Everyday Health Healthy Living
Personal Takes
Standing vs. Sitting: Why Movement Boosts Our Health
By Dr T. Jared Bunch, MD
At a series of physician lectures on our modern work environments, I learned why all
the physicians who presented there had thrown away their office chairs and replaced
them with working treadmills:
•
Continuous body motion, including fidgeting, is a healthy use of daily calories and
keeps our weight in check.
•
Our modern workplace environments that promote desk jobs and prolonged sitting
are harmful to our health.
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The question I want to pose to you is this: Are our modern workplaces, and the
advances that make them possible, directly responsible for our worsening societal
health? This question was the focus of a July 2015 study published in the European
Heart Journal, conducted by researchers at the University of Queensland in
Brisbane, Australia. Francisco Lopez-Jimenez, MD, also addressed the issue in an
excellent editorial about the same study, Standing for Healthier Lives – Literally.
Standing during work as a health benefit is not a new concept, Dr. Lopez-Jimenez
points out. In a 1953 study from London published in the British Medical Journal, bus
conductors who often stood during their work shifts had less coronary artery
disease and lower total mortality than bus drivers who spent their days seated.
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But in our society, jobs in which we are seated are often viewed as more advanced than
jobs that require prolonged standing.
Ask yourself a few simple questions:
•
If you see two employees traveling to work — one on a regular bicycle and the
other in a luxury automobile — who likely has the higher position in the company?
•
Consider a second scenario: If one of your neighbors hires a maid and a lawn
service, and the other spends a lot of time on the yard and doing household chores,
who probably has a higher economic status?
The Bigger the Chair, the Better — Not Healthier — the Job
Perception is important in our society. Leaders of large corporations often have very
large offices with vast comfortable seating. They also spend extensively to make large
open lobbies with big couches and chairs. “In every country, the bigger and more
comfortable the chair, the more important the job. Interestingly, in the English
language, chair also means chief or director, making a piece of furniture a synonym of
power and rank,” writes Lopez-Jimenez.
Clearly, societies have placed value on sitting. But unfortunately, we live with the
health consequences of this misdirected value.
In most advanced societies, diabetes, sleep apnea, obesity, heart disease, and
stroke are becoming epidemics. Obesity also results in higher rates of many cancers,
including breast, uterine, pancreatic, and colon cancers. Even in less developed
countries, the wealthier people tend to suffer from higher rates of these diseases: Their
status has afforded them more opportunities to sit and, consequently, become less
active.
Standing Vs. Sitting Cuts Heart Disease Risks
In the interesting new study from Australia, researchers recognized that excessive
sitting time is associated with worse health. They asked if replacing sitting with
standing was sufficient, or if additional activity and motion was still required or would
provide additional benefits. Six hundred and nighty-eight people were enrolled in this
study — 57 percent of them women. The average age was approximately 58.
Most of us often overestimate our activity and exercise times and underestimate our
sedentary times. When I ask some of my older male patients about their activity, their
wives will often start laughing at the answers. But these researchers did not rely on
self-reported times of activity: They used posture and activity monitors to accurately
measure standing and activity times.
When people replaced two hours of sitting a day with standing, they had changes in
these important heart disease risk factors:
•
2 percent lower blood glucose ( blood sugar)
•
11 percent reduction in triglycerides
•
6 percent lower total cholesterol
•
Higher levels of HDL cholesterol (good cholesterol), by 0.06 millimoles per liter
(mmol/L)
This suggests that just by increasing how much you stand during the day, you can
greatly impact risk factors for heart disease.
The authors then looked at what happens when you replace two hours of sitting with
stepping. For those who have an office job, this means using a small stair stepper or
workplace treadmill. The rate they chose was three METs (METS are metabolic
equivalents, and the energy it takes to just sit is one MET). Three METs is a walking
rate of about 2.5 to 3 miles per hour.
Results associated with this simple lifestyle change from sitting to stepping were
profound:
•
11 percent lower blood sugar
•
11 percent lower body mass index (BMI)
•
7.5 cm smaller waist circumference
•
14 percent lower triglycerides
•
Even higher levels of good (HDL) cholesterol, by 0.10 mmol/L.
These findings don’t reflect the other many positive benefits with increasing your time
standing and stepping: Lower stress and anxiety, better sleep, lower risk of muscle and
bone loss, improved posture, improved breathing mechanics, and more.
I agree completely with Lopez-Jimenez that we need to “Stand for Healthier Lives.” We
also need to recognize that our societal value of sitting is misplaced and has caused
public harm.
Many of my colleagues, like other researchers in this area, have given up their office
chairs. Some now do their computer work and charting on a walking treadmill. Australia
now has an active public health campaign to reduce sitting times. These healthy choices
will have far-reaching, positive consequences.
I applaud companies and organizations that have already started to promote the use of
standing desks and walking treadmill desks. The great news from the Australian study
is that many of the negative effects of prolonged sitting can be reversed.
So start now! Stand up!
PHOTO CREDIT: Ezra Bailey/Getty Images
T. Jared Bunch, MD is a native of Logan, Utah, and directs heart rhythm research at
the Intermountain Medical Center Heart Institute. You can follow @TJaredBunch on
Twitter. Dr. Bunch is also a frequent guest on The Dr. John Day Show, available on
iTunes.
Last Updated:8/7/2015
Important: The views and opinions expressed in this article are those of the author and not
Everyday Health. See More
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Causation vs Correlation
by Rebecca Goldin | Aug 19, 2015 | Causality, Correlation is not causation, Savvy stats reporting | 23 comments
J
ournalists are constantly being reminded that “correlation doesn’t imply causation;” yet, conflating the
two remains one of the most common errors in news reporting on scientific and health-related studies. In
theory, these are easy to distinguish—an action or occurrence can cause another (such as smoking causes
lung cancer), or it can correlate with another (such as smoking is correlated with high alcohol consumption).
If one action causes another, then they are most certainly correlated. But just because two things occur
together does not mean that one caused the other, even if it seems to make sense.
Unfortunately, intuition can lead us astray when it comes to distinguishing between the two. For example,
eating breakfast has long been correlated with success in school for elementary school children. It would be
easy to conclude that eating breakfast causes students to be better learners. Is this a causal relationship—does
breakfast by itself create better students? Or is it only a correlation: perhaps not having breakfast correlates
highly with other challenges in kids’ lives that make them poorer students, such as less educated parents,
worse socio-economic status, less focus on school at home, and lower expectations.
It turns out that kids who don’t eat breakfast are also more likely to be absent or tardy—and absenteeism plays
a significant role in their poor performance. This may lead one to believe that there is not a causal relationship.
Yet breakfast may encourage kids to come to school (and on-time), which then improves their performance in
school, and so perhaps encourages attendance, which then results in better performance. In a recent literature
review, there were mixed results suggesting that the advantages of breakfast depend on the population, the type
of breakfast provided, and the measurement of “benefit” for the kids. Breakfast seems to have an overall
positive impact on cognitive performance, especially memory tasks and focus. Not surprisingly, the benefit
seems greater for kids who are undernourished. But the clear message here is that a causal relationship has
been extremely hard to establish, and remains in question.
Many studies are designed to test a correlation, but cannot possibly lead us to a causal conclusion; and yet,
obvious “reasons” for the correlation abound, tempting us toward a potentially incorrect conclusion. People
learn of a study showing that “girls who watch soap operas are more likely to have eating disorders”— a
correlation between soap opera watching and eating disorders—but then they incorrectly conclude that
watching soap operas gives girls eating disorders. It is entirely possible that girls who are prone to eating
disorders are also attracted to soap operas.
There are several reasons why common sense conclusions about cause and effect might be wrong. Correlated
occurrences may be due to a common cause. For example, the fact that red hair is correlated with blue eyes
stems from a common genetic specification that codes for both. A correlation may also be observed when there
is causality behind it—for example, it is well established that cigarette smoking not only correlates with lung
cancer but actually causes it. But in order to establish cause, we have to rule out the possibility that smokers
are more likely to live in urban areas, where there is more pollution—and any other possible explanation for
the observed correlation.
In many cases, it seems obvious that one action causes another; however, there are also many cases when it is
not so clear (except perhaps to the already-convinced observer). In the case of soap-opera watching anorexics,
we can neither exclude nor embrace the hypothesis that the television is a cause of the problem—additional
research would be needed to make a convincing argument for causality. Another hypothesis might be that girls
inclined to suffer poor body image are drawn to soap operas on television because it satisfies some need
related to their poor body image. Or it could be that neither causes the other, but rather there is a common
trait—say, an overemphasis on appearance in the girls’ environment—that causes both an interest in soap
operas and an inclination to develop eating disorders. None of these hypotheses are tested in a study that
simply asks who is watching soaps and who is developing eating disorders, and finding a correlation between
the two.
How, then, does one ever establish causality? This is one of the most daunting challenges of public health
professionals and pharmaceutical companies. The most effective way of doing this is through a controlled
study. In a controlled study, two groups of people who are comparable in almost every way are given two
different sets of experiences (such one group watching soap operas and the other game shows), and the
outcome is compared. If the two groups have substantially different outcomes, then the different experiences
may have caused the different outcome.
There are obvious ethical limits to controlled studies: it would be problematic to take two comparable groups
and make one smoke while denying cigarettes to the other in order to see if cigarette smoking really causes
lung cancer. This is why epidemiological (or observational) studies are so important. These are studies in
which large groups of people are followed over time, and their behavior and outcome is also observed. In these
studies, it is extremely difficult (though sometimes still possible) to tease out cause and effect, versus a mere
correlation.
Typically, one can only establish a causal relationship if the effects are extremely notable and there is no
reasonable explanation that challenges causality. This was the case with cigarette smoking, for example. At the
time that scientists, industry trade groups, activists and individuals were debating whether the observed
correlation between heavy cigarette smoking and lung cancer was causal or not, many other hypotheses were
considered (such as sleep deprivation or excessive drinking) and each one dismissed as insufficiently
describing the data. It is now a widespread belief among scientists and health professionals that smoking does
indeed cause lung cancer.
When the stakes are high, people are much more likely to jump to causal conclusions. This seems to be doubly
true when it comes to public suspicion about chemicals and environmental pollution. There has been a lot of
publicity over the purported relationship between autism and vaccinations, for example. As vaccination rates
went up across the United States, so did autism. And if you splice the data in just the right way, it looks like
some kids with autism have had more vaccinations. However, this correlation (which has led many to conclude
that vaccination causes autism) has been widely dismissed by public health experts. The rise in autism rates is
likely to do with increased awareness and diagnosis, or one of many other possible factors that have changed
over the past 50 years.
Language further contorts the distinction, as some media outlets use words that imply causality without saying
it. A recent example in Oklahoma occurred when its Governor, Mary Fallin, saidthere was a “direct
correlation” between a recent increase in earthquakes and wastewater disposal wells. She would have liked to
say that the wells caused the earthquakes, but the research only shows a correlation. Rather than misspeak, she
embellished “correlation” with “direct” so that it sounds causal.
At times, a correlation does not have a clear explanation, and at other times we fill in the explanation. A recent
news story reports that housing prices in D.C. correlate with reading proficiency. Many stories can be crafted
to explain the phenomenon, but most people would be reluctant to conclude that a child’s reading proficiency
could cause the price of their house to be higher or lower, or vice-versa. In contrast, a news story reporting that
“30 years of research found a positive correlation between family involvement and a student’s academic
success” in Florida feels like it has the weight of causality. The big difference between these two different
correlations is our own belief in a likely mechanism for family to contribute to better grades.
In general, we should all be wary of our own bias: we like explanations. The media often concludes a causal
relationship among correlated observances when causality was not even considered by the study itself. Without
clear reasons to accept causality, we should only accept the existence of a correlation. Two events occurring in
close proximity does not imply that one caused the other, even if it seems to makes perfect sense.
Rebecca Goldin is Professor of Mathematical Sciences at George Mason University and Director of STATS.org.
She received her undergraduate degree from Harvard University and her Ph.D. from the Massachusetts
Institute of Technology. She taught at the University of Maryland as a National Science Foundation
postdoctoral fellow before joining George Mason in 2001. Her academic research is in symplectic geometry,
group actions and related combinatorics. In 2007, she received the Ruth I. Michler Memorial Prize, presented
by the Association for Women in Mathematics. Goldin is supported in part by NSF grant #1201458.
23 Comments
1.
Margaret Gorlin on September 3, 2015 at 1:20 pm
I discovered your stat stories this morning while poking around the internet looking for good examples of bad
statistics. I am still reading your stories with relish an hour after starting – they are interesting, fun, thought
provoking and at a level I can use in my intro stat classes this fall. Thank you for these gems – I am planning
to put them to good use this term.
Reply
•
statsorg on September 5, 2015 at 8:34 am
Thank you Margaret: We have big expansion plans in the next year!
Reply
•
vijay on December 19, 2017 at 4:05 pm
Great!!!!! Very informative and the style of presentation was extremely beautiful
Reply
2.
Zachary on October 8, 2015 at 4:31 pm
I’m a high school student in a college level Stats course and reading this has been so interesting. I’m starting to
think about a career in Stats
Reply
•
statsorg on October 9, 2015 at 6:48 pm
Good for you Zachary! The world needs more statisticians! And it’s willing to pay too!
Reply
•
David on March 24, 2016 at 1:41 am
See what you’ve caused!?
Reply
3.
Lexi on January 4, 2016 at 10:12 am
This was a great source of information. I’m currently writing an essay for extra credit and this is the first clear
explanation I have found that truly approaches the topic the way I need. Thank you so much.
Reply
4.
Mark on January 30, 2016 at 4:19 pm
Dr. Goldin, great piece. Is there a point where the size, scope or length of time scale of the data begins to
demonstrate such an overwhelming correlation that cause and effect is much more certain? I’m thinking about
this in the context of business, where most of the decisions we make each day are based on correlation v.
absolute certainty re cause and effect. It seems logical, for example, that correlations in larger bodies of data
over longer periods of time would be inherently more reliable and potential more indicative of cause and effect
than two quarters of data. Your thoughts would be greatly appreciated. Thanks!
Reply
5.
Yawning Enthusiast on February 24, 2016 at 1:35 pm
Most readers won’t know the difference between causation and correlation. In fact, many reporters who use
those two words don’t fully understand either. However, what would be the proper way to explain that not all
smokers suffer from lung cancer?
Reply
•
Uzoma on December 17, 2017 at 6:50 am
By doing a controlled experiment
Reply
•
Uzoma on December 17, 2017 at 7:01 am
x causes y if the lagged values of x improve the predectibilty of the current value of y
Reply
6.
Shanice F-J on May 2, 2016 at 11:20 am
Thank you for your help, the clearest example I’ve seen! I’m currently working on my dissertation and this
really helped! Thanks
Reply
7.
Shanice F-J on May 2, 2016 at 11:20 am
Thank you for your help, the clearest example I’ve seen! I’m currently working on my dissertation and this
really helped! Thanks
Reply
8.
ah ha on July 25, 2016 at 5:37 pm
go westfield, urban ed!!!
Reply
9.
ah ha on July 25, 2016 at 5:37 pm
go westfield, urban ed!!!
Reply
10.
morfara on November 4, 2016 at 8:12 pm
“If one action causes another, then they are most certainly correlated. “. Could you please elaborate a bit more
on that, i.e. can one action cause another, and not be correlated?
Reply
11.
Bastard Beard Co on May 13, 2017 at 7:11 am
Right away I am ready to do my breakfast, when having my breakfast coming over again to read more news.
Reply
12.
Lisa de Bie on September 19, 2017 at 1:46 pm
Great!
Reply
13.
Lisa de Bie on September 20, 2017 at 4:02 pm
Good!
Reply
14.
Tyler on February 13, 2018 at 3:59 am
Very insightful. Your article will change the way I discuss Causation vs Correlation. Thank you!
Reply
15.
車売る on March 25, 2018 at 10:45 pm
Thanks for finally talking about >Causation vs
Correlation
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