IntroductionGrowing evidence suggests that some common infections are causally associated with cognitive impairment; however, less is known about the burden of multiple infections.MethodsWe investigated the cross‐sectional association of positive antibody tests for herpes simplex virus, cytomegalovirus (CMV), Epstein‐Barr virus (EBV), varicella zoster virus (VZV), and Toxoplasma gondii (TOX) with Mini‐Mental State Examination (MMSE) and delayed verbal recall performance in 575 adults aged 41–97 from the Baltimore Epidemiologic Catchment Area Study.ResultsIn multivariable‐adjusted zero‐inflated Poisson (ZIP) regression models, positive antibody tests for CMV (p = .011) and herpes simplex virus (p = .018) were individually associated with poorer MMSE performance (p = .011). A greater number of positive antibody tests among the five tested was associated with worse MMSE performance (p = .001).DiscussionCMV, herpes simplex virus, and the global burden of multiple common infections were independently associated with poorer cognitive performance. Additional research that investigates whether the global burden of infection predicts cognitive decline and Alzheimer's disease biomarker changes is needed to confirm these findings.

BACKGROUND Delay discounting quantifies an individual’s preference for smaller, short-term rewards over larger, long-term rewards and represents a transdiagnostic factor associated with numerous adverse health outcomes. Rather than a fixed trait, delay discounting may vary over time and place, influenced by individual and contextual factors. Continuous, real-time measurement could inform adaptive interventions for various health conditions. OBJECTIVE This study sought to determine the validity and reliability of a novel, continuous, smartphone ecological momentary assessment (EMA)-based survey to measure delay discounting; to identify the fewest survey questions necessary to sufficiently capture delay discounting; and to determine the survey’s ability to reproduce established associations between delay discounting and substance use/cravings. METHODS Participants (n=97) were adults (age range=18-71 years), recruited on social media. In Phase 1, data were collected on participant sociodemographic characteristics and delay discounting was evaluated via the traditional Monetary Choice Questionnaire (MCQ) and our novel method (i.e., 7-item time- and 7-item monetary-selection scales). During Phase 2 (approximately 6 months later), participants completed the MCQ, our novel delay discounting measures, and health outcomes questions. We examined correlations between our method and the traditional MCQ within and across Phases. For scale reduction, we iteratively selected a random number of items and assessed the correlation between the full and random scales. We then examined the association between our time- and monetary-selection scales assessed during Phase 2 and the percent of assessments that participants endorsed using or craving alcohol, tobacco, or cannabis. RESULTS Six of the seven individual time-selection items were highly correlated with the full scale (r>0.89). Both time- (r=0.71, P<.001) and monetary-selection (r=0.66, P<.001) delay discounting rates had high test-retest reliability across Phases 1 and 2. Phase 1 MCQ delay discounting function highly correlated with Phase 1 (r=0.76, P<.001) and Phase 2 (r=0.45, P<.001) time-selection delay discounting scales. One or more randomly chosen time-selection items were highly correlated with the full scale (r>0.94). Greater delay discounting measured via the time-selection measure (adjusted mean difference = 5.89, 95% CI 1.99-9.79), but not the monetary-selection scale (adjusted mean difference = -0.62, 95% CI -3.57 to 2.32), was associated with more past-hour tobacco use endorsement in follow-up surveys. CONCLUSIONS This study evaluated a novel, EMA-based scale’s ability to validly and reliably assess delay discounting. By measuring delay discounting with fewer items and in situ via EMA in natural environments, researchers may be better able to implement real-time interventions to mitigate adverse health outcomes.