Parkinson’s Disease (PD) is characterized by motor and non‐motor symptoms that can overlap with other movement disorders, complicating accurate diagnosis and monitoring. Wearable technologies, such as smartwatches, offer continuous and objective assessment of motor function, but their clinical utility in multiclass classification and symptom prediction remains underexplored. This study aimed to determine whether smartwatch‐derived motor features can distinguish idiopathic PD from other movement disorders and whether motor variability is associated with non‐motor symptom burden in PD.

We analyzed data from the Parkinson’s Disease Smartwatch (PADS) dataset (N = 469), which includes accelerometer and gyroscope signals recorded during 20 standardized motor tasks. For each participant, mean and standard deviation values for each axis were averaged across tasks. Diagnostic group classification was assessed using multinomial logistic regression. Among individuals with idiopathic PD (n = 276), linear regression evaluated associations between motor variability and total non‐motor symptom scores from a 30‐item questionnaire.

Motor variability features, particularly accelerometer Y‐axis and gyroscope X‐axis standard deviations, significantly differentiated diagnostic groups (pseudo R² = 0.068). Age, sex, and handedness also contributed. In the PD subgroup, higher accelerometer X and gyroscope X variability were associated with greater non‐motor symptom burden, while greater stability in the mediolateral (Y) axis was linked to fewer symptoms (adjusted R² = 0.0081, p < 0.001).

Smartwatch‐derived motor variability features can modestly differentiate movement disorder diagnoses and are associated with non‐motor symptom severity in PD. Our findings support the complementary use of wearable sensors in clinical assessment and remote monitoring. Our findings also lay the foundation for future integration of wearable‐derived data into telemedicine workflows.