 10.1093/biostatistics/kxl009 19. Kellmann M . Preventing overtraining in athletes in high

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Paulo Felipe Ribeiro Bandeira, Michael Duncan, Maria Luiza Pessoa, Ívina Soares, Larissa da Silva, Jorge Mota and Clarice Martins

. , Hastie , T. , & Tibshirani , R. ( 2007 ). Sparse inverse covariance estimation with the graphical lasso . Biostatistics, 9 ( 3 ), 432 – 441 . PubMed ID: 18079126 doi:10.1093/biostatistics/kxm045 10.1093/biostatistics/kxm045 Fruchterman , T.M.J. , & Reingold , E.M. ( 1991 ). Graph drawing by

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Matthew D. Watson, George J. Davies and Bryan L. Riemann

measured in persons with chronic stroke . J Rehabil Med . 2015 ; 47 ( 8 ): 706 – 713 . PubMed ID: 26181580 doi: 10.2340/16501977-1990 26181580 10. Chan YH . Biostatistics 104: correlational analysis . Singapore Med J . 2003 ; 44 ( 12 ): 614 – 619 . PubMed ID: 14770254 14770254 11. Bland JM

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Patrick Ward, Johann Windt and Thomas Kempton

and conditioning, biomechanics, performance analysis, biostatistics, and data science. Regardless of their foundation and specific job title, we believe that effective sport scientists working in professional sport should be able to develop systematic analysis frameworks to enhance performance within