Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis

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Dosis A, Syversen AB, Kowal M, Grant D, Tiernan J, Wong D, Jayne DG

Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis

JMIR Mhealth Uhealth 2026;14:e69996

DOI: 10.2196/69996

PMID: 41592155

PMCID: 12841865

Current Preprint Settings (as selected by the authors) 1. When the manuscript is submitted, allow peer review from: (a) Anybody (open community peer review) (b) Editor-selected reviewers (closed peer review) 2. When the manuscript is submitted, display the preprint PDF to: (a) Anybody, anytime (b) Logged-in users only (c) Anybody, anytime (title and abstract only) (d) No one 3. When the manuscript is accepted, display the accepted manuscript PDF to: (a) Anybody, anytime (b) Logged-in users only (c) Anybody, anytime (title and abstract only) (d) No one Accepted for/Published in: JMIR mHealth and uHealth Date Submitted: Dec 13, 2024 Date Accepted: Dec 17, 2025

The final, peer-reviewed published version of this preprint can be found here:

Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis

Dosis A, Syversen AB, Kowal M, Grant D, Tiernan J, Wong D, Jayne DG

Exploiting Unsupervised Free-Living Data for Cardiorespiratory Fitness Estimation: Systematic Review and Meta-Analysis

JMIR Mhealth Uhealth 2026;14:e69996

DOI: 10.2196/69996

PMID: 41592155

PMCID: 12841865

Preprint Accepted Manuscript Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Exploiting unsupervised free-living data for cardiorespiratory fitness estimation, a systematic review Alexios Dosis; Aron Berger Syversen; Mikolaj Kowal; Daniel Grant; Jim Tiernan; David Wong; David G Jayne ABSTRACT Background:

Current methods of cardiorespiratory fitness (CRF) assessment may discriminate against frail individuals who are challenged to perform a maximal cardiopulmonary exercise test. CRF estimations from free-living wearable data, captured over extended time periods may offer a more accurate representation.

Objective:

This study aimed to review current evidence behind this novel concept and assess the accuracy of estimation models.

Methods:

Following PRISMA guidelines we systematically searched four databases (MEDLINE, EMBASE, Scopus and arXiv) for studies reporting the development of models to estimate CRF from continuous free-living wearable data. Studies conducted under controlled laboratory conditions were excluded. Performance metrics were combined in a meta-correlation analysis using a random effects model.

Results:

Of 1848 articles screened, 18 met the eligibility criteria with a total of 31072 participants. Weighted mean age was 46.9 ±1.46 years. Multiple computational techniques were used, with eight studies employing more advanced machine learning models. The meta-correlation analysis revealed a pooled overall estimate of 0.83 with a 95%CI [0.77; 0.88]. The I2 test indicated high heterogeneity at 97%. Risk of bias assessment found most concerns in the data analysis domain with studies often lacking clarity around the data handling process.

Conclusions:

Good agreement between CRF predictions and measured values was noted. Yet no definite conclusions can be drawn for clinical implementation, due to high heterogeneity among the included studies and lack of external validation. Nonetheless, continuous data streams appear a valuable resource that could lead to a step change in how we measure and monitor CRF. Clinical Trial: PROSPERO (CRD42024593878)