× Collective Allostatic Load

TECHNOLOGY AREA(S): Info Systems

OBJECTIVE: Design, develop, validate, and deploy integrated systems for collecting, aggregating, processing, and analyzing data related to Collective Allostatic Load (CAL), to provide quantitative and predictive measures of a team or groups performance resilience or dysfunction in the face of potentially multiple acute and chronic stressors. Envisioned capabilities will enable near-real time measurement of a groups state beyond the simple aggregation of individuals measures and behaviors, toward understanding the causes and consequences of internal and external factors on group performance over time.

DESCRIPTION: There is critical DoD need for the assessment of diverse, real-world human performance capabilities, particularly in novel, challenging, or adversarial contexts where individuals and teams likely face multiple stressors. The concept of allostasis has been introduced to describe an organisms response to one or more of these stressors in order to return to homeostasis, which can be thought of as a functional state of resilience and adaptability1The resulting wear and tear on the organism from this process, thought to accumulate over time and which can lead to a number of health and performance dysfunctions, is referred to as allostatic load [2-5]. Identifying allostatic load has demonstrated some value for trying to quantify and predict individual trajectories related to health, wellness, and behaviors [6-13]. Much of the research on allostatic load has been done in medical contexts, where associated measures, e.g., a composite index of indicators of cumulative strain on neurophysiological systems, are frequently associated with poor clinical and health outcomes. However, some research on performance in operational, competitive, and high stress environments has found seemingly paradoxical effects, where individual measures that would normally be associated with poor outcomes (such as low vagal tone) are actually associated with better performance [15]. In part, these findings may reflect the fact that current measures of allostatic load fail to incorporate the important influence a persons social context has on their biology. Group cohesion, leadership, morale, and trust have long been qualitatively, if not quantitatively, recognized as key elements in performance and resilience. These factors may shape whether a teams members are able to effectively deal with challenges or threats and are protected against distress and other negative impacts on performance and wellness [16-20]. Without accounting for these intangible but important social influences, conventional interpretation and prediction of any given individuals neurophysiological state and future performance may lead to conclusions that can be misleading, incomplete, or inaccurate. Being able to quantitatively measure a team or groups Collective Allostatic Load (CAL) at appropriate scales and frequencies may enable new capabilities for better predicting the current and future state and resilience of both a team and its individual members. This may further lead to capabilities for identifying and characterizing new design principles and assessment measures for human-machine teams, where “ because humans are involved “ such factors as trust, commitment, social support, and cohesion are likely to remain significant for shaping performance. The intent of this topic therefore is to solicit proposals for innovative quantitative and integrated approaches “ addressing a full pipeline of data collection, aggregation, processing, analysis, updating, visualization, and recommendation/intervention - that might rigorously advance the goal of making the important measurable, rather than making the measurable important for better understanding and predicting both team and individual resilience and performance. Proposers are encouraged to leverage a wide range of domains and technologies such as wearable and non-obtrusive sensors, data science and mathematics, machine learning, network science, cognitive neuroscience, experimental psychology, psychometrics and computational social science, while seeking to demonstrate the advantages and new capabilities their proposed approach may provide over current state of the art. Examples might include proposals that provide credible approaches to leveraging the growing volume and variety of personal and social data to enable new measures for quantifying CAL; new methods for integrating a suite of sensors that might include passive or social sensing platforms to enable repeated CAL measures; new reproducible experimental approaches to testing diagnostic and predictive validity of CAL measures; indirect assessments of CAL for longitudinal studies of teams or groups in different environments. This topic is generally not seeking to fund approaches that are tightly tied to narrow experimental protocols or sensor systems, rely on restricted or excessively costly software and/or data sets, or are likely to demonstrate only incremental improvements over current, largely qualitative, often non-predictive approaches towards trying to measure team performance. Hardware and sensor approaches should leverage widely-available existing platforms and any proposed development efforts must focus on range of application, ease of use, and low barriers of entry for adoption of the approach by DOD, USG, commercial, and academic communities.

PHASE I: Identify your specific approach to a research pipeline, including which CAL measures will be developed and how they will be collected, analyzed, validated and reproduced. Justify your approach via detailed specification of the degree of improvement over current practice, or a description of the new capabilities afforded. Identify the teams or groups for which you are proposing to initially develop CAL measures, and explain their relevance for the DoD. Demonstrate the key technical principles behind the proposed solution, and identify mitigations for any barriers to scale. The demonstrations should provide proof of principle both for credible CAL measures as well as significant diagnostic and/or predictive improvements over current approaches for determining a teams and its members resilience and performance. Phase I deliverables include a notional reference model that can achieve the core functionality of a complete product, credible experimental approaches to testing the generalizability of the CAL measures for more than one kind of team, validating and reproducing CAL measures, as well as an extensive commercialization/propagation plan for achieving widespread use. For this topic, DARPA will accept proposals for work and cost up to $225,000 for Phase I. The preferred structure is a $175,000, 8-month base period and a $50,000, 4-month option period. Alternative structures may be accepted if sufficient rationale is provided.

PHASE II: Demonstrate scale, generalizability, and usability of the proposed approach. The demonstration should validate the predicted improvements and/or new capabilities versus current state of practice, as well as the engineering and design work required to easily scale. This may include integrations into existing systems and processes and the development of institutional partnerships. The Phase II deliverables include the prototype system and a final report that includes demonstration system designs and appropriate experimental test results.

PHASE III: Developed technology may motivate a number of insertions into the academic, commercial, and government systems and communities. Commercial applications may include product development, collaboration and workforce productivity tools, and sports/athletic uses. Military applications may include team design, selection, assessment, and enhancement.

REFERENCES:

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KEYWORDS: Performance; Resilience; Teams; Social Science; Biology; Sensors; Stress; Human-machine Teaming