Biofeedback in team settings: A systematic review of applications and outcomes
Bibliographic record
Abstract
Biofeedback has shown great potential for enhancing individual performance, yet its application in team contexts remains underexplored. This systematic review examines how biofeedback functions within teams, identifying key design components and their impact on team effectiveness. We propose a framework that categorizes biofeedback into three phases: physiological data collection, processing, and feedback delivery. Our analysis of 30 empirical studies reveals that biofeedback can improve team processes by promoting balanced communication, enhancing awareness of team dynamics, and facilitating collaboration. Additionally, biofeedback fosters emergent team states such as connectedness, empathy, and social presence, supporting team cohesion. While evidence indicates that biofeedback enhances dyadic team performance, its impact on larger teams remains limited to subjective performance evaluations. The review identifies key research gaps, including limited study of autonomic nervous system activity, insufficient team-level data processing methods, and a narrow focus on visual feedback. We outline practical considerations for designing biofeedback systems that enhance team effectiveness across contexts. Future research should refine biofeedback designs, extend applications beyond the lab, and incorporate interdisciplinary insights to strengthen theoretical models. This review lays the groundwork for advancing team biofeedback research and practice. • Reviews 30 studies on biofeedback applications in team settings • Presents framework for data collection, processing, and feedback delivery • Shows biofeedback enhances team communication, coordination, and collaboration • Biofeedback improves team states like connectedness, empathy, and social presence • Finds biofeedback improves dyad performance and subjective outcomes in larger teams
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".