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. 2021 Jun 14;13(1):115.
doi: 10.1186/s13195-021-00852-1.

GPS driving: a digital biomarker for preclinical Alzheimer disease

Sayeh Bayat  1   2 , Ganesh M Babulal  3   4   5 , Suzanne E Schindler  3   4 , Anne M Fagan  3   4   6 , John C Morris  3   4   6   7   8   9 , Alex Mihailidis  10   11   12 , Catherine M Roe  3   4
Affiliations

GPS driving: a digital biomarker for preclinical Alzheimer disease

Sayeh Bayat et al. Alzheimers Res Ther. .

Abstract

Background: Alzheimer 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.

Methods: We 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.

Results: The 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.

Conclusion: The findings suggest that GPS driving may serve as an effective and accurate digital biomarker for identifying preclinical AD among older adults.

Keywords: Global positioning system; Machine learning; Naturalistic driving; Preclinical Alzheimer disease.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The final area under the receiver operating curves (AUC) for each model. Legends show the AUC as each feature is added to the model
Fig. 2
Fig. 2
Importance ranking of all features
See this image and copyright information in PMC

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