Submarine missions represent a unique operational environment characterized by restricted natural time cues, shift work, and high cognitive demands. Understanding how individuals adapt their sleep, caffeine consumption, and circadian timing under these conditions is essential for maintaining performance and wellbeing. We analyzed data from 30 participants before, during, and after a submarine mission. During the mission, participants were assigned to one of two operational shifts.

We investigated caffeine consumption patterns, sleep behavior, and circadian rhythmicity using wearable sensor data and mathematical models. Individual caffeine intake records were incorporated into a pharmacokinetic model to estimate caffeine concentration profiles over time. These estimates enabled us to examine whether caffeine consumption was primarily socially driven, habitually driven, or used strategically as a countermeasure against sleepiness.

Sleep timing was analyzed relative to operational shift schedules. Differences between shifts were observed, indicating shift-specific adaptations of sleep behavior during the mission. Furthermore, post-mission assessments revealed considerable variability in circadian timing across participants. We hypothesize that differences in sleep timing during the mission contribute to this variability in circadian phase following mission completion.

Circadian timing was estimated using established mathematical models driven by environmental and behavioral inputs. However, model predictions did not consistently align with physiological measurements, indicating an incomplete understanding of the effective zeitgeber input in the submarine environment. Ongoing work focuses on identifying the factors responsible for these discrepancies and improving the characterization of circadian entrainment under conditions of limited natural time cues.

These findings highlight the complex interactions between caffeine use, sleep scheduling, and circadian regulation in operational settings and underscore the need for improved modeling approaches to understand human adaptation in isolated and shift-work environments.