The SupportPrim project will develop and evaluate decision support systems for personalized care in patients with common musculoskeletal complaints in primary health care. Case-based reasoning, an artificial intelligence approach, and risk-stratified care will be evaluated in physiotherapy and general practice.

SmaRTWork project - a digital system for personalized return to work recommendations for sick-listed with musculoskeletal disorders. The decision support system will use "case-based reasoning" (CBR). The main principle of CBR is to use knowledge of previously successful "cases" to propose measures for new and similar "cases". The project is carried out in collaboration with the Department of Computer Technology and Informatics, and is financed by the Research Council of Norway. Project period 2021-2027. Contact: Lene Aasdahl

Non-pharmacological treatment of sleep problems in primary care is a study investigating the effectiveness of the municipal sleep course Sleep well on insomnia severity and other sleep- and health-related outcomes among participants with insomnia symptoms. Contact: Researchers Maria Hrozanova, Ingebrigt Meisingset and Eivind Schjeldrup Skarpsno.

The DE-PASS project is financed via the EU's COST Action program and involves more than 70 partners. The aim is to identify and measure factors that influence physical activity in different age groups and arenas (school, work, leisure etc.) and use this knowledge to develop targeted interventions. Contact: Professor Paul J. Mork

LABDA is a network project funded by the EU's Marie Skłodowska-Curie Actions programme. The aim of the project is to develop advanced analysis methods of 24/7 data on physical activity and sleep. This knowledge is used in various models to study the connection between physical activity and sleep and to develop targeted interventions. Contact: Professor Paul J. Mork

The physical activity behavior and sleep study is funded via NTNU Health - strategic programme, and is a collaboration with the Department of Computer Technology and Informatics. The project develops new analysis methods (machine learning) of sensor data on physical activity and sleep collected in HUNT4. The data is used, among other things, to study the connection between genetics and physical activity/sleep, as well as the connection between physical activity/sleep and various health outcomes. Contact: Professor Paul J. Mork

Better health for people with musculoskeletal pain is funded by DAM and, as the title says, aims to improve health and prevent an unfavorable outcome for people with musculoskeletal pain. The project makes use of rich data from the Health Survey in Trøndelag (HUNT) and links to national registers for sickness absence, health service use, and use of painkillers to study whether long-term health conditions/diseases, socio-economic conditions, and familial pain conditions, in interaction with lifestyle factors, affect the course of musculoskeletal pain. See more information about the project. Contact: Anna Marcuzzi