Discovery Early Career Researcher Award - Grant ID: DE230101174
Funder
Australian Research Council
Funding Amount
$443,154.00
Summary
Harnessing life-course transitions to optimise time-use behaviour habits. At every stage of life, how we use our time is one of the greatest determinants of our happiness, productivity, social wellbeing and quality of life. Time-use habits, for better or worse, are entrenched in daily routines that are difficult to break. This project aims to use existing population datasets to identify when during their life people are most likely to change their time-use habits, and to describe who may be at g ....Harnessing life-course transitions to optimise time-use behaviour habits. At every stage of life, how we use our time is one of the greatest determinants of our happiness, productivity, social wellbeing and quality of life. Time-use habits, for better or worse, are entrenched in daily routines that are difficult to break. This project aims to use existing population datasets to identify when during their life people are most likely to change their time-use habits, and to describe who may be at greatest risk of making unfavourable changes (e.g., replacing physical activity with sedentary time, not getting enough sleep). Expected outcomes include new analytical methods to understand time-use routines and new knowledge to inform future time-use improvement strategies to enable Australians to live their best life.Read moreRead less
Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects ....Mitigating bias in statistical analyses of data collected over time. This project aims to develop innovative nonparametric distribution and regression curve estimation techniques from data collected over time. These curves are key statistical tools for describing populations, but often, their estimators are inefficient when the data are massive, growing and change over time, or too restrictive when the data exhibit measurement errors and a fraction of them are equal to zero. The project expects to develop novel, less restrictive and more realistic nonparametric curve estimation methods in these complex settings. Outcomes include new practical statistical methods and software to benefit experts in diverse fields from nutrition and epidemiology, to environmental science and digital platforms, amongst others.Read moreRead less