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.
We are organized in four pillars:
- 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 two topic groups that embrace all three research pillars.
- Value-creation and research-based innovation
- User involvement group
Head of research group
Heads
Head of pillar I
Head of pillar II
Head of pillar III
Head of pillar IV
Members
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
Blog
Cooperation
We cooperate with the research group Applied Artificial Intelligence.
