Background: Promoting adaptation, improving well-being and maintaining an optimal quality of life (QOL) is an important aspect in dementia care. The purpose of this study was to identify determinants of QOL in young onset dementia, and to assess differences in QoL domains between people with Alzheimer's disease (AD) and frontotemporal dementia (FTD).
Methods: In total 135 persons with AD and 58 persons with FTD were included from two prospective cohort studies. QOL was assessed with the proxy reported quality of life in Alzheimer's disease questionnaire (QoL-AD). Possible determinants were explored using multiple linear regression and included sociodemographic variables, diagnosis, dementia severity, disease awareness, neuropsychiatric symptoms, met and unmet needs and hours of personal and instrumental care. Differences between QOL domains in people with AD and FTD were calculated using Mann-Whitney U tests.
Results: Lower QOL was associated with more depressive symptoms, lower disease awareness, and a higher amount of needs, both met and unmet. People with AD scored lower on the memory and higher on the friends' subscale. No differences were found for the other items.
Conclusion: This study demonstrates a unique set of determinants of QOL in AD and FTD. Interventions directed towards these specific factors may improve QOL.
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http://dx.doi.org/10.1080/13607863.2016.1232369 | DOI Listing |
Glob Health Res Policy
January 2025
Center for Public Health and Epidemic Preparedness and Response, Peking University, Haidian District, 38Th Xueyuan Road, Beijing, 100191, China.
Background: As population aging intensifies, it becomes increasingly important to elucidate the casual relationship between aging and changes in population health. Therefore, our study proposed to develop a systematic attribution framework to comprehensively evaluate the health impacts of population aging.
Methods: We used health-adjusted life expectancy (HALE) to measure quality of life and disability-adjusted life years (DALY) to quantify the burden of disease for the population of Guangzhou.
Pilot Feasibility Stud
January 2025
Department of Internal Medicine - Cardiology, Virginia Commonwealth University, West Hospital 8th Floor, North Wing, Richmond, VA, 23298, USA.
Background: To determine the feasibility, acceptability, and preliminary efficacy of a 6-month tailored non-linear progressive physical activity intervention (PAI) for lymphoma patients undergoing chemotherapy.
Methods: Patients newly diagnosed with lymphoma (non-Hodgkin (NHL) or Hodgkin (HL)) were randomized into the PAI or healthy living intervention (HLI) control (2:1). Feasibility was assessed by examining accrual, adherence, and retention rates.
Inj Epidemiol
January 2025
Injury Prevention Research Center, University of Iowa, 145 N Riverside Dr., Iowa City, IA, 52242, USA.
Background: Motor vehicle crashes are the second leading cause of injury death among adults aged 65 and older in the U.S., second only to falls.
View Article and Find Full Text PDFBMC Med Genomics
January 2025
Department of Otolaryngology, First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, WuHua District, Kunming City, Yunnan Province, China.
Hearing loss is a prevalent condition with a significant impact on individuals' quality of life. However, comprehensive studies investigating the differential gene expression and regulatory mechanisms associated with hearing loss are lacking, particularly in the context of diverse patient samples. In this study, we integrated data from 10 patients across different regions, age groups, and genders, with their data retrieved from a public transcriptome database, to explore the molecular basis of hearing loss.
View Article and Find Full Text PDFJ Transl Med
January 2025
School of Information and Communication Engineering, Dalian University of Technology, No. 2 Linggong Road, 116024, Dalian, China.
Background: Parkinson's Disease (PD) is a neurodegenerative disorder, and eye movement abnormalities are a significant symptom of its diagnosis. In this paper, we developed a multi-task driven by eye movement in a virtual reality (VR) environment to elicit PD-specific eye movement abnormalities. The abnormal features were subsequently modeled by using the proposed deep learning algorithm to achieve an auxiliary diagnosis of PD.
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