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At least 199 records · Page 11
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NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect. This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure in urban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗
NASA NTRS2020

Philadelphia Health & Air Quality: Assessing Land Surface Temperature, Vegetation Cover, and Compounding Vulnerability Factors to Identify High Priority Areas for Cooling Initiatives in Philadelphia, Pennsylvania

Heat is the leading cause of weather-related deaths in the US, with heat-related hospitalizations increasing by 2-5% between 2001-2010. In Philadelphia alone, 137 heat-related deaths were recorded between 2010-2018, while a total of 18 daily temperature records have been set since 2010. Temperature is relatively higher in cities compared to rural areas, a phenomenon known as the urban heat island effect.This effect exaggerates daytime maximum temperatures and nighttime heat retention in urban areas, which increases heat exposure inurban environments and especially impacts vulnerable populations. Vulnerability to heat-related illnesses is determined by a combination of risk factors, such as demographics, socioeconomic status, and preexisting health conditions. This project supported the Philadelphia Department of Public Health and Office of Sustainability by identifying priority areas for cooling interventions, such as heat danger educational outreach and urban tree planting. The team developed heat vulnerability scores for each census tract within Philadelphia. Remotely sensed land surface temperature, normalized difference vegetation index, normalized difference built-up index, normalized difference water index, and albedo data were calculated from Aqua Moderate Resolution Imaging Spectroradiometer and Landsat 8 Operational Land Imager/Thermal Infrared Sensor instruments. These variables were weighted against socioeconomic variables and preexisting health conditions using a principal component analysis. A total of 74 census tracts clustered were identified as high-risk areas for heat-related illnesses. 15 of these census tracts also had very low tree density (lower 20th percentile) and should be targeted for tree planting initiatives. The findings of this project will help target interventions to mitigate heat-related health issues and improve the overall wellness of Philadelphia residents.

Health & Air Quality↗
NASA NTRS2021

An Uncertainty Quantification Framework for Autonomous Flight System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle. This work has been accepted for publication at the International Journal of Prognostics and Health Management Jan 2021. Minor edits have been incorporated to this original submission to incorporate complete overview and software integration.

Uncertainty Quantification↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗
NASA NTRS2023

Health and Safety Effects of Airborne Soil Dust in the Americas and Beyond

Risks associated with dust hazards are often underappreciated, a gap between the knowledge pool and public awareness that can be costly for impacted communities. This study reviews the emission sources and chemical, physical, and biological characteristics of airborne soil particles (dust) and their effects on human and environmental health and safety in the Pan-American region. American dust originates from both local sources (western United States, northern Mexico, Peru, Bolivia, Chile, and Argentina) and long-range transport from Africa and Asia. Dust properties, as well as the trends and interactions with criteria air pollutants, are summarized. Human exposure to dust is associated with adverse health effects, including asthma, allergies, fungal infections, and premature death. In the Americas, a well-documented and striking effect of soil dust is its association with Coccidioidomycosis, commonly known as Valley fever, an infection caused by inhalation of soil-dwelling fungi unique to this region. Besides human health, dust affects environmental health through nutrients that increase phytoplankton biomass, contaminants that diminish water supply and affect food (crops/fruits/vegetables and ready-to-eat meat), spread crop and marine pathogens, cause Valley fever among domestic and wild animals, transport heavy metals, radionuclides and microplastics, and reduce solar and wind power generation. Dust is also a safety hazard to road transportation and aviation, in the southwestern US where blowing dust is one of the deadliest weather hazards. To mitigate the harmful effects, coordinated regional and international efforts are needed to enhance dust observations and prediction capabilities, soil conservation measures, and Valley fever and other disease surveillance.

health↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗
NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

behavioral health↗
NASA NTRSDate not supplied

Behavioral Health Benefits of the ISS Pick-and-Eat Crop Growth System

Summary: We summarize findings related to assessing the pick-and-eat salad-crop productivity and acceptability of the ISS food system as a countermeasure to maintain behavioral health in long-duration spaceflight. BACKGROUND Plants are a potential countermeasure for the stresses of living in space. Caring for plants and eating fresh food have been shown to serve as a psychological benefit for previous astronauts and for others in analogous environments such as Antarctica [1,2]. Gardening can be therapeutic in terrestrial settings [3]. These effects may carry over or be more pronounced in austere environments such as spaceflight. It can reduce stress and increase sensory stimulation and enjoyment [2]. Growing crops may offer astronauts meaningful and engaging work as they care for living things in an austere environment away from Earth’s nature and as they provide sustenance for their crew. We examined the extent to which interacting with plants (e.g., tending to, consuming) was related to behavioral health outcomes in long-duration space missions. METHOD Participants were 27 astronauts that interacted with the plant system VEG-04, VEG-05, and HRF-VEG (VEG-03 I-L, PH-04). In total, there were 106 in-flight observations. Participants completed monthly measures of mood and well-being, enjoyment and time spent performing specific crop growth and consuming the plants, meaningfulness, performance, connections with others and the Earth, desire to work with and eat plants, and experiences with struggling or dying plants. Higher ends of the 7-point Likert scale indicated more positive outcomes, lower ends of the scale were anchored with more negative outcomes, and the scale midpoints indicated neutral positions. Participants selected a specific survey version based on whether they interacted with the plant system(s) or not, and we included only the scores from the crewmembers that participated in any given activity. RESULTS & DISCUSSION Results indicated that participants generally rated tasks related to the plants and crop growth as enjoyable. Consuming the harvested plants and voluntary viewing were the most enjoyable activities. Tending to the plants was reported as moderately enjoyable, indicating BHP benefits from this work task. The BHP impacts of whether the task was engaging, meaningful, supported well-being, or was demanding for a crewmember were generally positive and increased over time. As shown in the Figure, working with plants was reported as more engaging, meaningful, and beneficial to well-being over time, while the demand from the task was moderate to low and consistent over time. Engaging, meaningful, and supporting well-being were strongly, positively correlated with each other, which is consistent with research that suggests engaging and meaningful work can support well-being. Plant activities such as voluntary viewing, watering, harvesting, and consuming, were positively related to task engagement and meaning, and enjoyment and well-being. Findings suggest that working with plants and consuming them can be a behavioral health countermeasure for long-duration spaceflight. REFERENCES [1] Schlacht I, et al. (2020) Impact of plants in isolation: The EDEN-ISS…in Antarctica. In Advances in Human Factors of Transportation, Washington DC. [2] Vessel E, Russo S (2015) Effects of reduced sensory stimulation and…countermeasures…. NASA/TM-2015-218576. [3] Odeh R, Guy C (2017) Gardening for therapeutic people-plant interactions during LDSM. Open Agriculture, 2(1), 1-13. ACKNOWLEDGEMENTS This research was supported by the Human Health and Performance Contract NNJ15HK11B, and NASA grant MTL #1075 (PI G.D. Massa).

countermeasure↗
NASA NTRS1972

Apollo experience report: Protection of life and health

The development, implementation, and effectiveness of the Apollo Lunar Quarantine Program and the Flight Crew Health Stabilization Program are discussed as part of the broad program required for the protection of the life and health of U.S. astronauts. Because the goal of the Apollo Program has been the safe transport of men to the moon and back to earth, protection of the astronauts and of the biosphere from potentially harmful lunar contaminants has been required. Also, to ensure mission success, the continuing good health of the astronauts before and during a mission has been necessary. Potential applications of specific aspects of the health and quarantine programs to possible manned missions to other planets are discussed.

Wooley, B. C.↗
NASA NTRS1969

Leave taking and overtime behavior as related to demographic, health, and job variables

An intra-installation model is formulated that correlates demographic, health and job related variables to the various types and amounts of leave and overtime taking behavior of employees. Statistical comparison of composite health ratings assigned to subjects based upon clinical criteria and bio-statistical data show that those employees who take the most annual leave as well as sick leave are the ones that have the poorest health ratings; employees who put in the most overtime have also the poorest health records. Stress effects of peak activity periods increase use of sick leave immediately after peak activity but not the use of annual leave.

Arnoldi, L. B.↗
NASA NTRS1969

Environmental health program in NASA

The NASA policy on environmental health uses medical and environmental concepts to: (1) Determine the health status of employees; (2) prevent illness and promote good health among employees; and (3) identify and control factors that affect the health of personnel and quality of environment. Evaluation and control of physical, chemical, radiological and biological factors surrounding personnel and which represent physiological and psychological stresses and impairment are considered.

Marrazzo, R. M.↗
NASA NTRS1975

Systems identification and application systems development for monitoring the physiological and health status of crewmen in space

The use of automated, analytical techniques to aid medical support teams is suggested. Recommendations are presented for characterizing crew health in terms of: (1) wholebody function including physiological, psychological and performance factors; (2) a combination of critical performance indexes which consist of multiple factors of measurable parameters; (3) specific responses to low noise level stress tests; and (4) probabilities of future performance based on present and periodic examination of past performance. A concept is proposed for a computerized real time biomedical monitoring and health care system that would have the capability to integrate monitored data, detect off-nominal conditions based on current knowledge of spaceflight responses, predict future health status, and assist in diagnosis and alternative therapies. Mathematical models could play an important role in this approach, especially when operating in a real time mode. Recommendations are presented to update the present health monitoring systems in terms of recent advances in computer technology and biomedical monitoring systems.

Leonard, J. I.↗
NASA NTRS1985

Estimating Health Services Requirements

In computer program NOROCA populations statistics from National Center for Health Statistics used with computational procedure to estimate health service utilization rates, physician demands (by specialty) and hospital bed demands (by type of service). Computational procedure applicable to health service area of any size and even used to estimate statewide demands for health services.

Alexander, H. M.↗
NASA NTRS1988

Integrated control and health management. Orbit transfer rocket engine technology program

To insure controllability of the baseline design for a 7500 pound thrust, 10:1 throttleable, dual expanded cycle, Hydrogen-Oxygen, orbit transfer rocket engine, an Integrated Controls and Health Monitoring concept was developed. This included: (1) Dynamic engine simulations using a TUTSIM derived computer code; (2) analysis of various control methods; (3) Failure Modes Analysis to identify critical sensors; (4) Survey of applicable sensors technology; and, (5) Study of Health Monitoring philosophies. The engine design was found to be controllable over the full throttling range by using 13 valves, including an oxygen turbine bypass valve to control mixture ratio, and a hydrogen turbine bypass valve, used in conjunction with the oxygen bypass to control thrust. Classic feedback control methods are proposed along with specific requirements for valves, sensors, and the controller. Expanding on the control system, a Health Monitoring system is proposed including suggested computing methods and the following recommended sensors: (1) Fiber optic and silicon bearing deflectometers; (2) Capacitive shaft displacement sensors; and (3) Hot spot thermocouple arrays. Further work is needed to refine and verify the dynamic simulations and control algorithms, to advance sensor capabilities, and to develop the Health Monitoring computational methods.

Holzmann, Wilfried A.↗
NASA NTRS1988

Plant health sensing

If plants are to be used as a food source for long term space missions, they must be grown in a stable environment where the health of the crops is continuously monitored. The sensor(s) to be used should detect any diseases or health problems before irreversible damage occurs. The method of analysis must be nondestructive and provide instantaneous information on the condition of the crop. In addition, the sensor(s) must be able to function in microgravity. This first semester, the plant health and disease sensing group concentrated on researching and consulting experts in many fields in attempts to find reliable plant health indicators. Once several indicators were found, technologies that could detect them were investigated. Eventually the three methods chosen to be implemented next semester were stimulus response monitoring, video image processing and chlorophyll level detection. Most of the other technologies investigated this semester are discussed here. They were rejected for various reasons but are included in the report because NASA may wish to consider pursuing them in the future.

Manukian, Ara↗
NASA NTRS1990

Psychological Health Maintenance on Space Station Freedom

The scheduling of crew rotations for up to 180 days on Space Station Freedom presents a special challenge for behavioral scientists who are tasked with providing psychological support for the crews, their families, and mission flight controllers. Preflight psychological support planning may minimize the negative impact of psychological and social issues on mission success, as well as assist NASA management in making real-time mission planning decisions in the event of a significant social event (for example, the death of a family member). During flight, the combined psychological, emotional, and social stressors on the astronauts must be monitored, along with other aspects of their health. The Health Maintenance Facility (HMF) will have the capability of providing preventive, diagnostic, and therapeutic assistance for significant psychiatric and interpersonal problems which may develop. Psychological support will not end with the termination of the mission. Mental health professionals must be part of the team of medical personnel whose job will be to facilitate the transition—physical and mental—from the space environment back to planet Earth. This paper reviews each phase of mission planning for Space Station Freedom and specifies those factors that may be critical for psychological health maintenance on extended-duration space missions.

Patricia A Santy↗