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Record W4412992862 · doi:10.2196/75417

The Effect of Discharge Planning Videos and Booklets on Quality of Life Among Patients With Heart Failure: Quasi-Experimental Study

2025· article· en· W4412992862 on OpenAlexvenueno aff
Fitri Arofiati, Fuji Dwi Lestari, Henri Setiawan

Bibliographic record

VenueJMIR Cardio · 2025
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintQuality (philosophy)GerontologyPsychologyMedicineComputer scienceWorld Wide WebPhysics

Abstract

fetched live from OpenAlex

Background: Heart failure remains a major global health issue, significantly impacting patients' quality of life due to its chronic and progressive nature. Effective discharge planning, including educational interventions such as videos and booklets, plays a crucial role in enhancing self-care management and overall patient well-being. Objective: The aim of this study is to evaluate the effects of discharge planning videos and booklets on the quality of life of patients with heart failure. Methods: This study used a quasi-experimental design and was conducted at PKU Muhammadiyah Gamping Hospital from July to November 2024. A total of 42 participants who met the inclusion criteria were selected based on sample size calculations using G*Power and were evenly assigned to intervention and control groups. Both groups received standard discharge planning provided by health care professionals. Discharge planning videos and booklets were developed as educational tools for the intervention group. The Minnesota Living With Heart Failure Questionnaire was used to assess quality of life. The independent sample t test was used to analyze the effect of the intervention using SPSS (version 29). This study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and was approved by the institutional review board (number 150/KEP-PKU/VII/2024). Results: The intervention significantly improved the quality of life of patients with heart failure, with the mean score decreasing from 39.00 (SD 8.11) to 24.76 (SD 4.02; P<.001) in the intervention group. In contrast, the control group showed minimal change, from 39.90 (SD 5.89) to 40.24 (SD 5.84), resulting in a statistically significant between-group difference of 15.58 (P<.001). Furthermore, the effect size was large (Cohen d=3.09), suggesting a strong practical significance of the intervention in enhancing the quality of life among patients with heart failure. Moreover, the mean Minnesota Living With Heart Failure Questionnaire scores across 4 domains-physical, mental, emotional, and social-also showed significant improvements after the intervention. The intervention group experienced reductions in all domains: physical (9.95 to 6.76), mental (7.81 to 5.62), emotional (13.19 to 7.48), and social (8.05 to 4.90), whereas the control group showed minimal or no change. These results indicate that the intervention effectively improved patients' quality of life across multiple dimensions. Conclusions: Discharge planning through videos and booklets may improve the quality of life of patients with heart failure compared to standard care. These findings highlight the potential clinical value of structured patient education. The intervention appeared to enhance patients' understanding of their condition and support self-management behaviors, including adherence to lifestyle recommendations. However, they should be interpreted with caution and confirmed through further studies with larger and more diverse populations.

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)0.008
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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