Digital measures may provide objective, sensitive, real-world measures of disease progression in Parkinson's disease (PD). However, multicenter longitudinal assessments of such measures are few. We recently demonstrated that baseline assessments of gait, tremor, finger tapping, and speech from a commercially available smartwatch, smartphone, and research-grade wearable sensors differed significantly between 82 individuals with early, untreated PD and 50 age-matched controls. Here, we evaluated the longitudinal change in these assessments over 12 months in a multicenter observational study using a generalized additive model, which permitted flexible modeling of at-home data. All measurements were included until participants started medications for PD. Over one year, individuals with early PD experienced significant declines in several measures of gait, an increase in the proportion of day with tremor, modest changes in speech, and few changes in psychomotor function. As measured by the smartwatch, the average (SD) arm swing in-clinic decreased from 25.9 (15.3) degrees at baseline to 19.9 degrees (13.7) at month 12 (P = 0.004). The proportion of awake time an individual with early PD had tremor increased from 19.3% (18.0%) to 25.6% (21.4%; P < 0.001). Activity, as measured by the number of steps taken per day, decreased from 3052 (1306) steps per day to 2331 (2010; P = 0.16), but this analysis was restricted to 10 participants due to the exclusion of those that had started PD medications and lost the data. The change of these digital measures over 12 months was generally larger than the corresponding change in individual items on the Movement Disorder Society-Unified Parkinson's Disease Rating Scale but not greater than the change in the overall scale. Successful implementation of digital measures in future clinical trials will require improvements in study conduct, especially data capture. Nonetheless, gait and tremor measures derived from a commercially available smartwatch and smartphone hold promise for assessing the efficacy of therapeutics in early PD.
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http://dx.doi.org/10.1038/s41531-024-00721-2 | DOI Listing |
J Med Internet Res
January 2025
Biomedical Informatics & Data Science Section, The Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Background: Mobile devices offer an emerging opportunity for research participants to contribute person-generated health data (PGHD). There is little guidance, however, on how to best report findings from studies leveraging those data. Thus, there is a need to characterize current reporting practices so as to better understand the potential implications for producing reproducible results.
View Article and Find Full Text PDFFront Robot AI
January 2025
Interactive Robotics Laboratory, School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU), Tempe, AZ, United States.
We present WearMoCap, an open-source library to track the human pose from smartwatch sensor data and leveraging pose predictions for ubiquitous robot control. WearMoCap operates in three modes: 1) a Watch Only mode, which uses a smartwatch only, 2) a novel Upper Arm mode, which utilizes the smartphone strapped onto the upper arm and 3) a Pocket mode, which determines body orientation from a smartphone in any pocket. We evaluate all modes on large-scale datasets consisting of recordings from up to 8 human subjects using a range of consumer-grade devices.
View Article and Find Full Text PDFCyberpsychol Behav Soc Netw
January 2025
Department of Communication, University of Delaware, Newark, Delaware, USA.
Smartwatches are digital devices, similar to smartphones, and come with the possibility of problematic use. Problematic technology use is the experience of psychological distress or reduced daily functioning in response to excessive or addictive technology use. The purpose of this study was to explore whether problematic use of smartwatch devices for exercise tracking influences user's health behaviors such as eating habits and sedentary activity.
View Article and Find Full Text PDFJ Neurol
January 2025
Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
Background: Longitudinal qualitative data on what matters to people with Parkinson's disease are lacking and needed to guide patient-centered clinical care and development of outcome measures.
Objective: To evaluate change over time in symptoms, impacts, and relevance of digital measures to monitor disease progression in early Parkinson's.
Methods: In-depth, online symptom mapping interviews were conducted with 33 people with early Parkinson's at baseline and 1 year later to evaluate (A) symptoms, (B) impacts, and (C) relevance of digital measures to monitor personally relevant symptoms.
Curr Obes Rep
January 2025
Metabolism and Body Composition, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
Background: Recent technological advances have introduced novel methods for measuring body composition, each with unique benefits and limitations. The choice of method often depends on the trade-offs between accuracy, cost, participant burden, and the ability to measure specific body composition compartments.
Objective: To review the considerations of cost, accuracy, portability, and participant burden in reference and emerging body composition assessment methods, and to evaluate their clinical applicability.
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