Background: Although an efficacious dementia-risk score system, Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) was derived using midlife risk factors in a population with low educational attainment that does not reflect today’s US population, and requires laboratory biomarkers, which are not always available. Objective: Develop and validate a modified CAIDE (mCAIDE) system and test its ability to predict presence, severity, and etiology of cognitive impairment in older adults. Methods: Population consisted of 449 participants in dementia research (N = 230; community sample; 67.9±10.0 years old, 29.6%male, 13.7±4.1 years education) or receiving dementia clinical services (N = 219; clinical sample; 74.3±9.8 years old, 50.2%male, 15.5±2.6 years education). The mCAIDE, which includes self-reported and performance-based rather than blood-derived measures, was developed in the community sample and tested in the independent clinical sample. Validity against Framingham, Hachinski, and CAIDE risk scores was assessed. Results: Higher mCAIDE quartiles were associated with lower performance on global and domain-specific cognitive tests. Each one-point increase in mCAIDE increased the odds of mild cognitive impairment (MCI) by up to 65%, those of AD by 69%, and those for non-AD dementia by > 85%, with highest scores in cases with vascular etiologies. Being in the highest mCAIDE risk group improved ability to discriminate dementia from MCI and controls and MCI from controls, with a cut-off of ≥7 points offering the highest sensitivity, specificity, and positive and negative predictive values. Conclusion: mCAIDE is a robust indicator of cognitive impairment in community-dwelling seniors, which can discriminate well between dementia severity including MCI versus controls. The mCAIDE may be a valuable tool for case ascertainment in research studies, helping flag primary care patients for cognitive testing, and identify those in need of lifestyle interventions for symptomatic control.

Accurate and scalable gait assessment is essential for clinical and research applications, including fall risk evaluation, rehabilitation monitoring, and early detection of neurodegenerative diseases. While electronic walkways remain the clinical gold standard, their high cost and limited portability restrict widespread use. Wearable inertial measurement units (IMUs) and markerless depth cameras have emerged as promising alternatives; however, prior studies have typically assessed these systems under tightly controlled conditions, with single participants in view, limited marker sets, and without direct cross-technology comparisons. This study addresses these gaps by simultaneously evaluating three sensing technologies—APDM wearable IMUs (tested in two separate configurations: foot-mounted and lumbar-mounted) and the Azure Kinect depth camera—against ProtoKinetics Zeno™ Walkway Gait Analysis System in a realistic clinical environment where multiple individuals were present in the camera’s field of view. Gait data from 20 older adults (mean age $70.06\pm 9.45$ years) performing Single-Task and Dual-Task walking trials were synchronously captured using custom hardware for precise temporal alignment. Eleven gait markers spanning macro, micro-temporal, micro-spatial, and spatiotemporal domains were compared using mean absolute error (MAE), Pearson correlation (r), and Bland–Altman analysis. Foot-mounted IMUs demonstrated the highest accuracy (MAE $=0.00$–$6.12$, $r=0.92$–$1.00$), followed closely by the Azure Kinect (MAE $=0.01$–$6.07$, $r=0.68$–0.98). Lumbar-mounted IMUs showed consistently lower agreement with the reference system. These findings provide the first comprehensive comparison of wearable and depth-sensing technologies with a clinical gold standard under real-world conditions and across an extensive set of gait markers. The results establish a foundation for deploying scalable, low-cost gait assessment systems in diverse healthcare contexts, supporting early detection, mobility monitoring, and rehabilitation outcomes across multiple patient populations.

Purpose: To assess age, sex, race and ethnicity disparities in cognitive function in community-dwelling older adults and identify factors that contribute to these disparities. Patients and Methods: Cognitive performance (global and domain-specific) and self-reported cognitive function were compared among Black (N=57), Hispanic (N=139), and White (N=108) older adults. The impact of socioeconomic status (SES), physical functionality, and mood indicators was assessed with a combination of hierarchical general linear models and mediation analysis. Results: Poorer cognitive performance and higher levels of impairment were found in older adults from racial and ethnic backgrounds. The contribution of lower SES to the observed racial and ethnic disparities in objective cognitive performance was 33% in Hispanics and about 20% in Blacks, while poorer physical functionality explained over half of the differences between Black and White participants. Higher self-reported cognitive impairment in minorities was explained by lower SES and higher depressive symptoms in Hispanics but not in Blacks. Conclusion: Performance on objective memory testing and self-reported cognition are greatly influenced by relevant biological, sociodemographic and medical variables. Dementia screening programs should be tailored to individual sociodemographic groups based on contributors that are specific to each group.

A random digit dialing sample from Missouri (USA) was used to compare associations between psychosocial factors and Alzheimer’s disease (AD) screening intention based on residential locale. Linear regression associations between demographics and five psychosocial constructs (dementia knowledge, perceived screening benefits, preventive health behaviors, perceived susceptibility, and self-efficacy) and screening intention were compared by residential locale. Participants (n = 932) had a mean age of 62 years (urban: n = 375; suburban: n = 319, rural: n = 238). African Americans more often lived in urban than suburban/rural neighborhoods, and more urban than suburban/rural residents reported insufficient income. Preventative health behaviors (e.g., dentist visits) were higher in urban and suburban versus rural participants. AD screening intention did not differ by residential locale. Among urban participants, self-efficacy to get screened was associated with screening intention. Among rural participants, dementia knowledge was associated with screening intention. Perceived screening benefits and perceived susceptibility to AD were associated with screening intention regardless of locale. Unlike urban participants, rural participants demonstrated greater screening intention with greater dementia knowledge. Our findings suggest that psychosocial factors associated with AD screening intention differ depending on residential locale. Strategies to increase dementia screening may need to account for regional variations to be maximally effective.

Background: Healthy lifestyles such as regular exercise, diet, social and cognitive activity may reduce dementia risk by helping build greater cognitive reserve across the life course, promoting resilience to neuropathology and better cognitive outcomes in late‐life. However, tracking these health behaviors throughout the life‐course (age 25, 40, 65) is difficult. We captured self‐reported health behaviors through the life‐course and evaluated their relationship with cognitive performance, resilience, and disease biomarkers in late‐life. Method: In 378 adults aged 50+yrs, we assessed the impact of change in physical activity (PA), cognitive activity (CA), social activity (SA), and diet from age 25 to present age on global cognitive performance (MoCA), resilience (Resilience Index), and biomarkers (plasma ptau217% ratio, NFL, GFAP; MR volumetric data). Behaviors were categorized as improved/no change vs declined and pairs of lifestyle behavior (e.g., physical activity and diet) were categorized as improved/no change in both, improved/no change in one, vs decline in both. Relationships between healthy behavioral change and MoCA, resilience, and biomarkers, and mediation of relationships between behavioral changes and MoCA were tested with age‐adjusted GLM models. Result: Self‐reported healthy change in diet from age 25 was positively associated with MoCA score (β=1.51±0.48;p=0.002), accounting for age differences and multiple comparisons. A synergistic effect was found when changes in diet were combined with healthy changes in PA (β=3.20±0.76;p<0.0001) and SA (β=2.74±0.71;p<0.0001). Healthy changes in PA (β=13.79±4.23;p=0.001) and SA (β=17.93±3.97; p <0.0001) were related to resilience and their effects were synergistically increased when healthy changes in diet were also reported. Beta coefficients were reduced by 15‐43% in resilience mediation models. Amygdala volume was positively associated with healthy changes in SA (β=0.13±0.05;p=0.007) with a trend for greater amygdala (β=0.10±0.05;p=0.054) and entorhinal area volume (β=0.24±0.14;p=0.088) in those reporting healthy change in PA. Conclusion: Findings suggest that improving/maintaining diet into late‐life, alone and especially in combination with improved physical and social activity level, increases resilience to neurodegenerative diseases and leads to better cognitive performance in later life. Evidence of greater amygdala volume in those reporting improved social and physical activity provides further support for the beneficial effect of maintaining lifelong positive lifestyle practices on brain health.

Background: Gait and balance deficiencies may be important indicators of cognitive impairment, distinguishing dementia from normal cognition (NC). It is unclear whether this extends to pre‐dementia stages of disease. Study goals were to: assess patterns of mobility across early stages of disease and identify specific measures that distinguish individuals with subjective cognitive impairment (SCI) and mild cognitive impairment (MCI).Method: We assessed whether mobility differentiates SCI (n = 35) from NC (n = 78) and MCI (n = 41). Using ANCOVA, we compared diagnostic groups on global (mini‐PPT; TUG) and individual (gait speed (GS), flexibility, balance, step length, single‐leg support) measures of mobility, controlling for age and baseline physical activity levels. Group separation was evaluated with logistic regression. Group differences in mobility while distinguishing between MCI etiology (AD vs non‐AD) and relationships between mobility and MRI cortical atrophy score (CAS) were analyzed and results validated against biomarker‐confirmed diagnoses (true controls/preclinical/MCI).Result: Participants were 69±9yr old, highly educated (16±3y), mostly non‐Hispanic White (77%), and performed within normal parameters for mobility. Mobility performance decreased with disease stage (MCI>SCI>NC). All mobility measures distinguished MCI from NC (p<0.01). Dual task mobility distinguished MCI from SCI (e.g., OR_TUG = 1.24, 95%CI:1.055‐1.457). AD vs non‐AD etiology accounted for most group differences in mobility. Higher mini‐PPT score was linked to lower odds of SCI vs NC (OR = 0.705 (0.543‐0.916)) stemming from significantly poorer balance in SCI (β = ‐0.3±0.1, p = 0.041). Lower GS explained MCI differences from NC (β = ‐0.1±0.03, p = 0.003) and SCI (β = ‐0.1±0.04, p = 0.027). Results were not explained by differences in age and physical activity. Small‐to‐moderate correlations between CAS and mobility (e.g., r_{TUG dual} = 0.34, p<0.001) were found. In analyses using biomarker‐confirmed diagnoses, mobility was lower in MCI vs true controls (all p<0.01) and vs preclinical disease (p<0.05), with a trend for preclinical vs true controls (p = 0.054) validating findings on clinical diagnoses.Conclusion: Mobility parameters can distinguish NC from SCI and MCI highlighting: 1) dual task testing and subtle changes in balance help detect early cognitive impairments; 2) changes in GS mark more significant cognitive impairment. Mobility testing may be an important tool in early detection of ADRD in the clinical setting and can help identify targets for early intervention.