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.
Highlights
Worldwide, around 50 million individuals are living with dementia, and this number is projected to increase to 152 million by 2050 [1]
The objective of this paper is to use machine learning techniques to test the ability of global positioning system (GPS) data to distinguish persons with and without preclinical Alzheimer disease (AD), defined using cerebrospinal fluid, in a cohort of cognitively intact older adults from a longitudinal driving study
Participants who met the following criteria were included in the study: (1) were age 65 years or older, (2) had normal cognition at a clinical assessment that included assignment of the Clinical Dementia RatingTM (CDRTM) [22], (3) underwent Cerebrospinal fluid (CSF) collection, (4) possessed a valid driving licence, and (5) drove at least weekly, on average
Summary
Around 50 million individuals are living with dementia, and this number is projected to increase to 152 million by 2050 [1]. Given the growing socioeconomic impacts of AD, many studies have focused on the development of specific treatment strategies aimed at slowing down or even preventing the onset of symptomatic AD [7, 8]. These strategies may require AD to be diagnosed at an early stage before significant damage to the brain has occurred, when patients are still cognitively normal. Alzheimer disease (AD) is the most common cause of dementia. The presence of AD brain changes can be ascertained by molecular biomarkers obtained via imaging and lumbar puncture. 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
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