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.
> 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).
Get a few sites to pick up a bogus scientific study article supporting a new product they're releasing is a thing.
For those interested, here's the study the article quotes: https://www.ncbi.nlm.nih.gov/pubmed/30763169
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).
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.
> 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
> 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.
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.
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.
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
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.
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.
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.
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.
More info here: https://roguehealthandfitness.com/importance-exercise-intens...
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.
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.
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.
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'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.