LAUSR

People living with HIV (PLWH) are at greater risk of cognitive impairment than their HIV-negative counterparts. In fact, 30–50% of PLWH meet the neuropsychological criteria for HIV-Associated Neurocognitive Disorder (HAND; Bonnet et al., 2013; Wei et al., 2020). Cognitive impairment in PLWH is associated with more difficulty with everyday functioning and poorer quality of life (QoL; Vance et al., 2017). Cognitive impairment may be exacerbated by age-related cognitive declines (Waldrop et al., 2021). In the United States, this represents a public health concern as nearly half of PLWHare 50 years and older, and the percentage of older PLWH is expected to be 70% by 2030 (Brooks et al., 2012). Therefore, it is important to prevent cognitive impairment to maintain optimal cognitive function in PLWH as they age. Emerging research supports cognitive training as a viable option for protecting cognitive health in PLWH. In a systematic review of 13 articles regarding cognitive training for PLWH, Vance, Fazeli, et al. (2019) found that cognitive training is beneficial for improving cognitive function in the targeted cognitive domain (e.g., executive functioning, attention, speed of processing). For example, in an experimental study of 46 PLWH, Vance et al. (2012) randomized participants to either an intervention group (n 5 22) or a no-contact control group (n 5 24). Ten hours of computerized cognitive exercises targeting training in speed of processing (SOP) were applied to the intervention group. The results demonstrated that SOP-targeted training significantly improved cognitive function in this domain (p 5 .04, power 5 0.53), as measured by a neuropsychological test of visual SOP and visual attention. Improvements also translated to a speed-based laboratory performance measure of instrumental activities of daily living (e.g., counting change, finding items on a food shelf). In the Training on Purpose Study (TOPS), Vance et al. (2021) conducted a two-group preexperimental/ postexperimental design study using individualizedtargeted computerized cognitive training with PLWH experiencing HAND, as measured by the Frascati criteria (Vance, Fazeli, Azuero, Wadley et al., 2021). More specifically, using norm-based (i.e., controlling for age/ education) cognitive performance measures, HAND at baselinewas defined if participants scored 1 ormore SDs below their expected norm-based performance in at least two cognitive domains (i.e., SOP, attention, executive function, spatial learning and memory, delayed spatial learning and memory, spatial visualization, verbal learning and memory, and delayed verbal learning and memory). This algorithm for defining HAND is referred Jun Y. Byun, MSN, is a PhD Student, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA. Andres Azuero, PhD, is a Professor, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA.Pariya L. Fazeli, PhD, is anAssociateProfessor, School ofNursing,University of Alabama at Birmingham, Birmingham, Alabama, USA. Wei Li, PhD, is an Associate Professor, Department of Clinical and Diagnostic Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, Alabama, USA. Crystal Chapman Lambert, PhD, CRNP, is an Associate Professor, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA. Victor A. Del Bene, PhD, is an Assistant Professor, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Kristen Triebel, PsyD, ABPP, is an Associate Professor, Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA. Alexandra Jacobs,MS, is a PhDStudent, Department of Psychology, University of Alabama at Birmingham, Birmingham, Alabama, USA. David E. Vance, PhD, MGS, is a Professor, School of Nursing, University of Alabama at Birmingham, Birmingham, Alabama, USA.