Programmed aging refers to the idea that mammals (and some other multiparous sexually reproducing organisms) evolved complex biological mechanisms that cause gradually increasing fitness deterioration and internally limit individual organism lifetimes. The rationale is that this process enhances the survival (non-extinction) of a species population. Evolvability theories propose that mammals have evolved design characteristics that aid their ability to evolve, that is, reduce the time required for a particular increment of evolutionary adaptation and/or increase the precision with which the adaptation can be performed. Because an increase in evolvability aids a population in escaping extinction, evolution selects the associated design characteristics. Both of these concepts conflict with traditional (Darwinian) theory regarding the nature of evolution. However, more recent genetics discoveries have exposed rich detail regarding the nature of the evolution process including the fact that biological inheritance involves the transmission of organism design information from parent to descendant in digital form. These discoveries have acted to support both programmed aging and evolvability theories. Acquisition traits are those that depend for their evolutionary (fitness) value on the acquisition of something that gradually accumulates during an organism’s life but is not transmitted genetically to descendants. This situation causes these traits including language, immunity, intelligence, and social status to represent a special need for evolvability and programmed aging.

As recently as 2002 programmed aging in mammals was widely thought to be theoretically impossible based on generally accepted concepts regarding the evolution process. However, as described in this article, genetics discoveries, results of selective breeding, and other direct evidence strongly support the idea that aging creates an evolutionary advantage and that therefore complex biological mechanisms evolved that control mammal aging. Like similar life-cycle programs that control reproduction, growth, and menopause the aging program can adjust the aging trait during an individual’s life to compensate for temporary or local changes in external conditions that alter the optimum lifespan for a particular species population. In addition, genetics discoveries strongly support the evolvability concept to the effect that sexually reproducing species can evolve design features that increase their ability to evolve, and that aging is one such feature.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

Background: With a rapid rise in our older adult population globally and due to their multimorbidities, our older adults are more likely to engage in the services provided in the emergency department at a higher rate than younger adults. The current emergency service delivery model may be ineffective against such an ageing phenomenon. Research reports most older adults having one or more co-morbidities including functional decline, dementia, and frailty. Studies have shown that older adults have been undertriaged with physicians not being able to comprehend their complex needs related to their presenting complaints in the emergency department. Geriatric emergency departments have incepted worldwide to better manage this care deficiency in the rapidly ageing society around the world. Data Sources: A search of published literature from 2010-2020 using (keywords) as described below was undertaken of which, relevant literature were selected for an informed review. Implications for Nursing: Understanding geriatric emergencies can enable healthcare workers to reduce undertriaging and provide appropriate care that improves patient's health outcomes currently and in the future in the emergency department. Further education in gerontology can also be a platform for our nurses to enhance their care and thought process, likewise upskilling themselves for the future geriatric population seeking treatment. Older healthcare workers will also be able to enhance their current job scope before retirement. In-house teachings from trained gerontologists or certified programs can shed light on the special care needs of our senior citizens globally. Conclusion: With our rapidly increasing population, we can expect an influx of our older patients both from home and long-term care facilities to present to the emergency department with a wide range of geriatric emergencies. By being able to create a geriatric screening process and tailored care models, healthcare workers will be able to understand their care process and in turn, improve patients' health outcomes and provide a quicker transition of care.