Clinical InvestigationPredictors of Medication Adherence Using a Multidimensional Adherence Model in Patients With Heart Failure
Section snippets
Study Design
This study was conducted in the context of a prospective study in which we determined the role of medication adherence in determining the outcomes in patients with HF.43 In the current analysis, we determined whether factors making up the 5 dimensions of the MAM predicted medication adherence. At baseline, patients completed questionnaires and started medication adherence monitoring. Medication adherence was monitored for 3 months, and outcomes were tracked for 6 months.
Sample and Settings
Patients were enrolled
Sample Characteristics
A total of 134 patients with HF were included in this study. The mean age of patients in the sample was 61 ± 11 years. The most common HF cause was ischemic heart disease. The sample consisted largely of patients with advanced HF as reflected by their NYHA functional class. The average left ventricular ejection fraction reflected the enrollment of patients with and without systolic dysfunction. Only 9% of patients had no comorbidities, whereas the remainder had 1 or more, with 44% of patients
Discussion
This is the first study in patients with HF in which the factors hypothesized to contribute to adherence hypothesized in the MAM, a multivariate medication adherence model, were tested.40 The findings of this study supported some, but not all, of the relationships hypothesized in the MAM. In multivariate analyses, worse NYHA functional class, more barriers to medication adherence, minority ethnicity, lower financial status, and lack of perceived social support were related to objectively
Limitations
Although there was sufficient power to demonstrate significant differences, a larger sample size and more heterogeneous sample may be needed to more thoroughly investigate predictors of adherence and to generalize the result to a larger population.
Conclusions
Although patient adherence is often difficult for a variety of patient, provider, and health care system-related factors, it is essential to optimal patient outcomes. Thus, health care providers and researchers must identify ways to enhance medication adherence. One important way is to identify the factors that contribute to poor adherence.
Barriers to medication adherence were the only significant predictor of all 3 indicators of objectively measured adherence. In addition to barriers to
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Patient-centered mobile health technology intervention to improve self-care in patients with chronic heart failure: Protocol for a feasibility randomized controlled trial
2021, Contemporary Clinical TrialsCitation Excerpt :Data are collected for 1 HF medication for each patient because it is impractical and may be burdensome to patients to use multiple MEMS. Previous research has demonstrated that monitoring of 1 medication with the MEMS provides a valid indicator that patients took all of their medications even when they are prescribed multiple medications per day [78–81]. Three indicators of medication adherence are assessed with the MEMS: (1) dose-count (taking adherence), defined as the percentage of prescribed number of doses taken and measures deviation from the physician's prescription; (2) dose-days, defined as the percentage of days the correct number of doses were taken; and (3) dose-time (scheduling adherence), defined as the percentage of doses taken on schedule.
Adherence to diet and medication and the associated factors among patient with chronic heart failure in a multi-ethnic society
2020, Heart and LungCitation Excerpt :The lack of encouragement and support would result in a poorer self-efficacy to manage their condition.42 Previous studies have demonstrated that factors such as minority ethnicity, lack of social support, lower income, older age, higher comorbidities and worse NYHA classification were predictors of poorer medication adherence.18,42 However, this was inconsistent with the findings of our study, whereby non-MI causes of CHF, absence of hypertension and moderately diminished EF were identified as predictors of poorer medication adherence.
This study was supported by funding from the Philips Medical-American Association of Critical Care Nurses Outcomes Grant; University of Kentucky General Clinical Research Center (M01RR02602), and Gill Endowment. Also the project was supported by grant number R01 NR008567 from the National Institute of Nursing Research. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Nursing Research or the National Institutes of Health.