Mobile Apps to Support the Self-Management of Hypertension: Systematic Review of Effectiveness, Usability, and User Satisfaction
Notice bibliographique
Résumé
BACKGROUND: Hypertension is a chronic disease that is considered to be a public health problem and requires efforts by patients to manage themselves. The global growth in the use of mobile phones and tablets has been accompanied by the increased use of health apps. Many of these apps support the self-management of hypertension and, therefore, they have the potential benefits of lowering blood pressure. Despite this, there is currently a lack of evidence for their effectiveness, usability, and patient satisfaction with their use. OBJECTIVE: A systematic review was conducted to assess the effectiveness of apps in lowering blood pressure, as well as their usability and patients' satisfaction with their use. METHODS: We conducted searches in the following databases: MEDLINE (OVID), EMBASE (OVID), PsycINFO (OVID), CINAHL, the Cochrane Central Register of Controlled Trials (CENTRAL, The Cochrane Library), IEEE Xplore ASSIAN, Google Scholar and the main Arabic databases Al Manhal, AskZad, and Mandumah. We looked for studies that used apps in the self-management of hypertension from 2008-2016. We also checked the reference lists of the review papers and all the primary studies for additional references. RESULTS: A total of 21 studies with a total of 3112 participants were included in the review. Of the 14 studies that assessed the effectiveness of the apps in lowering blood pressure, 10 (71.4%) studies (6 RCTs and 4 nonrandomized studies) reported that using the apps led to significant decreases in blood pressure and seemed to be effective in the self-management of hypertension. Of these 10, only 2 (20%) RCTs and 3 (30%) nonrandomized studies had a low-moderate risk of bias. The results of this review are inconclusive regarding which combinations of functionalities would be most effective in lowering blood pressure because of variation in the studies' quality, but the data suggest that apps incorporating more comprehensive functionalities are likely to be more effective. In all the studies that assessed the usability of the apps and users' acceptance of them, all the apps seemed to be accepted and easy to use. CONCLUSIONS: Most of the studies reported that apps might be effective in lowering blood pressure and are accepted by users. However, these findings should be interpreted with caution, as most of the studies had a high risk of bias. More well-designed, large-scale studies are required to evaluate the real effect of using apps in lowering blood pressure and to identify the most effective functionality combinations for lowering blood pressure.
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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 | 0,009 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,005 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».