Advances in geriatric medicine and research最新文献

It is predicted that the growth in the U.S. elderly population alongside continued growth in chronic disease prevalence will further strain an already overburdened healthcare system and could compromise the delivery of equitable care. Current trends in technology are demonstrating successful application of artificial intelligence (AI) and machine learning (ML) to biomarkers of cardiovascular disease (CVD) using longitudinal data collected passively from internet-of-things (IoT) platforms deployed among the elderly population. These systems are growing in sophistication and deployed across evermore use-cases, presenting new opportunities and challenges for innovators and caregivers alike. IoT sensor development that incorporates greater levels of passivity will increase the likelihood of continued growth in device adoption among the geriatric population for longitudinal health data collection which will benefit a variety of CVD applications. This growth in IoT sensor development and longitudinal data acquisition is paralleled by the growth in ML approaches that continue to provide promising avenues for better geriatric care through higher personalization, more real-time feedback, and prognostic insights that may help prevent downstream complications and relieve strain on the healthcare system overall. However, findings that identify differences in longitudinal biomarker interpretations between elderly populations and relatively younger populations highlights the necessity that ML approaches that use data from newly developed passive IoT systems should collect more data on this target population and more clinical trials will help elucidate the extent of benefits and risks from these data driven approaches to remote care.

Over the past few decades, interest has begun to surge in understanding the role of emotion in decision making, and more recently in studies across the adult life span. Relevant to age-related changes in decision making, theoretical perspectives in judgment and decision making draw critical distinctions between deliberative versus intuitive/affective processes, as well as integral versus incidental affect. Empirical findings demonstrate the central role of affect in various decision-related domains such as framing and risk taking. To situate this review within an adult life-span context, we focus on theoretical perspectives in adult development regarding emotion and motivation. As a result of age differences in deliberative and emotional processes, taking a life-span perspective is critical to advance a comprehensive and grounded understanding of the role of affect in decision making. Age-related shifts in information processing from negative toward positive material also have consequential implications. By taking a life-span perspective, not only will decision theorists and researchers benefit, but so too will practitioners who encounter individuals of various ages as they make consequential decisions.

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

Background: Strength asymmetries are a type of muscle function impairment that is associated with several health conditions. However, the prevalence of these asymmetries among adults from the United States remains unknown. We sought to estimate the prevalence and trends of handgrip strength (HGS) asymmetry in American adults.

Methods: The unweighted analytic sample included 23,056 persons aged at least 50-years with information on HGS for both hands from the 2006-2016 waves of the Health and Retirement Study. A handgrip dynamometer measured HGS, with the highest recorded values for each hand used to calculate asymmetry. Persons were categorized into the following asymmetry severity categories: (1) >10%, (2) >20.0%, and (3) >30.0%. Survey weights were used to generate nationally-representative asymmetry estimates.

Results: Overall, there were no statistically significant trends in HGS asymmetry categories over time. The prevalence of HGS asymmetry in the 2014-2016 wave was 53.4% (CI: 52.2-54.4), 26.0% (CI: 25.0-26.9), and 11.7% (CI: 10.9-12.3) for asymmetry at >10%, >20%, and >30%, respectively. HGS asymmetry was generally higher in older Americans compared to middle-aged adults at each wave. In the 2014-2016 wave, >30% asymmetry prevalence was 13.7% (CI: 12.7-14.6) in females and 9.3% (CI: 8.4-10.2) in males. Some differences in asymmetry prevalence by race and ethnicity were observed.

Conclusions: The prevalence of asymmetry was generally high, especially in women and older adults. Ongoing surveillance of strength asymmetry will help monitor trends in muscle dysfunction, guide screening for disablement, identify subpopulations at risk for asymmetry, and inform relevant interventions.

Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by inflammatory cell infiltration of the salivary and lacrimal glands, resulting in acinar epithelial cell atrophy, cell death, and loss of exocrine function. At least half of SS patients develop extraglandular inflammatory disease and have a wide range of systemic clinical manifestations that can affect any organ system, including connective tissues. As many as 3.1 million people in the U.S. suffer from SS, a disease that causes severe impairment. Women are nine times more likely than men to be affected by this condition. Unfortunately, there is currently no effective treatment for SS, and the available options only provide partial relief. Treatment involves using replacement therapies such as artificial saliva and eye lubricants, or immunosuppressive agents that have limited efficacy. The medical community recognizes that there is a significant need for more effective treatments for SS. Increasing evidence demonstrates the links between the dysfunction of the human microbial community and the onset and development of many human diseases, signifying the potential use of microorganisms as an alternative strategy to conquer these issues. The role of the microbiome in controlling immune function of the human host in the context of autoimmune diseases like SS is now becoming better understood and may help to enable new drug development strategies. Natural probiotics and synthetic biology applications hold promise for novel treatment approaches to solve the encryption of many complex and multifactorial immune disorders, like SS.

Fall prevention is critical for older adults. Stopping Elderly Accidents, Deaths, and Injuries (STEADI) is a fall prevention initiative, promoted by the Center for Disease Control (CDC). The purpose of this review aims to discuss the predictive validity, improve the predictive validity of STEADI, and apply STEADI in clinical settings.