Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression.

Background: Substance use disorders affect 36 million people globally, but only a small proportion of them receive the necessary treatment. E-health interventions have been developed to address this issue by improving access to substance use treatment. However, concerns about participant engagement and adherence to these interventions remain. This review aimed to evaluate adherence to e-health interventions targeting substance use and identify hypothesized predictors of adherence. Methods: A systematic review of literature published between 2009 and 2020 was conducted, and data on adherence measures and hypothesized predictors were extracted. Meta-analysis and meta-regression were used to analyze the data. The two adherence measures were (a) the mean proportion of modules completed across the intervention groups and (b) the proportion of participants that completed all modules. Four meta-regression models assessed each covariate including guidance, blended treatment, intervention duration and recruitment strategy. Results: The overall pooled adherence rate was 0.60 (95%-CI: 0.52-0.67) for the mean proportion of modules completed across 30 intervention arms and 0.47 (95%-CI: 0.35-0.59) for the proportion of participants that completed all modules across 9 intervention arms. Guidance, blended treatment, and recruitment were significant predictors of adherence, while treatment duration was not. Conclusion: The study suggests that more research is needed to identify predictors of adherence, in order to determine specific aspects that contribute to better exposure to intervention content. Reporting adherence and predictors in future studies can lead to improved meta-analyses and the development of more engaging interventions. Identifying predictors can aid in designing effective interventions for substance use disorders, with important implications for e-health interventions targeting substance use.

Treatment approaches and outcome trajectories for youth with high-risk opioid use: A narrative review.

AIM: First use of opioids often happens in adolescence and an increasing number of opioid overdoses are being reported among youth. The purpose of this narrative review was to present the treatment approaches for youth with high-risk opioid use, determine whether the literature supports the use of opioid agonist treatment among youth and identify evidence for better treatment outcomes in the younger population. METHODS: A search of the literature on PubMed using MeSH terms specific to youth, opioid use and treatment approaches generated 1436 references. Following a screening process, 137 papers were found to be relevant to the treatment of high-risk opioid use among youth. After full-text review, 19 eligible studies were included: four randomized controlled trials, nine observational studies and six reviews. RESULTS: Research for the different treatment options among youth is limited. The available evidence shows better outcomes in terms of retention in care and cost-effectiveness for opioid agonist treatment than abstinence-based comparisons. Integrating psychosocial interventions into the continuum of care for youth can be an effective way of addressing comorbid psychiatric conditions and emotional drivers of substance use, leading to improved treatment trajectories. CONCLUSIONS: From the limited findings, there is no evidence to deny youth with high-risk opioid use the same treatment options available to adults. A combination of pharmacological and youth-specific psychosocial interventions is required to maximize retention and survival. There is an urgent need for more research to inform clinical strategies toward appropriate treatment goals for such vulnerable individuals.

Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry.

INTRODUCTION: Machine learning capability holds promise to inform disease models, the discovery and development of novel disease modifying therapeutics and prevention strategies in psychiatry. Herein, we provide an introduction on how machine learning/Artificial Intelligence (AI) may instantiate such capabilities, as well as provide rationale for its application to psychiatry in both research and clinical ecosystems. METHODS: Databases PubMed and PsycINFO were searched from 1966 to June 2016 for keywords:Big Data, Machine Learning, Precision Medicine, Artificial Intelligence, Mental Health, Mental Disease, Psychiatry, Data Mining, RDoC, and Research Domain Criteria. Articles selected for review were those that were determined to be aligned with the objective of this particular paper. RESULTS: Results indicate that AI is a viable option to build useful predictors of outcome while offering objective and comparable accuracy metrics, a unique opportunity, particularly in mental health research. The approach has also consistently brought notable insight into disease models through processing the vast amount of already available multi-domain, semi-structured medical data. The opportunity for AI in psychiatry, in addition to disease-model refinement, is in characterizing those at risk, and it is likely also relevant to personalizing and discovering therapeutics. CONCLUSIONS: Machine learning currently provides an opportunity to parse disease models in complex, multi-factorial disease states (e.g. mental disorders) and could possibly inform treatment selection with existing therapies and provide bases for domain-based therapeutic discovery.