People implying that the study shows only correlation really don't seem to understand how we establish causality in science.
Or perhaps you don’t fully understand the challenges of establishing causality? Just because an intervention causes an improvement in some bio markers that are associated with lower mortality (unfortunately) does not mean that the intervention will cause lower mortality.
The classic example is vitamin D supplements: Higher vitamin D levels are associated with lower mortality in many medical conditions. Vitamin D supplements increase vitamin D levels. But vitamin D supplements seldom lower mortality.
Why? Probably because vitamin D is produced in the skin when we are in the sun. The more healthy subpopulation of any study will typically spend more time outside, so they will have higher vitamin D levels. But it’s (relative) health that causes higher vitamin D, not the other way around.
The only way to reliably establish causality is really an end-to-end randomized controlled trial (RCT). Stitching together two RCTs is not sufficient.
(Not saying that exercise does not lower mortality BTW, just that it’s complicated, and a study such as this is probably picking up two signals: one causal and one purely correlational.)
> Or perhaps you don’t fully understand the challenges of establishing causality? Just because an intervention causes an improvement in some bio markers that are associated with lower mortality (unfortunately) does not mean that the intervention will cause lower mortality.
> The classic example is vitamin D supplements: Higher vitamin D levels are associated with lower mortality in many medical conditions. Vitamin D supplements increase vitamin D levels. But vitamin D supplements seldom lower mortality.
That is quite a simplification. The research as far as I know is that too low Vitamin D levels are associated with higher mortality, but that supplementation above a certain level is meaningless (and most caucasians can get those levels through normal sun exposure). So yes vitamin D supplements in general don't improve mortality.
Note also the situation here is completely different. The link between mortality and morbidity markers and exercise have been established in other studies. The study here establishes that this directly translates to a correlation between fitness and mortality. So in a way it's the opposite of the vitamin D case.
> Why? Probably because vitamin D is produced in the skin when we are in the sun. The more healthy subpopulation of any study will typically spend more time outside, so they will have higher vitamin D levels. But it’s (relative) health that causes higher vitamin D, not the other way around.
You realise that you are proclaiming causality here?
> The only way to reliably establish causality is really an end-to-end randomized controlled trial (RCT). Stitching together two RCTs is not sufficient.
A RCT does not establish causality. That's essentially my point. A single study/experiment never proofs causality. You need a theory to explain the causality and multiple studies that falsify other possible causality mechanisms. That has been done extensively for excercise and morbity/mortality, the current study just establishes that this also correlates in the bigger picture. So yes the study itself does not "proof" causality, it's just a piece in the bigger puzzle of causality.
> (Not saying that exercise does not lower mortality BTW, just that it’s complicated, and a study such as this is probably picking up two signals: one causal and one purely correlational.)
> You realise that you are proclaiming causality here?
Yes, with emphasis on the word probably. Just like I will happily proclaim that exercise probably lowers mortality.
> A RCT does not establish causality. That's essentially my point. A single study/experiment never proofs causality. You need a theory to explain the causality and multiple studies that falsify other possible causality mechanisms. That has been done extensively for excercise and morbity/mortality, the current study just establishes that this also correlates in the bigger picture. So yes the study itself does not "proof" causality, it's just a piece in the bigger puzzle of causality.
The beauty of an end-to-end RCT is that it effectively neutralizes other possible causality mechanisms. You do not seem to appreciate this. My impression is that your reasoning is more in line with evidence based medicine (EBM), rather than the hypothetico-deductive method that I personally subscribe to. In my way of thinking there will never be definitive “proof” of causality, but I will happily take a drug that has gone through sufficiently powerful RCTs that failed to prove its ineffectiveness (and harm).
This might be a good heuristic for assigning confidence to results, but in theory, an RCT absolutely does establish causality, assuming internal, external, construct, and statistical validity of the study.
Multiple studies may not be better than one good study (assuming the above), which can be tested by looking at the leverage in a meta-anlaysis.
Having a theory is kind of orthogonal to a study finding a true positive result. Almost every published study, true or false, invokes some kind of theory, true or false. There is a joke in the soft sciences along the lines that you make up a new theory for every study.
I think that in science we have to be extra careful just what we are saying. And saying that this study proves causality would be wrong. I am sure there are other studies that could (do?) prove it but this statistical analysis can only prove correlation.
There's no reasonable path to reverse-causality with smoking. "People prone to developing lung cancer are more likely to take up smoking" doesn't really make sense.
On the other hand, "Fat and unhealthy people are more prone not to exercise" is a reasonable reverse-causality position, for all kind of reasons. Your knees hurt, you're self-conscious, it's extremely uncomfortable, your fat jiggles, etc.
I'm not saying direct causality doesn't exist here, I'm just saying that reverse-causality is reasonable, whereas with smoking it is not. Assuming causation from this particular correlation is harder than it is with smoking.
I think that we just have to look at the best explanation for the current data to determine causality, but it’s always a guess.
While "causality" is a strong word in the sentence above, the data is much stronger than simple correlation. Of course, outright causality has not been established, but the evidence to determine a predictive association is strong.
This is where explaining how causality is established really helps people understand what they are reading better, especially when confronted with something that is not light reading and summarizes a much larger, even heavier read. In other words, instead of saying people don't understand, maybe help people to understand.
If you’re on the fence and reading this definitely give it another shot
- a tiny step to do small one leg squat
- a kettle bell or any weight for deadlift
once in the morning, once in the evening.. or maybe more as one sees fit, until you feel the drive to do more (usually a month of slow and pleasurable exercise will naturally lead to a desire to try more)
[0] real slow, like taichi slow.. no muscle burn, no fast breathing..
It's stats on stats on stats and essentially creates a "character health" panel for your own body. Charts, metrics, real time tracking and exportable data. There are achievement badges and social connections if that's your thing too (good for you and your SO).
For me, being able to see real-time metrics and real time improvement really amped up my motivation to go out an exercise. Rewards were no longer this mysterious ethereal thing that will maybe show up sometime in the future if I keep grinding. Day by day, I could see improvement and sure enough could feel it too.
Also, for most of the weenies here, an $800 purchase isn't too rough. They have decent $500 and $300 dollar options, but the available amount stats and tracking goes down.
IMO, it's all about what drives someone - if they are going to be more motivated by someone training them, then they should do that. But if they are going to have more longevity by paying for an overpriced watch, maybe that's the better choice. Maybe even both? One thing is for sure - speaking in absolutes doesn't apply here.
Pretty much any of the more recent Forerunner series will do. The Forerunner 165 starts at $250 and the Forerunner 255 has been discounted to this level several times as well. They both have nearly all the available metrics and many sports modes, as well as triathlon modes most of us probably never need. They sync with the same (free) Garmin Connect smartphone app and cloud service.
So with any of these watches, you can sample the Garmin features. Upgrading to a more expensive watch later would mostly be for case material and size, aesthetics, or conspicuous consumption reasons.
The biggest functional difference between their lower price and expensive watches is that they limit on-watch mapping to only the expensive watches starting around $400-600 when on sale. The cheaper watches can only show a "breadcrumb" trail of your path in an ongoing activity, but no mapping of the surrounding terrain, roads, or landmarks.
There is also a funny distinction where their "outdoor" watches work a bit differently than their "health fitness" watches, developed in separate product divisions. But, these differences seem to be narrowing in recent years.
The other major feature tier is "music" which is a roughly $50-100 premium for the Forerunner 165 and 255 having non-music and music variants. This is where the watch can store and play music through bluetooth headphones, without a phone being present.
Keeping it unchanged is another kind of logical error.
The same scepticism would not be brought against many "hard science" experiments, even though they do the exact same thing, falsify alternative explanations until they have high certainty that they have causality.
It sounds like I can hit you a 1,000 times, you feel pain a 1,000 times and you still don't believe there's a causality.
"Every time we send 5 fire trucks to a fire, the damage is 10x than when we only send one fire truck. We've observed this 1000 times. And still you don't believe that the fire trucks are causing the damage."
In this case it should be absolutely clear that A (lots of trucks) aren't causing B (lots of damage), but rather a third aspect, C (size of fire) is causing both A and B. Insisting that A causes B will result in completely counterproductive interventions, like "send only one truck to all fires".
The same thing could be true for cardiovascular fitness. If people are sick, they're much less likely to running or hiking up a mountain. So rather than poor cardio fitness (A) causing high mortality (B), it could be that a third thing, sickness (C) is causing both A and B. If that is the case, then shaming people who are sick into trying to exercise, instead of making them healthy enough so that they feel like running, is likely to make things worse rather than better.
How do you tell the difference? Well the "gold standard" is randomized controlled trials. Pick 3,000 random people. Tell 1000 of them to exercise more, and 1000 of them to exercise less, and 1000 leave alone, and compare. If the "exercise more" group is healthier at the end of 10 years, that's decent evidence that "exercise more" is a useful intervention.
Failing that, you can think of other possible confounding factors and control for them. Don't just ask how much they exercise; ask how old they are, and how well they are, how stressed they are, and loads of other factors which might both cause both A and B, and use statistical methods to detect whether one of those factors is actually a better explanation than "A -> B".