Music therapy (MT) and music-based interventions (MBIs) are increasingly used for the treatment of substance use disorders (SUD). Previous reviews on the efficacy of MT emphasized the dearth of research evidence for this topic, although various positive effects were identified. Therefore, we conducted a systematic search on published articles examining effects of music, MT and MBIs and found 34 quantitative and six qualitative studies. There was a clear increase in the number of randomized controlled trials (RCTs) during the past few years. We had planned for a meta-analysis, but due to the diversity of the quantitative studies, effect sizes were not computed. Beneficial effects of MT/ MBI on emotional and motivational outcomes, participation, locus of control, and perceived helpfulness were reported, but results were inconsistent across studies. Furthermore, many RCTs focused on effects of single sessions. No published longitudinal trials could be found. The analysis of the qualitative studies revealed four themes: emotional expression, group interaction, development of skills, and improvement of quality of life. Considering these issues for quantitative research, there is a need to examine social and health variables in future studies. In conclusion, due to the heterogeneity of the studies, the efficacy of MT/ MBI in SUD treatment still remains unclear.
This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models.
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