ArticleSensors (Basel, Switzerland)2026
Integrating GPS and Accelerometry to Capture Life-Space Mobility in Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
Abstract
Mobility is key to independent living, and life-space mobility (LSM) describes the extent to which a person moves within and beyond their home. Restricted LSM may limit participation in life. Parkinson's disease (PD) can influence LSM through changes in physical function and social engagement. This study investigated LSM in people with mild-to-moderate PD using self-reported and objective measures, examining agreement between measures and relationships with physical activity and quality of life. The participants completed the Life-Space Assessment (LSA) and wore an accelerometer and GPS monitor for seven days. The accelerometer and GPS data were processed to derive GPS life-space and physical activity variables. The participants (n = 11) demonstrated variability in GPS-derived measures of LSM despite high levels of self-reported LSM (median 82). The participants spent approximately two thirds of wear time in close proximity to the home, where most activity time was sedentary. Higher levels of moderate-to-vigorous physical activity and step counts were observed beyond the home. Self-reported health-related quality of life showed weak associations with LSM. These findings suggest that while the LSA captures overall mobility, GPS data provides complementary insight into location-specific activity and individual movement patterns.
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