I understand that in the context of professional applications of statistics, such as when writing research papers, professional statisticians often use uninformative priors. I'm not talking about professional statistics, and I'm not talking about the supposed debate between "frequentists" and "Bayesians." I'm talking about how an individual lives their life, sample size: one. The Earth doesn't have enough resources to answer every question of cause and effect that will come up in your life. For the vast majority of these questions, you will use a three pound mass of mostly fat to determine causation. Teaching that mass of fat that credible mechanisms of action and pre-existing information provides prior context for evaluating newly received information helps it not to discard valuable insight.

If you provided me research that showed, weakly, that using racial epithets did not affect a politicians chances of re-election (in relevant countries and scenarios, etc.), I would not discard my existing prior that it does, and I would be right to do so. But I would keep it in mind for the next round of evidence in the event I could be wrong.

In this case, no professional statistican is going to perform a Bayesian analysis to tell you the probability that making yourself stronger will cause a decrease in your all-cause mortality. No one is going to aggregate that information and tell YOU what YOU need to do, in this case as in so many others. But there is enough information that you should nonetheless, and this study, like it or not, does add to that information, not detract from it.

P.S.: I disagree with using uninformative priors except when they are warranted. This is how alternative medicine masquerades as "Evidence-based Medicine."