Optimizing mHealth Instant Messaging-Based Smoking Cessation Support: A Sequential, Multiple Assignment, Randomized Trial (SMART)
Bibliographic record
Abstract
Abstract Mobile health (mHealth) messaging has been used to enhance quitting. Optimal sequencing of more personalized and intensive interventions may increase abstinence for mHealth non-responders (continuing smokers). We aim to test the effectiveness and cost of an adaptive design intervention based on chat-based personalized instant messaging (PIM) versus that based on regular instant messaging (RIM; non-personalized) on validated abstinence. Sequential, multiple assignment randomized trial proactively recruited adult daily cigarette smokers in Hong Kong. At baseline, participants received brief cessation advice plus referral assistance to cessation services and were randomized to receive PIM (PIM group, n = 422) or RIM (RIM group, n = 422). At 1 month, PIM non-responders were further randomized (ratio 3:1) to receive either combined cessation interventions (CCI, including multi-media messages, nicotine replacement therapy sampling, incentive for service referral, phone counselling, family/peer group chat) or maintained PIM for 2 months. RIM non-responders were further randomized (ratio 1:3) to receive PIM or maintained RIM. Responders (quitters) in either group continued to receive the respective initial intervention. Bio-validated abstinence at 6 months by intention-to-treat. The 844 participants were mostly male (82.3%). At 1 month, 370 (87.7%) and 373 (88.4%) non-responded to PIM and RIM, respectively. Of non-responders, 273 (73.8%) received CCI and 91 (24.4%) received PIM. At 6 months, PIM group had non-significantly higher validated abstinence than RIM group (10.2% vs. 8.3%, risk ratio [RR] 1.23, 95%CI 0.80 to 1.88) at doubled cost (US$33,228.8 vs. 15,985.5). In non-responders, receiving CCI (vs. maintained PIM: 4.8% vs. 6.2%, RR 0.77, 95%CI 0.30 to 1.97) or PIM (vs. maintained RIM: 3.3% vs 5.7%, RR 0.58, 95%CI 0.17 to 1.95) did not increase validated abstinence. The PIM-based adaptive intervention did not significantly increase validated abstinence than that of non-personalized IM. Non-responders to PIM or RIM did not benefit from more intensive interventions. ClinicalTrials.gov Identifier: NCT03992742
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".