Main Article Content

Models and theories of care applicable to predicting and improving adherence behaviours among Chronic Kidney Disease (CKD) patients


Geldine Chironda
Busisiwe Bhengu
Ancia Manwere

Abstract

Introduction: Models which relate to cognitive variables and processes may apply to adherence behaviours in patients with chronic conditions. Management of Chronic  Kidney Disease (CKD) requires incorporation of these models to facilitate the positive adherence behaviours among patients.
Aim: This article critiques models and theories of care applicable to predicting and improving adherence behaviours among patients with chronic kidney disease (CKD).
Methods: Medline, Ebsco, PubMed, Google Scholar, Cinahl and grey  literature were used to identify the relevant articles from the years  1990-2018. A conclusive search was done using the following key words: Models OR ‘Theories of care AND Prediction [tab]’ AND Improving AND adherence behaviours AND Chronic Kidney Disease
Results: The search identified 23 articles containing information on models and theories of care applicable to the management of CKD patients. The Common-Sense Model, Orem Self-care and Theory of Planned Behaviour Models were applied in predicting and improving adherence among CKD patients. Other identified possible models in predicting and improving adherence included Tran’s Theoretical Model, the Theory of Reasoned Action, and the Social Cognitive and Health Belief Models.
Conclusion: These theories and models provide a baseline assessment regarding predicting and improving adherence in chronic kidney disease patients. However, there is no model or theory which comprehensively explains an understanding of predicting and improving adherence of CKD patients to their management, hence the need to consider the available models/theories to effectively engage CKD patients with their integrated management to promote the highest level of adherent  behaviour.

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Key words: Models/theories of care, prediction, adherence, Chronic Kidney Disease stages 1 to 5.


Journal Identifiers


eISSN: 2616-9827
print ISSN: 2616-9819