Study of Everyday Physical Activities of People with Chronic Pain
Research type
Research Study
Full title
Multimodal Modelling of and Feedback to Movement Related States in Everyday Physical Activities of People with Chronic Pain
IRAS ID
269317
Contact name
Nadia Berthouze
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
To be confirmed following HRA approval, To be confirmed following HRA approval
Duration of Study in the UK
2 years, 11 months, 31 days
Research summary
The aim of the study is threefold: 1) better understanding of the movement of people with chronic pain in various contexts, 2) development of physical rehabilitation technology that can automatically detect pain and related emotional and cognitive states from movement and related physiological signals, 3) development of a movement-sound/music feedback and interaction framework aimed at increasing movement (qualities) awareness so as to foster engagement in everyday physical activities.
In the study, multimodal data will be collected from people with chronic pain, healthy people, and clinicians (e.g. physiotherapists and psychologists).
Sensor data will be captured from participants with chronic pain, healthy participants, and clinician participants while they perform harmless everyday physical activities (e.g. sit-to-stand), using body movement sensors (e.g. https://wearnotch.com/), video cameras, microphones, and physiological sensors (e.g. surface electromyography: https://www.biosignalsplux.com/en/products/sensors/), to objectively capture multimodal aspects of human movement.
Subjective data will also be captured based on interviews, other verbalisation, questionnaires, and diaries to provide insight into subjective experiences that affect or predict movement behaviour (e.g. confidence, anxiety).
The sensor and self-report data will be collected in both individual and group sessions. The data will be captured in our lab, participating sites (e.g. pain management clinics), and everyday settings (e.g. home, outdoors).
In the physical activity sessions, sound/music may be provided to accompany participant movements, to understand how these interact with and influence movement behaviour. The sound/music may be automatically tailored to predicted/estimated movement-related behaviour or cognitive/emotional experiences of a participant to enrich his/her engagement in the physical activity being performed.
Observer ratings of the acquired video data will be collected from clinicians, to obtain measures of the quality of movement behaviour in the physical activity sessions.
All collected data will be analysed using both quantitative and qualitative methods including visualisations, statistical analysis, machine learning and artificial intelligence algorithms, further video analysis, and thematic/framework analysis.REC name
London - Bloomsbury Research Ethics Committee
REC reference
20/LO/0284
Date of REC Opinion
20 Mar 2020
REC opinion
Unfavourable Opinion