| 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 |
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| 引用本文： | 耿青青,孙化玉,李晓霞. 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[J]. 中国组织工程研究, 2011, 15(39): 7234-7237. DOI: 10.3969/j.issn.1673-8225.2011.39.004 |
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| 作者姓名： | 耿青青 孙化玉 李晓霞 |
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| 作者单位： | 山东体育学院基础理论系，山东省济南市 250102 |
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| 基金项目： | 山东省科技厅科技攻关计划项目(2010GSF10803) |
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| 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。
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| 关 键 词： | 健步走 老年人 肥胖 减重 预测 数学模型 |
| 收稿时间： | 2011-07-09 |
A computer system to build a mathematical model for predicting the effect of walking on the weight loss of the obese elderly |
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| Geng Qing-qing,Sun Hua-yu,Li Xiao-xia. A computer system to build a mathematical model for predicting the effect of walking on the weight loss of the obese elderly[J]. Chinese Journal of Tissue Engineering Research, 2011, 15(39): 7234-7237. DOI: 10.3969/j.issn.1673-8225.2011.39.004 |
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| Authors: | Geng Qing-qing Sun Hua-yu Li Xiao-xia |
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| Affiliation: | Department of Basic Theory, Shandong Institute of Physical Education and Sports, Jinan 250102, Shandong Province, China |
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| 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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