A Feasibility Study of a Program Integrating Mindfulness, Yoga, Positive Psychology, and Emotional Intelligence in Tertiary-Level Student Musicians

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Abstract

Objectives

Higher education student musicians face high physical, psychological, and emotional demands affecting their well-being and academic experience. This study examined the feasibility and preliminary effectiveness of the so-called CRAFT program, based on mindfulness, yoga, positive psychology, and emotional intelligence, to improve psychological well-being, psychological distress, emotional regulation, and physical flexibility amongst tertiary education student musicians.

Methods

Using a single-arm pre-post study design, student musicians ( n = 25) at a royal conservatory of music in Spain followed a 25-week CRAFT program that was curricularly implemented during the academic year 2018/2019, once a week for 50 min. The outcome measures included were the Five Facet Mindfulness Questionnaire (FFMQ), the Subjective Psychological Well-Being Subscale (SPWS), the Emotional Regulation Questionnaire (ERQ), the Depression Anxiety and Stress Scale (DASS-21), and the Sit and Reach Test (SRT).

Results

Paired samples t-test and practical significance analyses revealed significant improvements for the total scale of the FFMQ ( g = 0.28), the Observe ( g = 0.44) and Describe ( g = 0.38) subscales of the FFMQ, the SPWS ( g = 0.32), the Reappraisal subscale of the ERQ ( g = 0.43), and the SRT ( g = 0.39). A similar pattern of results was observed in a filtered sample ( n = 15) when excluding participants simultaneously engaged in yoga/meditation activities other than the CRAFT program.

Conclusions

These results indicated that the CRAFT program is a promising intervention for improving mindfulness skills and health and well-being states and abilities amongst higher education student musicians. Further research is needed to substantiate these findings and extend them to similar settings and populations with complex psychophysical concerns.

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Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

Daniël Lakens (2013)

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.

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Effect size, confidence interval and statistical significance: a practical guide for biologists.

Shinichi Nakagawa, Innes C Cuthill (2007)

Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.