How people spend their finite time budget of 24 hours on daily activities is linked to their wellbeing. Yet, how to best allocate time to optimise multi-dimensional wellbeing (physical, mental, and cognitive) remains unknown. Here, we utilise a number of (objective) functions derived using compositional data analysis and a large child cohort ( \(n>1000\) ), to predict how time allocation is associated with wellbeing outcomes such as body mass index, life satisfaction, and cognition. We develop and advocate joint cumulative distribution function constraints to ensure the feasible solutions do not extrapolate the sampled data for which the objective function is derived from. Moreover, we incorporate quality diversity (QD) approaches to study these objective functions. We define two types of behavioural spaces (BSs), one based on the activities, called the variable-based behavioural space (VBS), and the other based on the objectives, called the objective-based behavioural space (OBS). The VBS allows us to generate a set of high-quality solutions with different activity durations, while the OBS allows us to trade off different wellbeing dimensions against each other. We also demonstrate a web application, Time allocation optimiser, for creating personalised, optimised time-use plans.
Nikfarjam et al. (Sat,) studied this question.