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Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2024 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 05/31/2024  
Task Last Updated: 10/16/2023 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Oswald, Fred  Ph.D. Rice University 
Salas, Eduardo  Ph.D. Rice University 
Key Personnel Changes / Previous PI: Per the Principal Investigator (PI): Drs. Sapirstien and Wick have left the project. A pediatric ICU (PICU) will be used instead of a surgical intensive care unit and neither Drs. Sapirstien nor Wick have involvement there. PICU faculty did not need support. (Ed., 2/15/23). January, 2023: Molly Kilcullen and Jaxon Wu have been added to the project to assist with data analysis in the final period. October 2021 report: Dr. Dietz and Nam Lee have left Johns Hopkins University (JHU) and are no longer working on the project.
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Whitmire, Alexandra  
Center Contact:  
alexandra.m.whitmire@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates: 2 
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees: 2 
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Flight Assignment/Project Notes: NOTE: End date changed to 05/31/2024 per A. Beitman/NASA JSC (Ed., 11/15/23)

NOTE: End date changed to 11/30/2023 per A. Beitman/NASA JSC (Ed., 10/20/2022)

NOTE: End date changed to 11/30/2022 per NSSC information (Ed., 11/2/2021)

NOTE: End date changed to 11/30/2021 per NSSC information (Ed., 10/23/2020)

NOTE: End date changed to 11/30/2020 per NSSC information (Ed., 1/30/2020)

Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generating guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits: Final products from this work will advance evidence-based selection practices for currently difficult to assess teamwork-related competencies. This will result in an astronaut corps more prepared to meet the demands of long duration space exploration. Additionally, these practices can benefit other professions where these competencies are germane.

Task Progress & Bibliography Information FY2024 
Task Progress: Key project tasks and progress: Early project efforts centered on clarifying program focus with research and operational sponsors and building the evidence base for assessment and selection practices through literature reviews, followed by publication of early project literature reviews, and empirical study design and execution in the NASA Human Exploration Research Analog (HERA) and intensive care unit (ICU) analogs. Due to the COVID pandemic, and resulting delays in HERA Campaign 6 and challenges with field data collection in ICU analogs, data collection has been delayed. In efforts to stretch the budget so that the full HERA Campaign 6 data collection can be included in this project, spending on this project has been minimized to a great extent. We have been successful at preserving funds, and the team has made progress with data analysis strategies. Our primary focus in the last year has been on refining data management and analytic tools to complete study analyses, as well as to distribute in a reusable, openly accessible R package. [Ed. Note: "R" is an open-source statistical programming language.]

Task 1.1. Finalize competencies, tasks, and timescales, and performance criteria for each analog. In prior project years, discussions with NASA research and operational sponsors guided us to focus on three main Long Duration Spaceflight Exploration (LDSE) competencies and their sub-competencies: 1) teamwork (including team orientation, team care, and communication), leadership / followership, and operational problem solving (including judgment and adaptability). These decisions were revisited with NASA operational sponsors and confirmed in a site visit three years ago.

Task 1.2 Finalize traditional competency measures. This task remains largely complete but will be revisited during data analysis to identify any poor performing measures.

Task 1.3.1 Map LDSE competencies to existing sociometric evidence and theory. This task remains largely complete but will be revisited during data analysis to identify which of the candidate sociometric measures demonstrate good validity evidence for use in astronaut selection.

Task 1.3.2 Reactive systems task analysis method. This task has been modified to focus on event-based measurement systems incorporating sociometric measures. This method has been published in the Handbook of Distributed Cognition.

Task 1.3.3 Sensor pilots. This task is complete and sensors for project studies have been selected and preliminary validity evidence generated.

Task 4. Develop open architecture assessment system. We have developed multiple iterations of database and scripts for linking and analyzing data from physiological, communication, and location-detection systems. We are finalizing scripts through our last phase of analysis and will deliver software as an open-source package in the R statistical language distributed via GitHub.

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
 None in FY 2024
Back to Search Results    
Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2023 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 11/30/2023  
Task Last Updated: 11/28/2022 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Oswald, Fred  Ph.D. Rice University 
Salas, Eduardo  Ph.D. Rice University 
Key Personnel Changes / Previous PI: Per the Principal Investigator (PI): Drs. Sapirstien and Wick have left the project. A pediatric ICU (PICU) will be used instead of a surgical intensive care unit and neither Drs. Sapirstien nor Wick have involvement there. PICU faculty did not need support. (Ed., 2/15/23). October 2021 report: Dr. Dietz and Nam Lee have left JHU and are no longer working on the project.
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Whitmire, Alexandra  
Center Contact:  
alexandra.m.whitmire@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates: 2 
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees: 2 
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Flight Assignment/Project Notes: NOTE: End date changed to 11/30/2023 per A. Beitman/NASA JSC (Ed., 10/20/2022)

NOTE: End date changed to 11/30/2022 per NSSC information (Ed., 11/2/2021)

NOTE: End date changed to 11/30/2021 per NSSC information (Ed., 10/23/2020)

NOTE: End date changed to 11/30/2020 per NSSC information (Ed., 1/30/2020)

Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generating guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits: Final products from this work will advance evidence-based selection practices for currently difficult to assess teamwork-related competencies. This will result in an astronaut corps more prepared to meet the demands of long duration space exploration. Additionally, these practices can benefit other professions where these competencies are germane.

Task Progress & Bibliography Information FY2023 
Task Progress: Early project efforts centered on clarifying program focus with research and operational sponsors and building the evidence base for assessment and selection practices through literature reviews, followed by the publication of early project literature reviews and empirical study design and execution in the NASA Human Exploration Research Analog (HERA) and ICU (intensive care unit) analogs. Due to the COVID pandemic and resulting delays in HERA Campaign 6 and challenges with field data collection in ICU analogs, data collection has been delayed. In efforts to stretch the budget so that the full HERA Campaign 6 data collection can be included in this project, spending on this project has been minimized. We have been successful at preserving funds, and the team has made progress with data analysis strategies. However, progress on other tasks has slowed. As HERA Campaign 6 finalizes and our ICU data collection is complete, we plan to restart efforts across project tasks and will be focusing on data analysis across analogs and final project deliverables in what we anticipate will be the final upcoming project year.

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
 None in FY 2023
Back to Search Results    
Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2022 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 11/30/2022  
Task Last Updated: 10/06/2021 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Oswald, Fred  Ph.D. Rice University 
Sapirstein, Adam  M.D. Johns Hopkins University 
Wick, Elizabeth  M.D. University of California, San Francisco 
Salas, Eduardo  Ph.D. Rice University 
Key Personnel Changes / Previous PI: October 2021 report: Dr. Dietz and Nam Lee have left JHU and are no longer working on the project.
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Whitmire, Alexandra  
Center Contact:  
alexandra.m.whitmire@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees: 2 
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Flight Assignment/Project Notes: NOTE: End date changed to 11/30/2022 per NSSC information (Ed., 11/2/2021)

NOTE: End date changed to 11/30/2021 per NSSC information (Ed., 10/23/2020)

NOTE: End date changed to 11/30/2020 per NSSC information (Ed., 1/30/2020)

Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generating guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits: Final products from this work will advance evidence-based selection practices for currently difficult to assess teamwork-related competencies. This will result in an astronaut corps more prepared to meet the demands of long duration space exploration. Additionally, these practices can benefit other professions where these competencies are germane.

Task Progress & Bibliography Information FY2022 
Task Progress: Key project tasks and progress: Early project efforts centered on clarifying program focus with research and operational sponsors and building the evidence base for assessment and selection practices through literature reviews, followed by publication of early project literature reviews, and empirical study design and execution in HERA and ICU (intensive care unit) analogs. Due to the COVID pandemic and resulting delays in HERA Campaign 6 and challenges with field data collection in ICU analogs, data collection has been delayed. In efforts to stretch the budget so that the full HERA Campaign 6 data collection can be included in this project, spending on this project has been minimized. We have been successful at preserving funds, and the team has made progress with data analysis strategies. However, progress on other tasks has slowed. As HERA Campaign 6 begins, we plan to re-start efforts across project tasks.

Task 1.1. Finalize competencies, tasks, and timescales and performance criteria for each analog. In prior project years, discussions with NASA research and operational sponsors guided us to focus on three main LDSE competencies and their sub-competencies: 1) teamwork (including team orientation, team care, communication), leadership / followership, and operational problem solving (including judgment, adaptability). These decisions were revisited with NASA operational sponsors and confirmed in a site visit three years ago.

Task 1.2. Finalize traditional competency measures. This task remains largely complete but will be revisited during data analysis to identify any poor performing measures.

Task 1.3.1 Map LDSE competencies to existing sociometric evidence and theory. This task remains largely complete but will be revisited during data analysis to identify which of the candidate sociometric measures demonstrate good validity evidence for use in astronaut selection.

Task 1.3.2 Reactive systems task analysis method. This task has been modified to focus on event-based measurement systems incorporating sociometric measures. A first draft of this method has been published in the Handbook of Distributed Cognition.

Task 1.3.3 Sensor pilots. This task is complete and sensors for project studies have been selected and preliminary validity evidence generated.

Task 4. Develop open architecture assessment system. One of the final project deliverables includes an open ‘middle layer’ system for extracting unobtrusive measures of LDSE competencies from multiple sensor systems. We have developed multiple iterations of database and scripts for linking and analyzing data from physiological, communication, and location-detection systems. We will finalize analysis scripts and deliver software as an open source system for analysis, delivered via GitHub or other code repository system.

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
Articles in Other Journals or Periodicals Bisbey TM, Kilcullen MP, Salas E. "Maintaining resilience in times of crisis: insights from high-reliability organizations." Oxford Research Encyclopedia of Politics. Published online: 31 August 2021. https://doi.org/10.1093/acrefore/9780190228637.013.1570 , Aug-2021
Articles in Peer-reviewed Journals Bisbey TM, Traylor AM, Salas E. "Transforming teams of experts into expert teams: eight principles of expert team performance." Journal of Expertise. 2021 Jun;4(2):190-207. , Jun-2021
Articles in Peer-reviewed Journals Kilcullen M, Bisbey T, Rosen MA, Salas E. "Does team orientation matter? A state of the science review, meta-analysis and multilevel framework." J Organ Behav. 2022 Mar 22. Review. https://doi.org/10.1002/job.2622 , Mar-2022
Back to Search Results    
Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2021 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 11/30/2021  
Task Last Updated: 01/18/2021 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dietz, Aaron  Ph.D. Johns Hopkins University 
Lee, Nam  Ph.D. Johns Hopkins University 
Oswald, Fred  Ph.D. Rice University 
Sapirstein, Adam  M.D. Johns Hopkins University 
Wick, Elizabeth  M.D. Johns Hopkins University 
Salas, Eduardo  Ph.D. Rice University 
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Whitmire, Alexandra  
Center Contact:  
alexandra.m.whitmire@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees:  
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Flight Assignment/Project Notes: NOTE: End date changed to 11/30/2021 per NSSC information (Ed., 10/23/2020)

NOTE: End date changed to 11/30/2020 per NSSC information (Ed., 1/30/2020)

Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generating guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits: Final products from this work will advance evidence-based selection practices for currently difficult to assess teamwork related competencies. This will result in an astronaut corps more prepared to meet the demands of long duration space exploration. Additionally, these practices can benefit other professions where these competencies are germane.

Task Progress & Bibliography Information FY2021 
Task Progress: Period Covered by the Report: 12/01/2019 – 11/30/2020

Overall project aims: To support astronaut selection practices for long duration space (LDSE) missions by addressing theoretical, methodological, and practical challenges for multi-level selection systems incorporating unobtrusive or sociometric measurements.

Key project tasks and progress: Early project efforts centered on clarifying program focus with research and operational sponsors and building the evidence-base for assessment and selection practices through literature reviews. This past year of project effort has focused on publication of early project literature reviews, and empirical study design and execution in Human Exploration Research Analog (HERA) and ICU analogs.

Task 1.1. Finalize competencies, tasks, and timescales and performance criteria for each analog. In prior project years, discussions with NASA research and operational sponsors guided us to focus on three main LDSE competencies and their sub-competencies: 1) teamwork (including team orientation, team care, communication), leadership / followership, and operational problem solving (including judgment, adaptability). These decisions were revisited with NASA operational sponsors and confirmed in a site visit two years ago.

Task 1.2. Finalize traditional competency measures. This task remains largely complete but will be revisited during data analysis to identify any poor preforming measures.

Task 1.3.1 Map LDSE competencies to existing sociometric evidence and theory. This task remains largely complete but will be revisited during data analysis to identify which of the candidate sociometric measures demonstrate good validity evidence for use in astronaut selection.

Task 1.3.2 Reactive systems task analysis method. This task has been modified to focus on event-based measurement systems incorporating sociometric measures. A first draft of this method has been published in the Handbook of Distributed Cognition.

Task 1.3.3 Sensor pilots. This task is complete and sensors for project studies have been selected and preliminary validity evidence generated.

Task 4. Develop open architecture assessment system. One of the final project deliverables includes an open ‘middle layer’ system for extracting unobtrusive measures of LDSE competencies from multiple sensor systems. We have developed multiple iterations of database and scripts for linking and analyzing data from physiological, communication, and location detection systems. We will finalize analysis scripts and deliver software as an open source system for analysis, delivered via github.

[Ed. note: compiled Jan 2021 from annual report submitted to Human Factors & Behavioral Performance (HFBP) element]

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
Articles in Peer-reviewed Journals Traylor AM, Tannenbaum SI, Thomas EJ, Salas E. "Helping healthcare teams save lives during COVID-19: Insights and countermeasures from team science." Am Psychol. 2020 Oct 29. Advance online publication. https://doi.org/10.1037/amp0000750 ; PMID: 33119329 , Oct-2020
Articles in Peer-reviewed Journals Shuffler ML, Salas E, Rosen MA. "The evolution and maturation of teams in organizations: Convergent trends in the new dynamic science of teams." Front Psychol. 2020 Sep 4;11:2128. https://doi.org/10.3389/fpsyg.2020.02128 ; PMID: 33013542; PMCID: PMC7499456 , Sep-2020
Articles in Peer-reviewed Journals Kazi S, Khaleghzadegan S, Dinh JV, Shelhamer MJ, Sapirstein A, Goeddel LA, Chime NO, Salas E, Rosen MA. "Team physiological dynamics: A critical review." Hum Factors. 2019 Sep 26:18720819874160. Online first. https://doi.org/10.1177/0018720819874160 ; PMID: 31557057 , Sep-2019
Articles in Peer-reviewed Journals Salas E, Bisbey TM, Traylor AM, Rosen MA. "Can teamwork promote safety in organizations?" Annual Review of Organizational Psychology and Organizational Behavior. 2020;7:283-313. https://doi.org/10.1146/annurev-orgpsych-012119-045411 , Jan-2020
Books/Book Chapters Kazi S, Khaleghzadegan S, Rosen MA. "Technological advances to understand and improve individual and team resilience in extreme environments." in "Psychology and Human Performance in Space Programs: Research at the Frontier." Ed. L.B. Landon, K.J. Slack, E. Salas. Boca Raton, FL: CRC Press, 2020. p. 87-106. Book: https://doi.org/10.1201/9780429440878 , Oct-2020
Books/Book Chapters Paoletti J, Kilcullen MP, Salas E. "Teamwork in space exploration." in "Psychology and Human Performance in Space Programs: Research at the Frontier." Ed. L.B. Landon, K.J. Slack, E. Salas. Boca Raton, FL: CRC Press, 2020. p. 195-216. Book: https://doi.org/10.1201/9780429440878 , Oct-2020
Books/Book Chapters Croitoru N, Bisbey TM, Salas E. "Team training for long-duration space exploration: A look ahead at the coming challenges." in "Psychology and Human Performance in Space Programs: Extreme Application." Ed. L.B. Landon, K.J. Slack, E. Salas. Boca Raton, FL: CRC Press, 2020. p. 81-99. Book: https://doi.org/10.1201/9780429440854 , Oct-2020
Books/Book Chapters Rosen MA, Kazi S, Khaleghzadegan S. "Microenvironmental influences on team performance in cancer care." in "Geospatial Approaches to Energy Balance and Breast Cancer. Energy Balance and Cancer, vol 15." Ed. D. Berrigan, N. Berger. Cham: Springer, 2019. p. 399-414. https://doi.org/10.1007/978-3-030-18408-7_17 , Jul-2019
Books/Book Chapters Khaleghzadegan S, Kazi S, Rosen MA. "Unobtrusive measurement of team cognition: a review and event-based approach to measurement design." in "Contemporary Research. Models, Methodologies, and Measures in Distributed Team Cognition." Ed. M.D. McNeese, E. Salas, M.R. Endsley. Boca Raton, FL : CRC Press, 2020. p. 95-113. Book: https://doi.org/10.1201/9780429459733 , Sep-2020
Back to Search Results    
Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2019 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 11/30/2019  
Task Last Updated: 09/20/2019 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dietz, Aaron  Ph.D. Johns Hopkins University 
Lee, Nam  Ph.D. Johns Hopkins University 
Oswald, Fred  Ph.D. Rice University 
Sapirstein, Adam  M.D. Johns Hopkins University 
Wick, Elizabeth  M.D. Johns Hopkins University 
Salas, Eduardo  Ph.D. Rice University 
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Williams, Thomas  
Center Contact: 281-483-8773 
thomas.j.will1@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees:  
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generate guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits: Final products from this work will advance evidence-based selection practices for currently difficult to assess teamwork related competencies. This will result in an astronaut corps more prepared to meet the demands of long duration space exploration. Additionally, these practices can benefit other professions where these competencies are germane.

Task Progress & Bibliography Information FY2019 
Task Progress: Reporting for the period 11/7/16-11/6/18

To date, project efforts have centered on clarifying program focus with research and operational sponsors, and building the evidence-base for assessment and selection practices through literature review.

Task 1.1. Finalize competencies, tasks, and timescales and performance criteria for each analog. Per discussions with NASA research and operational sponsors, this work focuses on three main LDSE competencies and their sub-competencies: teamwork (including team orientation, team care, communication), leadership / followership, and operational problem solving (including judgment, adaptability).

Task 1.2. Finalize traditional competency measures. This task is largely complete. We have reviewed the literature for team orientation, team care, communication, leadership / followership, and adaptability. Some of these competencies map clearly on to constructs reported in the literature (e.g., team orientation, communication) while others are more complex (e.g., team care). For these more complex competencies, we have generated a list of component constructs related to this competency and preformed literature reviews on those. The goal of these reviews was to identify specific scales or measurement practices used for focal constructs and their associated validity evidence. We are in the process of generating white papers detailing best measurement and assessment practices for each of these areas.

Task 1.3.1 Map LDSE competencies to existing sociometric evidence and theory. This task is largely completed--we have conducted two literature reviews to synthesize available validity evidence and measurement practices for unobtrusive measures. The first focuses on the use of physiological measurement within teams. This review was systematic, as search terms are relatively definable and the literature is reasonably well organized. The second review focused on unobtrusive measures of team communication including content-based analysis methods (lexical analysis, supervised learning, and generative modeling techniques) and paralinguistic features of speech (communication flow, vocal features, gesture and posture, facial expression, and gaze behavior). This review is narrative as the literature is not well organized and spread across multiple literatures. Each of these reviews has informed study design and measurement planning for this work and will be submitted as white papers to NASA and developed as peer reviewed articles.

Task 1.3.2 Reactive systems task analysis method. We had proposed to develop a method for team task analysis that mapped unobtrusive measurement practices to a given team’s configuration and workflow. After discussion with operational team, it was decided that a scenario design method would be more helpful. We are reframing the deliverables of this task to include guidance on how to design scenarios that tap targeted LDSE competencies by generating scenario events (task conditions) representing opportunities to enact competencies linked to traditional and unobtrusive measurement practices. We are currently developing this approach by linking the findings of literature reviews described above.

Task 1.3.3 Sensor pilots. As unobtrusive measurement methods are relatively new, and there are unanswered questions about their psychometric properties, we are designing and conducting a series of studies to rigorously assess the error structure of data generated with these methods. We have identified (through literature review and task analysis) four categories of measurement facets that could systematically influence data: 1) device, equipment, and processing factors, 2) environmental and physical layout factors, 3) team characteristics, and 4) task and work process factors. We will be conducting a G-study in the upcoming months to determine the magnitude of variance associated with each of these measurement facets.

Task 4. Develop open architecture assessment system. One of the final project deliverables includes an open ‘middle layer’ system for extracting unobtrusive measures of LDSE competencies from multiple sensor systems. We have developed the first iteration of database linking data from physiological, communication, and location-detection systems. This is necessary to enable upcoming data collection efforts and sensor pilot studies. This version of the database is implemented in SQLite for rapid prototyping. The next major iteration will be developed using PostgreSQL to improve scalability for data collection across multiple sites and afford the ability to include complex data extraction methods (e.g., pre-processing of physiological signals, generating measures of synchrony across data streams for a team) within the database itself.

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
Articles in Peer-reviewed Journals Rosen MA, DiazGranados D, Dietz AS, Benishek LE, Thompson D, Pronovost PJ, Weaver SJ. "Teamwork in healthcare: Key discoveries enabling safer, high-quality care." Am Psychol. 2018 May-Jun;73(4):433-50. Review. https://doi.org/10.1037/amp0000298 ; PubMed PMID: 29792459; PubMed Central PMCID: PMC6361117 , Jan-2019
Articles in Peer-reviewed Journals Shuffler ML, Diazgranados D, Maynard MT, Salas E. "Developing, sustaining, and maximizing team effectiveness: An integrative, dynamic perspective of team development interventions." Acad Manag Ann. 2018 Jun;12(2):688-724. https://doi.org/10.5465/annals.2016.0045 ; PubMed PMID: 30931078; PubMed Central PMCID: PMC6438631 , Jun-2018
Books/Book Chapters Rosen MA, Dietz AS, Kazi S. "Beyond Coding Interaction: New Horizons in Interaction Analysis." in "Cambridge Handbook of Group Interaction Analysis." Ed. E. Brauner, M. Boos, M. Kolbe. Cambridge University Press, 2018. p. 142-162. https://doi.org/10.1017/9781316286302.009 , Jul-2018
Back to Search Results    
Project Title:  Developing and Validating Sensor-based Measurement Strategies for Team Member Selection Reduce
Fiscal Year: FY 2017 
Division: Human Research 
Research Discipline/Element:
HRP HFBP:Human Factors & Behavioral Performance (IRP Rev H)
 Start Date: 12/01/2016  
End Date: 11/30/2019  
Task Last Updated: 01/03/2017 		 Download report in PDF pdf
Principal Investigator/Affiliation:   Rosen, Michael  Ph.D. / Johns Hopkins University 
Address:  750 E Pratt St, 15th Floor 
Armstrong Institute for Patient Safety and Quality 
Baltimore , MD 21202-3142 		 Email: mrosen44@jhmi.edu 
Phone: 407-620-1399  
Congressional District: 7 
Web:  
 Organization Type: UNIVERSITY 
Organization Name: Johns Hopkins University 
Joint Agency:  
Comments:  
Co-Investigator(s)
Affiliation: 
Dietz, Aaron  Ph.D. Johns Hopkins University 
Lee, Nam  Ph.D. Johns Hopkins University 
Oswald, Fred  Ph.D. Rice University 
Sapirstein, Adam  M.D. Johns Hopkins University 
Wick, Elizabeth  M.D. Johns Hopkins University 
Salas, Eduardo  Ph.D. Rice University 
Project Information: Grant/Contract No. NNX17AB55G 
Responsible Center: NASA JSC 
Grant Monitor: Williams, Thomas  
Center Contact: 281-483-8773 
thomas.j.will1@nasa.gov 
Unique ID: 11107  		Solicitation / Funding Source: 2014-15 HERO NNJ14ZSA001N-MIXEDTOPICS. Appendix E: Behavioral Health & Human Health Countermeasures Topics 
Grant/Contract No.: NNX17AB55G 
Project Type: GROUND 
Flight Program:  
TechPort: No  		No. of Post Docs:  
No. of PhD Candidates:  
No. of Master's Candidates:  
No. of Bachelor's Candidates:   		No. of PhD Degrees:  
No. of Master's Degrees:  
No. of Bachelor's Degrees:  
Human Research Program Elements: (1) HFBP:Human Factors & Behavioral Performance (IRP Rev H)
Human Research Program Risks: (1) BMed:Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders
(2) Team:Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team
Human Research Program Gaps: (1) BMed-101:We need to identify, quantify, and validate the key selection factors for astronaut cognitive and behavioral strengths (e.g., resiliency) and operationally-relevant performance threats for increasingly Earth independent, long-duration, autonomous, and/or long-distance exploration missions.
(2) Team-103:We need to identify psychological and psychosocial factors, measures, and combinations thereof for use in selecting individuals and composing highly effective crews most likely to maintain team function during shifting autonomy in increasingly earth independent, long duration exploration missions.
Task Description: Selection of astronauts for Long Duration Spaceflight Exploration (LSDE) missions poses challenges for NASA including the need to define and select candidates based on a new set of behavioral competencies underpinning effective performance in these extended and isolated missions. Additionally, an effective selection system will require new measurement methods capable of discriminating between individuals in a population already exhibiting extreme range restriction. Sensor-based, sociometric, and more generally, unobtrusive measurement methods hold promise as valuable tools for addressing these needs and complementing existing competency assessment methods. The proposed work seeks to advance the science and practice surrounding diagnostic measurement of LDSE competencies using a blended approach where sociometric techniques are combined with traditional assessment methods. We will leverage our team's extensive, transdisciplinary experience in signal processing and analysis of complex dynamic network data, psychometrics, performance assessment, and developing theory and strategies for LDSE team improvement to: (1) generate predictive validity evidence for LDSE behavioral competencies, (2) develop sociometric indices of those competencies and provide evidence of their validity, (3) develop an open architecture system for integrating sensor-based measurement systems and extracting sociometric indices, and (4) generating guidelines for the use of sociometric measures in the selection process. Our technical approach for achieving these aims involves metric development, metric validation, assessment architecture system design, and selection guideline development. First, metric development will involve updating our current literature review of unobtrusive measurement to incorporate findings from recent NASA efforts. We will also apply reactive systems modeling to systematically map sensor-based measurement system requirements with potential metrics for assessment. Next, we will conduct exploratory human in the loop analyses to identify additional candidate measures using tensor-decomposition methods of archival data to detect performance patterns. Metric validation will occur in a LDSE analog (HERA--Human Exploration Research Analog) as well as two clinical residency programs in order to increase the sample size needed for analysis. Specifically, we will prospectively collect the following from each context: traditional assessments of competencies (self-report, observation), sociometric assessments of competencies, and multi-dimensional outcomes (task outcome measures, multiple rater assessments). Validation studies will be conducted to establish the link between LDSE competencies and performance outcomes, demonstrate the relationships between sociometric and traditional measures of LDSE competencies, as well as index the amount of variability in outcome measures accounted for by sociometric indices above and beyond traditional measures. The next phase of this project involves advancing methodology and analytic capabilities for competency assessment using sociometric indices. The analytics developed will distill meaningful metrics from complex, dynamic sensor data that can be used across a range of sensor devices, providing a generalizable ‘middle layer’ architecture for processing these data. This analytic process involves integrating and mapping these diverse measurement sources to generate a valid and actionable depiction of performance (i.e., to guide selection). Findings from these efforts will result in evidence-based, practical, and validated guidelines for incorporating unobtrusive measurement into the astronaut selection process. Overall, successful completion of this project will advance the science and practice of multi-method individual and team LDSE competency assessment.

Research Impact/Earth Benefits:

Task Progress & Bibliography Information FY2017 
Task Progress: New project for FY2017.

Bibliography: Description: (Last Updated: 11/25/2023) 

Show Cumulative Bibliography
 
 None in FY 2017
Back to Search Results