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. Author manuscript; available in PMC: 2025 Jan 18.
Published in final edited form as: J Assoc Nurses AIDS Care. 2024 Jan 18;35(2):104–121. doi: 10.1097/JNC.0000000000000449

A 2-year Randomized Clinical Trial Examining the Effects of Speed of Processing Cognitive Training on Quality of Life Indicators in Adults with HIV-Associated Neurocognitive Disorder in Birmingham, Alabama: Results of the Think Fast Study

David E Vance 1, Pariya L Fazeli 2, Andres Azuero 3, Jennifer S Frank 4, Virginia G Wadley 5, James L Raper 6, Caitlin N Pope 7, Alexandra Jacob 8, Karlene K Ball 9
PMCID: PMC11217600  NIHMSID: NIHMS1950291  PMID: 38949906

Abstract

Speed of processing (SOP) cognitive training may improve indicators of quality of life (QoL) in people living with HIV (PLWH). In this 2-year longitudinal randomized controlled trial, 216 participants 40 and older with HIV-Associated Neurocognitive Disorder (HAND) or borderline HAND were assigned to one of three groups: 1) 10 hours of SOP training (n = 70); 2) 20 hours of SOP training (n = 73), or 3) 10 hours of Internet Navigation Control Training (a contact control group; n = 73). Participants completed several QoL measures at baseline, posttest, and year 1 and year 2 follow ups. Using linear mixed-effect models, no strong pattern of training effects across QoL outcomes was apparent, with small-magnitude, non-significant between-group differences in depression, locus of control, and Medical Outcomes Study-HIV scales. In conclusion, despite prior work showing some transfer of SOP cognitive training improving QoL, that was not observed. Implications for research and practice are posited.

Keywords: brain fitness, cognitive reserve, cognitive training, HIV-Associated Neurocognitive Disorder, neuroplasticity, speed of processing

As of 2018, 51% of people living with HIV (PLWH) in the United States were ≥ 50 years old, largely due to highly effective antiretroviral therapy that suppresses viral replication, thus extending the lifespan of PLWH to nearly to the same as those without HIV (Centers for Disease Control and Prevention, 2020). In fact, by 2030, 70% of PLWH will be over age 50 (Wing, 2017). Compared to those without HIV, as PLWH age, they are more at risk for diabetes, heart disease, hepatic/renal diseases, and neurological conditions that can accelerate or accentuate cognitive aging (Enogela et al., 2023; Waldrop et al., 2021). Since around 44% of PLWH experience HIV-Associated Neurocognitive Disorder (HAND), the prevalence and severity of HAND will likely increase, and thus, decrease quality of life (QoL) in PLWH (Smail & Brew, 2018; Vance et al., 2013; Wei et al., 2020). Such cognitive impairment in PLWH has been associated with several QoL indicators including subjective cognitive complaints (Vance et al., 2009), sleep quality (Campbell et al., 2020), depression and mood disturbance (Fazeli et al., 2011), and health-related QoL (Enogela et al., 2023). It is hypothesized that cognitive training approaches that mitigate cognitive loss or improve cognitive functioning may, in turn, improve QoL indicators. This is especially important because many PLWH with HAND already report low levels of QoL in general (Alford et al., 2021).

Computerized cognitive training approaches have been investigated extensively to improve and protect cognition in older adults and cognitively vulnerable adults (i.e., traumatic brain injury, mild cognitive impairment (MCI); Vance, McNees, & Meneses, 2009). In the Advanced Cognitive Training In Vital Elderly (ACTIVE) Study, the largest clinical trial of cognitive training, 2,802 community-dwelling older adults (65 and older) were randomized into one of four training conditions: 1) no-contact control group; 2) reasoning training group; 3) memory training group; or 4) speed of processing (SOP) training group (Ball et al., 2002). Participants were followed annually for ten years. These participants improved in the cognitive domain in which they were trained (i.e., SOP training improved SOP ability); the training effect of SOP training and reasoning training were particularly robust over time. In fact, reasoning training, and more so, SOP training improved QoL indicators over one to five years after training. Such QoL indicators included: 1) improved health-related quality of life (Wolinksky et al., 2006), self-rated health (Wolinsky, Mahncke et al., 2010), and internal locus of control (Wolinsky, Vander Weg et al., 2010), and 2) protection from depressive symptomatology (Wolinsky et al., 2009). These QoL indicators are also compromised in PWLH and likewise require intervention (Vance, Fazeli, Azuero et al., 2018), such as SOP training.

Cognitive training studies in PLWH have revealed several positive benefits. Based on a systematic review of 13 cognitive training studies in PLWH, it was observed that in general, cognitive ability improves after receiving cognitive training, particularly for domain-specific cognitive training (Vance et al., 2019). While many cognitive studies such as the ACTIVE Study and others (Hagovská et al., 2017) have included QoL indicators in their methodology, the cognitive training studies in PLWH by and large have lacked such QoL indicators (Vance et al., 2019). Yet, it is likely that improved QoL (i.e., mood, sleep quality, self-rated health) resulted from such cognitive training but has remained unexamined and undetected.

Cognitive training may promote brain plasticity that improves brain morphology and brain function (i.e., stronger connections between neurons) that, in turn, boosts cognitive functioning and, finally, translates to improvement in QoL indicators (Lampit et al., 2014; Vance, Fazeli, Cheatwood et al., 2019). For example, in a sample of 12 community-dwelling older adults (Mage = 71.43), Lampit et al. (2015) randomized them into two groups: 1) a computerized cognitive training group, or 2) a contact control (sham intervention) group. The computerized cognitive training group played games designed to improve cognitive functioning in several cognitive domains. To control for the effect of computer engagement, the contact control group watched National Geographic videos on the computer. Both groups spent three 1-hour sessions/week engaged in their protocols for 12 weeks, for a total of 36 hours. Participants in the computerized control group compared to the contact control group, improved significantly more in several cognitive domains. Based on MRIs conducted at baseline and posttest, the participants in the active experimental group also benefited from greater resting state functional connectivity between the superior frontal gyrus and the posterior cingulate; in fact, this connectivity correlated with cognitive change post-training for those who completed ≥ 9 hours of training. Similarly, SOP training has been observed to reduce dependence on frontally-oriented activity which may reallocate responses to posterior brain regions, thereby improving SOP and translating to increased QoL indictors (O’Brien et al., 2013). Taking this further, cognitive training may improve sleep quality. Haimov and Shatil (2013) randomized 51 older adults (Mage = 72.10) with insomnia to either: 1) a cognitive training group (n = 34) or 2) a contact control group (n = 17). Compared to the contact control group, the cognitive training group experienced improved sleep quality (i.e., sleep onset latency, sleep efficiency). Although it is unclear how cognitive training improved sleep quality, researchers posited that cognitive training either: 1) improved over neural function including neural regions utilized to regulate sleep, resulting in overall sleep quality, or 2) cognitive training required more cognitive exertion that results in the need for more sleep.

The Think Fast Study was a 2-year longitudinal randomized control trial (RCT) examining dosage of SOP training on cognitive (Aim 1), everyday functioning (Aim 2), and quality of life outcomes (Aim 3 – exploratory) in PLWH with HAND and borderline HAND. As presented in other articles, SOP training did improve SOP (Vance et al., under review) but unfortunately this improvement did not transfer to other cognitive domains and did not reduce the prevalence or severity of HAND (Vance et al., in press), but it did improve some aspects of everyday functioning (Vance, Fazeli, Azuero, Frank et al., 2023). Thus, the third aim of the Think Fast Study, reported in this article, was to examine whether SOP training would improve QoL indicators as other studies of cognitive training have demonstrated (Haimov & Shatil, 2013; Wolinsky, Vander Weg et al., 2010).

Methods

Participants and Procedure

This study was conducted in accordance with the Helsinki Declaration, and it received approval from our university’s institutional review board (F160122002). All participants provided written informed consent. Data collection for the study began in September 2016 and concluded in March 2020. Due to the COVID-19 pandemic, the study had to end prematurely with the loss of 15 participants not receiving their 2-year follow up assessments; thus, all data represent the pre-COVID period and are not influenced by the pandemic.

Recruitment of participants (N = 543) was carried out by distributing recruitment materials (i.e., flyers & brochures) in an HIV/AIDS clinic located in Birmingham, Alabama. Interested individuals called the contact number provided on the materials, and a telephone screener was administered to assess initial eligibility. The eligibility criteria included having an HIV diagnosis for at least one year, being over 40 years old, and the absence of major neuromedical comorbidities (e.g., multiple sclerosis, schizophrenia) or other conditions (e.g., brain trauma, deafness, blindness) that could impact cognitive training or assessment (Vance et al., 2017).

After establishing initial eligibility through the phone screening process, a baseline cognitive assessment was conducted on 260 participants to determine whether they met the operational definition of HAND or borderline HAND (see HAND diagnosis below). Subsequently, 216 PLWH met the full eligibility criteria and were randomly assigned to one of three groups: 10 hours of SOP training (n = 70), 20 hours of SOP training (n = 73), or 10 hours of sham internet navigation training (n = 73) (refer to intervention details below).

Participants underwent a comprehensive cognitive and everyday functioning battery at baseline, immediately after completing the training, and annually during the first and second years of the study (Vance et al., 2017). The determination of the sample size for the study was based on a power computation considering the expected gains in SOP performance with SOP training. The target recruitment was set at n=88 per group to detect an effect size of d = 0.4 between any two groups during any of the follow-up assessment visits.

Measures

Participants were administered an in-person 3-to-4 hour neuropsychological, psychosocial, and quality of life assessment battery at the research center at baseline, post-test (immediately after training), and annually at year 1 and year 2. The measures administered during these assessments relevant to the QoL aim of this article are listed below.

Demographic Background.

Participants self-reported demographic information including age, race (Black, Indigenous, and People of Color (BIPOC) = 0; white = 1), gender (female = 0; male = 1), years of education (1 = 1st grade…12 = 12th grade/GED…16 = bachelor’s degree), and annual household income (i.e., measured in $10,000 increments, ranging from $0 to above $100,000 US dollars.

HIV Characteristics.

The university HIV/AIDS clinic provided HIV characteristic information extracted from medical records closest to the baseline visit for this study (within +/− 3 months). This information included current viral load, nadir CD4+ lymphocyte count, and current CD4+ lymphocyte count.

Substance Use.

Participants were asked about current tobacco use (yes/no), number of cigarettes smoked/day, number of drinks on days drinking (1 = not applicable/don’t drink, 2 = 1–2 drinks…6 = 10 or more drinks), and frequency of alcohol use (0 = never; 5 = 4–7 times/week). A urine toxicology screen tested for marijuana, amphetamine, methamphetamine, opiates, and cocaine. From this, a variable was created indicating if participants were positive (i.e., UTOX+) for any of these substances on the day of the study visit.

Cognitive Assessment and HAND Diagnosis.

At the baseline and all subsequent follow-up visits, a comprehensive cognitive battery was administered, evaluating seven cognitive domains through the use of two or more tests per domain. These domains included: speed of information processing (assessed via Trail Making Test A, Digit Symbol Task, & Symbol Search); executive functioning (assessed via Wisconsin Card Sorting Test & Trail Making Test B); attention/working memory (assessed via Letter Number Sequencing & Paced Auditory Serial Addition Test); learning and delayed recall (assessed via Hopkins Verbal Learning Test & Brief Visuospatial Memory Test); verbal fluency (assessed via Controlled Oral Word Association Test & tests involving animal and action categories); and motor skills (assessed via Grooved Pegboard Test using both the dominant and nondominant hands). Raw scores were derived from each cognitive measure and then converted to T scores, taking into account demographic factors such as age, gender, education, and race when available. These T scores were further used to calculate clinical ratings using established algorithms. Clinical ratings of 5 or greater, indicating impairment in two or more domains, were considered indicative of HAND based on previous studies (Blackstone et al., 2012; Heaton et al., 2010). Additionally, participants with borderline HAND, characterized by clinical ratings of 4 or greater, were included to ensure a diverse sample of PLWH with a broader range of cognitive functioning and potential risk for future cognitive decline (Grant et al., 2014).

Depression.

Depressive symptoms were measured using the Center for Epidemiologic Studies Depression Scale-Revised (Radloff, 1977). Scores range from 0 – 60; a score of ≥ 16 indicates clinically relevant depressive symptomology.

Internal Locus of Control Scale.

This six-item measure utilizes a Likert-type scale (1 = strongly agree; 6 = strongly disagree) to gauge participants’ perception of their ability to exert influence over their lives, serving as an indicator of QoL. Higher scores signify a weaker internal locus of control. Cronbach’s α values for this measure range from 0.62 to 0.79 (Wolinsky, Vander Weg et al., 2010).

Pittsburgh Sleep Quality Index (PSQI).

The PSQI is a widely used measure to assess sleep quality as an indicator of QoL. It consists of 19 items that evaluate participants’ sleep quality over the past month. The questionnaire comprises seven subscales, including sleep efficiency, sleep duration, sleep disturbance, sleep latency onset, sleep medication use, sleepiness, and dysfunction due to sleepiness. Participants rate the extent to which various factors interfere with their sleep. To calculate the overall score, scores from the subscales are summed, resulting in a global score ranging from 0 to 21. A score greater than 5 indicates poor sleep quality. The PSQI is considered a gold standard measure and demonstrates excellent test-retest reliability with a correlation coefficient of r = 0.87 (Backhaus et al., 2002).

Medical Outcomes Study – HIV (MOS-HIV).

Adapted from a previous version of the Medical Outcomes Survey SF-36 Health-related QoL (HRQoL) Questionnaire, the MOS-HIV was employed to gather HRQoL data from individuals with HIV (Wu et al., 1997; Wu et al., 1991). This well-established and validated self-report instrument consists of 35 items distributed across 11 domains: general health perceptions, physical functioning, role functioning, pain, social functioning, mental health, energy/fatigue, health distress, cognitive functioning, quality of life, and health transition (Cooper et al., 2017). The MOS-HIV questionnaire incorporates two summary scales, namely the Physical Health Summary and the Mental Health Summary, which serve as comprehensive indicators of perceived HRQoL. Additionally, the study examined the general health perceptions subscale as a measure of self-reported overall health. Each scale is scored on a 100-point range, where higher scores correspond to better HRQoL.

Intervention Training Protocols

Participants with HAND or borderline HAND were randomly assigned to one of three groups: 10 hours of SOP training sessions, 20 hours of SOP training sessions, or 10 hours of Internet Navigation Control Training (serving as a control for social and computer engagement). To ensure balanced assignment between the groups, our randomization strategy considered factors such as baseline SOP performance categorized based on the Useful Field of View (UFOV®) test risk category (risk category = 1 for no SOP impairment, vs. 2–5 for subtle to severe SOP impairment) (Ball et al., 1993; Vance et al., under review) and race (BIPOC = 0; white = 1); as participants entered the study and met entry criteria, these characteristics were registered on our randomization protocol form, this information was then entered in a randomization database that assigned participants to their group assignment. All training groups completed their designated interventions in a computer lab located at our research center. A trained research assistant supervised the participants, providing guidance on accessing the computer program, offering support, monitoring progress, and ensuring treatment fidelity. To minimize the number of visits to the research center, participants could engage in 1–2 hour training sessions at a time. The overall training period spanned 10–12 weeks, during which participants completed their scheduled visits. Participants were compensated $20 per hour spent in the computer lab. Breaks were provided as necessary to accommodate participants’ needs.

SOP Training.

In the SOP training groups, participants engaged in either 10 or 20 hours of cognitive training modules available at www.brainhq.com. The SOP exercises involved multiple repeated trials, where stimuli were presented centrally on a computer screen, accompanied by peripheral target stimuli—a design based on the UFOV® test, which serves as the foundation for SOP training. During each trial, the participants’ objective was to correctly identify both the central target and the location of the peripheral target while filtering out distractors. This process was repeated numerous times, with the computer algorithm automatically adjusting the presentation of stimuli in milliseconds. The algorithm adapted the speed of stimulus presentation based on the participants’ response accuracy. This dynamic adjustment created a challenging cognitive environment that required continuous adaptation from participants. Importantly, the program ensured that participants were correct most of the time, aiming to foster positive neuroplasticity without being discouraging (Vance et al., 2014).

Internet Navigation Training.

Participants assigned to the Internet Navigation Training group participated in 10 hours of modules focused on utilizing the internet to search for various types of information, including health information, geographic data, community events, and other web-based activities. The design of these modules aimed to provide an enjoyable experience rather than mentally challenging tasks. Unlike the SOP training modules, there was no specific timing requirement for completing the internet navigation modules. Participants had the freedom to progress at their own pace and choose the modules they wanted to engage. However, they were encouraged to initially go through all 10 modules to ensure comprehensive exposure to the training material.

Treatment Fidelity.

To ensure treatment fidelity, several measures were taken. First, the number of training hours completed by each participant was carefully tracked. Additionally, the BrainHQ program automatically recorded the duration of engagement in each training module. Notably, there was a strong correlation between the program’s recorded cognitive training time and the tracking of time logged at the research centers (r = 0.91, p = 0.001). The study staff recorded the average percentage of training activity hours completed by participants, whether it was in the SOP or control group. On average, participants completed 87.92% of the assigned training hours, with a standard deviation of 30.59%. Regarding the SOP training specifically, the average number of hours reported by the software (representing actual engagement in the exercises) was 6.94 hours (SD = 2.97) for the 10-hour group and 13.88 hours (SD = 4.99) for the 20-hour group. However, when considering the hours actually spent engaging in SOP training, the average was 8.88 hours for the 10-hour group and 18.25 hours for the 20-hour group. In line with the ACTIVE Study protocol, which served as the basis for this SOP training approach, participants were considered “completers” when they successfully completed at least 70% of the prescribed training (Ball et al., 2002).

Sample Size

The a-priori target recruitment was n = 88 per group, estimated based on a projected 17% attrition (n = 75 per group after attrition), 3 follow-up repeated measurements, intra-subject correlation of 0.5, 80% power, and a significance level of .025. Under these assumptions, the standardized time-averaged detectable difference at follow-up between any two groups was d ~ .4.

Data Analysis

Balance between the three study groups was examined with respect to participant baseline characteristics (e.g., age, gender, race, education, HIV markers, etc.), baseline outcomes, and participation in follow-up data collection, utilizing descriptive statistics and measures of effect size, such as the proportion of variance explained, R2 (small ~0.02, medium ~0.13, large ~0.26) and Cramer’s V (small ~0.07, medium ~0.21, large ~0.35) for cross-tabulations comparing three groups (Cohen, 1988). Baseline between-group testing was not conducted as per CONSORT guidelines (Moher et al., 2010) since the null hypothesis of random assignment was known to be true. Next, participants’ characteristics and baseline outcomes were examined for association with study attrition (i.e., whether participants had complete follow-up data or not) using measures of effect size (i.e., Cohen’s d (small ~0.2, medium ~0.5, large ~0.8) and Cramer’s V (small ~0.1, medium ~0.3, large ~0.5, for cross-tabulations comparing two groups)) (Cohen, 1988). Characteristics with non-trivial magnitude of association with attrition (i.e., d ≥ 0.25) were used as controlling covariates in subsequent analysis steps to reduce potential bias caused by missing data (Groenwold et al., 2011). Then, for each of the outcome variables, linear mixed-effect models with subject-specific random effects were fitted to estimate between-group mean contrasts at the follow-up time-points, using as fixed effects: baseline values, indicator variables for time, group, time-by-group interaction, and adjusting covariates associated with missing data. The models used all available data at each time-point. Baseline data were used to estimate a pooled standard deviation for each outcome, which was then used to standardize the between-group contrasts and provide a measure of effect size (Cohen’s d). The multiple-degree-of freedom test for the time-by-group interaction terms was used to assess between-group differences in outcome trajectory over time. The Kenward-Roger approximation to degrees of freedom was used for the test statistics and therefore p-values computed from the model parameters and contrasts. A False Discovery Rate (FDR) approach with a target 10% FDR level (Benjamini & Hochberg, 1995) was used to control for multiple inferences. It was planned to first apply a correction to the tests of between-group comparisons in trajectory (a multiple-degree-of-freedom test for each outcome), and if a significant difference in trajectories was found, then a correction would be applied to the between-group contrasts at each follow-up time-point (nine contrasts for each outcome). Analyses were conducted using R software version 4.2.3. (R Core Team, 2023).

RESULTS

Participant Characteristics

Table 1 shows baseline characteristics of the N = 216 participants in the study. Descriptive statistics and measures of effect size did not suggest any major imbalance among the study groups. On average, participants in this study were 51.01 years old (SD = 6.53). Almost two-thirds of the participants were male (n = 134, 62.04%), and the majority identified as BIPOC (n = 179, 82.87%), with n = 172 (79.63%) identifying as African American; n = 1 (0.46%) Hispanic; n = 2 (0.93%) Native American, and n = 4 (1.85%) other. Participants had an average of 12.4 years of education (SD = 2.27) and reported an average income of approximately $18,300 (SD = $14,800). Participants reported taking an average of 6.76 (SD = 4.65) prescribed medications (including antiretroviral therapy). Among the participants who reported consuming alcohol (n = 123, 56.94%), the average number of alcoholic drinks per day was 2.86 (SD = 2.03). Among those who reported smoking (n = 119, 55.09%), the average number of cigarettes per day was 10.02 (SD = 7.10). Additionally, there were 93 participants (44.29%) with positive urine toxicology (UTOX+). In terms of selected HIV characteristics, on average, participants had been living with HIV for 16.05 years (SD = 8.50), had current CD4+ T lymphocyte count/mm3 of 649.61 (SD = 375.75), nadir CD4+ T lymphocyte count/mm3 of 270.14 (SD = 275.80), and the majority were on antiretroviral therapy (n = 192, 95.52%); overall, these participants were medically stable with HIV.

Table 1.

Participant Characteristics at Baseline by Group Assignment (N = 216)

Variable Control Group 10-hour Cognitive Training 20-hour Cognitive Training Effect size
(n = 73) (n = 70) (n = 73)
n (%) Mean (SD) n (%) Mean (SD) n (%) Mean (SD)
Age 50.2 (6.7) 51.4 (6.3) 51.5 (6.6) R2 = 0.01
Gender V = 0.06
 Female 30 (41.10%) 27 (38.57%) 25 (34.25%)
 Male 43 (58.90%) 43 (61.43%) 48 (65.75%)
Race/Ethnicity V = 0.03
 BIPOC 60 (82.19%) 59 (84.29%) 60 (82.19%)
 White 13 (17.81%) 11 (15.71%) 13 (17.81%)
Education (years) 12.3 (2.5) 12.6 (2.4) 12.3 (1.9) R2 = 0
Household income ($10K) 1.9 (1.6) 1.8 (1.4) 1.8 (1.4) R2 = 0
Years diagnosed with HIV 15.7 (8.9) 14.2 (8.1) 18.2 (8.0) R2 = 0.04
Current CD4+ T lymphocyte count/mm3 685.7 (414.9) 693.9 (360.5) 562.0 (335.7) R2 = 0.03
Nadir CD4+ T lymphocyte count/mm3 289.7 (275.6) 269.8 (263.9) 252.5 (288.8) R2 = 0
Number of prescribed medications 6.5 (4.0) 6.3 (4.0) 7.5 (5.6) R2 = 0.01
Prescribed ART V = 0.07
 No 4 (5.48%) 2 (2.86%) 3 (4.11%)
 Yes 65 (89.04%) 61 (87.14%) 66 (90.41%)
Unknown 4 (5.48%) 7 (10.00%) 4 (5.48%)
Alcohol use frequency V = 0.1
 Never 31 (42.47%) 32 (45.71%) 29 (40.28%)
 Monthly or less 15 (20.55%) 17 (24.29%) 15 (20.83%)
 Two to four times a month 14 (19.18%) 6 (8.57%) 15 (20.83%)
 Twice weekly or more 13 (17.81%) 15 (21.43%) 13 (18.06%)
Number of drinks on a drinking day* 3.1 (2.0) 2.8 (2.3) 2.8 (1.9) R2 = 0
Currently using tobacco 42 (57.53%) 40 (57.14%) 37 (50.68%) V = 0.06
Cigarettes per day* 11.8 (7.6) 8.2 (6.0) 10.1 (7.3) R2 = 0.04
UTOX+ 30 (42.25%) 28 (40.58%) 35 (50.00%) V = 0.06
Study Activities:
 Hours logged in training activities 8.4 (3.5) 8.9 (3.0) 18.2 (5.2) R2 = 0.56
 % of prescribed training activity hours 83.8 (35.2) 88.8 (29.6) 91.2 (26.1) R2 = 0.01
  Logged
Hours of computerized cognitive training NA (NA) 6.9 (3.0) 13.9 (5.0) R2 = 0.42
Baseline Outcomes:
 CES-Depression 19.9 (11.6) 18.0 (11.4) 17.2 (10.2) R2 = 0.01
 Locus of Control 26.8 (6.2) 27.7 (5.9) 28.5 (6.2) R2 = 0.01
 PSQI Sleep Quality 10.3 (4.6) 8.0 (4.6) 7.8 (3.8) R2 = 0.06
 MOS-HIV General Health Perceptions 53.4 (26.9) 55.1 (28.6) 57.1 (25.2) R2 = 0
 MOS-HIV HRQoL - Physical Summary 45.3 (11.6) 47.2 (11.5) 45.9 (11.5) R2 = 0
 MOS-HIV HRQoL - Mental Summary 47.2 (11.0) 47.3 (13.0) 48.7 (11.0) R2 = 0
Follow-up data collection V = 0.02
 Post 55 (75.34%) 55 (78.57%) 63 (86.3%)
 Year 1 47 (64.38%) 50 (71.43%) 53 (72.6%)
 Year 2 33 (45.21%) 40 (57.14%) 41 (56.16%)
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Notes. R2 = R-squared; V = Cramer’s V; R2: ~0.02 small, ~0.13 medium, ~0.26 large. In this table, Cramer’s V: ~0.07 small, ~0.21 medium, ~0.35 large. $10K = ten thousand dollars; ART = antiretroviral therapy; BIPOC = Black, Indigenous, and People of Color; CES-Depression = Center for Epidemiological Studies Depression Scale; HRQoL = health-related quality of life; MOS-HIV = Medical Outcomes Study – HIV; NA = not applicable; PSQI = Pittsburgh Sleep Quality Index; SD = standard deviation; UTOX+ = urine toxicology screen indicating a positive result.

Regarding baseline outcomes, participants’ scores on the Center for Epidemiologic Studies Depression Scale (CES-D) were on average 18.38 (SD = 11.08), with n = 112 participants (51.85%) reporting CES-D scores ≥ 16, indicating the presence of relevant depressive symptoms. Baseline sample averages for other outcomes were as follows: Locus of Control: M = 27.69 (SD = 6.14), PSQI: M = 8.71 (SD = 4.48), MOS-HIV General Health Perceptions: M = 55.19 (SD = 26.8), MOS-HIV Physical Summary: M = 46.10 (SD = 11.50), and MOS-HIV Mental Summary: M = 47.72 (SD = 11.63). All of these measures suggest low to moderate QoL at baseline.

Study Attrition

The bottom of Table 1 shows frequencies of participation at each follow-up data collection time-point by study group. Overall, n = 173 (80.09%), n = 150 (69.44%), and n = 114 (52.78%) participants were retained at the post, year 1, and year 2 time-points, respectively. Table 2 shows comparisons of baseline characteristics between the n = 114 participants with complete data vs. the n = 102 who did not complete all follow-up time-points. As per the measures of effect size, there was no single characteristic strongly associated with attrition, but moderate differences (d ≥ 0.25) were observed in nadir CD4+, number of prescriptions, and baseline MOS-HIV Physical Summary. These characteristics were used as covariates in outcome analyses.

Table 2.

Relationships Between Participant Characteristics and Study Attrition

Variable Incomplete data (n = 102) Complete data (n = 114) Effect size
n (%) Mean (SD) n (%) Mean (SD)
Age 50.4 (6.7) 51.5 (6.3) |d| = 0.17
Gender V = 0.01
 Female 39 (38.24%) 43 (37.72%)
 Male 63 (61.76%) 71 (62.28%)
Race/Ethnicity V = 0.06
 Non-white 82 (80.39%) 97 (85.09%)
 White 20 (19.61%) 17 (14.91%)
Education (years) 12.2 (2.4) 12.6 (2.1) |d| = 0.21
Household income ($10K) 1.9 (1.7) 1.8 (1.3) |d| = 0.07
Years diagnosed with HIV 15.6 (8.5) 16.5 (8.5) |d| = 0.10
Current CD4+ T lymphocyte count/mm3 634.7 (369.6) 664.3 (383.5) |d| = 0.08
Nadir CD4+ T lymphocyte count/mm3 311.5 (303.6) 237.0 (247.7) |d| = 0.27
Number of prescribed medications 6.1 (4.5) 7.3 (4.7) |d| = 0.25
Prescribed ART V = 0.17
 No 8 (7.84%) 1 (0.88%)
 Yes 87 (85.29%) 105 (92.11%)
 Unknown 7 (6.86%) 8 (7.02%)
Alcohol use frequency V = 0.12
 Never 49 (48.51%) 43 (37.72%)
 Monthly or less 21 (20.79%) 26 (22.81%)
 Two to four times a month 13 (12.87%) 22 (19.30%)
 Twice weekly or more 18 (17.82%) 23 (20.18%)
Currently using tobacco 58 (56.86%) 61 (53.51%) V = 0.03
UTOX+ 44 (44.44%) 49 (44.14%) V = 0
Baseline Outcomes:
 CES-Depression 17.6 (10.7) 19.1 (11.4) |d| = 0.13
 Locus of Control 28.2 (6.4) 27.2 (5.9) |d| = 0.15
 PSQI Sleep Quality 8.3 (4.5) 9.1 (4.4) |d| = 0.18
 MOS-HIV General Health Perceptions 58.9 (26.3) 51.8 (26.9) |d| = 0.27
 MOS-HIV HRQoL - Physical Summary 48.1 (11.6) 44.4 (11.2) |d| = 0.32
 MOS-HIV HRQoL - Mental Summary 49.2 (10.1) 46.4 (12.7) |d| = 0.24
Study group V = 0.11
 Control 40 (39.22%) 33 (28.95%)
 10 Hours training 30 (29.41%) 40 (35.09%)
 20 Hours training 32 (31.37%)   41 (35.96%)  
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Notes. |d| = absolute value of Cohen’s d; V=Cramer’s V; Cohen’s d: ~0.2 small, ~0.5 medium, ~0.8 large. In this table, Cramer’s V: ~0.1 small, ~0.3 medium, ~0.5 large. ART = antiretroviral therapy; BIPOC = Black, Indigenous, and People of Color; $10K = ten thousand dollars. CES-Depression = Center for Epidemiological Studies Depression Scale; HRQoL = health-related quality of life; MOS-HIV = Medical Outcomes Study – HIV; PSQI = Pittsburgh Sleep Quality Index; SD = standard deviation; UTOX+ = urine toxicology screen indicating a positive result.

Effects of Training

Table 3 presents model-estimated outcome means and between-group contrasts at the follow-up time-points. All models included baseline outcome values as a covariate. The rightmost column of the table shows the multiple-degree-of freedom tests of between-group differences in trajectories. A potential signal for between-group difference in trajectories was observed for the PSQI Index (F(6,490.32) = 2.44, p = 0.024); however, the test was not statistically significant after the multiple testing correction (corrected pFDR = 0.146). Therefore, no statistically significant effects could be claimed.

Table 3.

QOL Treatment Outcomes by Group at Follow-up

Outcomes by group and between-group contrasts POST Year 1 YEAR 2 Test of Trajectory Differences
Mean (SE) p d (95% CI) Mean (SE) p d (95% CI) Mean (SE) P d (95% CI) Time by Group Interaction
CES-Depression F(6, 485.49) = 0.36, p = 0.906
 Control 19.19 (1.04) - - 19.03 (1.11) - - 17.57 (1.32) - -
 10 Hrs. 19.26 (1.03) - - 19.18 (1.08) - - 18.99 (1.2) - -
 20 Hrs. 18.37 (0.96) - - 19.59 (1.05) - - 16.81 (1.19) - -
 Control vs. 10 Hrs. −0.07 (1.46) 0.961 −0.01 (−0.27, 0.25) −0.15 (1.55) 0.922 −0.01 (−0.29, 0.26) −1.43 (1.78) 0.424 −0.13 (−0.45, 0.19)
 Control vs. 20 Hrs. 0.82 (1.42) 0.564 0.07 (−0.18, 0.33) −0.56 (1.53) 0.715 −0.05 (−0.32, 0.22) 0.76 (1.78) 0.67 0.07 (−0.25, 0.38)
 10 Hrs. vs. 20 Hrs. 0.89 (1.41) 0.529 0.08 (−0.17, 0.33) −0.41 (1.51) 0.788 −0.04 (−0.30, 0.23) 2.18 (1.69) 0.197 0.20 (−0.10, 0.50)
Locus of Control F(6, 486.8) = 1.35, p = 0.234
 Control 28.47 (0.52) - - 28.67 (0.56) - - 29.9 0(0.67) - -
 10 Hrs. 28.31 (0.52) - - 27.91 (0.55) - - 27.57 (0.61) - -
 20 Hrs. 28.74 (0.49) - - 27.76 (0.53) - - 28.75 (0.60) - -
 Control vs. 10 Hrs. 0.17 (0.74) 0.819 0.03 (−0.21, 0.26) 0.76 (0.78) 0.333 0.12 (−0.13, 0.37) 2.33 (0.90) 0.01 0.38 (0.09, 0.67)
 Control vs. 20 Hrs. −0.26 (0.72) 0.712 −0.04 (−0.27, 0.19) 0.91 (0.78) 0.24 0.15 (−0.10, 0.40) 1.16 (0.90) 0.2 0.19 (−0.10, 0.48)
 10 Hrs. vs. 20 Hrs. −0.43 (0.71) 0.545 −0.07 (−0.3, 0.16) 0.15 (0.76) 0.842 0.02 (−0.22, 0.27) −1.18 (0.86) 0.169 −0.19 (−0.47, 0.08)
PSQI Sleep Quality F(6, 490.32) = 2.44, p = 0.024
 Control 9.1 (0.45) - - 9.54 (0.49) - - 8.14 (0.58) - -
 10 Hrs. 9.7 (0.45) - - 10.78 (0.47) - - 9.63 (0.53) - -
 20 Hrs. 9.04 (0.42) - - 8.83 (0.46) - - 9.65 (0.52) - -
 Control vs. 10 Hrs. −0.6 (0.64) 0.347 −0.14 (−0.43, 0.15) −1.24 (0.68) 0.068 −0.29 (−0.59, 0.02) −1.49 (0.78) 0.057 −0.34 (−0.70, 0.01)
 Control vs. 20 Hrs. 0.07 (0.62) 0.913 0.02 (−0.27, 0.3) 0.71 (0.67) 0.29 0.16 (−0.14, 0.47) −1.51 (0.78) 0.054 −0.35 (−0.70, 0.01)
 10 Hrs. vs. 20 Hrs. 0.67 (0.62) 0.28 0.15 (−0.13, 0.43) 1.96 (0.66) 0.003 0.45 (0.15, 0.75) −0.02 (0.74) 0.982 0 (−0.34, 0.33)
MOS-HIV General Health Perceptions F(6, 482.04) = 0.14, p = 0.992
 Control 52.5 (2.38) - - 52.49 (2.56) - - 53.96 (3.03) - -
 10 Hrs. 54.52 (2.38) - - 52.57 (2.49) - - 52.86 (2.77) - -
 20 Hrs. 53.55 (2.23) - - 52.6 (2.43) - - 55.18 (2.73) - -
 Control vs. 10 Hrs. −2.02 (3.36) 0.548 −0.08 (−0.32, 0.17) −0.08 (3.57) 0.982 0 (−0.26, 0.26) 1.11 (4.11) 0.788 0.04 (−0.26, 0.34)
 Control vs. 20 Hrs. −1.05 (3.27) 0.748 −0.04 (−0.28, 0.2) −0.12 (3.54) 0.974 0 (−0.26, 0.25) −1.22 (4.09) 0.765 −0.05 (−0.34, 0.25)
 10 Hrs. vs. 20 Hrs. 0.97 (3.27) 0.766 0.04 (−0.2, 0.27) −0.04 (3.49) 0.992 0 (−0.26, 0.25) −2.33 (3.89) 0.551 −0.09 (−0.37, 0.20)
MOS-HIV HRQoL - Physical Summary F(6, 482.14) = 0.79, p = 0.581
 Control 44.98 (0.95) - - 44.08 (1.01) - - 45.7 (1.20) - -
 10 Hrs. 44.77 (0.94) - - 46.74 (0.98) - - 46.5 (1.09) - -
 20 Hrs. 46.07 (0.88) - - 44.75 (0.96) - - 46.77 (1.08) - -
 Control vs. 10 Hrs. 0.22 (1.33) 0.872 0.02 (−0.21, 0.25) -2.66 (1.41) 0.06 −0.23 (−0.47, 0.01) −0.8 (1.62) 0.62 −0.07 (−0.35, 0.21)
 Control vs. 20 Hrs. −1.08 (1.30) 0.403 −0.09 (−0.31, 0.13) −0.67 (1.40) 0.63 −0.06 (−0.3, 0.18) −1.08 (1.62) 0.505 −0.09 (−0.37, 0.18)
 10 Hrs. vs. 20 Hrs. −1.30 (1.29) 0.315 −0.11 (−0.33, 0.11) 1.99 (1.38) 0.149 0.17 (−0.06, 0.41) −0.27 (1.54) 0.859 −0.02 (−0.29, 0.24)
MOS-HIV HRQoL - Mental Summary F(6, 479.08) = 0.61, p = 0.719
 Control 45.81 (1.05) - - 46.49 (1.12) - - 48.15 (1.33) - -
 10 Hrs. 48.12 (1.05) - - 48.39 (1.10) - - 48.6 (1.21) - -
 20 Hrs. 47.47 (0.98) - - 47.11 (1.06) - - 50.12 (1.20) - -
 Control vs. 10 Hrs. −2.31 (1.49) 0.121 −0.2 (−0.45, 0.05) −1.90 (1.57) 0.226 −0.16 (−0.43, 0.1) −0.45 (1.80) 0.803 −0.04 (−0.34, 0.26)
 Control vs. 20 Hrs. −1.66 (1.44) 0.249 −0.14 (−0.39, 0.10) −0.62 (1.55) 0.689 −0.05 (−0.31, 0.21) −1.97 (1.79) 0.271 −0.17 (−0.47, 0.13)
 10 Hrs. vs. 20 Hrs. 0.65 (1.44) 0.653 0.06 (−0.19, 0.30) 1.28 (1.53) 0.404 0.11 (−0.15, 0.37) −1.52 (1.71) 0.374 −0.13 (−0.42, 0.16)
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Notes. Estimates from longitudinal models adjusted for baseline outcome, baseline MOS-HIV General Health Perception, baseline MOS-HIV Physical Summary, baseline number of prescriptions, and Nadir CD4. Cohen’s d: ~0.2 small, ~0.5 medium, ~0.8 large. CES-Depression = Center for Epidemiological Studies Depression Scale; HRQoL = health-related quality of life; MOS-HIV = Medical Outcomes Study – HIV; PSQI = Pittsburgh Sleep Quality Index; SE = Standard Error. No test of trajectory differences was statistically significant at a 10% False Discovery Rate level.

Most observed effect sizes in the between-group contrasts were of small magnitude. A few exceptions included effects of moderate magnitude in some contrasts for PSQI and one contrast for Locus of Control.

Regarding CES-D, estimated group means ranged between 18.37 to 19.59 at the Post and Year 1 time-points and were slightly lower at Year 2 (ranging from 16.81 to 18.99); all groups showed clinically relevant depressive symptoms. All between-group standardized effects were of trivial-to-small magnitude (|d| ≤ 0.13).

For Locus of Control, estimated group means ranged between 27.76 to 28.67 at the Post and Year 1 time-points, with between-group standardized differences of trivial magnitude (|d| ≤ 0.15). At Year 2, the control group had a slightly higher mean (M = 29.9) and poorer Locus of Control than the 10-hour (M = 27.57, d = 0.38) and 20-hour (M = 28.75, d = 0.19) training groups.

In terms of PSQI, the groups had similar means at the post time-point (ranging from 9.04 to 9.1, |d| ≤ 0.15). At Year 1, the 10-hour group mean (M = 10.78) was higher than that of the control (M = 9.54, d = 0.29) and the 20-hour (M = 8.83, d = 0.45) group. At Year 2, the control group had a slightly lower mean (M = 8.14) than the 10-hour (M = 9.63, d = 0.34) and 20-hour (M = 9.65, d = 0.35) training groups. When considered together, these differences were not considered as following a consistent pattern of training effect.

Regarding MOS-HIV General Health Perceptions, estimated group means were similar across follow-up time-points, ranging from 52.49 to 55.18. All between-group standardized effects were of trivial magnitude (|d| ≤ 0.09).

In terms of MOS-HIV Physical Summary, all between-group standardized effects were of trivial-to-small magnitude (|d| ≤ 0.23), with estimated group means ranging from 44.08 to 46.77 across all time-points.

Similarly, for MOS-HIV Mental Summary, all between-group standardized effects were of trivial-to-small magnitude (|d| ≤ 0.16), with estimated group means ranging from 45.81 to 50.12 across all time-points. Group means were slightly higher at Year 2 compared to the other time-points.

DISCUSSION

The Think Fast Study aimed to investigate the effects of SOP training on QoL indicators in adults living with HAND and borderline HAND. The results of this 2-year longitudinal randomized controlled trial did not reveal a strong pattern of training effects on QoL measures. While there was a potential between-group difference observed in trajectories for the PSQI Index, this difference did not reach statistical significance after a multiple testing correction.

It is worth mentioning that previous research has shown the potential benefits of cognitive training, particularly SOP training, on QoL indicators in older adults and cognitively vulnerable populations. Studies such as the ACTIVE Study have demonstrated improvements in QoL indicators, including health-related quality of life, self-rated health, and internal locus of control, following cognitive training (Wolinksky et al., 2006; Wolinsky et al., 2009; Wolinsky, Mahncke et al., 2010; Wolinsky, Vander Weg et al., 2010). However, as seen here, these findings have not been replicated in PLWH; moreover, the inclusion of QoL indicators in other cognitive training studies focused on PLWH has been limited. In our sample, QoL indicators were low to moderate at baseline, as such, QoL may be more treatment resistant compared to those without HIV.

Upon reflection, SOP training may not be the best domain-specific cognitive training to improve QoL indicators in PLWH with HAND. In the Training on Purpose Study (TOPS), Vance et al. (2022) randomized 109 PLWH with HAND to a no-contact control group or to an individually-targeted cognitive training group. Based on the participant’s baseline neurocognitive performance, those in the individually-targeted group received domain-specific cognitive training targeting two cognitive domains (10 hours for each domain) that specifically contributed to their HAND diagnosis, based on a specific algorithm that favored attention training and/or SOP training. While findings were mixed, spatial learning and memory training and delayed spatial learning and memory training resulted in noticeably more improvements in QoL indicators than any of the other cognitive training protocols, including SOP training. Future studies should investigate the effect domain-specific cognitive training exerts on QoL indicators.

Implications for Practice

When considering the larger neuroHIV and cognitive training literature, several implications for clinical practice emerge. Firstly, as PLWH age, they may experience cognitive and everyday functional declines; however, some evidence suggests that domain-specific cognitive training could potentially improve QoL indicators (Wolinsky, Vander Weg, et al., 2010). Unfortunately, SOP training was not found to be effective in enhancing QoL in our sample. Nonetheless, consistent with other research (Vance et al., 2022), certain domain-specific cognitive training approaches have been shown to exert a greater influence on QoL indicators. Therefore, when clinicians provide recommendations regarding cognitive training for their HIV patients, it remains crucial to clearly communicate realistic expectations and limitations regarding transfer to QoL.

Secondly, clinicians may recommend cognitive training to patients concerned about cognitive decline as they age, aiming to alleviate anxiety and apprehension related to HAND and cognitive aging. Generally, cognitive training is a safe approach that shows promise in enhancing cognitive and everyday functioning, although our study failed to observe a therapeutic impact on QoL. Furthermore, in the Think Fast Study, participants with HAND were informed about their HAND status, and later qualitatively assessed during posttest about their reaction to this information. Various potentially negative QoL-related themes emerged, including anxiety, sadness, unexpected reactions, concerns, and lack of concern/no reaction. However, positive themes were also identified including, such as, confirmation of cognitive impairment, seeking knowledge, and a desire to improve (Vance, Jensen et al., 2019). Despite expressing unease and apprehension about having HAND, participants voiced a desire for tools to address and safeguard their cognitive abilities as they age. Cognitive training could serve as such a tool, empowering individuals, reducing anxiety, and potentially enhancing QoL over time.

Implications for Research

Participants in this study rated their QoL poorly on several measures. Research aimed at improving QoL in PLWS and HAND is thus critical. However, although SOP training wielded some effect on cognitive and everyday functioning in the Think Fast Study (Vance et al., under review; Vance, Fazeli, Azuero, Frank et al., 2023), these effects did not transfer to QoL indices. This result was surprising given the positive findings of other cognitive training studies (Vance et al., 2022; Wolinsky, Vander Weg et al., 2010). From a research perspective, this suggests several possibilities. First, SOP training may simply not generate an effect in improving QoL, thus reducing enthusiasm for studies investigating the effect of SOP training on QoL. Second, other types of cognitive training programs (i.e., spatial learning and memory training) may have a therapeutic effect on QoL and should be explored. Third, those who did experience a direct improvement in cognitive and everyday functioning from SOP training may have experienced an indirect improvement in QoL; however, this would need to be examined in several follow up statistical analyses that can examine such direct/indirect effects (i.e., structural equation modeling). And fourth, the QoL indices in this study may not have been sensitive enough to detect a change in QoL; thus, other types of QoL measurement may be needed, such as using experience method sampling (ESM) to measure a more moment-to-moment rating of QoL (i.e., 3 times a day for 7 days via smart-phones; Fazeli et al., 2017).

Strengths and Limitations

Like any study, this one has methodological strengths and limitations. Four strengths are noteworthy. First, cognitive function and HAND were assessed using standardized norm-based measures, enhancing generalizability. Second, standardized cognitive training modules were employed, ensuring consistency across targeted domains. Third, the study included a large sample with a 2-year follow-up. Last, the use of multiple measures for QoL acknowledges its multifaceted nature (Meeberg, 1993).

The findings of this study should be interpreted with these limitations. First, the study had to end prematurely due to the COVID-19 pandemic, which may have impacted the ability to assess long-term effects of SOP training on QoL. Second, the study sample was recruited from a single HIV/AIDS clinic in Birmingham, Alabama, which may limit generalizability.

Conclusion

In conclusion, the Think Fast Study did not find significant improvements in QoL indicators among PLWH with HAND and borderline HAND. While previous research has shown the potential benefits of cognitive training on QoL indicators in other populations, these findings have not been consistently observed in PLWH. This lack of finding suggests that SOP training may not have direct benefits to QoL, although it does have some impact on cognitive and everyday functioning.

Figure 1.

Figure 1

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Think Fast Consort Diagram

KEY CONSIDERATIONS.

  • Approximately 50% of PLWH experience HIV-associated neurocognitive disorder, which can have a significant influence on their quality of life (i.e., depression, sleep quality, locus of control).

  • Speed of processing training was not effective in improving quality of life indicators, which is surprising since other studies do show that speed of processing training and other types of cognitive training show they can improve quality of life.

  • Although cognitive training can improve cognitive and everyday functioning, when referring patients to speed of processing training and other types of cognitive training, nurses and clinicians must communicate realistic expectations of the cognitive training programs on quality of life indices.

Funding/Acknowledgements

The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). The following funding is acknowledged: NIH/National Institute of Mental Health R01-award (1R01MH106366-01A1; ClinicalTrials.gov; NCT02758093; PI: Vance) titled “An RCT of Speed of Processing Training in Middle-aged and Older Adults with HIV” NIH/National Institute on Aging (NIA) R00-award (R00 AG048762; PI: Fazeli), ORWH and NIH/NIDA BIRCWH grant (5K12DA035150; PI: Curry with Pope); NIH/NIA P30-award (Edward R. Roybal Center for Translational Research in Aging and Mobility; P30 AG022838; PI: Ball). Special thanks to our research team, especially Brittany Bradley, Delaney Diehl, Shyla Hossain, Michael Jenson, Peggy McKie, Josiah Robinson, Frida Tende, and Tess Walker.

Footnotes

Conflicts of Interest/Competing Interests

Non-financial Interests – David E. Vance, Pariya L. Fazeli, Andres Azuero, Jennifer S. Frank, Virginia G. Wadley, James L. Raper, Caitlin N. Pope, and Alexandra Jacob report no real or perceived vested interest that related to this article that could be construed as a conflict of interest.

Financial Interests – Karlene Ball owns stock in the Visual Awareness Research Group (formerly Visual Awareness, Inc.), and Posit Science, Inc., the companies that market the Useful Field of View Test and speed of processing training software. Posit Science acquired Visual Awareness, and Dr. Ball continues to collaborate on the design and testing of these assessment and training programs as a member of the Posit Science Scientific Advisory Board.

Declarations/Compliance with Ethical Standards

Research Involving Human Participants and/or Animals

Written informed consent was obtained using procedures approved by the investigational review board at each of the collaborating institutions. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Consent to Participate – Informed consent was obtained from all individual participants included in the study.

Consent to Publish – Participants consented that their data would be used in aggregate form for publication purposes.

Contributor Information

David E. Vance, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Pariya L. Fazeli, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Andres Azuero, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Jennifer S. Frank, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Virginia G. Wadley, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

James L. Raper, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Caitlin N. Pope, Department of Health, Behavior & Society, University of Kentucky, Lexington, Kentucky, USA,.

Alexandra Jacob, Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA,.

Karlene K. Ball, Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA..

Data Availability –

Data are available upon request.

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