Adherence and Decision Aids: A Model and a Narrative Review

Department of Radiation Oncology, Department of Epidemiology, Biostatistics and HTA, Radboud University Nijmegen Medical Center, Nijmegen, The Netherlands.
Medical Decision Making (Impact Factor: 3.24). 02/2010; 31(1):121-9. DOI: 10.1177/0272989X10370487
Source: PubMed


Patient adherence to medication or lifestyle interventions is a serious concern. Interventions to improve adherence exist, but their effects are usually small. Several authors suggested that decision aids may positively affect adherence.
. This presentation examines the role of decision aids in adherence research through development of a model and a narrative review.
. I: A model was developed to organize pathways relating decision aids and adherence. There is clinical evidence for these pathways, suggesting that decision aids may potentially improve adherence. The model is helpful when considering measures to study decision aids and adherence. II: A narrative review of decision aids and adherence was done. A systematic search resulted in 11 randomized studies. Two studies, both in the hypertension management domain, were positive. Shortcomings were identified regarding the range of adherence measures, the sample size, and the
It is argued that outcomes for the option "nonadherent" behavior should be described explicitly in the decision aid to inform patients about the costs and benefits of nonadherent behavior.
. A relation between decision aids and adherence is plausible in view of the psychological and medical literature. A systematic search showed that experimental evidence relating decision aids and adherence is inconclusive. Rigorous trials on this topic are worthwhile. Such trials should employ adequate sample sizes, multiple adherence measures, and a control arm delivering usual care. The decision aid should describe the option "being nonadherent" and its outcomes.

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    • "[27] Systematic reviews have shown that decision aids improve patient knowledge and values clarity, and increase the likelihood of making decisions [27]. However, few decision aids have been studied in the adherence context [26,28-31]. Further, none to our knowledge has focused on the choice among several similarly effective medications to reduce CHD risk, helped patients to clarify their values and communicate their treatment preferences to their provider, or coupled decision-making with tailored messages to overcome barriers to adherence. "
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    ABSTRACT: Decision aids offer promise as a practical solution to improve patient decision making about coronary heart disease (CHD) prevention medications and help patients choose medications to which they are likely to adhere. However, little data is available on decision aids designed to promote adherence. In this paper, we report on secondary analyses of a randomized trial of a CHD adherence intervention (second generation decision aid plus tailored messages) versus usual care in an effort to understand how the decision aid facilitates adherence. We focus on data collected from the primary study visit, when intervention participants presented 45 minutes early to a previously scheduled provider visit; viewed the decision aid, indicating their intent for CHD risk reduction after each decision aid component (individualized risk assessment and education, values clarification, and coaching); and filled out a post-decision aid survey assessing their knowledge, perceived risk, decisional conflict, and intent for CHD risk reduction. Control participants did not present early and received usual care from their provider. Following the provider visit, participants in both groups completed post-visit surveys assessing the number and quality of CHD discussions with their provider, their intent for CHD risk reduction, and their feelings about the decision aid. We enrolled 160 patients into our study (81 intervention, 79 control). Within the decision aid group, the decision aid significantly increased knowledge of effective CHD prevention strategies (+21 percentage points; adjusted p<.0001) and the accuracy of perceived CHD risk (+33 percentage points; adjusted p<.0001), and significantly decreased decisional conflict (-0.63; adjusted p<.0001). Comparing between study groups, the decision aid also significantly increased CHD prevention discussions with providers (+31 percentage points; adjusted p<.0001) and improved perceptions of some features of patient-provider interactions. Further, it increased participants' intentions for any effective CHD risk reducing strategies (+21 percentage points; 95% CI 5 to 37 percentage points), with a majority of the effect from the educational component of the decision aid. Ninety-nine percent of participants found the decision aid easy to understand and 93% felt it easy to use. Decision aids can play an important role in improving decisions about CHD prevention and increasing patient-provider discussions and intent to reduce CHD risk.
    BMC Medical Informatics and Decision Making 02/2014; 14(1):14. DOI:10.1186/1472-6947-14-14 · 1.83 Impact Factor
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    ABSTRACT: The purpose of this study was to examine the relationship between shared decision-making (SDM) and satisfaction with decision (SWD) within a larger survey of patient decision-making in health care consultations. A randomly selected age-proportionate national sample of adults (aged 21-70 years) stratified on race, ethnicity, and gender (N=488) was recruited from a health research volunteer registry and completed an online survey with reference to a recent health consultation. Measures included the shared decision making-9 questionnaire (SDM-Q-9), Satisfaction With Decision (SWD) scale, sociodemographic, health, and other standardized decision-making measures. Forward selection weighted multiple regression analysis was used to model correlates of SWD. After controlling for sociodemographic variables, SDM-Q-9 total score was associated with SWD, adjusted R(2)=.368, p<.001. Three of nine SDM-Q-9 items accounted for significant proportions of variance in SWD. SDM was positively associated with SWD and was strongest for three areas of SDM: patients being helped in a health care consultation with understanding information, with treatment preference elicitation, and with weighing options thoroughly. By identifying variables such as SDM that are associated with SWD, health care interventions can better target modifiable factors to enhance satisfaction and other outcomes.
    Patient Education and Counseling 03/2012; 88(1):100-5. DOI:10.1016/j.pec.2012.02.010 · 2.20 Impact Factor
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    ABSTRACT: Nonadherence to prescription medications has been shown to be significantly influenced by three key medication-specific beliefs: patients' perceived need for the prescribed medication, their concerns about the prescribed medication, and perceived medication affordability. Structural equation modeling was used to test the predictors of these three proximal determinants of medication adherence using the proximal-distal continuum of adherence drivers as the organizing conceptual framework. In Spring 2008, survey participants were selected from the Harris Interactive Chronic Illness Panel, an internet-based panel of hundreds of thousands of adults with chronic disease. Respondents were eligible for the survey if they were aged 40 years and older, resided in the US, and reported having at least one of six chronic diseases: asthma, diabetes, hyperlipidemia, hypertension, osteoporosis, or other cardiovascular disease. A final sample size of 1072 was achieved. The proximal medication beliefs were measured by three multi-item scales: perceived need for medications, perceived medication concerns, and perceived medication affordability. The intermediate sociomedical beliefs and skills included four multi-item scales: perceived disease severity, knowledge about the prescribed medication, perceived immunity to side effects, and perceived value of nutraceuticals. Generic health beliefs and skills consisted of patient engagement in their care, health information-seeking tendencies, internal health locus of control, a single-item measure of self-rated health, and general mental health. Structural equation modeling was used to model proximal-distal continuum of adherence drivers. The average age was 58 years (range = 40-90 years), and 65% were female and 89% were white. Forty-one percent had at least a four-year college education, and just under half (45%) had an annual income of $50,000 or more. Hypertension and hyperlipidemia were each reported by about a quarter of respondents (24% and 23%, respectively). A smaller percentage of respondents had osteoporosis (17%), diabetes (15%), asthma (13%), or other cardiovascular disease (8%). Three independent variables were significantly associated with the three proximal adherence drivers: perceived disease severity, knowledge about the medication, and perceived value of nutraceuticals. Both perceived immunity to side effects and patient engagement was significantly associated with perceived need for medications and perceived medication concerns. Testing the proximal-distal continuum of adherence drivers shed light on specific areas where adherence dialogue and enhancement should focus. Our results can help to inform the design of future adherence interventions as well as the content of patient education materials and adherence reminder letters. For long-term medication adherence, patients need to autonomously and intrinsically commit to therapy and that, in turn, is more likely to occur if they are both informed (disease and medication knowledge and rationale, disease severity, consequences of nonadherence, and side effects) and motivated (engaged in their care, perceive a need for medication, and believe the benefits outweigh the risks).
    Patient Preference and Adherence 11/2012; 6:789-804. DOI:10.2147/PPA.S36535 · 1.68 Impact Factor
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