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Examining the underlying processes of different dimensions of self-care behavior among persons with heart failure

PURPOSE: This study was conducted to compare how potential predictors differentially contribute to the different dimensions of self-care in persons with heart failure. METHODS: A cross-sectional design was used in this study. Purposive sampling was used to recruit a sample (N = 252) in this study. T...

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Detalles Bibliográficos
Autores principales: Al-Hammouri, Mohammed Munther, Rababah, Jehad A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9541077/
https://www.ncbi.nlm.nih.gov/pubmed/36203162
http://dx.doi.org/10.1186/s12872-022-02876-3
Descripción
Sumario:PURPOSE: This study was conducted to compare how potential predictors differentially contribute to the different dimensions of self-care in persons with heart failure. METHODS: A cross-sectional design was used in this study. Purposive sampling was used to recruit a sample (N = 252) in this study. The data were collected from three major referral hospitals in Jordan. Data analysis was performed using multiple linear regression. RESULTS: The results showed that around 89% of our sample had insufficiency in at least one dimension of self-care. The initial regression models showed that different combinations of predictors were significantly associated with different dimensions of self-care. These models were also different in terms of the variance explained and the coefficients of the significant predictors. After the follow-up analysis of the best fit models for these dimensions, these differences were maintained. CONCLUSION: Despite the similarities in the proposed variables predicting different self-care dimensions, their differences may suggest variations in the underlying processes controlling different self-care dimensions. The current study showed that seven out of the nine proposed variables predicted different dimensions of self-care either in the initial or best-fit models.