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Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study
Management of chronic diseases is complex and requires a long-term commitment to therapeutic medications. However, medication adherence is suboptimal. There is limited understanding of factors predicting medication adherence in chronic diseases in Oman. This study aimed to examine predictors of medi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151315/ https://www.ncbi.nlm.nih.gov/pubmed/37127692 http://dx.doi.org/10.1038/s41598-023-34393-4 |
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author | Al-Noumani, Huda Alharrasi, Maryam Lazarus, Eilean Rathinasamy Panchatcharam, Sathiya M. |
author_facet | Al-Noumani, Huda Alharrasi, Maryam Lazarus, Eilean Rathinasamy Panchatcharam, Sathiya M. |
author_sort | Al-Noumani, Huda |
collection | PubMed |
description | Management of chronic diseases is complex and requires a long-term commitment to therapeutic medications. However, medication adherence is suboptimal. There is limited understanding of factors predicting medication adherence in chronic diseases in Oman. This study aimed to examine predictors of medication adherence (i.e. patient clinical and demographic data, patient-physician relationship, health literacy, social support) among Omani patients with chronic diseases. This study used a cross-sectional correlation design. Data were collected from 800 participants using convenience sampling between December 2019 and April 2020. Arabic versions of the Brief Health Literacy Screening tool, Multidimensional Scale of Perceived Social Support, Patient-Doctor Relationship Questionnaire, and Adherence in Chronic Disease Scale were used to measure study variables. Descriptive statistics, independent t tests, one-way ANOVA, Pearson correlations, and multivariate linear regression were used for analysis. The study found that factors such as the patient-physician relationship, social support, disease duration, employment status, and medication frequency significantly predicted medication adherence. Medication adherence was higher among those who were unemployed, had a better patient-physician relationship, and greater social support. However, medication adherence was lower with longer disease duration and higher daily medication frequency. Additionally, medication adherence was positively associated with perceived social support and the patient-physician relationship, but not with health literacy. In conclusion, the study reveals that patient characteristics, social support, and patient-physician relationships are key factors in predicting medication adherence in patients with chronic diseases in the Middle East. It emphasizes the importance of improving these aspects, considering factors like employment status, disease duration, and medication frequency, and enhancing healthcare provider-patient relationships and social support systems to boost adherence. |
format | Online Article Text |
id | pubmed-10151315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101513152023-05-03 Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study Al-Noumani, Huda Alharrasi, Maryam Lazarus, Eilean Rathinasamy Panchatcharam, Sathiya M. Sci Rep Article Management of chronic diseases is complex and requires a long-term commitment to therapeutic medications. However, medication adherence is suboptimal. There is limited understanding of factors predicting medication adherence in chronic diseases in Oman. This study aimed to examine predictors of medication adherence (i.e. patient clinical and demographic data, patient-physician relationship, health literacy, social support) among Omani patients with chronic diseases. This study used a cross-sectional correlation design. Data were collected from 800 participants using convenience sampling between December 2019 and April 2020. Arabic versions of the Brief Health Literacy Screening tool, Multidimensional Scale of Perceived Social Support, Patient-Doctor Relationship Questionnaire, and Adherence in Chronic Disease Scale were used to measure study variables. Descriptive statistics, independent t tests, one-way ANOVA, Pearson correlations, and multivariate linear regression were used for analysis. The study found that factors such as the patient-physician relationship, social support, disease duration, employment status, and medication frequency significantly predicted medication adherence. Medication adherence was higher among those who were unemployed, had a better patient-physician relationship, and greater social support. However, medication adherence was lower with longer disease duration and higher daily medication frequency. Additionally, medication adherence was positively associated with perceived social support and the patient-physician relationship, but not with health literacy. In conclusion, the study reveals that patient characteristics, social support, and patient-physician relationships are key factors in predicting medication adherence in patients with chronic diseases in the Middle East. It emphasizes the importance of improving these aspects, considering factors like employment status, disease duration, and medication frequency, and enhancing healthcare provider-patient relationships and social support systems to boost adherence. Nature Publishing Group UK 2023-05-01 /pmc/articles/PMC10151315/ /pubmed/37127692 http://dx.doi.org/10.1038/s41598-023-34393-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Al-Noumani, Huda Alharrasi, Maryam Lazarus, Eilean Rathinasamy Panchatcharam, Sathiya M. Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title | Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title_full | Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title_fullStr | Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title_full_unstemmed | Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title_short | Factors predicting medication adherence among Omani patients with chronic diseases through a multicenter cross-sectional study |
title_sort | factors predicting medication adherence among omani patients with chronic diseases through a multicenter cross-sectional study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10151315/ https://www.ncbi.nlm.nih.gov/pubmed/37127692 http://dx.doi.org/10.1038/s41598-023-34393-4 |
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