Background: Intraindividual variability (IIV) on neuropsychological task performance has demonstrated enhanced sensitivity to neuropathological decline compared to mean performance. It is currently unknown whether increased IIV in everyday behaviors may also lend added sensitivity to detect early subtle changes seen in pre-clinical ADRD.
Methods: In a pilot smartphone digital phenotyping study, 34 participants (M = 71.6±5.5; M = 16.4±2.7; 68% female 57% non-Hispanic White) with healthy cognition or Mild Cognitive Impairment downloaded an open-source smartphone application (mindLAMP) to passively capture GPS trajectories for 4 weeks. Monthly mean and day-to-day standard deviation (IIV) metrics for three GPS features were generated to test a priori hypotheses based on a conceptual framework. Correlations examined the relationship between mean versus IIV mobility metrics and 11 baseline neuropsychological T-scores adjusted for age, sex, education, and estimated premorbid IQ. Hierarchical linear regressions examined whether mobility IIV was independently associated with cognition after adjusting for mean mobility, and vice versa, for relationships in which both mean and IIV were significantly correlated with the same cognitive outcome.
Results: Greater mean GPS activity (radius of gyration), less mean GPS routine (physical circadian routine), and greater IIV in these two GPS features were both significantly associated with a neuropsychological measure of language (Boston Naming Test; .39≤|r's|≤.55, p's<.05). IIV in GPS routine did not explain a significant amount of additional variance in Boston Naming scores after controlling for mean GPS routine, whereas mean routine explained an additional 11% of variance after controlling for IIV (Overall model: R = .34, F(2,31) = 8.07, p = .002). Unique associations were identified between IIV in GPS routine and animal fluency scores (r = .44, p<.05) and IIV in GPS location diversity and Boston Naming (r = .35, p<.05), whereas mean metrics of these features were not associated with corresponding outcomes.
Conclusions: Mobility-based digital phenotyping features appear to be selectively associated with demographically-corrected neuropsychological measures of language. Considering IIV in everyday mobility behaviors - in addition to overall mean mobility features - may lend added value. Next steps will evaluate and compare a greater array of digital phenotyping features (mean and IIV) in predicting cognition and other relevant outcomes.
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http://dx.doi.org/10.1002/alz.093386 | DOI Listing |
Background: Passively-obtained smartphone digital phenotypes may yield objective estimates of everyday cognition in older adults compared to traditional cognitive/self-report measures typically confounded by sociodemographics. However, it is currently unknown what covariates are relevant when interpreting smartphone sensor data. We aimed to clarify which intrinsic and extrinsic factors are associated with digital phenotyping versus traditional cognitive measures in a cohort of older adults.
View Article and Find Full Text PDFBackground: Intraindividual variability (IIV) on neuropsychological task performance has demonstrated enhanced sensitivity to neuropathological decline compared to mean performance. It is currently unknown whether increased IIV in everyday behaviors may also lend added sensitivity to detect early subtle changes seen in pre-clinical ADRD.
Methods: In a pilot smartphone digital phenotyping study, 34 participants (M = 71.
Sci Rep
December 2024
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
In today's technologically advanced landscape, precision in navigation and positioning holds paramount importance across various applications, from robotics to autonomous vehicles. A common predicament in location-based systems is the reliance on Global Positioning System (GPS) signals, which may exhibit diminished accuracy and reliability under certain conditions. Moreover, when integrated with the Inertial Navigation System (INS), the GPS/INS system could not provide a long-term solution for outage problems due to its accumulated errors.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.
An integrated navigation system is a promising solution to improve positioning performance by complementing estimated positioning in each sensor, such as a global positioning system (GPS), an inertial measurement unit (IMU), and an odometer sensor. However, under GPS-disabled environments, such as urban canyons or tunnels where the GPS signals are difficult to receive, the positioning performance of the integrated navigation system decreases. Therefore, deep learning-based integrated navigation systems have been proposed to ensure seamless localization under various positioning conditions.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Automotive Engineering, Gyeonggi University of Science and Technology, Siheung 15073, Republic of Korea.
In this study, we propose an enhanced LiDAR-based mapping and localization system that utilizes a camera-based YOLO (You Only Look Once) algorithm to detect and remove dynamic objects, such as vehicles, from the mapping process. GPS, while commonly used for localization, often fails in urban environments due to signal blockages. To address this limitation, our system integrates YOLOv4 with LiDAR, enabling the removal of dynamic objects to improve map accuracy and localization in high-traffic areas.
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