An mHealth App for Supporting Quitters to Manage Cigarette Cravings With Short Bouts of Physical Activity: A Randomized Pilot Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: While gains in reducing smoking rates in Finland have been made, prevalence rates are still substantial. Relapse rates among smokers engaged in quit-smoking programs are high. Physical activity has been proposed as one means to help smokers manage cravings. Software and apps on mobile phone and handheld devices offer an opportunity to communicate messages on how to use physical activity to manage cravings as part of quit-smoking programs. OBJECTIVE: We aimed to test the feasibility, acceptability, usability, and preliminary efficacy of an mHealth mobile phone app, Physical activity over Smoking (PhoS), to assist smokers in quitting smoking in a randomized controlled trial. The app was designed to prompt smokers to engage in physical activities to manage their smoking cravings. METHODS: Regular smokers (n=44) attended a group-based behavioral counselling program aimed at promoting physical activity as an additional aid to quit. After quit day, participants were randomly allocated to an intervention (n=25) or to a comparison (n=19) group. Participants in the intervention group were provided with the PhoS app and training on how to use it to assist with relapse prevention. Participants in the comparison condition were provided with generalized relapse prevention training. RESULTS: Some participants reported that the PhoS app was useful in assisting them to successfully manage their cigarette cravings, although compliance across the sample was modest and participants reported low levels of usability. Participants receiving the PhoS app did not report greater abstinence than those who did not receive the app. However, participants receiving the app were more likely to report greater abstinence if they did not use pharmacological support, while those who did not receive the app reported greater abstinence when using pharmacological support. Participants receiving the app reported greater levels of physical activity than those who did not. Results revealed that the app resulted in better retention. CONCLUSIONS: The PhoS app showed some potential to reduce abstinence among participants not using pharmacological therapy and to increase physical activity. However, problems with usability and lack of effects on abstinence raise questions over the app's long-term effectiveness. Future research should prioritize further development of the app to maximize usability and test effects of the intervention independent of quit-smoking programs. TRIAL REGISTRATION: International Standard Randomized Controlled Trial Number (ISRCTN): 55259451; http://www.controlled-trials.com/ISRCTN55259451 (Archived by WebCite at http://www.webcitation.org/6cKF2mzEI).
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".