INTRODUCTION:We investigated the association between retirement, age at retirement, and domain-specific cognitive decline. METHODS:Three hundred twenty-one participants aged 65+ from the Study of Healthy Aging in African Americans completed verbal episodic memory (VEM) and executive function (EF) assessments across three waves (every 16 months). Linear mixed-effects models with random intercepts and slopes examined associations of retirement status (retired, not retired) and timing (retired before age 65, retired at 65+, and not retired) with cognitive decline adjusting for demographics and parental education. RESULTS:Participants' mean age was 74.6 ± 6.9, 65% were women, and 87% were retired (33% at age 65+). Retirement status and timing were not associated with baseline cognition. Over 2.11 ± 0.66 years, non-retirees had slower EF decline than retirees (b = 0.11 [0.03, 0.19]). Participants who retired at 65+ had slower VEM decline than those who retired before 65 (b = 0.11 [0.02, 0.21]). DISCUSSION:Retirement and early retirement were associated with faster cognitive decline among Black/African American adults.

To support aging-in-place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug-and-play remote care solutions for privacy-preserving continuous monitoring that enabling real-time activity monitoring and intuitive, actionable information. This short paper presents insights from three iterations of deployment experience for remote monitoring system and the iterative improvement in hardware, modeling, and user interface guided by the Geriatric 4Ms framework (matters most, mentation, mobility, and medication). An LLM-assisted solution is developed to balance user experience (privacy-preserving, plug-and-play) and system performance.

Millions of people live with cognitive impairment from Alzheimer's disease and related dementias (ADRD). Voice-enabled smart home systems offer promise for supporting daily living but rely on automatic speech recognition (ASR) to transcribe their speech to text. Prior work has shown reduced ASR performance for adults with cognitive impairment; however, the acoustic factors underlying these disparities remain poorly understood. This paper evaluates ASR performance for 83 older adults across cognitive groups (cognitively normal, mild cognitive impairment, dementia) reading commands to a voice assistant (Amazon Alexa). Results show that ASR errors are significantly higher for individuals with dementia, revealing a critical usability gap. To better understand these disparities, we conducted an acoustic analysis of speech features and found that a speaker's intensity, voice quality, and pause ratio predicted ASR accuracy. Based on these findings, we outline HCI design implications for AgeTech and voice interfaces, including speaker-personalized ASR, human-in-the-loop correction of ASR transcripts, and interaction-level personalization to support ability-based adaptation.

Individuals with Alzheimer's disease (AD) and Alzheimer's disease-related dementia (ADRD) experience memory and thinking changes that impact their ability to use digital daily management tools. For example, adding an event to a digital calendar requires multiple steps that may act as barriers to independent use for individuals with AD/ADRD. This paper presents AI-Care, a conversational agentic artificial intelligence (AI) layer built on top of a remote caregiving platform co-designed with people with AD/ADRD. AI-Care is designed to reduce the cognitive load on individuals with AD/ADRD when managing everyday tasks such as setting calendar reminders and organizing to-do lists through natural-language interaction with a voice-first chatbot. The system uses a LangGraph-based stateful orchestration approach in which each request passes through sanitization, intent classification, context loading, safety checks, deterministic slot collection, tool execution, and response composition. Safety-critical responses, particularly around medications and allergies, are grounded in caregiver-verified records rather than free-form model generation. The system does not make autonomous medical or treatment decisions. Incomplete or ambiguous requests are handled through controlled multi-turn clarification rather than silent failure or guessing. The system supports both typed and spoken input, with voice output through ElevenLabs text-to-speech. Longer responses are chunked before synthesis to avoid rushed playback. A preliminary pilot with four individuals with mild-to-moderate AD/ADRD showed that users found the system trustworthy, competent, and likable, and were able to complete the evaluated coordination tasks through conversation. We describe the design goals, system architecture, safety controls, and findings from this formative evaluation.

INTRODUCTION:This study evaluated a multidomain 6-month intervention combining training in memory strategies (e.g., goal setting, to-do lists, calendar scheduling) and lifestyle modifications (physical exercise, cognitive/social engagement, well-being exercises) supported by a digital application. The target intervention group is compared to an education only group. METHODS:Participants were 270 older adults with subjective cognitive decline. Primary outcomes included global cognition and everyday function. Secondary outcomes included engagement in health behaviors, compensation strategy use, and other measures of health and well-being. We also examined baseline participant characteristics that predicted treatment response based on components of Self-Determination Theory (competence, relatedness, autonomous motivation), health literacy, demographics, and baseline dementia risk. Using an intent-to-treat framework, multiple linear regression models evaluated intervention effectiveness. RESULTS:At 6 months, no intervention group differences in cognition or everyday function were detected. However, the intervention group demonstrated significantly better well-being outcomes (i.e., gratitude) (p = .04). Lower baseline competence and autonomous motivation were associated with greater intervention effects on global cognition (ps <.04) and self-perceived stress (ps <.05), while higher autonomous motivation was associated with greater effects on everyday functioning (p = .026). Less education was associated with greater intervention benefits on social activity engagement (p = .03). Lower baseline dementia risk was associated with greater intervention benefits on moderate physical activity (p = .01). DISCUSSION:Combined training in healthy lifestyles and cognitive compensation may enhance aspects of emotional well-being. Further, particular participant characteristics may help determine who most benefits from this type of dementia risk reduction intervention.

BACKGROUND:Millions of people now regularly talk to voice technology (e.g., Siri, Alexa), which has sparked an interest in detecting a person's cognitive status from these interactions. While a slower speech rate has emerged as a potential behavioral biomarker of cognitive impairment, the vast majority of prior studies have examined human-directed tasks (e.g., describing a picture to an experimenter). However, our related work has shown that people slow their rate when talking to technology, compared to a real person. The current study investigates whether speech rate is a reliable biomarker of cognitive impairment in technology-directed tasks. METHODS:We analyzed recordings from the Voice Assistant System (VAS) corpus (Liang et al., 2022) wherein participants read commands to an Alexa voice assistant (e.g., "Alexa, what is today's date?")(see Figure 1). Participants (n = 89, ages 65-94, 46 women, 44 men) varied in MoCA score: as neurotypical (n = 30; MoCA 26+), having mild cognitive impairment (n = 35; MoCA 20-25), or having dementia (n = 24; MoCA < 20). We measured utterance-level speech rate (mean number of syllables per second) using a Praat script and modeled it with a mixed effects regression. Predictors included MoCA score, Trial, Gender, Age (and all possible interactions), as well as Geriatric Depression Scale, Generalized Anxiety Score, and Previous Voice Technology Experience (yes, no). Random effects included by-Participant and by-Command random intercepts and by-Participant random slopes for Trial Number. RESULTS:Individuals with higher MoCA scores produced their Alexa-directed commands with a faster speech rate [Coef = 0.02, p < 0.01] (see Figure 2A). There was also an effect of Gender: women tended to produce commands at a faster rate than men [Coef = 0.07, p < 0.05]. There were no other significant effects. However, there was considerable variation across participants in their speech rate for commands, leading to overlapping distributions for neurotypical adults and individuals with mild cognitive impairment and dementia (see Figure 2B). CONCLUSIONS:This study suggests that a reduced speech rate is associated with cognitive impairment in technology-directed commands, indicating that a slower rate might be a sensitive, albeit nonspecific, behavioral biomarker.

Millions of people now regularly talk to voice technology (e.g., Siri, Alexa), which has sparked an interest in detecting a person's cognitive status from these interactions. While a slower speech rate has emerged as a potential behavioral biomarker of cognitive impairment, the vast majority of prior studies have examined human-directed tasks (e.g., describing a picture to an experimenter). However, our related work has shown that people slow their rate when talking to technology, compared to a real person. The current study investigates whether speech rate is a reliable biomarker of cognitive impairment in technology-directed tasks. We analyzed recordings from the Voice Assistant System (VAS) corpus (Liang et al., 2022) wherein participants read commands to an Alexa voice assistant (e.g., “Alexa, what is today's date?”)(see Figure 1). Participants ( n  = 89, ages 65-94, 46 women, 44 men) varied in MoCA score: as neurotypical ( n  = 30; MoCA 26+), having mild cognitive impairment ( n  = 35; MoCA 20-25), or having dementia ( n  = 24; MoCA < 20). We measured utterance-level speech rate (mean number of syllables per second) using a Praat script and modeled it with a mixed effects regression. Predictors included MoCA score, Trial, Gender, Age (and all possible interactions), as well as Geriatric Depression Scale, Generalized Anxiety Score, and Previous Voice Technology Experience (yes, no). Random effects included by-Participant and by-Command random intercepts and by-Participant random slopes for Trial Number. Individuals with higher MoCA scores produced their Alexa-directed commands with a faster speech rate [Coef = 0.02, p  < 0.01] (see Figure 2A). There was also an effect of Gender: women tended to produce commands at a faster rate than men [Coef = 0.07, p  < 0.05]. There were no other significant effects. However, there was considerable variation across participants in their speech rate for commands, leading to overlapping distributions for neurotypical adults and individuals with mild cognitive impairment and dementia (see Figure 2B). This study suggests that a reduced speech rate is associated with cognitive impairment in technology-directed commands, indicating that a slower rate might be a sensitive, albeit nonspecific, behavioral biomarker.

Compensation has the potential to improve everyday functioning and delay or prevent conversion to dementia; thus, improvement of compensation is a desirable intervention target. In addition to cognition, research suggests personality and mood may affect compensation use. The primary aim of this study was to evaluate whether psychological factor(s) are significant predictors of compensation after accounting for cognition in a non-demented older adult sample. A secondary aim was to examine if there were differences in outcome depending upon whether self or informant report of compensation was included in the model. Participants included 100 cognitively healthy older adults and 26 individuals with mild cognitive impairment (N = 126; age M = 76.67, SD = 6.79; education M = 15.58, SD = 3.03) who completed the Revised NEO Personality Inventory (used to assess Agreeableness, Neuroticism, Openness, Conscientiousness, and Extraversion) and self-report measures from the National Institutes of Health (NIH) Toolbox (used to assess Sadness, Self-efficacy, Anger, and Positive affect). A measurement of Grit was also completed. Self- and informant-report from the Everyday Compensation (EComp) Scale were used as measures of compensation. Linear regression, controlling for cognition (Spanish and English Neuropsychological Assessment Scale Composite) was used to predict compensation (self or informant). For self-reported compensation, anger was associated with more compensation independent of cognition (B = .18, t = 2.60, p = .01). For informant-report, Openness was associated with greater compensation (B = .20, t = 2.02, p = .05). Participants with higher scores in Anger and Openness had increased compensation after accounting for cognitive status. A higher degree of Openness indicates a person is willing to try new things, which may result in an increased likelihood of trying compensatory strategies. While it is somewhat counterintuitive that greater negative emotion was associated with more compensation, anger is an activating and externalizing emotion which could facilitate compensation in response to worry about cognitive change. Results suggest that interventions should focus on ways to improve openness and activation to enhance compensation.

South Asian (SA) family caregivers living in the U.S. are understudied despite being a fast-growing population with a disproportionate risk of Alzheimer's disease. The primary aim of this study was to characterize care practices, preferences, and psychological wellbeing among SA caregivers providing care to older generation family members. Given the collectivist nature of SA cultures, we expected most caregivers to live with their care partners and prefer similar, family-based care for their future. Community outreach and an Asian American caregiver registry (CARES) were used for recruitment. Participants completed questionnaires asking about caregiving practices (e.g., frequency, involvement), perspective on their personal future care (evaluated on a 1-5 scale where 1=strongly agree and 5=strongly disagree), and caregiver stress/strain (Zarit Burden Interview). Participants included 14 SA caregivers (age M  = 49.5, SD  = 15.5; education M  = 17.1, SD  = 2.3,). Seventy-one percent identified as 1 st generation immigrant, 21% as 2 nd generation, and 7% did not specify. Most caregivers provided care to parents/in-laws (79%). Common tasks requiring assistance were transportation (100% of caregivers surveyed) medical appointments (93%), cooking and medication management (71% for both). Seventy-one percent provided physical support, whereas 57% provided support for cognitive limitations. Regarding future care, 43% of participants want to live with their family (non-spouse) in the future, and this desire was significantly correlated with currently co-residing with their care partner ( r  = -.68, p  = .01). Sixty-four percent indicate they would like to live alone/with spouse and 54% desire to not live in a retirement community. Mild to moderate levels of stress/strain were endorsed overall ( M  = 14.2, SD  = 9.4), and were significantly correlated with a future care preference of living alone/with spouse ( r  = -.820, p  < .001). In our sample, SA caregivers primarily live with the older generation family member they care for. Although less than half of the participants desire to live with family beyond their spouse in the future, currently providing in-home care increased likelihood of wanting similar care. Importantly, caregiver stress/strain strongly increased desire to live alone/with spouse. These findings suggest that caregiver wellbeing influences desires for future care, potentially resulting in perspectives that are not in-line with cultural values. As such, culturally tailored interventions are recommended to meet everyday care needs.

This study aimed to investigate the relationship between cognition, Alzheimer's disease (AD) biomarkers, and instrumental activities of daily living (IADL) performance among cognitively normal (CN) older adults, to identify key predictors of IADL performance. A better understanding of the combined influence of cognitive function and AD pathology on IADL performance in CN older adults could lead to the development of more precise functional screening tools for preclinical AD, improved functional outcome measures for AD and aging, and the development of interventions designed to maintain or improve IADL performance. CN older adults ( n  = 173) performed three IADL tasks (shopping, checkbook balancing, medication management) from the Performance Assessment of Self-Care Skills in their home. Cognitive and AD biomarker assessments were completed in an academic medical center. Amyloid and tau positron emission tomography (PET) were used to measure AD pathology. IADL performance was transformed into one composite score and dichotomized (better vs. worse, split at median). Cognitive assessments were transformed to create composites: Global, Episodic Memory, Semantic Memory, Attention & Processing, Working Memory, and Visuospatial. Logistic regression models were used to examine cognitive and biomarker predictors of IADL performance while controlling for demographic covariates. Cognitive domains, particularly the Global composite (OR=0.34, p  < 0.001) and Attention & Processing (OR=0.40, p  = 0.001), emerged as strong predictors of IADL performance. Higher amyloid burden (OR=1.82, p  = 0.015) was associated with worse IADL performance and tauopathy (OR=1.61, p  = 0.060) demonstrated a trend toward association. Comparison of predictive models identified additive influences of cognition and amyloid in predicting IADL performance. These findings suggest that cognitive function, particularly Global and Attention & Processing domains, and AD pathological burden play significant, complementary roles in IADL performance among CN older adults. Findings may be used to inform the development of sensitive functional assessments in preclinical AD that focus on key deficits, and later to develop interventions targeting important modifiable factors and compensatory rehabilitative methods to improve IADL performance in everyday life.

One in four people dementia live alone, leading family members to take on caregiving roles from a distance. Many researchers have developed remote monitoring solutions to lessen caregiving needs; however, limitations remain including privacy preserving solutions, activity recognition, and model generalizability to new users and environments. Structural vibration sensor systems are unobtrusive solutions that have been proven to accurately monitor human information, such as identification and activity recognition, in controlled settings by sensing surface vibrations generated by activities. However, when deploying in an end user's home, current solutions require a substantial amount of labeled data for accurate activity recognition. Our scalable solution adapts synthesized data from near-surface acoustic audio to pretrain a model and allows fine tuning with very limited data in order to create a robust framework for daily routine tracking.

Compensation (e.g., alarm setting) has the potential to improve or maintain everyday functioning and delay conversion to dementia. Various psychological characteristics may impact compensation and therefore could inform intervention development and response to intervention. This study aimed to evaluate whether personality and affective characteristics are associated with compensation after accounting for cognition in older adults without dementia. Measures of compensation (both self-report and informant-report), cognition, personality (neuroticism, openness, extraversion, conscientiousness and agreeableness), grit, self-efficacy, positive affect, and two types of negative affect (sadness, anger) were administered to a sample of 126 older adults (mean age = 76.6; mean education = 15.5 years; 61% female; 45% non-White). In analyses using the entire sample, higher ratings of anger were associated with self-reported compensation. In follow-up analyses stratified by ethnoracial group, neuroticism was a predictor of self-reported compensation in non-Hispanic White participants and openness was a predictor of informant-reported compensation in non-White participants. Our findings suggest that anger may facilitate activation to compensate for real or perceived cognitive change. Additionally, personality characteristics associated with compensation may vary across diverse ethnoracial groups and reporting source.

Purpose in life (PIL) refers to individuals’ derivation of meaning from life experiences, possession of a sense of direction and intentionality, and striving towards goals. PIL is associated with many positive health outcomes and reduced risk of cognitive decline and dementia in older adults. Importantly, PIL is potentially modifiable through intervention to reduce risk of Alzheimer’s disease and related disorders. However, it is a complex construct likely influenced by psychological, lifestyle, and demographic factors. This study examines factors contributing to PIL, with the long-range goal to inform the development of intervention approaches. This was a cross-sectional study of participants from the University of California Davis Alzheimer’s Disease Research Center Longitudinal Cohort with a PIL score (N = 191). The mean age was 76.3 (SD = 7.2). Most were female (65%) and cognitively normal (85.3%). A substantial percentage were from underrepresented groups (45%). Independent variables of interest were grit, positive affect, emotional support, self-efficacy, loneliness, and participation in physical and recreational activities. Covariates included age, sex, race, and education. Individual and joint linear regression models were run to examine associations. All variables were significantly associated with PIL in bivariate models, but only positive affect was associated with higher PIL in the joint model including all independent variables and covariates (B = 0.478, 95% CI [0.362, 0.655], p < .001). Loneliness was associated with lower PIL (B = -0.18, 95% CI [-0.384, -0.053], p = .01). Psychological, lifestyle, and demographic factors are important to consider in their association with PIL. Our findings showed that positive affect was associated with higher PIL and loneliness with lower PIL. These results have implications for future prevention and interventions to increase PIL in older adults, which may improve cognition and health. There are currently limited interventions that explicitly target PIL, and they are mostly in the cancer population. Conversely there are several evidence-based approaches to increase positive affect that may also increase sense of PIL. Additionally, the association between loneliness and PIL suggests that increasing social connection may also bolster PIL.

Alzheimer’s disease (AD) remains one of the most distressing public health challenges of our time, creating a critical need for tools that serve both care recipients and caregivers, especially in remote care settings. Interactive-Care (I-Care) is an innovative web-based remote caregiving platform designed to promote independence in AD patients while bridging both the physical and emotional gaps in caregiving. In this paper, we focus on I-Care’s calendar tool, developed to overcome the challenges presented by commonly used digital calendar platforms which impose high cognitive load and cause confusion among individuals with AD. We describe the iterative co-design process through which the calendar evolved, informed by multiple rounds of feedback and refinement.Participants/Methods: First, a calendar prototype was developed based on cognitive rehabilitation guidelines and existing calendar systems for individuals with mild cognitive impairment. The prototype was reviewed by experts in AD and dyads (care receiver and remote caregiver) who provided feedback and suggested modifications. The prototype was iteratively modified using this review-feedback-modification process 3 times. Next, two older adults (ages 84-88) with mild dementia (Montreal Cognitive Assessments of 19-20) participated in an iterative co-design process over the course of several interactions with the Calendar page. To quantitatively evaluate improvements, we conducted counterbalanced A/B testing comparing the pre-co-design and co-designed versions of the Calendar and additionally benchmarked its usability against Google Calendar. Participants also completed a custom Technology Acceptance Model (TAM) questionnaire that included Likert ratings (1-5, 5 being the highest) of Perceived Ease of Use, Perceived Usefulness, and Intention to Use. Results: Successive Calendar design refinements incorporated a shaded column highlighting the current day, step-by-step pop-up workflow for event creation, flashing notifications for new calendar entries, and multiple concomitant alarm options. The co-designed Calendar received high average TAM ratings in terms of Perceived Ease of Use = 5.0, Perceived Usefulness = 4.8, and Intention to Use = 5.0, indicating strong acceptance and usability. A/B testing also demonstrated substantial improvements. In the previous interface, built similarly to Google Calendar, participants were unable to complete key tasks without assistance. In contrast, with the co-designed Calendar, all tasks were completed independently, with a reduction in event creation time from 252 seconds to 94 seconds. Navigation between weeks and selecting today’s date also became faster and more accurate. Participants reported substantially higher satisfaction with co-design Calendar compared to the prior version, citing ease of navigation and clarity of visual cues. In contrast, Google Calendar task completion elicited very poor satisfaction ratings, with one participant refusing to continue using it due to its complexity.Conclusions: The I-Care Calendar design process demonstrates that individuals with cognitive impairment can engage in co-design to good effect resulting in a Calendar they can use independently. High satisfaction ratings highlight its clarity, intuitive design, and accessibility, emphasizing the value of tailoring digital tools to the cognitive needs of older adults. These findings underscore the importance of a co-design approach in developing assistive technologies that support daily routines, autonomy, and overall quality of life for older adults with cognitive impairments.

BACKGROUND:One-in-four people with Alzheimer's disease (AD) desire to age-in-place alone. Due to cognitive and functional declines, adult children of individuals with AD often take on remote caregiving roles. Recent advancements in monitoring technology provide new opportunities for remote caregiving; however, limitations, particularly for detecting information about activities of daily living (ADLs) remain. To address this gap, we evaluated the potential of structural vibration technology to detect both coarse- (e.g., activity label - hand washing) and fine- (e.g., specific step - turn water on) grained ADL information. METHOD:Our novel approach to ADL detection uses geophone sensors and machine learning (ML) to recognize unique vibration patterns relating to a given activity. We collected controlled data in a 1-bedroom simulation apartment testbed equipped with video cameras and sensors placed on the floor of the living room, bedroom, bathroom, and kitchen. As we were particularly interested in ADLs involving self-care, an additional sensor was placed on the bathroom counter (see Figure 1). Ten young adult participants each completed 30 total sequences involving 3 (of possible 10) activities (e.g., wash hands, brush teeth, take medication). The dataset was manually labeled based on the video ground truth and applied to the vibration sensing data. RESULT:Pairing our ground truth labels with the vibration data, we were able to automatically detect activities including walking, talking, and medication taking in real time (Figure 1). Furthermore, we were able to identify specific fine-grained actions of interest within a given activity (see Figure 2). Finally, we used Fast Fourier Transform t-Stochastic Neighborhood Embedding for dimensionality reduction and visualization of the dataset. As shown in Figure 3, specific activities clustered closely together suggesting they produced similar vibration patterns over multiple sequences. CONCLUSION:We present an innovative approach to detecting ADLs using unobtrusive vibration sensing technology. Results suggest that we can detect both when any activity occurres and the specific actions that contribute to the activity. As such, vibration sensing is a promising tool for both intervention (e.g., prompting when error or omission occurs) and outcome purposes in ADRD clinical trial research.

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

INTRODUCTION:The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development. METHODS:The analyses included patients ≥ 65 years with AD diagnosis in six University of California hospitals between January 2012 and October 2023. The controls were race/ethnicity, sex, and age matched without dementia. Data analyses used the Cox proportional hazards model and machine learning (ML). RESULTS:Hispanic/Latino and Native Hawaiian/Pacific Islander, but not Black subjects, had increased AD risk compared to White subjects. Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men. While underweight increased AD risk, overweight or obesity reduced risk. ML confirmed the importance of metabolic laboratory tests in predicting AD development. DISCUSSION:The data stress the significance of metabolism in AD development and the need for racial/ethnic- and sex-specific preventive strategies. HIGHLIGHTS:Hispanics/Latinos and Native Hawaiians/Pacific Islanders show increased hazards of Alzheimer's disease (AD) compared to White subjects. Underweight individuals demonstrate a significantly higher hazard ratio for AD compared to those with normal body mass index. The association between obesity and AD hazard differs among racial groups, with elderly Asian subjects showing increased risk compared to White subjects. Alcohol consumption and non-infectious hepatitis are significant hazards for AD. Machine learning approaches highlight the potential of metabolic panels for AD prediction.

Many older adults report subjective cognitive decline (SCD); however, the specific types of complaints most strongly associated with early disease detection remain unclear. This study examines which complaints from the Everyday Cognition Scales (ECog) are associated with progression from normal cognition to mild cognitive impairment (MCI)/dementia. 415 older adults were monitored annually for 5 years, on average. Cox proportional hazards models assessed associations between ECog complaints and progression to MCI/dementia. Follow-up models included depression as a covariate. Numerous Memory (5 items), Language (3 items), Visuospatial (1 item), Planning (2 items), and Organization (1 item) complaints were associated with diagnostic progression. After covarying for depression, remembering appointments and understanding spoken instructions remained significant predictors of diagnostic progression. While previous work has focused largely on memory-based SCD complaints, the current findings support a wider assessment of complaints may be useful in identifying those at risk for a neurodegenerative disease.

BACKGROUND:Compensation has the potential to improve everyday functioning and delay or prevent conversion to dementia; thus, improvement of compensation is a desirable intervention target. In addition to cognition, research suggests personality and mood may affect compensation use. The primary aim of this study was to evaluate whether psychological factor(s) are significant predictors of compensation after accounting for cognition in a non-demented older adult sample. A secondary aim was to examine if there were differences in outcome depending upon whether self or informant report of compensation was included in the model. METHOD:Participants included 100 cognitively healthy older adults and 26 individuals with mild cognitive impairment (N = 126; age M = 76.67, SD = 6.79; education M = 15.58, SD = 3.03) who completed the Revised NEO Personality Inventory (used to assess Agreeableness, Neuroticism, Openness, Conscientiousness, and Extraversion) and self-report measures from the National Institutes of Health (NIH) Toolbox (used to assess Sadness, Self-efficacy, Anger, and Positive affect). A measurement of Grit was also completed. Self- and informant-report from the Everyday Compensation (EComp) Scale were used as measures of compensation. Linear regression, controlling for cognition (Spanish and English Neuropsychological Assessment Scale Composite) was used to predict compensation (self or informant). RESULT:For self-reported compensation, anger was associated with more compensation independent of cognition (B = .18, t = 2.60, p = .01). For informant-report, Openness was associated with greater compensation (B = .20, t = 2.02, p = .05). CONCLUSION:Participants with higher scores in Anger and Openness had increased compensation after accounting for cognitive status. A higher degree of Openness indicates a person is willing to try new things, which may result in an increased likelihood of trying compensatory strategies. While it is somewhat counterintuitive that greater negative emotion was associated with more compensation, anger is an activating and externalizing emotion which could facilitate compensation in response to worry about cognitive change. Results suggest that interventions should focus on ways to improve openness and activation to enhance compensation.

BackgroundCompensation has the potential to improve everyday functioning and delay or prevent conversion to dementia; thus, improvement of compensation is a desirable intervention target. In addition to cognition, research suggests personality and mood may affect compensation use. The primary aim of this study was to evaluate whether psychological factor(s) are significant predictors of compensation after accounting for cognition in a non-demented older adult sample. A secondary aim was to examine if there were differences in outcome depending upon whether self or informant report of compensation was included in the model. MethodParticipants included 100 cognitively healthy older adults and 26 individuals with mild cognitive impairment (N = 126; age M = 76.67, SD = 6.79; education M = 15.58, SD = 3.03) who completed the Revised NEO Personality Inventory (used to assess Agreeableness, Neuroticism, Openness, Conscientiousness, and Extraversion) and self-report measures from the National Institutes of Health (NIH) Toolbox (used to assess Sadness, Self-efficacy, Anger, and Positive affect). A measurement of Grit was also completed. Self- and informant-report from the Everyday Compensation (EComp) Scale were used as measures of compensation. Linear regression, controlling for cognition (Spanish and English Neuropsychological Assessment Scale Composite) was used to predict compensation (self or informant). ResultFor self-reported compensation, anger was associated with more compensation independent of cognition (B = .18, t = 2.60, p = .01). For informant-report, Openness was associated with greater compensation (B = .20, t = 2.02, p = .05). ConclusionParticipants with higher scores in Anger and Openness had increased compensation after accounting for cognitive status. A higher degree of Openness indicates a person is willing to try new things, which may result in an increased likelihood of trying compensatory strategies. While it is somewhat counterintuitive that greater negative emotion was associated with more compensation, anger is an activating and externalizing emotion which could facilitate compensation in response to worry about cognitive change. Results suggest that interventions should focus on ways to improve openness and activation to enhance compensation.