Abstract FR445: Use of Digital Health Interventions to Control High Blood Pressure: A Systematic Review of Efficacy
Notice bibliographique
Résumé
Introduction: Improving BP control is now often done with digital tools, including apps, telemonitoring, wearables, and online coaching. They provide patients with new tools for self-care and give clinicians constant data about their progress. Yet, it is challenging to thoroughly review the complete results of digital health approaches for hypertension management. We systematically reviewed studies to see how digital health interventions affected blood pressure and patient outcomes. Hypothesis: Patients with hypertension who receive digital health interventions will experience greater improvements in blood pressure and control rates compared to those who receive usual care. Additionally, those using telemonitoring will have particularly favorable outcomes. Methods: According to the PRISMA guidelines, we searched online for studies that tested digital health tools in hypertension. The review analyzed 22 studies, with 15 carried out as randomized controlled trials and 7 done through observational means, all related to smartphone apps, telehealth, and remote blood pressure monitoring. Information on BP decrease, hypertension management success, and how many patients were involved was collected. Biases were assessed using the Cochrane tool for RCTs and the Newcastle-Ottawa tool for observational studies. Results: Most RCTs showed that digital health interventions, when used to control their BP, helped people control their BP better than they would with usual care or education alone. On average, reducing BP was more effective for those receiving an intervention than for those in the usual care group. The outcomes were the best when telemonitoring was closely followed by feedback or counseling. Several studies have found that digital health groups were more likely to follow their medication plans and manage their conditions. Researchers found that these programs made patients happy and maintained their BP levels in regular practice settings. The risk of bias was rarely high in RCTs as a whole. Conclusions: This review, which focused on an important area of hypertension care, showed that digital health tools can help manage blood pressure more effectively. What makes these interventions special is their ability to offer care outside a clinic setting. Our research suggests that using valid digital strategies can help update hypertension management guidelines, but additional research is needed to see their long-term impact.
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 | 0,021 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,013 | 0,012 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 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 ».