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Record 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 on OpenAlexvenueno aff
Jonathan Shih, Vivian E Kwok, Isabel Luna, Hyunjin Cindy Kim, Faviola Garcia, Christian Gutierrez, Courtney R. Lyles, Elaine C. Khoong

Bibliographic record

VenueJMIR Cardio · 2025
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintSafety netQualitative researchMedicineMedical emergencyPsychologySociologyComputer scienceEnvironmental healthWorld Wide WebSocial science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.768

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.320
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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