Background: Availability of large longitudinal population health and aging studies, such as the English Longitudinal Study on Ageing, hold many opportunities for the examination of the development of Frailty over time. Lacking are psychometrically robust longitudinal measurement of the concepts of frailty required for accurate and reliable estimates and identification of frail individuals. Objectives. Development of a psychometrically robust longitudinal research tool of Frailty. The tool is to be easy to replicate for future use in health and aging research. Participants/Measurements. Data comes from four waves of ELSA (2014-2017); N=39,528. Six domains of health status deficits that may measure frailty were used: Mobility, Daily Function, Self-rated general health, Self-rated pain, Depression, Self-report doctor diagnoses of health. Design. Exploratory factor analysis (EFA) of common-self-reported deficits available across four waves of longitudinal data were compared to determine which deficits were a best fit for a research-based frailty index. Standardized regression factor scores were calculated to represent individual’s placement in factors identified from EFA. Cross-sectional validation and reliability are reported. Results. Five-Factors of Frailty found were consistent across ELSA waves. Mobility/Severe Pain, Planning/Self Care, Depression, Moderate Pain/Movement/Arthritis and Cardio-Metabolic. The variable make-up of each factor differed between males and females. Psychometric properties of the five factor frailty model were taken into account at each stage of development (factorability/sampling adequacy, cross-sectional replicability, internal consistency, low interdeterminacy, relations with age).Conclusions. Five distinct factors of an accumulation of deficits approach to frailty were found. Use and development of the five factors of frailty research tool are outlined. This research tool will aid researchers in determining the complex risk factors and outcomes of empirically derived components of frailty in ELSA.

The use of Artificial Intelligence (AI) applications in a growing number of domains in the latest years has put into focus Ethical Legal and Societal Aspects (ELSA) of these technologies and the relevant challenges they pose. In this paper, we propose an ELSA Curriculum for Data Scientists aiming to raise awareness about ELSA challenges in their work, provide them with a common language with the relevant domain experts to cooperate to find appropriate solutions, and finally, incorporate ELSA in the data science workflow and not be seen as an impediment or a superfluous artifact, rather than an integral part of the Data Science Project Lifecycle. The proposed curriculum uses the CRISP-DM model as a backbone to define a vertical partition, expressed in modules corresponding to the CRISP-DM phases. The horizontal partition includes knowledge units belonging to three strands that run through the phases, namely Ethical and Societal, Legal, and Technical Rendering Knowledge Units (KUs). In addition to the detailed description of the aforementioned KUs, we also discuss the implementation, issues such as duration, form, and evaluation of participants, as well as the variance of the knowledge level and needs of the target audience.