Evaluation of the Acceptability of a Proposed, Instagram-Based, Randomized Controlled Trial for People With Asthma: Survey Study
Bibliographic record
Abstract
BACKGROUND: Asthma is a chronic lung disease that affects nearly 25 million individuals in the United States. More research is needed into the potential for health care providers to leverage existing social media platforms to improve healthy behaviors and support individuals living with chronic health conditions. OBJECTIVE: In this study, we assessed the willingness of Instagram users with poorly controlled asthma to participate in a pilot randomized controlled trial that will use Instagram as a means of providing social and informational support. In addition, we explored the potential for adapting the principles of photovoice and digital storytelling to Instagram. METHODS: We conducted a survey study of Instagram users aged 18-40 years with poorly controlled asthma in the United States. RESULTS: Over 3 weeks of recruitment, 457 individuals completed the presurvey screener; 347 (75.9%) were excluded and 110 (24.1%) were eligible and agreed to participate in the study. Of the 110 individuals, 82 (74.5%) completed the study survey. The mean age of the respondents was 21 (SD 5.3) years. Among respondents, 56% (46/82) were female, 65% (53/82) were non-Hispanic White, and 72% (59/82) had at least some college education. The majority of respondents (67/82, 82%) indicated that they would be willing to participate in the proposed study. CONCLUSIONS: Among young adult Instagram users with asthma, there is substantial interest in participating in a pilot randomized controlled trial that will use Instagram to connect participants with peers and a health coach to share information about self-management of asthma and build social connection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.111 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".