Lessons Learned and Call for Action From Building an International Consensus on Social Return on Investment Modeling of Physical Activity and Sport

Nieto, I ORCID: https://orcid.org/0000-0001-8902-3791, Mayo, X ORCID: https://orcid.org/0000-0002-4143-701X, Davies, L ORCID: https://orcid.org/0000-0003-0591-7507, Reece, L ORCID: https://orcid.org/0000-0003-2883-3963 and Jimenez, A ORCID: https://orcid.org/0000-0001-5295-9668 (2025) Lessons Learned and Call for Action From Building an International Consensus on Social Return on Investment Modeling of Physical Activity and Sport. Journal of Physical Activity and Health, 22 (7). pp. 768-771. ISSN 1543-3080

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Abstract

Background: The Social Return on Investment (SROI) model has been applied to physical activity and sports (PAS) inconsistently in the past. In order to demonstrate that PAS creates social value for society, consistent, scientific-based tools must be developed. Methods: In 2022, a group of interdisciplinary researchers started a project to standardize the application of SROI to global PAS activities. A Delphi study, informed by a systematic review on this topic, was used and the present commentary exposes the main conclusions. Results: Six main lessons can be drawn from the process of building a global SROI applied to PAS: the methodology to measure the impact of PAS at the population level is different from the methodology applied to specific interventions; there is consensus on the impact in health; there is knowledge, but also unanswered questions on the impact of PAS in education; the impact of PAS in population well-being as a promising area; the of impact on crime and social capital requires more research; and there is controversy in the relationship between PAS, and environment, and climate change. Conclusions: A global SROI applied to PAS is a powerful tool to demonstrate how an active population can bring value to society. For this purpose, researchers and policymakers are called to action to fill in the gaps that remain open in order to build a robust model.