Monitoring Parkinson’s Disease In-the-Wild

In Parkinson’s disease (PD), the development of accurate wearable biomarkers for real-world monitoring is a priority. Developers tend to prioritize agreement with clinical features (e.g., neurological tests). However, wearable biomarkers should also reflect the pathogenic processes underlying these clinical features. This critical aspect is often overlooked in validation studies, raising doubts about construct validity and limiting adoption of these biomarkers. Here, we propose a solution to address this gap. We examined whether a previously validated wearable biomarker, derived from a deep learning model trained on raw accelerometer signals during walking to estimate motor symptom severity scores, can also reflect the pathogenic processes associated with motor dysfunction in people with PD (PwP). The model was reproduced and evaluated in-the-wild, before being deployed on a subset of PwP for whom neuroimaging data were also available. Neuroimaging data were analyzed to extract the brain activity pattern associated with predicted motor symptoms severity scores. The topographic similarity between the extracted pattern and two established brain patterns (one underlying motor symptoms in PD and one not) was assessed. The model accurately estimated ground-truth motor severity scores (mean absolute error = 5.20). Despite not being explicitly trained for this purpose, the model was also able to capture pathogenic mechanisms specifically linked to motor dysfunction in PD (dice similarity = 0.653). These findings represent an initial step toward linking wearable biomarkers not only to clinical features, but also to underlying mechanistic representations. This supports the wider adoption of wearable biomarkers in clinical practice and trials.