Using a novel machine-learning approach, researchers recently created and demonstrated construct validity of an objective Parkinson disease (PD) severity score derived from smartphone assessments. This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. They used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. They found:

• The mPDS was derived from 6,148 smartphone activity assessments from 129 individuals. In addition, 23 individuals with PD and 17 without PD completed in-clinic assessments.
• Gait features contributed most to the total mPDS (33.4%).
• The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100.