Improving Adherence to PrEP Though Real-Time Monitoring Paired with Personalized, Automated Text Interventions
Notice bibliographique
Résumé
Background: Pre-exposure prophylaxis, or PrEP (brand name Truvada), is a once daily medication that reduces the chance of contracting HIV by more than 90% for those facing an elevated risk of HIV exposure. When starting PrEP, it takes at least seven days to reach high levels of protection and seven consecutive days to maintain protection. Unfortunately, many do not follow the guidelines for PrEP, leaving them vulnerable. Metro Wellness and Community Centers (Metro Wellness) provides comprehensive HIV services throughout Tampa Bay. Metro Wellness, with Mail-Meds Clinical Pharmacy (Mail-Meds), deployed a new technology, Nomi, to support patients in their journey of HIV prevention. Nomi accurately captures data through a connected prescription bottle to reveal how patients take their medications in real-time. Nomi translates data into actionable information for automatic patient interventions and engagement. Objective: To understand patients’ ability to adopt innovative technology to maintain adherence to the PrEP regimen. In addition, the study will determine if technology converts non adherent days to adherent through automated, personalized interventions. Methods: Patients enrolled in Nomi must be HIV negative, at high-risk for HIV infection, receiving PrEP for the first time through Metro Wellness, and have a cell phone with SMS capabilities. Patients receive their Truvada prescription in a connected bottle that measures the amount of medication taken by weight. The prescriptions are filled by Mail-Meds in clinic pharmacy. As patients take Truvada, the bottle sends data to Nomi. Nomi reviews the data and sends automatic text interventions and escalations based on the patient’s behavior. The texts sent are designed to be discrete to ensure patient privacy. Nomi also communicates with Metro Wellness staff. Staff receive escalations from Nomi to reach out the patents needing additional assistance. Results: The program is ongoing. All reported results are as of July 31, 2018. Forty-nine patients have been enrolled. Days on therapy range from 29 to 378. The average length of therapy is 107 days. Reasons for ending therapy include, patients changing prescribers, declining therapy, and side effects. A total of 1914 SMS text interventions have been sent. Patients need an intervention 24% of the time and convert 48% (n=917) of the time. Additionally, 45% of conversions (n=412) occurred within 1 hour of the intervention. Ninety percent (n=44) of the patients responded directly to Nomi at least once. A total of 564 total responses have been received. Each patient has sent an average of 13 responses. Conclusions: Patients had no perceived barriers to adopting Nomi. In fact, patients enjoy participating and interact frequently through responses. Personalized interventions, based on real-time data, quickly change patient behavior from what would have been a missed day, to a correct day. Metro Wellness integrated Nomi and medication adherence into the daily lives of their patients, through a direct communication channel. By using Nomi, Metro Wellness staff is able to connect with their patients more frequently, building stronger relationships, which has improved adherence in order to prevent HIV infection.
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 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 | 0,000 | 0,001 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».