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Record W4387426428 · doi:10.1007/s40279-023-01938-6

A Perspective on High-Intensity Interval Training for Performance and Health

2023· article· en· W4387426428 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSports Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaGran Sasso Science InstitutePepsiCo
KeywordsHigh-intensity interval trainingSports medicinePerspective (graphical)Training (meteorology)Interval trainingMedicineIntensity (physics)Physical therapyPhysical medicine and rehabilitationComputer scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Interval training is a simple concept that refers to repeated bouts of relatively hard work interspersed with recovery periods of easier work or rest. The method has been used by high-level athletes for over a century to improve performance in endurance-type sports and events such as middle- and long-distance running. The concept of interval training to improve health, including in a rehabilitative context or when practiced by individuals who are relatively inactive or deconditioned, has also been advanced for decades. An important issue that affects the interpretation and application of interval training is the lack of standardized terminology. This particularly relates to the classification of intensity. There is no common definition of the term "high-intensity interval training" (HIIT) despite its widespread use. We contend that in a performance context, HIIT can be characterized as intermittent exercise bouts performed above the heavy-intensity domain. This categorization of HIIT is primarily encompassed by the severe-intensity domain. It is demarcated by indicators that principally include the critical power or critical speed, or other indices, including the second lactate threshold, maximal lactate steady state, or lactate turnpoint. In a health context, we contend that HIIT can be characterized as intermittent exercise bouts performed above moderate intensity. This categorization of HIIT is primarily encompassed by the classification of vigorous intensity. It is demarcated by various indicators related to perceived exertion, oxygen uptake, or heart rate as defined in authoritative public health and exercise prescription guidelines. A particularly intense variant of HIIT commonly termed "sprint interval training" can be distinguished as repeated bouts performed with near-maximal to "all out" effort. This characterization coincides with the highest intensity classification identified in training zone models or exercise prescription guidelines, including the extreme-intensity domain, anaerobic speed reserve, or near-maximal to maximal intensity classification. HIIT is considered an essential training component for the enhancement of athletic performance, but the optimal intensity distribution and specific HIIT prescription for endurance athletes is unclear. HIIT is also a viable method to improve cardiorespiratory fitness and other health-related indices in people who are insufficiently active, including those with cardiometabolic diseases. Research is needed to clarify responses to different HIIT strategies using robust study designs that employ best practices. We offer a perspective on the topic of HIIT for performance and health, including a conceptual framework that builds on the work of others and outlines how the method can be defined and operationalized within each context.

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.

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 score0.965
Threshold uncertainty score0.371

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 head0.053
GPT teacher head0.337
Teacher spread0.284 · 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