The Project

The Project

The ATHLOS (Ageing Trajectories of Health: Longitudinal Opportunities and Synergies) Project officially began on the 1st of May 2015.

ATHLOS is a five-year project funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement number 635316. The Consortium is coordinated by Dr Josep Maria Haro (Parc Sanitari Sant Joan de Déu –PSSJD) and consists of 14 partners from 11 European countries.

Experts from the areas of demography, sociology, clinical medicine, epidemiology and public health, health statistics, economics, data management, and policy are involved in this research. ATHLOS will merge large sets of variables from longitudinal databases (at least 20 longitudinal studies) derived from several European and international studies. The Consortium will create and analyse a harmonised data set that includes the longitudinal studies identified, comprising more than 341,000 subjects. ATHLOS is a research and innovation action aimed at identifying the trajectories and determinants of healthy and active ageing, from early stages of development onwards. ATHLOS will ascertain risk and protective factors, their interactions, the stages in the lifespan in which they most greatly impact health, and how the modification of these factors—through promotion, prevention and treatment interventions—can change individual and population health. This deeper understanding of ageing will also result in a more realistic definition of ‘old age’ than the standard chronological approach.

Work packages


Creation of harmonised dataset

WP Leader: PSSJD; Dr Josep M. Haro.

A single harmonised dataset has been generated to include 18 prospective studies. In order to create a common data set for analysis, the first task was to map variables across all the datasets: a core set of variables were identified in each survey and they were categorised in different domains and response options remapped to account for differences within surveys. The original questions, design of each survey and characteristics of the sample were provided as metadata. Additional variables, including health-related outcomes and their determinants, were created and added to the harmonised dataset.


Development of analytical methods

WP Leader: Prof. Demosthenes Panagiotakos

A single scale of healthy ageing was created using an Item Response Theory approach. Several statistical models (based on Mixed Models with Repeated Measures, Generalised Estimating Equations, Structural Equation Modelling, Growth curve Mixture Modelling) were used (considering the single scale of health and other health-related outcomes as indicators of healthy ageing) to quantify trajectories of healthy ageing and their determinants. Finally, a microsimulation model was developed to predict trends in healthy ageing under alternative scenarios of determinants, interventions and health policy.


Describing patterns of trajectories, determinants and inequalities of healthy ageing

WP Leader: KCL; Prof Martin Prince.

Health trajectories and determinants of healthy ageing were assessed. The models created in the previous stage were employed to identify biological, lifestyle, medical and social determinants of healthy ageing across the life span and to identify variations in ageing trajectories associated to socio-demographic factors such as socioeconomic position and gender. Statistical methods such as Growth curve Mixture Modelling approach were used to identify groups of people that share a trajectory pattern and to explore variations in trajectories across groups at different stages of demographic transition. Age-Period-Cohort analysis was employed to analyse if subsequent cohorts of older adults are ageing in a healthier manner.


Characteristics-based approach to define ‘old age’

WP Leader: IIASA; Prof Sergei Scherbov and Prof Warren Sanderson.

Characteristics-based approach to defining old age: A reconceptualization of ‘old age’ was developed using projections of the proportion of older adults in a given population over time, exploring also the added value of health status in the new definition and the policy implications of different definitions of ageing.


Knowledge translation

WP Leader: UCL; Prof Martin Bobak.

Knowledge translation: This WP involves activities that use the research outcomes obtained from the previous WPs to conduct a series of applications to effectively improve the health of European citizens, providing more effective health services and strengthening the health care system. The microsimulation model developed in WP2 was used to: generate the economic and social consequences of policy value decisions on how best to allocate resources for interventions to modify ageing trajectories across the population (this exercise will enable policy-makers to be aware of the consequences of inevitable policy trade-offs); explore alternative healthy ageing scenarios in Europe and internationally, and to discuss their implications for policy, and; identify population-level or clinical interventions that can directly alter ageing trajectories towards those that optimise healthy ageing.



WP Leader: UAM; Prof José Luis Ayuso-Mateos.

This WP responds to the crucial need, from the initial deployment phase onward, to provide extensive visibility to the project in order to capitalise on the knowledge generated and advance the interests of concerned parties (public and private healthcare providers, technological companies, academia, pharmaceutical companies, public authorities, patients and caregivers). The dissemination activities have been intensively informed by the research conclusions and supporting evidence arising from the ATHLOS research, including especially the findings and policy recommendations arising from the project.


Coordination and management

WP Leader: PSSJD; Dr Josep M. Haro.

This WP is devoted to:

  • Making optimal use of resources by running agile structures and procedures linking together the project components.

  • Participating in the project’s meetings.

  • Ensuring quality assurance by means of proper internal reporting and follow-up tools.

  • Mitigating technical and managerial pitfalls.

  • Communicating with the EC’s relevant contact points.

  • Ensuring that all requirements regarding the reporting to EC are met.