Detection of driver inattention from fluctuations in vehicle operating data

Bando, Shizuka; Nozawa, Akio

doi:10.1007/s10015-014-0191-8

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Compared to cognitive dispersion, cognitive fluctuations remain understudied in this context. Minimizing cognitive fluctuations is important for everyday safety (e.g., driving) (23), and the magnitude of cognitive fluctuations may differentiate clinical conditions (e.g., cognitively intact from mild neurocognitive disorder) (24). Clarifying influences on cognitive fluctuations holds promise to inform evidence-based guidelines that enhance momentary cognition (9).…”

Section: Characterizing Momentary Cognition: Historical Challenges An...mentioning

confidence: 99%

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Hawks¹,

Beck²,

Jung³

et al. 2023

Preprint

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Type 1 diabetes (T1D) is a chronic condition characterized by glucose variability due to autoimmune destruction of insulin-secreting pancreatic beta-cells. Prior research (linking greater glucose variability to cognitive impairment and adverse health outcomes) and recent technological innovations (continuous glucose monitoring [CGM], cognitive ecological momentary assessment [EMA]) laid a foundation for the current study, which aimed to advance understanding of between- and within-person influences on physiological and cognitive processes relevant to health in T1D. We obtained intensive longitudinal, naturalistic measurements of cognition (EMA) and glucose (CGM) in N=199 adults with T1D. Given reduced cognitive performance during periods of low (hypo) and high (hyper) glycemia, our analyses focused on the strength of within-person quadratic associations between glucose and cognition—operationalized as the rate of change in the slope of the glucose-cognition relationship, hereafter, glucose acceleration. First, we used multilevel modeling to obtain group (i.e., aggregated across participants) and individual (i.e., specific to each participant) estimates of glucose acceleration. Next, we used machine learning to identify between-person predictors of individual estimates of glucose acceleration. Finally, we built personalized models to contextualize individual estimates of glucose acceleration in relation to time-varying processes. Group estimates of glucose acceleration were significant for processing speed but not sustained attention. Specifically, processing speed was fastest at glucose concentrations slightly above individuals’ means, regardless of absolute levels of the means. Between individuals, estimates of glucose acceleration varied in relation to several clinical and demographic variables, including age, microvascular complications, and percent time in hypoglycemia (< 70 mg/dL). Within individuals, fluctuations in glucose and processing speed were related to prior night’s sleep duration, self-reported assessment context (e.g., noise), and self-reported stress. Results generate testable insights about shared (across individuals) factors that impact cognitive fluctuations. They also highlight opportunities to support personalized medicine by increasing understanding about unique (person-specific) influences on cognitive fluctuations in T1D.

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Section: Characterizing Momentary Cognition: Historical Challenges An...mentioning

confidence: 99%

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Hawks¹,

Beck²,

Jung³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Laboratory studies indicate that very low (hypo) and, to a lesser extent, very high (hyper) glycemia both impair cognitive performance in those with T1D 8 – 15 . It remains unclear how continuous variation in glucose impacts cognitive fluctuations in naturalistic environments, with implications for everyday safety (e.g., driving, rapid decision-making) 16 . The magnitude of naturalistic cognitive fluctuations also shows promise to stratify individuals based on clinical risk 17 .…”

Section: Introductionmentioning

confidence: 99%

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Hawks,

Beck,

Jung

et al. 2024

npj Digit. Med.

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Type 1 diabetes (T1D) is a chronic condition characterized by glucose fluctuations. Laboratory studies suggest that cognition is reduced when glucose is very low (hypoglycemia) and very high (hyperglycemia). Until recently, technological limitations prevented researchers from understanding how naturally-occurring glucose fluctuations impact cognitive fluctuations. This study leveraged advances in continuous glucose monitoring (CGM) and cognitive ecological momentary assessment (EMA) to characterize dynamic, within-person associations between glucose and cognition in naturalistic environments. Using CGM and EMA, we obtained intensive longitudinal measurements of glucose and cognition (processing speed, sustained attention) in 200 adults with T1D. First, we used hierarchical Bayesian modeling to estimate dynamic, within-person associations between glucose and cognition. Consistent with laboratory studies, we hypothesized that cognitive performance would be reduced at low and high glucose, reflecting cognitive vulnerability to glucose fluctuations. Second, we used data-driven lasso regression to identify clinical characteristics that predicted individual differences in cognitive vulnerability to glucose fluctuations. Large glucose fluctuations were associated with slower and less accurate processing speed, although slight glucose elevations (relative to person-level means) were associated with faster processing speed. Glucose fluctuations were not related to sustained attention. Seven clinical characteristics predicted individual differences in cognitive vulnerability to glucose fluctuations: age, time in hypoglycemia, lifetime severe hypoglycemic events, microvascular complications, glucose variability, fatigue, and neck circumference. Results establish the impact of glucose on processing speed in naturalistic environments, suggest that minimizing glucose fluctuations is important for optimizing processing speed, and identify several clinical characteristics that may exacerbate cognitive vulnerability to glucose fluctuations.

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“…However, most studies measuring driver inattention have focused on behavioral metrics, such as lane deviation, speed variability, steering wheel reversal rate, and response time to time critical or potentially critical events (e.g., hazard detection). For example, decreases in self-regulatory awareness due to lapses in driver attention have been shown to increase mental fatigue and mind wandering while impairing performance (Bando & Nozawa, 2015; Naweed, 2013). Driver inattention has also been measured physiologically, mainly focusing on eye-tracking measures, such as percentage of eyelid closure over time, gaze dispersion, and gaze fixation (D’Orazio, Leo, Guaragnella, & Distante, 2007; Recarte & Nunes, 2000; Sakai, Shin, Kohama, & Uchiyama, 2012).…”

Section: Introductionmentioning

confidence: 99%

Validation of the Attention-Related Driving Errors Scale (ARDES) in an English-Speaking Sample

2016

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Detection of driver inattention from fluctuations in vehicle operating data

Cited by 8 publications

References 7 publications

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

Validation of the Attention-Related Driving Errors Scale (ARDES) in an English-Speaking Sample

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