How to help at risk women develop personalized physical activity programs that mitigate bone density loss and osteoporosis.
Next generation algorithms linked to biomechanical joint sensors to estimate activity effect on bone density.
Reduced osteoporosis population morbidity through a new generation of consumer wearable devices and apps.
Osteoporosis is a major health concern for older adults. Bone density is related to skeletal loading and thus weight-bearing activity has been considered a valuable intervention for osteoporosis. Activity modifying interventions have received significant attention as a potential means for increasing bone mass, but have had variable success due to the lack of attention to the specific mechanical stimuli provided to bone by each intervention protocol.
A mathematical model to explain the relationship between a habitual daily stress stimulus and bone density has been established in the literature. CPHM researchers have performed preliminary work to test this model in a cross-sectional population based study and are now developing more sophisticated algorithms and technology to quantify skeletal loading from physical activity measurements. By combining specific physical activity measures and knowledge of joint bio-mechanical loading during physical activity using the mathematical model for bone density, the team can first assess an individual’s habitual daily bone stress and then evaluate the potential for individualized activity protocols to best manage bone health.
Developing specific algorithms that can assess and log skeletal loading during everyday activities and incorporating these into wearable sensors can provide an objective assessment of the type, amount and intensity of physical activity individuals need to participate in each day to maintain healthy bone density throughout a lifetime. Next steps for translating this research include: evaluate the utility of the algorithm with existing consumer wearable sensors; development of next generation, joint load and activity-specific sensors; and validate the model/sensor combination in a larger population.