Perceptions of the Use of Mobile Technologies for Smoking Cessation: Focus Group Study With Individuals of Low Socioeconomic Status Who Smoke
Bibliographic record
Abstract
BACKGROUND: The use of mobile technologies to deliver behavioral health interventions, including smoking cessation support, has grown. Users' perceptions are important determinants of the adoption and use of new technologies. However, little is known about users' perceptions of mobile technologies as smoking cessation aids, particularly among disadvantaged individuals who smoke. OBJECTIVE: This study aimed to examine the acceptance of mobile technologies for smoking cessation among young adults with low socioeconomic status who smoke. METHODS: In total, 38 current cigarette smokers, 18 to 29 years old, who wanted to quit and did not have a 4-year college degree nor were enrolled in a 4-year college, participated in 12 semistructured digital focus groups. The moderation guide was guided by the Unified Theory of Acceptance and Use of Technology. Discussions were audio recorded, transcribed verbatim, and coded for the Unified Theory of Acceptance and Use of Technology constructs (ie, effort expectancy, facilitating conditions, performance expectancy, and social influence), sentiment (ie, negative, neutral, and positive), and purpose of using mobile technologies (ie, lifestyle and health management and smoking cessation) following a deductive thematic analysis approach. RESULTS: Participants had positive experiences using mobile technologies for lifestyle and health management, primarily for fitness and dietary purposes. Salient themes were facilitating conditions of use (44/80, 55%), with prior experiences and costs subthemes, followed by perceived usefulness of mobile technologies in helping users attain health goals (22/80, 27.50%), which were generally positive. Ease of use (11/80, 13.75%) and social influences (3/80, 3.75%) were minimally discussed. Conversely, participants had limited awareness of smoking cessation uses of mobile technologies, which was the primary barrier under facilitating conditions discussed (33/51, 64.70%). Participants expressed skepticism about the usefulness of mobile technologies in helping them quit smoking (14/51, 27.45%). Effort expectancy was not discussed, given participants' limited prior use. Social influences on mobile technology use for smoking cessation were minimally discussed (4/51, 7.84%). CONCLUSIONS: The use of mobile technologies for smoking cessation was unknown to young adults with low socioeconomic status who smoke. To reduce cigarette smoking and associated health disparities, increasing awareness and use of evidence-based mobile-based smoking cessation interventions are needed. Smoking cessation interventions should incorporate features perceived as useful and easy to use to capitalize on positive user experiences and the acceptability of mobile technologies for lifestyle and health management.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".