Technology-supported treatments for adjustment disorder: A systematic review and preliminary meta-analysis.

Background: The use of technology in psychological treatments can bring evidence-based interventions closer to more people using fewer resources. The aim of this systematic review and preliminary meta-analysis was to summarize all the available information about technology-supported psychological treatments for Adjustment Disorder (AjD) patients of all ages.
Method: Eligibility criteria included studies that tested a technology-supported treatment in patients with AjD and reported data on a mental health outcome. Case studies and case series were excluded. Searches were conducted in the PubMed, Web of Science, Scopus, and PsycINFO databases. Study quality was assessed using the Cochrane RoB 2.0. tool for Randomized Controlled Trials (RCTs) and the NHLBI tool for pre-post studies.
Results: Nine articles (8 RCTs and 1 pre-post study) were included, eight that tested computerised interventions and two that used virtual reality. The meta-analysis showed the superior efficacy of the intervention groups compared to control conditions in reducing mental health symptomatology and a significant improvement between pre- and post-treatment.
Limitations: The small number of studies included and the high heterogeneity among them were two of the main limitations.
Conclusions: These results are similar to those observed in previous systematic reviews on technology-supported treatments for other mental disorders and suggest that these interventions could be effective for patients with AjD. However, further research is needed to determine the advantages and disadvantages of these interventions for the treatment of AjD in different age populations such as children, adolescents or older adults, as well as effective means for improving treatment retention.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)