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

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 representative assessment and increase usability in clinical settings.

Objective: This study aimed to review current evidence behind this novel concept and evaluate the performance and quality of models developed to estimate CRF from free-living, unsupervised data.

Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched 4 databases (MEDLINE, Embase, Scopus, and arXiv) for studies reporting the development of models to estimate CRF from continuous free-living wearable data. Studies conducted entirely under controlled laboratory conditions were excluded. Performance metrics were combined in a meta-correlation analysis using a random-effects model and Fisher Z transformation.

Results: Of 1848 papers screened, 18 met the eligibility criteria, with a total of 31,072 participants. The weighted mean age was 46.9 (SD 1.46) years. Multiple computational techniques were used, with 8 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: A promising preliminary agreement between CRF predictions and measured values was noted. However, 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 to be a valuable resource that could lead to a step change in how we measure and monitor CRF.

Trial Registration: PROSPERO CRD42024593878; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024593878