Content Analysis of Smartphone Apps for Smoking Cessation in China: Empirical Study
Bibliographic record
Abstract
BACKGROUND: With 360 million smokers, China consumes more cigarettes than any other country in the world. Given that 620 million Chinese own smartphones, smartphone apps for smoking cessation are increasingly used in China to help smokers quit. OBJECTIVE: This study analyzed and evaluated the contents of all smoking cessation apps (iOS and Android) available in China, applying the China Clinical Smoking Cessation Guideline (CCSCG; identical to the US Clinical Practice Guideline for Treating Tobacco Use and Dependence) as a framework for analysis. METHODS: We conducted a content analysis of Chinese Android and iOS smoking cessation apps (N=64) designed to assist users in quitting smoking. Each app was independently coded by two raters for its approach to smoking cessation and adherence to the CCSCG. We also recorded the features of smoking cessation apps (eg, release date, size, frequency of downloads, user ratings, type, quality scores by raters, and designers). Linear regression was used to test predictors of popularity and user-rated quality. RESULTS: Chinese smoking cessation apps have low levels of adherence to guidelines, with an average score of 11.1 for Android and 14.6 for iOS apps on a scale of 0 to 46. There was no significant association between popularity, user rating, and the characteristics of apps. However, there was a positive relationship between popularity, user rating, and adherence score. CONCLUSIONS: Chinese apps for smoking cessation have low levels of adherence to standard clinical practice guidelines. New apps need be developed and existing apps be revised following evidence-based principles in China.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".