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. 2025 Apr 30:13:1546712.
doi: 10.3389/fpubh.2025.1546712. eCollection 2025.

Individual cardiorespiratory fitness exercise prescription for older adults based on a back-propagation neural network

Yiran Xiao  1 , Chunyan Xu  1   2 , Lantian Zhang  1 , Xiaozhen Ding  1
Affiliations

Individual cardiorespiratory fitness exercise prescription for older adults based on a back-propagation neural network

Yiran Xiao et al. Front Public Health. .

Abstract

Introduction: To explore and develop a backpropagation neural network-based model for predicting and generating exercise prescriptions for improving cardiorespiratory fitness in older adults.

Methods: The model is based on data from 68 screened studies. In addition, the model was validated with 64 older adults aged 60-79 years. The root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were used to evaluate the fitting and prediction effects of the model, and the hit rate was used to evaluate the prediction accuracy of the model.

Results: The results showed that (1) The mean error ratios for predicting exercise intensity, time and period were 7% ± 12, -5% ± 9% and - 7% ± 14%, respectively, indicating that the estimates were in good agreement with the expected results. (2) Of the 61 subjects who completed the assigned program, cardiorespiratory fitness improved significantly compared with pre-exercise. Improvements ranged from 9.2-10% and 8.9-15.8% for female and male subjects. (3) In addition, 71 and 94% of subjects (43/61) showed cardiorespiratory improvement within plus or minus one standard deviation and plus or minus 1.96 times standard deviation.

Discussion: A neural network-based model for exercise prescription for cardiorespiratory fitness improvement in older adults is feasible and effective.

Keywords: BP neural network; cardiorespiratory fitness; exercise prescription; experimental validation; older adults.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The research methodology.
Figure 2
Figure 2
PRISMA flow-chart.
Figure 3
Figure 3
BPNN training and testing process.
Figure 4
Figure 4
Flow through study.
Figure 5
Figure 5
The structure of BPNN.
Figure 6
Figure 6
(A) Fitting regression diagram of LM-BPNN model. (B) Training error diagram of LM-BPNN.
Figure 7
Figure 7
(A) Output difference in exercise intensity. (B) Output difference in exercise time. (C) Output difference in exercise volume.
Figure 8
Figure 8
Error ratio in exercise intensity, time and volume.
Figure 9
Figure 9
Difference in pre-E-VO2peak, post-E-VO2peak and expected-E-VO2peak.
Figure 10
Figure 10
(A) Linear Regression of post-E-VO2peak and expected-E-VO2peak in female. (B) Linear Regression of post-E-VO2peak and expected-E-VO2peak in male.
Figure 11
Figure 11
(A) Bland–Altman analysis of female’s CRF. (B) Bland–Altman analysis of male’s CRF.
See this image and copyright information in PMC

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