This study examined the change in 15K running performance for master runners over 21 years (1978–1998). Official times were collected for 60 male runners from the same running event. Trends in running performance were analyzed with several models (linear, polynomial, and segmented-line). A self-report questionnaire was used to quantify training and to characterize runners. Peak age of running performance was indirectly estimated at 33 years using a second-degree polynomial. The performance trend was also associated with an inflection point at age 41 directly estimated from a nonlinear, segmented, mixed-effects model (95% confidence interval: 38.77–42.44). After age 41, master runners ran nearly 1 min slower each year. Besides age, other parameters that influenced performance over time included type of training (interval training) and body weight. These data might be among the first to describe the trend in running performance for a group of master athletes, most of whom were noncompetitive runners.
Jill M. Slade, Hector De Los Santos-Posadas and M. Elaine Cress
Jordan L. Fox, Robert Stanton, Charli Sargent, Cody J. O’Grady and Aaron T. Scanlan
Statistical Analyses All outcome measures are reported as mean (SD). To independently determine differences in each workload variable between groups for each contextual factor investigated, separate linear mixed-effect models with Bonferroni post hoc tests were utilized. Specifically, 3 linear mixed-effect
Corrado Lupo, Alexandru Nicolae Ungureanu, Riccardo Frati, Matteo Panichi, Simone Grillo and Paolo Riccardo Brustio
Purpose: To monitor elite youth female basketball training to verify whether players’ and coaches’ (3 technical coaches and 1 physical trainer) session rating of perceived exertion (s-RPE) has a relationship with Edwards’ method. Methods: Heart rate of 15 elite youth female basketball players (age 16.7 [0.5] y, height 178  cm, body mass 72  kg, body mass index 22.9 [2.2] kg·m−2) was monitored during 19 team (268 individual) training sessions (102  min). Mixed effect models were applied to evaluate whether s-RPE values were significantly (P ≤ .05) related to Edwards’ data, total session duration, maximal intensity (session duration at 90–100% HRmax), type of training (ie, strength, conditioning, and technique), and whether differences emerged between players’ and coaches’ s-RPE values. Results: The results showed that there is a relationship between s-RPE and Edwards’ methods for the players’ RPE scores (P = .019) but not for those of the trainers. In addition, as expected, both players’ (P = .014) and coaches’ (P = .002) s-RPE scores were influenced by total session duration but not by maximal intensity and type of training. In addition, players’ and coaches’ s-RPE values differed (P < .001)—post hoc differences emerged for conditioning (P = .01) and technique (P < .001) sessions. Conclusions: Elite youth female basketball players are better able to quantify the internal training load of their sessions than their coaches, strengthening the validity of s-RPE as a tool to monitor training in team sports.
Eva D’Hondt, Fotini Venetsanou, Antonis Kambas and Matthieu Lenoir
The targeted continent and/or country driven promotion of physical activity and health from an early age onwards requires more insight into cross-cultural differences in motor competence. Using the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF), this study assessed and compared both fine and gross motor skill performances of 5- and 6-year-old children from Belgium (n = 325) and Greece (n = 245). Linear mixed effect models and a χ2 test analyzed between-country differences in BOT-2 SF scores and the distribution across descriptive performance categories. Overall, Belgian and Greek participants displayed quite similar levels of motor competence, with fewer children performing (well-)below average than could be expected. On test item level, however, several significant differences emerged. Large effect sizes were found for knee push-ups (Hedges’ g = 1.46) and copying a square (Hedges’ g = 2.59), which demonstrated a better outcome for Belgian and Greek preschoolers, respectively. These findings might be attributed to different (physical) education practices in both European countries. The present study also highlights the importance of using an assessment tool covering the entire range of motor skills as well as a focusing primarily on raw performance scores, containing and explaining more variance, for international comparative research purposes.
Keith R. Lohse, Jincheng Shen and Allan J. Kozlowski
observations, respectively). However, we want to point out that if our outcomes had only a small, fixed number of levels, we could run a generalized linear mixed-effect model as an ordinal logistic regression. There is not, however, an equivalent alternative in ANOVA when an ordinal outcome has only a small
Jesse C. Christensen, Caitlin J. Miller, Ryan D. Burns and Hugh S. West
outcome variable. Separate models were also run using PT visits as both a continuous and categorical variable. The initial general linear mixed effect model was computed with all potential moderator variables (age, sex, race, ethnicity, marital status, length of PT stay, and surgeon), but the final model
Deborah A. Cohen, Bing Han, Sujeong Park, Stephanie Williamson and Kathryn P. Derose
60 and above, and examined responses to questions on individual sociodemographic characteristics, park use, and personal health. Because the participants were largely Latino, interaction analyses between race–ethnicity and other variables were not conducted. We fitted a mixed-effect model to estimate
Pantelis T. Nikolaidis, Stefania Di Gangi and Beat Knechtle
Table 2 , we reported fixed effect estimate coefficients of our mixed effect model. All effects were significant, even interaction term between age and sex. Intraclass correlation coefficient, which is a measure of how strongly units in the same group resemble each other, was high (.96). This justifies
Hio Teng Leong and Siu Ngor Fu
measurement was assessed in a pilot study with 20 healthy individuals. Intraclass correlation coefficient (ICC 3,1 , 2-way mixed effect model, consistency) for absolute agreement was performed to calculate the standard error of measurement [SEM = SD × √(1 − ICC)] and minimal detectable change (MDC = 1
Jonathon Weakley, Carlos Ramirez-Lopez, Shaun McLaren, Nick Dalton-Barron, Dan Weaving, Ben Jones, Kevin Till and Harry Banyard
, interquartile range, and total range. Repetition counts were also log-transformed prior to analysis and subsequently back transformed postanalysis, with the resultant effect statistics given as accurate percentages. We used linear mixed-effect models (SPSS version 24; IBM, Armonk, NY) to compare kinetic