Conference Agenda

Sleep is commonly assessed using paper diaries in 24-hour movement behaviours research. However, paper diaries are increasingly considered outdated, are prone to recall bias, and require manual data entry. Missing data are also common, which can compromise the combination between sleep and accelerometer data. To overcome these limitations, this project uses a smartphone application (m-Path) to collect sleep diaries in the context of a 24-hour movement behaviours population study.

Methods: In 2026, 1200 adults aged 20-69 living in Belgium are being asked to wear 2 accelerometers and provide information on their sleep for 7 consecutive days, via a paper diary or m-Path. During a home visit, interviewers install m-Path on participants’ smartphones following a standardised protocol. Sleep diary entries are delivered through scheduled notifications adapted to participants’ potential wake times. Quality indicators include researcher burden, completion rates and moments, and usability perspectives (notification and direct messaging systems).

Results: Preliminary results indicate that 90% of participants chose the application. Real-time monitoring allows to reduce non-response via a direct messaging system to the participants. The in-built notification system provides higher control over completion moment, reducing recall bias. Research burden is low, despite more time dedicated to real-time monitoring. Further analyses will compare data quality between the paper and application-based diaries.

Conclusions: m-Path may offer a scalable and efficient alternative to paper sleep diaries in adult population studies. Its use could reduce researcher workload, improve data completeness, and support more accurate integration of sleep within 24-hour movement behaviours surveillance and research.