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Copyright 7997 by The Cerontological Society of America The Cerontologist Vol. 37, No. 5, 609-619 Although treatment for hypertension is readily available, poor control of hypertension is a major health problem frequently manifested in late life. Researchers believe that one of the major causes of uncontrolled hypertension is failure to take medication as directed. In this preliminary study, the medication-taking behaviors of 48 adults diagnosed with hypertension, ranging in age from 35 to 87, were recorded for 2 months with credit card-sized bar-code scanners. The social-cognitive model (Park, 1992) for understanding medication adherence, which proposes that medication adherence is governed by both beliefs and cognitive factors, was used as a basis for this research. Therefore, measures of health behaviors, attitudes about health and medication taking, and cognitive function were recorded, as well as blood pressure readings. The main findings were that (a) the oldest-old and groups of middle-aged adults were the most nonadherent, whereas the young-old were more likely to adhere than the other age groups; (b) high blood pressure readings predicted adherence to antihypertensive medications; and (c) medication beliefs influenced adherence in some situations. Key Words: Adherence, Aging, Hypertension, Self-care Adherence to Antihypertensive Medications Across the Life Span1 Roger W. Morrell, PhD,2 Denise C. Park, PhD,3 Daniel P. Kidder, MS,4 and Mike Martin, PhD5 Uncontrolled hypertension is a major risk factor blood pressure under effective control (Richardson, for heart attack and stroke and an antecedent of Simons-Morton, & Annegers, 1993; Sharkness & cardiovascular and renal disease (National High Blood Pressure Education Program Working Group, 1994). Hypertension is a serious health threat, as 50 to 60 million Americans are affected by the disease (Joint Committee on Detection, Evaluation, and Treatment of High Blood Pressure, 1993). Although hypertension can be treated effectively by oral medication, poor control of hypertension continues to be a major health problem. It has been estimated that 30-55% of patients with hypertension do not adhere to their prescribed medication regimen, with some patients taking less medication than prescribed and others taking more (Hamilton et al v 1993; Rudd, 1995; Rudd et alv 1989; Shaw, Anderson, Maloney, Jay, & Fagan, 1995). Furthermore, it has been reported that only one half to one fourth of all hypertensive patients have their Snow, 1992; Skaer, Sclar, Markowski, & Won, 1993). Because hypertension is a relatively common condition, adherence to antihypertensive therapies has been widely studied, with an emphasis on determining the psychosocial and medical predictors of nonadherence. A recent review suggests that although there are reliable findings on this topic, some of the results that have been reported are conflicting and, at times, confusing (see Dunbar-Jacob, Dwyer, & Dunning, 1991). One reason for the discrepancy in the findings is that the researchers have not used a reliable, sensitive measure of adherence. An electronic monitoring device is used in this study to address this problem. Another reason for this discrepancy is that few researchers in this area have utilized a comprehensive conceptual framework that incorporates a multidimensional view of medication taking to guide their research. Park and her colleagues, however (Park, 1992, 1994; Park & Jones, 1996), have developed a conceptual model for understanding medication adherence that proposes that medication adherence is governed by both beliefs and cognitive factors. This model integrates the Leventhal and Cameron (1987) self-regulatory view of medication adherence with basic cognitive psychology and will be here termed the social-cognitive model of medication adherence. Leventhal and Cameron (1987) have proposed that medication adherence is a self-regulatory be- 'This research was funded by a grant from the American Association of Retired Persons, Andrus Foundation, to Denise C. Park (principal investigator) and Roger W. Morrell. The authors would like to thank Gary Lautenschlauger and Christopher Hertzog for comments on previous versions of this article, and Joan Bennett and Kim Shifren for assistance with statistical analyses. 'Address correspondence to Dr. Roger W. Morrell, The Institute for Social Research, University of Michigan, 426 Thompson Street, Ann Arbor, Ml 48106-1248. 'The Institute for Social Research, University of Michigan. 'The Georgia Institute of Technology, Atlanta, GA. 5 The University of Mainz, Mainz, Germany. Vol. 37, No. 5,1997 609 havior. An individual will be adherent or nonadherent to a medication depending on what he or she believes to be true about a disease and the perceived relationship of taking medications to disease management. This illness representation is also dynamic and may change over the course of a disease. In particular, individuals who believe they have no control over their illness may be nonadherent because they may believe taking medication will have no effect on their outcome. Thus, it is important to understand individuals' personal beliefs about their illness because these types of beliefs will "drive" adherence behaviors (Park, 1994). In addition to illness beliefs, Park and Mayhorn (1996) have stressed that it is also important to measure beliefs about specific medications or a patient's medication representation. Persons will not take a medication if they do not consider it useful or if a decision is made to use the medication only when symptoms occur (Park & Mayhorn, 1996). Such decisions might ultimately affect adherence to antihypertensive medications if a patient views hypertension as a symptomatic condition (Meyer, Leventhal, & Cutmann, 1985). The social-cognitive model suggests that, in addition to beliefs, cognitive functioning also plays an important role in adherence, especially for older adults (Park, 1994). Remembering to take medications involves many components of cognitive processing including comprehension, working memory, long-term memory, prospective memory, and reasoning (Park, 1992; Park & Kidder, 1996). Age-related declines in some of these underlying cognitive mechanisms are well-documented, and may contribute to forgetting to take medication as prescribed or to not understanding how to properly take medication in older adults (see Salthouse, 1991). Furthermore, because older adults may be more likely than younger adults to be prescribed multiple medications, the process of taking medication correctly may be particularly difficult for older patients because of the cognitive demands required for organizing and maintaining complex medical regimens (Park, Willis, Morrow, Diehl, & Gaines, 1994). Other researchers have also proposed that health status and disease state are important components in understanding why people do or do not take their antihypertensive medications in particular (Cummings, Kirscht, Binder, & Codley, 1982; DeVon & Powers, 1984; Nagy & Wolf, 1984; Stanton, 1987). Furthermore, psychosocial factors such as satisfaction with one's physician might also determine how medication is taken (see Wolf, Putnam, James, & Stiles, 1978). Findings from research on adherence in general suggest that age is not a reliable factor for predicting adherence (see Lorenc & Branthwaite, 1993, for a discussion). Park and Jones (1996), however, suggest that age may be conceptualized as influencing medication adherence primarily through cognitive factors. In support of this, Morrell, Park, and Poon (1989) have reported that elderly adults consistently manifested poorer recall of prescription information than young adults. Moreover, Park, Morrell, Frieske, and Kincaid (1992) reported higher nonadherence rates in adults over the age of 75 (old-old adults) compared to individuals between the ages of 60 and 74 (young-old adults). In this preliminary study, the impact of beliefs and cognitive function on adherence to medication regimen by hypertensive adults is examined in order to guide future work in this area. This research differs from past efforts to study adherence in hypertensive patients in a number of ways. First, we collect measures of beliefs about hypertension and taking medications guided by the social-cognitive model (Park, 1992,1994; Park & Jones, 1996). Traditional demographic measures and health indices of the participant are also collected, as well as measures concerning participants' perceptions of their physician's behavior. The second unique characteristic of this research is a measure of cognitive functioning — working memory capacity. Working memory is an estimate of the amount of processing resources or mental energy that an individual possesses (Baddeley, 1986). Reliable findings suggest that working memory capacity declines with age (see Salthouse, 1991). Third, although Park et al. (1992) included only older adults in earlier adherence work, we also examine the adherence behaviors of middle-aged adults. Fourth, it is of special interest to determine what factors might influence adherence to antihypertensive medication as opposed to other types of medications in a patient's regimen. This comparison has not been made in past research and is examined in detail in this project. Finally, it is important that the method used for measuring adherence be unobtrusive, sensitive, and reliable, so that a true and accurate picture of medication-taking behavior can be obtained. Therefore, in the present work, adherence is recorded by an electronic monitoring technique — the Videx TimeWand, a portable bar-code reader. Leirer, Morrow, Pariante, and Sheikh (1988) have used this type of bar-code reader to measure simulated medication adherence in older adults in past research. Furthermore, Park and her colleagues have found this particular technique to be successful in measuring adherence to actual prescribed regimens of medications in 61 older adults and 15 patients diagnosed with rheumatoid arthritis over periods of 2 and 3 weeks (Park, 1992; Park et al., 1992, respectively). The monitoring period has been increased to 8 weeks in the present study, exceeding the time during which medication-taking behaviors were monitored in previous research by Park et al. The extended monitoring period allows the impact of the passage of time on adherence to be assessed. 610 Method Participants Forty-eight community-dwelling adults, ranging in age from 36 to 87 (M = 59.54, SD = 14.4), particiThe Gerontologist pated in this study. The sample was age-stratified such that there were 9 or 10 individuals in each 10year band from ages 35 to 75, and above. All participants were volunteers recruited from family and internal medicine practices located in the Athens, Georgia, area. Before participants could be admitted to the study, they were required to have their personal physician submit written verification that they had been diagnosed with essential hypertension. Their physician was also asked to rate the severity of their disease on a 5-point scale. They were also required to be taking at least one prescribed medication for hypertension on a daily basis. The number of medications taken by the participants ranged from 1 to 9 (M = 3.64, SD = 2.04). Additionally, the participants were required to be (a) managing their own medication regimen, (b) have at least 20/30 corrected binocular vision, (c) not have been hospitalized in the past 6 months, and (d) have no restriction in arm and hand movement. Individuals were paid a total of $175 for their participation. They received $40 at the initial visit, and additional payments of $45 during each of weeks 3, 5, and 7. A series of analyses of variance indicated that the participants in each of the 10-year age bands did not differ in the number of medications or daily dosages taken, years they had been diagnosed with hypertension, physician rating of disease severity, or perceived health. The groups of participants also did not differ in educational attainment. Older adults, however, were more likely to live alone than were younger adults (x2 = 13.04, p = .02). Scores on these variables are shown in Table 1 by age group. Equipment All participants received a Videx TimeWand to record their medication-taking behavior, the same device used by Leirer et al. (1988) and Park et al. (1992). The Videx TimeWand is a credit card-sized, portable bar-code reader. It has an optical sensor at one corner which, when swept across a bar code, records the bar code number into the memory of the TimeWand. The date and time of the scan is also recorded. Each participant was given a TimeWand placed inside a small wallet. Inside the wallet was a set of bar codes with corresponding labels that indicated the name and description of each of the medications in the participants' regimens. They were instructed to keep the wallet with their medications and to scan the appropriate bar code in the wallet immediately after taking each medication in their regimen, so that the drug's identity, and the date and time the medication was taken, could be recorded. Individual Differences Battery The following seven instruments were administered to each of the participants. The Medication Information Survey was developed for this study and was administered individually for each medication in the participants' regimens. Information collected included the reason for taking each medication, when (time of day or night) each of the medications was taken, the dosage of each of the medications taken each time, and the number of years that patients had been diagnosed with hypertension. Table 1 . Means and Standard Deviations of Medication, Illness, and Perceived Health Variables and Educational Attainment and Living Arrangement Breakdown by Age Group Age Group 35^4 (n = 10) 45-54 (n = 9) 55-64 (n = 10) 65-74 (n = 10) 75+ (n = 9) 2.8 (1.81) 10.9 (8.89) 2.90 (1.10) 3.5 (1.94) 11.22 (9.68) 2.55 (0.73) 4.3 (2.75) 11.60 (7.62) 2.50 (0.53) 4.1 (1.97) 14.20 (10.012) 2.60 (0.52) 3.6 (1.51) 10.44 (14.54) 2.56 (0.73) 2.0 (0.47) 1.80 (0.63) 2.44 (0.53) 1.77 (0.67) 2.20 (0.42) 1.70 (0.48) 2.40 (0.52) 1.90 (0.57) 2.25 (0.70) 1.63 (0.74) Education High school degree or less Some college or college degree Graduate degree 50.0% 40.0% 10.0% 55.5% 22.2% 22.2% 30.0% 50.0% 20.0% 40.0% 40.0% 20.0% 22.2% 33.3% 44.4% Live Alone Yes No 10.0% 90% 0.0% 100% 30% 70% 30% 70% 66.7% 33.3% Demographic Variables Number of medications Years diagnosed with hypertension Physician rating of severity of hypertension3 Perceived Health Present health rating6 Health as a barrier to activity" "1 = extremely mild; 2 = mild; 3 = moderate; 4 = severe; 5 = extremely severe. b 1 = excellent; 2 = good; 3 = fair; 4 = poor. C 1 = not at all; 2 = a little; 3 = a great deal. Note: Standard deviations are shown in parentheses. Vol. 37, No. 5,1997 611 A Demographic Questionnaire was adapted from the Older Americans Resources and Services (OARS) Instrument (Duke University Medical Center, 1975) and it included items about age, gender, education, profession, work status, living arrangements, marital status, and income. The Health Practices Questionnaire was also a newly developed instrument. It contained questions about exercise, diet, and use of vitamin supplements. The Hypertension Patient's Perception of Physician Behavior Scale was adapted from the Medical Interview Satisfaction Scale (Wolf et al., 1978), but was redesigned for this research to include items specifically about hypertension and the patientphysician relationship. The Patient's Hypertension Beliefs Questionnaire was derived from the Health Belief Questionnaire (Jette, Cummings, Borck, Phels, & Naessens, 1981). This instrument included questions designed to assess health beliefs about hypertension and included items on health concerns, perceived health, perceived barriers to taking medications, perceived control of hypertension, perceived control of overall health, self-care, perceived prognosis, detection of hypertension, perceived side effects, and knowledge of hypertension. Sample items from the instrument used in this study are shown in the appendix. A Present and Past Health Status Questionnaire was adapted from the OARS Instrument (Duke University Medical Center, 1975) to measure perceived health status. Finally, an assessment of the participants' comprehension of their medication regimen was conducted in which participants referred to their own medication regimen and completed an hour-by-hour plan of what medications they would take, the reason for taking each medication, and when they would take the medications over a 24-hour period. This instrument was used previously by Morrell, Park, and Poon (1989,1990). The second cognitive task was the 30-Point Word Familiarity Survey (Gardner & Monge, 1977), which was also administered to ascertain the participants' vocabulary ability. Procedures All participants were tested individually in a local physician's office. During their first visit, they completed the battery of instruments described above. After completing the package of measures, participants were trained to use the Videx TimeWand and practiced using it with their own medications. They were also given a wallet that had a section in which to keep the TimeWand when not in use and inserts that were clearly labeled with bar-code stickers and the name and description of each drug in their regimens. Participants were instructed to scan the bar code that corresponded to the medication they were taking whenever they took a particular medication. They were cautioned that a successful scan occurred only when a short beep was emitted from the TimeWand and the red light was also illuminated on the top of the TimeWand. Participants were also instructed to scan the individual bar codes the same number of times as the number of pills they took (i.e., two pills equaled two scans on the corresponding bar code). They were provided with a brochure that described how the TimeWand worked and what to do in case of problems with the device. Participants were also provided with a "Scanner's Diary" in which they could record scanning errors, problems using the TimeWand, or any other information about their adherence behavior they wanted to convey to the experimenters. The diary data were carefully cross-checked with actual scanning printouts, and accidents in scanning were deleted from the adherence scores used in the data analyses. Because the batteries in the TimeWands remained charged for about 7 days, participants were required to return for a brief appointment with the experimenter the same day each week over the next 8 weeks. The primary purpose of these visits was to Cognitive Battery exchange the TimeWand they had been using for Two cognitive tasks were administered to each of a freshly charged device. The participants' blood the participants. The first cognitive task was a mea- pressure was also recorded after resting 5 minutes sure of working memory, the Listening Span Task, during four of the appointments (i.e., every other which was collected to assess subjects' cognitive reweek). sources (Daneman & Carpenter, 1980; Salthouse & During the fifth-week visit, the measures of workBabcock, 1990). Working memory is a measure of ing memory and vocabulary ability were administotal storage and processing capacity available to an tered. At the end of the eighth week, participants individual. This task required participants to answer returned for a final visit with the experimenter. The questions about simple sentences that were orally last TimeWand that had been issued and all Scanpresented (the processing component) while they ner's Diaries were returned at this time simultaneously remembered the last word (the storage component) in each of the sentences. The number of sentences increased from one to six over Results trials so that the number of items they were to remember progressively increased. There were three Adherence Data Analyses trials at each level of memory load. The task was stopped whenever participants made errors on Data Scoring. — The adherence data were scored three consecutive trials. An individual's span was according to the following plan. Four types of addetermined by the total number of trials correct. herence errors (omission errors, commission er612 The Gerontologist rors, quantity errors, and total errors) were determined for each medication taken by the participants (a total of 175 medications over the 48 participants). Omission errors were recorded when participants did not take or omitted dosages from their regimens. Commission errors were noted when participants took extra dosages of a medication (e.g., taking a medication 4 times during a day instead of the prescribed 3 times a day). Quantity errors involved participants taking additional amounts of a medication during a dosing event (e.g., taking 3 pills instead of the prescribed 2 pills as reflected by the number of scans made with the TimeWand). Total errors were obtained by summing the number of omission, commission, and quantity errors. Proportions were then derived by dividing the absolute number of errors made in each of the four categories during each of the four biweekly periods by the absolute number of dosages participants were scheduled to take during each of the biweekly periods. The biweekly scores were used because few differences were observed between weekly and biweekly scores. made by all of the participants over the four biweekly monitoring periods, the data were graphed. Figure 1 demonstrates a positive linear relationship in the number of omission errors made over time, with the number of these types of errors increasing steadily as the monitoring period progressed. Nonadherence Over Time. — In order to ascertain if there was a systematic trend in the number of omission, commission, quantity, or total errors The Effects of Age and Monitoring Period on Adherence. — For the following sets of analyses, age was partitioned into five categories (35-44: n = 10; 45-54: n = 9; 55-64: n = 10; 65-74: n = 10; 75+: n = 9). Individual analyses of variance (ANOVAs) were conducted for omission, commission, quantity, and total errors, with age group as a between-subjects variable and the four biweekly monitoring intervals as a repeated measures variable. ANOVAs were used instead of regression analyses so that the effect of time of measurement could be determined. Results from the analyses conducted on commission and quantity errors are not reported because these types of errors were very rare (an average of about 2% for commission errors and an average of less than 1% for quantity errors across the age groups and biweekly periods). Mean proportion and standard deviations of omission and total errors are shown in Table 2 as a function of age group and biweekly monitoring interval. 0.16 0.14 0.12 0.08 0.06 0.04 0.02 Biweekly 1 Omission Errors Biweekly 3 Biweekly 2 Commission Errors Figure 1. Mean biweekly proportion of errors across age groups by type of error. Vol. 37, No. 5,1997 613 Quantity Errors Biweekly 4 Total Errors Total Errors. — Results from the ANOVA conducted on total errors indicated only a significant main effect of age, F(4,144) = 5.26, MSe = .111, p = .0006. The highest rates of nonadherence were observed in the oldest-old and the group of participants between the ages of 55 and 64 (an average of 21.5% and 18.3% across the four biweekly monitoring periods, respectively). The lowest proportion of total errors was made by participants 65 to 74 years old (an average of 3.8% across the four biweekly monitoring periods). The rate of nonadherence for the other groups (ages 35-54) fell between the two older groups (approximately 12.5%). The proportion of total errors for participants 65 to 74 years old was significantly different from the oldest age group and from the group of participants between the ages of 55 and 64, Tukey's Studentized Range = 3.907, p < .05. Omission Errors. — The analysis of omission errors, which were the most common errors made, yielded a significant main effect of age, F(4,144) = 5.35, MSe = 0.083, p = .0005, and biweekly monitoring interval, F(3,432) = 3.73, MSe = 0.010, p = .01. More omission errors were made by the oldest participants (about 19%) than the young-old participants (ages 65-74), whose proportion of nonadherence was extremely low (about 3%) across the monitoring period, Tukey's Studentized Range = 3.907, p < .05. A comparison test on the interval data indicated that more omission errors were also made in the last biweekly monitoring period (approxi- Table 2. Mean Proportion, Standard Deviations', and Marginal Means of Total and Omission Errors as a Function of Age Group and Biweekly Monitoring Interval Biweekly Intervals .. . . 1 Marginal Age Croup Biweeki Biweek2 Biweek3 Biweek4 Means Total Error; 35-44 45-54 55-64 65 74 75+ .112 (.171) .145 (.211) .159 (.251) .018 (.050) .155 (.184) Omission Errors 35^4 .100 (.169) .104 45-54 (.181) 55-64 .105 (.207) 65-74 .015 (.046) 75+ .143 (.188) .071 (.100) .161 (.250) .220 (.414) .020 (.038) .211 (.218) .092 (.134) .161 (.209) .204 (.387) .061 (.124) .252 (.224) .112 (.144) .144 (.239) .147 (.251) .054 (.110) .240 (.274) mately 12%) than in the first biweekly monitoring period (approximately 9%), F(1,144) = 6.97, MSe = 0.012, p = .009. Overall, these results suggest that the pattern of total errors was driven primarily by the number of omission errors made. Because total errors appeared to be the most global representation of the different types of medication-taking errors that were made, only total errors were analyzed in subsequent analyses. The Effects of Age and Monitoring Period on Adherence to Different Types of Medications. — To determine whether adherence was affected by different types of medications, the medications that the participants took were divided into two categories: antihypertensive medications and all other medications taken on a prescribed basis. Two 5 X 4 ANOVAs were conducted (one on the antihypertensive medications and one on the other medications in the regimen) on the proportion of total errors recorded, with age (divided into the five categories as above) as a between-subjects factor and the four biweekly monitoring periods as a within-subjects factor. Results from the ANOVA conducted on the antihypertensive medications revealed a significant main effect for age, F(4,42) = 2.57, MSe = 0.013, p = .05, with the oldest age group and the group of participants between the ages of 45 and 54 making significantly more errors with their antihypertensive medications (M = .16 and .15, respectively) over the four biweekly periods than the group of participants between the ages of 55 and 64 (M = .04), Tukey's Studentized Range = 4.03, p < .05, as shown in Table 3. The proportion of total errors committed by the remaining age groups were about the same (about 4-5%) across the four monitoring periods. No significant effects were observed from the ANOVA conducted on the proportion of total errors for the other prescribed medication category. .097 .153 Table 3. Mean Proportion and Standard Deviations' of Total Errors as a Function of Age Group Across Biweekly Monitoring Intervals for Antihypertensive Medications and Other Medications .183 .038 Type of Medication .215 Age Group .059 (.084) .160 (.250) .082 (.206) .011 (.034) .186 (.206) .081 (.129) .124 (.191) .141 (.258) .047 (.087) .202 (.176) .110 (.137) .121 (.229) .124 (.234) .030 (.077) .214 (.216) .088 35^4 .127 45-54 .133 55-64 .026 65-74 .186 75+ "Standard deviations are shown in parentheses. Antihypertensive Medications (n = 47) Other Medications (n = 37) .053 (.078) .148 (.181) .041 (.050) .048 (.063) .163 (.154) .035 (.047) .058 (.118) .211 (.275) .015 (.063) .038 (.140) "Standard deviations are shown in parentheses. 614 The Gerontologist Analyses of Age and Individual Difference Measures. — Individual one-way analyses of variance were conducted on the Health Practices subscales, the Perception of Physician Behavior subscales, the Hypertension Beliefs Questionnaire subscales, the two Health Status Questionnaire subscales, the measures of working memory and vocabulary ability, and the Medication Comprehension measure, with age as a between-subjects variable (using the five age categories previously described). No age differences were found in any of the psychosocial measures, with the exception of the Past Health scale. The oldest and youngest age groups reported having had a heart attack, stroke, or having been hospitalized for more than 5 days more often than the other age groups, Tukey's Studentized Range = 3.90, p < .05. There were also no significant differences in scores from the working memory task or the measure of vocabulary ability as a function of age. Mean scores on the cognitive measures for each of the age groups are outlined in Table 4. beliefs about self-care (items shown in the appendix), and the hypertension knowledge questionnaire subscale from the hypertension beliefs questionnaire (shown in Table 5). Although the systolic blood pressure reading in week 7 was correlated with total errors, it was also highly correlated with the blood pressure reading from week 3 (.72). Therefore, the week 7 reading was not included in the regression analyses conducted in order to avoid multicollinearity. Regression Analyses. — Because more errors in general were observed toward the end of the monitoring period, regression equations were calculated only for the last two biweekly monitoring periods. Separate regression analyses were conducted for the "antihypertensive medications only" category and the "other medications" category, with total errors included as the dependent variable in each case. Because the adherence scores for the antihypertensive medications and the other medications were highly correlated (.81 for both biweek 3 scores Correlations Between Individual Difference Mea- and biweek 4 scores), the same variables were included as predictors in both sets of the regression sures and Adherence. — Because the number of analyses according to the following plan. participants in this study was relatively small, including all of the demographic items, questionnaire The variables entered in the hierarchical regressubscales, and cognitive measures in a regression sion analyses were the scores from the measure of working memory, the scores from the beliefs about analysis would not be meaningful. Therefore, zeroself-care and the knowledge of hypertension suborder correlations were calculated between these scales, the measure of systolic blood pressure taken variables and the proportion of total errors for antiin week 3, and chronological age (outlined in Tables hypertensive medications only, and for the other 6 and 7). According to the social-cognitive model, medications categories recorded in the last two bicognitive functioning and personal beliefs about illweekly periods, to determine which variables ness were designated as possible predictors of nonshould be included as predictors in the regression adherence. Furthermore, other researchers have analyses that were conducted. Results from the corsuggested that disease state may also influence relational analyses revealed that only the measures medication-taking behavior. We used this as backof systolic blood pressure during weeks 3 and 7 ground to devise the plan for this set of analyses. In were significantly related to the proportion of total the first step, We assessed the effect of general cogerrors recorded for antihypertensive medications nitive functioning on adherence by entering the only in either of the biweekly periods (outlined in scores on the working memory task alone. The efTable 5). Five of the variables had significant relafects of illness beliefs and knowledge about illness tionships to the proportion of total errors recorded were examined in the second step, after the meafor other medications in either of the last two bisure of working memory. The third step included weekly monitoring periods: the measure of working the proximal measure of systolic blood pressure memory, systolic blood pressure readings taken taken in week 3 as a global indicator of disease during weeks 3 and 7 of the monitoring period, the Table 4. Means and Standard Deviations' of Cognitive Variables by Age Croup Age Group Cognitive Variables Vocabulary score Working memory Levels Trials 35^4 (n = 10) 45-54 (n = 9) 55-64 (n = 10) 65-74 (n = 10) 75+ (n = 9) 15.56 (5.57) 13.22 (4.71) 16.80 (6.01) 17.00 (6.53) 16.88 (6.68) 2.00 (1.15) 5.40 (2.59) 2.00 (0.87) 5.11 (2.42) 2.10 (1.20) 5.50 (3.27) 1.90 (0.99) 4.90 (2.51) 1.67 (1.22) 4.44 (3.36) 'Standard deviations are shown in parentheses. Vol. 37, No. 5,1997 615 Table 7. Outline of Multiple Regression Analyses Conducted on Other Prescribed Medications for Biweekly Periods Three and Four for Total Errors Table 5. Significant Correlations Between Cognitive Variables, Blood Pressure Readings, Questionnaire Subscales, and the Proportion of Total Errors for Biweekly Monitoring Periods Three and Four for Antihypertensive Medications and Other Medications Predictor Time Period Medication Category Biweek3 Antihypertensive Medications Only Systolic BP, week 3 Systolic BP, week 7 Other Medications Working memory Systolic BP, week 3 Systolic BP, week 7 Hypertension beliefs questionnaire Self-care subscale Hypertension knowledge subscale Biweekly Period Three Step One Working memory -.215 Step Two Working memory 078 Self-care subscale 252* Knowledge ,274* Step Three Working memory ,004 Self-care subscale -.283* Knowledge -.189 Blood pressure reading, week 3 .467** Step Four .004 Working memory Self-care subscale .313* .166 Knowledge Blood pressure reading, week 3 .432** .119 Age Biweek4 .48 .31 .43 n.s. n.s. .45 .36 -.30 .38 n.s. -.30 -.30 -.31 n.s. Nofe: *p < .05. Table 6. Outline of Multiple Regression Analyses Conducted on Antihypertensive Medications for Biweekly Periods Three and Four for Total Errors Predictor Biweekly Period Three Step One Working memory Step Two Working memory Self-care subscale Knowledge Step Three Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Step Four Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Age Biweekly Period Four Step One Working memory Step Two Working memory Self-care subscale Knowledge Step Three Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Step Four Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Age Beta R2Change Cumulative/?2 -.194 -.132 -.077 -.181 .038 .038 .076 Biweekly Period Four Step One Working memory Step Two Working memory Self-care subscale Knowledge Step Three Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Step Four Working memory Self-care subscale Knowledge Blood pressure reading, week 3 Age F 1.67 1.13 -.051 -.121 -.058 .466** .193** .269 3.68* -.043 -.150 -.031 .440* .105 .008 -.201 -.113 -.079 -.208 .277 2.99* .040 1.81 .049 .089 1.34 .142* .231 3.00* .002 .233 2.37* .046 2.15 .133* .179 3.05* .203** .382 6.34* .011 .393 5.19* .107 5.28* .199 3.49* .315 4.71' .321 3.78* -.328* -.211 -.231 -.209 F .092 -.157 -.254* -.145 .351** .115* -.151 -.276* -.128 .325* .088 .006 state, as well as the variables previously entered. Finally, chronological age was entered in the fourth step after all the variables to determine if this variable would explain any additional variance. The results of the regression analyses revealed that the blood pressure reading taken in week 3 was a predictor for adherence not only to antihypertensive medications but also to other prescribed medications included in the participants' regimens. Beliefs about self-care was a significant predictor only for other prescribed medications. Furthermore, chronological age did not significantly increase the observed R2 when it was entered after all of the variables in any of the sets of analyses conducted. These findings must be taken with caution, however, because the results reported here are subject to simultaneity bias, as blood pressure influences -.068 -.101 -.116 .414* -.057 R2 Change Cumulative/? 2 *p<.05;**p<.001. -.064 -.116 -.102 .400* Beta *p<.05;**p<.001. 616 The Gerontologist adherence behavior and vice versa. Unfortunately, limitations in our data prevent us from statistically controlling for this by conventional methods. The analyses conducted on the antihypertensive medications revealed similar results for both biweekly monitoring periods, as shown in Table 6. For the third biweekly period, approximately 28% of the variance (R2 = .277) was accounted for by the systolic blood pressure reading taken during week 3 in the fourth step. For the fourth biweekly period, the systolic blood pressure reading taken during week 3 accounted for 23% of the variance (R2 = .233) in the fourth step. None of the other variables were found to be significant predictors of total errors in either of the biweekly periods, when all of the variables were entered into the regression equation in the last step. Results from the analyses conducted on the other prescribed medications included in the participants' regimens also revealed similar findings for the last two biweekly monitoring periods. For the third biweekly period, approximately 39% of the variance {R2 = .393) was accounted for by the beliefs about self-care subscale and the systolic blood pressure reading taken during week 3 (as shown in Table 7) in the fourth step. For the fourth biweekly period, the same two variables accounted for approximately 32% of the variance (R2 = .321) in the fourth step. The knowledge of hypertension subscale and the measure of working memory were not found to be significant predictors of total errors in either of the biweekly periods by the time all of the variables had been entered into the regression equation. Discussion There are a number of important findings from this study and the results may be summarized as follows: (a) the oldest-old and middle-aged adults have the highest risk of nonadherence to medications, whereas the young-old are more likely to adhere than middle-aged adults; (b) high blood pressure readings predicted nonadherence to antihypertensive medications; and (c) beliefs about self-care were predictors of adherence to other types of medications only. Each of these major findings is discussed below. Age, Medication Type, and Adherence Clear effects of age on nonadherence were observed in this sample of hypertensive patients, as the oldest adults and the adults aged 55-64 made more errors in medication taking than the other age groups across the monitoring period. The types of errors they made were primarily omissions. The lowest number of errors was observed in the young-old age group (approximately 4%). The rate of nonadherence for the other participants fell in between the oldest groups (65-74 and 75+). Furthermore, there is some evidence to indicate that the oldest-old and those participants between the ages of 45 and 54 years of age were more likely to make Vol. 37, No. 5,1997 errors with their antihypertensive medications than the other age groups. These findings are consistent with previous work conducted by Park et al. (1992), which documented a greater likelihood of the oldest-old to omit dosages of medications relative to young-old adults. The present study suggests that this is a reliable finding, and also provides important data about middle-aged adults that Park et al. did not study. Middle-aged adults may also be at risk for nonadherence. It is tempting to speculate that the reason for the oldest-old not taking their medication as prescribed may be due to declines in cognitive functioning. We did not, however, find age differences in the measures of working memory and vocabulary that were administered, as is typically found in aging research. Working memory capacity has been reliably shown to decrease with age, and vocabulary ability generally remains stable or increases with age (see Salthouse, 1991). There is a trend in the expected direction for both of these measures in the data, but perhaps low statistical power, as a result of the limited number of participants in each cell, restricted the ability to detect significant differences. It should also be noted that the usual comparison in which age-related differences in working memory are reported is between young adults (ages 18-25) and older adults (over the age of 60). Our comparisons were between middle-aged and older adults, a factor that may have also contributed to our inability to detect these differences. Furthermore, there are significant moderate correlations between the working memory measures and the level of education of participants (correlation between working memory level score and education = .35, and correlation between working memory span score and education = .43). Therefore, it is possible that educational attainment moderated the decline in working memory, as the oldest age group was the most highly educated. Finally, the working memory span scores for the participants are somewhat low when compared to other samples, which have ranged from 2.91 to 2.33 for individuals between the ages of 60 and 87 (see Salthouse & Babcock, 1990). This finding may be due to our presenting the task verbally, which would increase the cognitive demands and probably lower scores relative to the standard method of presenting the task in a visual format. Thus, it is possible that our findings may not be completely generalizable to the total population because of these low scores. It is interesting that certain groups of middleaged adults were also more nonadherent than other groups in the sample. No evidence, however, is available from this study to suggest that beliefs about illness or medications might have influenced the adherence rates of the middle-aged differentially, as there were no main effects of age observed on any of the medication beliefs subscales. Thus, it is unlikely that differences in illness or medication beliefs between the age groups accounted for the findings that some of the middle-aged adults were also highly nonadherent. Perhaps the busy schedules of 617 same time. The results from the regression analyses also provide some support for the social-cognitive model as originally proposed by Park and her colleagues (see Park & Mayhorn, 1996). The measure of beliefs about self-care was shown to be the primary predictor for adherence to prescribed medications other than antihypertensive drugs. Thus, beliefs about taking medications may be more important for some types of medications than others. It would be interesting to conduct further research to ascertain which types of medications are affected by what kinds of beliefs. Additionally, the results from the correlational analyses suggested that working memory capacity might play a role in one's ability to adhere to a medical regimen, because the measure of working memory was correlated with nonadherence for the other prescribed medications the individuals were taking. It would be expected that working memory might be more influential in managing a regimen of multiple medications relative to taking a single medication because the organizational demands of maintaining a large regimen would be greater than with a small one (see Salthouse, 1991). This may be especially true for the elderly as the regimen becomes more complex over time (Cerrella, Poon, & Williams, 1980; Salthouse, Mitchell, Skovronek, & Babcock, 1989). This measure of working memory, however, was not found to be a significant predictor of total errors in the regression analyses conducted, possibly because of low variability of scores in this sample. Perhaps the use of a more sensitive measure of working memory or other measures of cognition might help to "tease out" this effect in future work. Therefore, in summary, the results from this research provide substantial information about adherence to prescribed medical regimens by individuals diagnosed with hypertension. These findings also clearly demonstrate the influence of the complex intermix of a number of different types of factors on adherence to prescribed medical regimens. the middle-aged adults might have interfered with their ability to take medications as prescribed. This possible influence on nonadherence was not addressed in this study. Therefore, future research is needed in this area to make this distinction. These data suggest that young-old adults are the most adherent of all of the age groups, as was shown in previous work by Park and colleagues. To account for this finding, it is possible that the young-old adults may be more focused on their medication-taking behaviors than middle-aged adults because of less hectic schedules. Furthermore, it is unlikely that young-old adults are experiencing major cognitive deficits that would undermine their ability to adhere. Therefore, they would be better able to handle the cognitive demands imposed on them by complex prescribed medical regimens than would the oldest-old. Finally, the most common mistakes made in medication taking by the participants were errors of omission. Few quantity or commission errors were observed. This finding suggests that cognitive aids such as telephone reminder systems (see Leirer, Tanke, & Morrow, 1993; Tanke & Leirer, 1993) or the use of organizers and organizational charts (see Park, Morrell, Frieske, Blackburn, & Birchmore, 1991) might be beneficial in helping individuals remember to take medications. Adherence to Antihypertensive Medications and Blood Pressure. — Results from the correlational and regression analyses conducted on only antihypertensive medications indicated that readings of high blood pressure were the primary predictor of nonadherence for this particular class of drugs. Higher systolic readings in week 3 of monitoring were shown to be related to later nonadherence. Because a major purpose of this study was to look at predictors of nonadherence, it is of clinical importance to demonstrate that high blood pressure readings may not only be a sign of uncontrolled hypertension. Such elevated readings may, in fact, be indicative of nonadherence, just as are beliefs, cognitive function, and other variables. Clinicians who detect high blood pressure readings in patients might be able to use this measure as a warning that medication has not been taken as prescribed. Adherence to Other Medications, Beliefs, and Cognition. — Another finding was that the best predictor (a measure of systolic blood pressure) for adherence to antihypertensive medications was also a significant predictor for other prescribed medications in the regimen. This finding suggests that individuals may form strategies for taking medications, such as taking all of them in the morning. In this sample, all participants were taking antihypertensive medications. 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Journal of Behavioral Medicine, 1, Received August 20,1996 Accepted April1,1997 Appendix. Items From the Self-Care Subscale of the Patient's Hypertension Beliefs Questionnaire 1. How likely is it that you will get other illnesses that are not life-threatening on a regular basis (such as a cold)? 1 = very likely; 2 = likely, 3 = neutral; 4 = unlikely, 5 = very unlikely. 2. How likely is it that you would stop taking medicine if it was costing a lot of money? 1 = very likely; 2 = likely; 3 = neutral; 4 = unlikely; 5 = very unlikely. 3. How well do you take care of your health? 1 = extremely well; 2 = very well; 3 = fairly well; 4 = not very well; 5 = not at all. 619