ArticleBMC research notes2022
The development and acceptability testing of an app-based smart survey system to record smoking behaviour, use of nicotine replacement therapy (NRT) and e-cigarettes.
Article in BMC research notes, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The Effectiveness of Text Support for Stopping Smoking in Pregnancy (MiQuit): Multi-Trial Pooled Analysis Investigating Effect Moderators and Mechanisms of Action.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Trial
- myCOtrak: an app which records smoking, nicotine use and exhaled carbon monoxide concentrations concurrently for use in smoking research.BMC research notes · 2025Article
- Article
- The Relationship Between Reported Daily Nicotine Dose from NRT and Daily Cigarette Consumption in Pregnant Women Who Smoke in an Observational Cohort Study.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Observational
- Development of "Baby, Me, & NRT": A Behavioral Intervention to Improve the Effectiveness of Nicotine Replacement Therapy in Pregnancy.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2023Article
- Comparison of a Daily Smartphone App and Retrospective Questionnaire Measures of Adherence to Nicotine Replacement Therapy Among Pregnant Women: Observational Study.JMIR formative research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
objectiveNicotine replacement therapy (NRT) helps people stop smoking. Monitoring treatment adherence is important as poor adherence to NRT limits its effectiveness. As e-cigarettes contain nicotine, their use ('vaping') is likely to affect both NRT use and smoking. We wished to measure adherence to NRT, and to investigate relationships between NRT, vaping and smoking so we developed 'NicUse', a smartphone App linked to a cloud database for collecting data relevant to NRT adherence. We report user-acceptability and investigate data validity among pregnant people by comparing heaviness of smoking reported to NicUse surveys with contemporaneous exhaled carbon monoxide readings.
resultsThirty five pregnant women participating in a pilot study were asked to install and use NicUse on their smartphones. 32/35 (91%) logged into NicUse, 31 (89%) completed one or more surveys, and 22 (63%) completed these on ≥ 20 of 28 study days. Twenty-four gave end-of-study user acceptability ratings; 23 (96%) agreed or strongly agreed NicUse was 'Easy to use' and 'Instructions were clear'. There was a strong correlation between the number of daily cigarettes reported on NicUse and exhaled CO readings taken on study Day 7 (Pearson's r = 0.95, p < 0.001). NicUse appears highly acceptable, and smoking data reported to it shows validity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.