Promoting resilience through neurocognitive functioning in youth living with HIV

ABSTRACT

Using a phased model of intervention development, we developed an intervention to promote resilience in youth living with HIV via improved neurocognitive resources. First, youth completed a naturalistic prospective memory (PM) task and were randomized into a visualization condition or control condition. Next, 47 of these participants completed another naturalistic PM task and were randomized into Calendaring condition, an Alarm condition, a Combined condition, or a Control condition. Youth with low PM demonstrated observable gains from the visualization technique. Youth in the Combined Calendaring and Alarm condition demonstrated significantly better performance than participants in the Control and Calendaring conditions. In a Proof-of-Concept study with 16 youth, the previous findings were translated into a single session in-person intervention followed by tailored text messaging to improve adherence and viral load via improved neurocognitive resources. The resulting intervention showed a signal of effect with viral load reductions in youth with available data. Targeting compensatory strategies to enhance neurocognitive functioning may promote resilience and health outcomes. A randomized pilot study with a control condition is the next step.

KEYWORDS:

• Neuropsychological
• memory
• self-management
• medication adherence
• text messaging
• viral load
• adolescent

Introduction

Youth ages 13–24 represent a quarter of the U.S. population living with HIV. Approximately 30%–50% of adults meet criteria for HIV-associated neurocognitive disorders (HAND) (Heaton et al., 2011). The few studies of HAND in youth living with HIV (YLWH), report rates between 40% and 70% (Hoare et al., 2016; Loft et al., 2014; Nichols et al., 2013). HAND represent biologically-based cognitive stressors that have the potential to overwhelm cognitive processes. For example, neurocognitive deficits can negatively impact HIV self-management, and, in turn, poor medication adherence, and resulting viral replication can exacerbate cognitive deficits (Anand, Springer, Copenhaver, & Altice, 2010). Promoting cognitive resilience, the ability to overcome threats to cognitive functioning or performance (Furniss, Back, & Blandford, 2012) may improve HIV self-management behaviors and health outcomes.

Many studies of non-adherence in YLWH focused on psychosocial factors (MacDonell, Naar-King, Huszti, & Belzer, 2013; MacDonell, Naar-King, Murphy, Parsons, & Harper, 2010), but “forgetting” is the most commonly cited barrier (MacDonell et al., 2013; Murphy et al., 2003). Executive function and memory appear to be most strongly predictive of medication adherence (Ettenhofer, Foley, Castellon, & Hinkin, 2010; Hinkin et al., 2002; Woods et al., 2009).

Prospective memory (PM) is the neurocognitive capacity to successfully form, maintain, and execute an intention in the future in response to a specific cue, see Zogg, Woods, Sauceda, Weibe, and Simoni (2011) for a review. In a series of studies, Woods and colleagues (Woods et al., 2008; Woods et al., 2009) have shown that better PM is associated with better ART adherence among individuals living with HIV. PM may therefore provide a critical cognitive target for developing a behavioral intervention to overcome “forgetting” thereby promoting cognitive resilience in YLWH of the cue-intention pairing (Kliegel, Jäger, Altgassen, & Shum, 2008). Research on PM among individuals living with HIV has documented deficits in all three components of PM, including encoding (Woods, Dawson, Weber, Grant, & Group, 2010), time monitoring (Doyle et al., 2013), and detection of cues with low salience (Zogg et al., 2011). Given documented deficits in each component, identifying which components are most amenable to change among YLWH is critical for designing an effective intervention to improve PM.

Using the NIH ORBIT model (Czajkowski et al., 2015) to methodically guide intervention development, we conducted a series of translational studies to develop a PM-based intervention to promote resilience and improve medication adherence and viral load (VL) among non-adherent YLWH. The ORBIT model carefully delineates two phases of early translational behavioral intervention research to maximize the likelihood of intervention success in the later phases of translational science. These include intervention design (Phase 1: Define and Refine) and preliminary testing (Phase 2: Proof of concept and Pilot studies). Specifically, we targeted the neurocognitive process of PM (i.e., basic science) to define and refine (ORBIT Phase I studies) and evaluate the feasibility (ORBIT Phase II Proof of Concept Study) of an intervention to promote cognitive resilience among non-adherent YLWH. Providing youth with compensatory strategies for overcoming cognitive barriers to adherence offers a potential avenue for promoting resilience in a context where neurocognitive deficits are common (Anand et al., 2010; Doyle et al., 2013; Morgan et al., 2012).

Phase 1 studies to develop the intervention: translating basic behavioral science into a cognitive resilience intervention

We conducted two experiments to examine the benefits of visualizing, encoding, cueing to improve prospective memory in YLWH (Faytell et al., 2017, 2018). Studies were approved by the University of California, San Diego and Wayne State University Institutional Review Boards (IRB#: 051312MP2F; 3 August 2012). All participants provided informed consent.

Methods

Sixty YLWH (ages 19-24; M = 22.6, SD = 1.5) were randomized to a visualization condition (n = 30) for which they imagined successful completion of a PM task for 30-seconds, or a control condition (n = 30) for which they repeated task instructions. The refusal rate was 10%. Participants were 86.7% male, more than half ethnic minorities (55.0% ) African American, 30.0% Hispanic, more than half (58.4%) evidenced problematic non-alcohol substance use, a majority (75%) met criteria for HAND, and 58.4% had a detectable VL. Participants completed a brief neurocognitive battery and questionnaires examining cognitive complaints.

A subsample of 47 participants (5 excluded for technology failures, 1 used a separate alarm, 5 did not have a cell phone, 1 refused, and 1 had missing data) were randomized into four conditions: Calendaring (n = 9), Alarm (n = 11), Combined (n = 13), or Control (n = 14). Participants completed a naturalistic PM task. They were instructed to send a text message with the number of hours they slept the night before to the examiner at a pre-specified time, once per day for one week. Participants in the Calendaring condition created a daily event in the calendar on their cell phone at the time specified for texting. Calendar alerts were disabled to create an encoding-only condition. Participants in the Alarm condition were instructed to set an alarm in their cell phone for the specified texting time, without text instructions, thereby creating a cueing-only condition. Participants in the Combined condition were instructed to create a calendar event and set an alarm in their cell phone, creating an encoding and cueing condition. The control condition received only the initial instructions to text message the examiner at the pre-specified time. Participants received 2 points for each message sent at the correct time, 1 point for a message sent at the incorrect time or completed another action at the correct time (e.g., called examiner), or 0 points if they did not send a text. Possible scores ranged from 0 to 14 with higher scores reflecting better PM performance.

Results and discussion

Logistic regression was utilized to examine the effect of the experimental condition (visualization vs. control), cognitive impairment (PM, learning), and the interaction of experimental condition by cognitive impairment on naturalistic PM (complete vs. incomplete). Race/ethnicity was included as a covariate in all models as it was significantly related to semi-naturalistic PM performance. A significant interaction was found between neurocognitive functioning and visualization. YLWH with low PM demonstrated significantly higher rates of PM accuracy from the visualization exercise (81.8% vs. 16.7% success; χ² = 7.20, p = .007, OR = 22.5, CI = 1.6–314.6) whereas YLWH with high PM did not evidence gains (73.7% vs. 79.2% success; χ² = 0.18, p = .673, OR = 0.737, CI = 0.2–3.0). Furthermore, a significant interaction was found between experimental condition and learning. YLWH with higher learning scores who were in the visualization condition showed a significantly higher rate of semi-naturalistic PM completion relative to those in the control condition (92.3% vs. 53.3% success; χ² = 5.72, p = .0167, OR = 10.5, CI = 1.1–102.3). In contrast, there was no effect of condition on PM completion for those with impaired learning (64.7% vs. 80.0% success; χ² = 0.94, p = .333, OR = 0.46, CI = 0.09–2.29). Neurocognitive functioning of YLWH moderated the response to a visualization exercise with differential effects demonstrated for the type of cognitive impairment examined (i.e., learning vs. PM). Moreover, findings suggest visualization provides cognitive support for PM functioning and may improve cognitive resilience for some YLWH.

Naturalistic PM performance was non-normally distributed (W = 0.74, p < .0001); therefore, the Wilcoxon rank sums test was used to compare PM performance across conditions. The omnibus test was significant suggesting an overall effect of condition on PM performance (χ² = 7.91, p = .048). Participants in the Combined condition had significantly better PM performance relative to the Control condition (χ² = 6.01, p = .0142, d = 1.2), marginally better performance relative to the Calendaring condition (χ² = 3.43, p = .0641, d = 1.1), and were not significantly different than participants in the Alarm condition (p = .261, d = 0.51). A trend-level difference was found for better PM performance among those in the Alarm condition relative to the Control condition (χ² = 2.94, p = .0862, d = 1.1). PM performance did not significantly differ between the Alarm and Calendaring conditions (p = .286), or between the Calendaring and Control conditions (p = .575). These results suggest combining encoding and cueing cognitive supports may benefit YLWH’s ability to execute future actions, such as taking medications. This study supports the inclusion of encoding and cueing components in an intervention designed to improve naturalistic PM.

Phase 2 study: proof-of-concept of PM intervention to improve medication adherence and viral load

Based on the Phase 1 studies, we designed a PM intervention that included encoding, specifically via visualization techniques, and cueing to improve PM for taking HIV medications. The purpose of this study was to test the proof-of-concept of a cueing and encoding PM intervention to improve medication adherence and health outcomes among YLWH. This study was approved by the Wayne State University Institutional Review Board (IRB#: 051312MP2F January 17, 2014), and participants provided informed consent.

Methods

Consistent with the NIH ORBIT model, proof-of-concept studies are typically with small samples in a pre–post test design, prior to proceeding with a pilot randomized trial. Sixteen behaviorally infected YLWH with detectable VL and an active ART prescription for at least 24 weeks participated in a 24-week intervention study. The refusal rate was 11%. YLWH were 16–24 years old (M = 21.0, SD = 2.31), predominantly male (93.4%), African American (100%), and nearly two-thirds reported moderate to high-risk substance use (62.5%). Following a 6-week multiple baseline assessing VL at the start of baseline and weekly self-reported medication adherence (percent of medication taken in a 7-day period), youth participated in a one-time intervention session conducted in their home or community, and 12-weeks of tapering text-based cue-intention reminders. Principals of motivational interviewing were utilized to increase youth involvement and motivation for behavior change (Borsari & Carey, 2000; Colby, Barnett, Monti, & Rohsenow, 1998; Lawendowski, 1998; Miller & Rollnick, 2012; Monti et al., 1999; Naar-King & Suarez, 2011). With the aid of an interventionist, youth identified salient cues that could be paired with taking medications. The cue-intention pairing, called a “When-Then Plan” was used as the content for the 12-week text-based reminders. For example, “When I see my phone for the first time in the morning, then I will take my medicine”. After selecting the cue-intention pairing, youth developed a contingency cue-intention pairing to overcome barriers termed their “If–Then Plan”. For example, “If I am rushing out the house, then I will grab a pill and take it once I get to school or work”. Finally, after identifying a cue-intention pairing and secondary plan in the event of a barrier, youth participated in a visualization encoding procedure to help solidify their When-Then and If–Then plans into memory. At the end of the one-hour intervention session, youth identified preferred tailoring for the text message reminders (e.g., time of day). Text message content was tailored to youths’ individual cue-intention pairing, and prompted use of the pairing rather than a reminder to take medication. Youth were instructed to practice the visualization (i.e., encoding) procedure when texts were received. Text messages were initially sent twice a day and were tapered to twice a week at the end of 12 weeks. Medication adherence, reported bi-weekly during the intervention and follow-up periods, and VL, assessed at 18- and 24-weeks, were the main outcomes measures.

Results and discussion

Mean medication adherence was 94.1%, 91.2% and 88.8% at baseline, intervention, and follow-up. Change in VL was examined with log drops, with a 0.5 log drop considered clinically significant change (Saag et al., 1996). At the end of intervention (18-weeks), more than half (60%) of participants had a 0.5 log drop in VL and 46.7% with a 1.0 log drop. By the end of the follow-up period (24-weeks), nearly three-quarters (73.3%) had a 1.0 log drop in VL. Change in the proportion of YLWH with detectable VL at baseline and end of follow-up was assessed with the McNemar test. A significant decrease in the proportion of youth with detectable VL (p = .013) supports an analytical signal of effect, in addition to the clinical signal in individual drop in VL. The intervention had a medium effect (d = 0.66) on VL from baseline to 24-weeks.

We also found the intervention was particularly helpful at improving viral function among YLWH with moderate to high risk substance use (80% 0.5 log drop) relative to YLWH with no or low risk substance use (60% 0.5 log drop). Finally, we investigated change in VL by performance-based PM which was measured at baseline using the Memory for Intentions Screening Test (MIST). A greater proportion of youth with MIST scores above the median (87.5%) had a 0.5 log drop in VL from baseline to 24-weeks than youth with MIST scores below the median (57.1%). However, when examining change in VL between study periods, youth with MIST scores above the mean demonstrated improved VL from baseline to 18-weeks with no improvement from 18 to 24 weeks. In contrast, among youth with MIST scores below the median, 28.6% had a 0.5 log drop in VL from baseline to 18-weeks, and 57.1% from baseline to 24-weeks. These findings may suggest the benefits of a cuing and encoding intervention to improve PM for taking medication may take longer in youth with poor PM functioning.

This proof-of-concept study provided initial signals of effect for reducing VL among non-adherent YLWH. Providing youth with a salient cue-intention pairing, visualization practice to encode the pairing, and text message reminders of the pairing bolstered youths’ memory and intention for taking medication. Medication adherence did not change over time; however, response bias and ceiling effect may have contributed to high rates of reported adherence and lack of change. VL, a biological proxy for adherence and main marker for health outcomes, declined over time, even among youth with the highest risk (i.e., moderate to high risk substance use, lower PM functioning). A brief intervention with signals of effect indicate promise for targeting PM to improve YLWH’s health outcomes and reduce transmission via viral suppression.

Summary and concluding discussion

Research on cognitive resilience and promotive factors to ameliorate the effects of biological stressors on neurocognitive functioning is limited in youth in general and in YLWH in particular. HIV is a biologically-based cognitive stressor that has the potential to overwhelm cognitive processes including PM. It may be possible that HIV-related cognitive deficits shift resources away from cognitive tasks such as prospective memory toward more immediate attentional needs (Healy & Bourne, 2005). As a result, individuals with deficits in PM demonstrate difficulty adhering to the HIV care continuum (e.g., attending medical appointments, adhering to medication) (Woods et al., 2009). The research presented herein provides preliminary support for utilizing compensatory strategies to improve cognitive resilience via improved PM performance resulting in improved HIV-related health outcomes.

The ORBIT model of intervention development provides a methodical approach to rigorously developing interventions across the translational research spectrum, and is particularly useful for advancing early-phase development (e.g., design and preliminary testing). The extant literature finds youth “forget” to take HIV-medications (MacDonell et al., 2013; Murphy et al., 2003), and deficits in PM are common among individuals living with HIV (Doyle et al., 2013; Morgan et al., 2012), suggesting deficits in PM may be an antecedent of non-adherence. Using the ORBIT model, the Phase 1 studies utilized the basic neurocognitive science of PM to define which PM components should be included in a PM-based intervention to improve outcomes in YLWH. Specifically, support was found for targeting cueing, and encoding, particularly via visualization. The Phase 2 study integrated these findings to develop a PM-based intervention for medication adherence. A small-N multiple baseline design was utilized to test the proof-of-concept of the intervention thereby benefiting from the relatively rapid testing of signals of efficacy, the ability to actively refine and further test the intervention over shorter periods of time, and greater cost-effectiveness. Signals of effect were evident via the improvement in VL in YLWH using both clinical and analytical benchmarks.

Processes or activities that are well-learned are completed more automatically, whereas novel ones often require more attention (Beilock, Carr, MacMahon, & Starkes, 2002). Medication adherence is likely to be novel to most YLWH and therefore not a deeply encoded task. The first two studies demonstrate, despite deficits in PM, improving encoding for a naturalistic PM task is possible. Interventions focused on the deeper encoding of behavioral intentions (e.g., taking medication), such as the intervention tested in Phase 2, may help not only improve PM for the intention but also routinize the action, increasing the likelihood the behavior occurs automatically in the future.

Although support was found for targeting encoding and cueing to improve naturalistic PM in laboratory settings among YLWH, these studies did not address whether improving PM for specific HIV self-management behaviors has real-world implications for youths’ health. Therefore, we developed a PM-based intervention incorporating a cue-intention pairing for medication adherence, a specific self-management behavior, and bolstered the encoding of the pairing via a visualization procedure. Signals of efficacy were evident through significant reductions in viral load across the 24-week study. Moreover, findings suggest the intervention is adept at improving the health of youth most at risk (i.e., lower PM functioning and high-risk substance use). The Phase 2 study provides real-world proof-of-concept for developing compensatory strategies for improving PM for health behaviors and outcomes in YLWH. Targeting cueing and encoding for medication adherence not only reduced VL, but it may be possible reducing VL decreases future deficits in PM thereby further improving future cognitive resilience. The next step is a pilot randomized trial comparing the intervention to a control in preparation for a full-scale trial.

Limitations include the small sample size for the Phase 2 study; although beneficial for rapid intervention development, findings should be interpreted as signals rather than demonstrations of efficacy. A pilot randomized trial to demonstrate feasibility of all study components, followed by a full-scale efficacy trial, is needed to determine the efficacy of the intervention. Finally, despite decreases in VL across the study, the Phase 2 study failed to find support for a change in adherence over time, likely due to a ceiling effect in self-reported adherence during baseline. Future studies should identify effective measurement of adherence for YLWH. Future research is needed to determine whether providing cognitive resources for PM earlier in the trajectory of infection to promote resilience helps not only prevent non-adherence but decreases the prevalence and severity of PM deficits over the life course. Studies are needed to identify other cognitive resources, and the processes through which they promote cognitive resilience, in the context of biological stress.

Acknowledgments

Amy L. Pennar contributed to the analysis of Phase 2 data, and the conceptualization and writing of the manuscript. Sylvie Naar contributed to the design and data collection of all 3 studies, and the conceptualization and writing of the manuscript. Steven Paul Woods contributed to the design and data collection of all 3 studies, and the conceptualization and writing of the manuscript. Sharon L. Nichols contributed to the design and data collection of Phase 1 studies and the conceptualization and writing of the manuscript. Angulique Y. Outlaw contributed to the design and data collections of all 3 studies, and the conceptualization and writing of the manuscript. Deborah A. Ellis contributed to the design and data collection of the Phase 2 study, and the conceptualization and writing of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research was funded by the National Institute on Drug Abuse [grant number 1RO1DA034497-01].