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Record W3024171629 · doi:10.2196/13637

Recruiting Student Health Coaches to Improve Digital Blood Pressure Management: Randomized Controlled Pilot Study

2020· article· en· W3024171629 on OpenAlexvenueno aff
Elena Vasti, Mark J. Pletcher

Bibliographic record

VenueJMIR Formative Research · 2020
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersUniversity of California, San Francisco
KeywordsMedicineDigital healthPsychological interventionBlood pressureRandomized controlled trialPhysical therapyEconomic shortageHealth educationHealth literacyIntervention (counseling)NursingHealth carePublic healthSurgeryGovernment (linguistics)Internal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hypertension is a significant problem in the United States, affecting 1 in 3 adults aged above 18 years and is associated with a higher risk for cardiovascular disease and stroke. The prevalence of hypertension has increased in medically underserved areas (MUAs). Mobile health technologies, such as digital self-monitoring devices, have been shown to improve the management of chronic health conditions. However, patients from MUAs have reduced access to these devices because of limited resources and low health literacy. Health coaches and peer training programs are a potentially cost-effective solution for the shortage of physicians available to manage hypertension in MUAs. Activating young people as student health coaches (SHCs) is a promising strategy to improve community health. OBJECTIVE: This pilot study aims to assess (1) the feasibility of training high school students as health technology coaches in MUAs and (2) whether the addition of SHCs to digital home monitoring improves the frequency of self-monitoring and overall blood pressure (BP) control. METHODS: In total, 15 high school students completed 3-day health coach training. Patients who had a documented diagnosis of hypertension were randomly assigned to 1 of the 3 intervention arms. The QardioArm alone (Q) group was provided a QardioArm cuff only for convenience. The SHC alone (S) group was instructed to meet with a health coach for 30 min once a week for 5 weeks to create action plans for reducing BP. The student+QardioArm (S+Q) group received both interventions. RESULTS: Participants (n=27) were randomly assigned to 3 groups in a ratio of 9:9:9. All 15 students completed training, of which 40% (6/15) of students completed all the 5 meetings with their assigned patient. Barriers to feasibility included transportation and patient response drop-off at the end of the study. Overall, 92% (11/12) of the students rated their experience as very good or higher and 69% (9/13) reported that this experience made them more likely to go into the medical field. There was a statistically significant difference in the frequency of cuff use (S+Q vs Q groups: 37 vs 17; P<.001). Participants in the S+Q group reported better BP control after the intervention compared with the other groups. The average BP at the end of the intervention was 145/84 (SD 9/18) mm Hg, 150/85 (SD 18/12) mm Hg, and 128/69 (SD 20/14) mm Hg in the Q, S, and S+Q groups, respectively. CONCLUSIONS: This pilot study demonstrates the feasibility of pairing technology with young student coaches, although challenges existed. The S+Q group used their cuff more than the Q group. Patients were more engaged in the S+Q group, reporting higher satisfaction with their SHC and better control of their BP.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.147
GPT teacher head0.522
Teacher spread0.374 · 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 designRandomized trial
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

Citations7
Published2020
Admission routes1
Has abstractyes

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