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Among modifiable lifestyle factors, diet may affect cognitive health. Cross-sectional and longitudinal associations may exist between dietary exposures [e.g., caffeine (mg/d), alcohol (g/d), and nutrient adequacy] and cognitive performance and change over time. This was a prospective cohort study, the Baltimore Longitudinal Study of Aging (n = 628-1305 persons depending on the cognitive outcome; ~2 visits/person). Outcomes included 10 cognitive scores, spanning various domains of cognition. Caffeine and alcohol intakes and a nutrient adequacy score (NAS) were estimated from 7-d food diaries. Among key findings, caffeine intake was associated with better baseline global cognition among participants with a baseline age (Agebase) of ≥70 y. A higher NAS was associated with better baseline global cognition performance (overall, women, Agebase <70 y), better baseline verbal memory (immediate and delayed recall, Agebase ≥70 y), and slower rate of decline or faster improvement in the attention domain (women). For an Agebase of <70 y, alcohol consumption was associated with slower improvement on letter fluency and global cognition over time. Conversely, for an Agebase of ≥70 y and among women, alcohol intake was related to better baseline attention and working memory. In sum, patterns of diet and cognition associations indicate stratum-specific associations by sex and baseline age. The general observed trend was that of putative beneficial effects of caffeine intake and nutrient adequacy on domains of global cognition, verbal memory, and attention, and mixed effects of alcohol on domains of letter fluency, attention, and working memory. Further longitudinal studies conducted on larger samples of adults are needed to determine whether dietary factors individually or in combination are modifiers of cognitive trajectories among adults. [ABSTRACT FROM AUTHOR]

• We demonstrate the use of multilevel modeling to examine variability in driving behaviors. • Distracted driving varied within and between drivers in a naturalistic driving study. • Cell phone use and reaching behaviors varied more across trips than across people. • Simulated daily sleep demonstrates how predictors of driving can vary within a person. Current methods of analyzing data from naturalistic driving studies provide important insights into real-world safety-related driving behaviors, but are limited in the depth of information they currently offer. Driving measures are frequently collapsed to summary levels across the study period, excluding more fine-grained differences such as changes that occur from trip to trip. By retaining trip-specific data, it is possible to quantify how much a driver differs from trip to trip (within-person variability) in addition to how he or she differs from other drivers (between-person variability). To the authors' knowledge, the current study is the first to use multilevel modeling to quantify variability in distracted driving behavior in a naturalistic dataset of older drivers. The current study demonstrates the utility of examining within-person variability in a naturalistic driving dataset of 68 older drivers across two weeks. First, multilevel models were conducted for three distracted driving behaviors to distinguish within-person variability from between-person variability in these behaviors. A high percentage of variation in distracted driving behaviors was attributable to within-person differences, indicating that drivers' behaviors varied more across their own driving trips than from other drivers (ICCs =.93). Then, to demonstrate the utility of personal characteristics in predicting daily driving behavior, a hypothetical model is presented using simulated daily sleep duration from the previous night to predict distracted driving behavior the following day. The current study demonstrates substantial variability in driving behaviors within an older adult sample and the promise of individual characteristics to provide better prediction of driving behaviors relevant to safety, which can be applied in investigations of current naturalistic driving datasets and in designing future studies. [ABSTRACT FROM AUTHOR]

Rationale Neighborhood characteristics may influence health and well-being outcomes through stressors in daily life. Objectives This study tested whether a varied set of perceived characteristics of neighborhood (i.e., social cohesion, safety, aesthetic quality, violence) predicted stressor frequency and severity as well as negative emotional responses to stressors. We predicted greater reported cohesion and safety and less violence would be associated with less frequent stressor exposure and severity and less intense negative affect following stressors; we conducted subsequent tests of neighborhood aesthetic quality as a predictor. Methods Participants ( n = 233, age 25–65 years) were residents in a socio-economically, racially, and ethnically diverse zip code in Bronx, New York, most who participated in the Effects of Stress on Cognitive Aging, Physiology and Emotion study between 2012 and 2013. They provided demographic information and neighborhood ratings, then participated in the EMA protocol in which they completed brief smartphone surveys of current negative affect and stressor exposure, severity, and recency, five times daily for 14 days. Results No coded neighborhood characteristic was related to the frequency of stressors. Individuals who reported greater neighborhood violence, however, rated their stressors as more severe. Individuals rating their neighborhood lower in safety or aesthetic quality, or higher in violence, had greater negative affect following stressors. Conclusion Even among people living within the same zip code, individual differences in perceptions of neighborhood predict how stressful they appraised stressors in daily life to be and how much negative affect they reported following stressors. [ABSTRACT FROM AUTHOR]

Aging populations are at increased risk of sleep deficiencies (e.g., insomnia) that are associated with a variety of chronic health risks, including Alzheimer's disease and related dementias (ADRD). Insomnia medications carry additional risk, including increased drowsiness and falls, as well as polypharmacy risks. The recommended first-line treatment for insomnia is cognitive behavioral therapy for insomnia (CBTi), but access is limited. Telehealth is one way to increase access, particularly for older adults, but to date telehealth has been typically limited to simple videoconferencing portals. While these portals have been shown to be non-inferior to in-person treatment, it is plausible that telehealth could be significantly improved. This work describes a protocol designed to evaluate whether a clinician-patient dashboard inclusive of several user-friendly features (e.g., patterns of sleep data from ambulatory devices, guided relaxation resources, and reminders to complete in-home CBTi practice) could improve CBTi outcomes for middle- to older-aged adults (N = 100). Participants were randomly assigned to one of three telehealth interventions delivered through 6-weekly sessions: (1) CBTi augmented with a clinician-patient dashboard, smartphone application, and integrated smart devices; (2) standard CBTi (i.e., active comparator); or (3) sleep hygiene education (i.e., active control). All participants were assessed at screening, pre-study evaluation, baseline, throughout treatment, and at 1-week post-treatment. The primary outcome is the Insomnia Severity Index. Secondary and exploratory outcomes span sleep diary, actiwatch and Apple watch assessed sleep parameters (e.g., efficiency, duration, timing, variability), psychosocial correlates (e.g., fatigue, depression, stress), cognitive performance, treatment adherence, and neurodegenerative and systemic inflammatory biomarkers. • Insomnia disorder is common in middle-aged and older adults. • Cognitive behavioral therapy for insomnia is underutilized in healthcare. • Teletherapy may improve treatment implementation, dissemination, and adherence. • Mobile app linked Smart devices and clinician dashboard may facilitate treatment. • Randomized clinical trial investigating mobile app on insomnia treatment outcomes. [ABSTRACT FROM AUTHOR]