Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573296
Title: Psychological adjustment to the onset of rheumatoid arthritis : a longitudinal evaluation of perceptions of, and adherence to, medication
Author: Hughes, Lyndsay Dawn
Awarding Body: University of Hertfordshire
Current Institution: University of Hertfordshire
Date of Award: 2012
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Abstract:
Rheumatoid arthritis (RA) is a chronic, progressive autoimmune disease causing inflammation of the synovium resulting in severe pain, joint disfigurement and disability as well as malaise, fatigue and a depressed immune system. Treatment consists of three broad phases; firstly, following diagnosis treatment is focussed on rapid reduction of pain and inflammation. Secondly, maintenance of quiescence is sought through medication. Finally, if disease activity remains high despite medication, escalation to anti-TNF α therapy is required to prevent permanent joint damage and disability. The primary course of treatment is prescription of disease modifying anti-rheumatic drugs (DMARDs) within 3 months of onset of symptoms. However, DMARDs can take 8-12 weeks to exhibit a noticeable benefit whereas unpleasant side effects can occur shortly after initiation. Also, DMARDs do not alleviate pain; therefore it is difficult for patients to attribute recovery to this medication. For these reasons, although it is imperative for future health and functioning to take DMARDs as prescribed, non-adherence is common at 30-50%. Non-adherence to treatment can be intentional, where a decision is made not to conform to the prescription, or unintentional which is often due to forgetting. To measure intentional non-adherence, a validated measure of adherence for rheumatoid arthritis was reduced through exploratory factor analysis from 19 items to 5 items by removing items that did not add to the explained variance of adherence. The CQR5 explained 53% of the variance in adherence and was shown to have a good fit to the data through confirmatory factor analysis. A discriminant function equation was generated that correctly identifies 88.5% of patients as high or low adherers and has high clinical utility due to the brevity for patients and unidimensionality for easy interpretation. The CQR5 was used throughout the programme of research to measure intentional non-adherence along with a separate measure of unintentional non-adherence. Four commonly used social cognition models of illness were measured in 227 RA patients to determine which had the best utility for predicting non-adherence to DMARDs. Patients were recruited to represent the three stages of illness including newly diagnosed, established on DMARD therapy and established with concurrent anti-TNF α therapy. Logistic regression analysis showed that the Self Regulatory Model best predicted intentional non-adherence as patients with perceptions of worse consequences of RA and longer disease duration were more likely to be highly adherent to DMARDs in cross-sectional analysis. In contrast, the Theory of Planned Behaviour better predicted patients who self-reported forgetting their DMARDs with patients with more confidence in being able to take their medications (Perceived Behavioural Control) being less likely to forget. 171 patients were successfully followed-up six months after baseline recruitment. The longitudinal results showed that the social cognition models differed for patients at different stages of the illness suggesting that their experience of living with rheumatoid arthritis influenced perceptions of their illness and medications. Newly diagnosed patients scored lower on factors measuring perceptions of disease chronicity and seriousness whereas patients that had escalated to anti-TNF α therapy scored higher on these factors. The newly diagnosed patients also showed more variability in the social cognition scores whereas the more established patients demonstrated stable models of illness. This supports Leventhal’s (1992) theory that illness representations will be regulated through integration of knowledge and experience of an illness. Structural equation modelling was used to establish the best predictors of intentional non-adherence at six month follow-up. In support of research in other chronic illnesses (Horne & Weinman, 2002; Niklas, Dunbar & Wild, 2010), the effect of perceptions of the consequences and chronicity of the illness on adherence are mediated by perceptions of the necessity of the medication. In addition, the impact of the emotional reaction to the illness on adherence to DMARDs is mediated by concerns about the medication. In addition, this study incorporated factors from the Theory of Planned Behaviour to explain medication adherence and found that the influence of friends and family impacts on the patient’s confidence to follow the prescription accurately which in turn as an effect on adherence to DMARDs. This large longitudinal study found that by combining factors from a number of social cognition models, it is possible to explain and predict intentional non-adherence and provides some evidence for best ways to intervene to improve adherence and prognosis. To provide a more comprehensive and clinically useful picture of non-adherence, a Cost of Illness study was carried which found that patients self-reporting low adherence to DMARDs also had significantly higher costs for this medication. This was caused by an increased incidence of Leflunamide prescribing for patients who often forget their medication and was maintained longitudinally. This association has not been previously reported in the literature and provides some evidence that non-adherence to DMARDs is having a concrete effect on the clinical management of patients. Finally, an SMS text message based reminder service designed to remind patients who self-report forgetting their medications was tested through a simulation study for the cost and likely benefit in health related quality of life using the health economic analysis of the longitudinal study and the results of a survey establishing the feasibility of implementing such a service in the rheumatology clinic. A sensitivity analysis testing the number of messages sent and the cost per message found that a reminder service for the sample of patients in this programme of research would cost between £1387.00 and £142.27 per year. This would equate to a cost per Quality Adjusted Life Year (QALY) gain of between £2889.58 and £296.40 by enabling patients to adhere more rigorously to their DMARD regimen. This programme of research is the first to test four commonly used social cognition models to predict adherence to DMARDs in a large, multi-centre longitudinal study of rheumatoid arthritis patients. Perceptions of the likely duration and consequences of the illness, as measured by the Illness Perceptions Questionnaire and the necessity of medications (measured by the Beliefs about Medications Questionnaire) along with self-efficacy (measured by the Theory of Planned Behaviour) explained 24% of the variance in intentional adherence over six months. The results show the importance of considering intentional and unintentional non-adherence separately as they appear to have different underlying mechanisms as well as patients in different phases of the illness as their experience influences their social cognition models of illness. A simple SMS based reminder service could act as a cue to action to reduce unintentional non-adherence whereas addressing issues surrounding maladaptive perceptions about the illness and the treatment could improve intentional non-adherence which has the potential to improve the prognosis and quality of life for patients as well as safe costs for the NHS.
Supervisor: Not available Sponsor: ESRC
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.573296  DOI: Not available
Keywords: medication adherence ; rheumatoid arthritis ; social cognition models ; self regulation models ; illness perceptions ; health belief model ; theory of planned behaviour ; chronic illness ; self management ; self-management ; self-regulation model
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