Enhancing fall risk assessment through real-world digital mobility biomarkers

Falls are a significant health concern for older adults, leading to severe injuries and loss of independence. The WHO ranks falls as the second leading cause of accidental injury deaths worldwide. Mobility assessment is crucial in evaluating fall risk. Traditional methods involve clinical measures that require specialized settings, but they may not accurately reflect daily mobility complexities. This thesis aimed to develop and validate tools for assessing fall risk through real-world digital mobility biomarkers using wearable sensors. First, this work presented an overview of fall risk factors, traditional assessments, and the potential of real-world digital mobility biomarkers. Then, it focused on the potential role of real-world walking, turning, postural transfers, and balance in fall risk. Finally, the implemented pipelines were applied in the automatic segmentation of the instrumented L-Test (iL-Test) in a multicohort study. Overall, this work demonstrated the potential of digital mobility biomarkers in real-world settings to enhance future fall risk assessments. By addressing gaps in traditional laboratory-based assessments, it contributed to ongoing efforts in developing clinically actionable outcomes. Future research should extend the work presented here in real-world digital mobility biomarkers, explore multivariate models, target multimodal approaches, and expand their application in diverse populations.