Estimating the Need For Medical Intervention Due To Sleep Disruption on the International Space Station
- Paper number
IAC-08.A1.1.8
- Author
Dr. Jerry Myers, NASA Glenn Research Center, United States
- Coauthor
Ms. Beth Lewandowski, National Aeronautics and Space Administration (NASA)/Glenn Research Center, United States
- Coauthor
Dr. Melissa Mallis, Institutes for Behavior Resources, Inc., United States
- Coauthor
Dr. Steve Hursh, Institutes for Behavior Resources, Inc., United States
- Coauthor
Mr. John Brooker, National Aeronautics and Space Administration (NASA)/Glenn Research Center, United States
- Year
2008
- Abstract
Disruption of ordinary sleep patterns during spaceflight is a well documented issue that affects more than half of all shuttle and International Space Station (ISS) mission crews. Both qualitative and quantitative measures of sleep patterns on ISS illustrate that a number of factors contribute to the disruption of normal sleep. These factors include, but are not limited to, insomnia, disrupted sleep schedules due to operations scheduled for off-nominal hours, noise levels within the spacecraft, physiological adaptation to microgravity, and the need to and complications with voiding. To avoid the increases in risk due to a sleep deficit, mitigation strategies have been implemented, including those that require pharmacological consumables. As part of the NASA Exploration Medicine Capability Project, the Integrated Medical Model (IMM) is being developed to study and optimize medical equipment and consumables requirements. The goal of the IMM is to minimize human health risks and maximize mission success. As part of the IMM task, NASA Glenn Research Center is leading the development of a forecasting model designed to determine the consumables required to mitigate risks related to sleep disruption. The model addresses the disruption of normal sleep patterns due to mission operations, environmental noise and off-nominal situations. The model uses an algorithm that assembles representative mission schedules, including nominal and off-nominal conditions considered to be important to the interruption of ordinary sleep patterns. This mission parameter information is fed into a well validate model that predicts human fatigue and performance, given a particular sleep/wake pattern (Sleep, Activity, Fatigue, and Task Effectiveness [SAFTE] Model, IBR Inc). SAFTE has been utilized by the United States Air Force and the transportation industry for work performance decrement predictions and for optimal sleep scheduling. For IMM, sleep and performance metrics generated by SAFTE are exported to a translation function. In the translation function the metrics are compared to thresholds as the method for determining if a remote or self diagnosis is likely to occur. The likelihood of a diagnosis is then correlated to the likelihood that a consumable mitigation approach is needed. By encapsulating this approach within a Monte Carlo engine, a statistically relevant representation of the rate of consumable use is developed. The predicted rate of consumables is compared to data (albeit sparse) from ISS flights for validation. The model information is then transferred to the parent IMM application for further weighting and integration with other medical conditions.
- Abstract document
- Manuscript document
IAC-08.A1.1.8.pdf (🔒 authorized access only).
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