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. Author manuscript; available in PMC: 2020 Jul 1.
Published in final edited form as: West J Nurs Res. 2019 Jan 17;41(7):990–1008. doi: 10.1177/0193945918823347

The Effect of an HIV Self-Management Intervention on Neurocognitive Behavioral Processing

Allison Webel 1, Nate Schreiner 1, Robert Salata 2, Jared Friedman 3, Anthony I Jack 3, Abdus Sattar 1, David M Fresco 4, Margaret Rodriguez 1, Shirley Moore 1
PMCID: PMC6570548  NIHMSID: NIHMS1002792  PMID: 30654713

Abstract

People living with HIV (PLHIV) are increasingly diagnosed with comorbidities which requires increasing self-management. We examined the effect of a self-management intervention on neurocognitive behavioral processing. Twenty-nine PLHIV completed a two- group, three-month randomized clinical trial testing a self-management intervention to improve physical activity and dietary intake. At baseline and 3 months later everyone completed validated assessments of physical, diet, neurocognitive processing (fMRI-derived network analyses). We used linear mixed effects modelling with a random intercept to examine the effect of the intervention. The intervention improved healthy eating (p=0.08) and but did not improve other self-management behaviors. There was a significant effect of the intervention on several aspects of neurocognitive processing including in the task positive network (TPN) differentiation (p=0.047) and an increase in the default mode network (DMN) differentiation (p=0.10). Self-management interventions may influence neurocognitive processing in PLHIV, but those changes were not associated with positive changes in self-management behavior.

Keywords: HIV, Exercise, fMRI, Behavioral Interventions

There are approximately 1.1 million people in the United States living with HIV and almost half of this population is over the age of 50 years (Centers for Disease Control and Prevention, 2018). Both HIV disease and aging, necessitate increased self-management skills to live and age well. In the beginning of the HIV epidemic, self-management interventions were focused on palliative care and as HIV treatment transformed the self-management interventions increasingly focused on HIV medication adherence, symptom management, and engagement with HIV care providers (Webel, Prince-Paul, et al., 2018). Today, self-management for persons living with HIV has expanded to include traditional healthy living behaviors, such as eating a healthy diet and engaging in regular, moderate-to-vigorous physical activity (Foster, Hillsdon, & Thorogood, 2005; Grinspoon et al., 2008; O’Brien, Nixon, Tynan, & Glazier, 2010). While we have seen self-management interventions successfully improve HIV medication adherence and symptom management, (Kanters et al., 2017) there has been less success improving physical activity and healthy eating among people living with HIV (PLHIV; Cutrono et al., 2016; Jaggers et al., 2016; Webel, Moore, et al., 2018). Further, responses even among successful self-management interventions for HIV medication adherence and symptom management, have been varied; suggesting that better tailoring may improve the effectiveness of self-management interventions (Tufts et al., 2015).

Self-management and Neurocognitive Behavioral Processing

One way to improve tailoring of self-management interventions is by modifying them to the individual PLHIV’s existing neurocognitive processes. Neurocognitive processes such as analytical reasoning, decision-making, and attention can influence how one receives and understands self-management information, and recalls and acts on this information. Self-management interventions can be tailored to an individual and their family based on their neurocognitive processes, however a better understanding of the relationship between neurocognitive constructs and self-management behaviors is necessary before this tailoring can occur.

Recent progress in neuroimaging now allows us to investigate the relationships among neurocognitive process, brain activity, and self-management behaviors. Functional magnetic resonance imaging (fMRI) couples a specific task with changes in blood flow in the brain. These quantifiable changes in blood flow, indicate changes in cortical brain activity and cognition, and now allow an examination of the neurocognitive mechanisms underpinning changes in the neurocognitive behavioral processes associated with changes in self-management behaviors.

Prior work indicates that changes in self-management behaviors may be associated with analytic (Task Positive Network [TPN]) and empathetic (Default Mode Network [DMN]) neural network activation (Jack et al., 2013). Specifically, our previous work identified the TPN as an important network in processing and acting upon analytic information while the DMN assists in processing and acting on empathetic information; Jack et al., 2013) –suggesting that an individual’s ability to differentiate the activation of TPN and DMN in response to analytic or empathetic information is associated with better [self]-management skills. This work informed the guiding a priori hypothesis of our neural network differentiation task, that greater TPN and DMN task differentiation would be associated with better self-management behaviors

Purpose

The purpose of this paper is twofold, to: describe the effect of a self-management intervention on neurocognitive behavioral processing variables and to describe the association between changes in these neural markers in relation to changes in self-management behavior (i.e., physical activity, diet, HIV medication adherence, and retention in care) in PLHIV. We hypothesized that that better cognitive neural network task differentiation would be associated with improved self-management behaviors over time (Jones et al., 2018).

Method

Design

These data derive from a pre-planned, nested pilot study of a larger two-group randomized clinical trial testing the effect of the SystemCHANGE self-management intervention (Boosting health By Changing AcTivity hereafter referred to as BOBCAT study) to improve physical activity and cardiometabolic outcomes in sedentary PLHIV (NCT02553291; Webel, Moore, et al., 2018).

Recruitment

Potential participants were recruited using recruitment letters sent to an HIV Research Registry in Northeast Ohio and flyers distributed at local HIV clinics and service organizations. The letters and flyers indicated the study’s purpose, time involvement, general inclusion criteria, and a phone number to call if someone was interested. Prospective participants responding to these advertisements were screened by a Research Assistant, using a standardized screening form, to ensure that they met study eligibility criteria. We consecutively enrolled all willing BOBCAT study participants meeting eligibility criteria for this nested study, until our we reached our a priori sample size of 30 participants. We selected a sample size of 30 participants based on financial constraints for pilot studies in the SMART Center (SMART Center, 2018).

Population

To be included in the BOBCAT clinical trial, potential participants had to 1) be diagnosed with HIV, 2) be ≥18 years of age, 3) be taking HIV anti-retroviral therapy, 4) had a recent HIV Viral Load less than 400 copies/mL, 5) be at high risk for developing cardiovascular disease (CVD; Framingham 30-year CVD risk score>20% for females and >30% for males), and 6) if taking a statin medication, be on it for at least 6 months. Potential participants were excluded if they 1) had a medical contraindication for exercise, 2) met the Department of Health and Human Service’s recommendations for weekly exercise (assessed with the 7-day physical activity recall), 3) were unable to understand spoken English, 4) were expecting to move out of the immediate area in the next six months, 5) had planned surgery in the next six months, 6) were pregnant or planned on becoming pregnant, or 7) were enrolled in a formal weight loss program. Additionally, to undergo an fMRI procedure participants had to have a Montreal Cognitive Assessment (MOCA) score of 22 or greater, indicating intact cognitive functioning, 2) be willing and able sit in the MRI scanner for 45 minutes, and 3) not have a medical contraindication for an MRI (e.g., aneurysm clips, MRI-incompatible devices, infusion pumps, chronic liver disease, metal or shrapnel in his or her body). Medical eligibility criteria (e.g., HIV status, medications, HIV viral load values) were confirmed, with the potential subject’s consent, using his or her electronic medical record.

Procedures

Participants meeting inclusion criteria were scheduled for an in-person baseline appointment during which participants completed a written informed consent. They then completed assessments of self-management behavior and neurocognitive processing, as described below. After completing all assessments, participants were randomized 1:1, stratified by gender and race, to either a self-management intervention or a control condition. The intervention condition was a six-session, in-person, group intervention teaching behavior change techniques and healthy lifestyle education. Participants randomized to the intervention completed six group sessions where they completed a standardized lifestyle education session led by a health educator. Approximately 90% of participants randomized ot the intervention attended 50% of the sessions and 60% of the participants attended at least five intervention sessions (Webel, Moore, et al., 2018). The health educator provided evidence-based information on the diet and exercise components of a healthy lifestyle (e.g., DASH and Mediterranean diets; types, frequency and intensity of physical activity). She also led them through a series of exercises (e.g., self-monitoring, cause an effect diagrams) to identify how to incorporate these behaviors into participants’ daily routines. The intervention primarily taught analytical self-management skills (e.g., self-monitoring and tracking) but also contains social/empathic features (e.g., social support activities). The control condition was a one-on-one session in which a research assistant provided information on healthy eating and physical activity using an American Heart Association pamphlet. A full description of the intervention and control condition components can be found in a previous publication (Webel, Moore, et al., 2018).

As previously reported, participants in the intervention did not increase their physical activity or improve their overall diet quality, but did reduce their daily carbohydrate intake which led to more weight loss over time than those in the control group (p<0.05; Webel, Moore, et al., 2018). All procedures for this nested pilot study, including a $100 incentive to complete the pilot study measures, were approved by the Institutional Review Board at University Hospitals Cleveland Medical Center and occurred between June 2015 and April 2016.

Measures

To examine the effect of the self-management intervention on neurocognitive behavioral processing variables and the association between changes in self-management behaviors and neurocognitive behavioral processing all participants completed assessments of self-management behavior and neurocognitive processing at baseline and three months later (immediately post-intervention).

Demographics and Health Characteristics.

All participants completed a self-reported demographic survey assessing gender, race, education and monthly income using REDCAP (Research Electronic Data Capture; Harris et al., 2009). Participants also consented to medical chart abstraction from which study staff abstracted detailed descriptive medical data including years living with HIV, current CD4+ T cell count, CD4+ T cell nadir, current medications, and current comorbid health conditions.

Self-management behaviors (physical activity and diet intake).

Physical Activity.

After completing the surveys, participants were given an ActiGraph accelerometer (Actigraph, LLC, Fort Walton Beach, FL) and a research assistant explained how to wear it for the next 7 consecutive days (Anastasopoulou et al., 2014; Berntsen et al., 2010; Strath et al., 2013). Among adults, the ActiGraph accelerometer has been found to have high reliability (r= 0.90–0.99) and valid (Aadland & Ylvisåker, 2015). Upon returning the accelerometer, we checked to ensure that data were recorded for at least 4 days and at least 8 hours per day (a valid wear day; Hamilton et al., 2011; Haskell et al., 2012; Hitz, Conway, Palcher, & McCarty, 2014). We defined moderate to vigorous physical activity as activity ≥ 2690 counts per minute for a minimum of 10 consecutive minutes (Caspersen, Powell, & Christenson, 1985). We used the ActiLife software to calculate the amount of physical activity per valid wear day using the Sasaki, John, and Freedson (2011) adult cutpoints for tri-axial accelerometers (Sasaki, John, & Freedson, 2011).

Diet Intake.

After receiving the Actigraph, participants completed an in-person 24-hour dietary recall with a trained dietician l. During this interview, each participant recalled what he or she consumed and the amount of each item in the previous 24 hours. Responses were simultaneously entered into the Nutrient Data System for Research (NDSR) software, (Buzzard & Feskanich, 1987; Dennis, Ernst, Hjortland, Tillotson, & Grambsch, 1980; Sievert, Schakel, & Buzzard, 1989) which calculated nutrients consumed and converted quantities to gram weight and corresponding nutrient values (Thompson & Subar, 2008). We used this software to calculate the Healthy Eating Index, a validated composite measure of diet quality assessing conformance to dietary guidelines and the preferred dietary outcome in the U.S. (Buzzard & Feskanich, 1987; Dennis et al., 1980; Freedman, Guenther, Krebs-Smith, & Kott, 2008; Guenther, Reedy, & Krebs-Smith, 2008; Guenther, Reedy, Krebs-Smith, & Reeve, 2008; Sievert et al., 1989).

Neurocognitive processing variables (fMRI-derived neural network differentiation and self-reported self-efficacy, patient activation, attention, and decision making).

Neural Network Differentiation.

To examine the neurocognitive behavioral processes associated with changes in self-management behaviors between the intervention and control groups, we designed an fMRI task to provoke our a priori regions of interest: the analytic (Task Positive Network) and empathetic (Default Mode Network) neural activation. To test our hypothesis, we developed an fMRI task that has been described in detail elsewhere (Jones et al., 2018). In brief, we designed four, seven minute and 40 second (total 30 minutes and 40 seconds) video conditions. These four conditions each contained 12 video clips emphasizing an analytic issue (e.g., an overview of HIV infects a CD4+ T cell), a social/empathy issue (e.g., someone telling a story about how they found out they were diagnosed with HIV), and health information interspersed with a rest condition (i.e., a red cross on a black background with no sound). All video clips were presented using Eprime software (v 2.0.10) to standardize the task in the scanner.

The fMRI images were acquired using a Siemens 3T Skyra scanner. To establish an anatomical baseline, participants first completed a T1-weighted structural MRI sequence. Participants then completed four T2-weighted fMRI sequences using the task described above. Participants were in the fMRI scanner for approximately 60 minutes at each time point.

Prior to analyzing the fMRI images, all data were preprocessed using the procedure described by Power et al. (2014). Then a target atlas was made to align our participant’s anatomy with the 711–2B space representing 3-D Talairach Space, which allowed us to locate and analyze the TPN and DMN. The specific coordinates used are presented in previous work.(Jack et al., 2013) As described by Boynton, Engel, Glover, and Heeger (1996) we used a general linear model with assumed hemodynamic response functions to estimate participants’ average response to the video conditions at baseline and over time. The rest condition was not explicitly modeled and was implicitly captured by the baseline estimate (Boynton et al., 1996). To analyze neural network differentiation, TPN differential activation was defined as the contrast of analytic - empathetic video conditions and the DMN was defined as the contrast of empathetic – analytic video conditions.

In addition to the mechanistic data provided by the fMRI images, we also assessed self-efficacy, patient activation, attention, and decision making. The six-item Chronic Disease Self-Efficacy Scale asks participants to self-report their confidence completing various self-management tasks (e.g., taking medications, managing symptoms; Lorig, Sobel, Ritter, Laurent, & Hobbs, 2001). Each item is scored from 0–10 and then summed to create an overall score. Higher scores indicate more self-efficacy.

The 10-item, psychometrically sound Patient Activation Measure (PAM-10) assesses one’s knowledge, skill, and confidence in managing his or her chronic health condition (Hibbard, Stockard, Mahoney, & Tusler, 2004). The items are scored on a 0–100 scale where higher scored indicated more patient activation. The PAM-10 had a Cronbach’s α of 0.91.

The Erikson Flanker Task (Eriksen & Eriksen, 1974) was used to assess participants’ selective attention, an important in skill in self-management (Hall & Marteau, 2014). This computer-administered task required that participants focus on five letters (s or h) despite sometimes being flanked by the incongruent letters, and to select the correct central letter. The endpoints for this task include the percent of time a respondent correctly identified the letter when the flankers were congruent and incongruent. A higher proportion of correct responses indicates more selective attention.

The Iowa Gambling Test was used to assess participant’s decision-making. In this computer-administered task, participants are presented with four virtual card decks which will either award or deduct money. The “good” decks tend to award money and the “bad” decks tend to deduct it. The endpoint of this task is a demographically-adjusted T score in which higher scores indicate better decision making. All data collectors were trained in administering the Erikson Flanker Test and the Iowa Gambling Tests using a standardized guide.

Analysis

We summarized important demographic and medical characteristics, neurocognitive processing, and self-management variables using means, standard deviations, medians, interquartile ranges, counts, and percentages of participants in each arm of the study. We examined variability and distributions of all neurocognitive processing measures using standard deviations, skewness, kurtosis, and box plots. To examine the effect of the intervention on our a priori defined neurocognitive processing variables we then created a change score (three months –baseline). We then created box plots of the variables at baseline, 3 months and the change score by intervention group. The difference in the changes in neurocognitive processing was assessed using a Wilcoxon rank-sum test. We also used a linear mixed effects model with a random intercept to examine the effect of the self-management intervention on neurocognitive behavioral processing. A time by group interaction term was created to model the effect of the intervention over time. Given the exploratory, pilot nature of this study and because we did not want to miss an effect, we selected a priori, a significance level of 10% for all hypothesis tests. This decision is consistent with the 2016 American Statistical Association Statement on Statistical Significance and p-values, with an emphasis on cautiously interpreting findings within the overall context. (Waserstein & Lazar, 2016). All analyses were conducted using Stata 14 and R.

Results

We enrolled 32 participants. Two did not complete time 2 measures and were replaced with additional participants and one did not have a valid baseline fMRI. A total of 29 participants completed all measures, including the fMRI, at baseline and 3 months later. The average age of the participants was approximately 53 (±8.3) years, most were African-American (96%), had a high school degree or less (57%)., and two (7%) were currently working. They had been living with HIV for an average of 14 years, were on HIV antiretroviral medications for 12.5 years and all had additional physical comorbidities (e.g., hypertension, hepatitis C). The intervention group had a higher body mass index (32.6 vs 27.1, p=0.10), but otherwise there were no differences in baseline characteristics between the two groups (p>0.10; Table 1). Participants engaged in a median of 31.9 (IQR: 0, 64.2) minutes of physical activity per week, well below the recommended average of 75–150 minutes per week. The median Healthy Eating Index score was 45.0 (IQR: 29,58), indicating low compliance with the U.S. recommended daily diet. Additional details can be found in Table 2.

Table 1:

Baseline Demographic and Medical Characteristics (n=29)

Intervention Group (n=16) Control Group (n=13) p-value
Mean Standard Deviation Mean Standard Deviation
Age (years) 52.3 8.0 53.2 8.6 0.75
Years Living with HIV 15.5 9.3 15.0 7.6 0.81
Years Taking HIV Antiretroviral Medications 11.4 7.8 13.5 8.5 0.37
Current CD4+ T-Cell Count (cells/mm3) 657 347 665 331 0.95
CD4+ T-Cell Nadir cells/mm3 294 212 209 117 0.24
Body Mass Index (kg/m2) 32.6 10.1 27.1 6.3 0.10*
Hip-to-Waist Ratio 0.95 0.05 0.93 0.11 0.57
Mean Blood Pressure (mmHG) 127/82 16.3 126/83 17.8 0.74
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Table 2:

Baseline Neurocognitive, Neurocognitive Processing, and Self-Management Variables

Theoretical
Range		 Recommend Range Median Interquartile
Range
(25%, 75%)
Neurocognitive Processing Variables
Chronic Disease Self-Efficacy 10–60 Higher scores indicate more self-efficacy 44 38, 52
Patient Activation Scale 0–100 Higher scores indicate more patient activation 75.5 56, 83.7
Decision Making Capacity (Iowa Gambling Test)
T-score Demographically Matched 0–100 ≤ 39: Impaired Decision Making Capacity
40–44: Less Than Average Decision Making Capacity
≥ 45: Nonimpaired Decision Making Capacity		 46 42, 47
Attention (Flanker Test) (n=8)
Percent of congruent trials correct 0–100 Higher scores indicate more attention 96 70, 98
Congruent trial reaction time (ms) Lower scores indicate more attention 523 506, 724
Percent of incongruent trials correct 0–100 Higher scores indicate more attention 95 72, 98
Incongruent trial reaction time (ms) Lower scores indicate more attention 534 499, 819
fMRI-Derived Neurocognitive Processing
Empathetic Network (DMN) Task Differentiation unlimited Higher scores indicate greater task differentiation 0.161 0.064, 0.259
Analytic Network (TPN) Task Differentiation unlimited 0.174 0.046, 0.240
Self-Management Variables
Physical Activity
Minutes in Moderate-to-Vigorous Physical Activity per week unlimited 75–150 minutes per week 31.9 () 0, 64.2
Steps per day unlimited 10,000 per day 5753 4334, 9701
Dietary Intake
Healthy Eating Index 0–100 Higher indicates better diet 45 39, 58
%of Daily Diet from Carbohydrates 0–100 Lower typically indicates better diet 50.4 40.4, 59.0
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Analysis of the effect of the intervention on self-management and neuroprocessing variables showed several significant relationships. Participants in the intervention group improved the quality of their overall diet intake over time (β= 10.02, p=0.08), however we did not see an effect of the intervention on physical activity (Table 3). We observed a decline in TPN task differentiation in patients receiving the control intervention as compared to patients receiving the SystemCHANGE intervention (z-value= −1.97, p=0.047) and an increase in DMN task differentiation in the control group (z-value = 1.65, p=0.10; Figure 1). General mixed modelling confirmed a group by time interaction on TPN task differentiation (β= 0.14, p=0.05 (Table 4). There were no differences between the intervention and control groups over time on self-efficacy, patient activation, selective attention and decision making over 3 months (p>0.10; data not shown). In analyzing how changes in self-management variables were related to changes in our fMRI-derived neuroprocessing variables we conducted a linear mixed effects model. We found that changes in self-management variables were not associated with changes in either the TPN or the DMN (group by time interactions p>0.10, data not shown).

Table 3:

The effect of the System Change intervention on Self-Management Outcomes (n=29)

Predictors Estimate Std. Error p-value
Moderate to Vigorous Physical Activity (minutes per day) Time −2.81 5.58 0.62
Intervention Group 5.83 4.98 0.25
time-by-group −3.37 7.29 0.65
Total Healthy Eating Index 2010 Time −8.84 4.05 0.04
Intervention Group −1.87 4.65 0.69
time-by-group 10.02 5.55 0.08
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Figure1: Differentiation in Neural Network Activation Over Three Months, by Intervention Group.

Figure1:

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Table 4:

Effect of the SystemCHANGE Intervention on fMRI-derived neurocognitive processing using linear mixed effects model

Task Positive Network Default Mode Network
Variable β Coefficient 95% Confidence
Interval		 p-value β Coefficient 95% Confidence
Interval		 p-value
Intercept 0.16 0.08,0.23 <0.01* 0.15 0.07, 0.23 <0.01*
Intervention −0.01 −0.10, 0.10 0.01* 0.04 −0.06, 0.15 0.47
Time −0.13 −0.24, −0.04 0.98 0.05 −0.08, 0.18 0.45
Time-by-group interaction 0.14 0.01, 0.27 0.05* −0.15 −0.33, 0.01 0.11
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Discussion

This is the first longitudinal study to examine associations between fMRI-derived neurocognitive processing variables and self-management behaviors (i.e. physical activity and diet intake) in PLHIV. In this nested pilot study, we found that, as expected, our self-management intervention improved diet intake and TPN task differentiation. However, these improvements were not associated with each other, suggesting that changes in neural network activation did not lead to the observed improvements in diet intake. This was surprising and in contrast to our center’s overarching hypothesis, that better cognitive neural network task differentiation would be associated with improved self-management behaviors over time (Jack et al., 2013). This novel hypothesis was built on promising literature in non-HIV populations, focusing on other behavioral outcomes. For example, studies in healthy college students found that those who balance these two neural networks tend to exhibit better leadership skills including, balancing creative thinking with analytic reasoning, avoiding ethical dangers, and properly motivating and incentivizing employees (Boyatzis, Rochford, & Jack, 2014; Friedman, Jack, Rochford., & Boyatzis, 2015). Others have reported that mindfulness training can reduce emotional reactivity (Default Mode Network) and increase attention in adolescents, (Sanger & Dorjee, 2015, 2016) thereby improving their mental health. While these studies suggest that improvements in neural network task differentiation would be associated with better health behaviors, none of these studies investigated this question, so it is challenging to contextualize our findings in this literature. However, as a nascent field of research, we need additional rigorous research to best understand if and how to use neuroimaging techniques to clarify the neurocognitive basis of chronic disease self-management; some of this research is ongoing (Jones et al., 2018).

Further, while fMRI analyses can provide a multitude of variables to investigate, as a pilot study it was important to investigate those that were pre-specified in our hypothesis. Our hypotheses was based on our prior work, (Boyatzis et al., 2014; Friedman et al., 2015) but this work was not conducted with PLHIV. HIV enters the brain early in HIV infection and remains there even when HIV medications suppress the HIV virus (Saylor et al., 2016). This leads to chronic inflammation and vascular dysfunction in the brain which may lead to the increased neurocognitive impairments experienced by PLHIV (Clifford, 2017). Up to 50% of PLHIV have neurocogntive impairments often impacting the fontostriatal neural systems diminishing important tasks needed for self-management including working memory, problem solving, and decision making (Casaletto, Weber, Iudicello, & Woods, 2017). We attempted to control for this in our eligibility criteria by using the MOCA brief screening tool and excluding those with a score indicating impaired cognition. However, the MOCA is not a diagnostic tool and without a full neuropsychiatric exam, it is hard to know if we enrolled anyone with mild cognitive impairment (Koenig, Fujiwara, Gill, & Power, 2016). Further, the elevated prevalence of neurocognitive dysfunction of PLHIV underscore a continued need to test and refine existing, successful self-management interventions and to continue tailoring them to the target population, including to their neurocognitive function. Future neuroimaging research could expedite this tailoring and should seek to describe neurological and cognitive correlates of known self-management mediators including self-efficacy, patient activation and self-regulation, (Moore et al., 2016) as well as self-management behavioral outcomes.

In addition to examining cognitive hypotheses to identify the neurocognitive mechanisms underpinning changes in the neurocognitive behavioral processes of self-management behavior, it may be helpful to employ paradigms that combine structural and cognitive hypotheses. Such studies would also require repeated functional and structural images longitudinally, with careful attention to the immunological and vascular effects of HIV. Further, the neurological consequences of HIV infection using contemporary HIV treatment are only starting to be characterized (Behrman-Lay et al., 2016; Su et al., 2016). As such, future research should consider enrolling homogenous samples to best describe the neurological and cognitive effects of HIV and examine their relationship to self-management behavior and responsiveness to self-management interventions. Imaging techniques such as Diffusion Tensor MRI might be particularly helpful in evaluating the integrity of white matter of PLHIV (Leite et al., 2013; Su et al., 2016) and can be integrated with functional and structural imaging protocols.

This nested pilot study was designed to help discover associations between the neurocognitive mechanisms underpinning changes in the neurocognitive behavioral processes and self-management behaviors of PLHIV. Though we found limited support for our hypothesis, using a rigorous, longitudinal study design with a high retention rate, we demonstrated that fMRI studies are acceptable to this population, indicating that future research using these techniques should be encouraged. Given the intense resources needed to conduct fMRI assessments, it is advisable to consider nesting his methodology into larger existing studies, and pooling data across populations to better understand these neurocognitive mechanisms.

While this study has several strengths, as a pilot, this work is limited by a small sample size, a single data collection site, and the inclusion of PLHIV who are virally suppressed; all of which limit generalizability of our findings. We also did not include a well-matched sero-negative comparison group, which limits our ability to examine HIV-related effects on our neurocognitive endpoints. If feasible, future studies should consider enrolling well-matched sero-negative participants to help understand the role of HIV on the neurocognitive mechanisms underpinning self-management behavior. Finally, we were guided by a novel a priori hypothesis to help us examine these mechanisms, yet failed to find support for this hypothesis. This may be related to our small, heterogenous sample or that this hypothesis may not be applicable to this population. Future work should consider additional neurocognitive approaches, additional imaging techniques to augment the functional imaging findings, larger and perhaps, more homogenous and better-characterized participants.

In conclusion, despite demonstrating that our self-management intervention improved an important behavioral outcome- diet intake, the neural activity in our pre-hypothesized regions cannot account for that improvement. However, the novel neuroimaging and cognitive assessment techniques used in this study are a promising way to help us identify the neurocognitive processes underpinning chronic disease self-management behavior. Future research using these techniques to better understand how to improve self-management behavior is warranted.

Acknowledgement:

We wish to acknowledge the substantial and invaluable contributions to this study by Shyla Urban, Jackson Currie and the participants of this study.

Source of Funding: This project was funded by grants from the American Heart Association (14CRP20380259) and a developmental grant from the National Institutes of Health Grants # P30 NR0153263.

Footnotes

Conflicts of Interest: The authors have no relevant conflicts of interest do declare.

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