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Record W3048321850 · doi:10.1080/02640414.2020.1801324

Affective responses to high-intensity interval training with continuous and respite music

2020· article· en· W3048321850 on OpenAlexaff
Leighton Jones, Matthew Stork, Liam S. Oliver

Bibliographic record

VenueJournal of Sports Sciences · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHigh-intensity interval trainingInterval trainingRespite carePsychologyMusic therapyContinuous trainingPhysical therapyMedicineAudiologyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Music has been shown to enhance affective responses to continuous exercise, but the most effective application of music during interval exercise is poorly understood. This study examined two contrasting applications of music designed to assuage the decline in pleasure often experienced during high-intensity interval training (HIIT). In a repeated measures crossover design, 18 recreationally active participants (10 men and 8 women; Mage = 25.1 ± 5.1 years; MBMI = 23.08 ± 2.01 kg/m 2; M VO2max = 38.82 ± 10.73 ml/kg/min) completed three HIIT sessions (10 x 60 s efforts at 100% Wmax, separated by 75 s recovery) on a cycle ergometer. Participants completed two experimental conditions: respite music (applied only during the recovery periods), continuous music (applied throughout the entire HIIT session); and a no-music control condition. Results indicated that music did not influence affective valence during the work bouts or recovery periods of the HIIT sessions (ps >.05), but that listening to music continuously elicited greater post-task enjoyment (p =.032, d = 0.66) and remembered pleasure (p =.044, d = 0.5). This study is the first to investigate the application of music during a practical HIIT protocol and to compare the effects of respite versus continuous music during interval exercise.

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 score0.759
Threshold uncertainty score0.199

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.268
Teacher spread0.233 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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