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Paul D. Loprinzi, Bradley J. Cardinal, Carlos J. Crespo, Gary R. Brodowicz, Ross E. Andersen and Ellen Smit

Background:

The exclusion of participants with invalid accelerometry data (IAD) may lead to biased results and/or lack of generalizability in large population studies. The purpose of this study was to investigate whether demographic, behavioral, and biological differences occur between those with IAD and valid accelerometry data (VAD) among adults using a representative sample of the civilian noninstitutionalized U.S. population.

Methods:

Ambulatory participants from NHANES (2003−2004) who were 20−85 years of age were included in the current study and wore an ActiGraph 7164 accelerometer for 7 days. A “valid person” was defined as those with 4 or more days of at least 10+ hrs of monitoring per day. Among adults (20−85 yrs), 3088 participants provided VAD and 987 provided IAD. Demographic, behavioral, and biological information were obtained from the household interview or from data obtained in a mobile examination center.

Results:

Differences were observed in age, BMI, ethnicity, education, smoking status, marital status, use of street drugs, current health status, HDL-cholesterol, C-reactive protein, self-reported vigorous physical activity, and plasma glucose levels between those with VAD and IAD.

Conclusions:

Investigators should take into consideration the potential cut-off bias in interpreting results based on data that excludes IAD participants.

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Jennifer M. Medina McKeon and Patrick O. McKeon

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Viviene A. Temple, P. Lynn Purves, Robyn Misovic, Coral J. Lewis and Carrie DeBoer

Many children with disabling conditions do not acquire the skills to successfully ride a 2-wheeled bicycle. The aim was to describe cycling patterns before and after an innovative learn-to-ride bike camp and factors that facilitate or hinder the generalization of skills developed at camp to home. Parents and children participated in semistructured interviews 3–4 mo postcamp. Transcripts were examined deductively for participation and contextual influences using a template of codes approach. None of the children were successfully riding a 2-wheeled bicycle before camp. Two patterns of participation were evident from narrative descriptions of postcamp riding: “riders” and “not there yet.” Major facilitating factors were the camp itself, the interaction between the camp and the health service, and continued parent involvement. The program transferred well to home for children who were riding independently on the last day of camp. Ongoing support is needed for children “not there yet.”

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Amador García-Ramos, Guy Gregory Haff, Francisco Luis Pestaña-Melero, Alejandro Pérez-Castilla, Francisco Javier Rojas, Carlos Balsalobre-Fernández and Slobodan Jaric

variety of resistance training exercises. 6 , 14 – 16 The load–velocity relationship of a given exercise has also proven to be very stable regardless of the 1RM value of the subject. 6 , 17 These results have encouraged researchers to propose a use of “generalized group equations” for different basic

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Stephen Crowcroft, Katie Slattery, Erin McCleave and Aaron J. Coutts

assessed for their model fit using generalized estimating equations (GEE) and then for their diagnostic accuracy from receiver operating characteristic curve analysis. Athlete-Monitoring Variables Subjective self-report measures recorded in this study included perceived fatigue rating (1 = much worse than

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Kazuhiro Harada, Sangyoon Lee, Sungchul Lee, Seongryu Bae, Yuya Anan, Kenji Harada and Hiroyuki Shimada

unimportant” and “somewhat unimportant,” we also categorized the perceived value item into three groups: (1) unimportant, (2) somewhat important, and (3) strongly important. Furthermore, generalized linear models were conducted to examine the associations of the expectation for physical activity and the

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Byron Lai, Eunbi Lee, Mayumi Wagatsuma, Georgia Frey, Heidi Stanish, Taeyou Jung and James H. Rimmer

. Rich and diverse adaptive programs 3. Strategies to increase intervention reach 1. Generalizability 2. Transferability 3. Scientific rigor 1. Long-term and sustainable postintervention outcomes 2. Precision-based interventions 3. Scalable interventions and recruitment strategies Promising Elements of

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Koren L. Fisher, Elizabeth L. Harrison, Brenda G. Bruner, Joshua A. Lawson, Bruce A. Reeder, Nigel L. Ashworth, M. Suzanne Sheppard and Karen E. Chad

information in the same demographic strata (e.g., age, sex, marital status, income, education). A multivariate analysis using generalized estimation equation (GEE) methods was conducted to examine the association between the aforementioned predictors and PA with the PASE score as the outcome of interest

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Casey Mace Firebaugh, Simon Moyes, Santosh Jatrana, Anna Rolleston and Ngaire Kerse

) matching with Ministry of Health mortality data. Data Analysis Function as measured using the NEADL scale was analyzed using repeated-measures generalized linear models controlling for gender, age, time in the study, number of medications, and baseline Charlson Index on samples of 256 Māori and 400 non

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Steve Amireault, John M. Baier and Jonathan R. Spencer

and offers additional contextual insight on preferences for physical activity. Third, this review considers the risk of bias in primary studies, allowing for an assessment of whether findings are consistent across methodologies and whether findings can be generalized across the older adult population