Evidence map›Paper›PMID 36250304›Full record

ArticleJournal of orthodontics2023

Orthodontic apps: An assessment of content accuracy and validity.

Dharshini Prithiviraj, Robert Sd Smyth, Mohammad Owaise Sharif

Abstract read
In one paragraph

Article in Journal of orthodontics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Dharshini PrithivirajOrthodontic Department, UCL Eastman Dental Institute, London, UK.
Robert Sd SmythOrthodontic Department, UCL Eastman Dental Institute, London, UK.ORCID 0000-0002-5763-1298
Mohammad Owaise SharifOrthodontic Department, Glan Clwyd Hospital, Rhyl, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the content accuracy of orthodontic treatment information in patient-focused apps.

designA cross-sectional review study.

settingOrthodontic apps available on the UK Android and Apple App Stores.

methodsApps identified in a previous research study and those identified via a questionnaire of specialist orthodontists were assessed for accuracy of content utilising an evidence-based checklist. The checklist covered five main orthodontically relevant themes and 32 codes with respective items.

resultsThe accuracy of information content for 16 patient-focused apps was assessed. Eight apps provided information related to orthodontic treatment and handling emergencies. Five apps were reminder apps and a small number (n = 3) contained games and timers for toothbrushing and aligners. With regard to the accuracy of information content, only two apps contained information across all five themes of the evidence-based checklist. Only one app received a score of 'fair - excellent' under the oral hygiene theme; interestingly, this app was the most commonly used patient-focused app. Eight apps containing orthodontic treatment information scored poorly as they had inaccurate information on handling emergency situations. None of the apps were deemed excellent with regard to accuracy of information content.

conclusionThe orthodontic mobile apps assessed in this study mostly contained information of poor accuracy. Therefore, there is a need for high-quality apps with credible information supported by evidence to be developed.

Indexed as

Mobile ApplicationsCross-Sectional StudiesHumansSurveys and Questionnairescontent accuracymobile appsorthodontic appssmartphones

Identifiers

PMID36250304
PMCPMC10226001

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.