NOTE: Element change to Human Factors & Behavioral Performance; previously Behavioral Health & Performance (Ed., 1/18/17)

NOTE: End date changed to 12/31/2017 per NSSC information (Ed., 6/16/16)

This ground-based research was designed to address the following Program Requirements Document (PRD) Risk and Behavioral Health and Performance (BHP) Integrated Research Plan (IRP; 2011).

PRD Risk: Risk of Performance Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team.

IRP (Integrated Research Plan) Gap – Team1: We need to understand the key threats, indicators, and life cycle of the team for autonomous, long duration and/or distance exploration missions.

The research targeted three specific aims that comprised an integrated approach for measuring, monitoring, and regulating teamwork processes and long-term team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research used Experience Sampling Methods (ESM; daily assessments) to assess team functioning across a range of ICE environments (short and long duration; Antarctica and NASA mission simulations). The purpose of this research aim was to characterize patterns of variation and dynamics for key teamwork processes (e.g., cohesion, collaboration, conflict). Benchmark data in ICE analog environments are important for developing insights into the nature of problems that may emerge that challenge team member interactions and team functioning. Findings from the benchmark studies are informative of the types of challenges that may be faced by space crews on long duration missions.

(2) Extend development of the team interaction sensor (TIS) technology (i.e., a wearable wireless sensor package). The purpose of this research aim was to advance development of a sensor technology to capture dynamic multimodal (i.e., physiological and behavioral) data that unobtrusively assesses team member interactions. Initial laboratory validation demonstrated the reliability and accuracy of the monitoring technology (Kozlowski, Biswas, & Chang, 2013) and its ability to predict affective reactions to stressed interactions (Kozlowski, Biswas, & Chang, 2014) sufficient to establish proof of concept. The extensions (a) added an additional sensing capability (i.e., swallow monitoring); (b) technology development to make the system more robust (i.e., packaging, energy efficiency; hardware, Bluetooth integration, algorithms, and software); and technology transfer to the NASA Wearable Electronics Application and Research Lab (WEAR Lab) at the Johnson Space Center (JSC).

(3) Develop a teamwork interaction metric and support system. The TIS provides high frequency data on team interaction indicators. The purpose of this research aim was to develop supporting components required for the data to be utilized as a countermeasure for team members to regulate psycho-social health: (a) Metrics – algorithms were developed to filter and parse the raw data streams into a meaningful measure that reflects teamwork functioning. The metric was then validated against prior laboratory data and in NASA mission simulation. (b) Distributed Networked Dashboard – a prototype system architecture / design was developed to distribute sensor information to computers and mobile devices, and (c) design concepts for a team effectiveness dashboard were developed for displaying teamwork interaction metrics and feedback to team members. The ultimate implementation and utilization of the system, however, will necessitate the direct involvement of NASA Operations personnel and astronaut end-users.

Products and findings from this research have the capability of reducing the risk of team performance decrements due to poor teamwork interactions by (a) characterizing normative and anomalous patterns of team functioning; (b) developing a technology to unobtrusively monitor team member interaction patterns; and (c) providing support to maintain teamwork.

References

Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and teams (Monograph). Psychological Science in the Public Interest, 7, 77-124.

Salas, E., Tannenbaum, S. I., Kozlowski, S. W. J., Miller, C., Mathieu, J. E., & Vessey, W. B. (2015). Teams in space exploration: A new frontier for the science of team effectiveness. Current Directions in Psychological Science, 24(3), 200-207.

Slack, K. J., Williams, T. J., Schneiderman, J. S., Whitmore, A. M., & Picano, J. J. (2016). Evidence report: Risk of adverse cognitive or behavioral conditions and psychiatric disorders. Human Research Program, Behavioral Health and Performance. National Aeronautics and Space Administration, Johnson Space Center. Houston, TX

Kozlowski, S. W. J., Biswas, S., & Chang, C.-H. (2013). Developing, maintaining, and restoring team cohesion. Final Report, National Aeronautics and Space Administration (NNX09AK47G). Houston, TX

Kozlowski, S. W. J., Biswas, S., & Chang, C.-H. (2014, February). Capturing and regulating the dynamics of team collaboration and cohesion. Presented at the NASA Human Research Program Investigators' Workshop, Galveston, TX

Project Summary

Teamwork processes –cognitive, motivational, affective, and behavioral – have been researched in the psychological sciences for well over a half century. Several lines of systematic research, large scale literature reviews, and meta-analytic summaries have firmly established that team processes, as key indicators of psycho-social team health, are critical contributors to team effectiveness, especially for “action” teams performing complex, interdependent tasks (Kozlowski & Ilgen, 2006). Disruptions to teamwork, due to conflict, low cohesion, or poor collaboration, have the potential to threaten team effectiveness. This is particularly the case under the isolated, confined, and extreme (ICE) conditions that can be anticipated for long duration space missions. These difficult operating environments are further challenged by high team autonomy and time lagged communications with ground. For high reliability teams, a disruption in good teamwork, especially at an inopportune time when well-coordinated teamwork is critical, can have disastrous consequences (Salas, Tannenbaum, Kozlowski, Miller, Mathieu, & Vessey, 2015; Slack, Williams, Schneiderman, Whitmire, & Picano, 2016). Thus, the capability for NASA to measure, monitor, and facilitate good teamwork interactions for flight crews is essential for overall mission effectiveness for the NASA strategic plan for space exploration. Developing this capability has been the goal of this research program.

This ground-based research addressed the following Program Requirements Document (PRD) Risk and Behavioral Health and Performance (BHP) Integrated Research Plan (IRP; 2011).

PRD Risk: Risk of Performance Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team. IRP Gap – Team1: We need to understand the key threats, indicators, and life cycle of the team for autonomous, long duration and/or distance exploration missions.

The research targeted three specific aims that comprised an integrated approach for measuring, monitoring, and regulating teamwork processes and long-term team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research used Experience Sampling Methods (ESM; daily assessments) to assess team functioning across a range of ICE environments (short and long duration; Antarctica and NASA mission simulations). The purpose of this research aim was to characterize patterns of variation and dynamics for key teamwork processes (e.g., cohesion, collaboration, conflict). Benchmark data in ICE analog environments are important for developing insights into the nature of problems that may emerge that challenge team member interactions and team functioning. Findings from the benchmark studies are informative of the types of challenges that may be faced by space crews on long duration missions.

(2) Extend development of the team interaction sensor (TIS) technology (i.e., a wearable wireless sensor package). The purpose of this research aim was to advance development of a sensor technology to capture dynamic multimodal (i.e., physiological and behavioral) data that unobtrusively assesses team member interactions. Initial laboratory validation demonstrated the reliability and accuracy of the monitoring technology (Kozlowski, Biswas, & Chang, 2013) and its ability to predict affective reactions to stressed interactions (Kozlowski, Biswas, & Chang, 2014) sufficient to establish proof of concept. The extensions (a) added an additional sensing capability (i.e., swallow monitoring), (b) technology development to make the system more robust (i.e., packaging, energy efficiency; hardware, Bluetooth integration, algorithms, and software), and technology transfer to the NASA Wearable Electronics Application and Research Lab (WEAR Lab) at the Johnson Space Center (JSC).

(3) Develop a teamwork interaction metric and support system. The TIS provides high frequency data on team interaction indicators. The purpose of this research aim was to develop supporting components required for the data to be utilized as a countermeasure for team members to regulate psycho-social health: (a) Metrics – algorithms were developed to filter and parse the raw data streams into a meaningful measure that reflects teamwork functioning. The metric was then validated against prior laboratory data and in NASA mission simulation. (b) Distributed Networked Dashboard – a prototype system architecture / design was developed to distribute sensor information to computers and mobile devices, and (c) design concepts for a team effectiveness dashboard were developed for displaying teamwork interaction metrics and feedback to team members. The ultimate implementation and utilization of the system, however, will necessitate the direct involvement of NASA Operations personnel and astronaut end-users.

Products and findings from this research have the capability of reducing the risk of team performance decrements due to poor teamwork interactions by (a) characterizing normative and anomalous patterns of team functioning; (b) developing a technology to unobtrusively monitor team member interaction patterns; and (c) providing support to maintain teamwork.

References

Kozlowski, S. W. J., & Ilgen, D. R. (2006). Enhancing the effectiveness of work groups and teams (Monograph). Psychological Science in the Public Interest, 7, 77-124.

Salas, E., Tannenbaum, S. I., Kozlowski, S. W. J., Miller, C., Mathieu, J. E., & Vessey, W. B. (2015). Teams in space exploration: A new frontier for the science of team effectiveness. Current Directions in Psychological Science, 24(3), 200-207.

Slack, K. J., Williams, T. J., Schneiderman, J. S., Whitmore, A. M., & Picano, J. J. (2016). Evidence report: Risk of adverse cognitive or behavioral conditions and psychiatric disorders. Human Research Program, Behavioral Health and Performance. National Aeronautics and Space Administration, Johnson Space Center. Houston, TX

Kozlowski, S. W. J., Biswas, S., & Chang, C.-H. (2013). Developing, maintaining, and restoring team cohesion. Final Report, National Aeronautics and Space Administration (NNX09AK47G). Houston, TX

Kozlowski, S. W. J., Biswas, S., & Chang, C.-H. (2014, February). Capturing and regulating the dynamics of team collaboration and cohesion. Presented at the NASA Human Research Program Investigators' Workshop, Galveston, TX

NOTE: Element change to Human Factors & Behavioral Performance; previously Behavioral Health & Performance (Ed., 1/18/17)

NOTE: End date changed to 12/31/2017 per NSSC information (Ed., 6/16/16)

This proposed ground-based research is designed to address the following Program Requirements Document (PRD) Risk and Behavioral Health and Performance (BHP) Integrated Research Plan (IRP; 2011).

PRD Risk: Risk of Performance Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team.

IRP Gap – Team1: We need to understand the key threats, indicators, and life cycle of the team for autonomous, long duration and/or distance exploration missions.

The proposed research has three specific aims and associated deliverables that represent an integrated approach for measuring, monitoring, and regulating teamwork processes and long-term team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research uses Experience Sampling Methods (ESM; daily assessments) to assess team functioning in ICE environments. The product of this research aim quantifies the expected range of variation in key teamwork processes (e.g., cohesion, collaboration, conflict), identifies internal and external shocks that influence variation, and assesses dynamic effects on team functioning. Benchmark data in ICE analog environments are essential for developing standards to distinguish expected variation in teamwork from anomalies indicative of a threat to team functioning. Such standards are essential for triggering countermeasures / interventions.

(2) Extend engineering development of an unobtrusive monitoring technology (i.e., a wearable wireless sensor package). The product of this research aim advances development of a prototype monitoring technology to capture dynamic multimodal (i.e., physical, physiological, and behavioral) data capturing team member and teamwork interactions. Initial validation demonstrated the reliability and accuracy of the monitoring technology sufficient to establish proof of concept. Extensions include making the system more robust (i.e., packaging, energy efficiency, hardware, algorithms, and software) and developing a Bluetooth module for integrating additional sensors.

(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology provides high frequency data streams on a range of team interaction and individual-level indicators. The product of this research aim develops supporting components (metrics, data fusion, and information display) required for these dynamic data streams to be utilized as a countermeasure for teams to regulate teamwork interactions and psycho-social health.

To accomplish the product objectives highlighted above we have developed a multidisciplinary research team composed of experts spanning team development, regulation, and effectiveness (Kozlowski); psychological measurement of motivation, affect, and stress (Chang); and wireless monitoring of team member interactions (Biswas).

This research contributes to reducing the risk of team performance decrements due to poor teamwork interactions by (a) characterizing normative and anomalous patterns of team functioning; (b) monitoring team member interactions; and (c) providing regulation support to maintain teamwork and to trigger countermeasures when needed to aid team recovery.

Benchmark Long Duration Team Functioning in ICE Analog Environments

A significant portion of research effort was invested in continuing the multiple benchmarking data collections we have developed for this project. We have ongoing data collections in the Antarctic (i.e., science teams camped on the ice during the Antarctic summer and station teams for winter over-missions) and in NASA mission simulations (i.e., an asteroid transit simulation and a Mars surface exploration simulation).

Australian Antarctic Division (AAD) Stations. Our collaborative research with Dr. Jeff Ayton of the AAD was extended (see prior reporting) through the current deployment. In this reporting period, we concluded the 2015-2016 data collection of approximately 27 participants from Mawson, Davis, and Casey Stations, and Macquarie Island. Research protocols and IRB (Institutional Review Board) approvals with AAD, Michigan State University (MSU), and NASA were updated and extended. We are currently working with our collaborator to bring this data collection effort to closure.

This research assesses daily teamwork processes using Experience Sampling Methodology (ESM), which captures a snapshot of key individual and team reactions to daily events. Although the absolute sample sizes tend to be small, the primary focus of this research is on the dynamics of reactions over a period of nine months to one year (i.e., approximately 270 to 360 measurement periods), which yields insights into long duration individual and team functioning.

To date, we have collected data from 151 individuals who wintered over in the Antarctic. Participants spent between 9-15 months in their stations and reported a total of 5,738 daily survey data points. Descriptive results showed that individuals exhibit different characteristic patterns on different individual and team-level indicators over time. These patterns can be differentiated into four categories: rock solid, uni-varier, multi-varier, or stabilizer. Individuals with a rock solid pattern did not vary in their responses to most of the questions over time. Uni-variers primarily fluctuated on one variable, such as daily task or social cohesion. Multi-variers, conversely, varied in their responses across a range of indicators. This response, potentially, arose because of their heightened perceptions of and response to environmental factors. Finally, stabilizers varied initially on many variables, but then converged to equilibrium across the mission. These findings suggest that response pattern classification may be a useful way to characterize how different individuals adapt to the rigors of long duration missions.

Science Field Teams in Antarctica. We also extended our ongoing collaboration with science teams that deploy to the Antarctic ice. Marking the seventh season of data collection, we extended our research protocol, renewed MSU and NASA IRB approvals, and recruited participants. Originally, eight participants were recruited to provide daily ESM reports for the 2016-2017 season. However, the expedition experienced delays in federal grant funding (due to delays by in Congress passing a budget for fiscal year 2017) which required half of their team to return to the USA without deploying to the ice. Four participants did complete diaries as requested. However, the mix of respondents and small sample size make individual responses potentially identifiable. Thus, we are not reporting summary findings from those data, given the stipulations in the IRB protocols for this data collection.

In this reporting period we explored the use of linguistic analysis tools to transform previously collected qualitative responses (open-ended comments) collected as part of our protocol into quantitative measures. Our focus was on a tool called Linguistic Inquiry and Word Count (LIWC), which provides counts of the words that fall into linguistic categories. A number of these categories, such as first person singular pronouns (e.g., I, me) or first person plural (e.g., we, us) have, respectively, demonstrated promise for estimating team processes such as social conflict or cohesion. These results show that language use in communication may be a viable approach to the development of unobtrusive measures of team processes in ICE environments.

Human Exploration Research Analog (HERA). We continued benchmarking research in HERA, a NASA mission simulation located at the Johnson Space Center (JSC) that was initiated in 2014. HERA missions involve a crew of 4 members, selected from NASA volunteers. HERA simulates a transit mission for exploration of an asteroid. In Campaign 1, mission duration was approximately 7 days for 4 crews of 4 members each. Campaign 2, initiated in January 2015, extended the missions to 14 days for 4 crews of 4 members each. Campaign 3, initiated in January 2016, extended the missions to 30 days for 4 crews of 4 members each. Campaign 4, initiated in January 2017, extended the missions to 45 days for 4 crews of 4 members each. Campaign 4, mission 1 is currently about to conclude. This research involved extending our protocol, securing IRB approvals from NASA and MSU, training personnel, and coordinating research activities with several other investigator teams.

We also assumed the responsibility of coordinating the collection of several end-of-day measures across investigators and then compiling and sharing the data. Data analyses for Campaign 3, therefore, are in progress and are not reported here. This is largely because a substantial proportion of our time is devoted to data sharing, which has emerged as a significant investment of personnel resources. We share data with more than ten other research teams. Each data sharing effort requires the development of a joint data sharing agreement (MSU Technologies) with the provider or recipient institution, which necessitates extensive communication. The data then have to be parsed, encrypted, and shared in a secure manner. The considerable labor and time involved have diverted resources from primary research activities. This substantial load on research personnel was not anticipated when this research team originally agreed to coordinate the sharing of behavioral data and it has significantly impeded our ability to accomplish primary research tasks. Thus, we are behind schedule on efforts to analyze data from Campaign 3.

Hawai‘i Space Exploration Analog and Simulation (HI-SEAS). We continued our benchmarking research in a surface exploration simulation, HI-SEAS, which is located at 8200 feet on Mt. Mona Loa on the big island of Hawai‘i. We initiated our collaboration with HI-SEAS mission 2. This research involved extending our protocol, securing IRB approvals from the University of Hawai’i (under PI Kim Binsted), MSU, and NASA, and substantially aiding HI-SEAS mission design. We contributed to crew selection (we screened on the five factor model of personality and cognitive ability), the mission story / script, mission EVA (extravehicular activity) / scenario design, and problem-solving on a variety of issues that arose across the arc of the missions.

We have completed data collections from the five-person crew (1 team member withdrew shortly after the mission began) of mission 2 (4 months), the six-person crew of mission 3 (8 months), and the six-person crew of mission 4 (12 months). The M4 crew also used the MSU monitoring badge so that we can enlarge the pool of benchmarking data for interactions over time.

Although it is difficult to draw firm conclusions based on these three case studies, we observed that the most significant anomalies affecting team functioning occurred in the two longer duration missions that exceeded six months. Teams in both longer duration missions developed relationship conflict patterns at approximately the six-month point in the mission. The nature of the problems across the crews were based on different issues, but the conflicts persisted to influence subsequent team functioning through the remainder of both missions. If either of these crews had had to respond to an emergency, it is the opinion of this research team that their effectiveness would have been impeded by their ongoing conflict patterns.

Conclusions can only be tentative at this point, but these observations suggest that mission duration in excess of six months is a contributing factor to problems in team functioning. If so, the findings suggest that the most informative analog missions will also be in excess of six months in duration.

Extend Engineering Development of an Unobtrusive Monitoring Technology

The monitoring technology under development has been successfully validated in the laboratory and is under evaluation in NASA mission simulations. Engineering activity was mainly focused on the integration of a Bluetooth hardware module and development of associated software.

Bluetooth (BT) hardware and software integration. When we started this project, wearable sensors were a novelty. However, over the course of technology development, wearable sensor technology has exploded across a variety of activity monitors (mostly wrist mounted) in the commercial marketplace. As sensors are proliferating, they are also becoming increasingly sophisticated. We anticipate that in the future, it is likely that commercial monitors will provide sensor data that are useful for our effort to diagnose team member psychosocial health. Thus, to provide flexibility for the technology platform, we have initiated an effort (with NASA concurrence) to integrate a BT module with the badge sensor processor.

During this reporting period, the engineering team chose a Bluetooth Low Energy (BLE) module, supplied by Laird Technologies Inc., for integration with the MSU badge system. The objective of this integration was to add BLE connectivity to the badge so that it can: (1) collect data from external sensors (e.g., heart rate from smart watches, galvanic skin resistance [GSR]) and (2) communicate with data aggregator devices such as mobile phones, tablets, and PCs. BLE development was accomplished in two phases. In the first phase, we used a BLE Chipset that allowed the badge to be connected to one peripheral device (e.g., external sensor) at a time. This development is now complete and we have a working prototype. Using this BLE connectivity, the badge is now able to communicate with an external Bluetooth heart rate sensor (Polar H7 http://www.polar.com/us-en/products/accessories/H7_heart_rate_sensor ) for heart rate monitoring. Software for all the subsystems, namely, the badge, base station, and PC have been developed for end-to-end transportation of heart rate data from the H7 monitor over the BLE link, all the way to the PC-based badge dashboard software.

The next step of the BLE development is to use a more generalized chipset that will allow the badge to be able to communicate with many peripherals simultaneously. Work on this phase is underway.

Develop Teamwork Interaction Metrics and Support Systems.

Metrics. Depending on teamwork activity, interaction data streams can be dense (e.g., most members of the team are engaged in an intensive interaction) or quite sparse (e.g., members interact as dyads every so often but mostly work apart) or anywhere in between. Yet, even when team interactions are intensive, everyone on the team will not be interacting with everyone else at exactly the same time, which means that there will be many “holes” or “gaps” in the interaction data. At this point in the development of the system, we need to use standard statistical analyses to link the interactions and physiological indicators. Statistical software requires specific, dense data structures. Thus, in order to analyze interaction level data, the raw data collected by the badges have to be filtered to target those points in time when one member is interacting with another member and then the interaction and associated physiological data have to be parsed (i.e., extracted) and rewritten to a new data file without gaps that can then be analyzed with appropriate statistical tools (i.e., random coefficient models). Our ultimate aim is to accomplish the filtering process algorithmically. However, to develop appropriate algorithms, one needs to first develop the logic, instantiate it in code, and evaluate the integrity of the resulting data set. We have been working on this focus this reporting period.

Data filtering and parsing. We have previously developed computer code for our laboratory evaluation of the badges. That code was used to filter the raw data files from the badges and to parse and transform (i.e., recompile) it into a dataset that is appropriate for statistical analysis. During this reporting period, we have been systematically extending, generalizing, and evaluating the code for badge data collected in HERA and HI-SEAS.

In prior efforts, we extended the code to “test” our highly structured interactions in HERA and HI-SEAS. That is, participants worked on a task in which they followed a protocol specifying who interacted with whom and in what order the interactions occurred. That procedure allowed a phased validation of the badge as we transitioned it from the laboratory (3-person teams) to a controlled field setting (HERA; 4-person teams) and to a less controlled field setting (HI-SEAS; 6-person teams).

With that phased validation in place, the next major extension was to advance the code to assess dyadic interactions for fully unstructured or natural interactions. We have made preliminary progress and have a prototype algorithm under development and evaluation.

Data fusion. Having filtered, parsed, and transformed the badge data, the multivariate time series metrics need to be fused into a coherent assessment of ongoing individual and team functioning. As previously reported, we have preliminary evidence that positive and negative reactions based on interaction-level data can be predicted from heart rate (HR), HR variability (HRV), and their interaction. These data are collected by the badge system. As we continue to develop the badge technology system as a sensor integration platform (i.e., Bluetooth integration) that adds additional sensing modalities, the physiological data available for inferring psychological states will expand and reliability will improve.

Distributed networked dashboard. A system architecture is needed to integrate sensor information. A backend server infrastructure was developed during the prior reporting period for supporting the proposed distributed network dashboard. The server, which is hosted at MSU Engineering Building, has a JAVA based remote connection to the existing PC-based dashboard software. All data collected by the base station is pushed up to this remote server via the PC-based dashboard software. The server then makes the data available via a web service. This provides the opportunity for accessing badge-collected data to be exported to any remote web client running on PCs, tablets, phones, and other handheld devices.

During this reporting period, we have developed design concepts for a live dashboard 'app' that space crews could use to monitor and improve the functioning of their team based on badge data. This Team Dashboard will be designed track and improve targeted team interaction metrics deemed crucial for success on space missions by a panel of experienced astronauts (a future concept that is beyond the scope of the current project). The Team Dashboard will be designed using best practices in User Experience Design and the latest Gamification techniques to create a tool that space crews feel they can rely on and enjoy using.

NOTE: End date changed to 12/31/2017 per NSSC information (Ed., 6/16/16)

The proposed research has three specific aims and deliverables that yield an integrated approach for measuring, monitoring, and regulating teamwork processes and team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research will use Experience Sampling Methods (daily assessments) to assess team functioning in ICE environments. The goal is to quantify expected variation in key team processes, identify internal and external shocks, and assess dynamic effects on team performance. Such data are essential for developing standards to distinguish normative variation from anomalies indicative of a threat to team functioning which are necessary for triggering countermeasures.

(2) Extend engineering development of an unobtrusive monitoring technology (wearable wireless sensor package). The product is to further develop a prototype monitoring technology of teamwork interactions. Initial validation has demonstrated reliability and accuracy sufficient to establish proof of concept. Proposed extensions are designed to (a) add sensing capabilities (swallow monitoring for food intake, stress assessment) and (b) technology development to make the system more robust (packaging, energy efficiency; hardware, algorithms, and software) for out-of-lab field demonstration.

(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology provides continuous data on a range of teamwork processes. Three additional components are required for a countermeasure system. (a) Metrics: Algorithms need to be developed that parse the raw data streams into meaningful measures, then the metrics need to be validated; (b) Data Fusion and Team Regulation Protocols: The multivariate time series metrics need to be fused into a coherent assessment of individual and team functioning. Anomalies that signal a departure from normative functioning have to be classified to drive the provision of feedback and/or team regulation interventions; (c) Distributed Networked Dashboard: A system architecture is needed to integrate sensor information and data fusion, direct feedback to maintain good teamwork, and, when the system detects an anomaly in team functioning, trigger appropriate feedback and countermeasures to help an individual or the team regulate team processes. Flexible options for distributing and displaying team status assessments and countermeasures need to be provided (e.g., individual team member, dyads, team leader, ground control).

These specific aims will contribute to reducing the risk of team performance decrements by characterizing normative and anomalous patterns of team functioning; monitoring team member interactions; and providing regulation support to maintain teamwork and to trigger countermeasures when needed to aid team recovery.

Benchmark Long Duration Team Functioning in ICE Analog Environments

A significant portion of research effort was invested in continuing the multiple benchmarking data collections we have developed for this project. We have ongoing data collections in the Antarctic (i.e., science teams camped on the ice and station teams for winter over-missions) and in NASA mission simulations (i.e., an asteroid transit simulation and a Mars surface exploration simulation). In addition, we previously collected data in an underwater (i.e., low gravity) exploration simulation. A description of our research activities follows.

Australian Antarctic Division (AAD) Stations and Field Teams in Aurora Basin (AB). Our collaborative research with Dr. Jeff Ayton of the AAD was extended (see prior reporting) with approval to continue data collection through the 2016-2017 ADD deployment. This year, we continued and concluded the 2014-2015 data collection of approximately 49 participants from Mawson, Davis, and Casey Stations, and participants from Macquarie Island. We also initiated new data collections with station personnel who deployed to winter-over for 2015-2016. This involved extending our research protocol with AAD; renewing IRB approvals by the AAD, MSU (Michigan State University), and NASA; and working with our collaborator and his team to recruit participants. Approximately 24 participants from Mawson, Davis, and Casey Stations, as well as participants from Macquarie Island, are participating in this ongoing effort to benchmark individual and team functioning in ICE settings.

This ongoing research assesses daily teamwork processes using Experience Sampling Methodology (ESM), which captures a snapshot of key individual and team reactions to events of the day. Although the absolute sample sizes tend to be small, the primary focus of the research is on the dynamics of reactions over a period of nine months to a year (i.e., approximately 270 to 360 measurement periods), which yields insights into long duration individual and team functioning.

To date, we have completed data collection from 124 individuals who wintered over in the Antarctic. Participants spent between 9-15 months in their stations and reported a total of 5,161 daily survey data points. Additional data is currently being collected from 24 new participants. Descriptive data have shown that individuals have different dynamic patterns in terms of their daily reports on different individual and team-level indicators over time. These patterns can be differentiated into four categories: rock solid, uni-varier, multi-varier, or stabilizer. Individuals with a rock solid pattern did not vary in their responses to most of the questions over time. Individuals who are uni-variers primarily varied on one variable, such as daily task or social cohesion. Individuals who are multi-variers varied daily in their responses across a range of indicators. This may be because they perceived more variability in the environment or because they were more impacted by external factors in the environment. Finally, individuals who are stabilizers initially varied in many variables, but then achieved and maintained a stable equilibrium across the mission.

We previously conducted variance decomposition analysis and found that most of the variance in team process indicators (e.g., cohesion, conflict) was at the individual or day level, with little variance attributed to the teams (Baard, Kermond, Pearce, Ayton, Change, & Kozlowski, 2014). This indicates that the team level had little meaningful impact and suggests that individuals may not be working on highly interdependent tasks as a team. The nature of the winter-over crew—individuals with diverse occupations working in isolation from each other—likely explains this pattern. On the other hand, it also means that most of the variability was accounted for by the person and environment.

Finally, random coefficients modeling revealed that relationships between some variables were reciprocal, while relationships between other variables were directional. Cohesion and performance had reciprocal, positive relationship with each other. This suggests that prior cohesion predicted future performance positively, and prior performance positively contributed to future cohesion ratings. Positive affect and team conflict had reciprocal, negative relationship with each other, suggesting that prior positive mood was related to less future conflict, and conflict was predictive of lower positive mood. On the other hand, conflict management had directional relationship with cohesion, such that prior conflict management was positively related to subsequent team cohesion. This finding suggests that teams engaging in conflict management felt more cohesive the next day, but that being cohesive may not necessarily make the team more effective at managing their conflict. Finally, prior positive affect also had directional, negative relationship with subsequent negative affect, supporting the buffering role of positive mood.

Recently we began to compare descriptive team process data across the different AAD missions (Olenick, Santoro, Kozlowski, Chang, & Dixon, 2015). For the three years of available data, we observed that overall team cohesion was relatively high and stable over time for two teams, while the third year exhibited very low cohesion at the beginning and end of their missions, with a plateau of moderate cohesion in between, likely resulting from a high number of conflicts at the beginning and end points of the missions. Performance trends for all teams were stable and not significantly different from year to year. Additionally, social and task conflict for all teams was relatively low and stable for all three teams until approximately 80% of the way through the season. At that point, for one team, social and task conflict began to decline while it began to increase for the other two teams, including the team exhibiting deficiencies in cohesion. Relatedly, all three teams reported similar levels of conflict management for most of their seasons, until about 75% through their time together. At this point, the team which showed less conflict at the end of their season began to show slightly more conflict management.

Although (given the sample size) we consider these findings preliminary, they suggest that selection of individuals for winter-over deployments should consider the utility of assessing for positive affectivity, an individual disposition (i.e., personality characteristic) that supports positive mood states. It also suggests that conflict management skills, bolstered by training, could help individuals and teams maintain effective working relationships across the long span of winter-over deployments to the Antarctic.

Science Field Teams in Antarctica. We also extended our ongoing collaboration with science teams that deploy to the ice during the Antarctic summer for 2015-2016. This marks our sixth season of data collection with this research team. This involved extending our research protocol, renewing MSU and NASA IRB approvals, and recruiting participants from the science teams. Approximately 7 participants contributed to the data collection, providing daily Experience Sampling Methodology (ESM) reports. The data for 2015-2016 have been compiled and data analyses are in progress. We routinely report findings for each mission back to team leadership, although the report findings are not summarized for this NASA annual report.

Results based on the data collected through the prior mission year suggest that teams vary in the key indicators of team processes both in terms of the levels (e.g., average cohesion across members) and the degree of agreement (e.g., standard error of cohesion across members; Pearce, Baard, Harvey, Karner, Chang, & Kozlowski, 2014; Pearce, Baard-Perry, Harvey, Karner, & Ayton, 2015). Moreover, cohesion and adaptability showed reciprocal relationships over time, such that higher cohesion on the prior day predicted higher adaptability on the subsequent day and vice versa. In addition, cohesion and negative affect also showed reciprocal relationships over time, such that high negative affect on the prior day predicted low cohesion the subsequent day and vice versa. These results suggest that high cohesion may be crucial in buffering the negative effects of poor individual psychosocial well-being on team effectiveness.

Human Exploration Research Analog (HERA). We continued benchmarking research in HERA, a NASA mission simulation located at the Johnson Space Center (JSC), that was initiated in 2014. HERA missions involve a crew of 4 members, selected from NASA volunteers. HERA simulates a transit mission for exploration of an asteroid. In Campaign 1, mission duration was approximately 7 days for 4 crews of 4 members each. Campaign 2, initiated in January 2015, extended the missions to 14 days for 4 crews of 4 members each. Campaign 3, initiated in January 2016, extended the missions to 30 days for 4 crews of 4 members each. Data collection for Campaign 3, Mission 2 is just about to conclude. This research has involved extending our protocol, securing IRB approvals from NASA and MSU, training personnel, and coordinating research activities with several other investigator teams. We also have taken on the responsibility of coordinating several end-of-day measures across investigators and then compiling and sharing the data. We have also been the lead team for coordinating interaction badge data (the “SS” badge provides shared data; the other MSU badge is under development and evaluation).

In addition to the use of our standard ESM protocol, we also employ a “simulation within the simulation” that is used to evaluate our monitoring technology. Heretofore, the monitoring “badge” has only been evaluated in lab settings for basic validation. This effort is extending evaluation for field testing and user reactions.

We found that based on the data collected from HERA Campaign 1 (106 daily reports from 16 participants) and HERA Campaign 2 (220 daily reports from 16 participants) different teams had different experiences throughout the mission and responded differently to the same environmental stressors (e.g., sleep deprivation, communication delays; Dixon & Vessey, 2015; Dixon, Santoro, Lauricella, Chang, & Kozlowski, 2016). In addition, analysis of agreement between crew members’ ratings on team processes indicated that within the same team, individual members do not always view their team or their shared experiences the same way. Finally, across the four missions in Campaign 1 and Campaign 2, there was no apparent generalizable pattern of reactions to experiences across teams that can be identified, despite the seemingly similar exposure to the simulated stimuli. In other words, thus far, each team experience (based on descriptive data) has its own distinct ecology that does not replicate to other teams. In part, this is the challenge of identifying stable and reliable patterns with small sample data. It may be possible that with additional data and analyses, replicable patterns across team experiences will be identified. However, in the meantime, these preliminary results show that the continued data collections with more teams in the HERA environment are needed to determine a normative profile of the team experience in HERA.

Hawai‘i Space Exploration Analog and Simulation (HI-SEAS). We continued our benchmarking research in a surface exploration simulation, HI-SEAS, which is located at 8200 feet on Mt. Mona Loa on the big island of Hawai‘i. When we initiated our collaboration with HI-SEAS mission 2, this involved extending our protocol; securing IRB approvals from the University of Hawai’i (under PI Kim Binsted), MSU, and NASA; and substantially aiding HI-SEAS mission design. We contributed to crew selection (we screened on the five factor model of personality and cognitive ability), the mission story / script, mission EVA / scenario design, and problem-solving on a variety of issues that arise across the arc of the missions.

We have completed data collections from the five-person crew (1 team member withdrew shortly after the mission began) of mission 2 (4 months) and the six-person crew of mission 3 (8 months). We are currently collecting ESM data from the six-person crew of mission 4 (12 months). The crew is also using the MSU monitoring badge so that we can enlarge the pool of benchmarking data for interactions over time.

Overall, the five-person crew from mission 2 provided a total of 485 daily surveys (Santoro & Binsted, 2015) and the six-person crew from mission 3 provided a total of 1303 daily surveys. Random coefficient modeling showed that team processes were reciprocally related to one another. As we reported last year for mission 2, positive autoregressive effects were found for the indicators for individual members’ psychosocial health (e.g., positive and negative affect) and other team process and effectiveness indicators (e.g., cohesion, conflict, performance) from the previous day to the next. In addition, cohesion played a major role in affecting other team processes, such as positively impacting performance and affect. Additionally, improvements in particular team processes and individual psychosocial well-being (i.e., cohesion, negative affect, and positive affect) from one day to the next benefited team effectiveness on the next day.

In mission 3, positive autoregressive effects were found for the same indicators as in mission 2 (e.g., psychosocial health and other team process and effectiveness indicators). Moreover, improvements in particular team processes (i.e., cohesion and negative affect) benefited team effectiveness the next day, similarly to mission 2. Unlike mission 2, however, the prior day’s positive affect only had an autoregressive effect, not impacting other team processes or team effectiveness.

Thus, similar to the long duration findings for the AAD stations, we see evidence that positive affect (which can be selected based on individual dispositions) and cohesion (which can be bolstered by team interactions and team leadership) have potential buffering effects on negative factors that impede individual and team psychosocial health.

Extend Engineering Development of an Unobtrusive Monitoring Technology

The monitoring technology under development has been successfully validated in the laboratory and is now under evaluation in NASA mission simulations. Engineering activity was mainly focused around areas-- 1) Development of algorithms and software for run-time dynamic badge-id allocation, 2) Detection of swallow monitoring using wearable sensors, and 3) integration of a Bluetooth hardware module and development of the associated software.

Development of algorithms and software for run-time dynamic badge-id allocation. In the previous versions of the badge software, the unique identification (ID) number for a badge used to be statically pre-allocated. While providing a simpler boot and start-up sequence, this arrangement leads to complications and management overhead in the event of badge hardware failures. When a badge fails, a new badge needs to be programmed with the same identifier before it can be used as a replacement of the failed unit. To ameliorate this issue, the engineering team developed a new dynamic ID management algorithm which auto-allocates unique IDs to the badges at boot-time. The algorithm is implemented using the TinyOS operating system within the badge software, TinyOS in the base station, and in the PC-based dashboard software that is written in Java. This new system was thoroughly tested in the lab and then for the data collection sessions ongoing both in HERA and HI-SEAS.

Swallow monitoring. We have developed a wearable solid food intake monitoring system that analyzes human breathing signals and swallow sequence locality for solid food intake monitoring. Food intake is identified by the way of detecting a person’s swallow events. The system works based on a key observation that the otherwise continuous breathing process is interrupted by a short apnea during swallowing. A Support Vector Machine (SVM) is first used for detecting such apneas in breathing signals collected from a wearable chest-belt. The resulting swallow detection is then refined using a Hidden Markov Model (HMM) based mechanism that leverages known locality in the sequence of human swallows. Using the developed system in this reporting period we are experimentally able to demonstrate the effectiveness of the two-stage SVM-HMM based mechanism for solid food intake detection via analyzing breathing signal and human swallow sequence locality. Apnea detection also has potential as an additional data modality for assessing stress during team member interactions. As this badge capability develops, it will be integrated into our phased lab validation process.

During this reporting year, we have performed detailed in-lab validation of the proposed system. Swallow detection was performed using just the SVM classifier and also with HMM followed by SVM classifier. To evaluate the detection performance (i.e., both SVM-only and SVM followed by HMM), we adopted the metrics Precision and Recall, commonly used 614 in biomedical signal processing and information retrieval.

Recognized swallows (i.e., true positives, TP) indicates the number of swallow events that are correctly detected. Retrieved swallows correspond to the number of detected swallows including both the TPs and the incorrectly detected swallows (i.e., false positives, FP). Relevant swallows (i.e., positive, P) refer to the number of actual swallow events annotated from video observations reflecting ground truth.

Six participants were evaluated. The results demonstrate that SVM+HMM performs consistently better than the SVM-only solution when the optimum threshold of posterior probability is unknown. These results validate the overall usefulness of the proposed HMM processing by leveraging known swallow sequence locality information for removing certain classification errors that are introduced by the SVM-only approach.

Bluetooth (BT) hardware and software integration. When we started this project, wearable sensors were a novelty. However, over the course of technology development, wearable sensor technology has exploded across a variety of activity monitors (mostly wrist mounted) in the commercial market place. As they are proliferating, they are also becoming increasingly sophisticated. We anticipate that in the future, it is likely that commercial monitors will provide sensor data that are useful to our effort to diagnose team member psychosocial health. Thus, to provide flexibility for the technology platform, we have initiated a new effort (with NASA concurrence) to integrate a BT module with the badge sensor processor. This is supplemental effort that was initiated in January 2016.

The engineering team has started the integration process. As the first step, a Bluetooth Low Energy (BLE) module, supplied by Laird Technologies Inc., has been chosen for integration with the MSU badge system. The module can be used in Peripheral mode or in Central mode depending on the specific applications. We intend to use the Central mode for collecting sensor data (e.g., heart rate) from other sensors to the badge, and the Peripheral mode for uploading data from the badge to a base station or mobile phone. In the first phase, we started the development in the Central mode.

Thus far, a software driver module has been successfully developed for reading heart rate data from a Polar H7 ( http://www.polar.com/us-en/products/accessories/H7_heart_rate_sensor ) heart rate monitor over Bluetooth Low Energy (BLE) link. The driver is currently run on the Laird Technologies BLE module in an isolated mode. The next step will be to integrate the BLE module with the MSU badges over a serial link so that the collected heart rate data can be integrated with the existing badge database and uploaded to the base station. Then we will try out other peripheral devices for testing the general BLE based data collection ability.

Develop Teamwork Interaction Metrics and Support Systems.

Metrics. Depending on teamwork activity, interaction data streams can be dense (e.g., most members of the team are engaged in an intensive interaction) or quite sparse (e.g., members interact as dyads every so often but mostly work apart) or anywhere in between. Yet, even when team interactions are intensive, everyone on the team will not be interacting with everyone else at exactly the same time, which means that there will be many “holes” or “gaps” in the interaction data. At this point in the development of the system, we need to use standard statistical analyses to link the interactions and physiological indicators. Statistical software requires specific data structures. Thus, in order to analyze interaction level data, the raw data collected by the badges have to be filtered to target those points in time when one member is interacting with another member and then the interaction and associated physiological data have to be parsed (i.e., extracted) and rewritten to a new data file without gaps that can be analyzed with appropriate statistical tools (i.e., random coefficient models). Our ultimate aim is to accomplish the filtering process algorithmically. However, to develop appropriate algorithms, one needs to first develop the logic, instantiate it in code, and evaluate the integrity of the resulting data set. This is the current focus of our research activity.

Data filtering and parsing. We have previously developed computer code for our laboratory evaluation of the badges. That code was used to filter the raw data files from the badges and to parse and transform (i.e., recompile) it into a dataset that is appropriate for statistical analysis. We are now engaged in systematically extending, generalizing, and evaluating the code for badge data collected in HERA and then to HI-SEAS. The initial extension of the code to the HERA environment is challenging because it is a less controlled environment in which two interactions may be going on at the same time. Therefore, we had to elaborate the code to ensure that it uniquely captures both of those simultaneous interactions. As the code was extended, it was then evaluated to ensure that captured all valid interactions and filtered out spurious interactions. Discriminant analyses were used to identify predictors of valid interactions. We then used the predictors to filter out spurious interactions so they are omitted from the final output the code produces.

The next step – extending and applying the code to HI-SEAS – is particularly challenging because there are two additional participants relative to the HERA code extensions. This increases the number of possible dyadic interactions from 6 to 15. The challenge is allowing for the additional dyad combinations without significantly increasing the time the code takes to process the raw data. To address this issue, we extended the code to 15 dyads in a way that simultaneously enhances the efficiency of the code. Initial pilot evaluation indicates that the code is functioning, although a more comprehensive evaluation is in progress.

The next major challenge is to extend the code to apply to unstructured interactions. To date, we have evaluated badge interaction data collected in both laboratory and field environments in which dyadic interactions are highly structured. That is, participants have worked on a task in which they follow a specific protocol regarding who interacts with whom and in what order the interactions take place. This has allowed a phased validation of the badge as we transition it from the laboratory (3-person teams) to a controlled field setting (HERA; 4-person teams) and to a less controlled field setting (HI-SEAS; 6-person teams). With the phased validation in place, the next major extension will advance the code and parameters to assess dyadic interactions for unstructured or natural interactions.

Data fusion. Having filtered, parsed, and transformed the badge data, the multivariate time series metrics need to be fused into a coherent assessment of ongoing individual and team functioning. As previously reported (see Kozlowski, Biswas, & Chang, 2014), we have preliminary evidence that positive and negative reactions based on interaction-level data can be predicted from heart rate (HR), HR variability (HRV), and their interaction. These data are collected by the badge system. As we continue to develop the badge technology system as a sensor integration platform (i.e., the new initiative to integrate a BT module) that adds additional sensing modalities, the physiological data available for inferring psychological states will expand and reliability will improve.

Distributed networked dashboard. A system architecture is needed to integrate sensor information. A backend server infrastructure has been developed during this reporting period for supporting the proposed distributed network dashboard. The server, which is hosted at MSU Engineering Building, has a JAVA based remote connection to the existing PC-based dashboard software. All data collected by the base station is pushed up to this remote server via the PC-based dashboard software. The server then makes the data available via a web service. This provides the opportunity for accessing badge-collected data to be exported to any remote web client running on PCs, tablets, phones, and other handheld devices. The server part is completed and tested during this reporting period. The engineering team is now preparing to develop the client side as needed by various teams that are using the system.

References

Baard, S. K., Kermond, C., Pearce, M., Ayton, J., Chang, C.-H., & Kozlowski, S. W. J. (2014, August). Understanding team affect, cohesion and performance dynamics in long duration Antarctic missions. In J. Ayton (Chair), Human biology and Medicine. Symposium presented at 2014 Open Science Conference XXXIII SCAR Biennial Meetings, Auckland, New Zealand.

Dixon, A. J., Santoro, J. M., Lauricella, T. K., Chang, C.-H., & Kozlowski, S.W.J. (2016, February). An investigation into team dynamics within the Human Exploration Research Analog. Poster presented at the Human Research Program Investigators’ Workshop, Galveston, TX.

Dixon, A. J. & Vessey, W. B. (2015, April). Research on team processes in the Human Exploration Research Analog. In S. W. J. Kozlowski & C.-H. Chang (Chairs), Team dynamics: Capturing process phenomena in extreme teams. Symposium conducted at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.

Kozlowski, S. W. J., Biswas, S., & Chang, C.-H. (2014). Monitoring and regulating teamwork. Final Report, National Aeronautics and Space Administration (NNX12AR15G). Houston, TX.

Olenick, J., Santoro, J. M., Chang, C-H., Kozlowski, S.W.J., & Dixon, A. J. (2015). Investigating teams in isolated, confined, and extreme environments: A look into AAD missions. Department of Psychology, Michigan State University, East Lansing, MI.

Pearce, M., Baard-Perry, S. K., Harvey, R. P., Karner, J., & Ayton, J. (2015). The dynamics of teamwork in the Antarctic: A multi-year, multi-national effort. In S. W. J. Kozlowski and C.-H. Chang (Co-chairs), Team dynamics: Capturing process phenomena in extreme teams. Symposium presented at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.

Pearce, M., Baard, S. K., Harvey, R. P., Karner, J., Chang, C.-H., & Kozlowski, S. W. J. (2014, August). Tracking the psychosocial health of ICE teams. Poster presented at the XXXIII Scientific Committee on Antarctic Research (SCAR) Biennial Meetings and Open Science Conference, Auckland, New Zealand.

The proposed research has three specific aims and deliverables that yield an integrated approach for measuring, monitoring, and regulating teamwork processes and team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research will use Experience Sampling Methods (daily assessments) to assess team functioning in ICE environments. The goal is to quantify expected variation in key team processes, identify internal and external shocks, and assess dynamic effects on team performance. Such data are essential for developing standards to distinguish normative variation from anomalies indicative of a threat to team functioning which are necessary for triggering countermeasures.

(2) Extend engineering development of an unobtrusive monitoring technology (wearable wireless sensor package). The product is to further develop a prototype monitoring technology of teamwork interactions. Initial validation has demonstrated reliability and accuracy sufficient to establish proof of concept. Proposed extensions are designed to (a) add sensing capabilities (swallow monitoring for food intake, stress assessment) and (b) technology development to make the system more robust (packaging, energy efficiency; hardware, algorithms, and software) for out-of-lab field demonstration.

(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology provides continuous data on a range of teamwork processes. Three additional components are required for a countermeasure system. (a) Metrics: Algorithms need to be developed that parse the raw data streams into meaningful measures, then the metrics need to be validated; (b) Data Fusion and Team Regulation Protocols: The multivariate time series metrics need to be fused into a coherent assessment of individual and team functioning. Anomalies that signal a departure from normative functioning have to be classified to drive the provision of feedback and/or team regulation interventions; (c) Distributed Networked Dashboard: A system architecture is needed to integrate sensor information and data fusion, direct feedback to maintain good teamwork and, when the system detects an anomaly in team functioning, trigger appropriate feedback and countermeasures to help an individual or the team regulate team processes. Flexible options for distributing and displaying team status assessments and countermeasures need to be provided (e.g., individual team member, dyads, team leader, ground control).

These specific aims will contribute to reducing the risk of team performance decrements by characterizing normative and anomalous patterns of team functioning; monitoring team member interactions; and providing regulation support to maintain teamwork and to trigger countermeasures when needed to aid team recovery.

Benchmark Long Duration Team Functioning in ICE Analog Environments

A significant portion of research effort was invested in developing, initiating, and conducting benchmarking data collections over the last several months. A description of our research activities follows.

Australian Antarctic Division (AAD) Stations and Field Teams in Aurora Basin (AB). In the prior reporting period, we had extended our collaborative research with Dr. Jeff Ayton of the AAD, gaining approval to continue data collection through the 2016-2017 AAD deployment (assuming that our project continues to be funded by NASA). We initiated new data collections with station personnel who deployed to winter-over for 2014-2015. This involved extending our research protocol with AAD; renewing IRB (Institutional Review Board) approvals by the AAD, Michigan State University (MSU), and NASA; and working with our collaborator and his team to recruit participants. Approximately 46 participants from Mawson, Davis, and Casey Stations, as well as participants from Macquarie Island, are participating in this ongoing effort to benchmark individual and team functioning in ICE settings.

This ongoing research assesses daily teamwork processes using Experience Sampling Methodology (ESM), which captures a snapshot of key individual and team reactions to events of the day. Although the absolute sample sizes tend to be small, the primary focus of the research is on the dynamics of reactions over a period of nine months to a year (i.e., approximately 270 to 360 measurement periods), which yields insights into long duration individual and team functioning.

To date, we have completed data collection from 26 individuals who wintered over in the Antarctic (Baard, Kermond, Pearce, Ayton, Chang, & Kozlowski, 2014). Participants spent between 9-15 months in their stations and reported a total of 2,333 daily surveys. Descriptive data showed that individuals have different dynamic patterns in terms of their daily reports on different individual and team-level indicators over time. These patterns can be differentiated into four categories: rock solid, uni-varier, multi-varier, or stabilizer. Individuals with a rock solid pattern did not vary in their responses to most of the questions over time. Individuals who are uni-variers primarily varied on one variable, such as daily task or social cohesion. Individuals who are multi-variers varied daily in their responses across a range of indicators. This may be because they perceived more variability in the environment or because they were more impacted by external factors in the environment. Finally, individuals who are stabilizers initially varied in many variables, but then achieved and maintained a stable equilibrium across the mission.

We also conducted variance decomposition analysis and found that most of the variance in team process indicators (e.g., cohesion, conflict) was at the individual or day level, with little variance attributed to the teams. This indicates that the team level had little meaningful impact and suggests that individuals may not be working closely as a team. The nature of the winter-over crew—individuals with diverse occupations working in isolation from each other—likely explains this pattern. On the other hand, it also means that most of the variability was accounted for by the person and environment.

Random coefficients modeling revealed that relationships between some variables were reciprocal, while relationships between other variables were directional. Cohesion and performance had reciprocal, positive relationship with each other. This suggests that prior cohesion predicted future performance positively, and prior performance positively contributed to future cohesion ratings. Positive affect and team conflict had reciprocal, negative relationship with each other, suggesting that prior positive mood was related to less future conflict, and conflict was predictive of lower positive mood. On the other hand, conflict management had directional relationship with cohesion, such that prior conflict management was positively related to subsequent team cohesion. This finding suggests that teams engaging in conflict management felt more cohesive the next day, but that being cohesive may not necessarily make the team more effective at managing their conflict. Finally, prior positive affect also had directional, negative relationship with subsequent negative affect, supporting the buffering role of positive mood.

Although (given the sample size) we consider these finding preliminary, they suggest that selection of individuals for winter-over deployments should consider the utility of assessing for positive affectivity, an individual disposition (i.e., personality characteristic) that supports positive mood states. It also suggests that conflict management skills, bolstered by training, could help individuals and teams maintain effective working relationships across the long span of winter-over deployments to the Antarctic.

Science Field Teams in Antarctica. We also extended our ongoing collaboration with science teams that deploy to the ice during the Antarctic summer for 2014-2015. This marks our fifth season of data collection with this research team. This involved extending our research protocol, renewing MSU and NASA IRB approvals, and recruiting participants from the science teams. Approximately 7 participants are contributing to the research effort, providing daily ESM reports.

Results based on the data collected to date suggest that teams vary in the key indicators of team processes both in terms of the levels (e.g., average cohesion across members) and the degree of agreement (e.g., standard error of cohesion across members; Pearce, Baard, Harvey, Karner, Chang, & Kozlowski, 2014; Pearce, Baard-Perry, Harvey, Karner, & Ayton, 2015). Moreover, cohesion and adaptability showed reciprocal relationships over time, such that higher cohesion on the prior day predicted higher adaptability on the subsequent day and vice versa. In addition, cohesion and negative affect also showed reciprocal relationships over time, such that high negative affect on the prior day predicted low cohesion the subsequent day and vice versa. These results suggest that high cohesion may be crucial in buffering the negative effects of poor individual psychosocial well-being on team effectiveness.

Human Exploration Research Analog (HERA). We continued benchmarking research in HERA, a NASA mission simulation located at the Johnson Space Center (JSC), that was initiated in 2014. HERA missions involve a crew of 4 members, selected from NASA volunteers. HERA simulates a transit mission for exploration of an asteroid. In Campaign 1, mission duration was approximately 7 days for 4 crews of 4 members each. Campaign 2, initiated in January 2015, extended the missions to 14 days. This research has involved extending our protocol, securing IRB approvals from NASA and MSU, training personnel, and coordinating research activities with several other investigator teams. We also have taken on the responsibility of coordinating several end-of-day measures across investigators and then compiling and sharing the data. We are also the lead team for coordinating interaction badge data (the “SS” badge provides shared data; the other MSU badge is under development and evaluation).

In addition to the use of our standard ESM protocol, we also employ a “simulation within the simulation” that is used to evaluate our monitoring technology. Heretofore, the monitoring “badge” has only been evaluated in lab settings for basic validation. This effort is extending evaluation for field testing and user reactions. Since the last report, we have collected data from crew members of two HERA missions in Campaign 1 in June and September of 2014. We have also collected data for two HERA missions in Campaign 2 in January/February and April of 2015. Data collection for the third HERA mission of Campaign 2 will begin in June of 2015.

We found that based on the data collected from HERA Campaign 1 (106 daily reports from 16 participants), different teams had different experiences throughout the mission and responded differently to the same environmental stressors (e.g., sleep deprivation, communication delays; Dixon & Vessey, 2015). In addition, analysis of agreement between crew members’ ratings on team processes indicated that within the same team, individual members do not always view their team or their shared experiences the same way. Finally, across the four missions in Campaign 1, there was no clear pattern of reactions to experiences across teams that can be identified, despite the seemingly similar exposure to the simulated stimuli. These preliminary results show that the continued data collections with more teams in the HERA environment will be informative of how different teams and individuals experience these environments.

Hawai‘i Space Exploration Analog and Simulation (HI-SEAS). We continued benchmarking research in a surface exploration simulation, HI-SEAS, which is located at 8200 feet on Mt. Mona Loa on the big island of Hawai‘i, and was initiated in the prior reporting period. This has involved extending our protocol; securing IRB approvals from the University of Hawai’i (under the PI, Kim Binsted), MSU, and NASA; and substantially aiding the HI-SEAS mission design. We have contributed to crew selection (we screened on the five factor model of personality and cognitive ability), the mission story / script, and mission EVA (extravehicular activity) / scenario design.

We completed the data collection from the five-person crew of mission 1, and are currently collecting ESM data from the six-person crew during their 240-day mission that began in October 2014. The crew is also using the SS badge and the MSU monitoring badge so that we can enlarge the pool of benchmarking data for interactions over time. We are now preparing for the next HI-SEAS mission that will involve a crew of 6 for 360 days. The mission is set to begin in the fall of 2015.

Overall, the five-person crew from mission 1 provided a total of 485 daily surveys (Santoro & Binsted, 2015). Random coefficient modeling showed that team processes were reciprocally related to one another. Positive autoregressive effects were found for the indicators for individual members’ psychosocial health (e.g., positive and negative affect) and other team process and effectiveness indicators (e.g., cohesion, conflict, performance) from the previous day to the next. In addition, cohesion played a major role in affecting other team processes, such as positively impacting performance and affect. Finally, improvements in certain team processes and individual psychosocial well-being (i.e., cohesion, negative affect, and positive affect) from one day to the next benefitted team effectiveness on the next day.

Thus, similar to the long duration findings for the AAD stations, we see evidence that positive affect (which can be selected based on individual dispositions) and cohesion (which can be bolstered by team interactions and team leadership) have buffering effects on negative factors that impede individual and team psychosocial health.

NASA Extreme Environment Mission Operations (NEEMO) Mission 18. Finally, we conducted data collection from a crew of four astronauts and two habitat technicians for NEEMO18. The roughly one week mission was completed in July 2014. The astronauts represented a mix of international space agencies, so the crew was multicultural. NEEMO18 data collection involved extension / modification of our protocol; coordinating IRB approvals with NASA, MSU, and several international space agencies; training revisions; and coordination among investigating teams and NASA elements. We were, once again, responsible for coordinating end-of-day reactions and badge data. The NEEMO data have largely been helpful for feedback on user reactions to the badges.

Extend Engineering Development of an Unobtrusive Monitoring Technology

The monitoring technology under development has been successfully validated in the laboratory and is now under evaluation in NASA mission simulations. Primary objectives for engineering development center on: (1) improved packaging and enhancing robustness of the wearable interaction monitoring badges and (2) detection of swallow monitoring using the wearable sensor system.

Packaging and robustness. We have developed a 3-D printed case for the wearable badge. The case contributes to robustness in terms of the badge abilities to withstand rough handling in variable environmental conditions. The access point has also been repackaged. The new access point is also capable of all the sensing possible using the badges.

To improve energy-efficiency, we have employed a series of transmission power control protocols (developed by Co-PI Biswas’ group) running on the badge hardware. A novel measurement based link power control mechanism with closed-loop feedback control techniques was used. These protocols have improved the battery life of the system. The current version of the badge with all these new software and protocol can run up to 6 hours of data collection in one recharge. These new robust badges were used in the HERA data collection sessions during this reporting period.

Swallow monitoring. We have developed a wearable solid food intake monitoring system that analyzes human breathing signal and swallow sequence locality for solid food intake monitoring. Food intake is identified by the way of detecting a person’s swallow events. The system works based on a key observation that the otherwise continuous breathing process is interrupted by a short apnea during swallowing. A Support Vector Machine (SVM) is first used for detecting such apneas in breathing signals collected from a wearable chest-belt. The resulting swallow detection is then refined using a Hidden Markov Model (HMM) based mechanism that leverages known locality in the sequence of human swallows. Using the developed system in this reporting period we are experimentally able to demonstrate the effectiveness of such two-stage SVM-HMM based mechanism for solid food intake detection via analyzing breathing signal and human swallow sequence locality. Apnea detection also has potential as an additional data modality for assessing stress during team member interactions. As this badge capability develops, it will be integrated into our phased lab validation process.

Flexible badge identifier. The embedded software on the badges and on the access point has been upgraded to support dynamic determination of badge identification. In the earlier version, each badge had to be hardwired with a unique badge identifier. In contrast to the earlier version, the newly developed software allows each badge to be allocated an identifier by the access point at start time (when the system is booted). This feature brings a huge operational advantage in that any badge can be used with any identifier by turning it on in a given sequence. Currently, the engineering team is working to upgrade the software for the Graphical User Interface so that the flexible identification feature can be employed by end-users.

The proposed research has three specific aims and deliverables that yield an integrated approach for measuring, monitoring, and regulating teamwork processes and team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research will use Experience Sampling Methods (daily assessments) to assess team functioning in ICE environments. The goal is to quantify expected variation in key team processes, identify internal and external shocks, and assess dynamic effects on team performance. Such data are essential for developing standards to distinguish normative variation from anomalies indicative of a threat to team functioning which are necessary for triggering countermeasures.

(2) Extend engineering development of an unobtrusive monitoring technology (wearable wireless sensor package). The product is to further develop a prototype monitoring technology of teamwork interactions. Initial validation has demonstrated reliability and accuracy sufficient to establish proof of concept. Proposed extensions are designed to (a) add sensing capabilities (swallow monitoring for food intake, stress assessment) and (b) technology development to make the system more robust (packaging, energy efficiency; hardware, algorithms, and software) for out-of-lab field demonstration.

(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology provides continuous data on a range of teamwork processes. Three additional components are required for a countermeasure system. (a) Metrics: Algorithms need to be developed that parse the raw data streams into meaningful measures, then the metrics need to be validated; (b) Data Fusion and Team Regulation Protocols: The multivariate time series metrics need to be fused into a coherent assessment of individual and team functioning. Anomalies that signal a departure from normative functioning have to be classified to drive the provision of feedback and/or team regulation interventions; (c) Distributed Networked Dashboard: A system architecture is needed to integrate sensor information and data fusion, direct feedback to maintain good teamwork and, when the system detects an anomaly in team functioning, trigger appropriate feedback and countermeasures to help an individual or the team regulate team processes. Flexible options for distributing and displaying team status assessments and countermeasures need to be provided (e.g., individual team member, dyads, team leader, ground control).

These specific aims will contribute to reducing the risk of team performance decrements by characterizing normative and anomalous patterns of team functioning; monitoring team member interactions; and providing regulation support to maintain teamwork and to trigger countermeasures when needed to aid team recovery.

Benchmark Long Duration Team Functioning in ICE Analog Environments

A significant portion of research effort was invested in developing, initiating, and conducting benchmarking data collections over the last several months. A description of our research activities follows.

Australian Antarctic Division (AAD) Stations and Field Teams in Aurora Basin (AB). We extended our collaborative research with Dr. Jeff Ayton of the AAD. We initiated new data collections with station personnel who deployed to winter-over for 2013-2014. This involved extending our research protocol with AAD; renewing IRB (Institutional Review Board) approvals by the AAD, MSU (Michigan State University), and NASA; and working with our collaborator and his team to recruit participants. Approximately 44 participants from Mawson, Davis, and Casey Stations, as well as field science teams, are participating in this ongoing effort to benchmark individual and team functioning in ICE settings.

This ongoing research assesses daily teamwork processes using Experience Sampling Methodology (ESM), which captures a snapshot of key individual and team reactions to events of the day. Although the absolute sample sizes tend to be small, the primary focus of the research is on the dynamics of reactions over a period of nine months to a year (i.e., approximately 270 to 360 measurement periods), which yields insights into long duration individual and team functioning.

Science Field Teams in Antarctica. We also extended our ongoing collaboration with science teams that deploy to the ice during the Antarctic summer for 2013-2014. This involved extending our research protocol, renewing MSU and NASA IRB approvals, and recruiting participants from the science teams. Approximately 10 participants are contributing to the research effort, providing daily ESM reports.

Human Exploration Research Analog (HERA). We initiated new benchmarking research in a NASA transit mission simulation, HERA, which is located at the Johnson Space Center (JSC). HERA missions involve a crew of members, selected from NASA volunteers. HERA simulates transit for exploration of an asteroid. Thus far, mission duration is approximately 7 days. This research has involved extending our protocol, securing IRB approvals from NASA and MSU, training personnel, and coordinating research activities with several other investigator teams. We also have taken on the responsibility of coordinating several end-of-day measures across investigators and then compiling and sharing the data. We are also the lead team for coordinating interaction badge data (the “SS” badge provides shared data; the other MSU badge is under development and evaluation).

In addition to the use of our standard ESM protocol, we also have developed a “simulation within the simulation” that is used to evaluate our monitoring technology. Heretofore, the monitoring “badge” has only been evaluated in lab settings for basic validation. This effort is extending evaluation for field testing and user reactions. Thus far, we have collected data for the first two HERA missions in February and April of 2014. Mission 3 is about to commence and a fourth mission is planned for the Campaign this year; another Campaign of four missions is planned for next year.

Hawai’i Space Exploration Analog and Simulation (HI-SEAS). We initiated new benchmarking research in a surface exploration simulation, HI-SEAS, which is located at 8200 feet on Mt. Mona Loa on the big island of Hawai’i. This has involved extending our protocol; securing IRB approvals from the University of Hawai’i (under the PI, Kim Binsted), MSU, and NASA; and substantially aiding the HI-SEAS mission design. We have contributed to crew selection (we screened on the five factor model of personality and cognitive ability), the mission story / script, and mission EVA / scenario design.

We are currently collecting ESM data from the 5-person crew during their 4-month mission that started in April 2014. The crew is also using the SS badge so we can enlarge the pool of benchmarking data for interactions over time. We are now in preparation mode for the next HI-SEAS mission that will involve a crew of 6 for 8 months. A 12 month mission is planned for the following year.

NASA Extreme Environment Mission Operations (NEEMO) Mission 18. Finally, we are preparing for data collection from a crew of four astronauts for NEEMO18. Training at JSC for the crew is about to begin and the roughly one week mission will run in July. The astronauts represent a mix of agencies, so the crew is international. NEEMO18 data collection has involved extension / modification of our protocol; coordinating IRB approvals with NASA, MSU, and several international space agencies; training revisions; and coordination among investigating teams and NASA elements. We are, once again, responsible for coordinating EOD and badge data.

Extend Engineering Development of an Unobtrusive Monitoring Technology

The monitoring technology under development has been successfully validated in the laboratory and is now under evaluation in NASA mission simulations. Primary objectives for engineering development center on: (1) improved packaging and enhancing robustness of the wearable interaction monitoring badges and (2) detection of swallow monitoring using the wearable sensor system.

Packaging and robustness. We have developed a 3-D printed case for the wearable badge and for the radio access point. The case contributes to robustness in terms of the badge's abilities to withstand rough handling in variable environmental conditions. The new access point is also capable of all the sensing possible using the badges.

To improve energy-efficiency, we have employed a series of transmission power control protocols (developed by Co-PI Biswas’ group) running on the badge hardware. A novel measurement based link power control mechanism with closed-loop feedback control techniques was used. These protocols have improved the battery life of the system. The current version of the badge with all these new software and protocol can run up to 6 hours of data collection in one recharge. These new robust badges were used in the HERA data collection sessions during this reporting period.

Swallow monitoring. We have developed a wearable solid food intake monitoring system that analyzes human breathing signal and swallow sequence locality for solid food intake monitoring. Food intake is identified by the way of detecting a person’s swallow events. The system works based on a key observation that the otherwise continuous breathing process is interrupted by a short apnea during swallowing. A Support Vector Machine (SVM) is first used for detecting such apneas in breathing signals collected from a wearable chest-belt. The resulting swallow detection is then refined using a Hidden Markov Model (HMM) based mechanism that leverages known locality in the sequence of human swallows. Using the developed system in this reporting period we are experimentally able to demonstrate the effectiveness of such two-stage SVM-HMM based mechanism for solid food intake detection via analyzing breathing signal and human swallow sequence locality. Apnea detection also has potential as an additional data modality for assessing stress during team member interactions. As this badge capability develops, it will be integrated into our phased lab validation process.

The proposed research has three specific aims and deliverables that yield an integrated approach for measuring, monitoring, and regulating teamwork processes and team functioning:

(1) Benchmark long duration team functioning in ICE analog environments. This research will use Experience Sampling Methods (daily assessments) to assess team functioning in ICE environments. The goal is to quantify expected variation in key team processes, identify internal and external shocks, and assess dynamic effects on team performance. Such data are essential for developing standards to distinguish normative variation from anomalies indicative of a threat to team functioning which are necessary for triggering countermeasures.

(2) Extend engineering development of an unobtrusive monitoring technology (wearable wireless sensor package). The product is to further develop a prototype monitoring technology of teamwork interactions. Initial validation has demonstrated reliability and accuracy sufficient to establish proof of concept. Proposed extensions are designed to (a) add sensing capabilities (swallow monitoring for food intake, stress assessment) and (b) technology development to make the system more robust (packaging, energy efficiency; hardware, algorithms, and software) for out-of-lab field demonstration.

(3) Develop teamwork interaction metrics and regulation support systems. The monitoring technology provides continuous data on a range of teamwork processes. Three additional components are required for a countermeasure system. (a) Metrics: Algorithms need to be developed that parse the raw data streams into meaningful measures, then the metrics need to be validated; (b) Data Fusion and Team Regulation Protocols: The multivariate time series metrics need to be fused into a coherent assessment of individual and team functioning. Anomalies, that signal a departure from normative functioning, have to be classified to drive the provision of feedback and/or team regulation interventions; (c) Distributed Networked Dashboard: A system architecture is needed to integrate sensor information and data fusion, direct feedback to maintain good teamwork and, when the system detects an anomaly in team functioning, trigger appropriate feedback and countermeasures to help an individual or the team regulate team processes. Flexible options for distributing and displaying team status assessments and countermeasures need to be provided (e.g., individual team member, dyads, team leader, ground control).

These specific aims will contribute to reducing the risk of team performance decrements by characterizing normative and anomalous patterns of team functioning; monitoring team member interactions; and providing regulation support to maintain teamwork and to trigger countermeasures when needed to aid team recovery.