Perceptions of Using Instant Messaging Apps for Alcohol Reduction Intervention Among University Student Drinkers: Semistructured Interview Study With Chinese University Students in Hong Kong
Bibliographic record
Abstract
BACKGROUND: Mobile instant messaging (IM) apps (eg, WhatsApp and WeChat) have been widely used by the general population and are more interactive than text-based programs (SMS text messaging) to modify unhealthy lifestyles. Little is known about IM app use for health promotion, including alcohol reduction for university students. OBJECTIVE: This study aims to explore university student drinkers' perceptions of using IM apps for alcohol reduction as they had high alcohol exposure (eg, drinking invitations from peers and alcohol promotion on campus) and the proportion of IM app use in Hong Kong. METHODS: A qualitative study was conducted with 20 Hong Kong Chinese university students (current drinkers) with Alcohol Use Disorder Identification test scores of ≥8 recruited using purposive sampling. Semistructured individual interviews were conducted from September to October 2019. Interview questions focused on drinking behaviors, quitting history, opinions toward IM app use as an intervention tool, perceived usefulness of IM apps for alcohol reduction, and opinions on the content and design of IM apps for alcohol reduction. Each interview lasted approximately 1 hour. All interviews were audio-taped and transcribed verbatim. Two researchers independently analyzed the transcripts using thematic analysis with an additional investigator to verify the consistency of the coding. RESULTS: Participants considered IM apps a feasible and acceptable platform for alcohol reduction intervention. They preferred to receive IMs based on personalized problem-solving and drinking consequences with credible sources. Other perceived important components of instant messages included providing psychosocial support in time and setting goals with participants to reduce drinking. They further provided suggestions on the designs of IM interventions, in which they preferred simple and concise messages, chat styles based on participants' preferences (eg, adding personalized emojis and stickers in the chat), and peers as counselors. CONCLUSIONS: Qualitative interviews with Chinese university student drinkers showed high acceptability, engagement, and perceived utility of IM apps for alcohol reduction intervention. IM intervention can be an alternative for alcohol reduction intervention apart from traditional text-based programs. The study has implications for developing the IM intervention for other unhealthy behaviors and highlights important topics that warrant future research, including substance use and physical inactivity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04025151; https://clinicaltrials.gov/ct2/show/NCT04025151?term=NCT04025151.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".