Evaluation of the My Diabetes Care Patient Portal Intervention: Protocol for a Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: My Diabetes Care (MDC) is a multi-faceted intervention embedded within an established patient portal, My Health at Vanderbilt. MDC is designed to help patients better understand their diabetes health data and support self-care. MDC uses infographics to visualize and summarize patients' diabetes health data, incorporates motivational strategies, provides literacy-level appropriate educational resources, and links to a diabetes online patient support community and diabetes news feeds. OBJECTIVE: This study aims to evaluate the effects of MDC on patient activation in adult patients with type 2 diabetes mellitus. Moreover, we plan to assess secondary outcomes, including system use and usability, and the effects of MDC on cognitive and behavioral outcomes (eg, self-care and self-efficacy). METHODS: We are conducting a 6-month, 2-arm, parallel-design, pragmatic pilot randomized controlled trial of the effect of MDC on patient activation. Adult patients with type 2 diabetes mellitus are recruited from primary care clinics affiliated with Vanderbilt University Medical Center. Participants are eligible for the study if they are currently being treated with at least one diabetes medication, are able to speak and read in English, are 21 years or older, and have an existing My Health at Vanderbilt account and reliable access to a desktop or laptop computer with internet access. We exclude patients living in long-term care facilities, patients with known cognitive deficits or severe visual impairment, and patients currently participating in any other diabetes-related research study. Participants are randomly assigned to MDC or usual care. We collect self-reported survey data, including the Patient Activation Measure (R) at baseline, 3 months, and 6 months. We will use mixed-effects regression models to estimate potentially time-varying intervention effects while adjusting for the baseline measure of the outcome. The mixed-effects model will use fixed effects for patient-level characteristics and random effects for health care provider variables (eg, primary care physicians). RESULTS: This study is ongoing. Recruitment was closed in May 2020; 270 patients were randomized. Of those randomized, most (214/267, 80.1%) were non-Hispanic White, and 13.1% (35/267) were non-Hispanic Black, 43.7% (118/270) reported being 65 years or older, and 33.6% (90/268) reported limited health literacy. We obtained at least 95.6% (258/270) completion among participants through the 3-month follow-up assessment. CONCLUSIONS: This randomized controlled trial will be one of the first to evaluate a patient-facing diabetes digital health intervention delivered via a patient portal. By embedding MDC into Epic's MyChart platform with more than 127 million patient records, our intervention is directly integrated into routine care, highly scalable, and sustainable. Our findings and evolving patient portal functionality will inform the continued development of MDC to best meet users' needs and a larger trial focused on the impact of MDC on clinical end points. TRIAL REGISTRATION: ClinicalTrials.gov NCT03947333; https://clinicaltrials.gov/ct2/show/NCT03947333. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/25955.
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 | 0,038 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,004 |
| Méta-épidémiologie (sens large) | 0,011 | 0,005 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,081 | 0,012 |
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 ».