ISSN: 2155-6105

Revista de investigación y terapia de adicciones

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Abstracto

Recruitment and Retention in mhealth Interventions for Addiction and Problematic Substance Use: A Systematic Review

Background: Disordered and problematic addictions are significant public health issues. It has been proposed that mHealth interventions can provide new models and intervention delivery modalities. However, research shows that studies that evaluate mHealth interventions for addiction disorders have low recruitment and high attrition. This study aims to identify published peer-reviewed literature on the recruitment and retention of participants in studies of mHealth interventions for people with addiction or problematic use and to identify successful recruitment and retention strategies.

Methods: Relevant studies were identified through Medline, Embase, PsychINFO, and Cochrane Central Register of Controlled Trials (CENTRAL) after January 1998. Studies were limited to peer-reviewed literature and English language published up to 2023. The revised Cochrane Risk of Bias RoB 2 tool was used to assess the risk of bias.

Results: Of the 2135 articles found, 60 met the inclusion criteria and were included. The majority of studies were for smoking cessation. Only three studies retained 95% of participants at the longest follow-up, with ten studies retaining less than 80% at the longest follow-up, indicating a high risk of retention bias. Those studies with high retention rates used a variety of recruitment modalities; however, they also recruited from populations already partially engaged with health support services rather than those not accessing services.

Conclusions: This review of recruitment and retention outcomes with mHealth interventions highlights the need for multimodal recruitment methods. However, significant gaps in effective engagement and retention strategies limit the positive outcomes expected from mHealth interventions.

Descargo de responsabilidad: este resumen se tradujo utilizando herramientas de inteligencia artificial y aún no ha sido revisado ni verificado.