Remote Cognitive Screening Of Healthy Older Adults for Primary Care With the MyCog Mobile App: Iterative Design and Usability Evaluation
Notice bibliographique
Résumé
BACKGROUND: Annual cognitive screening in adults aged >65 years can improve early detection of cognitive impairment, yet less than half of all cases are identified in primary care. Time constraints in primary care settings present a major barrier to routine screening. A remote cognitive screener completed on a patient's own smartphone before a visit has the potential to save primary care clinics time, encourage broader screening practices, and increase early detection of cognitive decline. OBJECTIVE: We described the iterative design and proposed the implementation of a remote cognitive screening app, MyCog Mobile, to be completed on a patient's smartphone before an annual wellness visit. The research questions were as follows: What would motivate primary care clinicians and clinic administrators to implement a remote cognitive screening process? How might we design a remote cognitive screener to fit well with existing primary care workflows? What would motivate an older adult patient to complete a cognitive screener on a smartphone before a primary care visit? How might we optimize the user experience of completing a remote cognitive screener on a smartphone for older adults? METHODS: To address research questions 1 and 2, we conducted individual interviews with clinicians (n=5) and clinic administrators (n=3). We also collaborated with clinic administrators to create user journey maps of their existing and proposed MyCog Mobile workflows. To address research questions 3 and 4, we conducted individual semistructured interviews with cognitively healthy older adults (n=5) and solicited feedback from a community stakeholder panel (n=11). We also tested and refined high-fidelity prototypes of the MyCog Mobile app with the older adult interview participants, who rated the usability on the Simplified System Usability Scale and After-Scenario Questionnaire. RESULTS: Clinicians and clinic administrators were motivated to adopt a remote cognitive screening process if it saved time in their workflows. Findings from interviews and user journey mapping informed the proposed implementation and core functionality of MyCog Mobile. Older adult participants were motivated to complete cognitive screeners to ensure that they were cognitively healthy and saw additional benefits to remote screening, such as saving time during their visit and privacy. Older adults also identified potential challenges to remote smartphone screening, which informed the user experience design of the MyCog Mobile app. The average rating across prototype versions was 91 (SD 5.18) on the Simplified System Usability Scale and 6.13 (SD 8.40) on the After-Scenario Questionnaire, indicating above-average usability. CONCLUSIONS: Through an iterative, human-centered design process, we developed a viable remote cognitive screening app and proposed an implementation strategy for primary care settings that was optimized for multiple stakeholders. The next steps include validating the cognitive screener in clinical and healthy populations and piloting the finalized app in a community primary care clinic.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».