Presenting and Evaluating a Smartwatch-Based Intervention for Smoking Relapse (StopWatch): Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Despite the benefits of smoking cessation, maintaining abstinence during a quit attempt is difficult, and most attempts result in relapse. Innovative, evidence-based methods of preventing relapse are needed. We present a smartwatch-based relapse prevention system that uses passive detection of smoking to trigger just-in-time smoking cessation support. OBJECTIVE: This study aims to evaluate the feasibility of hosting just-in-time smoking cessation support on a smartwatch and the acceptability of the "StopWatch" intervention on this platform. METHODS: The person-based approach for intervention development was used to design the StopWatch smoking relapse prevention intervention. Intervention delivery was triggered by an algorithm identifying hand movements characteristic of smoking from the smartwatch's motion sensors, and the system-generated intervention messages (co-designed by smokers) were delivered on the smartwatch screen. A total of 18 smokers tested the intervention over a 2-week period, and at the end of this period, they provided qualitative feedback on the acceptability of both the intervention and the smartwatch platform. RESULTS: Participants reported that the smartwatch intervention increased their awareness of smoking and motivated them to quit. System-generated intervention messages were generally felt to be relevant and timely. There were some challenges with battery life that had implications for intervention adherence, and the bulkiness of the device and the notification style reduced some participants' acceptability of the smartwatch platform. CONCLUSIONS: Our findings indicate our smoking relapse prevention intervention and the use of a smartwatch as a platform to host a just-in-time behavior change intervention are both feasible and acceptable to most (12/18, 66%) participants as a relapse prevention intervention, but we identify some concerns around the physical limitations of the smartwatch device. In particular, the bulkiness of the device and the battery capacity present risks to adherence to the intervention and the potential for missed detections. We recommend that a longer-term efficacy trial be carried out as the next step.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".