The Use of Patient-Oriented Mobile Phone Apps in Oral Health: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Oral health is a significant part of general health. Poor oral health can influence an individual's appearance, self-esteem, eating, and speaking. The use of mobile phone apps has been growing in the field of medicine, including dentistry. However, to date, there is no evidence related to the availability of mobile apps focusing on various branches of dentistry. OBJECTIVE: The aim of this study was to review the scientific literature on the use of patient-oriented mobile phone apps in oral health and summarize the key findings. METHODS: A scoping review of published scientific literature on the use of patient-oriented mobile phone apps in oral health was conducted in accordance with the Joanna Briggs Institute. A search was performed in PubMed and Scopus for studies published between January 2000 and June 2021 that were written in English. All study types except for those reporting developmental protocols were included in this review. In total, 2 reviewers independently screened the studies using the eligibility criteria. The study protocol was registered in the Open Science Framework registries in June 2021. RESULTS: The initial search yielded a total of 977 studies, 45 (4.6%) of which met the inclusion criteria. All the studies (45/45, 100%) were published after 2009. Most studies (31/45, 69%) concerned oral health promotion using mobile phone apps, followed by behavior management (5/45, 11%). More than half (23/45, 51%) of the included studies were conducted in Asian countries. Overall, 31% (14/45) of the studies focused on adolescents. A total of 51% (23/45) of the studies were randomized controlled trials (RCTs). Approximately 39% (9/23) of the included RCT studies reported a substantial reduction in dental plaque, and 26% (6/23) of the studies reported significant improvement in gingival health. Regarding dental anxiety management, 13% (3/23) of the RCT studies reported a significant decrease in mean heart rate and lower Facial Image Scale scores. CONCLUSIONS: According to the literature, the use of mobile apps in oral health is increasing among patients, mainly children and adolescents. Many studies that have used mobile apps have focused on promoting oral health. However, other areas such as diagnostic and remote consultations (teledentistry) have until recently been neglected despite their great potential.
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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 | 0.016 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".