CIM consists of a transdisciplinary team of musculoskeletal health researchers and computer scientists, working together to develop innovative and intelligent musculoskeletal health interventions.
Musculoskeletal disorders are the second common cause of disability worldwide, and with the ageing population these disorders represent one of the largest future challenges for health and society welfare.
Therefore, CIM will bring together experts in complementary fields to develop, evaluate, and implement effective innovative and practical solutions to challenges faced by stakeholders involved in the musculoskeletal field.
We will explore different technological methods within AI – in particular machine learning – in order to enhance current understanding of development and risk of persistent disorders.
Our ambition is to develop innovative and effective personalized interventions that can be implemented in clinical, work and health education settings.
Our pillars
The scientific organization of the research in CIM is divided in three pillars, which are designed to form a translational trajectory from clinical research to implementation and dissemination of the innovative and effective interventions.
- Pillar 1: Epidemiology and clinical research
- Pillar 2: Applied artificial intelligence research
- Pillar 3: Personalized interventions and models of care
In addition, we have established three topic groups that embrace all three research pillars.
- Value-creation and research-based innovation
- User involvement group
- Dissemination and utilization
Our blog
Find out more about our current projects, research and publications. Check out the team, and read news from the centre.
Visit our blog (uni.oslomet.no)Research groups
Research projects
- Adolescent Idiopathic Scoliosis in a life-course perspective (AIS-Life)
- Adulthood healthcare use for co-occurring pain and mental distress – the roles of early life adversity and buffers (PAINMENT)
- A healthy back throughout life – a digital information bank (The BACK Bank)
- AI-driven patient avatar for physiotherapy education
- Applying Artificial Intelligence in Developing Personalized and Sustainable Healthcare for Spinal Disorders (AID-Spine, part I)
- Can Motivational Interviewing facilitate return to work in sick-listed people with musculoskeletal disorders? (MI-NAV)
- Co-occurrent pain and psychological distress: From adolescence to adulthood
- Designing an intelligent personalized vest for scoliosis brace to improve treatment and monitoring of scoliosis in adolescents
- Health in young adults (HEYoung)
- HEYoung intervention study
- Implementing a stratified vocational advice intervention in individuals at high risk of long-term sickness absence with musculoskeletal disorders (SVAI)
- Machine learning for the analysis of physiotherapy records
- Physiotherapy for non-surgical spine patients
- Promoting and inhibiting factors for education and employment among young adults receiving welfare benefits or health services due to persistent pain and psychological distress
- Somatocognitive therapy in treatment of provoked localized vulvodynia (ProLove)
- The Back Complaints in the Elders, Norway (BACE-N)
- Young user involvement group in musculoskeletal health
Our research team
Leaders
Senior adviser
Project coordinator
Members
- Steven Hicks
- Vajira Thambawita
- Zheng an Toh
- Raymond Ostelo, Vrije University, Amsterdam
- Kaja Smedbråten, Oslo Municipality
User representative
Thor Einar Holmgard
External board
- Kjersti Storheim, Oslo University Hospital
- John-Anker Zwart, Oslo University Hospital
- Birgitta Öberg, Linkøping University
- Allan Abbott, Linkøping University
- Robert James Froud, Warwick Clinical Trials Unit and Kristiania University College
- Morten Goodwin, University of Agder
- Jan Hartvigsen, University of Southern Denmark
- Paul Jarle Mork, Norwegian University of Science and Technology (NTNU)
- Tore Solberg, The Arctic University of Norway (UiT)
- Danielle Van der Windt, Keele University
