bluuebunny/crossref_metadata_2025_split huggingface dataset:
A COMPREHENSIVE DIGITAL SELF-CARE SUPPORT SYSTEM FOR CAREGIVERS AND PERSONS WITH DEMENTIA/MCI AND MCC
Authors: Nambisan Priya , Lee Yura , Ellis Julie , Taani Murad 
Published: January 2025

Abstract
Research indicates that 95% of people with Alzheimer’s and other dementias also suffer from one or more other chronic conditions, increasing the risk of hospitalizations and complications such as contraindicated health care and drug interactions. Effective self-management practices are vital for maintaining cognitive function and preventing the onset of new chronic conditions among those with Multiple Chronic Conditions (MCC). However, managing MCC alongside cognitive impairment (CI) can make it very challenging for both patients and caregivers. Studies suggest that computer usage can enhance cognitive functioning and potentially delay further deterioration and digital apps are beneficial for self-care support. Despite this, there is a severe lack of technological applications that support those with CI and MCC and are often digitally excluded due to lack of understanding of their needs. We present the findings of a pilot test of a Comprehensive Digital Self-care Support System (CDSSS) known as myHESTIA. This system offers caregivers and persons with Alzheimer’s/Dementia a range of personalized tools including: 1) Health management tools: daily trackers for monitoring health conditions, reminders, to- do lists and educational resources; 2) Healing technologies: e.g. breathing exercises for relaxation, music therapy, learning activities like dancing or Yoga, and memory games and 3) Online social support forum: for connecting with others. This qualitative study aims to shed light on the perceived benefits of the system for both caregivers and persons with dementia/MCI, its utility in addressing unmet needs among those with CI, and its potential to improve the quality of life of caregivers.
ADVANCING CULTURALLY INCLUSIVE RESEARCH AND TOOLS IN ALZHEIMER’S DISEASE AND RELATED DEMENTIAS (ADRD)
Authors: Magid Maryam Abdel , Taylor Jean , Turner Anne 
Published: January 2025

Abstract

Background and Significance
Existing research underscores cultural norms’ influence on healthcare decisions but lacks depth in exploring their impact on dementia care transitions, critical for improving ADRD care outcomes. Research Aims include (1) Analyze interviews with dementia care providers and staff to better understand how cultural norms influence the decision-making processes for care transitions. (2) Explore methods used to facilitate inclusive and engaging discussions regarding care transitions.

Methods
Nested in the NIA-funded Decision Making in Alzheimer’s Research (DMAR) study, we examined interviews with 18 providers and staff on dementia care transitions. Prioritizing inclusivity, we leveraged the Alzheimer’s Disease Research Center (ADRC) registry and community partnerships coupled with tailored methods including diversifying the team and targeted outreach efforts. We’re employing a novel framework to analyze dementia care transitions, integrating societal and cultural aspects for nuanced understanding. This approach accounts for intergenerational culture, cognition perception, and care expectations, evolving to capture cultural nuances. Qualitative findings will be enriched with quantitative sentiment analysis, validated manually for accuracy.

Key findings
Novel framework will allow us to identify and analyze cultural norms affecting care transitions, as perceived by ADRD providers. Implications: The study’s findings advocate for inclusive decision-making tools in ADRD care to accommodate diverse patient preferences. Future steps involve applying this framework to patients and caregivers and integrating findings into diverse settings involved in care decision-making. Funding statement: Funded in part by NIA grant #R01 AG066957 and the NLM T15 training grant #5T155LM007442-22. Content responsibility lies with authors, not representing NIH views.
ASSOCIATION BETWEEN GUT MICROBIOTA, DIET, AND COGNITION IN ELDERLY PATIENTS WITH MILD COGNITIVE IMPAIRMENT
Authors: Chen Lili , Hou Zhaoyi , Liu Jinxiu , Li Xiuli , Zhang Yuping , Yang Shuhui , Zhang Peiling 
Published: January 2025

Abstract

Objectives
To compare the gut microbiota in individuals with mild cognitive impairment (MCI) and normal controls (NCs) and explore the association between dietary intake and various cognitive domains across identified enterotypes, providing a basis for dietary interventions based on the Microbiota-Gut-Brain Axis.

Methods
We analyzed fecal samples and 16S ribosomal RNA sequences for microbiota among 100 participants (NCs: 50, MCI: 50). Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), and Auditory Verbal Learning Test (AVLT). A semi-quantitative food frequency questionnaire was used to assess participants’ dietary intake, and differences in dietary habits and cognitive function among distinct enterotypes were analyzed.

Results
Participants with MCI exhibited lower bacterial richness. Linear Discriminant Analysis of Effect Sizes (LEfSe-LDA) revealed different major microbial genera between the groups, and a random forest model based on dominant bacterial genera could distinguish between MCI and NCs (AUC=0.743, 95%CI:0.646-0.840). Cluster analysis identified two enterotypes: Escherichia-Shigella (enterotype E) and Bacteroides (enterotype B). When grouped by enterotypes, similar dietary intake was associated with different cognitive domains: notably, egg intake positively correlated with MOCA and AVLT scores for enterotype E, but not for enterotype B. Conclusions: Significant differences exist in the gut microbiota of individuals with MCI compared to those with normal cognition, with dominant genera showing good predictive performance. The impact of different enterotypes on the relationship between dietary intake and cognitive function should be considered when making dietary recommendations in clinical practice.
ENABLING PRIORITIZATION FOR AMYLOID POSITIVITY TESTING WITH DIGITAL COGNITIVE ASSESSMENT
Authors: Jannati Ali , Thompson Karl , Toro-Serey Claudio , Banks Russell , Showalter John , Bates David , Tobyne Sean , Pascual-Leone Alvaro 
Published: January 2025

Abstract
Primary care providers currently wait for a complaint to initiate a ‘for-cause’ cognitive evaluation and often refer the patient to a specialist. Specialist evaluation then leads to the diagnosis, but treatment occurs late and is unlikely to meaningfully prevent or reduce disability. We hypothesized a brief digital cognitive assessment (DCA), the Digital Clock and Recall (DCR), could concurrently identify CI and predict PET Aβ status, thereby providing an efficient means for timely identification and prioritization of patients for disease-modifying treatments. 930 participants (age 72.0±6.7; 56.8% female; 23% minorities) were classified as cognitively unimpaired, mild cognitive impairment, or probable Alzheimer’s dementia, and 35.1% were Aβ+ on 18F-florbetapir PET scan. DCR-based models were compared with blood-based biomarkers (BBMs) Aβ42/40, pTau-181, and pTau-217, which poorly classified CI (AUCs=0.63, 0.66, 0.72) but accurately classified Aβ status (AUCs=0.81, 0.78, 0.89). DCR accurately classified CI and predicted Aβ status (AUCs=0.85, 0.83). Moreover, combining the DCR with Aβ42/40, pTau-181, and pTau-217 improved their Aβ-status prediction performance to AUCs of 0.882, 0.835, and 0.905, respectively. DCR was superior to BBMs in detecting cognitive impairment and boosted their Aβ-PET prediction ability to levels comparable to CSF biomarkers. We propose a workflow in which implementation of DCAs such as the DCR facilitates early identification of individuals with MCI or early dementia likely due to AD, thanks to the concurrent classification of CI and accurate prediction of Aβ PET status.
EXAMINING KNOWLEDGE ABOUT FIVE ALZHEIMER’S DISEASE–RELATED SERVICE AREAS AND ITS CORRELATES IN KOREAN AMERICANS
Authors: Hong Michin , Lee Sang 
Published: January 2025

Abstract
This study examined knowledge about Alzheimer’s disease (AD) related services and its predictors among Korean Americans (KAs). A total of 268 KAs in the Greater Washington metropolitan area participated and completed a cross-sectional survey. Knowledge about AD related services was assessed by asking how well they knew about each of the following areas: Diagnostic services, Alzheimer’s specialists, community-based services, home-based services, and institutional care, with four categories of responses from don’t know at all to know well. Multiple regression analyses were conducted for each service area with predictors including education, English proficiency, exposure to AD, sources and frequency of health-related information, and AD knowledge about treatment and management. KAs were more knowledgeable about community- and home-based services and institutional care than diagnostic services and Alzheimer’s specialist. All regression models except for diagnostic services were significant: Having more exposure to AD is related to being more knowledgeable in Alzheimer’s specialist, community- and home-based services, and institutional care; having more sources and frequency of health information is related to being more knowledgeable about Alzheimer’s specialist; and being more knowledgeable about AD management and treatment is related to having more knowledge about community- and home-based services, respectively. Our findings revealed specific areas of services more and less known to KAs, which needs to be addressed in educational outreach. Multivariate analyses identified variations in the predictors of different service areas while confirming the robust role of exposure to AD across the service areas, suggesting ways to increase knowledge about certain types of AD services.
ART, AGING, AND ARTIFICIAL INTELLIGENCE: EXPLORING AGE SCRIPTS IN THE UNIVERSE OF AI-GENERATED ART
Authors: Allen Laura , Xu Wenqian , Nishikitani Mariko , Patil Vaishnavi Atul , Hule Sunil , Bradley Dana 
Published: January 2025

Abstract
This study investigates age-related biases in AI-generated art through a mixed method investigation of images of older and younger individuals. Employing 19 text prompts associated with social exclusion for older adults, we collected 465 images from an AI-art generator, MidJourney, and conducted quantitative and qualitative analyses to understand differences between age group depictions. We used Amazon Rekognition software to detect the numerical image properties. Results that were compared statistically (STATA and Python) revealed significant differences in attributes: images with older people exhibited lower overall brightness (M = 46.54) and sharpness (M = 58.58) compared to younger individuals (brightness: M = 60.50; sharpness: M = 72.55). Moreover, older people were more likely to be smiling (16%) and with eyeglasses (56%) compared to younger individuals (6% and 12%), all with p &lt; 0.001. Next, we conducted a qualitative semiotics analysis with a randomly selected sample of 76 images. Older people were often depicted with a strong emphasis on nostalgia, vulnerability, and aloneness, reflected in vintage-style clothing, worn objects, and worried expressions. In contrast, younger individuals were often portrayed with a focus on modernity, energy, and independence, characterized by vibrant clothing, digital technology, and confident expressions. Together, these findings demonstrate the perpetuation of negative and reductionist age scripts in AI-generated art, such as older adults’ incompetence, loneliness, fear, and outdatedness. Implications of this research extend to societal representations of aging, emphasizing the importance of challenging age-related biases in AI-generated content. Future research should explore refinement of generative models which foster more equitable outcomes.
CAN CHATGPT IDENTIFY AND MANAGE FALL RISKS IN OLDER ADULTS?
Authors: Cheng Huai 
Published: January 2025

Abstract
Fall is one of the most common geriatric syndromes in older adults. Identifying and managing fall risks in older adults is very important but can be time-consuming. ChatGPT can answer complex questions such as passing USMLE and clinical questions. In this study, the author prompted ChatGPT to identify and manage fall risks in one old adult via a clinical vignette from a published curriculum and test whether ChatGPT can be used as a screening and management tool in daily practice. ChatGPT was prompted to respond to this clinical vignette with three approaches. First, ChatGPT was prompted to answer one general question on fall risks in old adults; Second, ChatGPT was prompted to answer 5 pre-test questions on fall prevention in older adults; Last, ChatGPT was prompted to answer 5 questions based on this clinical vignette. The author was the only person to determine the correctness of output from ChatGPT. ChatGPT can provide a comprehensive summary of 10 common fall risks in older adults and correctly answer 5 out of 5 pre-test questions with only a few answers missing. For a clinical vignette, ChatGPT provided correct answers on all five questions with only a few answers missing. ChatGPT can be used as a screening and management tool in daily practice.
DEVELOPMENT OF THE SWALLOWING ACTIVITY CLASSIFICATION MODEL FOR OLD ADULTS BASED ON ACOUSTIC ANALYSIS
Authors: Li Dan , Zhang Yaqing , Wu Junhui , Luo Wei , Liu Tao , Lyu Benshuai , Shang Shaomei 
Published: January 2025

Abstract
Dysphagia poses a significant health threat for old people worldwide, often resulting in severe complications like aspiration, choking, and aspiration pneumonia, thereby increasing hospital readmission rates and mortality. Swallowing function assessment serves as an effective means to reduce the occurrence of complications and improve patients’ survival rate. However, current assessment tools have pronounced limitations. Acoustic diagnosis, as a non-invasive method, has shown promising effectiveness in dysphagia screening and aspiration detecting, rendering it a key technology for addressing this issue. This study aims to develop a swallowing activity classifier based on acoustic recording and analysis using machine learning algorithms, which constitutes an efficient tool for non-invasive, remote, and continuous assessment of swallowing activities in old adults. The study recorded and annotated swallowing audio data from 61 old individuals, resulting in 120 acoustic samples. Signal features in time, frequency, time-frequency, and statistical domains were analyzed and extracted as inputs. Multiple models, including the Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Convolutional Recurrent Neural Network (CRNN), were used for classification. The MLP, CNN, and CRNN models achieved accuracy values of 74%, 68%, and 54%, respectively, on the validation set. Results indicate that swallowing, coughing, throat clearing, choking, wet breathing, and speech are characterized by distinct acoustic patterns, which can be effectively classified and identified using machine learning methods. These findings hold promise for the research and practical application of acoustic and machine learning-based swallowing activity classification models, facilitating continuous monitoring in old people, thereby providing guidance for clinical treatment and care.
COMPARISON OF QUALITY INDICATORS IN RURAL AND URBAN ASSISTED LIVING FACILITIES
Authors: Stuifbergen Alexa , Morgan Stephanie , Trainum Katie , Becker Heather 
Published: January 2025

Abstract
As the population of older adults grows, the demand for assisted living facilities (ALFs) is expected to increase. This growth will disproportionately affect rural ALFs, as the proportion of older adults living in rural areas is growing at a faster rate than urban areas, and older adults living in rural areas are in poorer health compared to their urban counterparts. This exploratory descriptive study examined differences between facility characteristics and resident perspectives of care between rural and urban ALFs using data collected as part of a major statewide assessment of quality of care in ALFs. The research team conducted site visits to collect facility level data from staff and resident interviews regarding perspectives of quality of care. The sample included 517 facilities from 8 geographic regions of a large Southwestern state with 143 of the facilities located in rural settings. Overall, licensed bed size ranged from 3 to 177 with the number of occupied beds significantly greater in urban ALFs (Mean=33.71, SD 28.51) than in rural ALFS (Mean=22.54, SD 15.93) (t=5.37, p&lt;.001). Overall, residents rated their health as good, very good, or excellent (78%) although a greater percentage of rural residents (23%) rated their health as fair or poor compared to urban residents (16%). Resident satisfaction with quality of health care and quality of experience in the facility was higher among residents of rural vs. urban ALFs. Additional research is needed to more fully explore factors influencing quality of life and quality of care in rural and urban ALFs.
FAMILY PERCEPTIONS OF THE EFFECTS OF COVID-19 ON OHIO LONG-TERM CARE FACILITIES
Authors: Dikhtyar Oksana , Heston-Mullins Jennifer 
Published: January 2025

Abstract
Family and friends are often the main source of emotional support and social contact for residents in long-term care facilities. The visitation restrictions imposed during the COVID-19 pandemic negatively impacted staff, family members, and residents, particularly those with cognitive impairment. However, there is limited literature on the impact that COVID-19 had on communication between residents and their family and friends, and communication between long-term care facilities and residents’ family and friends. To understand more about the effects of COVID-19, an online, state-wide survey was conducted which included questions regarding communication. A total of 1,686 involved family members or friends responded to the survey between September 2021 to May 2022. Phone calls were the most common method of communication between residents and their family and friends during visitation restrictions, followed by window visits and dropping off food and snacks. The frequency of communication also increased as a way for families to compensate for the lack of regular visits. Staff in most facilities used emails or postal mail, phone calls or text messages, and social media to communicate with family members. The amount of information respondents received from the facility affected their perceptions about the facility. These study findings can help improve facility-family communication and help facilities explore new ways of working with and engaging residents and families beyond the pandemic.
OLDER ADULTS’ EXPERIENCES OF LONELINESS AND ITS UNDERLYING MECHANISMS IN RESIDENTIAL CARE FACILITIES
Authors: Gill Amarjot , Sullivan Suzanne 
Published: January 2025

Abstract
Older Adults’ (OAs’) transition into residential care facilities (RCFs) detaches them from their meaningful social relationships, often precipitating feelings of loneliness. This prevalent issue in RCFs can lead to poor health outcomes, significantly disrupting OAs’ well-being and quality of life. Consequently, loneliness in RCFs emerges as a serious public health issue, necessitating a comprehensive understanding of its mechanism. This systematic review aims to explore OAs’ experiences and perceptions of loneliness and characterize mechanisms underlying OAs’ loneliness experiences through a comprehensive loneliness model. This review adhered to PRISMA guidelines. Articles from CINAHL, PubMed, PsycINFO, and Web of Science, published in English between 2018 and 2023, were examined. Of the 7,285 articles identified, 10 met the inclusion criteria. Quality appraisal for the selected studies was conducted using the Critical Appraisal Skills Programme (CASP) checklist. Three core themes of loneliness were identified: relational and individualized loneliness experiences, perception and emotional distress, and the influence of context and cognitive processes in modulating loneliness. A conceptual model delineating the mechanisms of OAs’ loneliness in RCFs was developed. This review highlights the influence of OAs’ residential context and cognitive processes, particularly their perceptions, in triggering loneliness and associated behavioral responses. OAs’ perceptions of reduced social control and insufficient social connections in RCFs exacerbate their loneliness, precipitating distressing emotional responses and diminished quality of life. Future research must explore strategic transformations of OAs’ perceptions through mechanistic targets and tailored care plans to reshape their social expectations within the context of RCF, potentially mitigating loneliness.
ASSOCIATIONS OF PROTEIN INTAKE WITH THE RISK OF ALL-CAUSE, CARDIOVASCULAR, AND CANCER MORTALITY: A META-ANALYSIS
Authors: Yang Huili , Cao Meng , Huang Chongmei , Jin Yaping , Liu Minhui 
Published: January 2025

Abstract
The association of the amount and sources of protein consumption with mortality for cardiovascular diseases (CVD) and cancer has been extensively studied. However, the outcomes remains controversial. The present meta-analysis was conducted to synthesize findings on the associations between total, animal, and plant protein intake and the risk of mortality from all causes, and cancer. Pubmed and Web of Science were searched to identify prospective cohort studies published up to November 2022. Random effects models were used to calculate pooled effect sizes and 95% confidence intervals for the highest versus lowest categories of protein intake. Heterogeneity were evaluated with I2 statistic, and publication bias was evaluated with funnel plots and Egger’s test. A total of 28 cohort studies were identified. The results showed that total protein (HR=0.99, 95%CI: 0.93, 1.04; HR=0.95, 95%CI: 0.87, 1.02; HR=0.97, 95%CI: 0.84, 1.11), animal protein (HR=1.02, 95% CI: 0.98, 1.07; HR=1.05, 95% CI: 0.99, 1.12; HR=1.00, 95% CI: 0.98, 1.02) were not associated with the all-cause, CVD and cancer mortality. However, the intake of plant protein was inversely associated with the risk of all-cause (HR=0.92, 95% CI: 0.87, 0.96) and CVD mortality (HR=0.87, 95% CI:0.81, 0.93), but not cancer mortality (HR=0.98, 95% CI: 0.94, 1.01). This study demonstrates that higher plant protein intake is associated with a reduced risk of all-cause and CVD mortality. Increasing plant protein intake is a potential way to decrease the risk of death.