Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data

Milligan, Gregor; Harvey, John; Dowthwaite, Liz; Vallejos, Elvira Perez; Nica-Avram, Georgiana; Goulding, James

Abstract

Life satisfaction significantly contributes to wellbe-ing and is linked to positive outcomes for individual people and society more broadly. However, previous research demonstrates that many factors contribute to the life satisfaction of an individual person, including: demography, socioeconomic status, health, deprivation, family life, friendships, social networks, living environment, and the broad range of behaviours enacted by the person, such as helping or volunteering. Consequently, it is challenging to disentangle the factors that contribute most significantly to life satisfaction, and thus more importantly, inform public policies designed to help foster positive wellbeing. We analyse primary survey data (n=2849) on self-reported life satisfaction in relation to a range of self-reported and observed variables associated with wellbeing. Specifically, we draw on a massive paired dataset related to use of a food sharing application in London, to augment the analysis using additional socioeconomic , environmental, and behavioural variables. Through a random forest machine learning approach and variable importance measures, we evaluate how a range of factors, that are often only evaluated individually, provide relative contributions towards life satisfaction. Result reveal that factors such as employment and social reliance contribute most significantly towards the experience of life satisfaction.

Citation

Milligan, G., Harvey, J., Dowthwaite, L., Vallejos, E. P., Nica-Avram, G., & Goulding, J. (2023). Assessing relative contribution of Environmental, Behavioural and Social factors on Life Satisfaction via mobile app data. In Proceedings: 2023 IEEE International Conference on Big Data: Dec 15 - Dec 18, 2023 Sorrento, Italy. https://doi.org/10.1109/BigData59044.2023.10386740

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2023 IEEE International Conference on Big Data
• Start Date: Dec 15, 2023
• End Date: Dec 18, 2023
• Acceptance Date: Dec 15, 2023
• Online Publication Date: Jan 22, 2024
• Publication Date: Dec 15, 2023
• Deposit Date: Feb 23, 2024
• Publicly Available Date: Feb 26, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: Proceedings: 2023 IEEE International Conference on Big Data: Dec 15 - Dec 18, 2023 Sorrento, Italy
• ISBN: 9798350324457
• DOI: https://doi.org/10.1109/BigData59044.2023.10386740
• Keywords: Wellbeing; Life Satisfaction; Machine Learning; Deprivation; Variable Importance
• Public URL: https://nottingham-repository.worktribe.com/output/31616966
• Publisher URL: https://ieeexplore.ieee.org/document/10386740