Behavioral Factors Related to Participation in Remote Blood Pressure Monitoring Among Adults With Hypertension: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Remote blood pressure (BP) monitoring (RBPM) or BP telemonitoring is beneficial in hypertension management. People with hypertension involved in telemonitoring of BP often have better BP control than those in usual care. However, most reports on RBPM are from intervention studies. Objective: This study aimed to assess participant characteristics and technology health behaviors associated with RBPM participation in a wider population with hypertension. This study will help us understand the predictors of RBPM participation and consider how to increase it. Methods: This was a quantitative, cross-sectional survey study of people with hypertension in the United States. The inclusion criteria included people aged ≥18 years with a hypertension diagnosis or who self-reported they have hypertension, had a prescription of at least one hypertension medication, understood the English language, and were willing to participate. The survey included demographics, technology health behaviors, and RBPM participation questions. The survey was self-administered on the Qualtrics platform and followed the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) checklist. The primary dependent variable was participation in RBPM. Results: In total, 507 people with hypertension participated in the survey. The mean age for all respondents was 60 (SD 14.7) years. The respondents were mostly female (306/507, 60.4%), non-Hispanic (483/507, 95.3%), and White (429/507, 84.6%). A little over half of the respondents reported having had hypertension for 5 years or more (287/507, 56.6%). About one-third of participants were aware of RBPM (165/507, 32.5%), and 11.8% (60/507) were enrolled in RBPM. The mean age of those engaging in RBPM and non-RBPM was 46.2 (SD 14.7) and 62 (SD 13.7) years, respectively. The most common reasons for not participating in RBPM were because their health provider did not ask the participant to participate (247/447, 55.3%) and their lack of awareness of RBPM (190/447, 42.5%). Most respondents in the RBPM group measure their BP at home (55/60, 91.7%), and 61.7% (37/60) engage in daily BP measurement, compared with 62.6% (280/447) and 25.1% (112/447), respectively, among the non-RBPM group. A greater number of those in the RBPM group reported tracking their BP measurements with mobile health (mHealth; 37/60, 61.7%) than those in the non-RBPM group (70/447, 15.6%). The electronic health records or patient portal was the most common channel of RBPM communication between the respondents and their health care providers. The significant predictors of participation in RBPM were RBPM awareness (adjusted odds ratio [AOR] 34.65, 95% CI 11.35-150.31; P<.001) and sharing health information electronically with a health provider (AOR 4.90, 95% CI 1.39-21.64; P=.01) among all participants. However, the significant predictor of participation in RBPM among participants who were aware of RBPM was sharing health information electronically with a health provider (AOR 6.99, 95% CI 1.62-47.44; P=.007). Conclusions: Participation in RBPM is likely to increase with increased awareness, health providers' recommendations, and tailoring RBPM services to patients' preferred electronic communication channels.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».