Musculoskeletal tissues such as bone, muscle, tendons, and cartilage respond to environmental signals. In the case of bone its mechanical integrity is regulated via two process, modelling and remodelling. The osteogenic index has been adopted to quantify the bone loading in exercise which is a result of experiments performed to assess bone adaptation to mechanical stimulus.
In previous research findings show that there are three key factors associated to bone adaptation, magnitude, frequency and duration. Current assessment techniques such as force plates are limited due to environmental constraints and the measurement only occurring when in contact with the plate. Accelerometers have subsequently been utilised but have mainly used metrics aligned to force plates thus not incorporating the 3 key factors of bone loading.
It is very important to understand how different exercises performed (at varying intensities) can modulate skeletal loading in terms of magnitude and frequency as this will provide insight into appropriate interventions for those recovering from surgery or traumatic injury and what is required to maintain bone health.
Accelerometers that are used to provide a surrogate measure of loading generally aim to replicate the measurement of force. Various metrics have been shown to correlate with force but again are difficult to use in natural and dynamic environments such as walking.
Therefore there is a need, to be able to measure skeletal loading at specific sites on the body that incorporates the three key factors of loading. In addition, the requirements extend to the dynamic nature of activities in natural environments.
Right Step Health's skeletal loading algorithms incorporates magnitude and frequency to compute loading intensity using accelerometers meeting all of the requirements. It provides metrics in near real time, provides loading distribution and can be adapted to consider loading contribution in 3 axes of motion, various frequency bands and separate activity phases such as swing vs stance phases of gait.
Placing two sensors at the ankle of each limb makes Right Step Healths algorithm an ideal candidate for measuring and monitoring dynamic weight bearing suitable for assessing patients in recovery, tracking their progress and ensuring positive outcomes. It also presents numerous opportunities to further research in skeletal loading aimed at maintaining bone integrity and profiling the modulation of exercise.
Right Step is engaged in skeletal loading research in microgravity environments to support human space exploration both in terms of bone maintenance and recovery.
Link to core paper: Accelerometry-Based Approach to Assess Loading
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