Abstract

Existing meta-analyses on the effect of online psychological interventions (OPIs) have found small to medium effect sizes for the treatment of anxiety and depression. On the other hand, third-generation trans-diagnostic OPIs are very rare and, due to the large variability among disorders, symptoms or target populations, it is difficult to assess their overall effect. Other systematic reviews and meta-analyses have overly broad inclusion criteria that make the understanding of the findings more difficult. The current study aims to analyze the empirical evidence for third-wave trans-diagnostic OPIs designed to decrease symptoms and promote psychological flexibility, including studies that compare a OPI to some control condition ( e. g. , waiting list, treatment as usual or other that should not have any effect) and include a general symptomatology scale as dependent variable. A search without filters or timeframe was performed on Scopus and 1 408 articles were found, among which 21 were reviewed in depth and 6 were included for meta-analysis. Risk of bias was assessed by a quality and heterogeneity assessment. Separate meta-analyses were performed for general distress and psychological flexibility at post-treatment and last follow-up. Risk of bias analysis suggest low risk of threats to validity and attribute heterogeneity to between-study attrition rates. Additionally, meta-regression models for duration, attrition rate, and mean age are proposed for each time point. The results show significantly large effect sizes for both variables at both time points. According to the meta-regression models attrition rates are a mediating variable for the effect on general distress both at completion and at the last follow-up. On the other hand, duration, age and attrition rate are all mediating variables for the effect on psychological flexibility at the end of treatment. The findings suggest that the high attrition rates observed on tele-psychology need to be mitigated; if this is not possible, intention-to-treat approaches should be adopted for data analysis. https://doi.org/10.16888/interd.2023.40.2.5

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