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Record W4388866554 · doi:10.1186/s12966-023-01535-w

Rates of compliance and adherence to high-intensity interval training: a systematic review and Meta-analyses

2023· review· en· W4388866554 on OpenAlexaff
Alexandre Santos, Kyra Braaten, Megan MacPherson, Diego Vasconcellos, Mathew Vis‐Dunbar, Chris Lonsdale, David R. Lubans, Mary E. Jung

Bibliographic record

VenueInternational Journal of Behavioral Nutrition and Physical Activity · 2023
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMedicineHigh-intensity interval trainingMeta-analysisPhysical therapyCINAHLPsychological interventionInterval trainingMEDLINEInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: To determine rates of compliance (i.e., supervised intervention attendance) and adherence (i.e., unsupervised physical activity completion) to high-intensity interval training (HIIT) among insufficiently active adults and adults with a medical condition, and determine whether compliance and adherence rates were different between HIIT and moderate-intensity continuous training (MICT). METHODS: Articles on adults in a HIIT intervention and who were either insufficiently active or had a medical condition were included. MEDLINE, EMBASE, PsychINFO, SPORTDiscus, CINAHL, and Web of Science were searched. Article screening and data extraction were completed by two independent reviewers. Risk of bias was assessed using RoB 2.0 or ROBINS-I. Meta-analyses were conducted to discern differences in compliance and adherence between HIIT vs. MICT. Sensitivity analyses, publication bias, sub-group analyses, and quality appraisal were conducted for each meta-analysis. RESULTS: One hundred eighty-eight unique studies were included (n = 8928 participants). Compliance to HIIT interventions averaged 89.4% (SD:11.8%), while adherence to HIIT averaged 63% (SD: 21.1%). Compliance and adherence to MICT averaged 92.5% (SD:10.6%) and 68.2% (SD:16.2%), respectively. Based on 65 studies included in the meta-analysis, compliance rates were not different between supervised HIIT and MICT interventions [Hedge's g = 0.015 (95%CI: - 0.088-0.118), p = .78]. Results were robust and low risk of publication bias was detected. No differences were detected based on sub-group analyses comparing medical conditions or risk of bias of studies. Quality of the evidence was rated as moderate over concerns in the directness of the evidence. Based on 10 studies, adherence rates were not different between unsupervised HIIT and MICT interventions [Hedge's g = - 0.313 (95%CI: - 0.681-0.056), p = .096]. Sub-group analysis points to differences in adherence rates dependent on the method of outcome measurement. Adherence results should be interpreted with caution due to very low quality of evidence. CONCLUSIONS: Compliance to HIIT and MICT was high among insufficiently active adults and adults with a medical condition. Adherence to HIIT and MICT was relatively moderate, although there was high heterogeneity and very low quality of evidence. Further research should take into consideration exercise protocols employed, methods of outcome measurement, and measurement timepoints. REGISTRATION: This review was registered in the PROSPERO database and given the identifier CRD42019103313.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.049
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.099
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.046
Bibliometrics0.0080.011
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.526
GPT teacher head0.529
Teacher spread0.003 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations48
Published2023
Admission routes1
Has abstractyes

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