Developing the Transdisciplinary Aging Research Agenda: New Developments in Big Data

Abstract

Background: Advances in big data analytics can enable more effective and efficient research processes, with important implications for aging research. Translating these new potentialities to research outcomes, however, remains a challenge, as exponentially increasing big data availability is yet to translate into a commensurate era of ‘big knowledge,’ or exponential increases in biomedical breakthroughs. Some argue that big data analytics heralds a new era associated with the ‘end of theory.’ According to this perspective, correlation supersedes causation, and science will ultimately advance without theory and hypotheses testing. On the other hand, others argue that theory cannot be subordinate to data, no matter how comprehensive data coverage may ultimately become.Objective: Given these two tensions, namely (i) between exponential increases in data that have not translated into exponential increases in biomedical research outputs; and (ii) between the promise of comprehensive data coverage and inductive data-driven modes of enquiry versus theory-driven deductive modes, this critical review seeks to offer useful perspectives of big data analytics and to derive certain theoretical implications for aging research.Method: This work offers a critical review of theory and literature relating big data to aging research. Result: The rise of big data provides important insights into the theory development process itself, highlighting potential for holistic theoretical assemblage to ultimately enable near real time research capability. Conclusion: Big data may represent a new paradigm of aging research that can dramatically increase the rate of scientific breakthroughs, but innovative theory development remains key to this potential.

The Role of Big Data in Aging and Older People’s Health Research: A Systematic Review and Ecological Framework

“…Analysts likely remain new to aging and gerontology studies while gerontologists likely remain confused by neural networks. Given that the promotion in connotation, process, and mechanism of aging and old age falls behind the model and data analysis, the lack of producing knowledge and information [15] confront big data with criticisms. As Kwan argued, there are considerable influences that computerized algorithms might bring to research results for knowledge production [135], and it is rather crucial to develop theories on patterns and paths of physiological and psychological aging and the aging society with big data and algorithms revealing trends and exceptions.…”

“…It is to capture and interpret the exceptions that distinguishes big data as great science progress. Considering that algorithms are designed and manipulated as "discursively framed by previous findings, theories, and training" [15], it is indispensable that knowledge participation occur as an interdisciplinary cooperation by algorithm engineers and gerontologists to communicate in their domains and disclose the chain of "data-information-knowledge".…”

“…Studies are expected to present more inspirations to understand the coupling of human-environment systems in the process of aging. This is the gap between data and knowledge outcomes [15]. The role of big data as an analytical tool has been emphasized over its role as a data product and resolution service.…”

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review

“…Performing advanced analytics on big data is one approach to define big data analytics (BDA) [ 14 15 ]. In a broader sense all kinds of predictive or explorative models applied to big data would meet this definition, also including statistical methods [ 50 ] and most often when the aspect of high velocity is inconclusively. In a narrower sense only inductive approaches like data mining or machine learning suited for high-dimensional data sets define big data analytics [ 10 27 46 51 52 ].…”

Revisiting how scientific research drives technological change: The Fifth Industrial Revolution

“…Research on the topic has sought to discover core theoretical mechanisms, or fundamental causal drivers of contemporary technological changes. Recent studies of contributions of technological changes in scientific research and biomedicine 8,9 and disaster response 10 suggest some patterns in how roots of societal technological change might derive from innovations in the scientific or knowledge creation process itself. These patterns are relevant to the debates Moll, Marwala, and Ntlatlapa engage with.…”