This repository contains the (anonymized) supplementary material for our paper. This includes the survey questions we asked, as well as a video and screenshots of DayO. Abstract: Planning one’s workday was shown to be positively related to increased control of time, work and life satisfaction, and performance. However, planning for an efficient workday is challenging, due to the high complexity of tasks that knowledge workers have to perform and the many constraints that influence their plan. Few planning tools actively support users with the task scheduling process, but they do not take into account these time and productivity constraints. Hence, we have devised an automated task scheduling approach that recommends an optimized workday plan, by considering constraints such as task characteristics (e.g., difficulty, deadline, importance), scheduled appointments, the current stress level and sleep quality from the previous night. A preliminary analysis of the approach, using DayO as a technology probe in situ, showed that participants found the idea very promising, but a real-world application would additionally require further support to reduce the cognitive burden of planning and would let users to adapt their schedules interactively, when constraints change throughout a workday.