Identification of Real-World Trunk Biomarkers for Fall Risk Detection

Over one-third of people older than 65 years fall each year leading to injuries, hospitalization or fatality. Fall prevention increases quality of life, as well as minimizes the pressure of rising social costs. Identifying risk factors for falls is important for accurate fall risk detection. Guidelines from RIZIV uses clinical scales to assess fall risk, but low sensitivity is reported for these tools. Using trunk motion as a biomarker for fall detection has recently been introduced and supported by published literature. The currently proposed biomarkers are mostly assessed during gait-related falls and under tightly controlled laboratory conditions, therefore challenging the translation to clinical practice. Contradictory, real-life fall accidents mostly occur during functional activities (i.e. transfers) while research is still pre-occupied with gait-related falls. Besides accurately reacting on postural perturbations, one must also sense when the body is destabilized. Yet, this ability has never been related to falls. During this study, we aim to overcome these limitations by not only examining trunk biomarkers outside of the laboratory during functional activities, but also by including additional fundamental strategies to keep one’s balance originating from both the motor and sensory system. The novelty of this study is that more accurate risk predictions can be made by clinicians based on data gathered during activities of daily living.

Keywords:Fall prevention and mobility, Trunk biomechanics, Elderly care

Disciplines:Neurological and neuromuscular diseases, Biomechanics, Motor control, Physiotherapy, Rehabilitation