Medication Beliefs and Antihypertensive Adherence Among Older Adults: A Pilot Study

FEATURE ARTICLE Medication Beliefs and Antihypertensive Adherence Among Older Adults: A Pilot Study Todd M. Ruppar, PhD, RN, GCNS-BC Fabienne Dobbels, PhD Sabina De Geest, PhD, RN, FRCN, FAAN

Older adults with hypertension are dependent on medication to control blood pressure and reduce risk for cardiovascular disease and renal impairment. Unfortunately, adherence to antihypertensive regimens remains low. This pilot study examines the relation among medication beliefs, demographic variables, and antihypertensive medication adherence in a sample of older adults (median age 5 74 years). Medication beliefs were measured using the Beliefs About Medicines Questionnaire (BMQ), and medication adherence was measured by electronic monitoring. Among study participants (n 5 33), concerns about medications were found to be related to poorer antihypertensive adherence. In particular, older adults with lower medication adherence were concerned about dependency and long-term effects from their medications. When controlling for other factors that may influence antihypertensive adherence, beliefs about medication necessity were related to adherence (odds ratio: 2.027, 95% confidence interval: 1.10-3.75). (Geriatr Nurs 2012;33:89-95) dherence to antihypertensive medication regimens is an essential health behavior to control blood pressure (BP) effectively and reduce older adults’ risk for severe cardiovascular disease.1-3 Uncontrolled hypertension increases the risk for heart attack, stroke, heart failure, and renal disease.1-3 In contrast, controlled blood pressure has been associated with a greater probability of living to age 85 and of reaching age 85 without major health problems.4 Recent consensus guidelines from the American College of Cardiology Foundation and the American Heart Association reinforce the need for management of BP as people age to prevent or limit target organ damage from sustained hypertension while avoiding overcontrol

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that may lead to symptoms associated with hypotension.5 This requires careful selection and titration of an antihypertensive regimen by the health care provider coupled with reliable medication adherence behavior by the patient. Unfortunately, medication adherence (MA) in most, if not all, chronic illnesses tends to be low, with reported mean adherence rates ranging from 26% to 85%.6-9 Many health behavior theories identify beliefs as a powerful predictor of behavior (e.g., Theory of Planned Behavior, Health Belief Model, Integrated Model of Behavior Prediction, Self-Regulation Model). Previous studies of the role of medication beliefs on adherence found that greater concerns about medications are associated with poorer adherence.10-12 Weaker beliefs in the necessity of medications have been found to be related to lower adherence10-12; but in 1 study, this was true only for intentional nonadherence, in which patients made a decision not to follow their prescribed regimen, and not for unintentional nonadherence, in which patients intend to adhere to their regimen but fail to do so.12 Most studies of the relation between medication beliefs and medication adherence have used self-reported medication adherence, which tends to underestimate the extent of medication adherence problems. Few studies have focused on the medication beliefs of older adults with hypertension. Greater knowledge of the beliefs older adults hold about their medications can permit clinicians to target more effectively their monitoring and intervention efforts to those patients at highest risk. In this study, Leventhal’s SelfRegulation Model13,14 was used to evaluate how illness representations and outcome expectations (beliefs) about medications may affect older adults’ adherence to antihypertensive medication regimens. Stronger beliefs in the necessity of medications would be related to higher adherence, whereas greater concerns about

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medication taking would be related to lower adherence. The purpose of this pilot study was to examine whether positive medication beliefs might be related to higher antihypertensive medication adherence among a sample of older adults with hypertension.

Methods This prospective study analyzes baseline screening data collected before randomization in a pilot randomized controlled trial of an antihypertensive medication adherence intervention for older adults.15 A convenience sample of participants was recruited by posting flyers and giving short talks about the study in senior centers and senior-living facilities in the midwestern United States. Flyers were also placed in several primary care practices and churches where parish nurses promoted the study with their clients. To be eligible, participants were required to: 1) be aged 60 or older; 2) have a self-reported diagnosis of hypertension; 3) be taking at least 1 antihypertensive medication with no antihypertensive prescription changes for the prior 30 days; 4) self-administer his or her own medications; 5) be able to read, write, and converse in English; 6) be cognitively intact (based on a score #2 on the Short Portable Mental Status Questionnaire [SPMSQ]); and 7) have the manual dexterity needed to open and close Medication Event Monitoring System (MEMS) caps. For safety reasons, participants were excluded if they had severe hypertension at enrollment (BP above 180/120). Participants were also excluded if they resided in a residential care facility where the participant’s medications were managed by someone else. Participants with concomitant cardiovascular disease or other chronic conditions were included.

Measures Cognitive Function Cognitive function was screened at study enrollment using the SPMSQ. The SPMSQ is a 10-item screening tool designed to distinguish between normal cognitive function and varying levels of intellectual impairment.16 This instrument has been used in both clinical and research settings, including thousands of elderly research study participants with testeretest correlation 90

of 0.82.16,17 The instrument tests several aspects of orientation and memory, as well as executive function. Scoring is performed by summing the number of errors made and adjusting for the participant’s educational level. Medication Adherence MA was measured using MEMS caps (Aprex, Union City, CA). These caps record the date and time on a microchip each time the medication bottle is opened. The caps were used for 1 antihypertensive medication per participant. If a participant was taking more than 1 antihypertensive, the MEMS cap was used with the antihypertensive medication that was prescribed with the greatest number of daily doses. Prior research has shown that monitoring a single medication is an effective surrogate for adherence with a patient’s entire medication regimen.18 Participants who used pillboxes or medication organizers were asked to take each dose of the monitored antihypertensive medication directly from the MEMS bottle rather than their organizer for the duration of the study. These participants were offered the option of using a small marker in the pillbox to remind them to take the antihypertensive from the MEMS bottle, a method used successfully in prior studies by Russell et al.18 Because the MEMS caps do not distinguish between bottle openings for dosing versus bottle openings for other reasons such as refills, each participant was also issued a MEMS diary on which to record any nondosing bottle openings (e.g., bottle refills). These openings, along with any multiple openings within 15 minutes of each other, were excluded from data analysis. MA was calculated as timing adherence, which is calculated as the percent of prescribed doses taken within the prescribed dosing interval. A window of 25% of the prescribed interval was allowed, so that the interval for a once-daily schedule was 24  6 hours, twice-daily was 12  3 hours, and so on. The first 4 weeks of MEMS data were used as a run-in period to eliminate intervention effect from MEMS use observed in studies with other populations.19 The final 2 weeks were used for the calculation of medication adherence. Although the commonly used cutpoint for adherence is 80%, there is evidence that suggests that a higher rate of adherencedpotentially more than 90%dis actually necessary to achieve clinical effectiveness Geriatric Nursing, Volume 33, Number 2

of antihypertensive medications.20 This study used an adherence cutpoint of 85%, chosen as a middle ground between the conventional 80% and the potentially necessary 90%. Also, an 85% cutoff allows for an average of no more than 1 day per week of taking medication incorrectly, which can facilitate goal setting when conducting adherence interventions. Medication Beliefs Medication beliefs were measured with the Beliefs about Medicines Questionnaire-Specific, developed by Horne and Weinman to measure medication beliefs related to taking medications for chronic conditions.21,22 The BMQ is a 10-item scale that measures 2 concepts: patients’ beliefs about the necessity of their antihypertensive medications (Specific-Necessity subscale) and patients’ concerns about negative effects from taking their medications (Specific-Concerns subscale). All items are answered on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The BMQ is scored by summing the items on each subscale. Subscale scores can range from 5 to 25. Higher scores on the Specific-Necessity subscale indicate stronger beliefs in the necessity of medications, whereas higher scores on the Specific-Concerns subscale indicate the participant has stronger concerns about taking medications. Internal consistency alphas during instrument development ranged from 0.65 to 0.86 across cardiac and general medical patient populations.21 Testeretest correlation at 2 weeks was 0.77 for the Necessity subscale and 0.76 for the Concerns subscale. Since its development, the BMQ has been used widely in descriptive research on medication adherence.10,11,23-25

providing additional detail when the prescribed regimen did not match the pharmacy label on the medication packaging. Participants were screened for cognitive impairment using the SPMSQ. The BMQ was administered to assess participants’ medication beliefs. Participants were then issued a MEMS cap and bottle to record MA for their antihypertensive medication with the greatest number of daily doses. A second study visit occurred at the 6-week time point, when adherence data were collected from the MEMS device. This 6-week monitoring period allowed a run-in period to eliminate potential intervention effect from MEMS use.19,26 Data Analysis Data were first summarized using descriptive statistics. Demographic, medication regimen, and medication belief variables were then analyzed across adherent and nonadherent subject groups using Mann-Whitney U tests because of the non-normal data distribution. Logistic regression was then used to evaluate the effect of medication beliefs on antihypertensive medication adherence while incorporating other potential influences such as age, number of medications, antihypertensive dosing frequency, and level of education. Data analysis was conducted using SPSS version 18.0 (SPSS, Chicago, IL).

Results Forty-seven older adults expressed initial interest in study participation during the recruitment period from August to December 2008. Of these, 33 individuals were eligible and consented to participate (Figure 1). No participants were lost or withdrawn from the study. The sample

Procedures Ethical approval for the study was obtained from the local university institutional review board. Data collection occurred during home visits conducted by a master’s-prepared nurse. At the first study visit, participants’ demographic information was collected in a face-to-face interview along with self-reported length of time with hypertension and any other coexisting chronic or acute conditions. A list of all medications being taken, including doses and dosing frequencies, was generated from information on participants’ medication containers, with participants Geriatric Nursing, Volume 33, Number 2

Figure 1. Study flow diagram. 91

had a median age of 74 years (see Table 1). The sample was predominantly female (79%) and was 79% white/Caucasian and 18% black/African American. Nineteen (58%) participants had at least some college education, and 9 (27%) had a high school diploma or equivalent as their highest level of education. The remaining 5 (15%) completed grade school or some high school. Participants’ self-reported number of years with hypertension ranged from 9 months to 50 years. Including hypertension, participants reported a median of 5 chronic conditions. The most common comorbid conditions were osteoarthritis (n 5 17), hypercholesterolemia/hyperlipidemia (n 5 16), and diabetes mellitus type 2 (n 5 12). Participants took a median of 5 prescription and 2 over-the-counter medications per day. The number of daily antihypertensive doses ranged from once daily (n 5 21), twice daily (n 5 11), or 3 times per day (n 5 1). Median antihypertensive MA for the entire sample was 91.7% (interquartile range: 37.6). When analyzed separately by MA level, those who were considered adherent ($85%, n 5 17) had a median MA rate of 100%, whereas those considered nonadherent (\85%, n 5 16) had a median medication adherence rate of 62.4%. There were no significant differences between adherent and nonadherent participants with regard to the number of medications or years with hypertension (Table 2).

Medication Beliefs Internal consistency reliability alphas for the BMQ in this sample were 0.749 for the concerns subscale and 0.907 for the necessity subscale. In univariate analysis, nonadherent participants

Table 1.

Sample Demographics (n 5 33) Characteristic

Median

IQR

Median age (years) Years with hypertension Number of daily prescription medications Number of daily OTC medications Baseline adherence rate (%)

74.00 11.00 5.00

11.50 17.50 3.50

2.00

2.50

91.70

37.60

IQR 5 interquartile range; OTC 5 over the counter.

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reported more concerns about their antihypertensive medications than did adherent participants (U 5 81.00, P 5 .046; Table 2). This result has a statistical power of 0.48 (1-b) using power analysis for t test with the asymptotic relative efficiency adjustment for the U test as suggested by Lehmann.27 There were no differences in beliefs about the necessity of antihypertensive medications (U 5 129.00, P 5 .799). Binary logistic regression was then used to analyze the impact of medication beliefs on adherence when including covariates that may also influence medication adherence (i.e., age, minority status, antihypertensive dosing frequency, and number of daily medications). In this analysis, a stronger belief in the necessity of medications was related to being adherent to antihypertensive medication (odds ratio: 2.027, P 5 .024; confidence interval [CI]: 1.097e3.745). Concerns about medications were not significantly related to medication adherence (odds ratio: 0.835; P 5.295; CI: 0.595e1.171). In this analysis, the total number of daily medications and education beyond high school were also predictors of better antihypertensive medication adherence (see Table 3).

Discussion The findings from this study are consistent with previous reports showing that older adults with negative beliefs about their medications are more likely to have problems with the implementation of their antihypertensive medication regimen.10-12,28 These negative beliefs require interventions that help older adults to modify both their illness representations of hypertension and outcome expectations regarding antihypertensive therapy. More specifically, older adults have concerns about medication dependency, long-term effects, and lifestyle disruptions.10 Negative beliefs about the necessity of medications are less commonly related to adherence outcomes but have been linked to intentional nonadherence.10-12,29 Weaker beliefs in the necessity of medications may also contribute to an apathetic attitude toward one’s medication, leading to unintentional nonadherence. No differentiation was made between intentional and unintentional nonadherence in this study, but the prevalence of intentional nonadherence Geriatric Nursing, Volume 33, Number 2

Table 2. Adherent Versus Nonadherent Participants

Medication Beliefs (BMQ) Specific-Concerns Specific-Necessity No. of prescription medications No. of OTC medications Total no. of medications Years with HTN

Nonadherent (n 5 16) Median

Adherent (n 5 17) Median

U

P

14.50 19.00 5.50 3.00 10.00 11.50

12.00 19.00 5.00 2.00 7.00 10.00

81.00 129.00 92.00 116.50 87.00 117.00

.046* .799 .110 .476 .081 .493

BMQ 5 Beliefs About Medicines Questionnaire; HTN 5 hypertension; OTC 5 over the counter; U 5 Mann-Whitney U Test. *P \ .05.

would be expected to be low in this sample, because study participants were all on an established regimen. Intentional nonadherence, most commonly manifested through early discontinuation, is typically more common in the first months after starting a new medication regimen.30 Not all prior studies have shown relationships between medication beliefs and adherence, however.31,32 This may be due to sample differences or to the wide variety of instruments used to measure both beliefs and adherence. Differences in measurement methods make comparisons across studies difficult; however, mean BMQ scores from this study (19.24 for necessity, 13.52 for concerns) were similar to BMQ scores from a study by Ross et al. (18.42 for necessity, 13.26 for concerns).11 The total number of daily medications has previously been linked to both medication nonadherence and medication beliefs.11,33 Although often used as an indicator of medication regimen

complexity, this is not necessarily a reliable assumption. In this study, most medications were taken only once daily. Another possible explanation is that a greater number of daily medications may increase patients’ financial burden, leading to increased nonadherence due to inability to refill prescriptions. Prior literature has also linked education with adherence outcomes as well as medication beliefs.11,31 Unfortunately, these relationships have not yet been explored in studies with sufficiently large and diverse samples to determine whether such effects are truly from patients’ educational level, or through interaction between education and other factors such as age, cognitive status, or culture.31,34 Limitations The results of this study must be interpreted with caution because of the small sample size and use of a convenience sample. With an

Table 3. Logistic Regression Model 95% Confidence Interval Variable

Odds Ratio

Lower

Upper

P

Specific-Concerns Specific-Necessity Age No. of medications Antihypertensive doses per day Educational level

0.84 2.03 1.31 0.53 0.10 0.005

0.60 1.10 0.97 0.29 0.01 \0.01

1.17 3.75 1.76 0.96 1.55 0.33

.295 .024 .074 .036 .100 .013

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underpowered study such as this, Type II error is possible. Future work should include a larger sample with greater diversity to explore differences between older adults from different racial and ethnic groups. Study participants took a variety of antihypertensive medications, each of which may have varying influences on medication beliefs and medication adherence due to different regimens, side effect profiles, or cost. A larger sample would permit the inclusion of specific antihypertensive medication or medication classes as covariates. The results from this study would be further strengthened by a longer duration of electronic monitoring for determining adherence rates. A longer monitoring period provides a fuller picture of medication adherence patterns.

Summary This study adds support to earlier findings indicating that positive beliefs about antihypertensive medications are related to improved antihypertensive medication adherence among older adults. Further research is needed on the effects of beliefs on antihypertensive adherence and the degree to which beliefs alone or beliefs in conjunction with other factors influence adherence. There is evidence, however, to support targeting health beliefs as one component of interventions to improve antihypertensive medication adherence.

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6. Cramer JA. Consequences of intermittent treatment for hypertension: the case for medication compliance and persistence. Am J Manag Care 1998;4:1563-8. 7. DiMatteo MR. Variations in patients’ adherence to medical recommendations: a quantitative review of 50 years of research. Med Care 2004;42:200-9. 8. Botelho RJ, Dudrak R 2nd. Home assessment of adherence to long-term medication in the elderly. J Fam Pract 1992;35:61-5. 9. van Eijken M, Tsang S, Wensing M, et al. Interventions to improve medication compliance in older patients living in the community: a systematic review of the literature. Drugs Aging 2003;20:229-40. 10. Phatak HM, Thomas J 3rd. Relationships between beliefs about medications and nonadherence to prescribed chronic medications. Ann Pharmacother 2006;40:1737-42. 11. Ross S, Walker A, MacLeod MJ. Patient compliance in hypertension: role of illness perceptions and treatment beliefs. J Hum Hypertens 2004;18:607-13. 12. Unni EJ, Farris KB. Unintentional non-adherence and belief in medicines in older adults. Patient Educ Couns 2011;83:265-8. 13. Leventhal H, Safer MA, Panagis DM. The impact of communications on the self-regulation of health beliefs, decisions, and behavior. Health Educ Q 1983;10:3-29. 14. Leventhal H, Zimmerman R, Gutmann M. Compliance: a self-regulation perspective. In: Gentry WD, ed. Handbook of behavioral medicine. New York: Guilford Press; 1984. p. 369-436. 15. Ruppar TM. Randomized pilot study of a behavioral feedback intervention to improve medication adherence in older adults with hypertension. J Cardiovasc Nurs 2010;25:470-9. 16. Pfeiffer E. A short portable mental status questionnaire for the assessment of organic brain deficit in elderly patients. J Am Geriatr Soc 1975;23:433-41. 17. Fillenbaum G, Heyman A, Williams K, et al. Sensitivity and specificity of standardized screens of cognitive impairment and dementia among elderly black and white community residents. J Clin Epidemiol 1990;43:651-60. 18. Russell CL, Conn VS, Ashbaugh C, et al. Intrasubject medication adherence patterns. Clin Nurs Res 2007;16: 153-63. 19. Denhaerynck K, Schaefer-Keller P, Young J, et al. Examining assumptions regarding valid electronic monitoring of medication therapy: development of a validation framework and its application on a European sample of kidney transplant patients. BMC Med Res Methodol 2008;8:5. 20. Burnier M, Schneider MP, Chiolero A, et al. Electronic compliance monitoring in resistant hypertension: the basis for rational therapeutic decisions. J Hypertens 2001;19:335-41. 21. Horne R, Weinman J, Hankins M. The Beliefs about Medicines Questionnaire: the development and evaluation of a new method for assessing the cognitive representation of medication. Psychol Health 1999;14:1-24. 22. Horne R, Weinman J. Patients’ beliefs about prescribed medicines and their role in adherence to treatment in chronic physical illness. J Psychosom Res 1999;47:555-67. 23. Menckeberg TT, Bouvy ML, Bracke M, et al. Beliefs about medicines predict refill adherence to inhaled corticosteroids. J Psychosom Res 2008;64:47-54.

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24. Llewellyn CD, Miners AH, Lee CA, et al. The illness perceptions and treatment beliefs of individuals with severe haemophilia and their role in adherence to home treatment. Psychol Health 2003;18:185-200. 25. Brown C, Battista DR, Bruehlman R, et al. Beliefs about antidepressant medications in primary care patients: relationship to self-reported adherence. Med Care 2005; 43:1203-7. 26. Deschamps AE, Van Wijngaerden E, Denhaerynck K, et al. Use of electronic monitoring induces a 40-day intervention effect in HIV patients. J Acquir Immune Defic Syndr 2006;43:247-8. 27. Lehmann EL. Nonparametrics. Statistical methods based on ranks. San Francisco: Holden- Day; 1975. 28. Peltzer K. Health beliefs and prescription medication compliance among diagnosed hypertension clinic attenders in a rural South African hospital. Curationis 2004;27:15-23. 29. Pound P, Britten N, Morgan M, et al. Resisting medicines: a synthesis of qualitative studies of medicine taking. Soc Sci Med 2005;61:133-55. 30. Vrijens B, Vincze G, Kristanto P, et al. Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ 2008;336:1114-7. 31. Morrell RW, Park DC, Kidder DP, et al. Adherence to antihypertensive medications across the life span. Gerontologist 1997;37:609-19. 32. Patel RP, Taylor SD. Factors affecting medication adherence in hypertensive patients. Ann Pharmacother 2002;36:40-5.

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33. Conn VS, Hafdahl AR, Cooper P, et al. Interventions to improve medication adherence among older adults: meta-analysis of adherence outcomes among randomized controlled trials. Gerontologist 2009;49: 447-62. 34. Lewis LM, Askie P, Randleman S, et al. Medication adherence beliefs of community-dwelling hypertensive African Americans. J Cardiovasc Nurs 2010;25:199-206. 10.1097/JCN.0b013e3181c7ccde. TODD M. RUPPAR, PhD, RN, GCNS-BC, Assistant Professor and JAHF and Atlantic Philanthropies Claire M. Fagin Fellow, University of Missouri, Sinclair School of Nursing, Columbia, MO. FABIENNE DOBBELS, PhD, Assistant Professor, KU Leuven, Center for Health Services and Nursing Research, Leuven, Belgium. SABINA DE GEEST, PhD, RN, FRCN, FAAN, Professor of Nursing, KU Leuven, Center for Health Services and Nursing Research, Leuven, Belgium, and University of Basel, Institute for Nursing Science, Basel, Switzerland. ACKNOWLEDGMENTS This study was funded by the John A. Hartford Foundation Building Academic Geriatric Nursing Capacity Program, the University of Missouri Interdisciplinary Center on Aging, and the Alpha Iota Chapter of Sigma Theta Tau International. 0197-4572/$ - see front matter Ó 2012 Mosby, Inc. All rights reserved. doi:10.1016/j.gerinurse.2012.01.006

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