Predicting basal metabolic rate, new standards and review of previous work.

人体测量学 基础代谢率 统计 工作(物理) 体重 人口学 表(数据库) 数学 计量经济学 医学 计算机科学 数据挖掘 工程类 内分泌学 内科学 社会学 机械工程
作者
Schofield Wn
出处
期刊:PubMed [National Institutes of Health]
卷期号:39 Suppl 1: 5-41 被引量:2859
链接
标识
摘要

After reviewing the literature on basal metabolism, this paper discusses and reviews recent attempts to predict BMR from age, sex and anthropometric measurements. Criticism is made of the scientific and statistical integrity of a widely used table of standard metabolic rates for weight. The statistical screening of data from the literature of the past 50 years is described and equations computed from these screened data are presented. In these equations, BMR is predicted simply from weight or from weight and height with sex and age taken into account. Information is given on error, and tables estimating error for predictions on new data both for individuals and for means of groups of subjects are included. A table of BMRs for weights from 3 to 84 kg for males and females separately is also included. Cross-validation techniques are used to estimate possible threats to validity from various sources including, for example, different procedures of early workers. It was found that in the data available subjects from developing countries not only were smaller and had lower metabolic rates (as was expected) but also had lower rates per unit body weight than European or North American subjects. It is argued that at an individual level the error of prediction must be high since the global operationalisation of BMR confounds separate effects known to participate in complex relations with sex, age and anthropometric indices. The work reported is aimed at meeting a practical need for equations which are simple to apply. However, it was found that little was gained by the use of more complex equations, although they remain of scientific interest.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
moon完成签到,获得积分20
刚刚
小小怪发布了新的文献求助20
1秒前
云杉木发布了新的文献求助10
1秒前
1秒前
科研通AI6.2应助珍香采纳,获得10
1秒前
yan完成签到,获得积分10
1秒前
小张z完成签到,获得积分10
2秒前
Robert_g完成签到,获得积分10
2秒前
junzzz完成签到 ,获得积分10
2秒前
2秒前
Immunology发布了新的文献求助10
2秒前
无限棉花糖完成签到,获得积分10
3秒前
丘比特应助renee采纳,获得10
3秒前
3秒前
sagitar应助开心的小馒头采纳,获得20
3秒前
灰烬使者完成签到,获得积分10
4秒前
jing发布了新的文献求助10
4秒前
4秒前
六六发布了新的文献求助10
4秒前
4秒前
4秒前
5秒前
5秒前
幸存者发布了新的文献求助20
5秒前
WW完成签到,获得积分10
5秒前
ZQX关注了科研通微信公众号
6秒前
dd完成签到,获得积分20
6秒前
雪白丹雪发布了新的文献求助10
6秒前
orixero应助wsqg123采纳,获得10
6秒前
7秒前
科研通AI2S应助tejing1158采纳,获得10
7秒前
李健应助Hear采纳,获得10
7秒前
7秒前
8秒前
gfreezer完成签到,获得积分10
8秒前
dd发布了新的文献求助10
8秒前
liutao发布了新的文献求助30
8秒前
8秒前
清爽山河完成签到 ,获得积分10
8秒前
慕青应助简单的夜绿采纳,获得10
8秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Rosenblum, Global Change Biology 500
CLSI VET01S-2024 Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals (7th Ed) 500
DIPPR Project 801 - Full Version 380
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7766854
求助须知:如何正确求助?哪些是违规求助? 9310725
关于积分的说明 20318962
捐赠科研通 7351983
什么是DOI,文献DOI怎么找? 3315202
关于科研通互助平台的介绍 2464635
邀请新用户注册赠送积分活动 2329840