Computational protocol: Energy compensation after sprint- and high-intensity interval training

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Protocol publication

[…] Sample size for the original study was estimated based on prior research on interval training; additionally, based on an estimated β = 0.8, moderate effect size f = 0.25, and a correlation among repeated measures of 0.8 for resting metabolic rate, a total sample size of 21 participants was calculated using G*Power [].In the original study, repeated measures ANOVA were conducted to examine the effects of time (pre/post), group (Control, SIT, HIIT), and their interaction (time*group). While the SIT and HIIT groups had training-induced changes in RMR and VO2max, they were not significantly different from each other. Independent-samples t-tests at baseline and post-testing showed no significant differences between the exercise groups. Specifically, no significant differences for exercise-induced changes in body composition, physical activity, exercise energy expenditure, and energy intake were observed between the SIT and HIIT groups during post-testing. Therefore, in order to increase power, the training groups were pooled in the present analysis. Following procedures previously used by McNeil and colleagues, we conducted a multivariable linear regression to examine the strength of the associations between energy compensation and changes in VO2max, energy intake, RMR, and NEPA, with baseline fat mass and VO2max as covariates []. Additionally, an independent samples t-test was conducted to determine differences in change scores between individuals who had energy compensation levels < 100% and those who had levels ≥ 100%. Data were analyzed using SPSS v. 23 (IBM Corp, Chicago, IL, USA), and statistical significance was accepted at p < 0.05. […]

Pipeline specifications

Software tools G*Power, SPSS
Application Miscellaneous
Organisms Homo sapiens
Diseases Hypertension