Therapeutic processes in digital interventions for anxiety: A systematic review and meta-analytic structural equation modeling of randomized controlled trials

2021 ◽  
Vol 90 ◽  
pp. 102084
Author(s):  
Matthias Domhardt ◽  
Hannah Nowak ◽  
Sophie Engler ◽  
Amit Baumel ◽  
Simon Grund ◽  
...  
2019 ◽  
Vol 79 (6) ◽  
pp. 1075-1102
Author(s):  
Yaacov Petscher ◽  
Christopher Schatschneider

Complex data structures are ubiquitous in psychological research, especially in educational settings. In the context of randomized controlled trials, students are nested in classrooms but may be cross-classified by other units, such as small groups. Furthermore, in many cases only some students may be nested within a unit while other students may not. Such instances of partial nesting requires a more flexible framework for estimating treatment effects so that the model coefficients are correctly estimated. Although several recommendations have been offered to the field on handling partially nested data, few are comprehensive in their treatment of manifest and latent variables in the context of partial nesting, full nesting, and cross-classification. The present study introduces n-level structural equation modeling (SEM) as a flexible measurement and analytic framework for the estimation of treatment effects for complex data structures that frequently present in randomized controlled trials. In this tutorial, we explore how the notation of n-level SEM allows for parsimonious model specification whether data are observed or latent and in the presence of partial nested or cross-classified designs. By using the xxm package in R, the advantage of using n-level SEM framework is demonstrated through five examples for single outcome manifest variables, as in the traditional multilevel model, as well as latent applications as in multilevel SEM.


2018 ◽  
Author(s):  
Joao Ricardo Nickenig Vissoci

BackgroundHarmful alcohol use leads to a large burden of disease and disability which disportionately impacts LMICs. The World Health Organization and the Lancet have issued calls for this burden to be addressed, but issues remain, primarily due to gaps in information. While a variety of interventions have been shown to be effective at reducing alcohol use in HICs, their efficacy in LMICs have yet to be assessed. This systematic review describes the current published literature on alcohol interventions in LMICs and conducts a meta analysis of clinical trials evaluating interventions to reduce alcohol use and harms in LMICs.MethodsIn accordance with PRISMA guidelines we searched the electronic databases Pubmed, EMBASE, Scopus,Web of Science, Cochrane, and Psych Info. Articles were eligible if they evaluated an intervention targeting alcohol-related harm in LMICs. After a reference and citation analysis, we conducted a quality assessment per PRISMA protocol. A meta-analysis was performed on the 39 randomized controlled trials that evaluated an alcohol-related outcome.ResultsOf the 3,801 articles from the literature search, 87 articles from 25 LMICs fit the eligibility and inclusion criteria. Of these studies, 39 randomized controlled trials were included in the meta-analysis. Nine of these studies focused specifically on medication, while the others focused on brief motivational intervention, brain stimulation, AUDIT-based brief interventions, WHO ASSIST-based interventions, group based education, basic screening and interventions, brief psychological or counseling, dyadic relapse prevention, group counseling, CBT, motivational + PTSD based interview, and health promotion/awareness. Conclusion Issues in determining feasible options specific to LMICs arise from unstandardized interventions, unequal geographic distribution of intervention implementation, and uncertain effectiveness over time. Current research shows that brain stimulation, psychotherapy, and brief motivational interviews have the potential to be effective in LMIC settings, but further feasibility testing and efforts to standardize results are necessary to accurately assess their effectiveness.


Sign in / Sign up

Export Citation Format

Share Document