JMIR Publications

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)