Integrating Smoking Cessation Treatment Into Web-Based Usual Psychological Care for People With Common Mental Illness: Feasibility Randomized Controlled Trial (ESCAPE Digital)
Bibliographic record
Abstract
BACKGROUND: Stopping smoking can improve mental health, with effect sizes similar to antidepressant treatment. Internet-based cognitive behavioral therapy (iCBT) provides evidence-based treatment for depression and anxiety, and digital interventions can support smoking cessation. However, combined digital smoking and mental health support is not currently available in UK health services. OBJECTIVE: This feasibility trial aimed to investigate the acceptability and feasibility of a digital tailored smoking cessation intervention delivered alongside usual iCBT, and test trial procedures. METHODS: The study design was a 2-armed, parallel groups, pragmatic, feasibility randomized controlled trial. Eligible participants were adult (18 years and older), regular smokers referred to iCBT from National Health Service Talking Therapies services in England. Participants were screened, consented, and randomized via a web-based platform and allocated to intervention (integrated smoking cessation support) or control (usual care) arms. Fully automated processes ensured allocation concealment. It was not possible to blind participants or clinicians to the behavioral intervention. Follow-ups via web-based questionnaires were completed at 3- and 6-months. Prespecified progression criteria, to determine the feasibility of the integrated intervention and trial procedures for a definitive trial, were enrolment of eligible clients (≥20%); recruitment to the target (≥80%); outcome data completeness (≥70%); and self-reported quit attempts in the intervention arm (≥8%). RESULTS: A total of 309 participants were randomized: 154 to the intervention arm and 155 to the control arm. The proportion of eligible clients enrolled (309/1484, 21%) met the criteria for progression; however, the number randomized was below target (309/500, 62%). In the intervention arm, 18% (27/154) self-reported at least one quit attempt, which exceeded the progression criteria but was comparable to the control arm (32/155, 21%). High loss to follow-up meant data completeness was low (<30% across 6 key pilot clinical outcomes). CONCLUSIONS: Integrating smoking cessation within digital mental health treatment and using automated procedures to enroll and randomize participants appears feasible. Adjustments to site recruitment could improve participant recruitment; however, a large loss to follow-up undermines the feasibility of progression. TRIAL REGISTRATION: ISRCTN Registry ISRCTN10612149; https://www.isrctn.com/ISRCTN10612149. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2024.107541.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".