ActivNap

Discovering digital biomarkers of activity and sleep in ageing and diverse neurological populations

Principal Investigator: Dr Rachael Lawson

Co-Supervisors: Dr Lisa Alcock, Dr Siliva Del Din, Prof John-Paul Taylor

BAM PhD student: Mohammadreza Sedghi

Our study aims to:

Develop novel clinically relevant outcome metrics from wearable sensors to identify sleep and activity patterns
Establish the clinical validity of wearable sensor-derived activity and sleep metrics
Explore whether activity and sleep patterns detected by wearable sensors are generic or specific to a pathology.

Our Methods

We will utilize data collected from body-worn sensors placed on the participants' lower back and wrist over a period of up to seven days in real-world settings, derived from existing different datasets.
Accelerometer-based data will be segmented to extract sleep and activity metrics, which will be evaluated for their clinical utility.
Demographic data, co-morbidities, medication, validated sleep questionnaires/ patient-reported outcomes, and accelerometer-based data of different studies will be harmonised, with strategies implemented to address and mitigate missing data.
Machine learning techniques will be employed to analyse the sleep-activity profiles across various conditions and to assess change over time.