Developing a Data Driven Activity Chain Modelling Methodology for Geodemographic Profiling of Singapore Residents and Analysis of Attitudes Towards Active Lifestyles

This research proposal seeks to develop a data driven activity chain modelling methodology for geodemographic profiling of Singapore residents and conduct analysis of attitudes towards active lifestyles.

Our profiling methodology will take into consideration the type, location and frequency of physical activities that residents participate in, their social-demographic characteristics as well as their lifestyle patterns.

The profiles will provide Health Promotion Board (HPB) with an understanding of how activity chains differ between population segments, geographic locations, and time of day. The research product will serve as a baseline dataset for HPB to identify population segments that are not actively involved in Healthy365 initiatives and lays the foundations for subsequent data driven studies to inform the design of behavioural nudges tailored to specific population segments based on their lifestyle preferences.

Specifically, we will a) establish the modelling methodology to profile individuals into archetypes based on their activity and lifestyle patterns, b) assemble the necessary data for modelling, which includes the use of mobile phone applications to c) capture the preference and sensitivity of different population segments towards active lifestyle promotional programmes.

This research aligns with Singapore’s strategic vision of preventive healthcare by pioneering an advanced digital capability to deliver personalised health promotion at scale.