Computational protocol: Lower-extremity resistance training on unstable surfaces improves proxies of muscle strength, power and balance in healthy older adults: a randomised control trial

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Protocol publication

[…] An a priori power analysis using G*Power 3.1 [] with an assumed type I error of .05 and a type II error of .10 (90% statistical power, correlation among groups: .5, nonsphericity correction: 1) was computed to determine an appropriate sample size to detect medium (.50 ≤ d ≤ .79) interaction effects. The calculations were based on a study assessing the effects of core strength training using unstable devices in older adults []. The analysis revealed the requirement of 54 participants (18 per group) to obtain medium “time × group” interaction effects. Considering the likelihood of dropouts, 83 participants were recruited to compensate for a possible dropout rate of ~20%.Prior to the main analysis, normal distribution was checked by visual inspection and tested with the Shapiro Wilk test for each dependent variable. In addition, Levene’s test for equality of variance was conducted. Baseline differences were tested between groups with a one-way ANOVA or a Kruskal-Wallis test depending on distribution and homogeneity. No differences were found (p ≥ .067). A 3 (group: M-SRT, M-URT, F-URT) × 2 (time: pre-test, post-test) ANOVA with repeated measures on time was conducted. Ryan-Holm-Bonferroni [] adjusted post-hoc tests (dependent t-tests, Wilcoxon tests) were used to detect statistically significant pre- to post-test differences within each group. Ryan-Holm-Bonferroni corrected p-values were reported. In the case of distribution or homogeneity violations, Kruskal-Wallis one-way analyses of variance and Friedman tests were performed for non-parametrical variables and to control results of parametrical tests. If differences were detected, non-parametric test results would be expressed. In addition, differences in absolute training intensity within the last training block were analysed. Therefore, the training load of the squat movement, which was a common exercise to all groups, was used. Differences were computed using a one-way ANOVA. Post-hoc applied independent t-tests were used to identify differences between groups. Changes for all variables within groups were calculated with the formula ∆% = ((Meanpre/Meanpost) – 1) × 100.To improve readability and homogeneity, effect sizes (Cohen’s d) were calculated for all statistical tests []. Following Cohen [], d-values ≤ .49 indicate small effects, .50 ≤ d ≤ .79 indicate medium effects, and d ≥ .80 indicate large effects. Significance level was set at α = 5%. All analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL, USA). […]

Pipeline specifications

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