Sleep is a much-discussed topic in the shipping industry, with its irregular work hours, guaranteed impromptu activities and ever changing conditions. Mostly, such discussions are focused on two aspects: duration and shift regime. Advanced sleep analysis has revealed that quality of sleep, integrating aspects as type of sleep (REM, non REM) and analysis of the circadian cycle are as important. Again, technology comes to the rescue. With wearables, one can pattern sleep phases, and with longer term measurements have a good indication on how well rested or recuperated an individual is. Aggregation to group levels allow for team measurement and – if required – actions. It astonishes that so little is done, when considering the huge potential impact. Multiple researchers demonstrated that reaction time drops with 25 to 50% after 5 days of low quality sleep. Fatigue is influencing attention and reaction time, but also immediate priorities, expectations, decision-making and memory. Circadian de-synchrony is associated with numerous health risks, higher blood pressure, higher levels of cortisol hormone, and lowered production of antibodies. Recent reports of UC Berkeley indicate that sleep deprivation and quality deterioration excessively boosts the part of the brain most closely connected to depression, anxiety and other psychiatric disorders.
Energy and recovery are highly individual characteristics of a person, and hard to measure. Complex algorithmic tools and machine learning techniques nowadays use heart rhythm, heart rate variability, sleep quality and breathing patterns to detect individual patterns and trends. Using a battery analogy, data analysis can identify how much a body ‘charges’ or ‘depletes’. Insights allow for personal coaching, better task assignment in teams and self-management.
Stress exists in many forms and shapes. People casually talk about ‘good’ and ‘bad’ stress. Statistical methods can derive from the variability of heart rates a good indication of individual levels in so-called time and frequency domains. Machine learning techniques and longer-term analysis can reveal individual stress levels with surprising accuracy. Trials performed on board of coaster vessels show a clear pattern of individual crewmembers and team according to activities performed. Surfacing such data and trends is probably the most important step in longer-term health and risk prevention.
The impact of regular activity on preventive health, alertness, and general wellbeing is generally accepted. Crew on log sea voyages is particularly prone to lack of exercise and resulting chronically diseases. While many responsibilization programs have been trialled, the most impactful lever to pull here is self-management. Simple apps provide the necessary framework and facts to promote physical activity and have people adhere to programs.
In sum, technology helps us to bring together a number of factors, impacting greatly safety and performance of individuals in teams. The progressive insights gathered will improve greatly life and conditions in harsh working environments such as the maritime industry