Comparison of a Daily Smartphone App and Retrospective Questionnaire Measures of Adherence to Nicotine Replacement Therapy Among Pregnant Women: Observational Study
Bibliographic record
Abstract
Background Few studies have investigated how to best measure adherence to smoking cessation medications, but continuous usage measures are recommended. Objective In this first study of its kind, we compared methods for measuring adherence to nicotine replacement therapy (NRT) among pregnant women, investigating the completeness and validity of data collected from daily assessments using a smartphone app versus data collected from retrospective questionnaires. Methods Women aged ≥16 years who were daily smokers and <25 weeks pregnant were offered smoking-cessation counseling and encouraged to use NRT. For 28 days after setting a quit date (QD), women were asked to report NRT use daily to a smartphone app and to questionnaires administered in person or remotely at 7 and 28 days. For both data collection methods, we provided up to £25 (~US $30) as compensation for the time taken providing research data. Data completeness and NRT use reported to the app and in questionnaires were compared. For each method, we also correlated mean daily nicotine doses reported within 7 days of the QD with Day 7 saliva cotinine concentrations. Results Of the 438 women assessed for eligibility, 40 participated and 35 accepted NRT. More participants (31/35) submitted NRT usage data to the app by Day 28 (median 25, IQR 11 days) than completed the Day 28 questionnaire (24/35) or either of the two questionnaires (27/35). Data submitted to the app showed a lower reported duration of NRT use compared to that indicated in the questionnaire (median for app 24 days, IQR 10.25; median for questionnaire 28 days, IQR 4.75; P=.007), and there appeared to be specific cases of overreporting to the questionnaire. Mean daily nicotine doses between the QD and Day 7 were lower when calculated using app data (median for app 40 mg, IQR 52.1; median for questionnaire 40 mg, IQR 63.1; P=.001), and some large outliers were evident for the questionnaire. Mean daily nicotine doses, adjusted for cigarettes smoked, were not associated with cotinine concentrations for either method (app rs=0.184, P=.55; questionnaire rs=0.031, P=.92), but the small sample size meant that the analysis was likely underpowered. Conclusions Daily assessment of NRT use via a smartphone app facilitated more complete data (a higher response rate) than questionnaires, and reporting rates over 28 days were encouraging among pregnant women. App data had better face validity; retrospective questionnaires appeared to overestimate NRT use for some participants.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".