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. Author manuscript; available in PMC: 2022 Jun 1.
Published in final edited form as: J Clin Psychol Med Settings. 2021 Jun;28(2):301–312. doi: 10.1007/s10880-020-09715-7

Apathy is associated with critical psychological determinants of medication adherence in HIV disease

Michelle A Babicz a, Steven Paul Woods a,*, Pariya Fazeli b, Erin E Morgan c
PMCID: PMC7541422  NIHMSID: NIHMS1582352  PMID: 32253661

Abstract

Apathy is common in HIV, separable from depression, and has been associated with non-adherence to antiretroviral therapy (ART). We examined the associations between apathy and critical psychological determinants of ART adherence, per the Information-Motivation-Behavioral model, in 85 persons living with HIV. Apathy was measured using a composite of the apathy subscale of the Frontal Systems Behavioral Scale and the vigor-activation scale of the Profile of Mood States. Independent of major depressive disorder, apathy was related at small-to-medium effect sizes with motivation to adhere and self-efficacy for health-related decision making and medication management, but not with HIV knowledge or medication management skills. These findings suggest that apathy plays a unique role in several critical health adherence determinants and support the importance of assessment and management of apathy to maximize health outcomes among individuals with HIV disease.

Keywords: HIV/AIDS, apathy, motivation, neuropsychiatry, medication adherence

INTRODUCTION

Apathy is a pervasive, complex syndrome characterized by reduced motivation for self-initiated goal-directed behavior and cognitive activity, and blunted affect (Levy & Dubois, 2005). Current conceptual models of apathy reflect the complexity of this syndrome and outline its contributing behavioral, social, environmental and neurobiological factors in the setting of neurocognitive disorders (Massimo, Kales, & Kolanowski, 2018). The Massimo, Kales, & Kolanowski (2018) model of apathy posits that neurological disorders can contribute to apathy either directly through shared neurobiological underpinnings (e.g. fronto-striatal-thalamo-cortical network disruption; see Kos, 2016 for review) and/or indirectly through increased vulnerability to stressors that are associated with apathy symptoms. The model outlines personal (e.g., type of cognitive impairment, severity of disorder, genetics, co-morbid behavioral and psychiatric disorder, sleep problems, pain), caregiver/social (e.g., stress, burden, lack of education, communication issues), and environmental (e.g., overstimulation or understimulation, lack of structure or activity) factors that may exacerbate stressors or trigger apathy symptoms directly.

Of course, apathy demonstrates both conceptual and clinical overlap with depression, which is commonly defined as a sadness and/or loss of interest and pleasure in once enjoyed activities (American Psychiatric Association, 2015); however, there exists strong behavioral, psychological, and neuroimaging evidence of their separability (e.g., Paul, Brickman et al., 2005; Paul, Flanigan et al., 2005; Tate et al., 2003). Studies across several neurological and psychiatric disorders, for example, suggest that apathy can occur in the absence of depression and vice-versa (Kirsch-Darrow et al., 2011; Starkstein et al., 2001; Starkstein et al., 1992; Okada et al., 1997; Kant et al., 1998). There have also been many studies finding weak to null associations between measures of apathy and depression (e.g., Levy et al., 1998). Neuroimaging studies suggest that apathy is associated with medial prefrontal and deep subcortical pathology, while depression is associated with the left prefrontal cortex and limbic networks (Paul, Brickman et al., 2005; Hoare et al., 2010; Starkstein et al., 1992).

Indeed, evidence from studies in human immunodeficiency virus (HIV) disease also provides support for the separability of apathy and depression. Apathy was one of the first neuropsychiatric syndromes observed in HIV (e.g., Navia, 1986) and can emerge very early in the course of HIV infection, independent of depression and other neuropsychiatric syndromes (Kamat et al. 2016). While some studies have reported moderately strong, positive relationships between self-report measures of apathy and depression (e.g., Castellon et al., 1998; Rabkin et al., 2000), the magnitude of the associations suggests that the constructs are not collinear. In fact, excluding items common to both apathy and depression from the measures used to assess symptoms attenuate these associations (Rabkin et al., 2000) and many investigations report weak to null correlations between the two neuropsychiatric constructs (e.g., Tate et al., 2003; Paul et al., 2005). Three studies have reported elevated levels of apathy in the absence of depression in persons living with HIV (PLWH; Kamat et al., 2015; Hoare et al., 2010; Rabkin et al., 2000). Neuroimaging studies reveal structural alterations in HIV with apathy that are independent of depression, providing further evidence for their separability (e.g., Paul et al., 2005; Kamat et al., 2014).

In the modern era, clinical levels of apathy are apparent in between 12% and 65% (median = 46%; e.g., Kamat et al., 2012) of PLWH. A recent meta-analysis showed that HIV status is associated with apathy at a large effect size (Cohen’s d = −.87), and that apathy in PLWH is related to lower nadir CD4 counts (Walker & Brown, 2018). While there have been several studies suggesting that the high prevalence of apathy in HIV may be associated with the frontostriatal pathology implicated in both HIV and apathy (e.g., Kamat et al., 2016), less is known about the direct and indirect effects of the personal (e.g., genetics, disease severity), social (e.g., stigmatization, family support), and environmental (e.g., level of activity, access to medical care) factors that may be uniquely impacted by HIV and contribute to apathy symptoms.

Of clinical relevance, apathy is strongly associated with poorer real-world outcomes among PLWH. To date, five studies have demonstrated that higher levels of apathy were associated with lower quality of life and everyday functioning outcomes independent of depression as assessed through self-report measures (Tate et al., 2003; Kamat et al., 2013; Shapiro et al., 2013) and diagnostic interviews (Kamat et al., 2016; Kamat et al., 2012; Kamat et al., 2013). For example, higher levels of self-reported apathy were related to greater severity of decline in activities of daily living (ADLs; e.g., increased dependence in shopping and decreased social activity) and cognitive symptoms in daily life in 75 HIV+ adults, even when controlling for psychiatric, medical, and neurocognitive factors (Kamat et al., 2012). Similarly, higher levels of apathy were moderately associated with lower physical (e.g., bodily pain) and mental (e.g., emotional well-being) health-related quality of life among 80 HIV+ persons, independent of depression and other clinical variables (Kamat et al., 2016). Taken together, these findings suggest that apathy plays an important and independent role in everyday functioning outcomes in the setting of HIV disease.

Notably, apathy has been implicated in suboptimal medication adherence among PLWH. Adherence to HIV care and treatment recommendations is essential for positive health outcomes, including lower rates of virologic failure (e.g., Perno et al., 2002) and mortality (e.g., Lima et al., 2007). One study found that higher levels of apathy, but not depression, were associated with poorer medication adherence over a one-month period in younger (less than 50 years of age), but not older PLWH (Barclay et al., 2007). Another study of 207 PLWH found that apathy, but not depression, was related to poorer medication adherence over a six-month period, even when controlling for demographics, medication side effects, and substance use (Panos et al., 2014). In fact, apathy has been associated with medication adherence in other clinical populations, including diabetes (e.g., Padala et al., 2008) and schizophrenia (e.g., Velligan et al., 2007).

The present study aimed to further investigate the association between apathy and medication management among PLWH as guided by an information-motivation-behavioral (IMB) model (Fisher, Fisher, Amico, & Harmon, 2006; Starace, Massa, Amico, & Fisher, 2006). Antiretroviral therapy (ART) adherence is a complex health behavior. Previous studies have demonstrated that medication knowledge and related management skills alone are insufficient to promote behavioral change for health behaviors such as adherence (e.g., Kelly & Barker, 2016). Thus, other potentially modifiable targets such as apathy may be relevant to promote medication adherence in HIV. We are not aware of any studies investigating the relationship of apathy to attitudes and behaviors that may impact health outcomes in PLWH. The IMB model suggests that critical determinants of successful ART adherence include: accurate information regarding HIV disease and treatment, motivation to adhere to a treatment plan, and both subjective and objective self-efficacy for treatment behavior. Each component has been associated with aspects of ART adherence both individually (e.g., Miller et al., 2003; Kennedy, Goggin, & Nollen, 2004; Wolf et al., 2007) and collectively within a single model (Starace, Massa, Amico, & Fisher, 2006). The intercorrelations among the core components of IMB model suggest that both accurate information and higher motivation to adhere to ART treatment are associated with stronger objective and subjective medication management skills, and these medication management skills are related to higher medication adherence rates (Starace, Massa, Amico, & Fisher, 2006). The concept of self-efficacy can also be extended to the perceived ability to chose a treatment plan. In a study by Kremer, Ironson, Schneiderman, & Hautzinger (2007) of 79 PLWH, more than half expressed decisional conflict about a recent treatment plan decision. As apathy is characterized by reduced motivation for self-initiated goal-directed behavior, it is important to understand how this reduction in motivation extends to health attitudes and behaviors in PLWH, as maintenance of health behaviors are critical for successful treatment.

The IMB model would predict that because apathy is associated with ART non-adherence, apathy is likely negatively interfering with one or several critical psychological determinants of adherence. As apathy is largely unrelated to episodic and semantic memory in PLWH (Rabkin et al., 2000), it would not be expected to negatively affect knowledge of HIV disease and treatment. Similarly, when PLWH with apathy perform neurocognitive tasks in the laboratory under the supervision of technicians who provide structure and motivation to initiate and complete tasks (e.g., Rabkin et al., 2000), the association between apathy and performance on measures is relatively weak. As such, apathy would not be expected to associate with the actual medication management skill levels of PLWH. Rather, the model might suggest that apathy in PLWH would be more closely linked to critical determinants involving motivation levels and self-directed goal behaviors including: decreased motivation to carry out health behaviors and decreased perceived self-efficacy to carry out health behaviors. The present study aimed to take a comprehensive approach in examining the associations between apathy and critical determinants of medication adherence, as guided by the IMB model. We investigate the relationship between apathy and: 1) knowledge of HIV disease; 2) decisional conflict; 3) motivation to adhere to treatment; 4) perceived self-efficacy in medication management skills; and 5) objective medication management skills. In doing so, we are able to follow logically and sequentially the multiple steps leading to successful treatment (i.e., learning about the disease and treatment; making a decision regarding treatment plan; forming intentions to adhere to the treatment plan; using skills to carry out the treatment plan). We hypothesize that higher levels of apathy will be associated with higher decisional conflict, and lower motivation to carry out health behaviors, and perceived self-efficacy and medication management skills, but not with knowledge of HIV disease or medication management skills.

METHODS

Participants

The institutional human research protections review board approved the study protocol. These data were drawn from a parent project (R21-MH098607) aimed at developing a series of novel, web-based tasks of health behaviors and everyday functioning for use in PLWH, data from which have been previously reported (e.g., Doyle et al., 2016; Kordovski et al., 2017; Woods et al., 2016; 2017; Woods and Sullivan, 2019). The current sample included 85 participants with HIV that were enrolled in the University of California San Diego HIV Neurobehavioral Research Program (HNRP), which has a participant accrual and retention unit that provides outreach to local HIV clinics in southern California, community-based organizations, and the general community. For this cross-sectional study, persons enrolled in the HNRP during February 2013 to August 2014 were contacted by research staff to determine their interest in the participating. Potential participants who expressed interest and were deemed eligible were provided with informed, written consent about the nature of the study and procedures were fully explained to them. Enzyme-linked immunosorbent assay (ELISA) tests or MedMira Rapid Tests were used to determine HIV status. Demographic and clinical characteristics of the sample are shown in Table 1. Study exclusions included severe psychiatric disorder (e.g., psychosis), severe neurological conditions known to affect neurocognition (e.g., head injury with loss of consciousness greater than 30 minutes), reading-based estimated verbal IQ less than 70, current substance use disorder, and positive screening on a Breathalyzer or urine toxicology test for alcohol or illicit drugs on the day of testing.

Table 1.

Study sample demographic and clinical characteristics

Variable M (SD)
Sociodemographics
 Age (years) 46.3 (9.8)
 Education (years) 13.9 (2.3)
 Sex (% women) 10.0
 Self-Reported Sexual Orientation (%)
  Heterosexual 19.0
  Gay/Lesbian 73.8
  Bisexual 7.2
 Ethnicity (%)
  African American 21.2
  Asian 4.7
  Caucasian 54.1
  Hispanic 18.8
  Native American 1.2
Neuropsychiatric
 FrSBE Apathy raw score 30.2 (8.6)
 POMS Vigor/Activation raw score 16.0 (7.7)
 Lifetime Major Depressive Disorder (%) 63.5
 Lifetime Substance Use Disorder (%) 68.2
 CogState (% impaired) 59.5
Medical
 Current CD4 631.0 (298.3)
 Nadir CD4 221.8 (206.5)
 Viral Load (% undetectable) 89.0
 AIDS (%) 56.1
 cART (%) 93.8
 Estimated duration of HIV Infection (years) 13.1 (9.1)
 Years on cART 12.6 (8.9)
 # of Prescribed Medications 4.8 (4.7)
 # Pills/Day 6.0 (6.1)
 # of Medical Conditions (out of 8) 1.0 (1.1)
 # of Health Care Visits in Past Year^ 5.0 (7.0)
Health Attitudes and Behaviors
 HIV Knowledge Questionnaire (of 18) 15.4 (2.6)
 Decisional Conflict Scale (of 100) 15.9 (13.0)
 Motivation
  Health Motivation Questionnaire (of 40) 29.7 (5.6)
  ADQ Intentions (of 20) 18.6 (2.1)
 Perceived Self-Efficacy
  Perceived HIV Self-Management Scale (of 35) 28.1 (4.7)
  BERMA Memory for Medications (of 100) 79.7 (13.1)
 MMT-R (% Fail) 42.0
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Notes: FrSBE = Frontal Systems Behavior Scales; POMS = Profile of Mood States; CD4 = cluster of differentiation four; AIDS = acquired immunodeficiency syndrome; cART = combination antiretroviral therapy; HIV = human immunodeficiency virus; ADQ Intentions = Adherence Determinants Questionnaire – Intentions subscale; BERMA = Beliefs Related to Medication Adherence; MMT-R = Medication Management Task-Revised;

^

= Median (Interquartile Range).

Procedure

Apathy.

As there is no single “gold standard” measure for apathy, a composite score was derived from two well-validated questionnaires that measure apathy symptomology, the Frontal Systems Behavior Scales (FrSBe; Grace & Malloy, 2001) and the Profile of Mood States (POMS; McNair, Lorr, & Droppleman, 1981). Raw scores from the FrSBe Apathy scale and the POMS Vigor-Activation scale were converted to sample-based z-scores and averaged to calculate the apathy composite, for which higher scores were indicative of higher levels of apathy (e.g., Kamat et al., 2016). The Pearson product moment correlation for the two scales was large (r = 0.54, p < .001), providing evidence of convergent validity and thus supported their use as a composite.

Frontal Systems Behavior – Apathy subscale.

Participants completed the FrSBE, including a 14-item apathy subscale (Grace & Malloy, 2001), in which they rated statements (e.g., “Sit around doing nothing”, “Have lost interest in things that used to be fun or important to me”) on a scale from 1 (“almost never”) to 5 (“almost always”). The 14 FrSBe items were summed (five items were reverse scored) to generate a current apathy scale (current study sample Cronbach α = .85), which in the current sample ranged from 14 to 52.

Profile of Mood States – Vigor Activation subscale.

Participants completed the POMS including the eight-item Vigor-Activation subscale (McNair, Lorr, & Droppleman, 1981), in which they reported on current mood states (i.e., Lively, Energetic, Alert) on a five-point Likert scale ranging from 0 (“not at all”) to 4 (“extremely). The eight POMS items were summed to generate a current apathy scale (current study sample Cronbach α = .92), which in the current sample ranged from 0 to 32.

Information.

The HIV Knowledge Questionnaire-18 (HIV-KQ-18) is a measure assessing knowledge needed for HIV prevention (Cary & Schroder, 2002). Participants read 18 items about HIV and indicate whether they think the statement is true or false, or “don’t know.” Participants were given one point for each correct answer and zero points for an incorrect response or “don’t know.” Possible scores range from 0 to 18 and the range in the current sample was 1 to 18 (current study sample Cronbach’s α = .77).

Self-efficacy for health-related decision-making.

The Decisional Conflict Scale (DCS; O’Connor, 1995, 2010) is a 16-item measure of self-efficacy for health-related decision-making. Briefly, participants were presented with a hypothetical scenario in which they were asked to imagine that they reported mild memory and attention problems to their healthcare provider who then presented the potential risks and benefits of four treatment options: 1) medication; 2) cognitive training with a psychologist; 3) combination of 1 and 2; or 4) no treatment (see Doyle et al,. 2016). After making their decision, participants answered 16 questions assessing self-efficacy in relation to their decision (e.g., “I know which options are available to me,” “I am clear about the best choice for me”) using a Likert-type scale (range 0 to 4; 0 = strongly agree, 4 = strongly disagree) with higher total values indicating less confidence in their health-related decision making. Raw responses were transformed to be on a 0 to 100 scale (sample range 0 to 51.6) such that 0 indicated the lowest level of decisional conflict and 100 indicated the highest decisional conflict (current study sample Cronbach’s α = .93).

Motivation.

Raw scores from the Health Motivation Questionnaire and Adherence Determinants Questionnaire – Intentions (described below) were converted to sample-based z-scores and averaged to calculate the Motivation composite score so that higher scores were indicative of higher levels of motivation to adhere to their health plan. The Pearson product moment correlation for the two scales was medium (r = 0.30, p = .004), providing evidence of convergent validity and thus supported their use as a composite.

Health Motivation Questionnaire.

Participants completed the Health Motivation Questionnaire (HMQ; Moorman, 1993), an eight-item scale in which participants rated their level of agreement with statements regarding motivation for health behaviors (e.g., “I try to prevent health problems before I feel any symptoms.”) on a scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Possible scores ranged from 5 to 40, with higher scores indicating higher levels of motivation (current study sample Cronbach’s α = .81).

Adherence Determinants Questionnaire – Intentions Subscale.

Participants completed the Adherence Determinants Questionnaire – Intentions Subscale (ADQ-I; DiMatteo et al., 1993), a four-item subscale that assessed the extent to which participants intend to adhere to their HIV treatment plan. Participants rated four statement (e.g., “I intend to follow my treatment plan.”) on a scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Possible scores ranged from 4 to 20, with higher scores indicating more intent to adhere to treatment (current study sample Cronbach’s alpha = .85).

Perceived Self-Efficacy for Medication Management.

Raw scores from the Perceived HIV Self-Management Scale and Beliefs Related to Medication Adherence – Memory for Medications subscale (BERMA Memory for Medications; McDonald-Miszczak, Maris, Fitzgibbon, & Ritche, 2004; described below) were converted to sample-based z-scores and averaged to calculate the perceived self-efficacy for medication management composite score so that higher scores were indicative of higher levels of perceived self-efficacy. The Pearson product moment correlation for the two scales was medium (r = 0.42, p < .001), providing evidence of convergent validity, and thus supported their use as a composite.

Perceived HIV Self-Management Scale.

The Perceived HIV Self-Management Scale (PHIVMS; Wallston, Osborn, & Wagner, 2011) assessed the participants’ perceived self-efficacy in managing their HIV disease. Participants rated each item statement (e.g., “I succeed in the projects I undertake to manage my HIV infection”) from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). The PHIVMS traditionally contains eight items but due to a transcription error, one item from the scale (“It is difficult for me to find effective solutions for problems managing my HIV infection”) was not included in the questionnaire form. Possible scores ranged from 5 to 35, with higher scores indicating higher levels of perceived ability to self-manage HIV disease (current study sample Cronbach’s alpha = .85).

Beliefs Related to Medications Survey – Memory for Medications subscale.

The BERMA Memory for Medications subscale is a 20-item measure of perceived ability to remember information relevant to successful medication adherence. Participants rated each statement (e.g., “I am good at remembering to take my medications”) on a scale from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”). Possible scores ranged from 20 to 100, with higher scores indicated higher levels of confidence in memory skills for medication management (current study sample Cronbach’s alpha = 91).

Objective Medication Management Skills.

A subset of 50 participants completed a modified version of the Medication Management Task-Revised (MMT-R), a behavioral measure of one’s ability to accurately dispense medications according to a hypothetical prescription regimen. Participants were observed and scored with respect to their ability to dispense and organize mock pills from five labeled prescription bottles to a medication organizer designed to hold a one-week’s supply of medication. Two to three points could be earned for correctly dispensing each medication. For example, on the “Zinofuvine” prescription bottle, the label instructed participants to take one pill, two times a day, at meal times. Participants received one point for dispensing the correct dosage of pills in each compartment, one point for dispensing the pills in the correct number of compartments for each day, and one point for considering any special time instructions for pills, if applicable (e.g., placing a sleep medication in an evening compartment) Possible scores ranged from 0 to 13. In the present sample, 58% received full points (i.e., 13) on the task. Thus, a dichotomous variable was created such that “High Skill Level” indicated a score of 13 and “Low Skill Level” indicated a score less than 13.

Mood disorders.

Lifetime major depressive disorder (MDD) and substance use disorder (SUD) diagnoses were determined with the Composite International Diagnostic Interview (CIDI; Wittchen, 1994), which is a lay interview that derives diagnoses consistent with DSM-IV.

Neurocognitive Assessment.

Participants completed the CogState (www.cogstate.com), which includes measures of psychomotor speed, attention and working memory, and episodic learning and has been validated for use in HIV (e.g., Maruff et al., 2009). The CogState subtests administered included: Detection, Identification, One-Back, Two-Back, One Card Learning, and Continuous Paired Associated Learning. Raw scores for each domain were converted to demographically-adjusted T-scores, which were then used to generate a Global Deficit Score (GDS; see Carey et al., 2004). Participants with a GDS score ≥ .50 were categorized as neurocognitively impaired.

Medical Evaluation.

A neuromedical evaluation was conducted for all participants by a research nurse which included a blood draw and assessment of current medications. Participants provided plasma samples from which current CD4 cell counts and viral load were derived and completed a clinical interview from which information on nadir CD4 count, AIDS diagnosis, estimated duration of HIV, comorbid conditions, and pill counts was collected.

Data Analyses

The apathy composite was normally distributed (Shapiro-Wilk W test p > .01); to determine the need for covariates for this measure, we conducted univariate analyses using ANOVA and Pearson correlations. However, four other measures were non-normally distributed, the HIV-KQ-18, DCS, Motivation composite, and Perceived Self-Efficacy for Medication Management composite (Shapiro-Wilk W test p < .01 for each measure); to determine the need for covariates for these measures, we used Wilcoxon rank-sum tests and Spearman’s rho correlation coefficients. We selected key sociodemographic, neuropsychiatric, and medical variables to evaluate as potential covariates (see Table 1 for the full listing). Any variable that was significantly related to both the main predictor (i.e., apathy) and criterion variable of interest (i.e., HIV-KQ-18, DCS, Motivation composite, Perceived Self-Efficacy for Medication Management composite, and MMT-R) using a liberal critical alpha of .10 were included as a covariate in that model. Of note, lifetime MDD was chosen a priori as a covariate for all multivariate models, which is consistent with prior work in this area (e.g., Kamat et al., 2016). Given the relatively small sample size, we used a critical alpha of .05 for the multiple regression analyses, which provided acceptable power (.84) to detect medium effect sizes (f = .15) for up to three predictors. Analyses were conducted in JMP Pro 14.0.0 (SAS Institute Inc., 2018).

RESULTS

Descriptive Analyses for Determination of Covariates

Correlates of Apathy.

Higher levels of apathy were associated at a significant level with lifetime MDD (F[1, 83] = 9.20, p = .003, Cohen’s d = .70) and at a trend level with history of HCV (F[1, 80] = 3.03, p = .08, Cohen’s d = .66) and higher number of pills taken per day (Spearman’s ρ = .22, p = .093). Apathy was not associated with any other sociodemographic, neuropsychiatric or medical variables in Table 1 (ps > .10).

Correlates of Dimensions of Medication Management.

Univariate analyses were used to investigate the relationship between sociodemographic, neuropsychiatric, and medical variables in Table 1 and critical determinants for medication management outcome measures.

Information.

Neurocognitive impairment (Z = −1.91, p = .056) and lower CD4 counts (Spearman’s ρ = .28, p = .012) were associated with lower scores on the HIV-KQ-18. There were no other Table 1 variables associated with the HIV-KQ-18 (ps > .10).

Self-Efficacy for Decision Making.

There were no sociodemographic, neuropsychiatric, or medical variables associated with DCS (ps > .10).

Motivation.

There were no sociodemographic, neuropsychiatric, or medical variables associated with the Motivation composite (ps > .10).

Perceived Self-Efficacy for Medication Management.

Higher perceived Self-Efficacy for Medication Management was associated with lower number of prescriptions (Spearman’s ρ = −.32, p = .012), and pills taken per day (Spearman’s ρ = −.35, p = .006), and fewer number of years on prescription medication (Spearman’s ρ = .21, p = .056). Participants with neurocognitive impairment had lower perceived self-efficacy for medication management (Z = 2.51, p = .012). There were no other Table 1 variables associated with perceived self-efficacy for medication management (ps > .10).

Objective Medication Management Skills.

Older (Z = 1.87, p = .060, Cliff’s d = .31) and neurocognitively impaired participants (X2 = 7.70, p = .006, Odd’s ratio = 5.25) had poorer performance on the MMT-R. There were no other Table 1 variables associated with the objective medication management skills (ps > .10).

Final Covariates.

Number of pills taken per day was associated with both the apathy composite and Perceived Self-Efficacy for Medication Management (ps < .05). As such, number of pills taken per day was included as a covariate, along with Lifetime MDD, in the multiple regression conducted to examine the association between apathy and Perceived Self-Efficacy for Medication Management. None of the other sociodemographic, neuropsychiatric or medical variables in Table 1 were associated with both the apathy composite and outcome variables (ps > .10). Thus, in the models for the other outcome measures, lifetime MDD was the only covariate.

Apathy and Information

Apathy scores were not associated with HIV-KQ-18, Spearman’s ρ = −.12, p = .260 (see Figure 1), thus a further model was not conducted.

Figure 1.

Figure 1.

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Associations between apathy and medication management determinants. ^ = Cohen’s d transformed to Spearman’s rho. * = p < .05.

Apathy and Perceived Self-Efficacy for Health-Related Decision-Making

A Spearman’s correlation test showed that higher apathy scores were associated with higher levels of decisional conflict at a small effect size (Spearman’s ρ = .24, p = .031; see Figure 1). A regression with apathy and Lifetime MDD predicting DCS neared significance F(2,82) = 3.05, p = .053, and apathy was the only significant predictor (t [82] = 2.24, p = .028; Lifetime MDD p = .785.

Apathy and Motivation

A Spearman’s correlation test showed that higher apathy scores were associated with lower motivation at a small effect size (Spearman’s ρ = −.28, p = .009; see Figure 1). A regression model with apathy and Lifetime MDD predicting motivation was significant, F(2, 82) = 3.29, R2 = .07, p = .042 and apathy was the only significant predictor (t [82] = −2.44, p = .017; Lifetime MDD p = .994).

Apathy and Perceived Self-Efficacy for Medication Management

A Spearman’s correlation test showed that increased apathy was associated with lower levels of Perceived Self-Efficacy for Medication Management at a medium effect size (Spearman’s ρ = −.42, p < .001; see Figure 1). A regression was conducted with apathy, lifetime MDD, and number of pills taken per day predicting Perceived Self-Efficacy for Medication Management. The overall model was significant, F(3, 81) = 7.62, p < .001, R2 = .22, and apathy was the only significant predictor, t(81) = −4.01, p < .001 (Lifetime MDD p = .439; pills/day p = .059).

Apathy and Objective Medication Management Skills

Apathy scores were not associated with medication management ability within the 50 participants that completed the MMT-R, F(1, 48) = .001, p = .968; see Figure 1. Thus, a further model was not conducted.

DISCUSSION

There is growing evidence to suggest that higher levels of apathy may be associated with poorer medication non-adherence in HIV (Barclay, 2007; Panos 2014). However, medication adherence is a complex health behavior involving multiple components (e.g., Fisher, Fisher, Amico, & Harman, 2006; Simoni et al., 2006), and little is known about the role of apathy in the various psychological determinants of medication management. The present study utilized a theory-driven approach (Fisher, Fisher, Amico, & Harman, 2006) to investigate the association between apathy and several critical psychological components of medication adherence (i.e., information, motivation, objective and subjective self-efficacy in medication management). Consistent with our hypothesis, higher levels of apathy were associated with lower motivation to adhere to health behaviors, at a small effect size. Notably, the association between apathy and motivation was independent of lifetime MDD, converging with evidence from Panos et al. (2014) and Barclay (2007), which suggested that apathy was independently associated with medication adherence in HIV. Personal motivation to adhere to medication can be based on one’s beliefs about the outcomes of medication adherence (e.g., taking medication will improve my health) and evaluations of those outcomes (e.g., it would be good to improve my health) (Fishbein & Ajzen, 1975). Thus, PLWH with higher levels of apathy may have more negative, or simply lack positive, beliefs regarding and evaluating medication adherence outcomes. Indeed, apathy is commonly described as a “syndrome of motivational loss (Marin, 1991),” which in this case was associated with specific aspects of medication adherence.

Broadly, the IBM model posits that information and motivation to adhere to medication function through objective and subjective self-efficacy in medication management to affect the maintenance of medication adherence (Starace, Massa, Amico, & Fisher, 2006). Consistent with hypotheses, PLWH with higher levels of apathy demonstrated lower confidence in their abilities to make health-related decisions and manage medications, at small and medium effect sizes, respectively. PLWH are regularly charged with making complex decisions regarding their treatment regimen that can greatly impact health outcomes and involve weighing a host of factors including possible side effects, financial cost, time burden, and potential increase in lifespan (see Gardner, McLees, Steiner, del Rio, & Burman, 2011 for review). Relatedly, underlying pathophysiological (e.g., involvement of the ventral striatum and anterior cingulate) and social and environmental correlates (e.g., severity of cognitive impairment and education level) of apathy are also related to behaviors such as decision-making and emotion regulation (see Balleine, Delgado, & Hikosaka, 2007; Toplak et al., 2010), which are known to be implicated in HIV (e.g., Doyle et al., 2016). After making these complex decisions, they are faced with carrying out their medication treatment plan which can require attending doctor appointments, organizing correct times and dosages for the medications, setting up reminders, and refilling prescriptions. PLWH with higher levels of apathy may experience difficulty carrying out goal-directed behavior generally in other areas of their life, and extend these beliefs to their perceived self-efficacy in health-related areas.

Apathy was not related to information or objective skills for medication management in the current sample. Taken together, these findings suggest that PLWH with higher levels of apathy do not lack the knowledge or skills to properly adhere to their treatment plan, but rather may have difficulty applying their knowledge and skills to successfully make decisions and manage their medications. These findings are consistent with Rabkin et al. (2000), which suggests that apathy does not affect semantic memory, and provides preliminary evidence that apathy may not interfere with functional capacity, but rather the use of these skills and knowledge in daily life. As such, in terms of the IBM model, our findings suggest that apathy is associated with decreased motivation to adhere to treatment and lower perceived self-efficacy for medication management and making healthcare decisions, which are factors that may interfere with successful ART adherence.

It is unlikely that the observed associations between apathy and components of medication adherence were an artifact of depression. Only 4% of the HIV+ participants in this sample had clinical diagnoses of current depression, whereas 34% had clinically elevated apathy scores. Moreover, 13% of our HIV+ sample with clinically elevated apathy (per the FrSBe normative standards) did not have lifetime histories of depression. The FrSBe apathy scale showed medium correlations with the POMS Depression-Dejection scale (r = .44), which did not differ significantly from the correlations between apathy and Fatigue (r = .38; z = .64, p = .26) or Confusion-Bewilderment (r = .46; z = −.30; p = .76), both of which are also symptom components in many models of depression. Similar findings were observed for the composite apathy score, which has a higher risk of inflated correlations given that it includes a POMS scale.

Nevertheless, the primary limitation of this study was the exclusive reliance on self-report questionnaires of apathy which may be less robust than other approaches, such as informant- or clinician-rated measures of apathy (e.g., Guercio et al., 2015). That being said, there is strong evidence of the construct validity of the self-report apathy measures, including associations with neuroanatomical abnormalities (e.g., Kamat et al., 2014) and impaired everyday functioning (e.g., Kamat et al., 2012). While there is no “gold standard” for measuring apathy, the FrSBE self-report apathy subscale is a widely used, well-validated apathy measure (e.g., see Clarke et al., 2011 for review) that corresponds closely with informant-report apathy measures (Grace & Malloy, 2001). We included the Vigor-Activation scale from the Profile of Mood States in an apathy composite measure employed in previous studies (Kamat et al., 2016; Marquine et al., 2014). Notably, there were no significant differences in the findings when using the apathy composite or its individual subscales (i.e., FrSBE apathy subscale or POMS Vigor-Activation subscale) as our measures of apathy.

Another limitation concerns the generalizability of these data, which were derived from a predominately male cohort with fairly well-controlled HIV disease. While we did not include medication adherence measures, 95% of participants had CD4 counts greater than 200, which suggests that that the current sample was relatively healthy (and likely largely adherent to their medication regimens). That apathy was related to several critical determinants of medication adherence at small-to-medium effect sizes in the present sample speaks to the robustness of the findings, as we would expect these effect sizes to be larger in samples with low CD4 counts and/or low rates of medication adherence. Nonetheless, future studies may wish to leverage larger and more medically diverse samples with medication adherence data to determine the extent to which apathy may moderate the relationship between critical determinants of medication adherence and medication adherence. The present findings might suggest that apathy might moderate the relationship between motivation and subjective, but not objective, medication management skills which are directly related to antiretroviral adherence outcomes (Fisher et al., 2006; Starace et al., 2006).

While several imaging studies provide a neurobiological context for the proposed shared underpinnings of HIV and apathy pathology, there exists a gap in the literature (and the present study) regarding the direct and/or indirect contributions of other personal, social, and environmental factors to apathy in HIV (e.g., Massimo, Kales, & Kovanoski, 2018). One potential social factor that may be worth further exploration is the relationship between apathy and stigmatization. One study of persons with schizophrenia demonstrated that there was more passive-apathetic social withdrawal among those that reported higher perceived stigmatization (Ertugrul, 2004). Studies have also suggested individual differences in aspects of social cognition (e.g., empathy, intellectual curiosity, and general enthusiasm) may be a personal factor that contributes to apathy (e.g., Lockwood et al., 2017; Drijgers et al., 2010; Sockeel et al., 2006). In the literature, apathy has commonly been explored through examining its associations with outcome measures (e.g., quality of life, functional impairment, medication adherence) using a cross-sectional study design (e.g., Kamat et al., 2012). Future studies may wish to employ a longitudinal study design to examine the trajectory of apathy to further elucidate whether apathy contributes to or is secondary to these outcomes.

Despite the emerging recognition of the clinical importance of apathy in neurological disorders and injuries (see Lanctot et al., 2017 for review), apathy is not often measured in clinic. There were no measures of apathy among a list of the top 15 mood and personality assessments used by neuropsychologists (Rabin et al., 2016). Clinicians working with PLWH may consider assessing symptoms of apathy due to its high prevalence among PLWH and its association with critical determinants of medication adherence, everyday functioning (e.g., Kamat et al., 2012), and quality of life (e.g., Tate et al., 2013). There are limited data on the effectiveness of apathy treatments, and to date the efficacy of apathy interventions have not been studied in PLWH. However, preliminary evidence in persons with dementia and schizophrenia suggests partial benefits of pharmacological intervention (e.g., Padala, Petty & Bhatia, 2005; Roth, Flashman, & McAllister, 2007; Mega et al., 1999; Bodkin et al., 2005), as well as individualized non-pharmacological intervention including recreational (e.g., Politis et al., 2004) and music therapies (e.g., Raglio et al., 2008). Future studies should explore the effectiveness of these interventions in PLWH.

Our results suggest that while PLWH with high levels of apathy may have the knowledge and skills necessary to manage their medications, they still demonstrate lower levels of motivation to adhere and perceived self-efficacy for health behaviors. While this study limited its scope of health behaviors to determinants of medication adherence, future studies should broaden the scope to explore the impact of apathy on other important health behaviors such as healthcare engagement, rate of follow-up with recommendations, and treatment cascade.

ACKNOWLEDGMENTS

The authors are grateful to Dr. Scott Letendre for overseeing the neuromedical aspects of the parent project, Dr. J. Hampton Atkinson and Jennifer Marquie Beck for participant recruitment, and Donald R. Franklin, Stephanie Corkran, Jessica Beltran, and Javier Villalobos for data processing.

Footnotes

This study was supported by NIH grants R21-MH098607, R01-MH073419, and P30-MH62512. The authors report no conflicts of interest. The views expressed in this article are those of the authors and do not reflect the official policy or position of the Department of the Navy, Department of Defense, nor the United States Government. The present study involved human participants, all of which provided informed, written consent after the nature of the study and procedures were fully explained to them.

References

  1. American Psychiatric Association. (2015). Depressive Disorders: DSM-5® Selections. American Psychiatric Publication. [Google Scholar]
  2. Barclay TR, Hinkin CH, Castellon SA, Mason KI, Reinhard MJ, Marion SD, … & Durvasula RS (2007). Age-associated predictors of medication adherence in HIV-positive adults: Health beliefs, self-efficacy, and neurocognitive status. Health Psychology, 26(1), 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Balleine BW, Delgado MR, & Hikosaka O (2007). The role of the dorsal striatum in reward and decision-making. Journal of Neuroscience, 27(31), 8161–8165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bodkin JA, Siris SG, Bermanzohn PC, Hennen J, & Cole JO (2005) Double-blind placebo-controlled, multicenter trial of selegiline augmentation of antipsychotic medication to treat negative symptoms in outpatients with schizophrenia. American Journal of Psychiatry, 162(2), 388–390. [DOI] [PubMed] [Google Scholar]
  5. Carey CL, Woods SP, Gonzalez R, Conover E, Marcotte TD, Grant I, & Heaton RK (2004). Predictive validity of global deficit scores in detecting neuropsychological impairment in HIV infection. Journal of clinical and experimental neuropsychology, 26(3), 307–319. [DOI] [PubMed] [Google Scholar]
  6. Carey MP, & Schroder KE (2002). Development and psychometric evaluation of the brief HIV Knowledge Questionnaire. AIDS education and prevention, 14(2), 172–182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Castellon SA, Hinkin CH, Wood S, & Yarema KT (1998). Apathy, depression, and cognitive performance in HIV-1 infection. The Journal of Neuropsychiatry and Clinical Neurosciences, 10(3), 320–329. [DOI] [PubMed] [Google Scholar]
  8. Castellon SA, Hinkin CH, & Myers HF (2000). Neuropsychiatric disturbance is associated with executive dysfunction in HIV-1 infection. Journal of the International Neuropsychological Society, 6(3), 336–347. [DOI] [PubMed] [Google Scholar]
  9. Clarke DE, Ko JY, Kuhl EA, van Reekum R, Salvador R, & Marin RS (2011). Are the available apathy measures reliable and valid? A review of the psychometric evidence. Journal of Psychosomatic Research, 70(1), 73–97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. DiMatteo MR, Sherbourne CD, Hays RD, Ordway L, Kravitz RL, McGlynn EA, … & Rogers WH (1993). Physicians’ characteristics influence patients’ adherence to medical treatment: results from the Medical Outcomes Study. Health Psychology, 12(2), 93. [DOI] [PubMed] [Google Scholar]
  11. Doyle KL, Woods SP, Morgan EE, Iudicello JE, Cameron MV, Gilbert PE, … & HIV Neurobehavioral Research Program (HNRP) Group. (2016). Health-related decision-making in HIV disease. Journal of Clinical Psychology in Medical Settings, 23(2), 135–146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Drijgers RL, Dujardin K, Reijnders JS, Defebvre L, & Leentjens AF (2010). Validation of diagnostic criteria for apathy in Parkinson’s disease. Parkinsonism and Related Disorders, 16(10), 656–660. [DOI] [PubMed] [Google Scholar]
  13. Ellis R, Langford D, & Masliah E (2007). HIV and antiretroviral therapy in the brain: neuronal injury and repair. Nature Reviews Neuroscience, 8(1), 33. [DOI] [PubMed] [Google Scholar]
  14. Ertugrul A, & Uluğ B (2004). Perception of stigma among patients with schizophrenia. Social Psychiatry and Psychiatric Epidemiology, 39(1), 73–77. [DOI] [PubMed] [Google Scholar]
  15. Fishbein MA, Ajzen I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, Addison-Wesley, MA. [Google Scholar]
  16. Gardner EM, McLees MP, Steiner JF, del Rio C, & Burman WJ (2011). The spectrum of engagement in HIV care and its relevance to test-and-treat strategies for prevention of HIV infection. Clinical infectious diseases, 52(6), 793–800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Grace J (2001). Frontal systems behavior scale: professional manual. Psychological Assessment Resources. [Google Scholar]
  18. Guercio BJ, Donovan NJ, Munro CE, Aghjayan SL, Wigman SE, Locascio JJ, … & Marshall GA (2015). The Apathy Evaluation Scale: a comparison of subject, informant, and clinician report in cognitively normal elderly and mild cognitive impairment. Journal of Alzheimer’s Disease, 47(2), 421–432. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Heaton RK, Marcotte TD, Mindt MR, Sadek J, Moore DJ, Bentley H, … & HNRC Group. (2004). The impact of HIV-associated neuropsychological impairment on everyday functioning. Journal of the International Neuropsychological Society, 10(3), 317–331. [DOI] [PubMed] [Google Scholar]
  20. Hoare J, Fouche JP, Spottiswoode B, Joska JA, Schoeman R, Stein DJ, & Carey PD (2010). White matter correlates of apathy in HIV-positive subjects: a diffusion tensor imaging study. The Journal of Neuropsychiatry and Clinical Neurosciences, 22(3), 313–320. [DOI] [PubMed] [Google Scholar]
  21. Kamat R, Woods SP, Marcotte TD, Ellis RJ, & Grant I (2012). Implications of apathy for everyday functioning outcomes in persons living with HIV infection. Archives of Clinical Neuropsychology, 27(5), 520–531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kamat R, Brown GG, Bolden K, Fennema-Notestein C, Archibald S, Marcotte TD, … & Heaton RK (2014). Apathy is associated with white matter abnormalities in anterior, medial brain regions in persons with HIV infection. Journal of clinical and experimental neuropsychology, 36(8), 854–866. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kamat R, Woods SP, Cameron MV, Iudicello JE, & HIV Neurobehavioral Research Program (HNRP) Group. (2016). Apathy is associated with lower mental and physical quality of life in persons infected with HIV. Psychology, Health & Medicine, 21(7), 890–901. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kant R, Duffy JD, & Pivovarnik A (1998). Prevalence of apathy following head injury. Brain Injury, 12(1), 87–92. [DOI] [PubMed] [Google Scholar]
  25. Kelly MP, & Barker M (2016). Why is changing health-related behaviour so difficult?. Public Health, 136, 109–116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Kennedy S, Goggin K, & Nollen N (2004). Adherence to HIV medications: Utility of the theory of self-determination. Cognitive Therapy and Research, 28(5), 611–628. [Google Scholar]
  27. Kirsch-Darrow L, Marsiske M, Okun MS, Bauer R, & Bowers D (2011). Apathy and depression: separate factors in Parkinson’s disease. Journal of the International Neuropsychological Society, 17(6), 1058–1066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kordovski VM, Woods SP, Verduzco M, & Beltran J (2017). The effects of aging and HIV disease on employment status and functioning. Rehabilitation Psychology, 62(4), 591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kos C, van Tol MJ, Marsman JBC, Knegtering H, & Aleman A (2016). Neural correlates of apathy in patients with neurodegenerative disorders, acquired brain injury, and psychiatric disorders. Neuroscience & Biobehavioral Reviews, 69, 381–401. [DOI] [PubMed] [Google Scholar]
  30. Kremer H, Ironson G, Schneiderman N, & Hautzinger M (2007). “It’s My Body”: Does Patient Involvement in Decision Making Reduce Decisional Conflict?. Medical Decision Making, 27(5), 522–532. [DOI] [PubMed] [Google Scholar]
  31. Lanctôt KL, Agüera-Ortiz L, Brodaty H, Francis PT, Geda YE, Ismail Z, … & Politis AM (2017). Apathy associated with neurocognitive disorders: recent progress and future directions. Alzheimer’s & Dementia, 13(1), 84–100. [DOI] [PubMed] [Google Scholar]
  32. Levy ML, Cummings JL, Fairbanks LA, Masterman D, Miller BL, Craig AH, … & Litvan I (1998). Apathy is not depression. The Journal of neuropsychiatry and clinical neurosciences, 10(3), 314–319. [DOI] [PubMed] [Google Scholar]
  33. Levy R, & Dubois B (2005). Apathy and the functional anatomy of the prefrontal cortex–basal ganglia circuits. Cerebral cortex, 16(7), 916–928. [DOI] [PubMed] [Google Scholar]
  34. Lima VD, Geller J, Bangsberg DR, Patterson TL, Daniel M, Kerr T, … & Hogg RS (2007). The effect of adherence on the association between depressive symptoms and mortality among HIV-infected individuals first initiating HAART. AIDS, 21(9), 1175–1183. [DOI] [PubMed] [Google Scholar]
  35. Lockwood PL, Ang YS, Husain M, & Crockett MJ (2017). Individual differences in empathy are associated with apathy-motivation. Scientific Reports, 7(1), 1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Marin RS (1991). Apathy: a neuropsychiatric syndrome. The Journal of Neuropsychiatry and Clinical Neurosciences, 3(3), 243–254. [DOI] [PubMed] [Google Scholar]
  37. Marquine MJ, Iudicello JE, Morgan EE, Brown GG, Letendre SL, Ellis RJ, … & Translational Methamphetamine AIDS Research Center (TMARC) Group. (2014). “Frontal systems” behaviors in comorbid human immunodeficiency virus infection and methamphetamine dependency. Psychiatry Research, 215(1), 208–216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Maruff P, Thomas E, Cysique L, Brew B, Collie A, Snyder P, & Pietrzak RH (2009). Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Archives of Clinical Neuropsychology, 24(2), 165–178. [DOI] [PubMed] [Google Scholar]
  39. Massimo L, Kales HC, & Kolanowski A (2018). State of the science: apathy as a model for investigating behavioral and psychological symptoms in dementia. Journal of the American Geriatrics Society, 66, S4–S12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Mcdonald-Miszczak L, Maris P, Fitzgibbon T, & Ritchie G (2004). A pilot study examining older adults’ beliefs related to medication adherence: the BERMA survey. Journal of Aging and Health, 16(5), 591–614. [DOI] [PubMed] [Google Scholar]
  41. McNair DM, Lorr M, & Droppleman LF (1981). Profile of mood states manual. San Diego, CA: EDITS manual. [Google Scholar]
  42. Mega MS, Masterman DM, O’Connor SM, Barclay TR, & Cummings JL (1999). The spectrum of behavioral responses to cholinesterase inhibitor therapy in Alzheimer disease. Archives of Neurology, 56(11), 1388–1393. [DOI] [PubMed] [Google Scholar]
  43. Miller LG, Liu H, Hays RD, Golin CE, Ye Z, Beck CK, … & Wenger NS (2003). Knowledge of antiretroviral regimen dosing and adherence: a longitudinal study. Clinical Infectious Diseases, 36(4), 514–518. [DOI] [PubMed] [Google Scholar]
  44. Moorman C, & Matulich E (1993). A model of consumers’ preventive health behaviors: The role of health motivation and health ability. Journal of Consumer Research, 20(2), 208–228. [Google Scholar]
  45. Navia BA, Jordan BD, & Price RW (1986). The AIDS dementia complex: I. Clinical features. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 19(6), 517–524. [DOI] [PubMed] [Google Scholar]
  46. O’Connor AM (1995). Validation of a decisional conflict scale. Medical Decision Making, 15(1), 25–30. [DOI] [PubMed] [Google Scholar]
  47. Okada K, Kobayashi S, Yamagata S, Takahashi K, & Yamaguchi S (1997). Poststroke apathy and regional cerebral blood flow. Stroke, 28(12), 2437–2441. [DOI] [PubMed] [Google Scholar]
  48. Padala PR, Petty F, & Bhatia SC (2005). Methylphenidate may treat apathy independent of depression. Annals of Pharmacotherapy, 39(11), 1947–1949. [DOI] [PubMed] [Google Scholar]
  49. Padala PR, Desouza CV, Almeida S, Shivaswamy V, Ariyarathna K, Rouse L, … & Petty F (2008). The impact of apathy on glycemic control in diabetes: a cross-sectional study. Diabetes research and clinical practice, 79(1), 37–41. [DOI] [PubMed] [Google Scholar]
  50. Panos SE, Del Re AC, Thames AD, Arentsen TJ, Patel SM, Castellon SA, … & Hinkin CH (2014). The impact of neurobehavioral features on medication adherence in HIV: evidence from longitudinal models. AIDS care, 26(1), 79–86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Paul RH, Brickman AM, Navia B, Hinkin C, Malloy PF, Jefferson AL, … & Flanigan TP (2005). Apathy is associated with volume of the nucleus accumbens in patients infected with HIV. The Journal of Neuropsychiatry and Clinical Neurosciences, 17(2), 167–171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Perno CF, Ceccherini-Silberstein F, De AL, Cozzi-Lepri A, Gori C, Cingolani A, … & Scasso A (2002). Virologic correlates of adherence to antiretroviral medications and therapeutic failure. Journal of Acquired Immune Deficiency Syndromes (1999), 31, S118–22. [DOI] [PubMed] [Google Scholar]
  53. Politis AM, Vozzella S, Mayer LS, Onyike CU, Baker AS, & Lyketsos CG (2004). A randomized, controlled, clinical trial of activity therapy for apathy in patients with dementia residing in long-term care. International Journal of Geriatric Psychiatry, 19(11), 1087–1094. [DOI] [PubMed] [Google Scholar]
  54. Rabin LA, Paolillo E, & Barr WB (2016). Stability in test-usage practices of clinical neuropsychologists in the United States and Canada over a 10-year period: A follow-up survey of INS and NAN members. Archives of Clinical Neuropsychology, 31(3), 206–30. [DOI] [PubMed] [Google Scholar]
  55. Rabkin JG, Ferrando SJ, van Gorp W, Rieppi R, McElhiney M, & Sewell M (2000). Relationships among apathy, depression, and cognitive impairment in HIV/AIDS. The Journal of Neuropsychiatry and Clinical Neurosciences, 12(4), 451–457. [DOI] [PubMed] [Google Scholar]
  56. Raglio A, Bellelli G, Traficante D, Gianotti M, Ubezio MC, Villani D, & Trabucchi M (2008). Efficacy of music therapy in the treatment of behavioral and psychiatric symptoms of dementia. Alzheimer Disease & Associated Disorders, 22(2), 158–162. [DOI] [PubMed] [Google Scholar]
  57. Roth RM, Flashman LA, & McAllister TW (2007). Apathy and its treatment. Current Treatment Options in Neurology, 9(5), 363–370. [PubMed] [Google Scholar]
  58. Shapiro ME, Mahoney JR, Zingman BS, Pogge DL, & Verghese J (2013). Apathy correlates with cognitive performance, functional disability, and HIV RNA plasma levels in HIV-positive individuals. Journal of Clinical and Experimental Neuropsychology, 35(9), 934–945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sockeel P, Dujardin K, Devos D, Deneve C, Destée A, & Defebvre L (2006). The Lille apathy rating scale (LARS), a new instrument for detecting and quantifying apathy: validation in Parkinson’s disease. Journal of Neurology, Neurosurgery and Psychiatry, 77(5), 579–584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Starace F, Massa A, Amico KR, & Fisher JD (2006). Adherence to antiretroviral therapy: an empirical test of the information-motivation-behavioral skills model. Health Psychology, 25(2), 153. [DOI] [PubMed] [Google Scholar]
  61. Starkstein SE, Mayberg HS, Preziosi T, Andrezejewski P, Leiguarda R, & Robinson RG (1992). Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. The Journal of Neuropsychiatry and Clinical Neurosciences, 4(2), 134–139. [DOI] [PubMed] [Google Scholar]
  62. Starkstein SE, Petracca G, Chemerinski E, & Kremer J (2001). Syndromic validity of apathy in Alzheimer’s disease. American Journal of Psychiatry, 158(6), 872–877. [DOI] [PubMed] [Google Scholar]
  63. Tate D, Paul RH, Flanigan TP, Tashima K, Nash J, Adair C, … & Cohen RA (2003). The impact of apathy and depression on quality of life in patients infected with HIV. AIDS Patient Care and STDs, 17(3), 115–120. [DOI] [PubMed] [Google Scholar]
  64. Toplak ME, Sorge GB, Benoit A, West RF, & Stanovich KE (2010). Decision-making and cognitive abilities: A review of associations between Iowa Gambling Task performance, executive functions, and intelligence. Clinical Psychology Review, 30(5), 562–581. [DOI] [PubMed] [Google Scholar]
  65. Velligan DI, Diamond PM, Maples NJ, Mintz J, Li X, Glahn DC, & Miller AL (2008). Comparing the efficacy of interventions that use environmental supports to improve outcomes in patients with schizophrenia. Schizophrenia Research, 102(1–3), 312–319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wagner JH, Justice AC, Chesney M, Sinclair G, Weissman S, & Rodriguez-Barradas M (2001). Patient-and provider-reported adherence: toward a clinically useful approach to measuring antiretroviral adherence. Journal of clinical epidemiology, 54(12), S91–S98. [DOI] [PubMed] [Google Scholar]
  67. Walker KA, & Brown GG (2018). HIV-associated executive dysfunction in the era of modern antiretroviral therapy: A systematic review and meta-analysis. Journal of Clinical and Experimental Neuropsychology, 40(4), 357–376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wallston KA, Osborn CY, Wagner LJ, & Hilker KA (2011). The perceived medical condition self-management scale applied to persons with HIV/AIDS. Journal of Health Psychology, 16(1), 109–115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wittchen HU (1994). Reliability and validity studies of the WHO-Composite International Diagnostic Interview (CIDI): a critical review. Journal of Psychiatric Research, 28(1), 57–84. [DOI] [PubMed] [Google Scholar]
  70. Woods SP, Iudicello JE, Morgan EE, Cameron MV, Doyle KL, Smith TV, … & Ellis RJ (2016). Health-related everyday functioning in the internet age: HIV-associated neurocognitive disorders disrupt online pharmacy and health chart navigation skills. Archives of Clinical Neuropsychology, 31(2), 176–185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Woods SP, Iudicello JE, Morgan EE, Verduzco M, Smith TV, Cushman C, & HIV Neurobehavioral Research Program (HNRP) Group. (2017). Household everyday functioning in the internet age: Online shopping and banking skills are affected in HIV− associated neurocognitive disorders. Journal of the International Neuropsychological Society, 23(7), 605–615. [DOI] [PMC free article] [PubMed] [Google Scholar]

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