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Record W3211595458 · doi:10.2196/32550

A Text Messaging–Enhanced Intervention for African American Patients With Heart Failure, Depression, and Anxiety (TXT COPE-HF): Protocol for a Pilot Feasibility Study

2021· article· en· W3211595458 on OpenAlexvenueno aff
Judith B. Cornelius, Charlene Whitaker‐Brown, Jaleesa Smoot, Sonia Hart, Zandria Lewis, Olivia Smith

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
FundersUniversity of North Carolina at Charlotte
KeywordsAnxietyDepression (economics)Intervention (counseling)MedicineQuality of life (healthcare)Ethnic groupCognitive behavioral therapyClinical psychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

BACKGROUND: African Americans have a higher incidence rate of heart failure (HF) and an earlier age of HF onset compared to those of other racial and ethnic groups. Scientific literature suggests that by 2030, African Americans will have a 30% increased prevalence rate of HF coupled with depression. In addition to depression, anxiety is a predictor of worsening functional capacity, decreased quality of life, and increased hospital readmission rates. There is no consensus on the best way to treat patients with HF, depression, and anxiety. One promising type of treatment-cognitive behavioral therapy (CBT)-has been shown to significantly improve patients' quality of life and treatment compliance, but CBT has not been used with SMS text messaging reminders to enhance the effect of reducing symptoms of depression and anxiety in racial and ethnic minority patients with HF. OBJECTIVE: The objectives of our study are to (1) adapt and modify the Creating Opportunities for Personal Empowerment (COPE) curriculum for delivery to patients with HF by using an SMS text messaging component to improve depression and anxiety symptoms, (2) administer the adapted intervention to 10 patients to examine the feasibility and acceptability of the approach and modify it as needed, and (3) examine trends in depression and anxiety symptoms postintervention. We hypothesize that patients will show an improvement in depression scores and anxiety symptoms postintervention. METHODS: The study will comprise a mixed methods approach. We will use the eight steps of the ADAPT-ITT (assessment, decision, administration, production, topical expert, integration, training, and testing) model to adapt the intervention. The first step in this feasibility study will involve assembling individuals from the target population (n=10) to discuss questions on a specific topic. In phase 2, we will examine the feasibility and acceptability of the enhanced SMS text messaging intervention (TXT COPE-HF [Texting With COPE for Patients With HF]) and its preliminary effects with 10 participants. The Beck Depression Inventory will be used to assess depression, the State-Trait Anxiety Inventory will be used to assess anxiety, and the Healthy Beliefs and Lifestyle Behavior surveys will be used to assess participants' lifestyle beliefs and behavior changes. Changes will be compared from baseline to end point by using paired 2-tailed t tests. An exit focus group (n=10) will be held to examine facilitators and barriers to the SMS text messaging protocol. RESULTS: The pilot feasibility study was funded by the Academy for Clinical Research and Scholarship. Institutional review board approval was obtained in April 2021. Data collection and analysis are expected to conclude by November 2021 and April 2022, respectively. CONCLUSIONS: The study results will add to the literature on the effectiveness of an SMS text messaging CBT-enhanced intervention in reducing depression and anxiety among African American patients with HF. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/32550.

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)0.021
metaresearch head score (Gemma)0.014
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: Non-randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.051
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.014
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0510.007

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 head0.151
GPT teacher head0.518
Teacher spread0.367 · 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
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

Citations3
Published2021
Admission routes1
Has abstractyes

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