Interactive causal diagram of habitat design impacts on behavioral health and performance in extreme environments
Name
s41526-026-00618-9.pdf
Description
Published version
Size
1.31 MB
Format
Adobe PDF
Checksum (MD5)
cc918060ed1c4da7dd4ccea0840ee44b
Author(s)
Lin, Mich
Chen, Lu
Arquilla, Katya
Date Issued
August 12, 2026
Journal
npj Microgravity
Publisher
Springer Science and Business Media LLC
Citation
Lin, M., Chen, L. & Arquilla, K. Interactive causal diagram of habitat design impacts on behavioral health and performance in extreme environments. npj Microgravity 12, 68 (2026).
Version
Final published version
Abstract
As crewed long-duration space exploration missions become increasingly Earth-independent and reliant on onboard technology, risks due to human-system incompatibility become critical to address. In a space habitat, establishing objective and meaningful relationships between the individual and the environment has been challenging due to multidirectional influences between system components. Feedback loops between behavioral health processes and outcomes complicate characterization of influence within the system. Yet, it remains crucial for habitat designers and stakeholders to trade design decisions and their associated risks. In this work, we have created a risk mapping of the impact of habitat design to behavioral health and performance outcomes in isolated, confined, and extreme environments. We leverage a Directed Acyclic Graph (DAG) to formalize habitat design parameters as powerful mediators between mission stressors and behavioral health outcomes. To represent the DAG accessibly, we created the Human-Environment Connection & Interaction Atlas (https://hecia.space), an interactive open-source platform. We conducted expert and user interviews to evaluate the underlying DAG and the usability of the tool. Herein, we present our novel development of a risk map that links habitat design and behavioral health, as well as an interactive visualization that can provide a basis for accessible communication of complex systems.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41526-026-00618-9
