Predictors of treatment response in a web-based intervention for cannabis users

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders

BACKGROUND AND AIMS

Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non-responders.

METHODS

This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. Adults with CUD (n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N-Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric and substance use information.

RESULTS

Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics.

CONCLUSIONS

Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.

Correlates of treatment engagement and client outcomes: results of a randomised controlled trial of nabiximols for the treatment of cannabis use disorder

INTRODUCTION AND AIMS

There is increasing interest and evidence for the use of cannabinoid medications in the treatment of cannabis use disorder, but little examination of the correlates of successful treatment. This paper is a secondary analysis of a randomised placebo-controlled trial of nabiximols for the treatment of cannabis use disorder (CUD), aiming to identify which client and treatment characteristics impact treatment engagement and outcomes.

METHOD

Bayesian multiple regression models were used to examine the impact of age, gender, duration of regular cannabis use, daily quantity of cannabis, cannabis use problems, self-efficacy for quitting, sleep, mental health, pain measures, and treatment group upon treatment engagement (retention, medication dose, and counselling participation) and treatment outcomes (achieving end-of-study abstinence, and a 50% or greater reduction in cannabis use days) among the 128 clients participating in the 12-week trial.

RESULTS

Among the treatment factors, greater counselling attendance was associated with greater odds of abstinence and ≥ 50% reduction in cannabis use; nabiximols with greater odds of ≥ 50% reduction and attending counselling, and reduced hazard of treatment dropout; and higher dose with lower odds of ≥ 50% reduction. Among the client factors, longer duration of regular use was associated with higher odds of abstinence and 50% reduction, and lower hazard of treatment dropout; greater quantity of cannabis use with reduced hazard of dropout, greater odds of attending counselling, and higher average dose; greater pain at baseline with greater odds of ≥ 50% reduction and higher average dose; and more severe sleep issues with lower odds of ≥ 50% reduction. Males had lower odds of attending counselling.

DISCUSSIONS AND CONCLUSIONS

These findings suggest that counselling combined with agonist pharmacotherapy may provide the optimal treatment for cannabis use disorder. Younger clients, male clients, and clients with sleep issues could benefit from extra support from treatment services to improve engagement and outcomes.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry (ACTRN12616000103460) https://www.anzctr.org.au.

Environmental conditions, mobile digital culture, mobile usability, knowledge of app in COVID-19 risk mitigation: A structural equation model analysis

INTRODUCTION

The mobile digital culture (MDC) supports individual lives, communities, and real-time organizational surveillance during COVID-19 emergencies. Hence, the study examined the advancement in smart health devices evidence in smartphone apps technologies in surveillance, control, and tracking potential virus areas among high-risk populations.

OBJECTIVE

The study explored how environmental condition and MDC mediates between knowledge of App and mobile usability in the prevention of COVID-19 infection in high-risk areas.

METHODS

Using the concept of UTAUT, the study conceptualized that mobile usability, MDC, knowledge of App and environmental condition, are essential for COVID-19 mitigation. A cross-sectional method was adopted through an online survey to assess data from n = 459 mobile users. The association of the study models was appraised through structural equation models (Amos v.24.0).

RESULT

We found mobile usability, knowledge of App, and MDC were statistically significant to COVID-19 mitigation. Environment condition as mediator had no effect in the study models. However, moderating effect of MDC shows a negative influence on the association between COVID-19 mitigation and knowledge of apps.

CONCLUSION

Future policies should consider the development of mHealth technology to improve end-user experience. Also, future policies should entail data privacy to reduce the infringement of data collected. This approach will lead to a confidential, high acceptance of usability of mHealth apps infectious disease prevention.

The Impacts of Social Support and Relationship Characteristics on Commitment to Sobriety Among People in Opioid Use Disorder Recovery

OBJECTIVE

Despite evidence that social support is beneficial for people living with opioid use disorders (OUDs), research has yet to investigate whether social support within certain relationships is more or less effective. The current study examined whether social support, relationship closeness with a disclosure partner, and/or the history of joint substance use between participants and disclosure partners affect commitment to sobriety among people receiving medications for OUD.

METHOD

Over a period of 3 months (two time points), participants taking OUD medications took part in a mixed-methods egocentric social network study exploring their relationships with disclosure partners before and following OUD disclosure (i.e., first telling a disclosure partner about one's OUD history or treatment). Data included 131 disclosure events/relationships clustered within 106 participants.

RESULTS

Greater relationship closeness was associated with increased commitment to sobriety over time. Further, significant interactions were found between social support and disclosure partner closeness, partner closeness and history of joint substance use, and social support and history of joint substance use. Higher social support was associated with greater commitment to sobriety among those disclosing to close partners. In contrast, receiving social support or disclosing to a close partner with whom there was a history of joint substance use was associated with decreased commitment to sobriety.

CONCLUSIONS

Findings highlight the complexities of social support among people in treatment for OUD and demonstrate that relationship closeness and a history of joint substance use with a disclosure partner may be important factors to consider before disclosure.

Technological Interventions for the Treatment of Substance Use Disorders

Substance use disorders (SUDs) pose a major public health concern. In recent years, technological (i.e., e-health) interventions have emerged and are increasingly offered in a variety of settings, including substance use treatment. E-health interventions encompass a wide variety of advantages depending on the chosen delivery format. This chapter discusses existing interventions and the effectiveness of delivering them as an e-health intervention, with a focus on randomized controlled trials, for the treatment of alcohol, cannabis, opioid, psychostimulant, or poly-substance use, as well as in transdiagnostic interventions. Based on the literature, suggestions for future research and clinical implications are discussed.

Internet-based cognitive-behavioural therapy for tinnitus: secondary analysis to examine predictors of outcomes

OBJECTIVES

The current study examined predictors of outcomes of internet-based cognitive-behavioural therapy (ICBT) for individuals with tinnitus.

DESIGN

Secondary analysis of intervention studies.

SETTING

Internet-based guided tinnitus intervention provided in the UK.

PARTICIPANTS

228 individuals who underwent ICBT.

INTERVENTIONS

ICBT.

PRIMARY AND SECONDARY OUTCOME MEASURES

The key predictor variables included demographic, tinnitus, hearing-related and treatment-related variables as well as clinical factors (eg, anxiety, depression, insomnia), which can have an impact on the treatment outcome. A 13-point reduction in Tinnitus Functional Index (TFI) scores has been defined as a successful outcome.

RESULTS

Of the 228 subjects who were included in the study, 65% had a successful ICBT outcome. As per the univariate analysis, participants with a master's degree or above had the highest odds of having a larger reduction in tinnitus severity (OR 3.47; 95% CI 1.32 to 12.51), compared with the participants who had education only up to high school or less. Additionally, the baseline tinnitus severity was found to be a significant variable (OR 2.65; 95% CI 1.50 to 4.67) contributing to a successful outcome with the intervention. Both linear and logistic regression models have identified education level and baseline tinnitus severity to be significant predictor variables contributing to a reduction in tinnitus severity post-ICBT. As per the linear regression model, participants who had received disability allowance had shown a 25.3-point lower TFI reduction compared with those who did not experience a decrease in their workload due to tinnitus after adjusting for baseline tinnitus severity and their education level.

CONCLUSIONS

Predictors of intervention outcome can be used as a means of triaging patients to the most suited form of treatment to achieve optimal outcomes and to make healthcare savings. Future studies should consider including a heterogeneous group of participants as well as other predictor variables not included in the current study.ClinicalTrial.gov Registration: NCT02370810 (completed); NCT02665975 (completed).