A Novel Remote Patient and Medication Monitoring Solution to Improve Adherence and Persistence With Inflammatory Bowel Disease Therapy (ASSIST Study): Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Inflammatory bowel diseases (IBDs) are chronic inflammatory conditions of the gastrointestinal tract. Although adherence to IBD therapies is associated with improved clinical outcomes, overall adherence is poor. Consequently, there is a critical need to develop interventions that monitor adherence in real time and identify reasons for nonadherence to support clinical teams in initiating effective interventions. Recently, electronic- and web-based platforms have been developed to monitor adherence and guide interventions. A novel remote therapy monitoring (RTM) technology, the Tappt digital health system, has been developed to monitor real-time medication adherence patterns through smart label technologies, capture patient-reported outcomes and barriers to care, and process patient data through algorithms that trigger personalized digital and human touch points between clinical visits. Such a digital health solution enables care teams to proactively identify and mitigate nonadherence and worsening clinical outcomes. OBJECTIVE: We propose a 12-month multicenter randomized controlled trial to assess the effectiveness of the Tappt digital health system on adherence, clinical outcomes, and health care use among patients diagnosed with IBD starting a new oral or subcutaneous therapy. METHODS: The digital health system intervention will provide automatic measurement of medication adherence via smart labels for pill bottles or injectors as well as a monitoring platform for providers. The system will prompt patients to complete a two-item assessment of symptoms monthly using the PRO-2 scales for UC and Crohn disease, from which increased symptoms will be alerted to providers. Participants will be randomized 2:1 to the intervention group or the control group, which will receive standard of care. All participants are required to complete questionnaires at baseline as well as at 12, 26, and 52 weeks. Assuming an adherence rate of 0.65 and 0.9 among control and intervention participants, respectively, we will need to enroll 123 participants: 82 (66.7%) in the intervention group and 41 (33.3%) controls. We will compare adherence as measured by the medication possession ratio, defined as the number of days of supply of medication obtained during the observation period out of the total number of days in the observation period, in participants using the RTM versus those receiving standard of care. We will also compare clinical outcomes and health care use in participants using the RTM versus those receiving standard of care. RESULTS: We anticipate starting recruitment in December 2022. CONCLUSIONS: Effective medication adherence monitoring and intervention programs need to be cost-efficient, pose little or no burden to the patient, record reliable data in real time, and provide actionable insights to the health care team. We anticipate the Tappt digital health system to improve the medication possession ratio, clinical outcomes, and health care use compared with standard of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05316584; https://clinicaltrials.gov/ct2/show/NCT05316584. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/40382.
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 | 0,031 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,007 | 0,003 |
| Méta-épidémiologie (sens large) | 0,012 | 0,006 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,079 | 0,013 |
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 ».