Improving the measurement of self-reported medication nonadherence: Response to Authors

Morisky, Donald E.; DiMatteo, M. Robin

doi:10.1016/j.jclinepi.2010.09.002

Cited by 311 publications

(220 citation statements)

References 13 publications

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“…Though some have questioned the correlation between self-reported and objective medication adherence, 23,24 self-reporting remains among the most common strategies for assessing medication adherence because it is simple and reliable. [25][26][27] Factors Not Associated with Medication Adherence Importantly, we found that several potential modifying factors may not be associated with medication nonadherence in the post-myocardial infarction patient population. Although factors such as financial insecurity, low health literacy, depression, and self-identification as nervous or tense had significant or near-significant bivariate associations with medication non-adherence, all of these lost significance on multivariable modeling.…”

Section: Younger Agementioning

confidence: 85%

“…As mentioned above, our measure of stroke-related worry has potential limitations, as does our medication adherence measure. Although the adherence measure is validated, 21,26,27 it may be susceptible to social desirability bias as a self-reported measure. Further, our adherence measure assessed general medication-taking behavior and did not distinguish between specific medication classes for which adherence may differ.…”

Section: Limitationsmentioning

confidence: 99%

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Medication Non-Adherence After Myocardial Infarction: An Exploration of Modifying Factors

et al. 2014

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BACKGROUND: Medication non-adherence is a major impediment to the management of cardiovascular disease risk factors. A better understanding of the modifying factors underlying medication non-adherence among individuals with known cardiovascular disease may inform approaches for addressing non-adherence. OBJECTIVE: The purpose of this study was to identify demographic and patient characteristics, medical comorbidities, psychosocial factors, and health belief-related factors associated with medication non-adherence among patients with known cardiovascular disease. DESIGN: We performed secondary analysis of baseline data from a randomized trial. PATIENTS: The study included 405 patients with a diagnosis of hypertension and history of acute myocardial infarction that was diagnosed within a three-year period prior to enrollment. MAIN MEASURES: Baseline demographics and patient characteristics, medical comorbidities, psychosocial factors, health belief-related factors, and patient-reported medication non-adherence were analyzed. KEY RESULTS: Of 405 patients, 173 (42.7 %) reported medication non-adherence. Factors associated with nonadherence in bivariate analysis included younger age, non-white race, having less than 12 years of education, smoking, financial insecurity, identifying as nervous or tense, higher life chaos score, greater worry about having a myocardial infarction, and greater worry about having a stroke. Using multivariable modeling, we determined that age (OR 0.97 per additional year, 95 % CI, 0.95-0.99), life chaos (OR 1.06 per additional point, 95 % CI, 1.00-1.11), and worry about stroke (OR 1.12 per additional point, 95 % CI, 1.01-1.25) remained significantly associated with self-reported medication non-adherence. CONCLUSIONS: We found that worry about having a stroke, higher life chaos, and younger age were all significantly associated with self-reported medication nonadherence in patients with cardiovascular disease and a history of myocardial infarction. Further research exploring these factors as targets for intervention is needed, as is additional research examining modifiable causes of medication non-adherence among patients with cardiovascular disease.KEY WORDS: cardiovascular disease; medication non-adherence; psychosocial; health beliefs.

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Section: Younger Agementioning

confidence: 85%

Section: Limitationsmentioning

confidence: 99%

Medication Non-Adherence After Myocardial Infarction: An Exploration of Modifying Factors

et al. 2014

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“…The MMAS-8 is a simple, reliable, and widely used instrument for determining adherence to prescribed medications [22]. The MMAS-8 has been demonstrated to be useful in identifying low adherence in clinical settings [23].…”

Section: Methodsmentioning

confidence: 99%

Adherence and perceived barriers to oral antiviral therapy for chronic hepatitis B

Liu

Farazi

et al. 2018

Global Health Action

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Background: Globally, of the 248 million people chronically infected with the hepatitis B virus (HBV), 74 million reside in China. Five oral nucleot(s)ide analogs (NUCs) have been approved for the treatment of chronic hepatitis B (CHB) in China. Objectives: The aims of this study were to determine rates of adherence to NUC therapy in patients with CHB, to identify the self-perceived barriers to adherence, and to examine the factors associated with adherence. Methods: Questionnaire-based interviews were administered among Chinese patients with CHB at hepatology clinics of a tertiary hospital in the city of Wuhan, China. Adults aged 18 years or older prescribed with NUCs were recruited and interviewed to complete a 27-item questionnaire in a private setting, and adherence was measured using the Morisky Medication Adherence Scale (MMAS-8). Results: Among 369 participants, only 16.5% had high adherence (score of 8), 32.2% had medium adherence (score of 6 to <8), and 51.2% were measured with low adherence (score of <6). A logistic regression model was used to determine the factors associated with medication adherence. Significant predictors of high adherence consisted of urban residency, non-cirrhotic status, not using prescribed pills other than HBV medications, and reminders from family members. The five most common reasons for skipping NUCs were that medication(s) are expensive (48.7%), forgetfulness (45.1%), have experienced or worry about potential side effects (19.8%), do not want others to know about my medication(s) usage (18.5%), and ran out of pills and do not have time to refill (15.9%). Conclusions: This study revealed that adherence rates to oral antiviral therapy were far from optimal. This finding should generate public attention, and it would be beneficial for interventional programs to target Chinese patients from rural regions, as well as patients with low socioeconomic status, cirrhosis, and taking multiple medications.

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“…A secondary outcome was self-reported adherence measured using the Morisky Medication Adherence Scale (MMAS), a validated 8-item self-reported adherence questionnaire. [22][23][24] The MMAS was administered at enrollment and end of study. At enrollment, participants also completed the Zimet Multidimensional Scale of Perceived Social Support.…”

Section: Outcomes and Measurementmentioning

confidence: 99%

Two Randomized Controlled Pilot Trials of Social Forces to Improve Statin Adherence among Patients with Diabetes

et al. 2015

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BACKGROUND:Medication nonadherence is an important obstacle to cardiovascular disease management. OBJECTIVE: To improve adherence through real-time feedback based on theories of how social forces influence behavior. DESIGN: Two randomized controlled pilot trials called PROMOTE and SUPPORT. Participants stored statin medication in wireless-enabled pill bottles that transmitted adherence data to researchers. PARTICIPANTS: Adults with diabetes and a history of low statin adherence based on pharmacy refills (i.e., Medication Possession Ratio [MPR] <80 % in the prerandomization screening period). INTERVENTION: In PROMOTE, each participant was randomized to 1) weekly messages in which that participant's statin adherence was compared to that of other participants (comparison), 2) weekly summaries of that participant's statin adherence (summary), or 3) control. In SUPPORT, each participant identified another person (the Medication Adherence Partner [MAP]) to receive reports about that participant's adherence, and was randomized to 1) daily reports to MAP, 2) weekly reports to MAP, 3) reports to MAP only if dose was missed, or 4) control. MAIN OUTCOMES MEASURE: Adherence measured by pill bottle. KEY RESULTS: Among 45,000 health plan members contacted by mail, <1 % joined the trial. Participants had low baseline MPRs (median=60 %, IQR 41-72 %) but high pill-bottle adherence (90 % in PROMOTE, 92 % in SUP-PORT) during the trial. In PROMOTE (n=201) and SUP-PORT (n=200), no intervention demonstrated significantly better adherence vs. control. In a subgroup of PRO-MOTE participants with the lowest pre-study MPR, pillbottle-measured adherence in the comparison arm (89 %) was higher than the control (86 %) and summary (76 %) arms, but differences were non-significant (p=0.10). CONCLUSIONS:Interventions based on social forces did not improve medication adherence vs. control over a 3-month period. Given the low percentage of invited individuals who enrolled, the studies may have attracted participants who required little encouragement to improve adherence other than study participation.

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Improving the measurement of self-reported medication nonadherence: Response to Authors

Cited by 311 publications

References 13 publications

Medication Non-Adherence After Myocardial Infarction: An Exploration of Modifying Factors

Medication Non-Adherence After Myocardial Infarction: An Exploration of Modifying Factors

Adherence and perceived barriers to oral antiviral therapy for chronic hepatitis B

Two Randomized Controlled Pilot Trials of Social Forces to Improve Statin Adherence among Patients with Diabetes

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