Mobile Health Technology for Personalized Tobacco Cessation Support Among Cancer Survivors and Caregivers in Laos (Project SurvLaos): Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Tobacco use remains a major cause of preventable deaths and evidence suggests that smoking cessation offers considerable benefits for patients with and survivors of cancer. In the Lao People's Democratic Republic (Lao PDR), approximately 60% of male patients and 15% of female patients with cancer smoke cigarettes. Nevertheless, there is no tobacco treatment program for this population. OBJECTIVE: This pilot randomized controlled trial (RCT) aims to evaluate the feasibility and preliminary efficacy of our mobile health-based automated treatment (AT) program to help Lao cancer survivors and caregivers quit smoking cigarettes. METHODS: We used an intervention mixed methods research design, which included a pilot 2-group interventional RCT and an embedded qualitative component to explain RCT outcomes. In the pilot RCT, cancer survivors or caregivers (n=80, no dyads) who smoked were recruited from national hospitals in Vientiane. Recruited participants were randomized to 1 of 2 treatment groups: standard care (SC) or AT. SC consisted of brief advice to quit smoking delivered by research staff, self-help written materials, and a 2-week supply of nicotine replacement therapy (transdermal patches). AT consisted of all SC components plus our fully automated, interactive, smartphone-based behavioral treatment program personalized and tailored to cancer survivors or caregivers and delivered by our Insight app. Feasibility outcomes of interest include the percentages of intervention messages delivered and viewed, and participant retention at the 3-month follow-up. The preliminary efficacy outcome is biochemically confirmed self-reported 7-day point prevalence abstinence at 3 months post study enrollment. During the interventional RCT and after the 3-month follow-up assessment, we used additional open-ended questions to explore why and how the participants did or did not successfully quit smoking and stay abstinent. RESULTS: Data collection occurred from April 2022 to May 2023. Outcome analyses are ongoing, and results are expected to be published in 2025. CONCLUSIONS: Our course of research will address the critical need of having a scalable and sustainable tobacco cessation treatment program for patients with cancer and their caregivers in Lao PDR. The preliminary data from this pilot project will lay a foundation for a subsequent fully powered RCT to evaluate the actual efficacy of our mobile health-based AT program. Ultimately, our course of research will contribute to reducing tobacco-related complications in cancer treatments, comorbidities, tobacco-related cancer recurrence, and mortality rates in Lao PDR. TRIAL REGISTRATION: ClinicalTrials.gov NCT05253573; https://clinicaltrials.gov/study/NCT05253573. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66517.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.099 | 0.013 |
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".