GPS driving: a digital biomarker for preclinical Alzheimer disease

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.

Going round in circles—The Papez circuit in Alzheimer's disease

“…Different approaches have been made recently to assess whether spatial navigation is sensitive to detect preclinical AD stage (Bayat et al, 2021; Levine et al, 2020) and at‐genetic‐risk ( APOE4 ) (Coughlan et al, 2020). Real‐world paradigms (Bayat et al, 2021) and virtual navigation task studies (Coughlan et al, 2020; Howett et al, 2019; Levine et al, 2020) have proved effective in identifying preclinical AD among older adults (Bayat et al, 2021; Coughlan et al, 2020; Howett et al, 2019; Levine et al, 2020). Spatial navigation and orientation processes emerge, therefore, as potential more sensitive cognitive marker of AD pathophysiology than solely relying on episodic memory measures.…”

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“…Bayat et al ( 113 ) evaluated the ability of in-vehicle GPS data loggers and driving behavior to distinguish cognitively normal older drivers with preclinical AD from those without preclinical AD using machine learning algorithms ( 113 ). For 1 year, 139 subjects (64 with preclinical AD; 75 without preclinical AD, as determined by cerebrospinal fluid biomarkers) were monitored while they drove with a commercial in-vehicle GPS data logger.…”

“…Random Forest models were trained on the GPS data. The receiver operating curve (ROC) area under the curve (AUC) for predicting preclinical AD from driving features alone was 0.82, with the addition of age alone increased to 0.94, and with the additions of age and APOE ε4 status increased to 0.96 ( 113 ).…”

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“…Digital biomarkers can continuously monitor patients to assess therapy response and disease progression without the need for clinical assessment [4,14]. Moreover, they often exhibit higher sensitivity than traditional clinically used methods, enabling early predictive diagnostics by identifying patients at risk of overt clinical disease [15].…”