Behavioral Factors Related to Participation in Remote Blood Pressure Monitoring Among Adults With Hypertension: Cross-Sectional Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".