Dataset Information

Cross-validation of the recumbent stepper submaximal exercise test to predict peak oxygen uptake in older adults.

ABSTRACT:

Background

Submaximal exercise testing can have a greater application in clinical settings because peak exercise testing is generally not available. In previous work, a prediction equation was developed to estimate peak oxygen consumption (Vo2) using a total body recumbent stepper (TBRS) and the Young Men's Christian Association (YMCA) protocol in adults who were healthy.

Objective

The purpose of the present study was to cross-validate the TBRS peak Vo2 prediction equation in older adults.

Design

A cross-sectional study was conducted.

Methods

Thirty participants (22 female, 8 male; mean age=66.8 years, SD=5.52; mean weight=68.51 kg, SD=13.39) who previously completed a peak exercise test and met the inclusion criteria were invited to participate in the cross-validation study. Within 5 days of the peak Vo2 test, participants completed the TBRS submaximal exercise test. The TBRS submaximal exercise test equation was used to estimate peak Vo2. The variables in the equation included age, weight, sex, watts (at the end of the submaximal exercise test), and heart rate (at the end of the submaximal exercise test).

Results

A strong correlation was found between the predicted peak Vo2 and the measured peak Vo2. The difference between the values was 0.9 mL·kg(-1)·min(-1), which was not statistically different. The standard error of the estimate was 4.2 mL·kg(-1)·min(-1).

Limitations

The sample included individuals who volunteered to perform a peak exercise test, which may have biased the results toward those willing to exercise to fatigue.

Conclusion

The data suggest the TBRS submaximal exercise test and prediction equation can be used to predict peak Vo2 in older adults. This finding is important for health care professionals wanting to provide information to their patients or clients regarding their fitness level.

SUBMITTER: Herda AA

PROVIDER: S-EPMC4016679 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Cross-validation of the recumbent stepper submaximal exercise test to predict peak oxygen uptake in older adults.

Herda Ashley A AA Lentz Angela A AA Mattlage Anna E AE Sisante Jason-Flor JF Billinger Sandra A SA

Physical therapy 20140116 5

<h4>Background</h4>Submaximal exercise testing can have a greater application in clinical settings because peak exercise testing is generally not available. In previous work, a prediction equation was developed to estimate peak oxygen consumption (Vo2) using a total body recumbent stepper (TBRS) and the Young Men's Christian Association (YMCA) protocol in adults who were healthy.<h4>Objective</h4>The purpose of the present study was to cross-validate the TBRS peak Vo2 prediction equation in olde ...[more]

PMID: 24435104

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Project description:Purpose: To compare peak oxygen uptake (VO2peak) between the asynchronous arm crank ergometry (ACE), and synchronous wheelchair ergometry (WERG), wheelchair treadmill (WTR), and upper-body poling (UBP) mode. Methods: PubMed, Scopus, CINAHL, and SPORTDiscus™ were systematically searched, and identified studies screened based on title, abstract, and thereafter full-text. Studies comparing VO2peak between ≥2 of the modes were included. A meta-analysis was performed by pooling the differences in VO2peak between upper-body exercise modes. The quality of the included studies was assessed and the level of evidence (LoE) established for each mode comparison. Meta-regression analyses investigated the effect of total body mass and participant-related characteristics (% of able-bodied participants, % of participants with tetraplegia and % of participants who are wheelchair athletes) on differences in VO2peak between modes. Results: Of the 19 studies included in this review, 14 studies investigated the difference in absolute and body-mass normalized VO2peak between ACE and WERG, and 5 studies examined the differences between ACE and WTR. No significant difference in absolute or body-mass normalized VO2peak was found between ACE and WERG (overall effect ±95% CI: 0.01 ± 0.06 L·min-1 and 0.06 ± 1.2 ml·kg-1·min-1, both p > 0.75; LoE: strong). No significant difference in absolute or body-mass normalized VO2peak was found between ACE and WTR (overall effect ±95% CI: -0.10 ± 0.18 L·min-1 and -1.8 ± 2.5 ml·kg-1·min-1, both p > 0.14; LoE: moderate). Absolute and/or body-mass normalized VO2peak did not differ between WERG and WTR in one study with 13 participants (LoE: limited) and between ACE and UBP in one study with 18 participants (LoE: moderate). In the meta-regression analyses, there was no significant effect of the investigated factors on differences in VO2peak. Conclusions: The differences between the asynchronous ACE and synchronous WERG propulsion, including possible differences in trunk involvement, do not seem to influence VO2peak. Therefore, ACE and WERG can be used interchangeably to test VO2peak. Possible differences in VO2peak in all other mode comparisons remain unclear due to the wide CIs and limited to moderate LoE.

Project description:Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min-1, [-262, 218]), spanning transitions from low-moderate (-23 ml min-1, [-250, 204]), low-high (14 ml min-1, [-252, 280]), ventilatory threshold-high (-49 ml min-1, [-274, 176]), and maximal (-32 ml min-1, [-261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted [Formula: see text] was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.

Dataset Information

Cross-validation of the recumbent stepper submaximal exercise test to predict peak oxygen uptake in older adults.

Background

Objective

Design

Methods

Results

Limitations

Conclusion

Publications

Cross-validation of the recumbent stepper submaximal exercise test to predict peak oxygen uptake in older adults.

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