A Video-Based Mobile App as a Health Literacy Tool for Older Adults Living at Home: Protocol for a Utility Study
Notice bibliographique
Résumé
BACKGROUND: People aged ≥65 years are more likely to have health problems related to aging, polypharmacy, and low treatment adherence. Moreover, health literacy levels decrease with increasing age. OBJECTIVE: The aim of this study is to assess an app's utility in promoting health-related knowledge in people aged ≥65 years. METHODS: We developed a simple, intuitive, and video-based app (DigiAdherence) that presents a recipe, nutritional counseling, and content on physical activity, cognitive exercise, motivation to adhere to treatment, fall prevention, and health literacy. A convenience sample of 25 older adults attending the Personalized Health Care Unit of Portimão or the Family Health Unit of Portas do Arade (ACeS Algarve II - Barlavento, ARS Algarve, Portugal) will be recruited. Subjects must be aged ≥65 years, own a smartphone or tablet, be willing to participate, and consent to participate. Those who do not know how to use or do not have a smartphone/tablet will be excluded. Likewise, people with major cognitive or physical impairment as well as those living in a long-term care center will not be included in this study. Participants will have access to the app for 4 weeks and will be evaluated at 3 different timepoints (V0, before they start using the app; V1, after using it for 30 days; and V2, 60 days after stopping using it). After using the app for 30 days, using a 7-point Likert scale, participants will be asked to score the mobile tool's utility in encouraging them to take their medications correctly, improving quality of life, increasing their health-related knowledge, and preventing falls. They will also be asked to assess the app's ease of use and visual esthetics, their motivation to use the app, and their satisfaction with the app. Subjects will be assessed in a clinical interview with a semistructured questionnaire, including questions regarding user experience, satisfaction, the utility of the app, quality of life (EQ-5D-3L instrument), and treatment adherence (Morisky scale). The proportion of participants who considered the app useful for their health at V1 and V2 will be analyzed. Regarding quality of life and treatment adherence perceptions, comparisons will be made between V0 and V1, using the t test for dependent samples. The same comparisons will be made between V0 and V2. RESULTS: This study was funded in December 2019 and authorized by the Executive Board of ACeS Algarve II - Barlavento and by the Ethics Committee of NOVA Medical School (99/2019/CEFCM, June 2020). This protocol was also approved by the Ethics Committee for Health (16/2020, September 2020) and the Executive Board (December 2020) of the Regional Health Administration of the Algarve, IP (Instituto Público). Recruitment was completed in June 2021. CONCLUSIONS: Since the next generation of older adults may have higher digital literacy, information and communication technologies could potentially be used to deliver health-related content to improve lifestyles among older adults. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/29675.
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 | 0,023 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,048 | 0,009 |
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 ».