A randomized controlled trial was conducted to examine eight weeks of resistance training (RT) with and without time-restricted feeding (TRF) in order to assess nutrient intake and changes in body composition and muscular strength in young recreationally active males. The TRF programme consisted of consuming all calories within a four-hour period of time for four days per week, but included no limitations on quantities or types of foods consumed. The RT programme was performed three days per week and consisted of alternating upper and lower body workouts. For each exercise, four sets leading to muscular failure between 8 and 12 repetitions were employed. Research visits were conducted at baseline, four, and eight weeks after study commencement. Measurements of total body composition by dual-energy X-ray absorptiometry and muscle cross-sectional area by ultrasound were obtained. Upper and lower body strength and endurance were assessed, and four-day dietary records were collected. TRF reduced energy intake by ∼650 kcal per day of TRF, but did not affect total body composition within the duration of the study. Cross-sectional area of the biceps brachii and rectus femoris increased in both groups. Effect size data indicate a gain in lean soft tissue in the group that performed RT without TRF (+2.3 kg, d = 0.25). Upper and lower body strength and lower body muscular endurance increased in both groups, but effect sizes demonstrate greater improvements in the TRF group. Overall, TRF reduced energy intake and did not adversely affect lean mass retention or muscular improvements with short-term RT in young males.

No abstract

Bi-factor confirmatory factor models have been influential in research on cognitive abilities because they often better fit the data than correlated factors and higher-order models. They also instantiate a perspective that differs from that offered by other models. Motivated by previous work that hypothesized an inherent statistical bias of fit indices favoring the bi-factor model, we compared the fit of correlated factors, higher-order, and bi-factor models via Monte Carlo methods. When data were sampled from a true bi-factor structure, each of the approximate fit indices was more likely than not to identify the bi-factor solution as the best fitting. When samples were selected from a true multiple correlated factors structure, approximate fit indices were more likely overall to identify the correlated factors solution as the best fitting. In contrast, when samples were generated from a true higher-order structure, approximate fit indices tended to identify the bi-factor solution as best fitting. There was extensive overlap of fit values across the models regardless of true structure. Although one model may fit a given dataset best relative to the other models, each of the models tended to fit the data well in absolute terms. Given this variability, models must also be judged on substantive and conceptual grounds.

No abstract

No abstract

Whereas general sample size guidelines have been suggested when estimating multilevel models, they are only generalizable to a relatively limited number of data conditions and model structures, both of which are not very feasible for the applied researcher. In an effort to expand our understanding of two-level multilevel models under less than ideal conditions, Monte Carlo methods, through SAS/IML, were used to examine model convergence rates, parameter point estimates (statistical bias), parameter interval estimates (confidence interval accuracy and precision), and both Type I error control and statistical power of tests associated with the fixed effects from linear two-level models estimated with PROC MIXED. These outcomes were analyzed as a function of: (a) level-1 sample size, (b) level-2 sample size, (c) intercept variance, (d) slope variance, (e) collinearity, and (f) model complexity. Bias was minimal across nearly all conditions simulated. The 95% confidence interval coverage and Type I error rate tended to be slightly conservative. The degree of statistical power was related to sample sizes and level of fixed effects; higher power was observed with larger sample sizes and level-1 fixed effects.

No abstract

ObjectiveTo investigate the efficacy of interventions designed to train and develop mental toughness (MT) in sport.DesignSystematic review and meta-analysis.Data sourcesJournal articles, conference papers and doctoral theses indexed in Embase, Scopus, PubMed and SPORTDiscus from inception to 22 November 2019.Eligibility criteria for selecting studiesObservational and pre–post experimental designs on the efficacy of physical and/or psychological interventions designed to promote MT in athletes.ResultsA total of 12 studies, published between 2005 and 2019, were included in the review. A majority of the studies included a sample comprised exclusively of male athletes (54.55%), MT interventions were primarily psychological (83.33%) and most studies measured MT via self-report (75%). The Psychological Performance Inventory (25%), the Mental Toughness Questionnaire-48 (16.67%), and the Mental, Emotional and Bodily Toughness Inventory (16.67%) were the most popular inventories used to measure MT. Methodological quality assessments for controlled intervention studies (k=7), single group pre-test–post-test designs (k=4) and single-subject designs (k=1) indicated that the risk of bias was high in most (75%) of the studies. The meta-analysis involving k=10 studies revealed a large effect (d=0.80, 95% CI 0.30 to 1.28), with variability across studies estimated at 0.56.ConclusionAlthough the findings of this review suggest there are effective, empirically based interventions designed to train MT in sport, practitioners should be aware of the level of validity of intervention research before adopting any of the MT training programmes reported in the applied sport psychology literature.