Difference between revisions of "Predicting maximal strength"
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− | <jsxgraph width= | + | This little application tries to predict the ''maximal strength'' (1RM) based on a |
− | var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-1,1. | + | ''repetitions to fatigue'' (RTF) value. |
− | + | ||
− | var | + | The calculation is based on the so called ''KLW formula'': |
− | + | :<math> | |
− | ], {strokeColor:'black'} | + | 1RM = w\cdot(0.988+0.0104\cdot x+0.00190\cdot x^2-0.0000584\cdot x^3) |
+ | </math> | ||
+ | The horizontal axis denotes the number of repetitions, the vertical axis denotes the ratio 1RM/RTF. | ||
+ | |||
+ | '''How to use this graphical calculator?''' | ||
+ | Suppose you managed to do 9 repetitions with a weight of 80 kilograms. In the graphical calculator below you have to drag the black dot to r=9 and the blue dot to weight=80. Now, you can read of the 1RM prediction of 95.43. | ||
+ | |||
+ | <jsxgraph width="700" height="500"> | ||
+ | var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-1,1.8,30,0.8], axis: true}); | ||
+ | |||
+ | var w = brd.create('slider',[[24,0.92],[24,1.7],[0,50,200]],{name:'weight w',snapWidth:1}); | ||
+ | |||
+ | f = function(x){ return (0.988+0.0104*x+0.00190*x*x-0.0000584*x*x*x); }; | ||
+ | |||
+ | var c = brd.create('functiongraph',[ | ||
+ | f, | ||
+ | 1,22 | ||
+ | ], {strokeColor:'black', highlightStrokeColor:'black'} | ||
); | ); | ||
+ | |||
+ | var r = brd.create('glider',[10,1,c],{name:'',fillColor:'black',strokeColor:'black',style:6}); | ||
+ | var t = brd.create('text',[function(){return r.X()+1;}, | ||
+ | function(){return r.Y();}, | ||
+ | function(){return "repetitions r = " + Math.floor(r.X());}]); | ||
+ | |||
+ | brd.create('text',[5,1.6, | ||
+ | function(){return "predicted 1RM = " + (w.Value()*f(Math.floor(r.X()))).toFixed(2);}], | ||
+ | {fontSize:24,strokeColor:'red'}); | ||
+ | |||
</jsxgraph> | </jsxgraph> | ||
+ | |||
+ | ===References=== | ||
+ | |||
+ | * W. Kemmler, D. Lauber, J. Mayhew, and A. Wassermann: "Predicting Maximal Strength in Trained Postmenopausal Woman", ''Journal of Strength and Conditioning Research'' 20(4), (2006), pp. 838-842. | ||
+ | |||
+ | === The underlying JavaScript code === | ||
+ | <source lang="javascript"> | ||
+ | var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-1,1.8,30,0.8], axis: true}); | ||
+ | |||
+ | var w = brd.create('slider',[[24,0.92],[24,1.7],[0,50,200]],{name:'weight w',snapWidth:1}); | ||
+ | |||
+ | f = function(x){ return (0.988+0.0104*x+0.00190*x*x-0.0000584*x*x*x); }; | ||
+ | |||
+ | var c = brd.create('functiongraph',[ | ||
+ | f, | ||
+ | 1,22 | ||
+ | ], {strokeColor:'black', highlightStrokeColor:'black'} | ||
+ | ); | ||
+ | |||
+ | var r = brd.create('glider',[10,1,c],{name:'',fillColor:'black',strokeColor:'black',style:6}); | ||
+ | var t = brd.create('text',[function(){return r.X()+1;}, | ||
+ | function(){return r.Y();}, | ||
+ | function(){return "repetitions r = " + Math.floor(r.X());}]); | ||
+ | |||
+ | brd.create('text',[5,1.6, | ||
+ | function(){return "predicted 1RM = " + (w.Value()*f(Math.floor(r.X()))).toFixed(2);}], | ||
+ | {fontSize:24,strokeColor:'red'}); | ||
+ | |||
+ | </source> | ||
+ | |||
+ | [[Category:Examples]] |
Latest revision as of 17:45, 20 February 2013
This little application tries to predict the maximal strength (1RM) based on a repetitions to fatigue (RTF) value.
The calculation is based on the so called KLW formula:
- [math] 1RM = w\cdot(0.988+0.0104\cdot x+0.00190\cdot x^2-0.0000584\cdot x^3) [/math]
The horizontal axis denotes the number of repetitions, the vertical axis denotes the ratio 1RM/RTF.
How to use this graphical calculator? Suppose you managed to do 9 repetitions with a weight of 80 kilograms. In the graphical calculator below you have to drag the black dot to r=9 and the blue dot to weight=80. Now, you can read of the 1RM prediction of 95.43.
References
- W. Kemmler, D. Lauber, J. Mayhew, and A. Wassermann: "Predicting Maximal Strength in Trained Postmenopausal Woman", Journal of Strength and Conditioning Research 20(4), (2006), pp. 838-842.
The underlying JavaScript code
var brd = JXG.JSXGraph.initBoard('jxgbox',{boundingbox:[-1,1.8,30,0.8], axis: true});
var w = brd.create('slider',[[24,0.92],[24,1.7],[0,50,200]],{name:'weight w',snapWidth:1});
f = function(x){ return (0.988+0.0104*x+0.00190*x*x-0.0000584*x*x*x); };
var c = brd.create('functiongraph',[
f,
1,22
], {strokeColor:'black', highlightStrokeColor:'black'}
);
var r = brd.create('glider',[10,1,c],{name:'',fillColor:'black',strokeColor:'black',style:6});
var t = brd.create('text',[function(){return r.X()+1;},
function(){return r.Y();},
function(){return "repetitions r = " + Math.floor(r.X());}]);
brd.create('text',[5,1.6,
function(){return "predicted 1RM = " + (w.Value()*f(Math.floor(r.X()))).toFixed(2);}],
{fontSize:24,strokeColor:'red'});