A systematic review and behaviour change technique analysis of remotely delivered alcohol and/or substance misuse interventions for adults

Howlett, Neil; García-Iglesias, Jaime; Boncroft, Charis; Breslin, Gavin; Bartington, Suzanne; Freethy, Imogen; Huerga, Monica; Jones, Julia; Lloyd, Nigel; Marhsall, Tony; Williams, Stefanie; Wills, Wendy; Brown, Katherine

doi:10.1016/j.drugalcdep.2022.109597

Cited by 13 publications

(17 citation statements)

References 86 publications

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“…This nding suggests remote service delivery options may help people to achieve a reduced exposure to cues for their substance use in future. Notably, in our systematic review of remote interventions for drug and alcohol support, reducing exposure to social and environmental cues for substance use was identi ed as a promising approach (9). Likewise, a systematic review with meta-analysis looking at factors which may in uence motivation to use substances, showed that cue exposure was one of several contextual factors that can contribute to substance use motivation (22).…”

Section: Discussionmentioning

confidence: 96%

Finding the Right Balance: A Framework analysis of staff and service user experiences of moving to remote drug and alcohol support during the COVID-19 pandemic

Brown,

Lloyd,

Fakoya

et al. 2024

Preprint

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Background Dependence on alcohol and other substances contributes significantly to poor health globally. Deaths associated with substance misuse have increased since the COVID-19 pandemic period and providing effective support for recovery is critical. The pandemic required an unprecedented change to recovery services for substance misuse, from in-person care to mainly telephone and online support. The qualitative study described here forms part of a wider evaluation and was one of the first to explore the move to remote models of recovery services. Methods This was a mixed-methods qualitative study, employing in-depth interviews, focus groups, text-based asynchronous interviews, digital timelines and creative vignettes. Framework analysis was undertaken, with focus on service user out-of-pocket expenses as part of a health economic evaluation. A Public Involvement in Research group, including people with lived experience, was involved in study design and analysis. Staff (N=36) and drug and alcohol service users (N=28) in an English city participated in one or more of the data collection methods. Findings were shared with staff and service users and their input shaped the final analysis. Results There was a strong desire to retain remote service delivery alongside the return of in-person provision, post-pandemic. Remote service delivery was not appropriate for everyone however, and not all aspects of service delivery work well remotely. Findings are organized under two main themes: Finding the Right Balance and Resourcing Hybrid. Four sub-themes sit within Finding the Right Balance. These are: Lockdown and remote services: from supportive context to excuse to ‘go wild’; Maintaining therapeutic effects remotely: trust, rapport and motivation; Characteristics of people and components of service delivery; and Efficiency and convenience of remote. Two sub-themes sit within Resourcing Hybrid. These are: Investing in Hybrid; and Service users’ resource needs. Conclusions Optimal delivery of drug and alcohol services should include remote support options, alongside in-person and outreach or home-visiting, where needed. Services may need to invest in training and resourcing for a hybrid offer. Service user preferences and characteristics including their personal resources, health and wellbeing, and readiness to address substance misuse should be central to deciding to whom remote care should be offered. Study registration ISRTCN50804656 https://doi.org/10.1186/ISRCTN50804656

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Section: Discussionmentioning

confidence: 96%

Finding the Right Balance: A Framework analysis of staff and service user experiences of moving to remote drug and alcohol support during the COVID-19 pandemic

Brown,

Lloyd,

Fakoya

et al. 2024

Preprint

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“…The Behavior Change Technique Taxonomy version 1 (BCTTv1) [ 48 , 49 ] was developed to provide a shared, standardized terminology for characterizing complex behavior change interventions and their active ingredients. Several systematic reviews with meta-regressions that used the BCTTv1 have found interventions with certain BCTs to be more effective than those without [ 50 - 53 ]. A better understanding of the BCTs used in digital interventions for young adult cannabis users would help not only to establish the key ingredients of such interventions but also develop and evaluate effective interventions.…”

Section: Introductionmentioning

confidence: 99%

Digital Interventions for Recreational Cannabis Use Among Young Adults: Systematic Review, Meta-Analysis, and Behavior Change Technique Analysis of Randomized Controlled Studies

Côté,

Chicoine,

Vinette

et al. 2024

J Med Internet Res

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Background The high prevalence of cannabis use among young adults poses substantial global health concerns due to the associated acute and long-term health and psychosocial risks. Digital modalities, including websites, digital platforms, and mobile apps, have emerged as promising tools to enhance the accessibility and availability of evidence-based interventions for young adults for cannabis use. However, existing reviews do not consider young adults specifically, combine cannabis-related outcomes with those of many other substances in their meta-analytical results, and do not solely target interventions for cannabis use. Objective We aimed to evaluate the effectiveness and active ingredients of digital interventions designed specifically for cannabis use among young adults living in the community. Methods We conducted a systematic search of 7 databases for empirical studies published between database inception and February 13, 2023, assessing the following outcomes: cannabis use (frequency, quantity, or both) and cannabis-related negative consequences. The reference lists of included studies were consulted, and forward citation searching was also conducted. We included randomized studies assessing web- or mobile-based interventions that included a comparator or control group. Studies were excluded if they targeted other substance use (eg, alcohol), did not report cannabis use separately as an outcome, did not include young adults (aged 16-35 y), had unpublished data, were delivered via teleconference through mobile phones and computers or in a hospital-based setting, or involved people with mental health disorders or substance use disorders or dependence. Data were independently extracted by 2 reviewers using a pilot-tested extraction form. Authors were contacted to clarify study details and obtain additional data. The characteristics of the included studies, study participants, digital interventions, and their comparators were summarized. Meta-analysis results were combined using a random-effects model and pooled as standardized mean differences. Results Of 6606 unique records, 19 (0.29%) were included (n=6710 participants). Half (9/19, 47%) of these articles reported an intervention effect on cannabis use frequency. The digital interventions included in the review were mostly web-based. A total of 184 behavior change techniques were identified across the interventions (range 5-19), and feedback on behavior was the most frequently used (17/19, 89%). Digital interventions for young adults reduced cannabis use frequency at the 3-month follow-up compared to control conditions (including passive and active controls) by −6.79 days of use in the previous month (95% CI −9.59 to −4.00; P<.001). Conclusions Our results indicate the potential of digital interventions to reduce cannabis use in young adults but raise important questions about what optimal exposure dose could be more effective, both in terms of intervention duration and frequency. Further high-quality research is still needed to investigate the effects of digital interventions on cannabis use among young adults. Trial Registration PROSPERO CRD42020196959; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=196959

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“…BDEs are associated with multiple adverse alcohol-related harms, such as traffic fatalities and violent behavior [1,2]. Digital behavioral interventions demonstrate some promise in reducing hazardous alcohol consumption among young adults [3][4][5][6]. Digital behavioral interventions using various communication modalities (eg, app, text messaging, and interactive voice response) can deliver content and collect responses over time, potentially adapting the timing and content of the intervention to specific contexts and needs (eg, motivational enhancement after self-regulation failure) [7].…”

Section: Introductionmentioning

confidence: 99%

“…Digital behavioral interventions using various communication modalities (eg, app, text messaging, and interactive voice response) can deliver content and collect responses over time, potentially adapting the timing and content of the intervention to specific contexts and needs (eg, motivational enhancement after self-regulation failure) [7]. Systematic reviews of digital behavioral interventions generally find modest effects on reducing unhealthy alcohol use [3][4][5][6], suggesting the need to boost the effectiveness of technological interventions. One of the ways to potentially increase the effects of digital intervention is to provide just-in-time (JIT) support when it will have an optimal impact [8] to modify drinking intentions and behavior.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

Bae¹,

Suffoletto²,

Zhang³

et al. 2023

JMIR Form Res

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Background Digital just-in-time adaptive interventions can reduce binge-drinking events (BDEs; consuming ≥4 drinks for women and ≥5 drinks for men per occasion) in young adults but need to be optimized for timing and content. Delivering just-in-time support messages in the hours prior to BDEs could improve intervention impact. Objective We aimed to determine the feasibility of developing a machine learning (ML) model to accurately predict future, that is, same-day BDEs 1 to 6 hours prior BDEs, using smartphone sensor data and to identify the most informative phone sensor features associated with BDEs on weekends and weekdays to determine the key features that explain prediction model performance. Methods We collected phone sensor data from 75 young adults (aged 21 to 25 years; mean 22.4, SD 1.9 years) with risky drinking behavior who reported their drinking behavior over 14 weeks. The participants in this secondary analysis were enrolled in a clinical trial. We developed ML models testing different algorithms (eg, extreme gradient boosting [XGBoost] and decision tree) to predict same-day BDEs (vs low-risk drinking events and non-drinking periods) using smartphone sensor data (eg, accelerometer and GPS). We tested various “prediction distance” time windows (more proximal: 1 hour; distant: 6 hours) from drinking onset. We also tested various analysis time windows (ie, the amount of data to be analyzed), ranging from 1 to 12 hours prior to drinking onset, because this determines the amount of data that needs to be stored on the phone to compute the model. Explainable artificial intelligence was used to explore interactions among the most informative phone sensor features contributing to the prediction of BDEs. Results The XGBoost model performed the best in predicting imminent same-day BDEs, with 95% accuracy on weekends and 94.3% accuracy on weekdays (F1-score=0.95 and 0.94, respectively). This XGBoost model needed 12 and 9 hours of phone sensor data at 3- and 6-hour prediction distance from the onset of drinking on weekends and weekdays, respectively, prior to predicting same-day BDEs. The most informative phone sensor features for BDE prediction were time (eg, time of day) and GPS-derived features, such as the radius of gyration (an indicator of travel). Interactions among key features (eg, time of day and GPS-derived features) contributed to the prediction of same-day BDEs. Conclusions We demonstrated the feasibility and potential use of smartphone sensor data and ML for accurately predicting imminent (same-day) BDEs in young adults. The prediction model provides “windows of opportunity,” and with the adoption of explainable artificial intelligence, we identified “key contributing features” to trigger just-in-time adaptive intervention prior to the onset of BDEs, which has the potential to reduce the likelihood of BDEs in young adults. Trial Registration ClinicalTrials.gov NCT02918565; https://clinicaltrials.gov/ct2/show/NCT02918565

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A systematic review and behaviour change technique analysis of remotely delivered alcohol and/or substance misuse interventions for adults

Cited by 13 publications

References 86 publications

Finding the Right Balance: A Framework analysis of staff and service user experiences of moving to remote drug and alcohol support during the COVID-19 pandemic

Finding the Right Balance: A Framework analysis of staff and service user experiences of moving to remote drug and alcohol support during the COVID-19 pandemic

Digital Interventions for Recreational Cannabis Use Among Young Adults: Systematic Review, Meta-Analysis, and Behavior Change Technique Analysis of Randomized Controlled Studies

Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation Study

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