Mobile App Usage Patterns of Patients Prescribed a Smoking Cessation Medicine: Prospective Observational Study
Bibliographic record
Abstract
BACKGROUND: Cigarette smoking is the leading preventable cause of death and is responsible for more than 480,000 deaths per year in the United States. Smoking cessation is challenging for many patients. Regardless of available treatment options, most quit attempts are unaided, and it takes multiple attempts before a patient is successful. With the ever-increasing use of smartphones, mobile apps hold promise in supporting cessation efforts. This study evaluates the ease of use and user satisfaction with the Pfizer Meds app to support smoking cessation among patients prescribed varenicline (Chantix). OBJECTIVE: Study participants included varenicline users who downloaded and used the app on their personal smartphone. The main objectives were to report mobile app download frequency and usage details and to describe the participant-reported satisfaction with and usefulness of the app over the 14-week follow-up study period. METHODS: Adults aged 18 years or older who had been prescribed varenicline were identified from the Express Scripts Incorporated pharmacy claims database. After meeting privacy restrictions, subjects were sent an invitation letter and second reminder letter with instructions on how to download the Pfizer Meds mobile app. Participants received a push notification to complete a smartphone-enabled survey regarding the utility of the app 12 weeks after downloading the app. Descriptive statistics summarized sociodemographics, use of varenicline, and details of use and satisfaction with the mobile app. RESULTS: Of the 38,129 varenicline users who were sent invitation letters, 1281 participants (3.35%) downloaded the Pfizer Meds app. Of the 1032 users with demographic and other data, 585 (56.68%) were females, and 446 (43.22%) were males; mean age was 46.4 years (SD 10.8). The mean number of app sessions per participant was 4.0 (SD 6.8). The end-of-study survey was completed by 131 survey respondents (10.23%, 131/1281); a large number of participants (117/131, 89.3%) reported being extremely, very, or moderately satisfied with the app. A total of 97 survey respondents (97/131, 74.0%) reported setting up a quit date in the app. Of those, 74 (74/97, 76%) reported quitting on their quit date. CONCLUSIONS: Positive patient engagement was observed in this study based on app download and usage. This study quantified how the Pfizer Meds app performed in an observational real-world data setting. The findings demonstrate the willingness of participants to set a quit date and use the app for support in medication adherence, refill reminders, and information regarding how to take the medication. This study provides real-world evidence of the contribution apps can make to the continued encouragement of smokers to improve their health by smoking cessation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".