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How Sitting For Long Periods Affects Your Metabolism (theglobeandmail.com)
132 points by Cyclone_ 28 days ago | hide | past | web | favorite | 36 comments



This report triggered several of my alarm bells, so I investigated further. I had a look at the quoted study.

"RESULTS: No differences between trials ( P > 0.05) were found in the overall plasma triglyceride, glucose, or insulin responses during the HFGTT. "

It takes some balls to dismiss a study based on one sentence in the abstract, but this is an admission of a common and fatal mistake, so I'm going to do exactly that.

They're claiming there is no difference between groups because they were unable to show that, if there was an inherent difference, the chance they'd see these results is at most 5%. There's an easy way to obtain such a result: small sample size. And, indeed, they only compared 2 groups of 5 people.

There is a proper way to show two groups are the same, and it's called "equivalence testing." It has the same kind of statistical requirements that normal testing does. What they did instead is totally statically invalid.

There's not a soft way to put it: this study proves nothing. Let it be an example in training your defenses against bad science.


The results themselves are not wrong per se. Reaching this conclusion from those results, however, is frankly insulting and should make all authors of the paper ashamed.

> These data indicate that physical inactivity (e.g., sitting ~13.5 hrs/day and <4,000 steps/day) creates a condition whereby people become "resistant" to the metabolic improvements that are typically derived from an acute bout of aerobic exercise (i.e., exercise resistance).

Also note that the researchers _immediately_ re-fed the group who participated in the intense exercise:

> The additional energy expenditure from the exercise session was estimated via indirect calorimetry. This energy was then replaced with additional caloric intake with the post-exercise dinner (Clif® Builder’s®; ~40%/30%/30% of calories from carbohydrate, fat, and protein, respectively).

This is especially bad because it isn't mentioned anywhere in the abstract (I checked the full-text).

Shit science.


Makes you wonder what they're selling.

Get a few sites to pick up a bogus scientific study article supporting a new product they're releasing is a thing.


Right? This feels like a foundational article used to build bullshit credentials somewhere else by planting bogus citations that no one will check because they are >1 level deep. Hell, people don't even check 1-level deep citations.


Excellent analysis!

For those interested, here's the study the article quotes: https://www.ncbi.nlm.nih.gov/pubmed/30763169


I also wanted to dig into this. The null hypothesis and burden-of-proofs change between p tests and equivalence testing, right?

P tests: null hypothesis is that there is nothing different, burden of proof to suggest there is a difference.

Equivalence testing: null hypothesis is that there is something different, burden of proof to suggest there is no difference.

But maybe this is just a misapplied p-test? So while the "RESULTS" section of the paper is fine:

No differences between trials ( P > 0.05) were found in the overall plasma triglyceride, glucose, or insulin responses during the HFGTT.

The "CONCLUSION" section is where they invert the statistics of their own study and literally suggest a p-test-able difference despite their results section literally concluding a p-test-able non-difference:

These data indicate that physical inactivity (e.g., sitting ~13.5 hrs/day and <4,000 steps/day) creates a condition whereby people become "resistant" to the metabolic improvements that are typically derived from an acute bout of aerobic exercise (i.e., exercise resistance).


Yes, they used a test designed to find a difference between two groups, and it failed to report a difference.

Your analysis of that is correct, although the terminology "p-test" to describe "a statistical test designed to find a difference" is not. I don't know the general term for this class of tests.

The definition of p-values is a bit wonky (there's actually a set of null hypotheses rather than a single one, and it involves a supremum over that set), but the line "if we're wrong, then the chance of seeing this data is at most p" is pretty accurate. All kinds of frequentist statistical tests use them, including equivalence tests.


Can you explain this common mistake further?

> They're claiming there is no difference between groups because they were unable to show that, if there was an inherent difference, the chance they'd see these results is at most 5%.

It seems to me that they're claiming that they failed to reject the null hypothesis, the end. I.e. according to this statistic, there was no significant difference in the means between all groups in the ANOVA. This was a small sample size, but the analysis seems to include multiple trials for each participant.

This seems like a standard use of ANOVA to me. So how can you say that the alpha value is wrong just based on this one sentence from the abstract? It seems to me that you'd have to know the domain, know what effect to expect, and so on


Not OP, but [0]:

> If the data do not contradict the null hypothesis, then only a weak conclusion can be made: namely, that the observed data set provides no strong evidence against the null hypothesis. In this case, because the null hypothesis could be true or false, in some contexts this is interpreted as meaning that the data give insufficient evidence to make any conclusion; in other contexts it is interpreted as meaning that there is no evidence to support changing from a currently useful regime to a different one.

Also, keep in mind that in this study, the intervention (the difference between both groups) was, basically: perform an intense 1-hour bout of exercise and immediately replenish all calories spent.

The null hypothesis is, thus:

"In sedentary people, performing an intense bout of exercise and immediately replenishing spent calories doesn't affect next day metabolism."

Being unable to reject that statement doesn't immediately confirm it in statistical terms, but even if that were the case, it is such a lousy statement to begin with, that any headlines coming out of it are outright misinformation. Both groups were equally sedentary and that might have nothing to do with any observed effects.

[0]: https://en.wikipedia.org/wiki/Null_hypothesis


Ah thanks, I understand this aspect. As I say, they simply failed to rejected the null hypothesis, and their approach here seems perfectly valid.

The authors did overreach in their interpretation of the result. But OP said ANOVA is wrong, and that the alpha value is wrong. I don't understand how they can say this without understanding this domain, the intervention, and knowing what effect size could be expected. Maybe α=0.05 is perfectly reasonable here.


Hi, actual OP here.

I said that failing to show a statistically significant difference is not the same as showing equivalence with statistical significance. Tests designed for the former cannot do the latter with any amount of data. You need a different test for that, or at least the same underlying statistical test used in a different manner. Can you explain what about this implies that "ANOVA and the alpha value are wrong?"

I'll confess to having never really learned ANOVA, but it sounds like it's a family of models generalizing the t-test. You can indeed perform equivalence and non-inferiority testing with the t-test, as I did in my OOPSLA 2018 paper. You just have to use it in a different way than it sounds like they did here.


Thanks for reply. Regarding alpha value: you seem to say that with a small sample size, it'd be possible to see no effect between groups. I think you were implying that they should've been testing p<0.0001 or something? But I'm not sure how to choose this value, without knowing the difference between means that you expect for the medical intervention.


So, as I understand it: how do you pick a P such that, if you fail to prove there is a difference with p<P, then there is no difference?

If you compute the power of your test, you can do this, so long as you know the expected difference of your means. Like you implied, that is not realistic. Do you do something else entirely: https://en.m.wikipedia.org/wiki/Equivalence_test


Exercise should only be for cardiovascular health. If you have an unfavorable energy imbalance, i.e. you're gaining weight, then you need to eat less by extending your time between meals. It's as 'simple' as that. Not easy, but simple. The 'easiest' solution is to go through periods of intermittent fasting. Either skip breakfast+lunch most days, or take a few days off eating entirely. People need to stop dieting, and they need to start 'not eating'. Our society consumes too much food.


Depending on your present calorie consumption you can start reducing calories by really concentrating on the fats you're including in your everyday diet. I'm not saying fats are bad, but that there is double the calories per unit of fat versus carbs and protein. Start concentrating on replacing some of those fats with purer protein and high fiber foods. This way you've already reduced calories, but not how much you're eating. I started with getting out of the habit of always having whole milk on hand at home, for example. You'd be surprised by how much you can curb your appetite by just leaning a bit more on high fiber foods and protein.

I combined this with being more mindful of how much I exercised that day. If I did exercise that day I don't use it as an excuse to binge eat anything.

I also try to push breakfast out until around 10am (I wake up around 7am, and usually stop eating by 8pm the previous day). Not sure if that counts as fasting, but it works for me. I tried 16 hours of fasting (basically no eating until lunch) and I just ended up binge-eating the calories I saved by not eating. Your mileage may vary.

After getting to work I have about a quart of water, then a coffee. If I feel on the edge of "I need a snack" sometimes it can be enough to just have another pint of water in response to that feeling.

Last, I really tried to get an understanding for how many calories are in particular foods and how many calories I really need per day. Use a calorie calculator to get a feeling for how much you burn while doing certain exercises. Learn how many calories are in common items that you eat all the time. Find optimizations if they are particularly high in calories. Then, after you've learned a bit, stop stressing about the numbers, take your newfound knowledge and see how you do.


My opinion, from my own experience: Sure, that definitely works. But the choice of cutting fat is kind of arbitrary. You can do the same with sugar & carbs and get basically the same result w.r.t weight loss. I think this illuminates the real underlying issue with modern diets which is not that we eat "too much of X", but rather "too much of everything, too frequently". Approaches like this work not because "fat is bad" or "carbs are bad", but because they provide a simple, concrete framework for diet choices that ends up reducing overall calorie consumption.


It's just way easier to rack up calories if the thing you're eating is higher in fats. Remember that fats have no fiber (neither does protein, but that's another conversation), so you won't 'feel' as full, which can easily lead to a much higher calorie ingestion rate that you're body won't be able to keep up with.

Another big thing is portion control! You hit on the nose with eating too much of everything. Something else I've done is making myself smaller plates, but also taking much smaller bites and taking a much longer time to finish the plate. I make it take as long as possible. That way by the end of the plate I have a more accurate sense of how hungry I still am or not.

It's tough to summarize all of points of my current regimen since I've pulled bits and pieces from lots of different sources and put some personal experience into the mix.


Heh... What's interesting is I personally feel the opposite. I can eat thousands of calories of carbs/sugar no problem, but I fill up on fat pretty quick. I did the keto diet for about 4 months and almost never went over my daily calorie budget, basically without even trying that hard. Most days I had to remind myself to eat more because I was way under.

I wonder if this is a case for emphasizing that "one size fits all" doesn't work in dieting, either. It seems likely that satiety for various food sources might vary based on biology and lifestyle too.


Exercise has a multitude of benefits beyond cardiovascular health. And exercise absolutely does impact your energy balance, if you hit it hard enough. Timing of meals is largely unimportant; some people find it easier to limit intake by consuming fewer larger meals, others have the opposite experience.


Yes, there are also neuroprotective effects of cardio. And I'm not talking about meal timing, I'm talking about skipping meals entirely.


I am always conflicted when I see something in here linking to globe n mail, particularly when the article is basically utter garbage, without any amount of research involved in it whatsoever. While there may be truth in there, it is certainly not worthy of even scanning the piece.


This kind of articles are just another excuse for paralysis by analysis and for people to stay fat.

Most of the time, your metabolism doesn't matter.

And it never matters if you train with high intensity and have a decent muscle mass.


13 hrs is a f*ton of sitting even for an office job. Anyway I was just given a standing desk and I feel way better at 5PM.


I wonder if intensity matters. What would the response be if instead of treadmill one lifted weights with high intensity.


It depends on the fitness level of the subjects. The more fit they are, the less low intensity exercise (treadmill) is effective.

More info here: https://roguehealthandfitness.com/importance-exercise-intens...


Also depends on how you use it.

If slow, long jogs aren't cutting it—hit the sprints. 20 seconds on (full out), 30 seconds off (medium-light pace), or some similar interval.


Very confusing article. At first I thought it is trying to say that if you are sitting for long periods, exercise can be bad for you. What it's actually saying is that more you sit, less effective the exercise is.


Maybe this explains why exercise never makes me feel "better" just more tired.


Do you stick with it on a regular schedule?

No science here, but anecdotally if I've been slacking off on exercise and then restart, I feel pretty tired/terrible at first. After a few weeks of regular routine exercise though, it flips and I feel tired/terrible when I don't exercise and energized when I do exercise.


I have the same experience. My threshold seems to be about 8 days - meaning that I can resume exercise more or less ok after slacking (or being ill) for 8 days, but after 10 days it costs a lot more and I feel worse afterwards. Of course it is more like a ramp than a binary thing, but there's a "step" around the 9th day in the graph.


Same. About three weeks of consistent exercise (three times a week) and I'm back in, "fighting" shape. Those three weeks suck but after that, it almost sucks not to work out.


The study in question limited participants to 4,000 steps a day for four days. That's an absurdly low level of exercise to undertake.


Is it that absurd?

During the coldest months of winter I would wake up and eat breakfast (sitting), jump in the car (sitting), commute to the office (sitting).

Once at the office I sit for 8h.

Then car again (sitting). Back home I prepare food (standing!) and then eat it (sitting). I then spent the rest of the day either sitting on the couch or at the computer.

Weekends could get even worst if it was a snowstorm day. Wake up, eat, sit in front of Netflix and then back to sleep.

Now that spring is in the air my steps are raising back up.


You sit for 8 hours...straight? No. You also seem somewhat proud for not being active...why make your family go through the drawbacks earlier than necessary? Not to mention the future medical costs (not just money but time)


It's not about being proud, it's about showing how having such a behavior is not absurd. I don't walk around talking about my inactivity.

In my case, that's only two months a year. Someone else might be that inactive for the entire year.

Think older people, people with disabilities, physical or mental health issues, etc.

Sure, I don't sit for 8 hours a day while at the office but it's pretty close to it. I have a 30 minute lunch where I walk to the cafeteria and stand while waiting for the microwave. I also have a 15 minute break in the afternoon where I walk to the most comfortable spot of the building and back again.

When it's not -22ºF outdoor I squeeze some outdoor walking time in during those breaks.

I hate stationary cardio on machines and can't stand harsh cold weather. My exercise is done hiking up trails during spring, summer and fall.


I'm working remote since 4 years, and I have had periods spanning weeks where I have moved less than 2000 steps during 90% of the days. When I started working remote, I went from walking 8k steps per day to ~1k consistently for a 6 months or so. My schedule when I started was 1) Go up from bed 2) Go to computer 3) Sit for ~8h straight, only pause for toilet and making lunch 4) After 8h make dinner, eat, feel like a vegetable in both mind and body.

I've since then re-arranged my day to at least walk 5k-8k steps per day. I've found that the step count doesn't matter much, I need at least 30min high intensity workout every day to not go numb in my mind and body. Luckily, I have two daughters, so I'm forced to do more excersices than I did when I worked from outside of my home.




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