We won't meet ever. You are without a doubt the least useful person on ST, and if it were not for your elite triathlon squad I would have zero respect for you. I have tons of respect for what you've done with them, but other than that you do nothing but increase the overall entropy of the universe at a far greater rate than any other human being who has walked this earth.

---

__________________________

I tweet!

I believe Zack may is right. The assumption 1kcal = 1Kj is that an assumption. It may be based on labratory data of over 10 000 people, but when comparing it to my efficiency on any given day on the rollers it is an assumption.

I believed it to be a better assumption the the Suunto as it is based of a measured value.

What do you need to measure it then? A tape measure?

You need to go to the lavender room.

The power you output in cycling is but a small fraction of the overall calorie expenditure of your body!

Human beings can put out something like 3000W of power in cycling, most of which is wasted as heat. PT has to guess at exactly how much is wasted as heat. Only a small percentage of your overall calorie expenditure is accounted for by mechanical work!

I invite you to read the following article:

Measurement of energy expenditure
James A Levine*
Mayo Clinic, Endocrine Research Unit, 5-194 Joseph, 1st Street SW, Rochester, MN 55902, USA

---

__________________________

I tweet!

That is not the assumption. The assumption is that efficiency is 25%. That is a fairly accurate assumption.

---

-

The Triathlon Squad

Like us on Facebook!!!

Nah, 1kcal=4.2kJ. That's just physics, but that's not the issue here.

---

__________________________

I tweet!

Zack, if studies have been done and a metabolic cart has been used to evaluate the average efficiency of a person while biking (with a large n) i.e measured input (Kcal) to measured output (Kj) ,would this not constitute a better parameter for prediction then what suunto has to offer?

A CALORIMETER!

---

__________________________

I tweet!

I believe it may be when you look at that ratio and compare it to the assumed efficiency of a human cycling.

Lmao, im not sure if you know what a calorimeter is

Trying to corellate weight loss against the outputs of a device is a tricky business. I started off at 115kg about 16 months ago and have lost 41kg so far. I have used VidaOne as a software programme to measure and set a diet over that period. Into the software I have downloaded exercise data from my polar RS400 and CS400 computers as well as data on calories burned from my pool mate pro. I effect I could reasonably accuratly predict what calories I have put into my body and in what form and what calories I have suposedly burned as a predicition based on HR from my various measuring devices.

At first I needed to adjust figures quite considerably in the software for my base metabolic rate to get a predictable weight loss prediction which matched up to reality. These figure varied up to -35% and differed between exercise metabolic rate and normal everyday activities. Once it was dialed in it was a very steady and predictable weight loss calories out equated to calories in with a deficiency to allow weight loss. All my initial exercise was cycling for the first 8 months until I had lost a good bit of weight got fitter and then switched to three disciplines.

Once I started running and swimming the predictability of my system started to be far more difficult even being able to set different training zones for my RS400 to reflect running more specificaly. I eventuly playing arond with values in the software got it back to being predictable again. I then took a break from pretty intensive training over the summer holidays and in effect my weight loss switch was turned off and again the software needed adjustment as I think my metabolic rate had reverted and without constant pressure of training I was very prone to putting on bounce back weight.

Since getting back into training 20hrs plus per week things are again starting to get more predictable after a couple of months.

I have read 'racing weight' and have been but not so much now very focused on weight loss. Without doubt being aware of what is was burning up in training did help a lot in keeping me focused on what I was eating in trying to hit the numbers. It is worth pointing out I am very autistic and so it is not in my nature to cheat and not record everything I put into my body. At first I really expected it to be an exact science and something I could more or less predict exactly when I would hit weight targets. Unfortunatly it has not been like this, as I have got fitter and have lost weight my body has gone through phases and at times simply does not give up the weight even though the numbers say it should. When I have had periods of a few weeks which have been heavily focused on new types of training eg. running more than cycling weight loss results have been very different as my body adapts.

From a personal perspective use your data to get a feel for what is happening but try and find a happy balance over an extended period, I think this will be different depending on the exercise form. Your body will do what it will do and all you can do is hopefully prod it in the right direction. As a warning over the past year there have been times where I have been guilty of trying to force my body into giving me the results I wanted, often these times have been highly counterproductive when I believe my body simply decided to horde enrgy as fat as if it were an animal about to be starved through a cold and long winter or go on a long migration. My perspective today is one of being far more respectful to myself and understanding that I ask a lot of hard questions from my body on a daily basis and as such I should treat it with care and attention.

of course strictly speaking all measuring devices are doing this. even a thermometer is taking input, doing some math (via the lines carved onto its surface) and producing a guess =)

the power meter takes torque and rpm input and does some math to guess at power.

---

---

Kat Hunter reports on the San Dimas Stage Race from inside the GC winning team
Aeroweenie.com -Compendium of Aero Data and Knowledge
Freelance sports & outdoors writer Kathryn Hunter

It's possible, but the measured range of efficiencies during cycling is enormous. There's obviously a correlation of some sort, but it's not that great. IMO a biological marker like HR would be better than rear wheel power for predicting calorie expenditure--there's a much larger disconnect between the rear wheel power and the body than between the HR and the body. From wikipedia:

The efficiency of human muscle has been measured (in the context of rowing and cycling) at 18% to 26%.

From the study I cited above:



Heart rate monitoring
In humans, there is a significant relationship between heart
rate and energy expenditure, at least in the absence of
exercise. Heart rate monitors are portable, non-restraining
and unobtrusive and measurements can be carried out over
several days. A number of devices of varying complexity
have been used to record heart rate in free-living
subjects
37 – 45
. The conceptual limitation of this approach is
thatenergy expenditure and heartrate are notlinearly related
for an individual in part because cardiac stroke volume
changes with changing heart rate and even posture. There is
a substantial inter-individual variance for the relationships
between heart rate and energy expenditure in terms of slope,
intercept and curve characteristics. Furthermore, variance in
covariables thataffectheartrate,such as emotion,also impact
the ‘heart rate/energy expenditure’ relationship. Hence,
precision of heart rate prediction of energy expenditure is
improved where a separate regression equation is derived to
relate heart rate to energy expenditure for each individual.
Some investigators use multiple regression equations for
each subject. At best, the mean (^95% confidence limits)
error for estimating energy expenditure using heart rate
monitoring is 3 ^ 20% during light activity.

Kinematic measurements
In kinematic measurements, a subject’s movements are
quantified and these measurements are usually performed
in conjunction with other measures of energy expenditure.
These tools are used primarily to estimate the energy cost
of NEAT (‘spontaneous physical activity’).
Some techniques are specific for confined spaces such as
radar tracking and cine photography
56,57
. Other techniques
have been used in free-living individuals and generally
focus on pedometers and accelerometers of varying
sophistication. Pedometers typically detect the displacement of a subject with each stride. However, pedometers
tend to lack sensitivity because they do not quantify stride
length or total body displacement and overall, therefore,
become poor predictors of activity thermogenesis
58
.
Accelerometers detect body displacement electronically
with varying degrees of sensitivity; uniaxial accelerometers
in one axis and triaxial accelerometers in three axes.
Portable uniaxial accelerometer units have been widely
used to detect physical activity
59 – 61
. Careful evaluation
demonstrates that these instruments are not sufficiently
sensitive to quantify the physical activity of a given freeliving subject but rather they are more valuable for
comparing activity levels between groups of subjects.
Greater precision has been obtained using triaxial
accelerometers
62 – 64
. In free-living subjects, data from
these devices correlate well with the total daily energy
expenditure, measured using doubly labelled water,
divided by basal metabolic rate
65
. The utility of motion
tracking using approaches such as Global Positioning
Systems has not been fully defined for human studies.

---

__________________________

I tweet!

The assumption becomes 1kcal of energy utilized by cyclist = 1kjoule of work measured at rear wheel. Pardon my poor initial wording.

I hear you, I need to spend more time on that argument than a superficial reading!

Come on man, read the whole thread. You've got a lot of catching up to do!

People are trying to poke holes in my argument that rear wheel power isn't some gold standard for measuring calorie expenditure. They're failing miserably, but are comforted by their fallacious arguments or the notion that they are smarter than me.

---

__________________________

I tweet!

That says a range of 18-26% efficiency, which I think backs up the point ZackC was trying to make.

It's not a direct measurement. It is a calculated measurement based on an assumption of your body's efficiency. Taking kJ from the PT as calories burned means you assume the body is 23.9% efficient (1/4.184). If you are actually 18% efficient and use 24% instead, you are underestimating calories by 33%. If you are actually 26% efficient you are overestimating by about 8%.

It's a better method than anything else really, but that doesn't mean it's an exact measurement.

using Zack's reasoning, one can simply take hr and extrapolate power from it, assuming the body's efficiency stays pretty much the same. however, we all know that doesn't work well, as too many variables affect HR.


Saris(powertap) has a device now that estimates power off of HR and a power test, but they also indicate it is a guess and why that guess may be off. You can go to their website and read a bit more, but there isn't a whole lot there. Just enough to make one dangerous.

Holy shit! A constructive response!

I was using the 1000kcal per day simply as a daily goal during the off season.

What a can of worms I opened.

its certainly no gold standard but it is not practical to do much better =)

---

---

Kat Hunter reports on the San Dimas Stage Race from inside the GC winning team
Aeroweenie.com -Compendium of Aero Data and Knowledge
Freelance sports & outdoors writer Kathryn Hunter

Thank you!!! Finally, there is someone on here who is willing to look past the fallacious argument that has been proposed:
A. PT measures power (kJ/s or J/s)
B. 1kj/s=4.2kcal/s
Therefore: C. Pt measures kcal/s
D. By extension, the calorie measurements made by powertap are representative of the calorie expenditure of the body.

People seemed to think I was getting hung up on points A, B, and C, which I'm not. The glaring issue with the above argument is D, and I've documented that with a peer-reviewed journal article.


Thank you! Finally, someone figured it out!

---

__________________________

I tweet!

The question then becomes:

Which method makes the assumption with the gretest variability and therefore potential error?


Edit: Based on the semantics focus I figured it was best to not use a often misunderstood statistics term!!!!

Give me some credit, I replied that your reasoning was correct.

powermeter is repeatable, HR not so much