Mobile Apps for Eye Care in Canada: An Analysis of the iTunes Store
Notice bibliographique
Résumé
BACKGROUND: Mobile phone screens can facilitate stimulation to various components of the visual system and many mobile apps are accepted as a means of providing clinical assessments for the oculo-visual system. Although many of these apps are intended for use in clinical settings, there is a growing number of apps in eye care developed for self-tests and eye exercises for lay people. These and other features, however, have not yet been well described. OBJECTIVE: Our objective was to identify, describe, and categorize mobile apps related to eye care that are available to users in the Canadian iTunes market. METHODS: We conducted an extensive search of the Apple iTunes Store for apps related to eye care. We used the terms "eye," "eye care," "vision," and "eye test" and included apps that are targeted at both lay people and medical professionals. We excluded apps whose primary function is not related to eye care. Eligible apps were categorized by primary purpose, based on how they were described by their developers in the iTunes Store. RESULTS: Our search yielded 10,657 apps, of which 427 met our inclusion criteria. After removing duplicates, 355 unique apps were subject to further review. We assigned the eligible apps to three distinct categories: 39/355 apps (11.0%) were intended for use by medical professionals, 236 apps (66.5%, 236/355) were intended for use by lay people, and 80 apps (22.5%, 80/355) were intended for marketing eye care and eye-care products. We identified 9 subcategories of apps based on the descriptions of their primary functions. Apps for medical professionals fell into three subcategories: clinical calculators (n=6), clinical diagnostic tools (n=18), and education and networking apps for professionals (n=15). Apps for lay people fell into four subcategories: self-testing (n=153), eye exercises (n=30), patient tools and low vision aids (n=35), and apps for patient education (n=18). Mixed-use apps (n=80) were placed into two subcategories: marketing of individual practitioners or eye-care products (n=72) and marketing of multiple eye-care products or professional services. CONCLUSIONS: The most extensive subcategory pertaining to eye care consisted of apps for use by lay people, especially for conducting self-tests (n=236). This study revealed a previously uncharacterized category of apps intended for use by doctors and patients, of which the primary goal is marketing of eye-care services and products (n=80).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,027 | 0,046 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».