Review of Alzheimer’s Disease Focused Mobile Applications
Notice bibliographique
Résumé
Background: As of 2017, an estimated 5.5 million Americans are living with Alzheimer’s disease and related dementias (ADRD). Information and support for individuals with ADRD and their caregivers are critically needed. Technological advancements have provided patients and caregivers with tools that can provide information and education in areas such as improving awareness about the disease, disease management, and caregiving skills training. Mobile applications (apps) are an example of these tools. Studies have been conducted to assess the content of mobile apps focused on other health issues such as diabetes, weight management, and cancer; however, little is known about ADRD-related mobile apps. To our knowledge, this is the first comprehensive review of apps focused on ADRD. Objective: The objective of this study was to review the content of ADRD-related mobile apps. Methods: ADRD-related mobile apps were searched using keywords such as “Alzheimer”, “Alzheimer’s Disease” and “Dementia” in the App store for iOS-supported apps and Google Play Store for Android-supported apps. Apps were included for final review based on description, and inclusion and exclusion criteria. Three reviewers coded characteristics of the app (e.g. developer, version, number of installations, user ratings), target users, purpose, content of information provided, and technical aspects. Descriptive statistics, including frequencies and percentages, were used to analyze the data. Results: A total of 38 apps were included in the review (16 were only available in iOS; 9 were only available in Android; 13 apps were available in both operating systems). IT companies developed 36.8% of the apps reviewed, followed by non-profit organizations (18.4%), and health-consulting organizations (10.5%). Very few apps were developed by government agencies (5.3%) or pharmaceutical companies (5.3%). Most apps were intended for caregivers of individuals with ADRD (63.2%), followed by the general population (44.7%). The main purpose of the apps was for disease management (55.3%), skills training (42.1%), disease and treatment information (34.2%), and to improve disease awareness (29.0%). Very few apps had a goal of providing peer support (2.6%). Most of the content was focused on caregiving (63.2%) and disease management (50.0%). Other information frequently presented included signs and symptoms of ADRD (34.2%), types of ADRD (31.6%), financial and legal issues (29.0%), resources for supporting patients (29.0%), and healthy lifestyle for ADRD prevention (29.0%). Few apps contained information about differences between typical aging and ADRD symptoms (13.2%), and health insurance option for ADRD patients (10.5%). Few apps had video (23.7%) or audio (2.6%) lectures or tutorials. Interactive features were limited; few apps had a function of sharing (18.4%), an app community (10.5%), or sending reminders (7.9%). Conclusions: ADRD mobile apps that provide caregiving information can potentially benefit individuals who are supporting ADRD patients. Most ADRD mobile apps reviewed did not cover certain aspects related to ADRD, such as how to differentiate ADRD symptoms from typical aging. In addition, information provided by the apps was mainly presented in the form of text with limited audio/video options. There are opportunities for further development of ADRD apps with respect to content and format.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».