ICan, an Internet-based intervention to reduce cannabis use: study protocol for a randomized controlled trial

A guided digital intervention to reduce cannabis use: The ICan randomized controlled trial

AIMS

To test the effectiveness of a digital intervention to reduce cannabis use (ICan) with adherence-focused guidance compared with educational cannabis information.

DESIGN

This was a single-blind randomized controlled trial. Follow-up clinical outcome measurements took place 3 and 6 months after randomization.

SETTING

The trial was conducted in the Netherlands. The intervention and guidance took place on-line, with recruitment via Facebook/Instagram advertisement campaigns.

PARTICIPANTS

Inclusion criteria were ≥ 18 years, cannabis use on ≥ 3 days/week, the desire to reduce/quit cannabis and using a smartphone. Participants were allocated to either ICan (n = 188) or control (n = 190) (69% male, mean age = 27.5 years).

INTERVENTION AND COMPARATOR

ICan is a mobile (web-)application based on motivational interviewing and cognitive behavioural therapy and includes three main components: screening, brief intervention (six modules) and referral to treatment. The control condition consisted of non-interactive educational cannabis information.

MEASUREMENTS

Primary outcome was the number of cannabis use days in the 7 days prior to the 6-month follow-up measurement. Secondary outcome measures at 3- and 6-month follow-up were the number of grams of cannabis used and attitudes towards seeking professional help for cannabis use related problems.

FINDINGS

Intention-to-treat analysis showed that 6 months after randomization the mean number of cannabis use days in the past 7 days was reduced in both conditions (time P < 0.001), with no significant group × time interaction effect [ICan = 4.17 days, control = 4.31 days, Cohen's dbetween  = 0.06, 95% confidence interval (CI) = -0.15, 0.26, P = 0.93]. Three months after randomization the mean number of grams used in the past 7 days was reduced in both conditions, with a significantly larger reduction in the ICan condition (P = 0.009, Cohen's dbetween  = 0.15). At 6-month follow-up the significant group × time interaction effect was no longer present (P = 0.30). In both conditions, attitudes towards seeking professional help remained virtually unchanged over time.

CONCLUSIONS

A digital intervention to reduce cannabis use (ICan) was more effective than non-interactive educational cannabis information in reducing grams of cannabis used over 3 months, but not more effective at reducing cannabis use days at 6-month follow-up. Cannabis use reductions were maintained in both conditions between 3 and 6 months' follow-up.

Reducing Cannabis Use in Young Adults With Psychosis Using iCanChange, a Mobile Health App: Protocol for a Pilot Randomized Controlled Trial (ReCAP-iCC)

BACKGROUND

Cannabis use is the most prevalent among adolescents and young adults; frequent consumption is associated with cannabis use disorder (CUD) and psychosis, with a high prevalence (up to 50%) of CUD in individuals with first-episode psychosis (FEP). Early Intervention Services (EIS) for psychosis include face-to-face psychosocial interventions for CUD, because reducing or discontinuing cannabis use improves clinical and health care service use outcomes. However, multiple barriers (eg, staff availability and limited access to treatment) can hinder the implementation of these interventions. Mobile health (mHealth) interventions may help circumvent some of these barriers; however, to date, no study has evaluated the effects of mHealth psychological interventions for CUD in individuals with FEP.

OBJECTIVE

This study describes the protocol for a pilot randomized controlled trial using a novel mHealth psychological intervention (iCanChange [iCC]) to address CUD in young adults with FEP. iCC was developed based on clinical evidence showing that in individuals without psychosis, integrating the principles of cognitive behavioral therapy, motivational interviewing, and behavioral self-management approaches are effective in improving cannabis use-related outcomes.

METHODS

Consenting individuals (n=100) meeting the inclusion criteria (eg, aged 18-35 years with FEP and CUD) will be randomly allocated in a 1:1 ratio to the intervention (iCC+modified EIS) or control (EIS) group. The iCC is fully automatized and contains 21 modules that are completed over a 12-week period and 3 booster modules available during the 3-month follow-up period. Validated self-report measures will be taken via in-person assessments at baseline and at 6, 12 (end point), and 24 weeks (end of trial); iCC use data will be collected directly from the mobile app. Primary outcomes are intervention completion and trial retention rates, and secondary outcomes are cannabis use quantity, participant satisfaction, app use, and trial recruiting parameters. Exploratory outcomes include severity of psychotic symptoms and CUD severity. For primary outcomes, we will use the chi-square test using data collected at week 12. We will consider participation in iCC acceptable if ≥50% of the participants complete at least 11 out of 21 intervention modules and the trial feasible if attrition does not reach 50%. We will use analysis of covariance and mixed-effects models for secondary outcomes and generalized estimating equation multivariable analyses for exploratory outcomes.

RESULTS

Recruitment began in July 2022, and data collection is anticipated to be completed in July 2024. The main results are expected to be submitted for publication in 2024. We will engage patient partners and other stakeholders in creating a multifaceted knowledge translation plan to reach a diverse audience.

CONCLUSIONS

If feasible, this study will provide essential data for a larger-scale efficacy trial of iCC on cannabis use outcomes in individuals with FEP and CUD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05310981; https://www.clinicaltrials.gov/ct2/show/NCT05310981.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/40817.

Digital interventions to enhance readiness for psychological therapy: A scoping review (Preprint)

Background

Psychological therapy is an effective treatment method for mental illness; however, many people with mental illness do not seek treatment or drop out of treatment early. Increasing client uptake and engagement in therapy is key to addressing the escalating global problem of mental illness. Attitudinal barriers, such as a lack of motivation, are a leading cause of low engagement in therapy. Digital interventions to increase motivation and readiness for change hold promise as accessible and scalable solutions; however, little is known about the range of interventions being used and their feasibility as a means to increase engagement with therapy.

Objective

This review aimed to define the emerging field of digital interventions to enhance readiness for psychological therapy and detect gaps in the literature.

Methods

A literature search was conducted in PubMed, PsycINFO, PsycARTICLES, Scopus, Embase, ACM Guide to Computing Literature, and IEEE Xplore Digital Library from January 1, 2006, to November 30, 2021. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) methodology was applied. Publications were included when they concerned a digitally delivered intervention, a specific target of which was enhancing engagement with further psychological treatment, and when this intervention occurred before the target psychological treatment.

Results

A total of 45 publications met the inclusion criteria. The conditions included depression, unspecified general mental health, comorbid anxiety and depression, smoking, eating disorders, suicide, social anxiety, substance use, gambling, and psychosis. Almost half of the interventions (22/48, 46%) were web-based programs; the other formats included screening tools, videos, apps, and websites. The components of the interventions included psychoeducation, symptom assessment and feedback, information on treatment options and referrals, client testimonials, expectation management, and pro-con lists. Regarding feasibility, of the 16 controlled studies, 7 (44%) measuring actual behavior or action showed evidence of intervention effectiveness compared with controls, 7 (44%) found no differences, and 2 (12%) indicated worse behavioral outcomes. In general, the outcomes were mixed and inconclusive owing to variations in trial designs, control types, and outcome measures.

Conclusions

Digital interventions to enhance readiness for psychological therapy are broad and varied. Although these easily accessible digital approaches show potential as a means of preparing people for therapy, they are not without risks. The complex nature of stigma, motivation, and individual emotional responses toward engaging in treatment for mental health difficulties suggests that a careful approach is needed when developing and evaluating digital readiness interventions. Further qualitative, naturalistic, and longitudinal research is needed to deepen our knowledge in this area.

Predicting Success of a Digital Self-Help Intervention for Alcohol and Substance Use With Machine Learning

Background

Digital self-help interventions for reducing the use of alcohol tobacco and other drugs (ATOD) have generally shown positive but small effects in controlling substance use and improving the quality of life of participants. Nonetheless, low adherence rates remain a major drawback of these digital interventions, with mixed results in (prolonged) participation and outcome. To prevent non-adherence, we developed models to predict success in the early stages of an ATOD digital self-help intervention and explore the predictors associated with participant's goal achievement.

Methods

We included previous and current participants from a widely used, evidence-based ATOD intervention from the Netherlands (Jellinek Digital Self-help). Participants were considered successful if they completed all intervention modules and reached their substance use goals (i.e., stop/reduce). Early dropout was defined as finishing only the first module. During model development, participants were split per substance (alcohol, tobacco, cannabis) and features were computed based on the log data of the first 3 days of intervention participation. Machine learning models were trained, validated and tested using a nested k-fold cross-validation strategy.

Results

From the 32,398 participants enrolled in the study, 80% of participants did not complete the first module of the intervention and were excluded from further analysis. From the remaining participants, the percentage of success for each substance was 30% for alcohol, 22% for cannabis and 24% for tobacco. The area under the Receiver Operating Characteristic curve was the highest for the Random Forest model trained on data from the alcohol and tobacco programs (0.71 95%CI 0.69-0.73) and (0.71 95%CI 0.67-0.76), respectively, followed by cannabis (0.67 95%CI 0.59-0.75). Quitting substance use instead of moderation as an intervention goal, initial daily consumption, no substance use on the weekends as a target goal and intervention engagement were strong predictors of success.

Discussion

Using log data from the first 3 days of intervention use, machine learning models showed positive results in identifying successful participants. Our results suggest the models were especially able to identify participants at risk of early dropout. Multiple variables were found to have high predictive value, which can be used to further improve the intervention.