Illustration of a brain split into two halves, with the left side showing a stylized brain and the right side depicted as a microchip with circuit lines, overlaid with an orange heartbeat line. The word 'Activnap' is written in bold orange text below.

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
Icon of a person with surrounding dotted outline and four 'X' marks, resembling a schematic or diagram of a person surrounded by other components.
Icons representing time management and scheduling, including a ruler, clock, and a checkmark inside a circle on a black background.
Worker in protective gear walking past a sleeping person under a blanket in a hazardous area with caution tape. 
Explore whether activity and sleep patterns detected by wearable sensors are generic or specific to a pathology.

Our Methods

Illustration of a person wearing a wearable health device, with a house on the left and a hospital on the right.
Line drawing of a person sleeping in bed with Z's, another person walking away, and a giant martini glass with an electrocardiogram line above it, all on a black background. 
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.
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Accelerometer-based data will be segmented to extract sleep and activity metrics, which will be evaluated for their clinical utility.
A digital illustration of a human brain combined with a circuit board, symbolizing artificial intelligence, inside concentric circles on a black background. 
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.
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