Researchers put together an incredible workplace wellness program that provided thousands of workers with paid time off to receive biometric health screening, health risk assessments, smoking cessation help, stress management, exercise, etc.
What did this do for their health?🧵
So, for starters, this program had a large sample and ran over multiple years.
Because of it, we have evidence on what people do with clinical health info, with exercise encouragement and advice, with nutritional knowledge, through peer effects, and so on.
Participants in the treatment group were prompted to participate with cash rewards ranging from $50 to $350.
Go to screening? Earn some money, help yourself by bolstering your knowledge about yourself and potentially improving your health.
What could be simpler?
The participants certainly seemed to think so.
The cash rewards did get more people into screenings and advising, and they even got some people moving more.
If estimates from earlier studies were to be believed, this effort should even do enough to save employers money!
But that didn't work.
Average monthly medical spending didn't change when comparing the treatment to the control group.
In fact, this study stands out in the literature, as getting nulls across basically every outcome relevant to the employer.
Health and wellness incentives and opportunities did not make people less absent or medically costly, or much else (which we'll get to).
Before getting to other outcomes, we have to ask: Why trust this over other results? A few reasons:
For one, it was bigger than other studies in the experimental literature.
For two, it was preregistered, publicly archived, and independently analyzed by outside researchers.
All of that on its own is really good. But what really takes the cake is that the prior literature was impacted by p-hacking and publication bias, whereas these researchers committed to publishing their results regardless.
Who do you trust more?
"We aren't financially conflicted and we'll publish regardless of what happens and of course we provide data and code."
or "p = 0.04, this program is life-changing (ignore my financial conflicts of interest :))"
I know my answer, you know my answer.
Now let's talk other outcomes.
Medical spending: not affected in total, admin-wise, drug-wise, office-wise, hospital-wise, or in terms of any utilization metric.
Employment and productivity: Didn't affect employee retention, salaries, promotions, sick leave, overtime, etc.
More employment and productivity: Didn't affect job satisfaction or feelings of productivity. BUT, did affect views about management priorities on health (increased) and the likelihood of engaging in a job search (increased).
That's backfiring, potentially.
Participants failed to increase their number of gym visits, didn't participate in the IL marathon, 10k, or 5k more often, despite smoking cessation advice and help they didn't smoke less, they didn't report better health, hell, they became (marginally-significantly) fatter!
Across basically every metric, the results were null, null, and--my favorite--null.
And this is what we expect with credible intervention evaluations of high-quality samples. This is so common, in fact, that it's been dubbed the "Stainless Steel Law":
But the most amazing detail, in my opinion, is that this study went further:
It explained why prior observational work showed such large benefits for workplace wellness programs.
The reason is selection: health-conscious employees selected into the program and stuck with it!
These programs' effectiveness is a classic example of selection leading to results that simply cannot be trusted.
But... how?! Why?! After all, this program had all the ingredients that so many prominent people think will solve America's public health issues.
The answer is that they misunderstand people.
Most people are lazy, commitment is hard
My recommendation to ppl who haven't learned that is to do a clinical rotation or read abt the thousands of programs across America that have done food delivery coaching, etc., with no effect
This leads me to something important:
Do you know why Ozempic works so well and has enjoyed such incredible popularity of late?
If you can understand these headlines, you'll get it.
Ozempic makes it automatic to lose weight.
It takes out the effort, and people have an easier time doing more (in this case, work) than they do being asked to eat less or doing things that simultaneously bore and fatigue them (exercise) without a commitment mechanism like a boss
For this reason, GLP-1RAs are going to decisively beat all efforts to advise people, to provide them with healthy food and instructions on how to prepare it, and all of that tried-and-true advice that's been around and in vogue for decades, but clearly hasn't worked.
To top this all off, here's the result of a contemporaneous large, cluster-randomized controlled trial of workplace wellness programs at BJ's Wholesale Club.
Similar intervention, somewhat optimistic effects, and, once again, no results to show for it.
Some of you who are familiar with medicine no doubt do, but if you don't, no worries: This is James Lind, the man most often credited with finding the cure for scurvy.
Scurvy is one of humanity's great historical killers.
It's a gruesome condition that culminates in your life's wounds reappearing on your flesh. If you want a picture, go look it up.
You never hear about it today though, because it's so easy to cure.
This research directly militates against modern blood libel.
If people knew, for example, that Black and White men earned the same amounts on average at the same IQs, they would likely be a lot less convinced by basically-false discrimination narratives blaming Whites.
Add in that the intelligence differences cannot be explained by discrimination—because there *is* measurement invariance—and these sorts of findings are incredibly damning for discrimination-based narratives of racial inequality.
So, said findings must be condemned, proscribed.
The above chart is from the NLSY '79, but it replicates in plenty of other datasets, because it is broadly true.
For example, here are three independent replications:
A lot of the major pieces of civil rights legislation were passed by White elites who were upset at the violence generated by the Great Migration and the riots.
Because of his association with this violence, most people at the time came to dislike MLK.
It's only *after* his death, and with his public beatification that he's come to enjoy a good reputation.
This comic from 1967 is a much better summation of how the public viewed him than what people are generally taught today.
And yes, he was viewed better by Blacks than by Whites.
But remember, at the time, Whites were almost nine-tenths of the population.
Near his death, Whites were maybe one-quarter favorable to MLK, and most of that favorability was weak.
The researcher who put together these numbers was investigated and almost charged with a crime for bringing these numbers to light when she hadn't received permission.
Greater Male Variability rarely makes for an adequate explanation of sex differences in performance.
One exception may be the number of papers published by academics.
If you remove the top 7.5% of men, there's no longer a gap!
The disciplines covered here were ones with relatively equal sex ratios: Education, Nursing & Caring Science, Psychology, Public Health, Sociology, and Social Work.
Because these are stats on professors, this means that if there's greater male variability, it's mostly right-tail
Despite this, the very highest-performing women actually outperformed the very highest-performing men on average, albeit slightly.
The percentiles in this image are for the combined group, so these findings coexist for composition reasons.