Folkbiology refers to people’s everyday understanding of the biological world. The early twentieth-century pioneers of public health C.-E.A Winslow (1877–1957), and his mentor H. Biggs (1859–1923), conceptualized public health as the ‘purchasable’ science of preventing disease and death from unfavorable economic and living conditions. Their ideas were foundational in shaping public health’s strategy of a ‘war against disease’ (Winslow, 1903), a strategy that was very successful in preventing the early-life mortality risks from infectious diseases, and was eventually extended to combating (...) the chronic diseases of late life (like cancer). However, the initial framing of public health, through the lens of sanitation science, was predicated upon folkbiological premises that geroscience must abate in order to direct public health interventions toward the goal of improving the quality of life for older persons in the twenty-first century. Three folkbiological premises of sanitation science’s ‘war against disease’ are identified and critiqued: (i) the belief that health is the ‘normal’ condition of the human mechanism and disease ‘unnecessary’; (ii) the belief that the proximate causes of disease are the only modifiable risk factors public health interventions can alter; and (iii) the belief that the rate of biological aging is universal. (shrink)

Recent progress in geroscience holds the promise of significantly slowing down or even reversing ageing and age-related diseases, and thus increasing our healthspans. In this paper, I offer a novel argument in favour of developing such technology and making it unconditionally available to everyone. In particular, I argue that justice requires that each person be provided with sufficient opportunities to have a ‘complete life’, that many people currently lack such opportunities, and that we would substantially improve the status quo (...) by giving them access to anti-ageing technology. (shrink)

Geroscience and philosophy of aging have tended to focus their analyses on the biological and chronological dimensions of aging. Namely, one ages with the passage of time and by experiencing the cellular-molecular deterioration that accompanies this process. However, our concept of aging depends decisively on the social valuations held about it. In this article, we will argue that, if we study social aging in the contemporary world, a novel phenomenon can be identified: the paradox of aging in the digital (...) era. If the social understanding of aging today is linked to unproductivity and obsolescence; then there is a possibility that, given the pace of change of digital technologies, we become obsolete at an early chronological and biological age, and therefore, feel old at a younger age. First, we will present the social dimension of aging based on Rowe and Kahn's model of successful aging. We will also show that their notion of social aging hardly considers structural aspects and weakens their approach. Second, departing from social aging in its structural sense, we will develop the paradox of aging in the digital era. On the one hand, we will explain how the institutionalization of aging has occurred in modern societies and how it is anchored in the concepts of obsolescence and productivity. On the other hand, we will state the kind of obsolescence that digitalization produces and argue that it can make cohorts of biologically and chronologically young individuals obsolete, and thus they would be personally and socially perceived as old. (shrink)

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. (shrink)