Evidence Supporting the Management of Medical Conditions During Long-Duration Spaceflight: Protocol for a Scoping Review.

BACKGROUND: Future long-duration space exploration missions, such as traveling to Mars, will create an increase in communication time delays and disruptions and remove the viability of emergency returns to Earth for timely medical treatment. Thus, higher levels of medical autonomy are necessary. Crew selection is proposed as the first line of defense to minimize medical risk for future missions; however, the second proposed line of defense is medical preparedness and crew member autonomy. In an effort to develop a decision support system, the Canadian Space Agency mandated a team of scientists from Thales Research and Technology Canada (Québec, QC) and Université Laval (Québec, QC) to create an evidence-based medical condition database linking mission-critical human conditions with key causal factors, diagnostic and treatment information, and probable outcomes. OBJECTIVE: To complement this database, we are currently conducting a scoping review to better understand the depth and breadth of evidence about managing medical conditions in space. METHODS: This scoping review will adhere to quality standards for scoping reviews, employing Levac, Colquhoun, and O'Brien's 6-stage methodology; the reported results will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. In stage 1, we identified the research question in collaboration with the Canadian Space Agency (CSA), the main knowledge user. We prioritized 10 medical conditions: (1) acute coronary syndrome, (2) atrial fibrillation, (3) eye penetration, (4) herniated disk, (5) nephrolithiasis, (6) pulmonary embolism, (7) retinal detachment, (8) sepsis, (9) stroke, and (10) spaceflight associated neuro-ocular syndrome. In stage 2, with the help of an information specialist from Cochrane Canada Francophone, papers were identified through searches of the following databases: ARC, Embase, IeeeXplore, Medline Ovid, PsychINFO, and Web of Science. In stage 3, studies will be selected and assessed using a 3-step process and emerging, refined exclusion criteria. In stage 4, the data will be charted in a table based on parameters required by the CSA and developed using Google spreadsheets for shared access. In stage 5, evidence-based descriptive summaries will be produced for each condition, as well as descriptive analyses of collected data. Finally, in stage 6, the findings will be shared with the CSA to guide the completion of this project. RESULTS: This study was planned in December 2018. Stage 1 has been completed. The initial database search strategy with all target conditions combined identified a total of 10,403 citations to review through title and abstract screening and after duplicate removal. We plan to complete stages 2-6 by the beginning of 2021. CONCLUSIONS: This scoping review will map the literature on the management of 10 priority medical conditions in space. It will also enable us to identify knowledge gaps that must be addressed in future research, ensuring successful and medically safe future missions as humankind embarks upon new frontiers of space exploration. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24323.

WAUC: A Multi-Modal Database for Mental Workload Assessment Under Physical Activity.

Assessment of mental workload is crucial for applications that require sustained attention and where conditions such as mental fatigue and drowsiness must be avoided. Previous work that attempted to devise objective methods to model mental workload were mainly based on neurological or physiological data collected when the participants performed tasks that did not involve physical activity. While such models may be useful for scenarios that involve static operators, they may not apply in real-world situations where operators are performing tasks under varying levels of physical activity, such as those faced by first responders, firefighters, and police officers. Here, we describe WAUC, a multimodal database of mental Workload Assessment Under physical aCtivity. The study involved 48 participants who performed the NASA Revised Multi-Attribute Task Battery II under three different activity level conditions. Physical activity was manipulated by changing the speed of a stationary bike or a treadmill. During data collection, six neural and physiological modalities were recorded, namely: electroencephalography, electrocardiography, breathing rate, skin temperature, galvanic skin response, and blood volume pulse, in addition to 3-axis accelerometry. Moreover, participants were asked to answer the NASA Task Load Index questionnaire after each experimental section, as well as rate their physical fatigue level on the Borg fatigue scale. In order to bring our experimental setup closer to real-world situations, all signals were monitored using wearable, off-the-shelf devices. In this paper, we describe the adopted experimental protocol, as well as validate the subjective, neural, and physiological data collected. The WAUC database, including the raw data and features, subjective ratings, and scripts to reproduce the experiments reported herein will be made available at: http://musaelab.ca/resources/.

PASS: A Multimodal Database of Physical Activity and Stress for Mobile Passive Body/ Brain-Computer Interface Research.

With the burgeoning of wearable devices and passive body/brain-computer interfaces (B/BCIs), automated stress monitoring in everyday settings has gained significant attention recently, with applications ranging from serious games to clinical monitoring. With mobile users, however, challenges arise due to other overlapping (and potentially confounding) physiological responses (e.g., due to physical activity) that may mask the effects of stress, as well as movement artifacts that can be introduced in the measured signals. For example, the classical increase in heart rate can no longer be attributed solely to stress and could be caused by the activity itself. This makes the development of mobile passive B/BCIs challenging. In this paper, we introduce PASS, a multimodal database of Physical Activity and StresS collected from 48 participants. Participants performed tasks of varying stress levels at three different activity levels and provided quantitative ratings of their perceived stress and fatigue levels. To manipulate stress, two video games (i.e., a calm exploration game and a survival game) were used. Peripheral physical activity (electrocardiography, electrodermal activity, breathing, skin temperature) as well as cerebral activity (electroencephalography) were measured throughout the experiment. A complete description of the experimental protocol is provided and preliminary analyses are performed to investigate the physiological reactions to stress in the presence of physical activity. The PASS database, including raw data and subjective ratings has been made available to the research community at http://musaelab.ca/pass-database/. It is hoped that this database will help advance mobile passive B/BCIs for use in everyday settings.

Prediction of Stress and Mental Workload during Police Academy Training Using Ultra-Short-Term Heart Rate Variability and Breathing Analysis.

Heart rate variability (HRV) has been studied in the context of human behavior analysis and many features have been extracted from the inter-beat interval (RR) time series and tested as correlates of constructs such as mental workload, stress and anxiety. Such constructs are crucial in assessing quality-of-life of individuals, as well as their overall performance when doing critical tasks. Most studies, however, have been conducted in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate settings remains unknown. Additionally, it is desirable for such mental state monitoring systems to have high temporal resolution, thus allowing for quick feedback and adaptive decision making. In this article, we explore the use of features computed from time windows much shorter than typically reported in the literature. More specifically, we evaluate the potential of HRV and breathing features computed over so-called ultra-short-term segments (i.e., < 5 minutes) for stress and mental workload prediction. Experiments with 27 police academy trainees show that short time windows as low as 60 seconds can provide useful insights, in particular for mental workload assessment. Moreover, the fusion of HRV and breathing features showed to be an important aspect for reliable behavioural assessment in highly ecological settings.

Multi-Scale Heart Beat Entropy Measures for Mental Workload Assessment of Ambulant Users.

Mental workload assessment is crucial in many real life applications which require constant attention and where imbalance of mental workload resources may cause safety hazards. As such, mental workload and its relationship with heart rate variability (HRV) have been well studied in the literature. However, the majority of the developed models have assumed individuals are not ambulant, thus bypassing the issue of movement-related electrocardiography (ECG) artifacts and changing heart beat dynamics due to physical activity. In this work, multi-scale features for mental workload assessment of ambulatory users is explored. ECG data was sampled from users while they performed different types and levels of physical activity while performing the multi-attribute test battery (MATB-II) task at varying difficulty levels. Proposed features are shown to outperform benchmark ones and further exhibit complementarity when used in combination. Indeed, results show gains over the benchmark HRV measures of 24.41 % in accuracy and of 27.97 % in F1 score can be achieved even at high activity levels.

A cognitive prosthesis for complex decision-making.

While simple heuristics can be ecologically rational and effective in naturalistic decision making contexts, complex situations require analytical decision making strategies, hypothesis-testing and learning. Sub-optimal decision strategies - using simplified as opposed to analytic decision rules - have been reported in domains such as healthcare, military operational planning, and government policy making. We investigate the potential of a computational toolkit called "IMAGE" to improve decision-making by developing structural knowledge and increasing understanding of complex situations. IMAGE is tested within the context of a complex military convoy management task through (a) interactive simulations, and (b) visualization and knowledge representation capabilities. We assess the usefulness of two versions of IMAGE (desktop and immersive) compared to a baseline. Results suggest that the prosthesis helped analysts in making better decisions, but failed to increase their structural knowledge about the situation once the cognitive prosthesis is removed.

Dealing with task interruptions in complex dynamic environments: are two heads better than one?

OBJECTIVE: This study examined whether teaming up mitigates individual vulnerability to task interruptions in complex dynamic situations. BACKGROUND: Omnipresent in everyday multitasking environments, task interruptions are usually detrimental to individual performance. This is particularly crucial in dynamic command and control (C2) safety-critical contexts because of the additional challenge imposed by the continually evolving situation during the interruption. METHOD: We employed a firefighting microworld to simulate C2 in the context of supervisory control to examine the relative impact of interruptions on participants working in a functional dyad versus operators working alone. RESULTS: Although task interruption was detrimental to participants' efficacy of monitoring resources, the negative impact of interruption was reduced for those working in teams. Teaming up translated into faster resumption time, but only if both teammates were interrupted simultaneously. Interrupting only one team member was associated with increased postinterruption communications and slower resumption time. CONCLUSION: These findings suggest that in complex dynamic situations working in a small team confers more resistance to task interruption than working alone by virtue of the reduced individual workload typical of teamwork. The benefit of collaborative work seems nevertheless mediated by the coordination and communication overhead associated with teamwork. APPLICATION: The present findings have practical implications for operators dealing with unexpected events such as task interruptions in C2 environments.

The ubiquitous nature of the Hebb repetition effect: error learning mistaken for the absence of sequence learning.

Sequence learning is essential in cognition and underpins activities such as language and skill acquisition. One classical demonstration of sequence learning is that of the Hebb repetition effect, whereby serial recall improves over repetitions on a repeated list relative to random lists. When addressing the question of which mechanism underlies the effect, the traditional approach is to prevent the action of processes thought to be responsible for sequence learning: If the typical Hebb repetition effect is reduced, these processes are key to the effect, researchers claim. By reanalyzing the data of F. B. R. Parmentier, M. T. Maybery, M. Huitson, and D. M. Jones (2008)-who reported no Hebb effect for sequences of auditory-spatial stimuli-we revealed that error learning can be mistaken for the absence of sequence learning. Indeed, incorrect responses are reproduced increasingly over repetitions. Our findings suggest that the Hebb repetition effect can be associated with response learning as well as stimulus processing.

Decision-tree models of categorization response times, choice proportions, and typicality judgments.

The authors present 3 decision-tree models of categorization adapted from T. Trabasso, H. Rollins, and E. Shaughnessy (1971) and use them to provide a quantitative account of categorization response times, choice proportions, and typicality judgments at the individual-participant level. In Experiment 1, the decision-tree models were fit to reaction time and choice proportion data from a study reported by A. L. Cohen and R. M. Nosofsky (2003). In Experiment 2, participants were also asked to provide typicality ratings for each stimulus. A process-tracing method called the "4-questions game" (Y. Sayeki, 1969) was used in a posttest phase to identify a decision tree for each participant. In both experiments, the decision-tree models explained a very high proportion of variance in the data and compared favorably with 2 leading exemplar models.

Learning correct responses and errors in the Hebb repetition effect: two faces of the same coin.

In a serial recall task, the Hebb repetition effect occurs when recall performance improves for a sequence repeated throughout the experimental session. This phenomenon has been replicated many times. Nevertheless, such cumulative learning seldom leads to perfect recall of the whole sequence, and errors persist. Here the authors report evidence that there is another side to the Hebb repetition effect that involves learning errors produced in a repeated sequence. A learning measure based on past recalls (correct or incorrect) shows that the probability of a given response increases with the number of prior occurrences of that response. The pattern of results reveals another manifestation of the Hebb repetition effect and speaks to the nature of implicit learning.