NUS1000 is a large scale freshman-year study of undergraduate sleep, well-being and learning patterns that has unique key features: (1) continuous objective multi-dimensional data gathered with passive sensing of sleep and stress over a semester, (2) utilisation of Learning Management System-based outcome data as a marker of study behaviour and academic achievement. The goal is to gather information that can be used to improve student sleep, mental wellbeing and performance.
The first year in university can be a challenging experience for undergraduates who must learn to cope with learning in an unfamiliar environment, form new relationships, live away from home and/or might have to manage personal finances for the first time. These academic, social, and personal demands can result in significant stress, affecting sleep, learning and mental/emotional wellbeing. Characterizing and understanding the time-course and inter-relationship of these demands and their consequences is crucial for making science-based improvements to a student's university experience. To this end, the investigators will longitudinally evaluate sleep, learning and wellbeing in \~1000 first-year students as they adapt to university life to understand how these behaviours fluctuate and interact throughout the academic term.
Study Type
OBSERVATIONAL
Enrollment
500
This study will track sleep, learning and wellbeing of 500 freshmen under free-living conditions for 20 weeks
Change in sleep duration and timing from the start to the end of the semester
The Oura ring is a sleep-tracking device worn on the finger. During the study, participants will need to sync their devices to a mobile app.
Time frame: 20 weeks
Change from baseline sleep habits from the start to the end of the semester
Participants will also be required to complete a set of sleep questionnaires probing sleep habits (bedtime, wake time, time taken to fall asleep, etc.)
Time frame: Week 1-2, Week 8-9, and Week 17-18
Change in daily well-being from the start to the end of the semester
Participants will be prompted to fill in brief questions on daily well-being. At the end of each assessment, participants will be required to record an audio clip of their day, without any personal identifiers, for mood/sentiment analysis.
Time frame: 20 weeks
Change from baseline well being from the start to the end of the semester
Participants will also be required to complete a set of well-being questionnaires (e.g., feelings of connectedness or isolation, levels of stress, etc.)
Time frame: Week 1-2, Week 8-9, and Week 17-18
Change in learning outcomes from the start to the end of the semester
Grades from quiz/assignments for each module will be collected.
Time frame: 20 weeks
Change in learning patterns from the start to the end of the semester
Student interactions with the LMS-system will be collected, such as the number of page/file views.
Time frame: 20 weeks
Change in mood from the start to the end of the semester
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.
At the end of each day, participants will be required to record an audio clip of their day, without any personal identifiers, for mood analysis.
Time frame: 20 weeks
Change in time-use patterns from the start to the end of the semester
Participants will be asked to fill out a time-use diary over three 2-week periods throughout the semester, indicating the activities they engaged in over the past day
Time frame: Week 1-2, Week 8-9, and Week 17-18
Change in smartphone touchscreen interaction patterns from the start to the end of the semester
Phone taps will be tracked automatically through an EU-GDPR (the European Union's stringent privacy regulation) compliant smartphone app (QuantActions). This application logs human-smartphone screen interactions during regular use by logging a location-specific timestamp for each screen contact.
Time frame: 20 weeks
Change in movement patterns from the start to the end of the semester
The investigator's in-house smartphone app (Z4IP) will extract movement patterns from location data that will only be kept on the participant's phone up to 96 hours for these computations. The extracted movement patterns (and not geographical locations) will then be uploaded to secure servers, tagged to subject IDs without any personal identifiers, i.e., names and emails will not be linked to their movement patterns. As this component is optional, participants will be asked to indicate if they would like to opt-in at the informed consent stage.
Time frame: 20 weeks