Mobile Apps for Dental Caries Prevention: Systematic Search and Quality Evaluation
Bibliographic record
Abstract
BACKGROUND: Dental caries is the most common multifactorial oral disease; it affects 60% to 90% of the global population. Dental caries is highly preventable through prevention behaviors aimed at improving oral hygiene, adequate fluoride usage, and dietary intake. Mobile apps have the potential to support patients with dental caries; however, little is known about the availability, target audience, quality, and features of these apps. OBJECTIVE: This review aims to systematically examine dental caries prevention apps; to describe their content, availability, target audience, and features; and to assess their quality. METHODS: We systematically identified and evaluated apps in a process paralleling a systematic review. This included a search strategy using search terms; an eligibility assessment using inclusion and exclusion criteria focused on accessibility and dental caries self-management behaviors, including oral hygiene, dietary intake, and fluoride usage; data extraction on app characteristics, including app store metrics; prevention behavior categorization; feature identification and description; a quality appraisal of all apps using the validated Mobile App Rating Scale (MARS) assessment tool; and data comparison and analysis. RESULTS: Using our search strategy, we retrieved 562 apps from the Google Play Store and iTunes available in Australia. Of these, 7.1% (40/562) of the apps fit our eligibility criteria, of which 55% (22/40) targeted adults, 93% (37/40) were free to download, and 65% (26/40) were recently updated. Oral hygiene was the most common dental caries prevention behavior domain, addressed in 93% (37/40) of the apps, while dietary intake was addressed in 45% (18/40) of the apps and fluoride usage was addressed in 42% (17/40) of the apps. Overall, 50% (20/40) of the apps addressed only 1 behavior, and 38% (15/40) of the apps addressed all 3 behaviors. The mean MARS score was 2.9 (SD 0.7; range 1.8-4.4), with 45% (18/40) of the apps categorized as high quality, with a rating above 3.0 out of 5.0. We identified 21 distinctive features across all dental caries prevention behaviors; however, the top 5 most common features focused on oral hygiene. The highest-ranking app was the Brush DJ app, with an overall MARS score of 4.4 and with the highest number of features (n=13). We did not find any apps that adequately addressed dental caries prevention behaviors in very young children. CONCLUSIONS: Apps addressing dental caries prevention commonly focus on oral hygiene and target young adults; however, many are not of high quality. These apps use a range of features to support consumer engagement, and some of these features may be helpful for specific patient populations. However, it remains unclear how effective these apps are in improving dental caries outcomes, and further evaluation is required before they are widely recommended.
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 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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".