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Record W3130343653 · doi:10.1177/1071181320641146

Effect of Guided Tactical Breathing with Biofeedback on Acute Stress Attenuation and Marksmanship Performance of Novice Shooters

2020· article· en· W3130343653 on OpenAlexaff
Ramtin Lotfabadi, Joshua A. Granek, Jiayuan He, Ning Jiang, Fan He, Junhan Bae, Hamid Boland, Catherine M. Burns

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsDefence Research and Development CanadaUniversity of Waterloo
Fundersnot available
KeywordsBiofeedbackArousalStressorBreathingWearable computerPsychologyTask (project management)AthletesPhysical medicine and rehabilitationSimulationComputer scienceMedicinePhysical therapyEngineeringClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

The current study introduced a novel approach to inducing stress, and examining effects of wearable and mobile technology-assisted tactical breathing with real-time heartrate biofeedback, on the attenuation of acute stress, post-stressor recovery and performance. 39 participants with no prior experience with firearms participated in a marksmanship task engaging stationary targets with a semi-automatic rifle, at a controlled indoor shooting range. Novice shooters applying guided tactical breathing with biofeedback following exposure to the shooting task, were able to maintain lower arousal (uninhibited parasympathetic system). Findings suggested significant effect of intervention with biofeedback on stress attenuation, however no significant improvement of marksmanship performance among novice shooters resulting from the intervention was found. This study provides insights into app-led tactical breathing training to control arousal levels during stress, recommending strategies on further evaluation of the effectiveness of mobile and wearable technologies on stress attenuation for varying levels of marksmanship skill and individual difference.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.247
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

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