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Abstract 13020: A Pilot Randomized Controlled Trial on a Technology-Based Family-Centered Empowerment Transitional Program Among Heart Failure Patients

2022· article· en· W4380795302 on OpenAlexaboutno aff
Doris Sau Fung Yu, Chi Wing Wong, Victor Cheng

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRandomized controlled trialEmpowermentHeart failurePhysical therapyNursingFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Heart failure is the most common cause of hospital readmission. While the increased acceptance of mobile health, a novel virtual platform, titled Technology-based Family-centered Empowerment (T-FAME) Program was developed to enhance post-discharge outcomes of HF patients. Hypothesis: The T-FAME was more effective than usual care to enhance self-care, perceived control, HRQoL, and family function of post-discharge HF patients . Methods: From Aug2021 to Jan2022, a pilot RCT randomized 60 post-discharge HF patients (mean age=66.4, SD=2.1; NYHA Grade II/III: 70/30%) to T-FAME or usual care. The 12-week nurse-delivered T-FAME comprises an interactive mobile Apps, which allows i) daily self symptom and clinical monitoring with tailored autonomous nurses' feedback , ii) family-centered goal-setting and attainment plan on self-care , iii) personalized drug education, iv) real-time live chat with nurse, v) video-based HF compass for information navigation. The nurse visited the family for three times and conducted the Calgary family assessment and counseling. Outcome evaluation was done by Self-care Heart Failure Index, Self-care Self-efficacy Scale, Control Attitude Scale, Family Functioning Device, and Minnesota Living with Heart Failure Questionnaire at baseline and post-test. The patients and primary caregivers were interviewed for engagement experience in T-FAME. Results: Two-way analysis of covariance showed significant greater improvement of T-FAME group on self-care maintenance (F=4.762,p=0.033, ƞp2=0.08), management (F=18.223,p<0.001,ƞp2=0.249) and symptom perception (F=9.33,p=0.003, ƞp2=0.145). They reported non-significant greater improvement in self-care self-efficacy, perceived control and HRQL with ƞp2=0.048-0.05, indicating almost medium effect in pilot trial. More families receiving T-FAME reported non-problematic family function (50%) than the control group (16.7%) (p=0.013). The qualitative findings indicated T-FAME is highly acceptable to enable proactive person-centered disease monitoring and management. Conclusions: The preliminary health benefits of T-FAME supports its stringent cost-benefit evaluation in full-scale RCT.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized triallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Randomized trialmedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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