Hätte dir gerne die MASS Website geschickt, aber LL erlaubt nur ein paar Dateitypen.
ABBC3_SPOILER_SHOW
MASS Research Review · Being Sedentary - More Fat And Less Lean Mass, Even In Athletes
By Eric Helms
Don’t think regular exercisers can be sedentary? In the modern lifting world where you can work from home and walk less often, it’s increasingly common to train hard and frequently, while being sedentary outside of the weight room. Unfortunately, this reality may have negative effects on body composition and health, even in athletes. Read on to learn more.
Study Reviewed: Sedentary Behaviours and Their Relationship with Body Composition of Athletes. Júdice et al. (2021)
Key Points
In the present study (1), a group of 65 female and 70 male athletes from various sports (including some strength and power sports) reported their time spent being sedentary and were DXA scanned. The researchers performed a multiple regression analysis to determine the relationships between time in sedentary behaviors and body composition.
Weekly training time was inversely related to time spent in transport, and total and trunk body fat percentage. Further, after adjusting for sex, age, weekly training time, years of sport practice, and sport type, total sedentary time and screen time were positively related to trunk fat percentage and inversely associated with fat-free mass percentage. Total screen time and cell phone screen time specifically were also positively associated with total body fat percentage.
Athletes who train less tend to have higher amounts of trunk and total body fat. Athletes who had to spend more time traveling, i.e. had longer commutes, tended to train less. However, body fat tends to be higher and fat free mass lower in more sedentary athletes, independent of time spent training, sport, years of sport practice, age, and sex.
The negative health effects of time spent being sedentary are often thought to be directly counteracted by exercise. This is true to some degree in outcome; rather than seeing exercise as directly counteracting sedentary behavior, a more accurate representation is that time spent being sedentary carries a negative health risk, while exercise has a separate, positive impact on health (2). However, most of the data that support the concept of this distinct relationship are non-athletes with obesity or who are overweight. The present study (1), however, was an assessment of sedentary behavior in a cohort of 65 female and 70 male athletes from multiple sports (including disciplines relevant to MASS readers). The researchers collected data pertaining to the athletes’ body composition and the amount of time they typically spent training and engaged in sedentary behaviors, then examined relationships between these variables. Weekly training time was inversely related to time spent in transport, as was trunk and total body fat percentage. Further, using multiple linear regression models adjusting for sex, age, weekly training time, years of sport practice, and sport type, total sedentary time and screen time were positively related to trunk fat percentage and inversely associated with fat-free mass percentage. Finally, total screen time and cell phone screen time specifically were also positively associated with total body fat percentage. While this cross-sectional analysis can’t unequivocally determine causation, these findings may indicate that even in the presence of a large volume of hard training, being very sedentary can increase the likelihood of an athlete carrying more fat mass and less lean mass. In this article I’ll discuss the implications of these findings and what they mean for lifters.
Purpose and Hypotheses
Purpose
The purpose of this study was to examine the associations between different types of self-reported sedentary behaviors and body composition in a mixed group of female and male athletes.
Hypotheses
I suspect that the authors expected a relationship between sedentary behavior and adiposity, given they cited previous research that found relationships between body fatness and sedentary behavior in highly trained athletes.
Subjects and Methods
Subjects
The characteristics of the participants in this study are shown in Table 1. Of the 135 Portuguese athletes (65 female, 70 male), 2.2% were top 10 internationally in their sport, 29.6% competed at the international level, and 68.1% competed nationally. Athletes came from a variety of sports including powerlifting, gymnastics, track and field, climbing, judo, karate, taekwondo, boxing, rugby, hockey, fencing, rowing, tennis, volleyball, handball, basketball, soccer, futsal, golf, motor biking, surfing, swimming, water polo, trampoline, and triathlon. Of the athletes, 44.4% were from team sports, 55.6% were from individual sports, 69.6% were from non-weight sensitive sports, and 30.4% were from weight sensitive sports.
Study Overview
The athletes were interviewed using a validated questionnaire to assess and quantify time spent in various sedentary behaviors. Additionally, using standardized protocols, the athletes had their body composition measured via DXA scans. The authors reported a metric of their lab’s reliability in assessing body composition, reporting a low coefficient of variation for both total fat mass (1.7%) and trunk fat mass (0.01%), respectively. Following data collection, the researchers ran basic correlations and utilized multiple linear regression to assess relationships between sedentary behavior and body composition adjusted for age, sex, weekly training time, years of sport practice, and sport type.
Criticisms and Statistical Musings
What follows is an explanation of how to interpret the regression results, one slightly confusing aspect of the data, and a preemptive warning of how this study might (but shouldn’t) be interpreted by some readers.
Looking at the results of the regression analyses in Table 2, you’ll see p-values, which you’re likely familiar with, and unstandardized β (95% CI) values, which you’re probably not familiar with. The unstandardized β value in a regression model represents the average change in the outcome variable you’d expect to see for every one-unit increase in the predictor variable. For example, looking at the first column of Table 2 for the outcome of fat mass percentage, we see the predictor variable with the most significant relationship is cell phone screen time (p = 0.001), with a β value of 1.447. Cross referencing Table 1 to get the units, we interpret this to mean that if everything else is held constant (and when adjusting for sex, age, weekly training time, years of sport practice, and sport type), for every additional hour of cell phone screen time per day, we’d expect an athlete to have a ~1.4% higher body fat percentage on average. The range provided in parentheses (95% CI) is the 95% confidence interval, and it means that we are 95% sure that the true average β value for the population this sample came from would fall in that range (in this case, 0.585-2.309).
Additionally, to help you conceptualize the impact that sedentary behavior has on body composition in terms you are more familiar with, Dr. Trexler did us a solid by calculating the estimated r2 values that correspond with each variable. These are reported in the findings section so you can get a more general idea of the impact of sedentary behavior, rather than just having the β values to rely on. As a brief reminder, an r-value is the strength of a basic, linear correlation between two variables. It can span from -1 to 1 with negative values representing inverse or “negative” relationships (one variable decreases as the other increases), while positive values represent a positive relationship. The closer an r-value is to 1 (or -1), the stronger the relationship (the more linear) between the two variables. An r2 value however, is just that, the r-value squared. Thus, it is always positive, and the way to interpret it is as the percentage of the change in one variable which is explained by the other. So for example, an r-value of 0.80 is a strong positive relationship; if you square 0.80, you get an r2 value (also called the coefficient of determination) of 0.64, which can be interpreted to mean that 64% of the change in one variable is associated with a change in the other variable.
For the final statistical aspect of this section, a minor point of confusion on our part is that we expected the relationships for fat-free mass percentage and total body fat percentage to be the inverse of one another, and to have identical p-values. That isn’t the case, and we aren’t sure why. The body fat and fat-free mass relationships tell similar stories, but it would make sense that they would tell inverse, identical stories, as they are normally calculated as a function of one another. My pet theory is that there was some unreported accounting for body water done separately, and then the column labels for total and trunk fat-free mass were accidentally swapped, as you can see that trunk fat free mass is roughly inverse of fat-free mass (when you’d expect total fat-free mass to be). Greg’s theory is that total body fat percentage was derived from DXA along with total fat-free mass, but then the percentage of fat-free mass was calculated by dividing the DXA-derived fat-free mass by body mass assessed on a separate scale. Either could explain this discrepancy and why total body fat percentage and fat-free mass percentage don’t quite add up to 100%, and why the p-values aren’t the same. However, either way, it probably doesn’t materially impact the interpretation.
With the statistical musings out of the way, I now want to preemptively urge readers to resist the natural tendency to assume that this is a straightforward, causative relationship. This is a cross-sectional analysis where the authors simply observed the relationships between variables in a group. This approach to research is very different from a controlled trial where they split the participants into groups to test how manipulating one variable impacts the other. In the example used above, while we know that the athletes with more cell phone screen time had significantly higher body fat percentages, we don’t necessarily know if an intervention to decrease their screen time would result in a reduction in body fat. Don’t get me wrong, this could be a causative relationship, but we can’t know that from this study alone. Even when there is an intuitive relationship between two variables that seems to make sense, like being more sedentary resulting in higher body fat due to lower total energy expenditure and poorer hunger regulation resulting in more energy consumption, we should be cautious in our interpretation of isolated cross-sectional relationships. While you can try to increase your confidence by correcting for other variables that might influence a relationship (note how the authors adjusted for sex, age, weekly training time, years of sport practice, and sport type), what if there was an unmeasured variable causing body fat to go up that is related to cell phone screen time? What if those who had higher cell phone screen times also spent more time indoors and had Vitamin D deficiencies that influenced the body fat differences (3)? I’m not saying this is the case (in fact, it’s probably not; the Vitamin D data on body composition is weak at best), but the point is that you can’t know for sure it’s not the case from a cross-sectional design.
On the other hand, this doesn’t mean you can simply dismiss all correlation and regression results with the oh-so-cliché “correlation doesn’t equal causation” line. Like all evidence, cross-sectional relationships exist on a continuum. There are barely significant, weak relationships with no known mechanistic link, and there are highly significant, very strong relationships that are mechanistically linked, and everything in between. A single study almost never acts as proof in isolation, but rather contributes to a body of data. Also, you have to consider what you are studying and use common sense; a controlled trial shouldn’t always get more weight than an association. For example, a well-done regression analysis in a large population showing a link between high consumption of a specific food and deaths from heart disease isn’t trumped by a 6-month trial comparing two small groups consuming different amounts of the food, because you wouldn’t expect many deaths in such a short time period anyway. In addition to the length of time issue in this hypothetical scenario, it may be impossible to do an actual controlled trial. A zero exposure control group for a common food item may not be feasible, or it could be unethical to give a group of people something that might harm them. In some cases, an observational study with a well-done regression analysis is as good as it's going to get.
Findings
Weekly time spent training had a significant inverse relationship with time spent sitting in transport (r = -0.33; p < 0.001), total fat mass percentage (r = -0.31; p < 0.001), and trunk fat mass percentage (r = -0.28; p = 0.001). But, weekly time spent training was not significantly related to any other body composition metric or sedentary behavior (p > 0.05).
While testing for potential covariates in the multiple linear regression analyses, the authors found a significant sport type by sedentary behavior interaction for body composition (p = 0.014). However, since each individual sport had a very low sample size, they opted not to stratify the analyses by sport and instead included sport type as a covariate in all the analyses (more on the relevance of this in the interpretation).
Table 2 displays the relationships between various sedentary behaviors and body fat percentage, trunk fat percentage, fat-free mass percentage, and waist circumference after adjusting for sex, age, weekly training time, years of sport practice, and sport type. Total screen time (p = 0.035) and specifically cell phone screen time (p = 0.001) both had positive significant relationships with total body fat percentage, and video game time had a low but nonsignificant p-value as well (p = 0.069). There were also positive significant relationships between total sedentary time (p = 0.038) and total screen time (p = 0.040) with trunk fat percentage, and cell phone (p = 0.095) and video game screen time (p = 0.085) also had low p-values. Finally, total sedentary time (p = 0.042) and total screen time (p = 0.026) were negatively associated with fat-free mass percentage, and while the p-values were pretty low, these negative relationships weren’t quite significant for cell phone (p = 0.081) and video game screen time (p = 0.069). There were no other significant or nearly significant relationships.
Finally, big ups to Dr. Trexler for doing some in-house calculations of the r2 values for the significant sedentary behaviors to help explain how much much of an impact they have on body composition (if you’d read the Criticisms and Statistical Musings section you’d have known this was coming … just saying). These values are presented in Table 3.
Interpretation
First, let’s go through the results. The basic correlational data is straightforward and makes intuitive sense; the athletes who trained more tended to have slightly less trunk and total fat mass, and athletes with more time spent in transport trained less. I would suppose that those who had long commutes trained less, as they probably weren’t able to dedicate as much time to training. However, it is also possible that some individuals may have trained more and commuted less if they were housed in a centralized training center. As I mentioned in the findings, the authors did find a significant interaction between sport type and body composition when looking for covariates, but didn’t feel it was appropriate to segregate the sample by sport due to the small number of athletes in each discipline and opted to adjust the model by sport type. I think this was the right move, and controlling for total training time isn’t enough. Remember, this was a sample of a diverse group of athletes. For example, there were both powerlifters and triathletes in this study. A powerlifter, depending on meet proximity, might train 6-14 hours a week (4), which isn’t that different from the 8-15 hours of training per week of the average triathlete (5). However, with 1-5 reps per set and 3+ minutes rest between sets, that means 80% or more of a powerlifting training session is spent sitting on a bench resting, while the entire training time for a triathlete is running, biking, or swimming at various aerobic intensities. Thus, you can probably see why there might be an influence of sport type, and why it’s important to control for both sport type and total training time. Moving on to the regression results, independent of time spent training (and sex, age, experience, and sport type), time in sedentary behaviors was associated with less fat-free mass and more fat mass. You might have noticed that total fat-free mass was only related to cell phone screen time and overall screen time, but wasn’t significantly related to total combined sedentary behavior. I don’t know exactly what to make of that, considering screen time accounted for ~60% of total time spent sedentary. I suppose it’s possible there is something uniquely bad about screen time, or it could be a statistical aberration (see the Criticisms and Statistical Musings section); however, trunk fat mass was related to total time spent being sedentary. Considering trunk fat mass is specifically associated with poorer metabolic health (6), no matter how you dice it, these are slightly troubling relationships.
If you’re like me, for many years I viewed myself as exempt from the negative effects of being sedentary because I exercised regularly. This is probably the typical view, that activity falls on a single continuum from sedentary to highly active. On one end of the spectrum you have a person with a desk job and a two-hour commute who doesn’t exercise, and on the other end of the spectrum you have someone exercising, playing sport, or doing hard manual labor daily for more than an hour. This single continuum view is implicit when using most energy expenditure equations. For example, the Schofield equation used by the WHO (the World Health Organization, not the band) multiplies your basal metabolic rate by an activity multiplier from 1.2-2.4 based on descriptions across this spectrum to determine total energy needs. In the modern world, it’s become increasingly common for lifters to be sedentary for 80-90% of the day, and then to also train an hour or more most days of the week. Likewise, for athletes in most sports, training and competition only take up so much time out of the day. But more important than the disconnect of equations with the realities of modern life, conceptualizing time spent being sedentary and time spent exercising on the same scale might not reflect physiological reality.
As I mentioned, I viewed myself as exempt from the negative effects of being sedentary (and I think most people who exercise do) until a few years ago when I started to read the more recent research on this topic. I no longer see my training as “cancelling out” my time spent sedentary; rather, I see training as a positive health signal, and time spent sedentary as a negative signal. For those interested, I covered the sizeable body of research indicating they may be independent factors influencing health in my video last month. Now, to be fair, most of the research on this topic is on individuals who are not lifting weights, don’t have a lot of muscle mass, and are higher in body fat than most MASS readers. Also, many MASS readers rank performance and/or body composition as a higher priority than health (it’s okay, just admit it). Thus, the present study provides an important link, as it is on a cohort of well-trained athletes which takes time spent training into account, and specifically evaluates the relationship between sedentary behavior and body composition. As I mentioned in the Criticisms and Statistical Musings section (it’s okay, I forgive you for not reading it), we shouldn’t conclude that this study in isolation unequivocally proves that being sedentary results in higher body fat and less lean mass in lifters. However, it’s not the only study out there on a reasonably representative population.
One proposed mechanism by which sedentary behavior has an independent negative effect on health is by blunting the acute metabolic benefits of exercise. Performing exercise typically results in lower glucose, triglycerides, and insulin after consuming meals subsequent to the exercise bout. However, one study showed that an hour of running the night prior to a high-fat, high-glucose tolerance test did not improve postprandial insulin, glucose, or triglyceride compared to doing no exercise the night prior. Importantly, this was only after the participants were sedentary for ~13.5 hours/day for four days prior. When the participants weren’t sedentary in the days prior, exercise did lower these levels following the tolerance test meal (7). Similar findings were reported among healthy men with a lower BMI (average of ~23), even when they were in a caloric deficit (8). Finally, the present study is not the first on this topic by this group of researchers. In 2014, the same group of researchers performed a similar analysis on a cohort of 82 male elite athletes who were ~14% body fat on average (9). Much like the present study, the authors found sedentary behavior predicted higher total and trunk fat mass independent of time spent training. The only caveat, that unfortunately doesn’t apply to strength athletes or bodybuilders, is that in one sub-analysis comparing athletes with a very high training load, they noted this relationship didn’t hold up. They speculated that when athletes are training 20+ hours a week, they may simply not have enough time to rack up enough sedentary behavior to be a problem. Nonetheless, ultimately, the collective data suggest that sedentary behavior has an independent negative effect on health and perhaps body composition as well.
To avoid scare mongering, I want to be clear on something. Even if you are sedentary outside of lifting, your absolute risk of having health issues is low compared to the non-exercising general populace, even if your relative risk is a bit worse than other lifters who are less sedentary (2). To help you understand just how much of an impact being sedentary might have relative to other athletes, this is where the assist from Dr. Trexler on generating the values in Table 3 comes in. As you can see, the highest estimated r2 value of 0.09 for total screen time means that ~9% of the variance in body fat can be accounted for by the amount of screen time these athletes had. Now that’s not nothing, but it also shows that other factors like training, nutrition, sleep and genetics play much larger roles. Further, the rest of the variables only explain 3-4% of the variance in the assessed body composition variables. So, rather than catastrophizing after reading this review if you’re sedentary outside of training, consider the relatively low absolute impact we’re talking about. I invite you to take a step back from the ledge and view becoming less sedentary as an opportunity to turn over a “small rock” in your ongoing quest toward self-improvement as a lifter. To take you one step further from the cliff, remember that this study didn’t assess the athletes’ nutrition. If you are sedentary but you also eat a healthy diet with sufficient protein and lots of fiber, fruits, and vegetables, you can expect the small negative impact of sedentary behavior on health and body composition to be even lower than what is represented here. However, based on the totality of evidence, it is true to say that relative to other athletes and lifters, if you are sedentary outside of training, you might be just a bit worse off in terms of health and body composition. So, what can you do about it? Fortunately, it doesn’t take that much to not be sedentary.
Up to this point I’ve quantified sedentary behavior in hours, but there are studies which quantify it as step count per day. In a similar design to the post-exercise metabolic challenge studies I mentioned earlier, Burton and colleagues used step count restriction to assess the effects of sedentary behavior on the acute metabolic effects of exercise (10). After five days of taking either 2675 ± 314, 4759 ± 276, or 8481 ± 581 steps per day, the participants performed an exercise bout and were given a high-fat meal tolerance test the following morning. When getting ~8500 steps on average, the participants had lower postprandial triglyceride levels and higher fat oxidation compared to lower step counts. Looking more broadly at the limited prospective epidemiological data on health and step counts, all-cause mortality risk in older women was notably reduced at 4400 steps per day compared to 2700 and steadily decreased until 7500 steps per day, after which the reduction in risk plateaued (11). Collectively, these two studies indicate that if you’re currently sedentary (getting 1-3k steps/day), getting up to just ~5000 steps may make a difference, and getting up to 7000-9000 steps may completely eliminate any negative effects of sedentary behavior. This can be accomplished by taking one to two twenty-minute walks per day, or less artificially by combining walks and hikes with using automotive transportation less, and walking (or biking) to any place you regularly go that’s a 15-30 minute walk (or bike ride) away, like the post office, grocery store, class, work, gym, etc. As a side note in case you’re wondering, the minimal amount of cardiovascular activity just mentioned is highly unlikely to have a negative effect on your lifting goals.
Next Steps
This is a case where there is a well-established relationship via multiple lines of evidence, but there isn’t yet a population-specific intervention to lean on to tell us how to tackle the problem effectively. While I’m convinced that sedentary behavior has an independent detrimental effect on health and body composition, even in athletes, I don’t know the most effective intervention to reliably reduce sedentary behavior in athletes (or better framed psychologically, to reliably increase their non-exercise activity). Thus, I would love to see a series of studies where researchers collaborate with sports psychologists to implement behavior change methods to determine what can reliably induce a sustained reduction in athletes’ sedentary behavior, and to see the resultant effects on health and body composition.
Application and Takeaways
Even if you regularly train hard, racking up hours of being sedentary might make it harder to be lean. The effects of exercise and the effects of being sedentary may be independent. While sedentary athletes are leaner and healthier compared to the non-exercising general public, when compared to other athletes, sedentary athletes have higher body fat and lower lean mass, independent of time spent training. Fortunately, eliminating the negative effects of being sedentary doesn’t take much; two 15-20 minute walks per day will do it for most people, corresponding to a step count between 7000-9000 steps per day.
