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Record W3131400687 · doi:10.2196/18984

Text Messaging and Home Blood Pressure Monitoring for Patients with Uncontrolled Hypertension: Proposal for a Feasibility Pilot Randomized Controlled Trial

2021· article· en· W3131400687 on OpenAlexvenueno aff
Claudia L. Campos, Deanna N. Jones, Beverly Snively, Michael Rocco, Carolyn F. Pedley, Sara Atwater, Justin B. Moore

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersNational Center for Advancing Translational Sciences
KeywordsRandomized controlled trialText messagingBlood pressureMedicinePhysical therapyMedical emergencyComputer scienceWorld Wide WebInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A decrease in blood pressure, even modestly (ie, 2 mmHg), lowers cardiovascular morbidity and mortality. Low patient adherence to antihypertensive medication is the most significant modifiable patient-related barrier to achieving controlled blood pressure. Preliminary studies have shown that SMS text messaging and home blood pressure monitoring (HBPM) can be effective in promoting medication adherence and blood pressure control. The best strategy to engage with older patients of low socioeconomic status who are low adopters of technology and disproportionally affected by uncontrolled hypertension is still unknown. OBJECTIVE: The objective of this study is to improve blood pressure control in the older, low socioeconomic status population. The study will test two aims: First, we aim to evaluate the feasibility of conducting a randomized controlled trial by using an SMS-based approach among nonadherent, older patients of low socioeconomic status who have uncontrolled hypertension. Feasibility will be assessed in terms of recruitment rates per month (primary outcome); patient acceptability will be evaluated by monitoring retention rates and SMS response rates and using the validated Systems Usability Scale (secondary outcomes). Second, we aim to estimate the effects of the SMS approach on lowering blood pressure and adherence to antihypertensive medications. METHODS: We will recruit 24 patients of low socioeconomic status with uncontrolled hypertension (systolic BP>140 mmHg or diastolic BP>90 mmHg) showing low medication adherence and taking at least two antihypertensives, who have presented to two outpatient clinics of Wake Forest Baptist Health (Winston Salem, North Carolina, USA). Participants will be randomly assigned to either SMS and HBPM (n=12) or usual care and HBPM (n=12) intervention. Clinicians adjusting the patients' medications will be blinded to the study assignment. Text messages will be sent from a secure platform to assess medication adherence and HBPM on a weekly basis. The content and delivery frequency of the proposed SMS intervention are based on input from three focus groups conducted in Spring 2019. Participants in both study arms will receive education on HBPM and using an HBPM device. We hypothesize that we will successfully recruit 24 participants and the intervention will be acceptable to the participants. It will also improve medication adherence (assessed by question Medication Adherence Questionnaire scores) and blood pressure control. RESULTS: Our study was funded in July 2020. As of May 2021, we have enrolled 6 participants. CONCLUSIONS: Our findings will help design a larger efficacy trial to advance the field of eHealth delivery systems particularly for older adults of low socioeconomic status. This study addresses a highly significant topic and targets a population of high morbidity and mortality that has been traditionally underrepresented in clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT03596242; https://clinicaltrials.gov/ct2/show/NCT03596242. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/18984.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.038
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.039
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0020.002
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.178
GPT teacher head0.541
Teacher spread0.363 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2021
Admission routes1
Has abstractyes

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