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基于反向传播神经网络的老年人个体心肺适能运动处方

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

作者信息

Xiao Yiran, Xu Chunyan, Zhang Lantian, Ding Xiaozhen

机构信息

Department of Sport Science Institute, Beijing Sport University, Beijing, China.

Beijing Higher School Engineering Research Center of Sport Nutrition, Beijing Sport University, Beijing, China.

出版信息

Front Public Health. 2025 Apr 30;13:1546712. doi: 10.3389/fpubh.2025.1546712. eCollection 2025.

DOI:10.3389/fpubh.2025.1546712
PMID:40371295
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12074945/
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 (R) 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.

摘要

引言

探索并开发一种基于反向传播神经网络的模型，用于预测和生成运动处方，以改善老年人的心肺适能。

方法

该模型基于68项筛选研究的数据。此外，该模型在64名年龄在60 - 79岁的老年人中进行了验证。使用均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R）来评估模型的拟合和预测效果，并使用命中率来评估模型的预测准确性。

结果

结果表明：（1）预测运动强度、时间和周期的平均误差率分别为7%±12、-5%±9%和 -7%±14%，表明估计值与预期结果高度一致。（2）在完成指定计划的61名受试者中，与运动前相比，心肺适能有显著改善。女性和男性受试者的改善范围分别为9.2 - 10%和8.9 - 15.8%。（3）此外，71%和94%的受试者（43/61）在正负一个标准差和正负1.96倍标准差范围内显示心肺功能有所改善。

讨论

一种基于神经网络的用于改善老年人心肺适能的运动处方模型是可行且有效的。

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2
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Sensors (Basel). 2024 Apr 22;24(8):2656. doi: 10.3390/s24082656.
3
Estimating PM utilizing multiple linear regression and ANN techniques.利用多元线性回归和人工神经网络技术估算颗粒物（PM）。
Sci Rep. 2023 Dec 19;13(1):22578. doi: 10.1038/s41598-023-49717-7.
4
Effects of aging and endurance exercise training on cardiorespiratory fitness and cardiac structure and function in healthy midlife and older women.衰老和耐力运动训练对健康中年及老年女性心肺适能以及心脏结构和功能的影响。
J Appl Physiol (1985). 2023 Dec 1;135(6):1215-1235. doi: 10.1152/japplphysiol.00798.2022. Epub 2023 Oct 19.
5
Non-Invasive Blood Pressure Sensing via Machine Learning.基于机器学习的无创血压检测
Sensors (Basel). 2023 Oct 9;23(19):8342. doi: 10.3390/s23198342.
6
ELoran Propagation Delay Prediction Model Based on a BP Neural Network for a Complex Meteorological Environment.基于 BP 神经网络的复杂气象环境下 ELoran 传播时延预测模型。
Sensors (Basel). 2023 May 29;23(11):5176. doi: 10.3390/s23115176.
7
Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data.利用基于非侵入性数据的机器学习方法来描述心血管健康状况。
PLoS One. 2023 Mar 2;18(3):e0282398. doi: 10.1371/journal.pone.0282398. eCollection 2023.
8
Early stopping by correlating online indicators in neural networks.通过关联神经网络中的在线指标实现提前停止。
Neural Netw. 2023 Feb;159:109-124. doi: 10.1016/j.neunet.2022.11.035. Epub 2022 Dec 14.
9
Research on Diagnosis and Management of Postgraduates Mental Health Status Based on BP Neural Network.基于 BP 神经网络的研究生心理健康状况诊断与管理研究。
Front Public Health. 2022 May 10;10:897565. doi: 10.3389/fpubh.2022.897565. eCollection 2022.
10
Effects of Exercise Training on Anabolic and Catabolic Hormones with Advanced Age: A Systematic Review.运动训练对增龄期合成代谢和分解代谢激素的影响：系统综述。
Sports Med. 2022 Jun;52(6):1353-1368. doi: 10.1007/s40279-021-01612-9. Epub 2021 Dec 22.