Changes in Functional Health, Changes in ...

Health Psychology 2011, Vol. 30, No. 1, 31–39

© 2011 American Psychological Association 0278-6133/11/$12.00 DOI: 10.1037/a0021881

Changes in Functional Health, Changes in Medication Beliefs, and Medication Adherence Benjamin Schu¨z and Susanne Wurm

Jochen P. Ziegelmann

German Centre of Gerontology

Freie Universita¨t Berlin

Lisa M. Warner

Clemens Tesch-Römer

German Centre of Gerontology and Freie Universita¨t Berlin

German Centre of Gerontology

Ralf Schwarzer Freie Universita¨t Berlin Objective: Medication adherence often lies below recommendations although it is crucial for effective therapies, particularly in older adults with multiple illnesses. Medication beliefs are important factors for individual adherence, but little is known about their origin. We examine whether changes in functional health predict changes in medication beliefs, and whether such changes in beliefs predict subsequent medication adherence. Design: At three points in time over a 6-month period, 309 older adults (65– 85 years) with multiple illnesses were assessed. Latent true change modeling was used to explore changes in functional health and medication beliefs. Adherence was regressed on changes in beliefs. Main Outcome Measures: Medication beliefs were measured by the Beliefs About Medicines Questionnaire; medication adherence by the Reported Adherence to Medication Scale. Results: Functional health and medication beliefs changed over time. Increasing physical limitations predicted increases in specific necessity and specific concern beliefs, but not in general beliefs. Changes in specific necessity beliefs predicted intentional adherence lapses, changes in general overuse beliefs predicted unintentional adherence lapses. Conclusions: Medication beliefs partly depend on health-related changes, and changes in beliefs affect individual adherence, suggesting to target such beliefs in interventions and to support older adults in interpreting health changes. Keywords: medication adherence, medication beliefs, older adults, functional health, symptoms

Rosemann, & Szecsenyi, 2008; Schu¨z, Wurm, Warner, & TeschRömer, 2009). Successful treatment of these illnesses, which means stable or improved quality of life and physical functioning, requires, among others, the adherence to prescribed medication. Systematic reviews have shown that poor adherence to medication is associated with increased health problems and mortality (Simpson et al., 2006). Despite this, however, between 25% (DiMatteo, 2004) and 50% (World Health Organization, 2003) of adults are not adherent to their prescribed medication. It is important to distinguish whether medication nonadherence is the result of accidental slips (e.g., forgetting) or a purposeful behavioral decision. Accordingly, unintentional nonadherence and intentional nonadherence have been differentiated (Horne, 2006; Wroe, 2002). While unintentional nonadherence mainly means accidental nonadherence episodes, intentional nonadherence relates to changing or skipping medication on purpose. Unintentional nonadherence is assumed to be affected by demographic and clinical factors, whereas intentional nonadherence is assumed to be dependent on individual beliefs about medication such as expecting side effects or doubts in the necessity of medication (Wroe, 2002). This underlines the importance of research on factors that can shed light on individual decisions relating to medication adher-

Sooner or later, increasing age is almost inevitably associated with a growing number of chronic and acute illnesses (Fried, 2000). This individual accumulation of illnesses (multimorbidity) affects the majority of individuals over 60 years of age. Epidemiological data suggest that around 61% of men and 65% of women over 60 years suffer from two or more co-occurring diseases (van den Akker, Buntinx, Metsemakers, Roos, & Knotterus, 1998). Among the most common conditions in multimorbidity are cardiovascular diseases, diabetes, and arthritis (Laux, Kuehlein,

Accepted under the editorial term of Robert M. Kaplan. Benjamin Schu¨z and Susanne Wurm, German Centre of Gerontology; Jochen P. Ziegelmann, Department of Health Psychology, Freie Universita¨t Berlin; Lisa M. Warner, German Centre of Gerontology and Department of Health Psychology, Freie Universita¨t Berlin; Clemens Tesch-Römer, German Centre of Gerontology; and Ralf Schwarzer, Department of Health Psychology, Freie Universita¨t Berlin. This study (PREFER) was funded by the German Federal Ministry of Education and Research (Grant No. 01ET0702). The first and fourth author are funded within this project, the third author is funded by Grant No. 01ET0801 by the same funding body. The content is the sole responsibility of the authors. Correspondence concerning this article should be addressed to Benjamin Schu¨z, Ph.D., German Centre of Gerontology, Manfred-von-RichthofenStr. 2, 12101 Berlin, Germany. E-mail: [email protected] 31

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ence. While demographic and clinical factors seem to play only a minor role in explaining who is more likely to intentionally skip medication (DiMatteo, 2004), individual beliefs about medication are more likely to determine individual adherence behavior (Horne, 2006). The most commonly used psychosocial approach to explore such medication beliefs is the Necessity-Concerns Framework (NCF; Horne, 2003), a treatment-oriented extension of the Common-Sense Model of health and illness (CSM; Leventhal, Brissette, & Leventhal, 2003). The theory assumes that individual adherence behavior is an attempt to cope with the threat caused by illnesses and results from a cognitive and emotional appraisal process. Accordingly, people are more likely to be adherent if they perceive their conditions as severe and threatening enough to warrant treatment, and at the same time perceive a specific treatment to have more positive than negative consequences. For example, a person with cardiovascular disease who experiences worsening in symptoms, such as increasing difficulties to climb stairs, and who perceives these symptoms to be serious enough to treat them, might come to the conclusion that adhering to the prescribed cardiovascular medication promises more positive (e.g., symptom relief) than negative (e.g., drowsiness) results and thus take the medication more regularly than before. These beliefs can be measured with the Beliefs about Medicines Questionnaire (BMQ; Horne, Weinman, & Hankins, 1999), that assesses cognitions both specific to an individual’s prescriptions (specific necessity: individual beliefs in the necessity and beneficial effects of medication; specific concerns: individual beliefs in negative consequences of medication such as side effects) and general for all medicines (general harm: beliefs that medicines do more harm than good; general overuse: beliefs that too many medicines are being prescribed). The BMQ has been applied in a multitude of clinical settings from medication for cardiovascular disease (Bane, Hughes, & McElnay, 2006) and arthritis (Neame & Hammond, 2005), or diabetes (Tibaldi et al., 2009). A result consistent in most illness groups was that specific beliefs were better predictors of adherence than general beliefs.

Medication Beliefs in Older Adults With Multiple Illnesses Older adults with multiple illnesses have specific need to adhere to their prescribed medication, yet adherence falls below desirable levels. Our study therefore aims at exploring whether medication beliefs predict medication adherence in this specific sample. Individual beliefs about medication might be especially important factors for adherence in older people with multiple illnesses: The likelihood of potentially inappropriate medication, drug interactions and application problems increases with the number of medicines prescribed (Milton, Hill-Smith, & Jackson, 2008). This can affect how older people think about their medication (Modig, Kristensson, Kristensson Ekwall, Rahm Hallberg, & Midlov, 2009), which in turn changes individual adherence (Benner et al., 2009; Hughes, 2004). One challenge in examining older individuals with multiple illnesses using the CSM and NCF is that individuals who suffer from multiple illnesses experience both symptoms that are specific to one disease and symptoms that can be caused by several diseases, which complicates assessing specific attributions of symptoms to illnesses and treatments.

The illnesses most prevalent in multimorbid older adults are cardiovascular disease, diabetes or arthritis (Laux et al., 2008; Schu¨z et al., 2009). These illnesses have in common that they are often accompanied by functional limitations (Kadam & Croft, 2007; Noe¨l et al., 2007). The majority of individuals affected by multiple illnesses experience functional limitations as highly prevalent symptoms during their illness trajectories, as many illnesses share risk factors and symptoms (Laux et al., 2008). Thus, our study, rather than focusing on a list of symptoms as often employed in studies examining the CSM and the NCF, examines functional limitations because they reflect shared symptoms of multimorbidity. The CSM and NCF assume that on experiencing an increase in illness-related symptoms, individuals enter a contemplation process, which finally results in taking up adaptive behavioral responses (in this case, medication adherence) or not. There is evidence for this process: Halm, Mora, and Leventhal (2006) have shown that a substantial proportion of older adults only consider themselves as suffering from asthma and adhere to treatment when experiencing concrete symptoms, whereas adherence was poor during asymptomatic episodes. For the target group of multimorbid older adults, it is therefore crucial to examine whether changes in physical functioning lead to changes in cognitions, which in turn predict changes in behavior. Such change-change associations are inherent in most behavior-oriented theories, yet have rarely been examined empirically (Weinstein & Rothman, 2005).

Research Questions Our study aims at examining the associations between changes in health, changes in medication beliefs, and changes in medication adherence implied in the Common-Sense Model (Leventhal, Brissette, & Leventhal, 2003) and the Necessity-ConcernsFramework (Horne, 2003) in a sample of older adults suffering from multiple illnesses, a group at specifically high risk for further health deteriorations. To our knowledge, these change-change associations have not been tested before. We rely on latent true change modeling (Geiser, Eid, Nussbeck, Courvoisier, & Cole, 2010; Steyer, Partchev, & Shanahan, 2000) to model individual change-change associations using a structural equation modeling framework. In particular, we will examine whether changes in physical functioning as a symptom prevalent in most multimorbidity patterns can predict changes in individual beliefs about medication. We will further examine the shape of change, that is, whether decreases or increases in physical functioning predict decreases or increases in medication beliefs. Finally, we will examine whether such changes in medication beliefs can predict subsequent medication adherence.

Method These research questions were addressed in a longitudinal study with three measurement points in time over a 6-month period.

Participants and Procedure Participants for this study were recruited from the third assessment wave of the German Aging Survey (Wurm, Tomasik, & Tesch-Römer, 2010), a population-based representative survey in

CHANGES IN MEDICATION BELIEFS

the German population between 40 and 85 years of age. Participants were considered eligible if they fulfilled the following inclusion criteria: a) being 65 years of age or older; b) suffering from at least two conditions mentioned either in the Charlson Comorbidity Index (Charlson, Szatrowski, Peterson, & Gold, 1994) or the Functional Comorbidity Index (Groll, To, Bombardier, & Wright, 2005); and c) having provided written consent to be contacted for further studies. From the initial sample of 6,205 people (aged 40 – 85), n ⫽ 443 (7.14%) fulfilled all inclusion criteria. Of these 443, n ⫽ 309 (69.7%) gave informed consent for the study. Participants in this baseline sample were on average 73.27 years old (SD ⫽ 5.1), and 41.7% of them were women. Around 12.6% indicated low, 52.1% medium, and 35.3% high education according to the International Standard Classification of Education (Unesco, 1997). Geographically, participants came from all regions of Germany, with n ⫽ 108 (35%) living in the Eastern federal states (former German Democratic Republic). After giving informed consent, participants made appointments for the first measurement (Time 1, March 2009). They were then visited at home by trained interviewers, completed a 30-min interview and were given a questionnaire with a prepaid return envelope. The second measurement point (Time 2, June 2009) was a questionnaire only with prepaid return envelope. Here, n ⫽ 252 (81.6% of the initial sample) took part. The third measurement point (Time 3, September 2009) consisted of another personal interview and questionnaire. This assessment was completed by n ⫽ 271 participants (87.7% of the initial sample). At every measurement point, participants not returning the questionnaire received one postal reminder.

Measures A full medication inventory (Psaty et al., 1992) was conducted at Time 1: Participants were asked to bring all medicines which were then recorded according to the medication code (if available) or the drug brand name and dosage. Beliefs about Medicines were assessed at Times 1, 2, and 3 using a 8-item short version of the Beliefs about Medicines Questionnaire (Horne et al., 1999). This short version was developed from data of a pilot study in N ⫽ 104 older individuals with multiple illnesses by reducing the original questionnaire to the best-discriminating items for the four BMQ subscales (two items each): General Harm (e.g., “Most medicines are addictive,” interitem correlation rii ⫽ .44, p ⬍ .01); General Overuse (e.g., “Doctors use too many medicines,” rii ⫽ .64, p ⬍ .01); Specific Necessity (e.g., “My health, at present, depends on my medicines,” rii ⫽ .79, p ⬍ .01); and Specific Concerns (e.g., “My medicines disrupt my life,” rii ⫽ .56, p ⬍ .01). All items were answered on 5-point Likert-type scales ranging from 1 “totally disagree” to 5 “totally agree.” Medication Adherence was measured by two items from the Reported Adherence to Medication scale (RAM; Horne et al., 1999) asking about altering and forgetting to take medication. Physical functioning was measured in the questionnaire by using the 10-item Physical Functioning subscale of the SF-36 (Ware & Sherbourne, 1992). The degree of limitation in activities such as lifting or carrying groceries, kneeling, walking, bathing, dressing, and so forth was rated on a three-point scale from 1 “severely limited” to 3 “not limited at all.” In order to gain more reliable estimates, the 10 items were parceled into two parcels of

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five items each (Bandalos, 2002). Illnesses were assessed by asking participants to indicate their illnesses on a list of 23 conditions informed by the Charlson Comorbidity Index (Charlson, Szatrowski, Peterson, & Gold, 1994) and the Functional Comorbidity Index (Groll et al., 2005). Covariates included age, sex, number of illnesses, and educational status according to the International Standard Classification of Education (Unesco, 1997).

Analytical Procedure Changes in physical functioning and changes in medication beliefs were analyzed using Latent True Change Modeling (LTC; Geiser et al., 2010; Reuter et al., 2010; Steyer, Eid, & Schwenkmezger, 1997; Steyer et al., 2000). Separate models were computed for the four medication beliefs domains specific necessity (Model 1), specific concerns (Model 2), general harm (Model 3), and general overuse (Model 4). LTC factor scores were saved, and adherence (log-transformed RAM scores due to severe skewness) was subsequently regressed on the LTC factors. Analyses of variance were computed in order to illustrate the shape of change in physical functioning and medication beliefs. Dropout analyses were conducted to examine differences between participants dropping out of the study and those remaining in the study. All latent analyses were done in MPlus 5 (Muthe´n & Muthe´n, 2007), with missing data specified in the model using the Maximum Likelihood Estimator with robust standard errors. All other analyses were done in SPSS. Goodness of fit was evaluated using the ␹2-test, the Comparative Fit Index (CFI) and the Root Mean Square Error of Approximation (RMSEA). Good model fit is indicated by a nonsignificant ␹2-test, a CFI ⬎ .95 and a low RMSEA value ( p ⬍ .05). Latent True Change Modeling. Latent True Change Modeling (LTC) is an approach to modeling individual differences in intraindividual change based on structural equation modeling with all advantages of latent variables, in particular greater reliability due to the control of measurement error. Compared to growth curve modeling, LTC has the advantage of allowing for individual differences in change. In principle, LTC models are an extension of the basic longitudinal measurement model of confirmatory factor analysis that additionally decomposes the time-specific factors of subsequent measurement points into the state factor of the previous measurement point plus a latent change factor representing latent change between the measurement occasions. (Geiser et al., 2010; Reuter et al., 2010). This allows for identifying and measuring the latent change factor using the indicators for the state factor and relating this latent change factor to other variables within a model. In addition, indicator-specific factors were specified in order to control for indicator-specific effects due to for example, unique item wordings, that longitudinally can amount to a serious problem in model specification, as this would lead to higher autocorrelations of a specific indicator over time than the correlations with other indicators measuring the same construct (Eid, Schneider, & Schwenkmezger, 1999; Reuter et al., 2010). The generic LTC model in Figure 1 illustrates that changes in physical functioning between Time 1 and Time 2 predict changes in medication beliefs between Time 2 and Time 3 (␤123). In addition, changes in physical functioning between Time 1 and Time 2 were also specified as predictors of concurrent changes in

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Figure 1. Generic Latent True Change Model for Physical Functioning (PF) and Medication Beliefs (MB) at Time 1, 2, and 3.2

medication beliefs (␤122), and changes in physical functioning between Time 2 and Time 3 predicted concurrent changes in medication beliefs (␤133).

Results General Characteristics Participants suffered on average from 5.55 illnesses (SD ⫽ 2.99), with hypertension (67.64%), ostheoarthritis (63.11%), hyperlipidemia (49.19%), arthritis (31.07%), and peripheral vascular disease (30.74%) being the five most prominent conditions. On average, participants consumed 4.26 (SD ⫽ 2.96) medicines per day.

Dropout Analyses Participants dropping out between Time 1 and Time 2 or between Time 2 and Time 3 were examined for significant differences in the study variables at Time 1. Drop-outs at Time 2 indicated significantly higher specific necessity beliefs and significantly lower general overuse beliefs at Time 1 (all ps ⬍ .05). No significant differences were found for those who dropped out between Time 1 and Time 3.

Goodness of Fit Model 1 (LTC model for changes in specific necessity) fitted the data well with CFI ⫽ .99 and RMSEA ⫽ .03, however, the ␹2-test was significant: ␹2 ⫽ 99.51, df ⫽ 77, p ⬍ .05. Model 2 (changes

in specific concerns) showed a similarly good fit with CFI ⫽ .99, RMSEA ⫽ .03 and ␹2 ⫽ 97.14, df ⫽ 77, ns. Fit for general medication beliefs was less promising: Model 3 (changes in general overuse beliefs) yielded CFI ⫽ .99, RMSEA ⫽ .03, but ␹2 ⫽ 101.83, df ⫽ 77, p ⬍ .05. Model 4 (changes in general harm) yielded CFI ⫽ .95, RMSEA ⫽ .08 and ␹2 ⫽ 256.80, df ⫽ 77, p ⬍ .01.1

Latent Change Models All means of the latent change variables were significantly different from zero, which indicates substantial change in the factors over the three measurement points in total and between neighboring measurement points. Table 1 shows that changes in specific necessity beliefs (Model 1) between Time 2 and Time 3 were significantly predicted by changes in physical functioning between Time 1 and Time 2 (␤123 ⫽ ⫺.25, p ⬍ .01) in the manner that increases in physical functioning predicted decreases in specific needs. Similarly, 1 Detailed factor loading and variance component tables are available from the first author upon request. 2 In order to identify the model, the first indicator of each factor is specified as reference indicator and is accordingly fixed at one. Note that the T2 and T3 factors are perfectly determined by the T1 state factors and the T1–T2 change factors (for the T2 factors) and in addition the T2–T3 change factors (for the T3 factors). The factor loadings ␭ have no occasion index k, because factor loadings are time-invariant.

CHANGES IN MEDICATION BELIEFS

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Table 1 Parameter Estimates for Latent True Change Models 1– 4 Parameter

Model 1 (specific necessity)

Model 2 (specific concerns)

Model 3 (general harm)

Model 4 (general overuse)

␳111 ␳112 ␳113 ␳122 ␳123 ␤122 ␤123 ␤133

⫺.19ⴱⴱ ⫺.21ⴱⴱ .01 ⫺.38ⴱⴱ .13 ⫺.02 ⫺.25ⴱⴱ ⫺.05

⫺.12 ⫺.22ⴱⴱ .04 ⫺.50ⴱⴱ .18ⴱ ⫺.11 ⫺.01 ⫺.21ⴱⴱ

⫺.08 ⫺.23ⴱⴱ .03 ⫺.31ⴱⴱ ⫺.48ⴱ ⫺.14 .27 .07

.06 ⫺.23ⴱⴱ .03 ⫺.46ⴱⴱ .05ⴱ ⫺.07 .02 ⫺.06

ⴱ

p ⬍ .05.

ⴱⴱ

p ⬍ .01.

changes in specific concerns (Model 2) between Time 2 and Time 3 were significantly predicted by changes in physical functioning between Time 2 and Time 3 (␤133 ⫽ ⫺.21, p ⬍ .01). Changes in general medication beliefs (general harm, Model 3 and general overuse, Model 4) on the other hand were not significantly predicted by changes in physical functioning. Analyses of variance were conducted using quartiles of LTC scores in physical functioning between Time 1 and Time 2 as factor, and LTC scores in medication beliefs between Time 2 and Time 3 as dependent variables in order to illustrate these relations. Both ANOVAs for changes in specific necessity, F(3, 303) ⫽ 5.84, p ⬍ .01 (linear term) and changes in specific concerns, F(1, 303) ⫽ 2.89, p ⬍ .01 (quadratic term) were significant. Figure 2 shows that in particular individuals in the lower quartile of physical functioning change (i.e., those with decreases) perceived increases in specific necessity of their medication. Specific concern beliefs decreased strongest in individuals in the fourth quartile of physical functioning change (improvements).

Covariates of Medication Beliefs Table 2 shows that the health-related covariates (number of illnesses and number of medicines) affected all medication beliefs

in that more medicines were associated with higher specific necessity and specific concern, but with less general harm and general overuse. A higher number of illnesses were associated with higher specific concern, higher general harm and higher general overuse. From the socioeconomic covariates, only education predicted medication beliefs in that less education was associated with higher specific concerns and higher general harm beliefs.

Changes in Medication Beliefs and Changes in Medication Adherence In a third set of analyses, self-reported intentional and unintentional nonadherence (log-transformed due to skewness) were regressed on the LTC scores (see Table 3). The hierarchical multiple regression analyses showed that intentional nonadherence at Time 3 could be predicted by baseline behavior (nonadherence) in the first step and the number of medications in the second step, with more medicines making intentional nonadherence less likely. In the third step, changes in general harm and specific necessity predicted nonadherence, with increasing general harm beliefs making nonadherence more likely, and increasing specific necessity beliefs making nonadherence less likely. For unintentional nonadherence Time 3, baseline behavior was predictive in the first step,

Figure 2. LTC Scores of Specific Necessity and Specific Concern in Quartiles of LTC Scores Physical Functioning.

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Table 2 Parameter Estimates for Medication Beliefs Time 1 and Physical Functioning Time 1 Regressed on Covariates in Models 1– 4 Model 1

Model 2

Model 3

Model 4

Covariates (T1)

SN T1

PF T1

SC T1

PF T1

GH T1

PF T1

GO T1

PF T1

Age Sex (1 ⫽ Male, 2 ⫽ Female) Education Number of illnesses Number of medicines

.02 .02 ⫺.01 .09 .47ⴱⴱ

⫺.20ⴱⴱ ⫺.14ⴱⴱ .13ⴱ ⫺.38ⴱⴱ ⫺.09

⫺.07 ⫺.05 ⫺.19ⴱⴱ .29ⴱⴱ .15ⴱⴱ

⫺.18ⴱⴱ ⫺.14ⴱⴱ .12ⴱ ⫺.39ⴱⴱ ⫺.09

.03 ⫺.11 ⫺.24ⴱⴱ .20ⴱⴱ ⫺.20ⴱⴱ

⫺.20ⴱⴱ ⫺.14ⴱ .13ⴱ ⫺.40ⴱⴱ ⫺.10

⫺.05 ⫺.11 ⫺.08 .24ⴱⴱ ⫺.23ⴱⴱ

⫺.19ⴱⴱ ⫺.13ⴱ .13ⴱ ⫺.37ⴱⴱ ⫺.10

Note. SN ⫽ specific necessity; SC ⫽ specific concerns; GH ⫽ general harm; GO ⫽ general overuse; PF ⫽ physical functioning. p ⬍ .05. ⴱⴱ p ⬍ .01.

ⴱ

and increases in general overuse perceptions were predictive of increases in the likelihood to forget to take medication.

Discussion This study examined whether changes in subjective functional health caused changes in medication beliefs in a sample of older adults suffering from multiple illnesses using a latent true change modeling approach, and whether such changes in beliefs could predict self-reported medication adherence behavior. The operationalisation of medication beliefs in this article relies on the Beliefs about Medicines Questionnaire (BMQ; Horne et al., 1999), which assumes that individuals hold both specific necessity and concern beliefs about their own medication and general beliefs about medication. We found that changes in functional health predicted changes in specific medication beliefs of the participants, but not changes in general beliefs about medicines. Changes in specific necessity beliefs predicted intentional adherence behavior, whereas changes in general overuse beliefs predicted unintentional adherence lapses.

Changes in Functional Health and Changes in Medication Beliefs In particular, we found that decreases in functional health predicted subsequent increases in specific necessity beliefs and concurrent increases in specific concern beliefs.

Participants with decreasing functional health perceived increasing necessity for their medication, whereas participants with increasing functional health perceived decreasing necessity for their medication. These findings can be interpreted in line with the Necessity-Concerns Framework for medication adherence (NCF; Horne, 2003), an extension of the treatment control facet of the Common-Sense Model of health and illness behavior (CSM; Leventhal et al., 2003). In terms of the CSM, an increase in symptoms of the illness can lead to adaptive responses such as higher perceived necessity of medication. This means that the perceived necessity for medication due to increased symptoms outweighs possible concerns about the medication (Clifford, Barber, & Horne, 2008; Horne & Weinman, 2002). Our study adds to previous work in that it establishes a longitudinal perspective on the genesis of such beliefs in treatment necessity, which allows for stronger causal interpretations. The finding that, on the other hand, in participants with improving functional health, specific necessity beliefs decreased, might seem counterintuitive at first sight—after all, symptoms have improved, which could also be seen as an indicator of the effectiveness of medication. However, previous work in asthma patients has shown that the experience of illness symptoms is central to perceiving a necessity of treatment for chronic illness (Halm et al., 2006). In addition, this finding can be interpreted in line with Horne’s (2003) observation that most individuals taking medication seem to hold negative medication schemes: As soon as symptoms alleviate, the specific need to take

Table 3 Intentional and Unintentional Nonadherence to Medication Time 3 Regressed on LTC Scores and Covariates Intentional nonadherence time 3

Unintentional nonadherence time 3

Step

Predictor

␤ Step 1

␤ Step 2

␤ Step 3

⌬R2

␤ Step 1

␤ Step 2

␤ Step 3

⌬R2

1 2

Nonadherence Time 1 Sex Age Education Number medicines Number illnesses Functional health time 1 LTC general harm LTC general overuse LTC specific necessity LTC specific concern

.29ⴱⴱ

.27ⴱⴱ .00 .08 ⫺.02 ⫺.13 .14 .04

.26ⴱⴱ ⫺.01 .07 ⫺.02 ⫺.15ⴱ .16ⴱ .04 .15ⴱ .03 ⫺.15ⴱ .06

.08ⴱⴱ .02

.31ⴱⴱ

.30ⴱⴱ ⫺.10 ⫺.05 ⫺.05 .00 .14 .06

.29ⴱⴱ ⫺.10 ⫺.07 ⫺.05 .02 .12 .04 ⫺.09 .17ⴱⴱ ⫺.05 ⫺.03

.09ⴱⴱ .02

3

ⴱ

p ⬍ .05.

ⴱⴱ

p ⬍ .01.

.05ⴱ

.03ⴱ

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further medication decreases, and potentially negative general medication schemes overwrite cognitive representations of positive effects of medication adherence as expressed in the necessity items. We found a synchronic effect of changes in functional health on changes in specific concerns about medication. In particular, decreasing functional health was accompanied by increases in concerns about medication, and increasing functional health was accompanied by decreases in medication concerns. This is consistent with the few previous studies examining the effects of symptom change on medication beliefs (Aikens, Nease, & Klinkman, 2008; Gonzalez et al., 2007; Schrimshaw, Siegel, & Lekas, 2005), which suggest that participants who have experienced positive effects of treatment decrease possible concerns such as expecting side effects. Our finding that changes in functional health concurrently predict changes in medication beliefs gives further support to the idea that individuals do take into account their specific illnessrelated experiences when evaluating possible concerns about their medication, which is in contrast to Horne and Weinman’s (2002) hypothesis that such concerns are mainly fueled by general beliefs about medication. The finding that general beliefs about medicines were not affected by changes in functional health is in accordance with the CSM, as such general beliefs are thought to rely on general schemes about medicines, that in turn are fueled by media, previous experience, and general critical views toward science and medicine (Horne, 2003). In addition to these change-change associations, we found that a higher number of illnesses was associated with more specific concerns, more general harm beliefs and more general overuse beliefs. This suggests that individuals with worse health are more concerned about their medicines. Previous research suggests that such associations might be due to an unclear attribution of symptoms and impairments to either illnesses or treatment side effects: Individuals could thus attribute symptoms to their medication and, in turn, gain more negative beliefs about their medication (Schrimshaw, Siegel, & Lekas, 2005). The number of medicines an individual was taking was negatively related to general harm and general overuse beliefs, but positively related to specific necessity and specific concerns. This could, on the one hand, suggest that individuals who take more medicines are in fact in greater need, but also that they might worry more about possible side effects of their medication regimen (Modig et al., 2009). We also found that education was negatively related to specific concerns and general harm beliefs, which suggests that particularly individuals with lower education are at risk for negative treatment beliefs. Similar results have been reported by Mårdby, Åkerlind, and Jörgensen (2007), who found education to negatively affect medication beliefs.

Changes in Medication Beliefs and Changes in Medication Adherence Apart from examining changes in medication beliefs, our study goes beyond previous studies on the relation between medication beliefs and adherence in that its longitudinal latent true change design supports a causal chain from changes in illness symptoms (functional limitations) over changes in medication beliefs (in

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particular specific necessity and specific concerns) to adherence behavior. Intentional nonadherence. In particular, we found that increases in specific necessity beliefs predicted better adherence to medication in that individuals reported less intentional changes in their adherence behavior. This corroborates a large body of research on medication beliefs that consistently shows that necessity beliefs are associated with better adherence (Clifford et al., 2008; Horne, 2003). The change-change associations implied in the NCF have however not yet been examined. Individuals with improving health, however, perceive less specific necessity and, thus, are at greater risk for nonadherence (Horne & Weinman, 2002). In addition to that, increases in the general belief that medication is harmful predicted a greater likelihood to intentionally change or skip prescribed medication regimens. Although the BMQ suggests specific medication beliefs to be better predictors of adherence than general beliefs, some previous studies (e.g., Mårdby et al., 2007) reported similar effects of general medication beliefs on adherence. Unintentional nonadherence. With regard to unintentional adherence problems, we found that in particular individuals who increased their beliefs in physicians prescribing too many medicines were more likely to forget to take their medication. This finding suggests that there might be motivated memory effects at work, in that people are more likely to forget medication adherence if they value medication negatively.(congeniality hypothesis; Eagly, Chen, Chaiken, & Shaw-Barnes, 1999).

Functional Health and Symptoms in Multimorbidity Our study relies on the CSM (Leventhal, Brissette, & Leventhal, 2003) as theoretical background. Experiencing symptoms is the central input in the process depicted in the CSM, and studies usually assess this part by presenting lists of symptoms and asking individuals to indicate whether these symptoms were caused by their illness or not (e.g., Kaptein et al., 2007). The specific focus of our study on multimorbid older adults, however, makes examining such attributions to specific illnesses difficult. All participants in our study suffered from two or more illnesses, at least in part sharing risk factors and symptoms (Laux, Kuehlein, Rosemann, & Szecsenyi, 2008). Thus, we relied on the physical functioning subscale of the SF-36 (Ware & Sherbourne, 1992) as a well-evidenced indicator for functional limitations, which are experienced by a majority of older individuals suffering from multiple illnesses (Kadam & Croft, 2007).

Limitations and Strengths There are limitations to our study that merit discussion. First, adherence behavior was assessed via self-report. Socially desirable behaviors such as medication adherence are likely to be overreported (Stafford, Jackson, & Berk, 2008), and the positively skewed distribution of self-reported adherence scores in our study seems to confirm this hypothesis. However, comparative studies have shown that self-reports are, nevertheless, valid indicators of individual differences in adherence (Hansen et al., 2009). Furthermore, the sample of our study consisted of unpaid volunteers, which makes it likely that our sample overrepresents relatively healthy individuals with multiple illnesses. Nevertheless, the Ger-

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man Aging Survey, from which our sample was selected, is a population-representative study (Wurm et al., 2010), which implies that our sample goes beyond mere convenience samples. The operationalization of symptoms as changes in functional health lacks the specific attribution of symptoms to illnesses as in the IPQ, but it allows to assess changes in symptoms shared by a multitude of illnesses in older individuals and, thus, model a chain of changes in health, cognitions and behavior that could be generalized to the population of older individuals, who in their majority are affected by multiple illnesses rather than single, isolated illnesses (van den Akker, Buntinx, Metsemakers, Roos, & Knotterus, 1998). Apart from these limitations, our study has some specific strengths. First, it focuses on older adults with multiple illnesses, an underresearched high-risk group with high need for medication adherence (Noe¨l et al., 2007). A second strength is the use of latent true change methodology, which allows to relate true measurement-error-free changes in functional health to changes in medication beliefs and, thus, provides stronger support for the assumed causal chain between health, cognitions, and behavior.

Implications Our study showed for the first time that changes in functional health predict changes in medication beliefs, and that these changes in medication beliefs can predict medication adherence in multimorbid older adults. In particular the finding that improvements in functional health lead to decreases in specific necessity beliefs and that such necessity beliefs are important predictors of intentional adherence suggests targeting such beliefs in interventions. Such interventions might stress that even relatively few or improving symptoms warrant medication adherence. This might be complicated by the representations of symptomless illnesses (Meyer, Leventhal, & Gutmann, 1985), but interpreting receding symptoms as effects of the medication has been shown to improve adherence (Gonzalez et al., 2007). In particular for the target group of older adults with multiple illnesses, such interventions fostering adaptive medication beliefs are promising for improving adherence.

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