Increased sedentary behaviour is common in neurodegenerative diseases and CNS disorders characterised by reduced aerobic capacity, fatigue, postural imbalance and pain. This can have considerable implications for the worsening of symptoms and the development of comorbid conditions such as mood disturbance, hypertension, hypercholesterolaemia and obesity. Relevant measures that we can extract from actigraphy data include total sedentary time during the day, total activity/24 hours, activity in the least active hours, activity in the most active hours, the start times of the least and most active hours, and the difference between the most and least active periods.
Measurement of gait (walking) provides valuable information on functional performance in patients with neurodegenerative diseases and other CNS disorders affecting gait. In particular, measurements of walking speed are relatively easy to derive, but can bridge between standard in-clinic walking tests and measures of walking that are more representative of daily living. Disease-specific walking models built from and applied to actigraphy data can provide real world measures of walking. Sophisticated algorithms are used for more detailed characterization of gait, including measures of contact, stance, step, stride and swing.
Current measures of disease stage and progression in movement disorders largely rely on in-clinic assessments. As these provide a snapshot view into a patient’s functioning, they lack sensitivity to intra- and inter-daily fluctuations in symptoms and independence vs. dependence in activities of daily living. Advances in wearable device technology have enabled clinically relevant parameters to be measured continuously in real world settings and with relatively low patient burden. In particular, some devices fitted with 3-axial accelerometers allow for continuous raw data collection for up to several weeks, without the need to recharge the device. Additional insights can be derived from sensors capable for continuous recording of heart rate/heart rate variability, skin temperature, orientation, magnetic field strength, etc. Such measures can complement the accelerometry measurements and help to eliminate false signals. The use of raw data allows us to apply the optimal algorithms for each endpoint, as well as for re-analysis of the data as improved methods become available.