Rapid, Repeated FISH in Human Amniocytes and Fibroblasts for ...

Rapid, Repeated FISH in Human Amniocytes and Fibroblasts for ...

Edward Tovar, B.A. & Ramani S. Durvasula, Ph.D.
California State University Los Angeles, Department of Psychology

The present study examines adherence to highly active antiretroviral therapy (HAART) in
a multiethnic community sample of 50 HIV positive women. Rates of infection continue
to grow in women, particularly ethnic minority women, however, little research on
adherence has focused solely on HIV+ women (see Figure 1). A framework
encompassing knowledge, beliefs, and self-efficacy, all of which have been shown to
influence medication adherence and general adherence, was tested. Medication adherence
was indexed on the basis of a pill count, and represents the average percent adherence to
all HAART medications during a one week period. The study revealed that the strongest
and only significant predictor of medication adherence was self-efficacy for adherence.
Results suggest that in addition to addressing issues such as availability of medications
and practical supports, adherence interventions for women should focus on development
and maintenance of self-efficacy, particularly for self-care behaviors.

The advent of highly active antiretroviral therapy (HAART) has significantly changed the
face of HIV treatment improving health, enhancing quality of life, and prolonging life
expectancy. However, the medications carry with them liabilities including side effects
and requirements of strict adherence. In the face of non-adherence, complications such as
treatment non-response and development of resistant viral strains may arise (Vanhove et
al., 1996). It is known that rates of adherence to HIV medication range from 30-70%
(Luscher et al., 1990).
Numerous models, theories, and methods have been mobilized in the study of
adherence. In this study, a theoretical framework encompassing cognitive factors
including knowledge and beliefs will be tested. This has been suggested by other
researchers who assert that health behavior modification requires knowledge, skills, and
self-efficacy (Cervone, 2000; Markus & Nurius, 1986). Knowledge has been defined
variously as awareness of personal health status (OBrian & Petrie, 1992; Sorenson &
Mascovich, 1998) as well as general knowledge of the afflicting condition (Solomon et
al., 1999). Studies have found that understanding of medication effectiveness is
associated with better adherence (Bennet et al., 1998), while inadequate knowledge and
confusion have been associated with lower adherence (OBrien & Petrie, 1992). In
contrast, some studies have described no significant lasting benefits of knowledge upon
adherence (Sorenson & Mascovich, 1998; Park & Willis, 1994).
Health beliefs have also been implicated in understanding of health behaviors. The
Health Beliefs Model (HBM) (Rosenstock, 1974) postulates that health behavior is a
function of the individuals perception of their susceptibility to a condition, the severity of
the condition, and the individuals assessment that a given action will result in a positive
outcome, while taking into account barriers which may impede engagement in healthier
behaviors. Studies employing the HBM have demonstrated that perceived severity and
susceptibility contribute to adherence (Janz & Becker,1984).
Another variable known to affect health behavior is self-efficacy, which has been
described as a persons belief in their ability to successfully engage in a given behavior
(Cervone, 2000), and that positive self-efficacy, knowledge and skill are required for an
individual to change and maintain health behaviors. Thus, the present study utilizes a
cognitive framework to understand adherence behavior and specifically focuses on the
contributions of knowledge, health beliefs and self-efficacy to adherence in HIV positive

E s tim a te d # o f w o m e n liv in g w ith A ID S
(1 9 9 1 -1 9 9 9 )
7 0 00 0
6 0 00 0
5 0 00 0
4 0 00 0
3 0 00 0
2 0 00 0
1 0 00 0

1 99 1

1 99 3

19 95

1 99 7

All participants were administered a health interview, cognitive testing, and a
pill count by a doctoral or masters level interviewer. Specific measures
examined in the present study were as follows:
Demographic factors: age, education
Personal health knowledge: was measured using a series of open ended
items which addressed awareness of health markers (e.g. viral load, T-cell
General health knowledge: was assessed with a series of open-ended items
(e.g. "What does it mean if your viral load is undetectable?") and closed
ended items (e.g. "If my viral load is undetectable it means I'm HIV free")

19 9 9

Figure 2: Linear relationship
between self efficacy and adherence

Mean adherence level in the sample was 81.4%
(SD = 30.1). Mean self-efficacy was 8.9 (SD =
2.2) out of a possible 10 (higher scores were
indicative of greater adherence). Results of
simultaneous multiple regression revealed that only
self-efficacy made a significant contribution to
medication adherence in this sample ( = .60, p
= .000), with higher self-efficacy associated with
better adherence. Table 1 provides mean and SDs
for all predictors and outcomes, table 2 provides a
summary of regression coefficients, and figure 2
provides graphical depiction of findings.







Rsq = 0.3336


Health beliefs: were indexed using sub-scales of the Adherence
Determination Questionnaire (DiMatteo et al., 1994). Subscales used in the
present study include: (1) Perceived utility of treatment, (2) Perceived
severity of the illness, (3) Perceived susceptibility to AIDS and opportunistic
Self-efficacy: was based on answers to the following statement: (rated on a
1-10 scale) "How sure are you that you will be able to take most or all of
your antiretroviral medication as directed?" Higher scores were indicative of
greater self-efficacy
Medication Adherence: A pill count of participants' HIV medication was
conducted at the time of the study visit in order to determine medication
adherence. A follow-up pill count was conducted by phone 7 days later.
Mean adherence was calculated as follows:

Table 1. Means & SDs for predictors and outcomes

Std. Deviation


Mean percent adherence
(range =




(max =10)




Perceived Utility
(max =40)




Perceived Severity
(max =18)




Perceived susceptibility




General health




Personal health




PPercent Adherence =
Nactual (# of pills in bottle at study visit (-) # of pills in bottle at
follow-up pill count) minus
Nexpected (# of pills prescribed per day (x) # of days between the
study visit and the follow-up pill count) x 100 = PERCENT ADHERENCE.
SScores were calculated for each of the HIV medications women were
receiving. Mean adherence was calculated by taking the mean of percent
adherence scores


Figure 1: Changing trends in AIDS epidemiology



Mean adherence


The present study draws from a pilot study examining neuropsychological performance,
psychosocial variables, and medication adherence in HIV+ women. The sample was
comprised of 50 HIV positive women currently residing in Los Angeles County.
Participants were recruited from the UCLA/Drew Women and Family Project , a 5 year
longitudinal study of HIV+ women or through community agencies. Mean age of women
was 40.5 years (SD = 9.0 years). Mean level of education was 12.7 years (SD = 2.4). Only
26.9% of the sample endorsed either being married or in a relationship lasting 3 months or
longer, and 26.9 % were employed part time or full time. The ethnic breakdown is 48%
African American, 29 % Caucasian, 19 % Latina, 2 % Asian Pacific Islander, and 2 %
American Indian.

Table 2. Regression coefficients











Self efficacy




Perceived utility












Personal health




General health










Self efficacy for ART

The current study examined a theoretical framework encompassing cognitive
factors (knowledge, beliefs and self efficacy) and the relationship of these
factors to HAART adherence in HIV positive women. Medication adherence,
particularly for complex regimens such as HAART, is a complex behavior,
requiring patients to remember multiple medications and dosing schedules.
Results of this study reveal that the strongest and only significant predictor of
medication adherence in this model was self-efficacy with greater self
efficacy associated with better adherence. Self efficacy is a
multidimensional construct and represents the degree to which an individual is
confident that they are capable of managing a problem. Bandura stressed that
perception of self-efficacy is a cognitive concept which is associated with
behavioral change (Bandura, Adams, & Beyer, 1977). A heightened sense of
self-efficacy may also engender a belief that most events are controllable and
result in an increased willingness to tackle problems (e.g. stick with a difficult
medication regimen). The overall levels of self-efficacy in this sample were
high (M = 8.9). A case-by-case examination of the data revealed that those
women who endorsed self-efficacy scores of less than 4 all had adherence
levels of 30% or less indicating an awareness of their manifested inability to
take their medications. Thus, in this sample, self-efficacy may serve as a
marker of both past adherence behavior as well as beliefs about the future
ability to adhere.
Given that the literature on self-efficacy appears to suggest that knowledge is
an integral part of understanding the self-efficacybehavior relationship, it is
puzzling that knowledge did not make a significant contribution to adherence
in this study. This may reflect the possibility that knowledge alone is
insufficient to explain a complex behavior such as adherence, and that
sufficient knowledge is available to an individual who maintains a high sense
of perceived self-efficacy. The lack of association between health beliefs and
adherence was also inconsistent with past studies (e.g. Janz and Becker,
1984). The more general nature of the HBM items (e.g. following my
treatment plan will help me to be healthy) may have provided a less direct
assessment of the cognitions and beliefs underlying the actual adherence
behavior of the participants than the measure of self-efficacy. Finally, the
HBM has not been shown to be associated with other health behaviors (e.g.
condom use) in women of color (Wyatt et al., 1998). Finally, this study had
some methodological limitations. The small sample size may have limited our
ability to adequately test this multivariate model. In addition, a single
question addressing self-efficacy may have been insufficient, and a measure
which addresses multiple dimensions of self-efficacy should be used in future
Overall, this study suggests that within the spectrum of knowledge,
beliefs, and self-efficacy, self-efficacy was the strongest predictor of
medication adherence behavior. This finding has significant implications for
the design of interventions to improve adherence in HIV positive individuals.
Interventions which work with patients to bolster beliefs about their capacity
to adhere to medication regimens, and other health behaviors (e.g. safer sex
practices, diet) may have the greatest likelihood of engendering behavioral
change. Many of the women in this sample present with histories of
socioeconomic deprivation and poor health care, despite this, the levels of
self-efficacy for medication adherence in this sample were high. Thus, a twopronged approach to adherence interventions may be suggested with one arm
reinforcing ongoing levels of self-efficacy for those who endorse a capacity to
adhere and a second arm targeting those women with poor adherence and low
self-efficacy in order to build skills and beliefs with a goal of behavior change.
Given that other work on HAART adherence suggests the impact of other
factors (e.g. neurocognitive performance) on adherence (Durvasula et al.,
1999), further work using more comprehensive models to examine this
behavior and construct intervention programs is necessitated.
The authors would like to acknowledge the State of California Universitywide
AIDS Research Program for their support.

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