Remote smartphone monitoring of Parkinson’s disease and individual response to therapy

• Published in: Nature biotechnology Vol. 40; no. 4; pp. 480 - 487
• Main Authors: Omberg, Larsson, Chaibub Neto, Elias, Perumal, Thanneer M., Pratap, Abhishek, Tediarjo, Aryton, Adams, Jamie, Bloem, Bastiaan R., Bot, Brian M., Elson, Molly, Goldman, Samuel M., Kellen, Michael R., Kieburtz, Karl, Klein, Arno, Little, Max A., Schneider, Ruth, Suver, Christine, Tarolli, Christopher, Tanner, Caroline M., Trister, Andrew D., Wilbanks, John, Dorsey, E. Ray, Mangravite, Lara M.
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.04.2022; Nature Publishing Group
• Subjects: 631/61; 692/308/409; 692/699/375/1718; Agriculture; Balance; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Data collection; Gait; Humans; Life Sciences; Mental task performance; Movement; Movement disorders; Neurodegenerative diseases; Observational studies; Parkinson Disease - diagnosis; Parkinson's disease; Phonation; Quality assessment; Quality control; Remote monitoring; Remote sensors; Severity of Illness Index; Signs and symptoms; Smartphone; Smartphones
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/s41587-021-00974-9

Remote health assessments that gather real-world data (RWD) outside clinic settings require a clear understanding of appropriate methods for data collection, quality assessment, analysis and interpretation. Here we examine the performance and limitations of smartphones in collecting RWD in the remote mPower observational study of Parkinson’s disease (PD). Within the first 6 months of study commencement, 960 participants had enrolled and performed at least five self-administered active PD symptom assessments (speeded tapping, gait/balance, phonation or memory). Task performance, especially speeded tapping, was predictive of self-reported PD status (area under the receiver operating characteristic curve (AUC) = 0.8) and correlated with in-clinic evaluation of disease severity ( r  = 0.71; P  < 1.8 × 10 −6 ) when compared with motor Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS). Although remote assessment requires careful consideration for accurate interpretation of RWD, our results support the use of smartphones and wearables in objective and personalized disease assessments. Smartphone sensors that monitor disease symptoms enable remote assessment of Parkinson’s patients.