Fatigue is prevalent in immune-mediated inflammatory and neurodegenerative diseases, yet its assessment relies largely on patient-reported outcomes, which capture perception but not fluctuations over time. Wearable sensors, like inertial measurement units (IMUs), offer a way to monitor daily activities and evaluate functional capacity. We have shown that kinematic features from sit-to-stand and stand-to-sit transitions are promising indicators to objectively assess fatigue in these patient populations.
Background
For patients with chronic diseases such as neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID), any successful therapeutic intervention is its ability to improve the patients’ activities of daily living (ADL) and health-related quality of life (HRQoL). Current evaluations of ADL and HRQoL rely mainly on subjective self-reports, typically using standardized questionnaires provided by patients every few months. This approach is prone to recall bias, reliability issues and poor sensitivity to change. The use of wearable sensors provides an opportunity to objectively assess phyical (e.g., with IMUs) and physiological (e.g., heart rate monitor) state, that is likely linked to fatigue, sleepiness and ADLs.
Methods
Using a previously validated algorithm to detect sit-to-stand and stand-to-sit movements, we determined whether specific kinematics of these movements, e.g., duration, speed, and angular range, change with increased fatigue. A linear mixed effects model was used to estimate the relation between the kinematic features and physical and mental fatigue.