Greater effect of diet than exercise training on the fatty acid profile of rat skeletal muscle

We determined the interaction of diet and exercise-training intensity on membrane phospholipid fatty acid (FA) composition in skeletal muscle from 36 female Sprague-Dawley rats. Animals were randomly divided into one of two dietary conditions: high-carbohydrate (64.0% carbohydrate by energy, n = 18) or high fat (78.1% fat by energy, n = 18). Rats in each diet condition were then allocated to one of three subgroups: control, which performed no exercise training; low-intensity (8 m/min) treadmill run training; or high-intensity (28 m/min) run training. All exercise-trained rats ran 1,000 m/session, 4 days/wk for 8 wk and were killed 48 h after the last training bout. Membrane phospholipids were extracted, and FA composition was determined in the red and white vastus lateralis muscles, Diet exerted a major influence on phospholipid FA composition, with the high-fat diet being associated with a significantly (P < 0.01) elevated ratio of n-6/n-3 FA for both red (2.7-3.2 vs. 1.0-1.1) and white vastus lateralis muscle (2.5-2.9 vs. 1.2). In contrast, alterations in FA composition as a result of either exercise-training protocol were only minor in comparison. We conclude that, under the present experimental conditions, a change in the macronutrient content of the diet was a more potent modulator of skeletal muscle membrane phospholipid FA composition compared with either low- or high-intensity treadmill exercise training.

Predicting psychological and subjective well-being from personality: incremental prediction from 30 facets over the Big 5

This study investigated the relationship between the Big 5, measured at factor and facet levels, and dimensions of both psychological and subjective well-being. Three hundred and thirty-seven participants completed the 30 Facet International Personality Item Pool Scale, Satisfaction with Life Scale, Positive and Negative Affectivity Schedule, and Ryff’s Scales of Psychological Well-Being. Cross-correlation decomposition presented a parsimonious picture of how well-being is related to personality factors. Incremental facet prediction was examined using double-adjusted r2 confidence intervals and semi-partial correlations. Incremental prediction by facets over factors ranged from almost nothing to a third more variance explained, suggesting a more modest incremental prediction than presented in the literature previously. Examination of semi-partial correlations controlling for factors revealed a small number of important facet-well-being correlations. All data and R analysis scripts are made available in an online repository.

Incremental criterion prediction of personality facets over factors: obtaining unbiased estimates and confidence intervals

 Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-. r-squared bootstrapping. We also provide an R package that implements the proposed methods.