Think about how much time you sit during your day. If you are like the average college student, you are likely sitting for long periods of time sitting in classes, studying for tests, and writing blogs. A friend of mine was recently expressing his opinion to me that sitting is like the new smoking. As a curious (and worrisome) individual, I wanted to see for myself whether sitting for long periods of time can have a negative effect on our health. I wondered, is there a correlation between sitting too much and negative health effects such as early death? And if so, could the correlation be causal or is it due to chance?
First, I needed to find a non-biased, recent study that could at least give me a better understanding to what we are looking at. I first looked at the US National Health and Nutrition Examination Survey that studied people’s actions throughout their day using accelerometers. According to this study, U.S. adult citizens spend on average more than half of their day in a sedimentary position. Below is a chart depicting the average time one spends in a sedimentary position.Figure 2: http://www.sciencedirect.com/science/article/pii/S0168822712002082
Next I looked at a study conducted by Canadian researchers studying the link between sitting time and mortality rates. The study accounted for multiple possible confounding variables such as physical activity, body mass index, smoking status, and alcohol consumption. As seen in figure 3, the results show a particularly strong correlation between time spent sitting and survival rate over a span of 14 years.
The P-value in this particular case was determined to be less than 0.0001. This study makes a strong inference that the link between sitting for too long and early death is causal because it studied a large group of people (17,013 Canadians), accounted for multiple potential confounding variables, and has a considerably low P-value.
Although the researchers have conducted what seems to be a thorough and convincing study, more studies should be taken into consideration in case of the extremely small possibility that the correlation between sedimentary position and higher mortality rate are due to chance.
I do not have the time nor the resources available to me to conduct a thorough meta-analysis. However with that being said, meta-analyses can be very helpful in proving correlation between two possible occurrences. Australian researchers included six studies ranging from 1989-2013 in a thorough meta-analysis. In total, more than 595,000 people were involved with the studies, accumulating more than 3,500,000 person-years of data (Chau). The study concluded that it is very likely that sitting too much decreases one’s life-span. However, it should be noted that no P-value was made evident.Figure 3: http://www.ergotron.com/portals/0/literature/other/english/acsm_sittingtime.pdf
Though the mechanism is not clear, some scientists believe that when muscles are not used frequently, lipoprotein lipase activity, an essential process of the human body becomes suppressed (Dunstan).
Although the effects may not be immediate, there seems to be a positive correlation between sitting and negative health effects. The more time spent sitting per day generally associates you with a higher likelihood of developing serious health issues and earlier mortality. After looking into this myself, I will attempt to stand up and move around to break up my studying. So next time you are cramming for a test or trying to write 5 blogs the day before the deadline, make it a point to move around.
Dunstan, David W. “Too much sitting – A health hazard.” Elsevier, vol. 97, Sept. 2012. Science Direct, www.sciencedirect.com/science/article/pii/S0168822712002082. Accessed 13 Oct. 2016.
Katzmarzyk, Peter T. “Sitting Time and Mortality from all Causes, Cardiovascular disease, and cancer.” . Google Scholar, www.ergotron.com/portals/0/literature/other/english/acsm_sittingtime.pdf.
Chau JY, Grunseit AC, Chey T, Stamatakis E, Brown WJ, Matthews CE, et al. (2013) Daily Sitting Time and All-Cause Mortality: A Meta-Analysis. PLoS ONE 8(11): e80000. doi:10.1371/journal.pone.0080000