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Record W4394586677 · doi:10.2196/54010

Exploring the Acceptability and Feasibility of Remote Blood Pressure Measurements and Cognition Assessments Among Participants Recruited From a Safety-Net Emergency Department (Reach Out Cognition): Nonrandomized Mobile Health Trial

2024· article· en· W4394586677 on OpenAlexvenueno aff
Mackenzie Dinh, Chun Chieh Lin, Candace Whitfield, Zahera Farhan, William J. Meurer, Sarah Bailey, Lesli E. Skolarus

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Institutes of Health
KeywordsCognitionEmergency departmentMedicineSafety netBlood pressureMedical emergencyGerontologyEnvironmental healthPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Hypertension is a prevalent cardiovascular risk factor disproportionately affecting Black Americans, who also experience a higher incidence of Alzheimer disease and Alzheimer disease-related dementias. Monitoring blood pressure (BP) and cognition may be important strategies in reducing these disparities. OBJECTIVE: The objective of the Reach Out Cognition study was to explore the feasibility and acceptability of remote cognitive and BP assessments in a predominantly Black, low-income population. METHODS: Reach Out was a randomized, controlled, mobile health-based clinical trial to reduce BP among patients with hypertension at an emergency department in a safety-net hospital (ie, a US hospital in which 25% of the patients are Medicaid recipients). Upon conclusion of Reach Out, participants were given the option of continuing into an extension phase, Reach Out Cognition, that included Bluetooth-enabled BP monitoring and digital cognitive assessments for 6 months. Digital cognitive assessments were text message-linked online surveys of the Self-Administered Gerocognitive Exam and Quality of Life in Neurological Disorders scale. BP assessments were measured with Bluetooth-enabled BP cuffs paired with an app and the data were manually sent to the research team. Outcomes were feasibility (ie, enrollment and 3- and 6-month completion of digital cognitive and BP assessments) and acceptability of assessments using a 4-item validated survey, ranging from 1 (not acceptable) to 5 (completely acceptable). RESULTS: Of the 211 Reach Out participants, 107 (50.7%) consented and 71 (33.6%) completed enrollment in Reach Out Cognition. Participants had a mean age of 49.9 years; 70.4% were female and 57.8% identified as Black. Among the 71 participants, 51 (72%) completed cognitive assessments at 3 months and 34 (48%) completed these assessments at 6 months. BP assessments were completed by 37 (52%) and 20 (28%) of the 71 participants at 3 and 6 months, respectively. Participants were neutral on the acceptability of the digital cognitive assessments (mean 3.7) and Bluetooth self-measured BP (SMBP) monitoring (mean 3.9). Participants noted challenges syncing the BP cuff to the app, internet connection, and transmitting the data to the research team. CONCLUSIONS: Enrollment and assessment completion were low, while acceptability was moderate. Technological advances will eliminate some of the Bluetooth SMBP barriers and offer new strategies for cognitive assessments. Subsequent studies could benefit from offering more comprehensive support to overcome Bluetooth-related hurdles, such as personalized training materials, video conferencing, or in-person research team support. Alternatively, strategies that do not require pairing with an app and passive transmission of data could be considered. Overall, further research is warranted to optimize participant engagement and overcome technological challenges. TRIAL REGISTRATION: ClinicalTrials.gov NCT03422718; https://clinicaltrials.gov/study/NCT03422718.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.326
GPT teacher head0.499
Teacher spread0.172 · 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 designNon-randomized 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

Citations1
Published2024
Admission routes1
Has abstractyes

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