JMIR Publications

ABSTRACT

Background:

Cardiorespiratory fitness (CRF) plays an important role in coping with hypoxic stress at high altitude (HA). However, the association of CRF with the development of acute mountain sickness (AMS) remains unclear. Wearable technology devices provide a feasible assessment of CRF, which is quantifiable as maximal oxygen uptake (VO2max), and may contribute to AMS prediction.

Objective:

We aimed to determine the validity of VO2max estimated by the smartwatch test (SWT) and evaluate the performance of a VO2max-SWT-based model in predicting susceptibility to AMS.

Methods:

Both SWT and cardiopulmonary exercise test (CPET) were performed for VO2max measurement in 46 healthy participants at low altitude (LA, 300 m) and subsequently in 41 of them at HA (3900 m) respectively. The characteristics of red blood cells and hemoglobin in all participants were analyzed by routine blood examination before the exercise tests. The Bland–Altman method was used for bias and precision assessment. Multivariate logistic regression was used to analyze the correlation between AMS and the candidate variables. A receiver operating characteristic curve was used to evaluate the efficacy of VO2max in predicting AMS.

Results:

VO2max decreased after acute HA exposure measured by CPET (25.20 ± 6.46 vs. 30.17 ± 5.01, P < 0.001) and SWT (26.17 ± 6.71 vs. 31.28 ± 5.17, P < 0.001). VO2max was slightly overestimated by SWT but had considerable accuracy as the mean absolute percentage error (< 7.0%) and mean absolute error (< 2.0 mL·kg−1·min−1), with a relatively small bias compared with VO2max-CPET both at LA and HA. Twenty of the 46 participants developed AMS at 3900 m, whose VO2max was significantly lower than that of those without AMS (CPET: 27.80 ± 4.55 vs. 32.00 ± 4.64, P = 0.004; SWT: 28.50 ± 4.42 vs. 33.42 ± 4.73, P = 0.001). VO2max-CPET, VO2max-SWT, and red blood cell distribution width-coefficient of variation (RDW-CV) were independent predictors of AMS. To increase the prediction accuracy, we used combination models, including VO2max-CPET combined with RDW-CV and VO2max-SWT combined with RDW-CV, which indicated that the VO2max-SWT-based model (area under the curve = 0.839, 95% confidence interval = 0.720–0.959) was more effective and accurate in AMS prediction than the other models.

Conclusions:

The smartwatch device can be a feasible approach for estimating VO2max in both LA and HA. SWT-based VO2max at LA is an effective indicator of AMS and helps to better identify susceptible individuals following acute HA exposure, especially by combining the RDW-CV. Clinical Trial: This study was registered at www.chictr.org.cn (ChiCTR2200059900).