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Ok smart people! How would you evaluate macro impact on overeating (and possibly satiety)?
EvgeniZyntx
Posts: 24,208 Member
in Debate Club
Recently someone wanted to look into their macros and see how it impacted their recent binges.
I wrote a method to do this out of MFP data and will post it below. I'm looking for criticism and input. Does it make sense to you? Who would you do this differently?
General Method: Take the days that are high and look at macro distribution over the prior x days.
First Results: I see a shift where I tend to eat less fat the few days prior to a "large" day. (Note, I don't have a binge issue, so these are just days with a few hundred cals over.)
And this is purely correlation - I'm not saying that at this time that it actually impacts my satiety. I'm not tracking "satiety" in my dairy.
Detailed Method:
Select only days with calorie data for the prior x days.
For the days with high calories, take average of protein, fat and carb macros for the prior x days.
Normalise these averages against all days with calories.
Look at distribution of these days - do they tend to be the same as general eating or is there a shift towards higher or lower days?
Thoughts? Comments?
I wrote a method to do this out of MFP data and will post it below. I'm looking for criticism and input. Does it make sense to you? Who would you do this differently?
General Method: Take the days that are high and look at macro distribution over the prior x days.
First Results: I see a shift where I tend to eat less fat the few days prior to a "large" day. (Note, I don't have a binge issue, so these are just days with a few hundred cals over.)
And this is purely correlation - I'm not saying that at this time that it actually impacts my satiety. I'm not tracking "satiety" in my dairy.
Detailed Method:
Select only days with calorie data for the prior x days.
For the days with high calories, take average of protein, fat and carb macros for the prior x days.
Normalise these averages against all days with calories.
Look at distribution of these days - do they tend to be the same as general eating or is there a shift towards higher or lower days?
Thoughts? Comments?
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Replies
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That's pretty neat. Does each person's diary in this case equals N, or an entirely separate experiment? I wonder because "high calorie day", for example would have to be determined on an individual basis, and would also likely be randomly selected. Or not. I suppose you Could use a formula like 1.XX TDEE, who knows.
By analyzing this statistically, you should be able to show if there's significant impact of macro distribution in days preceding an overage day. This should also indicate if there's no link, that is, other factors such as a holiday gathering, emotional eating, lack of planning, I'm in Momma's house so I better eat ALL THE THINGS, ETC have come into play. However, macros seem like an easy one to evaluate given our data
Oh also the individual would of course need to have been tracking macros properly. Even now, I mostly double check macros on individual food items when one entry throws my pie chart WAY out of whack. This has happened a total of two times0 -
Is there a way to input the number of days before the high cal day that you want to look at so that you can try different options? For example, first look at day before then the 2 days before?
Also, would the user input what a high cal day is? Days above 2000, say? Or would it take the average calories for a period and look at days that are more than some percentage above the average?
I don't think it would work for me, though, since I have lots of days that are planned highs -- either an evening out or a big exercise day. But that aside, it seems like a really great tool.0 -
The idea is a personal analysis - not research across the database - 1) because I do not want to look at other people's data without permissions or manage passwords - one just downloads the excel sheet and runs it off their own data 2) it is pretty easy to see high days - just take target cals and identify days that are above that or use a limit that is manually set. Currently, I'm working with a manual set limit because that allows me to play with it.That's pretty neat. Does each person's diary in this case equals N, or an entirely separate experiment? I wonder because "high calorie day", for example would have to be determined on an individual basis, and would also likely be randomly selected. Or not. I suppose you Could use a formula like 1.XX TDEE, who knows.
Since I can't exclude causes, this is not a causal analysis but a correlative stats. If you can't eat to a target, the solution might be to move out of Momma's but this will tell you if you are eating more carbs or fat on the days before.By analyzing this statistically, you should be able to show if there's significant impact of macro distribution in days preceding an overage day. This should also indicate if there's no link, that is, other factors such as a holiday gathering, emotional eating, lack of planning, I'm in Momma's house so I better eat ALL THE THINGS, ETC have come into play. However, macros seem like an easy one to evaluate given our data
You can see that my protein distribution is bi-modal. That is because I also have high days were I purposefully eat above cals to reach my protein goals. The analysis won't give the context.Oh also the individual would of course need to have been tracking macros properly. Even now, I mostly double check macros on individual food items when one entry throws my pie chart WAY out of whack. This has happened a total of two times
Yes, bad data makes bad analysis.
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lemurcat12 wrote: »Is there a way to input the number of days before the high cal day that you want to look at so that you can try different options? For example, first look at day before then the 2 days before?
Also, would the user input what a high cal day is? Days above 2000, say? Or would it take the average calories for a period and look at days that are more than some percentage above the average?
I don't think it would work for me, though, since I have lots of days that are planned highs -- either an evening out or a big exercise day. But that aside, it seems like a really great tool.
Absolutely, the idea is to let the user set the number of consecutive days to consider. It recalculates the curve as one changes the number of day to consider.0 -
are the binges logged accurately ? If so some form of multivariable regression might be helpful.0
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Ok, I'll be th dumb one. What are the X and Y axes on the graph? I see Y goes up to 16, but can't really tell what unit that is. Being normalized probably means there's no unit, but perhaps there's some context I'm missing0
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Ok, I'll be th dumb one. What are the X and Y axes on the graph? I see Y goes up to 16, but can't really tell what unit that is. Being normalized probably means there's no unit, but perhaps there's some context I'm missing
It's a normalised histogram
y is a count - number of days,
x is the average of days prior to a high days / the average of all days. It is unit-less.
So think of it this way - your looking at a sample of days (days before the high cal day). If they are similar to the distribution of all days then the curve should look like a bell curve with a median of 1. If the high days have a tendency to be driven by, say, low fat days prior, then we should see the curve shifted to the left. Like in that sample curve.
This is a good question. I need to be clear on it.
Let's say you cut a bunch of strings and you are trying to make them 5 inches long. Some will be little shorter, others a little longer. If you take the length of each, divide by the average, then count all the strings that are between .9 and 1.0 or 1.0 and 1.1, etc you can build a histogram, that is the count for each value (y axis).0 -
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My thoughts aren't 100% clear on this so ask for clarification if I am not making sense.
Are you writing this to pick out certain high calorie days and then look at the day(s) preceeding it? Or will the user select the day that a binge occurs and analysis will happen on the previous day(s)? If it's the latter, then I think there won't be an issue determining which day(s) are being analyzed.0 -
ClosetBayesian wrote: »
Let's keep it real, this runs off Excel.0 -
My thoughts aren't 100% clear on this so ask for clarification if I am not making sense.
Are you writing this to pick out certain high calorie days and then look at the day(s) preceeding it? Or will the user select the day that a binge occurs and analysis will happen on the previous day(s)? If it's the latter, then I think there won't be an issue determining which day(s) are being analyzed.
Currently written to review all days above a threshold - but in such a manner that you can select one or more specific days.
So the answer is "yes" to both questions.0 -
Fascinating. I don't binge, but I think it'd be neat to check out when you're done.
For the first scenario, are you going with a certain threshold above the daily goal?0 -
Fascinating. I don't binge, but I think it'd be neat to check out when you're done.
For the first scenario, are you going with a certain threshold above the daily goal?
Two thresholds - a lower one (say 400) anything under is considered a day that wasn't fully logged. A higher one - user set, above daily goal.
The current version of the worksheet is here: https://www.dropbox.com/s/10mhbzig1v4v7h2/mfp6v5.xlsm?dl=0
(to use your own data, go to the "Start" worksheet, press "Reset" and then "Go")
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Thanks!0
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This would be something interesting to see in a larger group sample. Self-reporting data does have it's flaws, but it would be interesting to see the general direction it takes.0
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This would be something interesting to see in a larger group sample. Self-reporting data does have it's flaws, but it would be interesting to see the general direction it takes.
Possibly but I also think that would require some cluster classifications - we have people losing, others maintaining, bulking, active, sedentary, athletes, people that binge, people that don't... making inferences from random user data is going to be very difficult. However, if we had two groups - bingers vs non-bingers it might give some interesting results.0 -
I'd find it interesting if exercise calories were an option for comparison as well.EvgeniZyntx wrote: »This would be something interesting to see in a larger group sample. Self-reporting data does have it's flaws, but it would be interesting to see the general direction it takes.
Possibly but I also think that would require some cluster classifications - we have people losing, others maintaining, bulking, active, sedentary, athletes, people that binge, people that don't... making inferences from random user data is going to be very difficult. However, if we had two groups - bingers vs non-bingers it might give some interesting results.
I believe there is some research that depending on a gene or two some people have different appetite responses to exercise. My own personal feeling lately is that my appetite as a function is almost a normal curve. I could be okay eating nothing all day if I was laying in bed, if I was sitting at work all day and sitting at around 3K steps I'd like to eat 3K kcal of food, but if I'm actually moving 20K+ steps and/ burning an estimate of over 2.5K+, I'd mentally feel okay eating under 1K kcal of food and have to ignore my un-appetite.0 -
I think you need overlays of liquid intake, exercise - cardio/ resistance and hormonal shift (specifically in women)0
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EvgeniZyntx wrote: »Recently someone wanted to look into their macros and see how it impacted their recent binges.
I wrote a method to do this out of MFP data and will post it below. I'm looking for criticism and input. Does it make sense to you? Who would you do this differently?
General Method: Take the days that are high and look at macro distribution over the prior x days.
First Results: I see a shift where I tend to eat less fat the few days prior to a "large" day. (Note, I don't have a binge issue, so these are just days with a few hundred cals over.)
And this is purely correlation - I'm not saying that at this time that it actually impacts my satiety. I'm not tracking "satiety" in my dairy.
Detailed Method:
Select only days with calorie data for the prior x days.
For the days with high calories, take average of protein, fat and carb macros for the prior x days.
Normalise these averages against all days with calories.
Look at distribution of these days - do they tend to be the same as general eating or is there a shift towards higher or lower days?
Thoughts? Comments?
Seems to make sense to me, but I do think looking at total calorie intake as well as exercise loads might have impact as well. My heaviest eating days tend to follow a period of hard exercise after a trend of lighter days, so really I am just making up for prior excess deficit.0 -
Some good points there.
I see the following:
- calories in (easy, have the data)
- exercise/TDEE (requires data from a tracker, manually entered, easy to integrate)
From these it is possible to look at av. prior cals vs high days or av. prior deficit vs high days.
- liquids, hormones, stress
I can see that these non-cal related items would be very impactful to overeating but are more difficult to track and evaluate. I'm going to have to leave them out for now. But, certainly worth thinking about.
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I've integrated now the ideas behind
- calories
- macros
- exercise / TDEE (either from an estimator, external tracker or a Base + MSF exercise)
- Sleep might be another item to include, since data is sometimes available.
The results so far are interesting.
Here is a screen capture on an analysis off my data (over 6 days prior to overeat days) - It seems that I tend to overeat to catch up on larger deficits - keeping a 25% cut seems fine. 30% not so much. Exercise impacts this.
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One additional thing I think might be useful. Or at least interesting. Include the binge day - or have an option to look at that specific day
It might be useful to see two things: 1) what the person is binging on. Eating a lot more carbs? A lot more fats? A fairly even mixture of the two? Booze involved? and 2) the individual could maybe look at his/her diary and see if he/she is eating more of any particular macro earlier in the day, that may cause him/her to overeat later in the day.
I suppose that's easy enough to simply look at the individual day's diary, but it might be interesting/helpful to see if there's some sort of pattern between the preceding days and the preceding hours, which would likely be easier to see if the binge day's data is included in the graph(s).0 -
Following....
I may have to consider something like this, but in general my macronutrients are fairly consistent. The bigger driver for me is the days after a lifting day, I want to eat a house (especially leg days).0 -
Following....
I may have to consider something like this, but in general my macronutrients are fairly consistent. The bigger driver for me is the days after a lifting day, I want to eat a house (especially leg days).
The same here. I can do 12 miles on the elliptical, long bike rides, be moving stuff, etc... essentially any not too intense cardio or strength and keep eating in line just fine with calorie burn.
The more I push intensity on anything to the point of high effort, including lifting, the more it seems to make me hungry in the following days.0 -
holy crap
i need to learn some stats
any of you guys trade futures? these skills can come in handy.0 -
+1
Machine learning and prediction algorithms for 100, Alex.0 -
+1
Machine learning and prediction algorithms for 100, Alex.
So....are you saying you do trade futures?
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