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Record W4286008029 · doi:10.3390/healthcare10071346

Clinical and Biological Adaptations in Obese Older Adults Following 12-Weeks of High-Intensity Interval Training or Moderate-Intensity Continuous Training

2022· article· en· W4286008029 on OpenAlexafffund
Layale Youssef, Jordan Granet, Vincent Marcangeli, Maude Dulac, Guy Hajj‐Boutros, Olivier Reynaud, Fanny Buckinx, Pierrette Gaudreau, José A. Morais, Pascale Mauriège, Gilles Gouspillou, Philippe Noirez, Mylène Aubertin‐Leheudre

Bibliographic record

VenueHealthcare · 2022
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecCentre Hospitalier de l’Université de MontréalMcGill University Health CentreUniversité LavalUniversité de MontréalUniversité du Québec à MontréalInstitut Universitaire de Gériatrie de Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineHigh-intensity interval trainingInterval trainingContinuous trainingRowingPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Sarcopenia and obesity are considered a double health burden. Therefore, the implementation of effective strategies is needed to improve the quality of life of older obese individuals. The aim of this study was to compare the impact of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on functional capacities, muscle function, body composition and blood biomarkers in obese older adults. Adipose tissue gene expression and markers of muscle mitochondrial content and quality control involved in exercise adaptations were also investigated. Sixty-eight participants performed either HIIT (n = 34) on an elliptical trainer or MICT (n = 34) on a treadmill, three times per week for 12 weeks. HIIT produced significantly higher benefits on some physical parameters (six-minute walking test (HIIT: +12.4% vs. MICT: +5.2%); step test (HIIT: +17.02% vs. MICT: +5.9%); ten-repetition chair test (HIIT: −17.04% vs. MICT: −4.7%)). Although both HIIT and MICT led to an improvement in lower limb power (HIIT: +25.2% vs. MICT: +20.4%), only MICT led to higher improvement in lower limb muscle strength (HIIT: +4.3% vs. MICT: +23.2%). HIIT was more beneficial for increasing total lean body mass (HIIT: +1.58% vs. MICT: −0.81%), while MICT was more effective for decreasing relative gynoid fat mass (HIIT: −1.09% vs. MICT: −4.20%). Regarding adipose tissue gene expression, a significant change was observed for cell death-inducing DFFA (DNA fragmentation factor-alpha)-like effector A (CIDEA) in the HIIT group (A.U; HIIT at T0: 32.10 ± 39.37 vs. HIIT at T12: 48.2 ± 59.2). Mitochondrial transcription factor A (TFAM) content, a marker of mitochondrial biogenesis, increased significantly following HIIT (+36.2%) and MICT (+57.2%). A significant increase was observed in the HIIT group for Translocase of Outer Membrane 20 (TOM20; +54.1%; marker of mitochondrial content), Mitofusin-2 (MFN2; +71.6%; marker of mitochondrial fusion) and Parkin RBR E3 Ubiquitin Protein Ligase (PARKIN; +42.3%; marker of mitophagy). Overall, our results indicate that even though MICT (walking on treadmill) and HIIT (on an elliptical) are effective intervention strategies in obese older adults, HIIT appears to have slightly more beneficial effects. More specifically, HIIT led to higher improvements than MICT on functional capacities, lean mass and skeletal muscle markers of mitochondrial content, fusion, and mitophagy. Thus, MICT but also HIIT (time-efficient training) could be recommended as exercise modalities for obese older adults to maintain or improve mobility, health and quality of life.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.221
GPT teacher head0.406
Teacher spread0.185 · 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 designNon-randomized trial
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

Citations22
Published2022
Admission routes2
Has abstractyes

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