Body composition and hydration status changes in male and female open-water swimmers during an ultra-endurance event

Body mass changes during ultra-endurance performances have been described for running, cycling and for swimming in a heated pool. The present field study of 20 male and 11 female open-water swimmers investigated the changes in body composition and hydration status during an ultra-endurance event. Body mass, both estimated fat mass and skeletal muscle mass, haematocrit, plasma sodium concentration ([Na+]) and urine specific gravity were determined. Energy intake, energy expenditure and fluid intake were estimated. Males experienced significant reductions in body mass (-0.5 %) and skeletal muscle mass (-1.1 %) (P < 0.05) during the race compared to females who showed no significant changes with regard to these variables (P > 0.05). Changes in percent body fat, fat mass, and fat-free mass were heterogeneous and did not reach statistical significance (P > 0.05) between gender groups. Fluid intake relative to plasma volume was higher in females than in males during the ultra-endurance event. Compared to males, females' average increase in haematocrit was 3.3 percentage points (pp) higher, urine specific gravity decrease 0.1 pp smaller, and plasma [Na+] 1.3 pp higher. The observed patterns of fluid intake, changes in plasma volume, urine specific gravity, and plasma [Na+] suggest that, particularly in females, a combination of fluid shift from blood vessels to interstitial tissue, facilitated by skeletal muscle damage, as well as exercise-associated hyponatremia had occurred. To summarise, changes in body composition and hydration status are different in male compared to female open-water ultra-endurance swimmers.     

Auto-hermeneutics: A phenomenological approach to information experience

The need for methodologically rigorous approaches to the study of human experience in LIS has emerged in recent years. Auto-hermeneutics is a research approach that offers a systematic way to study one’s own experiences with information, allowing investigators to explore yet-undocumented contexts, setting precedents for further work in these areas and ultimately deepening our understanding of information experiences. This articulation of auto-hermeneutics is based on the phenomenological method of Heidegger and draws principles from systematic self-observation and interpretative phenomenological analysis. Similarities and differences among auto-hermeneutics and other automethodologies are discussed, along with guidelines for assessing auto-hermeneutic research. Finally, an example of an auto-hermeneutic study illustrates the unique contributions this approach affords.     

Quantifying Error and Uncertainty Reductions in Scaling Functions: An ITEMS Module

This module describes and extends X‐to‐Y regression measures that have been proposed for use in the assessment of X‐to‐Y scaling and equating results. Measures are developed that are similar to those based on prediction error in regression analyses but that are directly suited to interests in scaling and equating evaluations. The regression and scaling function measures are compared in terms of their uncertainty reductions, error variances, and the contribution of true score and measurement error variances to the total error variances. The measures are also demonstrated as applied to an assessment of scaling results for a math test and a reading test. The results of these analyses illustrate the similarity of the regression and scaling measures for scaling situations when the tests have a correlation of at least .80, and also show the extent to which the measures can be adequate summaries of nonlinear regression and nonlinear scaling functions, and of heteroskedastic errors. After reading this module, readers will have a comprehensive understanding of the purposes, uses, and differences of regression and scaling functions.     

A Delphi Study and Initial Validation of Counselor Supervision Competencies

The authors addressed the lack of supervision training standards for doctoral counseling graduates by developing and validating an initial list of supervision competencies. They used content analysis, Delphi polling, and content validity methods to generate a list, vetted by 2 different panels of supervision experts, of 33 competencies grouped into 5 categories.