Drowsiness and sedentary behaviour are common accompaniments to a wide range of systemic and neurological illnesses. However, it is unclear whether such information could be used within an Early Warning System. We developed a method for quantifying behaviours, and measured whether this behavioural measure improved prediction of clinical deterioration in stroke in-patients.
Participants wore 4 inertial movement unit watches on wrists and ankles, allowing them to engage in everyday activities. Watch data was paired with annotated videos to train a deep learning model, enabling classifications of five behaviours (lying flat, sitting up in bed, sitting upright, standing or walking). Subjects were either instructed to perform a series of behaviours, or left to act as they wished. Cross-validation F1 scores for instructed sessions were: 0.95 (SE ±0.01; n=18); and for free-living: 0.88 – 0.74 (SE ± 0.04 -0.11; n=41). Subsequently we applied our system to stroke inpatients during daytime hours and assessed whether behavioral quantification was an independent predictor of clinical deterioration.
Of 61 recruited subjects, 22 (36%) deteriorated over the following 5 days. The strongest clinical predictors of deterioration (stroke severity, baseline mobility, brain hemorrhage) together enabled a predictive accuracy of 72% (AUROCC: 0.64); while adding daytime behavioral metrics – particularly time spent in bed - increased this to 78% (AUROCC: 0.79).
In summary, we show that behavioral changes are common prior to clinical deterioration in stroke patients; often before other more classical signs of deterioration; and can be quantified using smartwatches.
Not applicable.