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Disease management knowledge by cardiac rehabilitation use globally and its correlates

2025· article· en· W7127615328 on OpenAlexaff
G Ghisi, S S Shaubnaum, M S Q Farias, P Chokalingam, N D Gaye, S L Grace

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsYork UniversityUniversity Health Network
Fundersnot available
KeywordsRehabilitationObservational studyDisease managementDiseaseMEDLINEProspective cohort studyHeart diseaseKnowledge level

Abstract

fetched live from OpenAlex

Abstract Background/Introduction Cardiovascular diseases (CVD) remain a leading cause of morbidity and mortality worldwide, with an increasing prevalence in low- and middle-income countries (LMICs). Cardiac rehab (CR) fosters disease management knowledge critical for empowering patients to achieve sustained secondary prevention. Purpose To investigate: (1) CVD management knowledge following CR, including variations by region, country income classification, and patient sociodemographic and clinical characteristics; (2) the association of post-CR knowledge with the number of CR sessions completed and setting of CR delivery; and (3) whether domain-specific knowledge post-CR is associated with corresponding health behaviors. Methods Secondary analysis of ICCPR’s International Cardiac Rehabilitation Registry (ICRR) data (to May 2024) was conducted for this prospective observational study. Eligible patients included those receiving phase II outpatient CR, with pre-program assessment completed at least 6 months prior. Knowledge was assessed via patient report at follow-up/post-program in 9 domains (y/n), as well as heart-health behaviors and work status. Programs reported CR use, functional capacity, and CV risk factors. Results 4,309 patients (mean age 60.2±32.2, 79% male) were included, from >20 programs, with 3,729 (86.5%) patients having any follow-up data (n=2,342 completed CR). Overall knowledge was high (90.0%), and greatest with regard to return to life roles, cardiac medications, and knowing care plan sent to primary (Figure 1). Overall knowledge varied significantly by country income classification (p<0.001), with lower knowledge levels observed in lower-middle-income countries. It also varied by region (p<0.001) with the highest overall knowledge scores observed in the Western Pacific, particularly in return to life roles, as well as lipid and blood pressure control, while the lowest scores were found in the African region, notably in lipid control, nutrition, and chest pain response (Figure 1). Male sex and higher educational attainment were associated with greater overall knowledge (both p<0.05). CR completion and greater sessions attended correlated with higher knowledge (p<0.01), particularly with hybrid delivery (p<0.001). Knowledge in specific domains correlated with applicable health behaviors: nutrition knowledge was associated with greater fruit and vegetable intake (p<0.01), medication knowledge with adherence (p<0.05), and exercise knowledge with physical activity (p<0.01). Conclusion(s) CR participants in diverse settings globally have high disease management knowledge, with variations based on regional, economic, and patient-level factors. Greater CR exposure was associated with higher knowledge acquisition, particularly in in-person models. Knowledge was associated with corresponding heart-health behaviors, which can support optimal patient outcomes.Figure 1

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.336
Teacher spread0.317 · 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 designObservational
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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