2006 Predicting treatment adherence among patients attending primary health care clinics: The...

Predicting treatment adherence

among patients attending primaryhealth care clinics: The utility of the

Theory of Planned Behaviour

Ashraf KageeDepartment of Psychology, University of Stellenbosch, Private Bag X1, Matieland, 7602, [email protected]

Marie van der MerweDepartment of Psychology, University of Stellenbosch, South Africa

Treatment regimens for conditions such as hypertension and Type II diabetes require strict adherence tomedical instructions. Yet, adherence among patients living with chronic medical conditions attendingpublic health clinics is typically low. The present study sought to determine the extent to which the Theoryof Planned Behaviour (TPB) was able to significantly explain variance in adherence intentions andbehaviour in a sample of 117 formerly disadvantaged South Africans living with diabetes and hypertensionin the Western Cape. The results showed that the linear combination of TPB variables — Attitudes,Perceived behavioural control, and Subjective norms— was able to account for 47 per cent of the variancein adherence intentions and 23 per cent of the variance in self-reported adherence behaviour. The additionof the variables Psychological distress and Social support to the two regression models, hypothesised toadd predictive power to the TPB, yielded non-significant results. The findings of the study are discussed inthe context of applying theoretical models of behaviour developed in the industrially developed world andtested on middle-class subjects to patient samples in developing countries such as South Africa. Ourfindings suggest that social cognitive models of health behaviour such as the TPB may be useful inpredicting treatment adherence but should be used in a critical and cautious manner.

Keywords: chronic illness; public health clinics; Theory of Planned Behaviour; treatment adherence

Treatment regimens for conditions such as hypertension and Type II diabetes require

strict adherence to medical instructions (Pladevall, Williams, Potts, Divine, Xi, &

Elston, 2004). As chronic illnesses, these conditions have much in common from the

standpoint of adherence to medical and behavioural regimens. Medications prescribed

following consultation with a health professional are usually dispensed with an

expectation of close to perfect adherence. Such expectations pertain to dosage, timing,

ingestion with specific foods, contra-indications regarding ingestion with other

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# Psychological Society of South Africa. All rights reserved. South African Journal of Psychology, 36(4), 2006, pp. 699–714ISSN 0081-2463

medicines, and consistent adherence to the treatment regimen over time (World Health

Organisation, 2003). These details are of crucial importance in maximising the health

benefits from medical treatment. Patient non-adherence to treatment regimens has been

repeatedly shown to have a negative effect on health and quality of life (Dick,

Schoeman, Mohamed, & Lombard, 1996). Indeed, non-adherence may have severe

implications for the control of symptoms, recovery time, quality of life, and mortality

(Zgibor et al., 2004). In terms of financial implications, there is likely to be considerable

wastage when resources are directed at medical consultations, purchase of medication,

transport to appointments, and other aspects surrounding medical care while adherence

to treatment remains low (World Health Organisation, 2003).

Despite the importance of adherence to treatment regimens, almost two-thirds of

chronic patients report low levels of adherence (Ogedegbe, Mancusi, Allegrante, &

Charlson, 2003). In South Africa, the barriers to adherence among historically

disadvantaged rural patient populations are poorly understood (Kagee, 2004) and little

research into treatment adherence has been conducted in rural and semi-rural

community clinics in South Africa. Indeed, public health in rural areas has received

less attention from researchers in comparison with urban and peri-urban areas. The

absence of a knowledge base concerning treatment adherence among rural primary care

patients in historically disadvantaged communities in South Africa suggests that a

primary research need is to identify the factors associated with non-adherence from the

perspective of patients.

Most South Africans from previously disadvantaged communities make use of

primary health care clinics for their medical needs (McIntyre, 1998). Due to the

country’s history of racial discrimination, public hospitals and clinics are generally

overcrowded, under-resourced, and understaffed (Mazibuko, Mckenzie, & Schneider,

1989). Moreover, competing demands on patients’ time and resources create conditions

under which adherence to medical recommendations is compromised. For example,

funds to pay for medication, transport to and from clinics, and child care may not be

readily available, leading to poor clinic attendance and non-compliance with

medication regimens. Compromised health also has attendant consequences such as

worker absenteeism and higher health care costs (Simoni, Fick, Lockhart, & Liebovitz,

2002), thereby placing further burdens on the nation’s health system and economy.

The Theory of Planned Behaviour

A variety of social-cognitive theoretical models have been developed to understand

decision making in various contexts. These have been applied in medical settings in

order to understand decision making regarding health behaviours such as adherence.

The Health Belief Model (Janz & Becker, 1984), the Theory of Reasoned Action

(Ajzen, 1988), and the Theory of Planned Behaviour (TPB) (Ajzen, 2002) are

examples of such models. The TPB has received considerable empirical support in

predicting health decision making, specifically the manner in which intentions mediate

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Ashraf Kagee and Marie van der Merwe

between psychosocial factors and health (O’Boyle, Henly, & Larson, 2001; Rhodes &

Courneya, 2004). The TPB postulates that the likelihood of an individual engaging in

a specific health behaviour is correlated with the strength of his or her intention to

engage in the behaviour. A behavioural intention represents an individual’s

commitment to act and is itself the outcome of a combination of several variables.

According to the TPB, the factors that directly influence intentions to engage in a

specific health behaviour include the person’s attitudes towards the behaviour, the

person’s perception of subjective group norms concerning the behaviour, and the

extent to which the person perceives him or herself to have behavioural control

concerning the behaviour (Ogden, 2000).

The variables that predict behavioural intentions are the individual’s attitude towards

the behaviour (including his or her beliefs about the outcomes of the behaviour and the

evaluations of these outcomes), the individual’s perception of existing subjective norms

concerning the behaviour (including his or her perception of others’ attitudes towards

adherence and motivation to comply with others), and the individual’s perceived

behavioural control (including an evaluation of internal factors such as perceived

capacity to engage in the behaviour and external factors such as the availability of

existing resources).

Attitudes: Attitudes towards treatment adherence are a person’s evaluative opinions,

both positive and negative, of the outcome of a behaviour (O’Boyle et al., 2001; Ogden,

2000). In general, a positive attitude toward a behaviour is associated with its practice

while a negative attitude is not (Horne, Clatworthy, Polmear, & Weinman, 2001).

Subjective norms: Subjective norms refer to a person’s evaluation of others’ (usually

family and friends’) expectations of a specific behaviour (O’Boyle et al., 2001). The

concept of subjective norms is based on the assumption that social pressure encourages

people to behave in a socially desirable manner and that people are in turn motivated to

comply with these social expectations (Ogden, 2000). Subjective norms have been

shown to be a robust predictor of health behaviour intentions (Finlay, Trafimow, &

Jones, 1997).

Perceived behavioural control: Perceived behavioural control refers to an individual’s

belief that he or she can engage in a specific behaviour, taking into account internal and

external control factors. Internal control factors refer to the abilities, skills, or

information that he or she possesses. External control factors refer to the opportunities

or barriers that he or she experiences. Internal and external control factors are linked

with past behaviour or experiences (Povey, Conner, Sparks, James, & Shepherd, 2000).

Perceived behavioural control is conceptually similar to self-efficacy (Ogden, 2000;

Povey et al., 2000; Rhodes & Courneya, 2004) which, in the context of the present

study, is the ability to take a proactive role in one’s health by taking medication as

prescribed, exercising, and maintaining a strict diet. Self-efficacy to engage in health

behaviours has been shown to be robustly associated with a healthy lifestyle in the

general patient population (Gillis, 1993), among patients with diabetes (Syrjala,

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Predicting treatment adherence among patients attending primary health care clinics

Ylostalo, Niskanen, & Knuuttila, 2004), and among those with hypertension (Ogedegbe

et al. 2003). The TPB also predicts a direct relationship between perceived behavioural

control and health behaviour without the mediating variable of intentions (Bryan,

Fisher, & Fisher, 2002).

The TPB has been used extensively to predict various health behaviours such as

medication adherence among psychiatric patients (Conner, Black, & Stratton, 1998),

exercise intentions among learners attending high school (Tsorbatzoudis, 2005), and

clinical glove use among health care workers (Watson &Myers, 2001). Few studies have

applied the TPB to predicting dietary and fluid adherence among patients with chronic

illnesses, and those that have were conducted mainly among North American and

European samples. The TPB has received little attention in the context of primary health

care in a developing country such as South Africa. Indeed, the TPB has been criticised as

being too individualistic in focus and thus inapplicable outside of western societies

(Campbell, 2003). In response to criticisms of this nature, Fishbein (2002) notes:

I have often heard people argue that theoretical models (such as TPB) are ‘western’ or

‘US’ models that don’t apply to other cultures or countries or that these types of models

are not culturally specific. In marked contrast, I would argue that when properly applied

these models are culturally specific. For example, the relative importance of each of the

variables in the model is likely to vary as a function of both the behaviour and the

population under consideration . . . When properly applied these models require one to

understand the behaviour from the perspective of the population being considered. I

would argue that each of the variables in the model can be found in almost any culture or

population (p. 121).

In multicultural psychology, it has been observed that communalism characterises

many non-western social contexts, while individualism is characteristic of industrially

developed nations (e.g., Pedersen, 1987). For this reason, we assumed that social and

family support would play an important role in the maintenance of health and well-

being and might therefore predict treatment adherence more robustly than the TPB

variables. In addition, as the prevalence of depression and psychological distress is

generally high among medical patients, we hypothesised that psychological distress

would be associated with poor adherence.

The aim of the present study was to determine the extent to which the TPB is

applicable in the context of a developing country, South Africa, among historically

disadvantaged patients attending semi-rural primary care clinics. The study sought to

determine whether TPB variables were able to significantly explain variance in

behavioural intentions to engage in treatment among patients attending rural primary

health care clinics in the Western Cape. An additional aim was to determine whether

the addition of the variables Social support and Psychological distress added to the

amount of variance explained by the TPB variables.

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METHOD

Participants

One hundred and twenty-four patients receiving treatment for diabetes or hypertension

at primary health care clinics in the Boland area of the Western Cape were asked to

participate in this study.

Procedures

All questionnaires were translated into Afrikaans by an employee at a professional

translation service at Stellenbosch University. The translation was back-translated by a

different employee and discrepancies were resolved.

The study was explained to patients in a private room at the clinics. Upon agreement

to participate, patients were asked to sign a form indicating informed consent. Each

participant was presented with a supermarket voucher valued at R20 as a gesture of

appreciation for their participation in the study. Participants completed the

questionnaire in a private room located on the clinic premises. A research assistant

administered the questionnaire to those patients who were unable to read. The

questionnaire was made available in both English and Afrikaans. The majority of

participants responded to the Afrikaans version of the questionnaire.

Measures

Attitudes towards treatment adherence: Attitudes towards treatment adherence were

tested with the Adherence Attitude Inventory (AAI), a 28-item, Likert-type instrument

(Lewis & Abell, 2002). The instrument assesses cognitive functioning, patients/health

worker communication, and commitment to treatment adherence. The AAI was

originally developed for use with AIDS patients but has since been used with other

illness conditions as well. The reliability of the instrument as measured by Cronbach’s

alpha was 0.75 (Lewis & Abell, 2002). In the present study, Cronbach’s alpha was 0.72.

Self-report treatment adherence: Treatment adherence was assessed using a six-item

questionnaire, asking whether the patient had taken his or hermedication that day, the day

before, and the week before. The response options on this measure are: ‘no’, ‘cannot

remember’, ‘as far as I remember’, or ‘yes’. In the present study,Cronbach’s alphawas 0.74.

Psychological distress: The 25-item version of the Hopkins Symptom Checklist

(HSCL-25) was used to assess global psychological functioning. This instrument is

derived from the 90-item list Symptom Checklist (HSCL-90), and has also been used to

test symptoms of depression and anxiety. The internal consistency of the HSCL-25 is

0.93 (Kaaya, Fawzi, Mbwambo, Lee, Msamanga, & Fawzi, 2002). In the present study,

Cronbach’s alpha was 0.89.

Perceived group norms: Perceived group norms was measured with the Normative

Beliefs about Adherence questionnaire, an 11-item five-response-option Likert-type

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scale based on social learning theory (Bandura, 2004). Typical items include ‘How

would you experience talking to other people about taking your medication?’ In the

present study, Cronbach’s alpha was 0.63.

Perceived behavioural control: Perceived behavioural control was measured with a

seven-item questionnaire assessing self-efficacy to engage in various adherence-related

activities, such as ‘to go for at least one month without sugar in my food’. In the present

study, Cronbach’s alpha was 0.57, following deletion of poor items.

Intentions to adhere to treatment: This variable was measured with a seven-item,

three-response-option Likert-type scale assessing intentions to engage in various

adherence-related activities such as ‘to reduce or eliminate sugar in my food and drinks

for the next month’. In the present study, Cronbach’s alpha was 0.72.

Social support from family: An adapted version of the 20-item 5-point Perceived

Social Support Family (PSS-Fam) questionnaire (Procidano & Heller, 1983) was used

to assess family support (Gavazzi, 1994). The internal consistency of the PSS-Fam as

measured by Cronbach’s alpha was 0.90 (Gavazzi, 1994). In the present study,

Cronbach’s alpha was 0.87.

Social support from friends: A modified version of the 20-item 5-response option

Perceived Social Support Friends (PSS-Friends) questionnaire (Procidano & Heller,

1983) was used to assess support from friends. The internal consistency of the PPS-

Friends was 0.88 (Gavazzi, 1994). In the present study, Cronbach’s alpha was 0.84.

Biological indicators: Blood pressure and blood glucose level readings for the last five

visits were recorded from patient charts.

Analysis

The data were entered into SPSS version 13. In order to assess for collinearity, the

Variance Inflation Factor (VIF) statistic and correlation coefficients between the

variables were calculated. Attitudes, Perceived group norms, Perceived behavioural

control, Behavioural intentions, Distress, and Social support were entered into a

hierarchical regression model as predictors in order of importance according to the

TPB, with Self-reported adherence as the criterion variable.

RESULTS

Description of the sample

A total of 117 (22 male; 95 female) patients attending five community public health

clinics in the Boland area of the Western Cape completed questionnaires for the study.

Of the total sample, 79 had a diagnosis of hypertension, 24 had a diagnosis of diabetes,

and 14 had a diagnosis of both hypertension and diabetes. Of the total sample, 8 per

cent had completed high school and 28 per cent were employed full time. Most

participants reported their annual family income to be in the R0 to R10 000 range,

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indicating that the majority of the sample were poor. All participants were historically

disadvantaged by South Africa’s former apartheid political system. The average age of

the sample was 52 years (SD: 13.24; range: 22 to 82 years). Of the total sample, 79 per

cent were born in a town; 1.7 per cent in a city; and 17.6 per cent on a farm. The rest of

the sample (1.7%) did not disclose where they were born.

Model 1

Kolmogorov-Smirnov and Shapiro-Wilk tests for normality showed significant results.

The data were then normalised by log transformation. Variance Inflation Factor (VIF)

calculations indicated that collinearity between predictor variables was not a concern.

Table 1 lists the mean scores and standard deviations of the sample on the measures

used, together with the correlation coefficients between the variables. Table 2 displays

the predictor variables as they were entered into the model, together with their

standardised regression coefficients and significance levels. Table 3 shows the summary

statistics for the regression analysis in which the relationship between the TPB variables

(Attitudes, Perceived group norms, and Perceived behavioural control), Social support,

Psychological distress, and Adherence intentions was investigated. The linear

combination of Attitudes, Perceived behavioural control, and Perceived group norms

significantly accounted for 47 per cent of the variance in adherence intentions (F =

35.54; p< 0.00), indicating that the TPB is robustly applicable among the sample under

study. The addition of Social support from family to the model yielded an R2 of 0.51

(DR2 = 0.02; p = 0.15); the addition of Social support from friends yielded an R2 of

0.52 (DR2 = 0.03; p= 0.06); and the addition of Psychological distress yielded an R2 of

0.55 (DR2 = 0.06; p = 0.38). Contrary to our expectations, the inclusion of these

variables added non-significantly to the variance in adherence intentions, suggesting

that in this sample the TPB alone best explained adherence intentions.

Table 1. Correlation matrix of intention to adhere to treatment (N = 117)

Mean SD Intentions Attitudes Subj.

norms

PBC SS (fam) SS (fr)

Intentions 32.98 3.19

Attitudes 49.01 9.45 0.09

Subj. norms 29.43 4.55 0.38* –0.20*

PBC 22.01 3.31 0.66* –0.01 0.33*

SS (family) 84.11 13.36 0.22* –0.11 0.10 0.09

SS (friends) 81.59 13.13 0.40* –0.16* 0.19* 0.29* 0.36*

HSCL 43.32 12.75 0.04 0.25* –0.28* 0.00 –0.06 0.02

Note: * Correlation is significant at the 0.05 level (1-tailed).

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Table 2. Parameters of the variables in regression model 1

Standardised model B SE B Beta t p

Constant 8.23 2.63 3.13 0.00

Attitudes 0.05 0.02 0.16 2.34 0.02

Subjective norms 0.15 0.05 0.21 2.87 0.01

PBC 0.51 0.07 0.53 7.51 0.00

SS (fam) 0.02 0.02 0.10 1.51 0.13

SS (fr) 0.05 0.02 0.19 2.67 0.01

HSCL 0.02 0.02 0.06 0.89 0.38

Table 3. Model summary predicting adherence intentions

Block R R2SE F p

A 0.70 0.47 2.32 35.54 0.00

B 0.72 0.51 2.27 29.37 0.00

C 0.73 0.54 2.20 26.45 0.00

D 0.74 0.55 2.20 22.13 0.00

Notes:

A. Predictors: (Constant), Attitudes towards treatment adherence, Subjective norms,Perceived behavioural control

B. Predictors: (Constant), Attitudes towards treatment adherence, Subjective norms,Perceived behavioural control, Social support from family

C. Predictors: (Constant), Attitudes towards treatment adherence, Subjective norms,Perceived behavioural control, Social support from family, Social support from friends

D. Predictors: (Constant), Attitudes towards treatment adherence, Subjective norms,Perceived behavioural control, Social support from family, Social support fromfriends, Psychological factors

Model 2

We also tested the relationship between the various predictor variables, adherence

intentions, and self-reported adherence. Table 4 shows the bivariate correlation

coefficients between these variables. As can be seen, Intentions was modestly correlated

with Self-reported adherence (0.36; p < 0.05), as were all the other predictors except

Social support from family. Table 5 shows the parameters of the final regression model

in which the variables Attitudes, Subjective norms, Perceived behavioural control,

Social support from friends and family, and Psychological distress were entered. Table

6 shows the summary statistics of regression model 2 at each step of the analysis. The

linear combination of Attitudes, Perceived group norms, and Perceived behavioural

control significantly explained 23 per cent of the variance in reported adherence

behaviour. Again, contrary to our expectations, the addition of the variables Social

support from friends and Psychological distress did not significantly explain any

additional variance in the self-reported adherence.

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Table 4. Correlation matrix of self-report treatment adherence behaviour

Self-reported

Adh

Attitudes Subjectivenorms

PBC SS (fam) SS (fr)

Self-reportedAdh

Attitudes –0.23*

Subjectivenorms

0.26* –0.20*

PBC 0.42* –0.01 0.33*

SOS (family) 0.14 –0.11 0.10 0.09

SOS (friends) 0.16* –0.16* 0.19* 0.29* 0.36*

Psychologicalfactors

–0.16* 0.25* –0.28* 0.00 –0.06 0.02

Intentions 0.36* –0.23* 0.26* 0.42* 0.14 0.16*

Note: * Correlation is significant at the 0.05 level (1-tailed).

Table 5. Parameters for all the variables in regression model 2

Model B se beta t p

Constant 10.37 3.72 2.78 0.01

Attitudes –0.07 0.03 –0.19 –2.12 0.04

Subjectivenorms

0.05 0.07 0.07 0.73 0.47

PBC 0.42 0.10 0.40 4.35 0.00

SOS (family) 0.02 0.02 0.09 0.98 0.33

SOS (friends) –0.01 0.03 –0.03 –0.33 0.75

Psychologicalfactors

–0.02 0.02 –0.09 –0.97 0.33

Table 6. Predictors of self-report treatment adherence behaviour

Block R R2 SE F p

A 0.48 0.23 3.11 11.46 0.00

B 0.49 0.24 3.12 7.03 0.00

C 0.50 0.25 3.13 6.02 0.00

Notes:

A. Predictors: (Constant), Attitudes towards treatment adherence, Perceived groupnorms and Perceived behavioural control

B. Predictors: (Constant), Attitudes towards treatment adherence, Perceived groupnorms, Perceived behavioural control, and Social support from friends

C. Predictors: (Constant), Attitudes towards treatment adherence, Subjective norms,Perceived behavioural control, Social support from friends, and Psychological distress.

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The relationship between intentions and biological indicators ofadherence

Adherence intentions was not significantly correlated with blood pressure among

patients with hypertension (r = –0.11; p = 0.35) nor with blood glucose level among

patients with diabetes (r = 0.39; p = 0.07). We were unable to explore the relationship

between intentions, blood pressure, and blood glucose levels with further analyses

because insufficient data were included in patient charts.

DISCUSSION

The purpose of the study was to examine the utility of the TPB in predicting and

explaining treatment adherence among patients attending public health clinics in the

Western Cape of South Africa. The study found support for the TPB as a framework

for understanding the determinants of treatment adherence. These findings may aid in

developing interventions to enhance adherence among primary care patients with

chronic illnesses. There have been mixed findings in the literature regarding the utility

of TPB variables in predicting health-promoting behaviour, particularly adherence to

treatment regimens. For example, only perceived behavioural control significantly

predicted exercise adherence among a Canadian sample of colorectal cancer survivors

(Courneya, Friendenreich, Quinney, Fields, Jones, & Fairey, 2004); no TPB variables

predicted exercise and dietary adherence among American women enrolled in a weight-

loss programme (Gardner & Hausenblas, 2004); and no TPB variables predicted

adherence to malaria prophylaxis among travellers receiving consultation in a travel

clinic (Farquharson, Noble, Barker, & Behrens, 2004). On the other hand, among a

sample of African American women, the attitude and perceived behavioural control

components of the TPB, but not perceived group norms, explained a significant

proportion of the variance in intentions (Bowie, Curbow, LaVeist, Fitzgerald, &

Zabora, 2003). Among a Canadian sample of cardiac patients, all TPB variables

significantly accounted for 38 per cent of the variance in adherence intentions, while

intentions in turn explained 23 per cent of the variance in exercise adherence

(Blanchard, Courneya, Rodgers, Daub, & Knapkik, 2002). Among travellers returning

to a malarious region, a model based on the TPB explained approximately 50 per cent

of the variance in reported adherence among mefloquine users and 40 per cent of the

variance among chloroquine and proguanil users (Abraham, Clift, & Grabowski, 1999).

Thus it appears from previous work that the TPB model may wholly or partly explain

both adherence intentions and behaviour.

Predicting adherence intentions

The present analysis showed that approximately 47 per cent of the variance in

adherence intentions was explained by TPB variables, a large effect size for multivariate

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models in the social sciences (Cohen, 1988). Perceived behavioural control was the

strongest predictor of adherence intentions (beta = 0.59). This is a potentially

important finding, suggesting that the enhancement of behavioural control may be a

way to increase adherence to treatment recommendations. Perceived group norms was

also significantly correlated (beta = 0.21) with behavioural intentions. Thus the extent

to which patients thought that other patients were similarly engaging in adherent

behaviour was predictive of their own intentions to adhere to treatment recommenda-

tions. Our analysis found no significant correlation between attitudes and adherence

intentions. Thus, similar to previous findings (e.g., Blanchard et al., 2002; Bowie et al.,

2003; Courneya et al., 2004), the TPB was found to only partly explain adherence

intentions among the sample.

The significant relationship between self-efficacy and adherence intentions is of some

importance. For many historically disadvantaged South African patients, perceived self-

efficacy is likely to be related to two factors, namely health literacy and social capital.

Health literacy implies an awareness of the importance of adherence despite the

absence of actual symptoms (Turk, Salovey, & Litt, 1986). Thus, perceived self-efficacy,

that is, the ability to take a proactive role in one’s health by taking medication as

prescribed, exercising, and maintaining a strict diet, is likely to be influenced by one’s

cognitive understanding of the importance of these issues on one’s health. Qualitative

data collected by Kagee, LeRoux, and Dick (in press) suggest that many patients only

consider behavioural regimens such as low salt, fat, and sugar intake, exercise, and

adherence to medication as tertiary measures following the onset of medical symptoms,

rather than as prophylactic interventions. Moreover, health literacy is often related to

educational level. Among poor communities in South Africa characterised by poor

educational opportunities, health literacy is likely to be low, accounting in part for low

levels of adherence.

Social capital involves the notion of ‘perceived citizen power’ (Campbell, 2003) and is

characteristic of communities where people have the perception that their opinions and

aspirations are acknowledged and accorded respect and value. The development of a

health-enabling community in which perceived self-efficacy is enhanced is likely to be

fostered by the notion of social capital. It is possible, given the context of public health

care in South Africa, that perceived self-efficacy of patients may be influenced largely

by extra-individual factors relating to health literacy and the extent to which the health

system helps to create a health-enabling environment. Even though social capital was

not specifically measured in this study, there may be a relationship between this variable

and perceived self-efficacy that warrants further exploration.

Contrary to our expectations, the addition of social support from family and

psychological distress did not add significantly to the model, suggesting that the TPB

may be sufficiently robust in predicting behavioural intentions. We were surprised by

this finding, as it is often assumed that in poor communities social support plays an

important role in enhancing coping and that individual well-being is less individua-

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listically experienced than in many western countries and more intimately tied with

perceptions of family, community, and societal well-being (Pedersen, 1987; Swartz,

1998). The high prevalence of major depressive disorder among primary care samples in

the United States (Coyne, Fechner-Bates, & Schwenk, 1994; Katon & Schulberg, 1992)

also led us to hypothesise that psychological distress would predict poor adherence.

This hypothesis was not supported by our data.

Predicting adherence behaviour

The linear combination of Attitudes and Perceived behavioural control was able to

explain 23 per cent of the variance in adherence behaviour. However, we found

paradoxically that a negative attitude was associated with greater adherence

behaviour. This result was unexpected and may be related to the fact that the

measure of Attitudes, the Adherence Attitude Inventory, was originally intended for

AIDS patients rather than patients with diabetes or hypertension. Similar to Model 1,

Subjective norms again failed to predict adherence behaviour, leaving only Perceived

behavioural control as a robust predictor of the adherence. Also similar to Model 1,

none of the additional variables — that is, Social support and Psychological distress

— were able to explain a significant proportion of the variance in behaviour. We also

found a strong association between intentions to engage in future adherent behaviour

and present adherent behaviour, suggesting that, consistent with the TPB, intentions

played an important role in behavioural adherence of the sample. Thus participants

with stronger volitions reported higher levels of adherence than those with weaker

volitions.

An important debate that is currently underway is whether theoretical models

conceptualised in industrially developed countries and tested on samples drawn from

predominantly white middle-class populations in those societies are applicable in the

context of developing countries. The uncritical application of theoretical formulations

in different geographical contexts has come under increasing criticism from researchers

working in developing countries. Indeed, much has been written about the fact that

people living in non-western cultures have a greater level of social interconnectedness

and interdependence than people living in the United States and Europe (Pedersen,

1987; Magwaza, 1999). To this extent, social cognitive models such as the TPB that

focus on individualistic constructs have been said to be of limited utility in many

developing country contexts (Campbell, 2003). Yet, as Fishbein (2002) has suggested,

the TPB may indeed be applicable in cultural contexts beyond North America and

Europe. Our data suggest that, among the sample in the present study, the linear

combination of two TPB variables, Perceived group norms and Perceived behavioural

control, were successful in explaining adherence intentions. These results lend some

credibility to the utility of applying the TPB among historically disadvantaged patients

attending South African public health clinics. Our data therefore provide an empirical

710


basis to respond to the question of the applicability of the TPB, at least among the

present sample.

Implications

Our results have some potentially important implications for conceptualising the way in

which treatment adherence among chronic disease patients may be conceptualised. The

fact that perceived behavioural control and perceived self-efficacy strongly predicted

adherence intentions suggests that interventions directed at increasing these factors

among patients may play a role in enhancing adherence. Similarly, the robust

relationship between intentions and adherent behaviour, as predicted by the TPB,

suggests that interventions aimed at enhancing behavioural intentions are likely to yield

positive outcomes regarding adherence to medication, exercise, and diet. Theoretically

driven and empirically supported interventions are likely to meet with greater success

than those that are based mainly on intuition or clinical experience (Kagee, 2006). The

benefits of increased levels of adherence in turn are likely to yield benefits that extend to

decreased employee absenteeism, reduced use of public health facilities, and increased

quality of life for patients living with chronic diseases of lifestyle.

Limitations of the study

Our original intention was to collect chart data on biological variables such as blood

pressure and blood glucose levels. However, too few charts were available for review,

yielding a potentially underpowered analysis if these variables had been included. Thus

the relationship between behavioural intentions, self-reported adherence, and biological

markers of adherence awaits further investigation. It is also possible that our sample may

be biased, as more women thanmen were recruited into the study. This phenomenonmay

be related to the likelihood that the times of data collection precluded the recruitment of

employed men in the study. Additional studies of this nature should ensure equal gender

representation in sample selection. An additional limitation is that a variable such as

Intentions to adhere to instructions might hide a certain element of social desirability.

A final limitation of the study pertains to the relatively low alpha reliabilities of the

scales measuring perceived group norms and perceived behavioural control. Such low

alpha reliabilities are not unusual with a small number of items, as was true for these

measures. Moreover, translation of the instruments into Afrikaans may also have been

responsible for the relatively low internal consistency of these measures.

Despite these limitations, our findings suggest some support for the use of the TPB

with a sample in a developing country. Further use and application of the TPB may be

of considerable use in enhancing treatment outcomes among South African primary

health care patients.

711


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