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Record W4387896526 · doi:10.2196/46019

Effects of Physical Exercise on Telomere Length in Healthy Adults: Systematic Review, Meta-Analysis, and Meta-Regression

2023· review· en· W4387896526 on OpenAlexvenueno aff
Juan Luis Sánchez‐González, Juan Luis Sánchez-Rodríguez, Sergio Varela-Rodríguez, Rogelio González‐Sarmiento, Cristina Rivera‐Picón, Raúl Juárez‐Vela, Clara Isabel Tejada-Garrido, Javier Martín‐Vallejo, Víctor Navarro‐López

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typereview
Languageen
FieldMedicine
TopicTelomeres, Telomerase, and Senescence
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisMeta-regressionPhysical exercisePhysical activityModalitiesMedicinePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Physical exercise is one of the main nonpharmacological treatments for most pathologies. In addition, physical exercise is beneficial in the prevention of various diseases. The impact of physical exercise has been widely studied; however, existing meta-analyses have included diverse and heterogeneous samples. Therefore, to our knowledge, this is the first meta-analysis to evaluate the impact of different physical exercise modalities on telomere length in healthy populations. OBJECTIVE: In this review, we aimed to determine the effect of physical exercise on telomere length in a healthy population through a meta-analysis of randomized controlled trials. METHODS: A systematic review with meta-analysis and meta-regression of the published literature on the impact of physical exercise on telomere length in a healthy population was performed. PubMed, Cochrane Library, SCOPUS, Web of Science, and Embase databases were searched for eligible studies. Methodological quality was evaluated using the Risk Of Bias In Nonrandomized Studies of Interventions and the risk-of-bias tool for randomized trials. Finally, the certainty of our findings (closeness of the estimated effect to the true effect) was evaluated using Grading of Recommendations, Assessment, Development, and Evaluations (GRADE). RESULTS: We included 9 trials that met the inclusion criteria with fair methodological quality. Random-effects model analysis was used to quantify the difference in telomere length between the exercise and sham groups. Meta-analysis showed that exercise did not significantly increase telomere length compared with the control intervention (mean difference=0.0058, 95% CI -0.05 to 0.06; P=.83). Subgroup analysis suggested that high-intensity interventional exercise significantly increased telomere length compared with the control intervention in healthy individuals (mean difference=0.15, 95% CI 0.03-0.26; P=.01). Furthermore, 56% of the studies had a high risk of bias. Certainty was graded from low to very low for most of the outcomes. CONCLUSIONS: The findings of this systematic review and meta-analysis suggest that high-intensity interval training seems to have a positive effect on telomere length compared with other types of exercise such as resistance training or aerobic exercise in a healthy population. TRIAL REGISTRATION: PROSPERO CRD42022364518; http://tinyurl.com/4fwb85ff.

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)0.022
metaresearch head score (Gemma)0.052
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 score0.024
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.052
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0240.056
Bibliometrics0.0080.008
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.093
GPT teacher head0.396
Teacher spread0.303 · 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

Citations19
Published2023
Admission routes1
Has abstractyes

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