Treatment retention and reductions in blood alcohol concentration (BAC) during the first 90 days of a telehealth program for alcohol use disorder

Designing and testing treatments for alcohol use disorder

This chapter provides a succinct overview of several recommendations for the design and analysis of treatments for AUD with a specific focus on increasing rigor and generalizability of treatment studies in order to increase the reach of AUD treatment. We recommend that researchers always register their trials in a clinical trial registry and make the protocol accessible so that the trial can be replicated in future work, follow CONSORT reporting guidelines when reporting the results of the trial, carefully describe all inclusion and exclusion criteria as well as the randomization scheme, and always use an intent to treat design with attention to analysis of missing data. In addition, we recommend that researchers pay closer attention to recruitment and engagement strategies that increase enrollment and retention of historically marginalized and understudied populations, and we end with a plea for more consideration of implementation science approaches to increase the dissemination and implementation of AUD treatment in real-world settings.

Experiences of telehealth among people receiving alcohol and other drug treatment during the COVID-19 pandemic: Implications for future telehealth delivery

INTRODUCTION

The novel coronavirus (COVID-19) pandemic necessitated the rapid uptake of telehealth to deliver treatment for alcohol and other drug (AOD) concerns. However, little is known about how the move from in-person to telehealth delivery impacted clients' experience of care. This qualitative study aimed to explore experiences of telehealth among people receiving alcohol and other drug treatment during the COVID-19 pandemic, and their preferences regarding future telehealth care.

METHODS

Participants were aged 34-66 years (M = 44 years, 60% male) and were recruited from Victorian AOD treatment services and consumer networks. A total of 20 semi-structured interviews were analysed using thematic analysis.

RESULTS

Three themes were identified: (i) experiences of the practical impacts of telehealth; (ii) experiences of telehealth interactions; and (iii) preferences for future telehealth. Contextual factors, including location and socioeconomic status, were found to impact clients' ability to access reliable telehealth with sufficient privacy. While telehealth was generally associated with increased treatment engagement (for a typically stigmatised population), participants noted varying effects on the therapeutic alliance. Although in-person treatment was generally favoured, participants often valued telehealth as a modality to provide empathic care during the pandemic. Participants expressed a preference for a hybrid treatment model in the future, in which they could choose a combination of telehealth and in-person services.

CONCLUSION

Client and clinician information and training are vital to improve the future delivery of telehealth for AOD treatment.

Patterns of drinking behavior around a treatment episode for alcohol use disorder: Predictions from pre-treatment measures

BACKGROUND

Alcohol use disorder (AUD) has been described as a chronic disease given the high rates that affected individuals have in returning to drinking after a change attempt. Many studies have characterized predictors of aggregated alcohol use (e.g., percent heavy drinking days) following treatment for AUD. However, to inform future research on predicting drinking as an AUD outcome measure, a better understanding is needed of the patterns of drinking that surround a treatment episode and which clinical measures predict patterns of drinking.

METHODS

We analyzed data from the Project MATCH and COMBINE studies (MATCH: n = 1726; 24.3% female, 20.0% non-White; COMBINE: n = 1383; 30.9% female, 23.2% non-White). Daily drinking was measured in the 90 days prior to treatment, 90 days (MATCH) and 120 days (COMBINE) during treatment, and 365 days following treatment. Gradient boosting machine learning methods were used to explore baseline predictors of drinking patterns.

RESULTS

Drinking patterns during a prior time period were the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle (incentive salience, negative emotionality, and executive function) were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during the treatment period and following treatment.

CONCLUSIONS

There is heterogeneity in drinking patterns around an AUD treatment episode. This study provides novel information about variables that may be important to measure to improve the prediction of drinking patterns during and following treatment. Future research should consider which patterns of drinking they aim to predict and which period of drinking is most important to predict. The current findings could guide the selection of predictor variables and generate hypotheses for those predictors.