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Yani L. Dickens, Judy Van Raalte and Russell T. Hurlburt

-aloud methods presume that thinking translates easily and contemporaneously into words, typically focus on thoughts at the expense of other inner experiences such as emotions or sensations, lack random and representative sampling in favor of ongoing, continuous real-time reporting ( Hurlburt, 2011 ), and may be

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Megan S. Patterson and Patricia Goodson

compulsive exercise scores? Based on the tripartite model and previous literature, the investigators expect relationships with peers and family members to significantly impact compulsive exercise scores in the sample. They also hypothesize that having greater body dissatisfaction and exercising more

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Daniel J. Madigan, Thomas Curran, Joachim Stoeber, Andrew P. Hill, Martin M. Smith and Louis Passfield

 al., 2011 ). Notably, Gotwals et al. ( 2010 ) also found coach pressure to predict perfectionistic strivings and perfectionistic concerns in a sample of late-adolescent athletes. Although current findings are suggestive of a link between coach pressure and athlete perfectionism, several issues remain

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Laura D. Ellingson, Paul R. Hibbing, Gregory J. Welk, Dana Dailey, Barbara A. Rakel, Leslie J. Crofford, Kathleen A. Sluka and Laura A. Frey-Law

data collection. Procedures At the initial visit, participants were fitted with a wrist-worn tri-axial accelerometer (ActiGraph GT3X+, dynamic range ±6 g, sampling rate 30 Hz) to wear on their non-dominant wrist 24 hrs/day for a week (including sleep and showering). Participants were instructed to

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Paul J. McCarthy and Marc V. Jones

This focus group study examined the sources of enjoyment and nonenjoyment among younger and older English children in the sampling years of sport participation (ages 7–12). Concurrent inductive and deductive content analysis revealed that, consistent with previous research, younger and older children reported sources of enjoyment such as perceived competence, social involvement and friendships, psychosocial support, and a mastery-oriented learning environment. Nonenjoyment sources included inappropriate psychosocial support, increasing competitive orientation, negative feedback and reinforcement, injuries, pain, and demonstrating a lack of competence. Differences between younger and older children’s sources of enjoyment and nonenjoyment also emerged. Younger children reported movement sensations as a source of enjoyment and punishment for skill errors and low informational support as nonenjoyment sources. Older children reported social recognition of competence, encouragement, excitement, and challenge as sources of enjoyment with rivalry, overtraining, and high standards as sources of nonenjoyment. These differences underscore the importance of tailoring youth sport in the sampling years to the needs of the child.

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Fuzhong Li and Peter Harmer

This study was designed to assess the factorial construct validity of the Group Environment Questionnaire (GEQ; Carron, Widmeyer, & Brawley, 1985) within a hypothesis-testing framework. Data were collected from 173 male and 148 female intercollegiate athletes. Based on Carron et al.’s (1985) conceptual model of group cohesion, the study examined (a) the extent to which the first-order four-factor model could be confirmed with an intercollegiate athlete sample and (b) the degree to which higher order factors could account for the covariation among the four first-order factors. The a priori models of GEQ, including both the first- and second-order factor models, were tested through confirmatory factor analysis (CFA). CFA results showed that the theoretically specified first- and second-order factor models fit significantly better than all alternative models. These results demonstrated that the GEQ possesses adequate factorial validity and reliability as a measure of the sport group cohesion construct for an intercollegiate athlete sample.

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Brian Cook, Trisha M. Karr, Christie Zunker, James E. Mitchell, Ron Thompson, Roberta Sherman, Ross D. Crosby, Li Cao, Ann Erickson and Stephen A. Wonderlich

The purpose of our study was to examine exercise dependence (EXD) in a large community-based sample of runners. The secondary purpose of this study was to examine differences in EXD symptoms between primary and secondary EXD. Our sample included 2660 runners recruited from a local road race (M age = 38.78 years, SD = 10.80; 66.39% women; 91.62% Caucasian) who completed all study measures online within 3 weeks of the race. In this study, EXD prevalence was lower than most previously reported rates (gamma = .248, p < .001) and individuals in the at-risk for EXD category participated in longer distance races, F(8,1) = 14.13, p = .01, partial eta squared = .05. Group differences were found for gender, F(1,1921) 8.08, p = .01, partial eta squared = .004, and primary or secondary group status, F(1,1921) 159.53, p = .01, partial eta squared = .077. Implications of primary and secondary EXD differences and future research are discussed.

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Susan C. Duncan, Lisa A. Strycker, Terry E. Duncan and Nigel R. Chaumeton

It is important that studies on youth health behavior obtain sufficiently large representative samples so that power is adequate and results are generalizable. However, few researchers have documented procedures and methods for recruitment of a random stratified youth sample for studies on health-related behavior, specifically physical activity. This study describes the recruitment methods used to attain a stratified sample of 360 target youth (boys and girls from 10-, 12-, and 14-year-old cohorts), and a parent of each child, representing families in 58 neighborhoods. A peer of each target youth was also invited to participate. Recruitment was conducted primarily by telephone, using computer-assisted telephone interviewing (CATI) software. Approximately 38% of calls resulted in person contact, of which about 98% of families did not qualify. Of those qualified, about 68% agreed to participate. The telephone recruitment was supplemented by door-to-door recruitment in selected neighborhoods. The average cost of recruitment was approximately $99 per family by telephone and $64 door to door. Advantages and limitations of the recruitment method are discussed.

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Timothy K. Behrens and Mary K. Dinger


The purpose of this study was to compare steps·d-1 between an accelerometer and pedometer in 2 free-living samples.


Data from 2 separate studies were used for this secondary analysis (Sample 1: N = 99, Male: n = 28, 20.9 ± 1.4 yrs, BMI = 27.2 ± 5.0 kg·m-2, Female: n = 71, 20.9 ± 1.7 yrs, BMI = 22.7 ± 3.0 kg·m-2; Sample 2: N = 74, Male: n = 27, 38.0 ± 9.5 yrs, BMI = 25.7 ± 4.5 kg·m-2, Female: n = 47, 38.7 ± 10.1 yrs, BMI = 24.6 ± 4.0 kg·m-2). Both studies used identical procedures and analytical strategies.


The mean difference in steps·d-1 for the week was 1643.4 steps·d-1 in Study 1 and 2199.4 steps·d-1 in Study 2. There were strong correlations between accelerometer- and pedometer-determined steps·d-1 in Study 1 (r = .85, P < .01) and Study 2 (r = 0.87, P < .01). Bland-Altman plots indicated agreement without bias between steps recorded from the devices in Study 1 (r = −0.14, P < .17) and Study 2 (r = −0.09, P < .40). Correlations examining the difference between accelerometer–pedometer steps·d-1 and MVPA resulted in small, inverse correlations (range: r = −0.03 to −0.28).


These results indicate agreement between accelerometer- and pedometer-determined steps·d-1; however, measurement bias may still exist because of known sensitivity thresholds between devices.

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Robert E. Davis and Paul D. Loprinzi


To examine whether accelerometer-measured physical activity–based reactivity was present in a nationally representative sample of U.S. children (6–11 yrs), adolescents (12–17 yrs), and adults (≥20 yrs).


Data from the 2003–2006 National Health and Nutrition Examination Survey (N = 674, 6–85 yrs) were used. Physical activity (PA) was assessed using the ActiGraph 7164 accelerometer, with PA assessed over 7 days of monitoring. Two PA metrics were assessed, including activity counts per day (CPD) and time spent in moderate-to-vigorous PA. Evidence of reactivity was defined as a statistically significantly change in either of these 2 PA metrics from day 1 of monitoring to days 2 or 3, with day 1 of monitoring being a Monday.


Suggestion of reactivity was observed only for the adult population where CPD from days 2 and 3 (297,140.6 ± 7920.3 and 295,812.9 ± 8364.9), respectively, differed significantly from day 1 (309,611.5 ± 9134.9) over the monitoring period (4.0% to 4.5% change). The analysis was conducted 2 additional times with differing start days (Tuesday and Wednesday), and this approach failed to demonstrated a reactive presence.


In this national sample of U.S. children, adolescents and adults, we did not observe sufficient evidence of accelerometer reactivity.