SEIZALARM
March 1, 2023
PathAI
March 1, 2023Article of the Month – March 2023
Using Smartphones and Machine Learning to Quantify Parkinson’s Disease Severity the Mobile Parkinson Disease Score
Importance
Current Parkinson’s disease (PD) measures are subjective, rater-dependent, and assessed in the clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings.
Objectives
To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy.
Design, Setting.
This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning–based approach to generate a mobile Parkinson’s disease score (mPDS) that objectively weighs features derived from each smartphone activity (e.g., 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.
Outcomes and Measures...
The ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication.
Results
The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed the most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease’s Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy.
Conclusions
Using a novel machine-learning approach, we created and demonstrated the construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics.
Relevance to Healthcare Field
Parkinson’s disease (PD) is the second most common neurological disorder after Alzheimer’s disease. It can be managed with medications, and one objective way of determining if the medication is working is through lengthy in-clinic assessments. In this study, researchers created an Android smartphone application called “HopkinsPD” for PD patients. HopkinsPD assesses five activities: voice, finger tapping, gait, balance, and reaction time. The purpose was to utilize the smartphone data gathered from the patients using machine learning methods and determine the mobile PD score severity of symptoms (mPDS) within intraday fluctuations of symptoms and the response to dopaminergic medications. Although the study’s population had demographic sampling limitations of solely white people that are Android users and with a college education, this AI system should be widely tested so we can use this objective method that is time and cost-effective for every patient.