Perceptions and Attitudes Toward the Use of a Mobile Health App for Remote Monitoring of Gingivitis and Willingness to Pay for Mobile Health Apps (Part 3): Mixed Methods Study
Bibliographic record
Abstract
Background Gum infection, known as gingivitis, is a global issue. Gingivitis does not cause pain; however, if left untreated, it can worsen, leading to bad breath, bleeding gums, and even tooth loss, as the problem spreads to the underlying structures anchoring the teeth in the jaws. The asymptomatic nature of gingivitis leads people to postpone dental appointments until clinical signs are obvious or pain is evident. The COVID-19 pandemic has necessitated social distancing, which has caused many people to postpone dental visits and neglect gingival health. iGAM is a dental mobile health (mHealth) app that remotely monitors gum health, and an observational study demonstrated the ability of iGAM to reduce gingivitis. We found that a weekly dental selfie using the iGAM app reduced the signs of gingivitis and promoted oral health in a home-based setting. Objective The aim of this mixed methods study is to assess perceptions, attitudes, willingness to pay, and willingness to use an mHealth app. Methods The first qualitative phase of the study included eight semistructured interviews, and the second quantitative phase included data collected from responses to 121 questionnaires. Results There was a consensus among all interviewees that apps dealing with health-related issues (mHealth apps) can improve health. Three themes emerged from the interviews: the iGAM app is capable of improving health, the lack of use of medical apps, and a contradiction between the objective state of health and the self-definition of being healthy. Participants were grouped according to how they responded to the question about whether they believed that mHealth apps could improve their health. Participants who believed that mHealth apps can enhance health (mean 1.96, SD 1.01) had a higher willingness to pay for the service (depending on price) than those who did not believe in app efficacy (mean 1.31, SD 0.87; t119=−2417; P=.02). A significant positive correlation was found between the amount a participant was willing to pay and the benefits offered by the app (rs=0.185; P=.04). Conclusions Potential mHealth users will be willing to pay for app use depending on their perception of the app’s ability to help them personally, provided they define themselves as currently unhealthy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".