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Enregistrement W4411774566 · doi:10.2196/60196

English- and Spanish-Speaking Patient Preferences on Home Blood Pressure Monitors in an Urban Safety Net Setting: Qualitative Study

2025· article· en· W4411774566 sur OpenAlexvenueno aff
Jonathan Shih, Vivian E Kwok, Isabel Luna, Hyunjin Cindy Kim, Faviola Garcia, Christian Gutierrez, Courtney R. Lyles, Elaine C. Khoong

Notice bibliographique

RevueJMIR Cardio · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueBlood Pressure and Hypertension Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPreprintSafety netQualitative researchMedicineMedical emergencyPsychologySociologyComputer scienceEnvironmental healthWorld Wide WebSocial science

Résumé

récupéré en direct d'OpenAlex

Background: Self-measured blood pressure monitoring is necessary for successful management of hypertension. However, disparities in blood pressure control persist, with low-income patients and racial and ethnic minorities more likely to have uncontrolled hypertension. These patients are also at increased risk for digital exclusion. Several validated blood pressure monitors for self-measured monitoring are available, but little is known about patient preferences between different device traits. Studies have shown that poor usability or technology design can lead to barriers to adoption. Objective: We investigated patient-reported barriers, preferences, and facilitators to self-measured blood pressure monitoring from a diverse population at an urban safety-net hospital. Methods: This qualitative study included English- and Spanish-speaking patients with hypertension. Participants completed a survey about sociodemographic traits, self-measured blood pressure monitoring practices and training, and experience with technology. Semi-structured interviews were conducted to elicit preferences about blood pressure devices, the accompanying mobile apps, and their experience sharing blood pressure measurements with their providers. Interviews included participant demonstration of home blood pressure measurement to evaluate baseline self-measured blood pressure monitoring technique. Two home blood pressure monitoring devices were presented: a Bluetooth-enabled device and a cellular-enabled device that syncs data directly. Surveys and interviews were conducted in participants' preferred language. Rapid qualitative data analysis was applied to analyze qualitative data. Results: Fifteen participants (8 English-speaking and 7 Spanish-speaking) were enrolled. Participants all identified as racial and ethnic minorities. Educational attainment varied, ranging from less than high school to college graduates. Eight exhibited some form of digital inaccessibility: lacking internet access, not activating their patient portal, or having difficulty connecting a device to Wi-Fi. Most required assistance with Bluetooth pairing and navigating app features. Overall, participants valued tracking their blood pressure, were motivated to engage in self-measured blood pressure monitoring practices, and desired training. Nearly all participants demonstrated inconsistencies in blood pressure education, displayed incorrect measurement techniques, and had not received formal training on self-measured blood pressure monitoring. Spanish-speaking participants reported that using apps was challenging because they were presented in English and wanted translated apps and resources. The cost of features was a key factor in device preference. Conclusions: Patient-reported barriers to successful self-measured blood pressure monitoring adoption include cost, insufficient training, digital inaccessibility, and language discordance. Addressing these challenges may enhance the adoption of self-measured blood pressure monitoring in safety net populations. Providers should evaluate patients' preferences and develop tailored interventions when recommending self-measured blood pressure monitoring. Cellular self-measured blood pressure monitoring devices that automatically transmit blood pressure readings may reduce digital complexity and promote sharing results with providers, though future studies are needed to evaluate usability and implementation.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,207
Score d'incertitude au seuil0,768

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,024
Tête enseignante GPT0,320
Écart entre enseignants0,295 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission1
Résumé présentoui

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