| Literature DB >> 34373643 |
Larsson Omberg1, Elias Chaibub Neto2, Thanneer M Perumal3, Abhishek Pratap3,4, Aryton Tediarjo3, Jamie Adams5,6, Bastiaan R Bloem7, Brian M Bot3, Molly Elson5, Samuel M Goldman8, Michael R Kellen3, Karl Kieburtz5,6, Arno Klein3, Max A Little9,10, Ruth Schneider5,6, Christine Suver3, Christopher Tarolli5,6, Caroline M Tanner8, Andrew D Trister11, John Wilbanks3, E Ray Dorsey5,6, Lara M Mangravite12.
Abstract
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.Entities:
Mesh:
Year: 2021 PMID: 34373643 DOI: 10.1038/s41587-021-00974-9
Source DB: PubMed Journal: Nat Biotechnol ISSN: 1087-0156 Impact factor: 54.908