Research group information
Research group leader
- ProfessorTimo Jämsä
Research group description
Risk factors for fractures
Osteoporosis and related fractures is a serious health problem in developed countries. Current clinical diagnostic tools have only limited ability to assess fracture risk at an individual level. Using a biomechanical approach and advanced image analysis, we have demonstrated that different hip fracture types have different biomechanical origins, and consequently, different intraskeletal and extraskeletal risk factors. Assessment of the trabecular structure using texture analysis of radiographs appears to be a promising method. Textural analysis enables discrimination of patients at risk for femoral neck fracture, even with better prediction accuracy than bone mineral density (BMD). We have developed computational finite element (FE) models and shown that FE analysis can yield reasonable accuracy in the assessment of experimental failure load. Currently, our main interest is in solving the biomechanical background for the controversial relationship between hip osteoarthritis and hip and acetabular fractures.
Detection and risk factors of falls
Falls and fall-related injuries are a serious problem in older people. We have performed an incremental set of studies on accelerometric detection of falls, suggesting that automatic fall detection systems might offer a tool for supporting independent living and improving safety among older people. In our long-term field test with elderly subjects, we monitored a total of 15,500 h of real-life data from older people. These data suggest that automatic accelerometric fall detection systems might offer a tool for improving safety among older people, especially in frequent fallers. Our current interest is in the different clinical and behavioural risk factors of falls.
Physical activity monitoring
We are studying different objective physical activity (PA) monitoring methods for assessing the dose-response of PA for different health outcomes, including osteoporosis, osteoarthritis, diabetes, and lipid metabolism. We have been able to demonstrate the different activity thresholds for different health outcomes. E.g., in the recent study with prediabetic subjects, we showed that even light PA as determined by a wearable motion sensor decreases insulin resistance, improves lipid homeostasis and reduces visceral fat. Currently we are studying the relationships between cardiovascular health and objectively measured PA and sedentary behavior in the data collected in the Northern Finland Birth Cohort 1966, using clustering analysis and machine learning methods.
Technology solutions for health promotion and healthy aging
Physical inactivity is an increasing challenge for health at the population level, especially in young population. In the multidisciplinary MOPO study we evaluated whether the use of an activity monitor providing feedback has an effect on PA in young men. The study demonstrates that a wrist-worn activity monitor providing feedback has a positive effect on PA and sedentary behaviour in young men. In collaboration with a multidisciplinary group of researchers we are analyzing the determinants of sedentary and non-sedentary lifestyles, with an aim to recognize and develop optimal motivational solutions to promote PA healthy behavior in different target groups.
One of the main global challenges is the aging of the population. Frailty and memory problems are typical problems related to aging. In a multidisciplinary international network or researchers, we are studying novel technological solutions to promote healthy aging, and to help older people to live at their own homes.