ArticleAlzheimer's research & therapy2021
GPS driving: a digital biomarker for preclinical Alzheimer disease.
Article in Alzheimer's research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
60 citing papers in PubMed, 3 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Early detection of diseases causing dementia using digital navigation and gait measures: A systematic review of evidence.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Pooled it
- Driving among individuals with chronic conditions: A systematic review of applied research using kinematic driving sensors.Journal of the American Geriatrics Society · 2024Pooled it
- Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Turns and downturns in aging drivers.Transportation research interdisciplinary perspectives · 2026Article
- Lifting the cage of consent a data perspective on evolvable trust relationships.Data in brief · 2026Article
- Driving the neural exposome: Latent mobility states from naturalistic GPS data in older adults.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through Quantitative Analysis.Research square · 2026Article
- Leveraging Naturalistic Driving Digital Biomarkers for Early Mild Cognitive Impairment Detection: Deep Learning Strategies.JMIR medical informatics · 2026Article
- Review of Artificial Intelligence for Clinical Use in Alzheimer's Disease and Related Dementias.Seminars in neurology · 2026Review
- Sensor-Detected Differences in Behaviors of Older Drivers with Pre-MCI and Mild Cognitive Impairment vs. Unimpaired Drivers.Sensors (Basel, Switzerland) · 2026Article
- Association of Daily Driving Behaviors With Mild Cognitive Impairment in Older Adults Followed Over 10 Years.Neurology · 2025Observational
- Detecting Alzheimer's disease using digital virtual reality cognitive tests.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The neighbourhood built environment affects driving behaviours of older adults: a combined geographic information systems and machine learning method.Cities & health · 2025Article
- The Impact of Cognitive Impairment on Pedal Control and Crash Risk Following Stroke: A Pilot Study.Restorative neurology and neuroscience · 2025Article
- Longitudinal associations between plasma biomarkers and changes in naturalistic driving behaviors in older adults.Journal of Alzheimer's disease : JAD · 2025Article
- Early identification of cognitive impairment in older adults from Advanced Activities of Daily Living (AADL): A scoping review.Public health · 2025Article
- Analyzing Mobility Indicators Using Machine Learning to Detect Mild Cognitive Impairment: A Systematic Scoping Review.Applied clinical informatics · 2025Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Identifying and Predicting Cognitive Decline Using Multi-Modal Sensor Data and Machine Learning Approach.Research square · 2025Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
Abstract
backgroundAlzheimer disease (AD) is the most common cause of dementia. Preclinical AD is the period during which early AD brain changes are present but cognitive symptoms have not yet manifest. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. However, the use of these methods is limited by cost, acceptability, and availability. The preclinical stage of AD may have a subtle functional signature, which can impact complex behaviours such as driving. The objective of the present study was to evaluate the ability of in-vehicle GPS data loggers to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning methods.
methodsWe followed naturalistic driving in cognitively normal older drivers for 1 year with a commercial in-vehicle GPS data logger. The cohort included n = 64 individuals with and n = 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers. Four Random Forest (RF) models were trained to detect preclinical AD. RF Gini index was used to identify the strongest predictors of preclinical AD.
resultsThe F1 score of the RF models for identifying preclinical AD was 0.85 using APOE ε4 status and age only, 0.82 using GPS-based driving indicators only, 0.88 using age and driving indicators, and 0.91 using age, APOE ε4 status, and driving. The area under the receiver operating curve for the final model was 0.96.
conclusionThe findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.