The Beliefs About Medication Scale: Development, Reliability, and Validity

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1 Journal of Clinical Psychology in Medical Settings, Vol. 9, No. 2, June 2002 ( C 2002) The Beliefs About Medication Scale: Development, Reliability, and Validity Kristin A. Riekert 1,3 and Dennis Drotar 2 The purpose of the study was to develop and evaluate a psychometrically sound health belief measure, the Beliefs About Medication Scale (BAMS) that can be used with adolescent chronic illness populations whose prescribed treatment includes oral medication. One hundred and thirty-three adolescents (age years) with asthma (n = 60), HIV (n = 31), or inflammatory bowel disease (n = 42) completed the BAMS and, along with their parent, a self-report medication interview. A confirmatory factor analysis supported the hypothesized subscales of Perceived Threat, Positive Outcome Expectancy, Negative Outcome Expectancy, and Intent to Adhere to treatment. The subscales evidenced good internal consistency and 3-week test-retest reliability. Univariate and multivariate analyses demonstrated that the health belief constructs accounted for 22% of the variance in medication adherence beyond demographic and illness characteristics. The study provides preliminary evidence of the reliability and validity of a theoretically based measure of health beliefs for adolescents. The BAMS may be a useful tool to evaluate the psychological barriers to adherence that place teenagers at risk for nonadherence. KEY WORDS: adherence; compliance; health beliefs; attitudes; social cognitive theory. An estimated 31% of children in the United States are affected by chronic health conditions (Newacheck & Taylor, 1992). Data from the pediatric chronic health conditions literature suggest that rates for medication nonadherence vary from 21 to 52% (e.g., Ettenger et al., 1991; Meyers, Thompson, & Weiland, 1996; Schoni, Horak, & Nikolaizik, 1995). Most studies find, when examined by broad developmental stages (e.g., child, adolescent, adult), that nonadherence is more prevalent during adolescence (e.g., Ettenger et al., 1991; Kovacs, Goldston, Obrosky, & Iyengar, 1992). Nonadherence can lead to unnecessary hospitalizations, diagnostic tests, increased medical complications, and risk to patients lives (DiMatteo, 1994; Dunbar-Jacob, 1993; Fotheringham 1 Department of Psychology, Case Western Reserve University, Cleveland, Ohio. 2 Division of Behavioral Pediatrics & Psychology, Rainbow Babies and Children s Hospital, Cleveland, Ohio. 3 Correspondence should be addressed to Kristin A. Riekert, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, 5501 Hopkins Bayview Circle, Baltimore, Maryland 21224; kriekert@welch.jhu.edu. & Sawyer, 1995). Moreover, in some instances, nonadherence may account for some of the unexplained relapses seen in children and adolescents with certain chronic health conditions (Davies & Lilleyman, 1995; Ettenger et al., 1991). Furthermore, during adolescence, children begin to assume more independent responsibility for their own treatment adherence. Consequently health professionals require the means to assess which adolescents and families will be at risk for nonadherence and evaluate their unique set of barriers to optimal adherence. An individual s beliefs and attitudes about one s illness and treatment have been found to be related to treatment adherence (Gochman, 1997; Janz & Becker, 1984; Norman & Conner, 1996). Health professionals, therefore, would benefit from tools to evaluate health beliefs that may place teenagers at risk for nonadherence with medication regimens. Unfortunately, the evaluation of health beliefs has been hampered by many methodological limitations. These problems include the use of unstandardized measures with unknown reliability and validity and a lack of theoretical consideration for the inclusion of items /02/ /0 C 2002 Plenum Publishing Corporation

2 178 Riekert and Drotar (Boer & Seydel, 1996; Maddux & DuCharme 1997; Strecher, Champion, & Rosenstock, 1997). One reason for the lack of well-validated measures is that many scales focus on combinations of adherence behaviors for a specific chronic health condition rather than one adherence behavior common across many chronic conditions. This research strategy necessitates developing a different measure for every illness. Despite the potential utility of a cross-disease measure of health beliefs about a specific adherence behavior (e.g., taking medication, dietary restrictions, exercise), to our knowledge, no such measure has been developed and validated for children or adolescents with chronic health conditions. To address this need, the primary goal of this study was to assess the reliability and construct/ criterion validity of the Beliefs About Medication Scale (BAMS). The BAMS was designed to assess health beliefs concerning one domain of adherence behaviors (i.e., oral medication use) that involves similar adherence behaviors across many chronic health conditions. This focus on a specific behavioral domain has the advantage of detailed measurement of medication-specific health beliefs and adherence. Moreover, this approach allows direct comparisons of adherence behaviors among adolescents with diverse chronic health conditions. Adolescents with three chronic health conditions were included in the sample to enhance and evaluate the generalizability of results across illness groups. To develop the BAMS, major health belief theories were critically reviewed, including the health belief model, protection motivation theory, theory of planned behavior/theory of reasoned action, and selfefficacy theory (Bandura, 1997; Boer & Seydel, 1996; Janz & Becker, 1984; Maddux & DuCharme, 1997; Rogers & Prentice-Dunn, 1997). An integrated conceptual model of the relationship between health beliefs and adherence behaviors was synthesized from these theories. Based on previous research and theory, primary constructs in this hypothetical model included Perceived Threat, Positive Outcome Expectancy, Negative Outcome Expectancy, and Intent. Perceived Threat is composed of two related concepts: perceived severity and perceived susceptibility of the illness. Adolescents with higher Perceived Threat have been found to have poorer adherence to diabetes regimens (e.g., Bond, Aiken, & Somerville, 1992). Positive Outcome Expectancy is defined as one s beliefs about the physical, emotional, and social benefits of taking the medicine. More perceived benefits (i.e., Positive Outcome Expectancy) are related to better adherence in teenagers with diabetes (e.g., Palardy, Greening, Ott, Holderby, & Atchison, 1998). Negative Outcome Expectancy includes beliefs about potential psychological barriers to performing the adherence behavior, and the consequences resulting from performing the adherence behavior. Higher perceived barriers (i.e. Negative Outcome Expectancies) to adherence are linked with poorer treatment adherence among adolescents with diabetes and cancer (e.g., Bond et al., 1992; Palardy et al., 1998; Tamaroff, Festa, Adesman, Walco, 1992). Although the relationship between adolescent s adherence intentions and actual adherence behaviors has not been studied, adults intention to perform adherence behaviors predicts the performance of such behaviors (Flynn, Lyman, & Prentice-Dunn, 1995; Randall & Wolfe, 1994). We hypothesized that a confirmatory factor analysis (CFA) would support the assumption that the BAMS measures these four health belief constructs. At the univariate level, it was hypothesized that Positive Outcome Expectancy and Intent would be positively correlated with adherence to treatment, whereas Perceived Threat and Negative Outcome Expectancy would be negatively correlated with adherence to treatment. It was hypothesized that these relationships would hold true for all illness groups. At the multivariate level, it was hypothesized that health beliefs would account for a significant proportion of variance in adherence behaviors beyond that predicted by demographic and illness variables. METHODS Participants Participants were 133 adolescents with one of the following chronic illness conditions: asthma (n = 60), human immunodeficiency virus (HIV; n = 31), and inflammatory bowel disease (IBD; n = 42). Participants were recruited from specialty clinics at Rainbow Babies and Children s Hospital, Cleveland, OH, and the HIV/AIDS Malignancy Branch at the National Cancer Institute, Bethesda, MD. These three chronic health conditions were chosen because they all require daily oral medication use. To be eligible to participate in the study, adolescents had to be between the ages of 11 and 18 years and prescribed oral medication (e.g., pills, liquids, inhaler) to be taken on a daily basis. Adolescents were excluded from the study if they had a disease duration

3 The Beliefs About Medication Scale 179 of less than 1 year or obvious developmental delays that would prevent them from completing the study requirements. One hundred and eighty-one adolescents were asked to participate in the study (82 with asthma, 32 with HIV, and 67 with IBD) and 75% agreed to participate in the study (73% with asthma, 97% with HIV, 66% with IBD). Reasons for refusal to participate in the study included no time (42%), teenager not interested (19%) and mother not interested (11%), and other (28%). Two adolescents with IBD were subsequently dropped from the study because they had been diagnosed less than 1 year. Fifty families were asked to participate in the test-retest portion of the study. Fifty families agreed, but only 27 mailed back the measures, representing 20% of the total sample. Sample demographic data are presented in Table I. The total sample included adolescents with a mean age of 14.2 years. The adolescents with asthma were significantly younger than the adolescents with IBD or HIV. The sample was almost evenly split between males and females. Overall, one fourth of the adolescents were members of ethnic minority groups, primarily African American. Adolescents with IBD were significantly more likely to be Caucasian than the teenagers with asthma or HIV, reflecting the epidemiology of the illness. The families in the total sample represented a wide range of socioeconomic status with annual household incomes ranging from less than $10,000 (8%) to over $100,000 (8.0%). The groups did not differ on physician rated illness severity, but did differ on duration of illness and number of medications prescribed, reflecting the epidemiology of the illnesses and the standard prescribed medication regimens. Measures Beliefs About Medication Scale The BAMS is a 59-item scale that asks adolescents to independently rate, on a 7-point Likert scale, how much they agree or disagree with statements about their illness and its treatment. The endpoint anchors of the scale were strongly disagree and strongly agree, except for four intent items, which had the anchors of definitely not likely and definitely likely. Previous health belief studies were utilized as the primary means of item generation. Therefore, many BAMS items are similar to those that have previously been found to be associated with adherence behaviors, except they have been rewritten to remove illness-specific references and to make them specific to medication use. Additional items were generated from the operational definitions of the health belief constructs and from informal consultation with health professionals involved in the treatment of adolescents with chronic health conditions (e.g., pediatric psychologists, social workers, and nurse practitioners) in an attempt to sufficiently represent the broad constructs. There were four hypothesized subscales: Perceived Threat (e.g., I do not think my illness is a serious illness, I think I will become sicker than I am right now ), Positive Outcome Expectancies (e.g., If I take my medicine the way the doctor says I should, it helps keep me feeling well, I want to take my medicine the way the doctor says I should because it matters to people I care about ), Negative Outcome Expectancies (e.g., The side effects of my medicine are so bad that I do not want to take it, It is embarrassing for me to take my medicine in front of people I do not know ), and Intent (e.g., I want to take every dose of Table I. Demographic and Illness Characteristics Total Asthma HIV IBD Variable (N = 133) (n = 60) (n = 31) (n = 42) Age, M (SD) 14.2 (2.0) 13.5 (1.9) 15.0 (2.0) 14.7 (1.7) F(2, 130) = 8.71, p <.001 Gender (% male) χ 2 (2) = 2.38, p =.304 Ethnicity (% Caucasian) χ 2 (2) = 9.87, p =.007 Yearly household income (%) χ 2 (4) = 6.94, p =.139 $19, $20,000 59, $60, Illness duration, M (SD) 8.1 (3.9) 10.0 (3.3) 10.0 (2.1) 4.4 (2.7) F(2, 121) = 51.67, p <.001 Physician rated severity (%) χ 2 (4) = 2.56, p =.634 Mild Moderate Severe # Medications prescribed, M (SD) 3.8 (2.5) 2.8 (1.6) 6.0 (2.9) 4.0 (2.4) F(2, 130) = 20.93, p <.001

4 180 Riekert and Drotar my medicine the way the doctor says I should, What are the chances that you will miss at least one dose of your medicine? ). Demographics Questionnaire The demographic questionnaire included questions such as the adolescent s age, gender, ethnicity, and age at diagnosis. The parent was asked questions about the highest level of schooling completed by the mother and father, mother and father s occupations, and mother s marital status. Physician Form The physician was asked, using a single item, to give a gross assessment (mild, moderate, or severe) of the adolescents illness severity relative to other adolescents with the same illness. Medication Interview The semistructured interview took approximately min and yielded a retrospective report of the adolescent s medication adherence during the week prior to enrollment in the study. For each medication prescribed, the adolescent was asked about doses missed, doses taken early or late (defined as ±1 hr), and doses where more or less medication than prescribed was taken. The questions were directed toward the teenager, but occasionally the teen asked the parent to help with the interview or a parent corrected the adolescent. The interviewer then asked the adolescent if he/she agreed with their parent and did not proceed until consensus was reached. The primary outcome variable for this study was the percentage of the total number of doses prescribed taken exactly as prescribed (e.g., the percent of prescribed doses taken at the correct time and correct amount per dose). This variable served as the primary variable to assess the criterion validity of the BAMS. The questions on this interview were based on an existing adult medication adherence interview (Williams, Rodin, Ryan, Grolnick, & Deci, 1998). Procedures The first author or a research assistant approached the family when they arrived for the clinic appointment. The first author or research assistant briefly explained this study to the family and assessed whether the adolescent met the eligibility criteria. If they agreed to participate in the study, the parent, typically the mother signed the consent form and the adolescent signed an assent form. The Medication Interview was completed with the parent and adolescent. The adolescent then completed the BAMS independently. After the medical appointment, the adolescent s physician (or nurse practitioner for the adolescents with HIV) was given the Physician Form. Because adolescents who are nonadherent to their medical regimen often choose not to participate or fail to complete research requirements (Riekert & Drotar, 1999; Roberts & Wurtele, 1980), a raffle for $20 gift certificates to a national retail electronics store was held as an incentive to participate in the study. The Institutional Review Boards at both sites approved this protocol. RESULTS The results of the study are presented in three sections. First is a description of the adolescents adherence to their medication regimens. The second section provides a detailed description of the development of subscales for the BAMS. A CFA was used to evaluate the construct validity of competing hypothetical models of the factor structure of the BAMS. Based on the results of the CFA, subscales were developed. Descriptive and reliability data are provided for each subscale. The final section provides data on the criterion validity of the BAMS by using correlations and hierarchical multiple regression analyses to test a priori hypothesized univariate and multivariate relationships between the BAMS subscales and medication adherence Description of Adolescents Medication Adherence The average adherence level based on the Medication Interview total number of doses taken exactly right was 73.25% (SD = 24.24, range 0 100%). Fifty percent of the sample reported taking less than 80% of their prescribed medication exactly right the week prior to their medical appointment. The three illness groups did not differ on adherence with 56, 37, and 50% of the adolescents with Asthma, HIV, and IBD respectively reporting <80% adherence with their medication regimen, χ 2 (2) = 2.99, p =.225.

5 The Beliefs About Medication Scale 181 Development of Subscales for the BAMS Initial data review resulted in the exclusion of three items because of poor variability. Because there were a large number of test items relative to the study sample size, a CFA could not be conducted at the item level because the model would be underidentified. That is, the number of parameters to be estimated exceeded the number of data points. Consequently, there would be an infinite number of solutions (Byrne, 1994). To prevent underidentification, items were grouped together to form nine-item packets. The packets were constructed using both theoretical and empirical rationale for including an item on a packet (see Riekert, 2000, for more discussion on the packet development). After the item packets were developed, they were reviewed to ensure that they did not violate statistical assumptions necessary for a CFA. Next, a CFA was performed to evaluate competing models of factor structures. Several a priori models were hypothesized and tested. The goal of the CFA was to determine the most parsimonious model from which to develop subscales. Because CFA had not been previously conducted for adolescent s health beliefs, post hoc model respecifications were permitted to further evaluate and improve model fit. Four a priori models were developed for the BAMS. The first hypothesized model included four factors that consisted of (1) Perceived Threat, (2) Positive Outcome Expectancy, (3) Negative Outcome Expectancy, and (4) Intent. This model is consistent with theoretical distinctions made between the constructs and was the a priori preferred model. Three other models were tested and involved combining factors from the fourfactor model into a three-factor, two-factor, and onefactor model (see Riekert, 2000, for more details on model specifications). The analyses were conducted using the EQS for Windows Version 5.7 program developed by Bentler (1995). Parameters were estimated via maximum likelihood (ML) using raw data as input. Two tests were run to assist in model respecification: the Wald Test and the Lagrange Multiplier Test (Bentler, 1995; Byrne, 1994). It is recommended that a number of fit indices be reviewed in the evaluation of the models (Kline, 1998). The standard reported fit indices include the chi-square fit index, the Nonnormed Fit Index (NNFI), the Comparative Fit Index (CFI), and Steiger s Root Mean Square Error of Approximation (RMSEA; Bentler, 1995; Kline, 1998). The chi-square fit index was high, χ 2 (21) = 38.08, p =.013, whereas the NNFI (.930), CFI (.959), and RMSEA (.078) indicated that four-factor health belief model fit the data well. Two other respecified models were statistically equivalent to this model, but not superior. As such, the four-factor model was selected because it was consistent with a priori theoretical expectations, it sufficiently fit the data, and there was no post hoc model fitting that increased the likelihood of capitalizing on chance (see Riekert, 2000, for more details on item factor loadings, the factor correlation matrix, and the fit indices for all tested models). Subscales of the BAMS Based on the results of the CFA, four subscales were developed for the BAMS. During the scale development phase, three additional items were deleted due to poor item-total correlations. The descriptive and reliability data for the BAMS subscales are presented in Table II. All four subscales were normally distributed and demonstrated good variability. The subscales demonstrated good reliability as evidenced by Cronbach s alphas (.79.87), mean item-total correlations (.44.53), and 3-week test-retest results (.71.77). Relationships Between the BAMS and Medication Adherence Correlations Nonparametric statistics were used (e.g., Spearman s rho) because medication adherence data, as a continuous variable, were nonnormally Table II. BAMS Subscale Descriptive Statistics and Reliability Data (N = 123) Theoretical Cronbach s Mean item-total Test-retest Subscale (number of items) M (SD) Range range alpha correlation reliability Perceived Threat (n = 13) (14.54) Positive Outcome Expectancy (n = 20) (16.48) Negative Outcome Expectancy (n = 13) (15.16) Intent (n = 7) (6.34)

6 182 Riekert and Drotar Table III. Correlation (Spearman s Rho) Between BAMS and Medication Adherence Medication adherence Total sample Asthma HIV IBD BAMS subscale (N = 119) (n = 52) (n = 28) (n = 39) Perceived Threat Positive Outcome Expectancy Negative Outcome Expectancy Intent p <.10. p <.05. p <.01. p <.001. distributed and transformation would lead to results that were difficult to interpret. In general, adolescent health beliefs correlated with adherence in the predicted direction (Table III). Lower Negative Outcome Expectancy and higher Positive Outcome Expectancy and Intent were significantly associated with better adherence (r =.29,.36, and.51 respectively, p <.01). In contrast to theoretical predictions, Perceived Threat was not significantly related to adherence (r =.17, p =.065), but it did approach significance. Correlations for each illness group were examined to assess whether the hypothesized relationships between health beliefs and adherence held true within illness groups. The magnitude of the correlations between adolescent health beliefs and adherence within each illness group were consistent or higher when compared to the results from the total sample (Table III.), except for Positive Outcome Expectancy for the asthma group and Intent for the HIV group. In both these instances, the correlations were lower and nonsignificant. Hierarchical Multiple Regression Regression analyses were calculated to test the hypothetical relationships between adolescents health beliefs and adherence behaviors. The analyses involving the adherence data were dichotomized into <80% and 80% adherence because the adherence data as a continuous variable violated the assumption of normality. The analyses were re-run using the continuous adherence data and yielded similar results. Table IV illustrates the results of the analyses using the dichotomous adherence variable. Demographic variables (age and ethnicity; Caucasian vs. non-caucasian) and illness variables (illness duration and number of medications prescribed) were entered prior to the health beliefs variables to Table IV. Summary of Hierarchical Multiple Regression Analysis With Dependent Variable = Medication Adherence (<80 vs. 80% Adherence; N = 110) Cumulative Variable β df R 2 R 2 change Step 1 Age.026 2, Ethnicity.186 Step 2 Illness duration.117 4, Number of.185 medications prescribed Step 3 Perceived Threat.005 8, Positive Outcome.060 Expectancy Negative Outcome.310 Expectancy Intent.219 p <.10. p <.05. p <.001. evaluate whether the BAMS subscales could account for variance in adherence above and beyond that accounted for by demographic and illness variables. The overall model was significant, R 2 =.30, F(8, 101) = 5.45, p <.001, with the health belief variables accounting for 22% of the variance in adherence beyond that accounted for by demographic and illness variables, F change (3, 111) = 7.04, p <.001. The results indicated that Intent (β =.219, t = 2.14, p <.05) and Negative Outcome Expectancy (β =.310, t = 2.58, p <.05) accounted for a significant proportion of the variance in adherence behaviors. In the final equation, ethnicity (β =.186, t = 2.08, p <.05) and number of prescribed medications (β =.185, t = 2.01, p <.05) were also significantly related to adherence, with Caucasian adolescents and those prescribed more medications reporting higher adherence. DISCUSSION This study was a preliminary evaluation of the reliability and validity of the BAMS for adolescents with a chronic illness. It extends the current health belief measurement research literature in several important ways. The BAMS was developed from a conceptual model based on prominent social cognitive theories and was subjected to a CFA as a more rigorous test of the construct validity than has previously been evaluated in pediatric chronic illness adherence studies.

7 The Beliefs About Medication Scale 183 The results of the CFA supported the hypothesis that the BAMS consisted of four health belief factors: Perceived Threat, Positive Outcome Expectancy, Negative Outcome Expectancy, and Intent. Furthermore, these subscales demonstrated good internal reliability and test-retest reliability. The hypotheses regarding the univariate relationship between the BAMS subscales and medication adherence were supported for both the total sample and the individual illness groups. All the BAMS subscales were significantly related to the adolescents self-report of medication adherence in the predicted direction except Perceived Threat, which approached significance. At the multivariate level, the data supported the hypotheses that health beliefs would account for a significant proportion of variance in adherence beyond demographic and illness variables. Taken together, the univariate and multivariate tests of the relationship between health beliefs and medication adherence provide preliminary support for the criterion validity of the BAMS. Overall, the final multivariate model accounted for a comparable amount of variance in adherence (30%) as found in other adolescent studies of health beliefs and adherence (19 52%; Bond et al., 1992; Brownlee-Duffeck et al., 1987; Palardy et al., 1998). As such, the BAMS appears to be a promising theoretically based measure of adolescent health beliefs about medications, with initial support for its construct and criterion validity and reliability. This study is one of the few to include multiple chronic health conditions and the first to use the same self-report adherence measure for all groups. The direction and magnitude of the univariate relationships held across the illness groups despite the different illnesses, the varying number of medications prescribed, and the differences in the types of medications the adolescents were taking. These results provide preliminary evidence of the generalizablity of the relationship between health belief constructs and adherence behaviors across illness groups. In contrast to other health belief instruments, the BAMS can be used to assess adolescents adherence to treatment for chronic health conditions that includes medication use as a component of the prescribed treatment regimen. These data suggest that the BAMS may be a useful research tool for evaluating the psychological beliefs about medication that place teenagers at risk for nonadherence with their medication regimen. It may be useful as a process measure to assess the fidelity of interventions designed to alter health beliefs and subsequently improve adherence (Rejeski, Brawley, McAuley, & Rapp, 2000). Nevertheless, future research is needed to test its utility for predicting which adolescents are at risk for nonadherence. Although it requires further assessment, the BAMS may also have the potential to be a useful clinical tool to facilitate the design and evaluation of interventions tailored to modify specific health beliefs about medication that may affect treatment adherence. Several limitations of the current study must be acknowledged. First, the sample size obtained was too small to perform the CFA at the item level and also prohibited randomly splitting the sample in half to test the validity of the derived factors on an independent sample. Furthermore, the small sample size of the separate illness groups prohibited testing whether the hypothesized multivariate relationships were consistent within illness groups. Second, there are limited data regarding the reliability and validity data for the adolescents self-report of medication adherence measure used in this study and no objective measure of adherence was used. Furthermore, adolescents may have been reluctant to report nonadherence in front of their parent. Finally, 25% of the families asked to participate in the study declined, and approximately 17% of those who agreed to participate were excluded from the regression analyses due to missing data. Research has consistently documented that individuals who refuse to participate in studies or do not complete all the requirements of the study differ systematically on variables of importance to the study, including adherence (Riekert & Drotar, 1999; Weinberger, Tublin, Ford, & Feldman, 1990). Consequently, it cannot be assumed that the results of the present study generalize to populations of less adherent adolescents. Future research is needed to replicate and cross validate the findings of this study on independent samples of adolescents with chronic illnesses. Larger samples of multiple chronic health conditions will allow for further evaluation of the construct and criterion validity of the BAMS within and between illness groups. Second, future research should examine the relationship between the BAMS subscales and objective measures of adherence (e.g., electronic monitoring devices), to minimize the likelihood that the findings obtained in this study were the result of self-report bias or shared method variance. Finally, the preliminary data based on the BAMS suggests that behavior-specific measures of health beliefs can account for a significant proportion of variance in measures of the corresponding adherence behavior. Consequently, future research may include the development of other behavior-specific health belief

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