中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (39): 7234-7237.doi: 10.3969/j.issn.1673-8225.2011.39.004

• 数字化骨科 digital orthopedics • 上一篇    下一篇

构建数学模型预测老年肥胖人健步走减重效果的计算机系统

耿青青，孙化玉，李晓霞   

  1. 山东体育学院基础理论系，山东省济南市  250102
  • 收稿日期:2011-07-09 修回日期:2011-08-16 出版日期:2011-09-24 发布日期:2011-09-24
  • 作者简介:耿青青☆，女，1981年生，山东省枣庄市人，汉族， 2010年华南师范大学体育科学学院毕业，博士，讲师，主要从事运动免疫学研究。 gqq1188@yahoo.com.cn
  • 基金资助:

    山东省科技厅科技攻关计划项目(2010GSF10803)

A computer system to build a mathematical model for predicting the effect of walking on the weight loss of the obese elderly

Geng Qing-qing, Sun Hua-yu, Li Xiao-xia   

  1. Department of Basic Theory, Shandong Institute of Physical Education and Sports, Jinan  250102, Shandong Province, China
  • Received:2011-07-09 Revised:2011-08-16 Online:2011-09-24 Published:2011-09-24
  • About author:Geng Qing-qing☆, Doctor, Lecturer, Department of Basic Theory, Shandong Institute of Physical Education and Sports, Jinan 250102, Shandong Province, China gqq1188@yahoo.com.cn
  • Supported by:

    the Science and Technology Tackle Key Program of Shandong Science and Technology Bureau, No. 2010GSF10803*

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328

被引次数

2

摘要：

背景：运动健身方案尚缺乏系统的、具有高度针对性的、疗效显著且容易被老年人接受的运动健身指导体系。
目的：在肥胖老年人健步走锻炼减肥的基础上，建立健步走锻炼减重后体质量预测数学模型。
方法：对50名单纯性肥胖老年人进行3个月健步走运动，受试者以最大摄氧量的40%~60%的运动强度进行健步走运动，靶心率控制在100~120次/min，40 min/次，5次/周，建立数学模型预测减重后的体质量。模型1：与实验前体质量m1，锻炼天数t有关的正比例函数预测；模型2：与实验前体质量m1，年龄a，身高h，性别sex，锻炼天数t有关的正比例函数预测。
结果与结论：与运动前相比，受试者运动后体质量、体脂百分比、肥胖度、体质量指数均显著下降(P < 0.01)，提示健步走减肥效果显著；测量受试者锻炼前体质量m1和6 min快步走消耗的能量e，通过模型1可预测肥胖老年人参加健步走运动t天后的体质量mt；若已知其年龄a、性别sex，测量受试者锻炼前体质量m1、身高h和6 min快步走消耗的能量e，通过模型2可预测肥胖老年人参加健步走运动t天后(t≥2)的体质量mt，且对于同一受试者，模型2的预测效果更为精确，模型1更易推广。

关键词: 健步走, 老年人, 肥胖, 减重, 预测, 数学模型

Abstract:

BACKGROUND: There are no systematic and high-targeted health guidance systems that are effective and easy to be accepted by the elderly.
OBJECTIVE: To establish a mathematical model for predicting the effect of walking on the weight loss of the obese elderly.
METHODS: Fifty obese old people received walking exercise under 40%-60% maximal oxygen uptake and with a heart rate of 100-120 beats/per minute for 3 months, 40 minutes once, 5 times a week. The mathematical model was established to predict the body mass. Model 1: a direct proportion function associated with pre-exercise body mass (m1) and training days (t); Model 2: a direct proportion function associated with pre-exercise body mass (m1), age (a), height (h), sex, and training days (t).
RESULT AND CONCLUSION: Compared with the data prior to the exercise, the body mass, fat percentage, body mass index and obesity degree were declined significantly after the exercise (P < 0.01), indicating that the weight-reducing effect of walking was rather outstanding. The model 1 could be used to predict the body mass (mt) after t-day walking exercise based on pre-exercise body mass (m1) and consuming energy of 6-minute quick walking (e). The model 2 could be used to predict the body mass (mt) after t-day walking exercise (t≥2) based on age (a), sex, pre-exercise body mass (m1), height (h) and consuming energy of 6-minute quick walking (e). For the same trainer, the model 2 is more accurate than the model 1 in the predictable results, but the model 1 is easier to practice.

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