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Record W4402494240 · doi:10.1016/j.jshs.2024.100986

Comparison of objectively measured and estimated cardiorespiratory fitness to predict all-cause and cardiovascular disease mortality in adults: A systematic review and meta-analysis of 42 studies representing 35 cohorts and 3.8 million observations

2024· review· en· W4402494240 on OpenAlexaff
Ben Singh, Cristina Cadenas‐Sánchez, Bruno Gonçalves Galdino da Costa, José Castro‐Piñero, Jean‐Philippe Chaput, Magdalena Cuenca‐García, Carol Maher, Nuria Marín‐Jiménez, Ryan McGrath, Pablo Molina-Garcí, Jonathan Myers, Bethany Gower, Francisco B. Ortega, Justin J. Lang, Grant R. Tomkinson

Bibliographic record

VenueJournal of sport and health science/Journal of Sport and Health Science · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsChildren's Hospital of Eastern OntarioMcGill University
FundersEuropean CommissionHorizon 2020Australian Government
KeywordsCardiorespiratory fitnessMedicineMeta-analysisCardiovascular healthDiseaseCohortCohort studyPhysical therapyInternal medicineGerontology

Abstract

fetched live from OpenAlex

• We meta-analyzed 42 studies representing 35 cohorts and 3.8 million adults to compare the associations of objectively measured, exercise-estimated, and non-exercise-estimated cardiorespiratory fitness (CRF) with all-cause and cardiovascular disease (CVD) mortality in adults. • We found 14% and 16% reductions in all-cause and CVD mortality risk per higher metabolic equivalent of task ((MET) i.e., 3.5 mL/kg/min), respectively, with no differences in risk reduction between objectively measured, exercise-estimated, and non-exercise-estimated CRF. • Exercise and non-exercise estimated CRF provide practical and robust alternatives to the more costly and time-consuming objectively measured CRF to enhance patient risk stratification in clinical settings. Cardiorespiratory fitness (CRF) is a powerful health marker recommended by the American Heart Association as a clinical vital sign. Comparing the predictive validity of objectively measured CRF (the “gold standard”) and estimated CRF is clinically relevant because estimated CRF is more feasible. Our objective was to meta-analyze cohort studies to compare the associations of objectively measured, exercise-estimated, and non-exercise-estimated CRF with all-cause and cardiovascular disease (CVD) mortality in adults. Systematic searches were conducted in 9 databases (MEDLINE, SPORTDiscus, Embase, Scopus, PsycINFO, Web of Science, PubMed, CINAHL, and the Cochrane Library) up to April 11, 2024. We included full-text refereed cohort studies published in English that quantified the association (using risk estimates with 95% confidence intervals (95%CIs)) of objectively measured, exercise-estimated, and non-exercise-estimated CRF with all-cause and CVD mortality in adults. CRF was expressed as metabolic equivalents (METs) of task. Pooled relative risks (RR) for all-cause and CVD mortality per 1-MET (3.5 mL/kg/min) higher level of CRF were quantified using random-effects models. Forty-two studies representing 35 cohorts and 3,813,484 observations (81% male) (362,771 all-cause and 56,471 CVD deaths) were included. The pooled RRs for all-cause and CVD mortality per higher MET were 0.86 (95%CI: 0.83–0.88) and 0.84 (95%CI: 0.80–0.87), respectively. For both all-cause and CVD mortality, there were no statistically significant differences in RR per higher MET between objectively measured (RR range: 0.86–0.90) and maximal exercise-estimated (RR range: 0.85–0.86), submaximal exercise-estimated (RR range: 0.91–0.94), and non-exercise-estimated CRF (RR range: 0.81–0.85). Objectively measured and estimated CRF showed similar dose–response associations for all-cause and CVD mortality in adults. Estimated CRF could provide a practical and robust alternative to objectively measured CRF for assessing mortality risk across diverse populations. Our findings underscore the health-related benefits of higher CRF and advocate for its integration into clinical practice to enhance risk stratification.

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)0.024
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0140.001
Bibliometrics0.0020.003
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.382
GPT teacher head0.496
Teacher spread0.114 · 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.

Study designObservational
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
Published2024
Admission routes1
Has abstractyes

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