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Vigilance usually deteriorates over prolonged driving at non-optimal times of day. Exposure to blue-enriched light has shown to enhance arousal, leading to behavioral benefits in some cognitive tasks. However, the cognitive effects of long-wavelength light have been less studied and its effects on driving performance remained to be addressed. We tested the effects of a blue-enriched white light (BWL) and a long-wavelength orange light (OL) vs. a control condition of dim light on subjective, physiological and behavioral measures at 21:45 h. Neurobehavioral tests included the Karolinska Sleepiness Scale and subjective mood scale, recording of distal-proximal temperature gradient (DPG, as index of physiological arousal), accuracy in simulated driving and reaction time in the auditory psychomotor vigilance task. The results showed that BWL decreased the DPG (reflecting enhanced arousal), while it did not improve reaction time or driving performance. Instead, blue light produced larger driving errors than OL, while performance in OL was stable along time on task. These data suggest that physiological arousal induced by light does not necessarily imply cognitive improvement. Indeed, excessive arousal might deteriorate accuracy in complex tasks requiring precision, such as driving.

Attention maintenance is highly demanding and typically leads to vigilance decrement along time on task. Therefore, performance in tasks involving vigilance maintenance for long periods, such as driving, tends to deteriorate over time. Cognitive performance has been demonstrated to fluctuate over 24 h of the day (known as circadian oscillations), thus showing peaks and troughs depending on the time of day (leading to optimal and suboptimal times of day, respectively). Consequently, vigilance decrements are more pronounced along time on task when it is performed at suboptimal times of day. According to research, light exposure (especially blue-enriched white) enhances alertness. Thus, it has been proposed to prevent the vigilance decrement under such adverse circumstances. We aimed to explore the effects of blue-enriched white light (vs. dim light) on the performance of a simulated driving task at a suboptimal time of day. A group of evening-types was tested at 8 am, as this chronotype had previously shown their largest vigilance decrement at that time. In the dim light condition, vigilance decrements were expected on both subjective (as increments in the Karolinska Sleepiness Scale scores) and behavioral measures [as slower reaction times (RTs) in the auditory Psychomotor Vigilance Task, slower RTs to unexpected events during driving, and deteriorated driving accuracy along time on task]. Physiological activation was expected to decrease (as indexed by an increase of the distal-proximal temperature gradient, DPG). Under blue-enriched white light, all these trends should be attenuated. Results from the control dim light condition replicated the vigilance decrement in all measures. Most important, the blue-enriched white light attenuated this decrement, leading to both lower DPG and faster RTs. However, it impaired accuracy of driving performance, and did not have any effect on subjective sleepiness. We conclude that exposure to blue-enriched light provides an effective countermeasure to enhance vigilance performance at suboptimal times of day, according to measures such as RTs. However, it should be considered that alerting effects of light could impair accuracy in precision tasks as keeping a proper car position. The current findings provide ergonomic implications for safety and fatigue related management systems.

The present study proposes a classification model for the differential diagnosis of primary insomnia (PI) and delayed sleep phase disorder (DSPD), applying machine learning methods to circadian parameters obtained from ambulatory circadian monitoring (ACM). Nineteen healthy controls and 242 patients (PI = 184; DSPD = 58) were selected for a retrospective and non-interventional study from an anonymized Circadian Health Database (https://kronowizard.um.es/). ACM records wrist temperature (T), motor activity (A), body position (P), and environmental light exposure (L) rhythms during a whole week. Sleep was inferred from the integrated variable TAP (from temperature, activity, and position). Non-parametric analyses of TAP and estimated sleep yielded indexes of interdaily stability (IS), intradaily variability (IV), relative amplitude (RA), and a global circadian function index (CFI). Mid-sleep and mid-wake times were estimated from the central time of TAP-L5 (five consecutive hours of lowest values) and TAP-M10 (10 consecutive hours of maximum values), respectively. The most discriminative parameters, determined by ANOVA, Chi-squared, and information gain criteria analysis, were employed to build a decision tree, using machine learning. This model differentiated between healthy controls, DSPD and three insomnia subgroups (compatible with onset, maintenance and mild insomnia), with accuracy, sensitivity, and AUC >85%. In conclusion, circadian parameters can be reliably and objectively used to discriminate and characterize different sleep and circadian disorders, such as DSPD and OI, which are commonly confounded, and between different subtypes of PI. Our findings highlight the importance of considering circadian rhythm assessment in sleep medicine.

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