Utilising digital health to improve medication-related quality of care for hypertensive patients: An integrative literature review
Notice bibliographique
Résumé
BACKGROUND: Hypertension or high blood pressure is considered as a significant contributor and risk factor to many serious conditions, approximately 1.13 billion people have hypertension globally. However, the integrated technologies can upscale health provisions and improve the effectiveness of the healthcare system. WHO has recommended that the digital health interventions (DHIs) and the Health System Challenges should be used in tandem in addressing health. AIM: To summarise the outcomes from a range of research which investigated the use of DHI to improve the medication-related quality of care (MRQOC) for hypertensive patients. METHODS: An integrative literature review was undertaken in October 2019 using the Medline, Cumulative Index of Nursing and Allied Health Literature, and Scopus databases for publications in English with no date limit. RESULTS: In total, 18433 participants were included in this review from 28 studies meeting the eligibility criteria. There were 19 DHI identified within eight countries: Australia, Canada, India, South Korea, Lebanon, Pakistan, the United Kingdom, and the United States of America. The DHI were provided as community-based, clinical-based and home-based program through mobile phone, mobile health system, short message service, and telehealth, digital medicine, and online healthcare (web-based). The mean age of participants was 59 ranging from 42 to 81 years with an average mean systolic blood pressure of 143.3 mmHg at baseline, ranging from 129.0 mmHg to 159.0 mmHg. The proportion of male participants ranged from 13.9% to 92.0%. Eighteen interventions showed evidence of reduction in blood pressure and improvement of self-management in relation to medication adherence and blood pressure control. The reduction of systolic blood pressure ranged between 1.9 mmHg and 26.0 mmHg, with a mean of 10.8 mmHg. The digital health was found positively associated with the MRQOC for hypertensive patients such as improvement in medication adherence and medication management; better blood pressure control; maintaining follow-ups appointment and self-management; increasing access to healthcare particularly among patients living in rural area; and reducing adverse events. However, some interventions found no significant effect on hypertensive care. The follow up duration varied between 2 mo and 18 mo with an average attrition rate of 10.1%, ranging from 0.0% to 17.4%. CONCLUSION: Utilising digital health innovation for hypertensive care in different settings with tailored interventions positively impacted on MRQOC leading to an improvement of patient outcomes and their quality of life. Nevertheless, inconclusive findings were found in some interventions, and inconsistent outcomes between DHI were noted. A future research and evidence-based DHI for hypertension or chronic diseases should be developed through the evidence-to-decision framework and guidelines.
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 | 0,004 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,014 | 0,011 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».