What I Learned About Injury Rates from Surveying 1,900 Powerlifters

The most surprising finding of this analysis was that no training variable meaningfully predicted injury risk, including weekly training volume, per-lift training frequency, or proportion of training with loads in excess of 85% of 1RM.
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Last year, I sent out a survey just to get a big picture overview of the state of powerlifting.  I meant to publish the results a long time ago, but never quite got around to it.  Luckily, my friend Andrew Patton has been going through the data – specifically the data on injuries (Andrew is a part of Cartolytics, a data science and spatial analysis consulting group; if you need help with data analysis, you should definitely get in touch with him).

This article is a cross-sectional analysis of the injury data that we collected.  Unfortunately, it probably wouldn’t be publishable in a journal because I wasn’t specific enough in the way I phrased the questions (I’m not an epidemiologist; live and learn, I suppose). Still, this article should still be interesting and useful, and it gives us a starting point for a more rigorous analysis.  Andrew and I are currently collaborating on a more thorough prospective study on the injury risks of powerlifting.  We’ll keep you guys updated on the results as we’re able.

First, some background about these data:

I asked if people had ever experienced an acute injury when training.  If they had, I asked what types of injuries they’d sustained.  The strength of this approach is that it allows you to get data on less-common types of injuries (for example, if you follow 100 people for one year, you may never see a neck injury or a triceps injury).  The drawbacks are that people may not remember old injuries (general recall issues you run into with any retrospective analysis), the frequency of each injury may be off (i.e. if someone had one acute pec injury and 3 acute SI joint injuries, we know they injured both their pec and their SI joint, but we only register one person with an SI joint injury versus 3 total SI joint injuries), and we can’t quantify injuries per 1,000 hours of training to compare the injury risk of powerlifting to other sports (we know what proportion of these lifters have been injured, but not how frequently injuries occur).

Here’s the big picture overview of the current analysis:

  1. Men were much more likely to have sustained an acute injury than women; roughly ⅔ of men had sustained at least one acute injury in their training career, whereas only about ½ of women had sustained at least one acute injury.  This held true even when controlling for training age and competitive success.
  2. Unsurprisingly, people who’d been training longer were more likely to have sustained an acute injury than newer lifters.
  3. Lifters who had sustained at least one acute injury were relatively stronger (assessed via allometric scaling), competed more frequently, and were more likely to have previously dealt with a chronic injury than lifters who had never sustained an acute injury.  All of those factors were also (unsurprisingly) associated with time spent training, but were still independently predictive of having experienced an acute injury, meaning stronger lifters and lifters who competed more often were more likely to have experienced an injury regardless of training experience.

The most surprising finding of this analysis was that no training variable meaningfully predicted injury risk, including weekly training volume, per-lift training frequency, or proportion of training with loads in excess of 85% of 1RM.

 

 

 

 

If you’re interested in the complete results, I’ll let Andrew take it from here.

Abstract

Context: Alteration of training style and methodology based on skill level and gender may change the risk of acute injury for competitive raw powerlifters.

Objective: To determine training-based risk factors for acute injury in competitive raw powerlifters.

Design, Setting, and Patients: Cross-sectional analysis of 1,543 responses to an online survey directed at competitive strength athletes on Facebook and Reddit. The survey was administered from April 10, 2016, to April 15, 2016. Acute injury was defined by answering in the affirmative to “Have you ever sustained an acute (sudden injury/pain) injury directly resulting from your training?”

Main Outcome Measure: Odds of having an acute injury resulting from powerlifting training while controlling for training age, sex, and absolute load.

Results: 63.9 percent of the respondents reported having an acute powerlifting-related injury during their powerlifting careers. 66.9 percent of men sustained an acute injury while 49.2 percent of women sustained an acute injury (p < 0.01). Multivariate risk factors positively associated with the odds of an acute injury included increased training age (p < 0.05), having a chronic injury (p < 0.01), increased frequency of competition (p < 0.05), and increased level of lifting ability (p < 0.05). The risk factor negatively associated with the odds of having an acute injury was being female. No significance was found with any variable associated with training style or methodology.

Conclusions: While the cross-sectional nature of the data set removes the ability to analyze temporality of exposure and outcome, it is somewhat surprising that no training methodologies were associated with the odds of injury risk. All other positive predictors are generally associated with length of training and load lifted. The cause for significant decrease in odds of having an acute injury for women was not determined and warrants further inquiry, although possible explanations include generally larger natural range of motion and better decision making.

Background

There is a substantial body of literature regarding the injury rates (injuries/hour played) in athletics at the recreational and professional level. However, these are generally “stick and ball” or Olympic sports. The few studies that have studied strength sports have found injury rates generally comparable to team sports. However, with the explosion in the popularity of recreational strength training in the United States in the recent years, characterizing the actual potential causes for acute injury is necessary. This analysis is far easier in strength sports, and particularly powerlifting, as the training is fully quantized with discrete numbers of sets, repetitions, and load. Anecdotally, there is evidence that extreme volumes and/or extreme intensities can produce injuries, but there is little established data. This study will attempt to characterize specific demographic and training variables that are plausibly related to acute injury risk in powerlifters.

Methods

In early April 2016, survey responses were solicited from Facebook and Reddit. Approximately 1,900 responses were collected in total. Questions covered a wide range of standard demographic information and a variety of training, injury, and lifestyle-related factors as well. The outcome, acute injury, was assessed by the dichotomous answers to “have you ever sustained an acute (sudden injury/pain) injury directly resulting from your training?” Respondents were excluded from the study population if they did not record injury status, age, sex, bodyweight, or a full three-lift total. In addition, respondents were excluded if they were ever users of anabolic steroids/PEDs or recorded exclusively non-raw totals. Following application of inclusion criteria, 1,543 responses were retained for analysis.

Given the dichotomous nature of the outcome, logistic regression was used to model the relationship between predictors and odds of acute injury. Certain model parameters including sex, strength levels, amount of training over 85% of 1RM, weekly volume, and weekly frequency were chosen a priori, based on biological evidence and experience in the field. Strength levels were categorized via adjusted strength score and standardized to results from 2016 Raw Nationals to develop rankings to make sexes comparable. As an example, men and women who had ASS scores in the same percentile (when compared within sexes) were placed in the same strength category.  Full population characteristics are presented in Table 1.

Table 1: Study Population Characteristics

Following univariate analysis, training age, strength category, competition frequency, chronic injury, weekly volume, and weekly frequency were found to have significant (p < 0.05) relationships, whereas the amount of heavy training over 85% of 1RM was not significant. During the course of the univariate analysis, a spline term for training age was created at greater than two years of training. With the full model, backward and forward stepwise regression was performed, returning the same results. The goodness of fit was assessed with Pearson’s Chi Squared (p > 0.05) and found to be significant with a mean variance inflation factor of 1.08. The odds ratios resulting from the regressions are presented in Table 2.

Table 2: Regression Results

Results

The regression results indicated that being a female was negatively associated with the presence of acute injury, whereas increased training age, increased competition frequency, increased strength ability, and the presence of a chronic injury were found to be positively associated with the presence of acute injury. Neither volume, frequency, or percent of training above 85% of 1RM were present in the final model. When comparing standardized Pearson residuals against the probability of acute injury, male outliers trended negative and female outliers trended positive. The residuals are plotted in Figure 1.

Figure 1: Standardized Pearson Residuals by Probability of Acute Injury and Sex

Discussion

While interesting and somewhat counterintuitive to conventional thinking, the results from this analysis should not be overinterpreted. The issue of temporality is a major concern. The exposure in this situation, training protocol, is likely to be adjusted either temporarily or permanently post-acute injury, and the wording of the questionnaire did not capture this nuance. Furthermore, the respondents gave fairly broad answers to their definition of an acute injury from a strained muscle to severe ligament and orthopedic injuries. Nonetheless, the primary outcome of interest from this analysis was the significant differential in predicted acute injury probability by sex across all training age levels, as seen in Figure 2.

Figure 2: Predicted Probability of Acute Injury by Training Age and Sex

It appears that to some degree, developing an acute injury is merely a function of time, in that 10 years of training creates more individual repetitions and chance for a random or fluke injury. Within that gradient though, the substantial probability differences based on sex is immediately apparent. This difference is hypothesized to be caused by one of three factors. First, women tend to have greater flexibility and range of motion, which could be protective to muscle and joint injuries associated with compromised movement patterns when performing exercises. Secondly, women handle lighter loads, which results in less absolute systemic load. Lastly, women tend to make fewer exceptionally poor choices regarding weight selection in training. However, further research is needed to validate these claims.

Acute Injury Study Part II

In an attempt to create a more robust and detailed analysis, a second study is under way. It’s a prospective study, requiring participants to fill a short survey every month for one year on their training, injury status, and lifestyle. 

We’ll keep you guys updated on the results as we’re able.  Complete results should be in by October, 2018.


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46 thoughts on “What I Learned About Injury Rates from Surveying 1,900 Powerlifters”

  1. Great article, glad to see you hitting this topic! I’d be very interested in seeing the role other factors play in injury rate, things like time spent warming up and warming down, sleep preceding injury, diet / supplements… I know you could go way overboard with it, but I’m inclined to believe some injury is related to factors like these.

    1. Hey Walker – we certainly are interested in those factors as well. Our follow-up study is planned to include those topics, so keep checking back here over the next several months to see what we find.

  2. Interesting read, thank you.

    Word of caution:
    Under ‘ Background’ you state as a reason for doing the study:
    ‘However, with the explosion in the popularity of recreational strength training in the United States in the recent years, characterizing the actual potential causes for acute injury is necessary. ‘

    Then deciding to restrict the study to a population of competitive powerlifters only needs to be emphasized much more for obvious reasons.

      1. Let’s then abstain from the fact that you stated ‘competitive’, we thus agree that it’s a subcategory, i.e. a restriction. Could you please explain which overall population you targetted when you wrote ‘explosion’?

        Also, the popularity is mainly through Crossfit. And there this topic is obviously very relevant.

  3. Some parts of the article stated that load, volume, and frequency didn’t appear to be predictive of acute injury but in other sections “total repetitions” over a career was given as a possible risk, and “lighter loads” as a reason why women may sustain less injuries. Are these not at odds? Or is Andrew simply stating that despite load, volume, and frequency not appearing to be predictive, the possibility still remains that they are? Thanks for the date and article either way

    1. For intensity, it’s the difference between absolute and relative intensity. Relative intensity is what we looked at here (amount of training above 85% of 1RM), but absolute intensity (total weight on the bar) would be different between men and women. For time spent training, it’s just a matter of risk exposure. If volume doesn’t really influence your odds of getting injured in a given session too much, each session would still be an instance of risk exposure, and you’ll have more of those exposures over time.

  4. Interesting read!

    As a noob research reader, there’s just a few things that confused me.

    In the results, it was stated:
    “Following univariate analysis, training age, strength category, competition frequency, chronic injury, weekly volume, and weekly frequency were found to have significant (p < 0.05) relationships"
    Then after it was stated :
    "Neither volume, frequency, or percent of training above 85% of 1RM were present in the final model"

    I understood that, weekly volume and frequency was not a significant determinant for injury. But I got confused with the follow up statement
    "Neither volume, frequency, or percent of training above 85% of 1RM were present in the final model"

    1. That’s a good question. Essentially, with a univariate analysis, you’re looking to see if one single factor impacts the odds ratio (in this case, the odds of having been injured if one criterion describes you, vs. the odds of having been injured if another criterion describes you). Volume and frequency were both found to have significant effects (p<0.05) when analyzed independently, but as you can see, they didn't actually have much of an independent effect (an OR of 1.0 means no effect; their ORs were 1.01 and 1.05) – essentially, the only reason there was a "significant" effect was that we were working with a pretty large sample.

      With the full model, you're asking whether each successive factor has a significant effect once other factors are already accounted for. In this case, once already accounting for sex, training age, competition frequency, etc., also accounting for volume and frequency didn't significantly alter the model, likely because they covaried with some of the other factors that had larger independent effects we'd already accounted for.

  5. Kind of dumb thing, but you just started grad school so it didn’t hurt to ask: did you get IRB approval for this new study? Just asking because the form doesn’t look like the standard IRB boilerplate. If you don’t have an IRB approval you might not be able to submit to many journals. It’s just survey data, but still human subjects research.

    1. Andrew Patton is a graduate of the University of California, Berkeley, where he earned a B.S. in Molecular Toxicology. He also holds an M.S. in Environmental Management from the University of San Francisco with an emphasis in Water Management and a certificate in Geospatial Technologies. Currently he is attending Johns Hopkins Bloomberg School of Public Health, where he is in the Exposure Sciences and Environmental Epidemiology PhD track.

  6. This isn’t related to the article, but I have a question (and I’d even be willing to pay for advice): How can someone know if they’re “ready” to take steroids? I want to be 90% sure I’ve maxed out my natty gains before using, but I’m finding it pretty difficult to determine where that is.

    Right now, I’m 22 years old, 5’11”, 175lbs morning weight post-defecation, and 10% bodyfat (I’m going to get it checked by a DEXA soon), which means I’m only at a 22FFMI, even though I’ve been training and dieting for eight years. Ideally, I want to end up doing some fitness modeling at 210lbs sub 10% bodyfat (somewhere between Steve Cook and Frank Zane in terms of size), but the gains are SLOW. And part of me is worried that, even with steroid use, I won’t be able to get there.

    Still, I’ve been training near-optimally for so long that it doesn’t make sense to make any rash decisions just because I’m impatient.

    TL;DR Casey Butt says my MAXIMUM natural muscular potential is 200lbs at 10% bodyfat, but I don’t see how I could ever accomplish that. Is there a MINIMUM that you would recommend before someone start juicing?

    1. I’m really not the person to ask about that, but the typical advice I’ve heard from people who are much more in that world is:

      1) Don’t do it until all growth and maturation is taken care of (mid-20s to be safe), and you’ve been training seriously for at least five years.

      2) Make sure you’re well-informed about the benefits AND the consequences, and have a good knowledge about how to use gear as safely as possible first.

      1. Thanks, Greg. And I know you’re not exactly an expert on steroid usage, but you’re definitely one of the most knowledgeable resources in the fitness industry I can contact — and you definitely know your fair share. I’m just having trouble finding reputable information on how to get the most out of your health and training career (for people with the desire to go down that road).

        Compared to most users I know, who started pinning in high school, it’s not something I’m treating lightly.

        But it’s uniquely dangerous when most of my “expertise” is pieced together from blog and forum posts. With 1-3 million Americans admitting to previous steroid usage, you would think SOMEONE would step up to talk about how to use in the safest way possible, working alongside a medical health professional. Eventually, I want to be that someone (not until a minimum 600 deadlift, 500 squat, 345 bench and 23.5-24FFMI because I think that’s within reach, but I don’t want to set those goals too low). I’m just sick of seeing subpar meatheads like John Doe Bodybuilding and Victor Pride lead the discussion on AAS, saying little more than “it’s not THAT bad for you.” It doesn’t make sense; it makes us all look like morons.

        Do you have any books you would recommend I read? Or any resources?

        1. I mean, the issue is that it’s illegal, so it’s very rare for a study on steroids to clear an IRB, and thus there’s very little human data from intervention trials. There’s a lot of cross-sectional data, but with that, it’s hard to know whether the steroids are the differentiating factor, or if users and nonusers simply differ in systematic ways. And, of course, those studies just compare people who’ve done gear to people who haven’t – there’s no control for people who’ve been “smart” about the way they’ve used.

          1. Thanks a lot, Greg! After reading the relatively recent “Single-Dose Testosterone Administration Impairs Cognitive Reflection in Men,” I’ve done a 180. I want more research done on the cognitive effects of steroids before I reconsider.

            I was also under the incorrect assumption that steroids were legal in Thailand (and not just incredibly easy to buy).

  7. Hi Greg,

    If you need help from an epidemiologist with your research questions, I’m your man.

    That being said, unless the questions are really trash, I’d be surprised that alone would disqualify the paper. You just might not get into top tier journals.

    Cheers,

    Carl

    1. Nah, they weren’t trash, but I know we can do better. The bigger issue with me is that we can’t estimate injury rates per unit of time, and that the stuff assessing current practices may not reflect conditions at time of injury.

  8. Quick question on the methodology for calculating injury rates — is an hour of weightlifting 1) an hour under tension, or 2) an hour in the gym with some rest assumptions? Thanks!

  9. Hi there! I’m wondering if it is possible to isolate if injuries that occur during powerlifting are due to age-related factors, or are because older competitors have (theoretically) just spent more time under the bar and are therefore statistically more likely to sustain injury? Great study. Can’t wait to hear more. Thanks!!

    1. We’re actually running a prospective study right now that will (hopefully) help answer than question!
      In this case, though, we were just asking if they HAD ever sustained an injury, which is probably just a matter of total time spent under the bar.

  10. Hey Greg,
    My forearm hurts when i do barbell curls. When i am doing them, it is generally ok but when i put the barbell down after the set, i get a sharp pain in the bone of the forearm, the wrist is ok though. It kinda feels like forearm splints. What could it be?
    Will dumbbell curls provide relief and do dumbbell curls provide same amount of gains as straight barbell curls?

  11. Hey Greg,
    Recently when i was squatting atg for a 5rm, i felt something in my left side of mid-lower back kinda tweak although i cannot exactly describe how it felt . I was able to do some more backoff sets and finish the workout but after the workout, the pain has increased. Now it hurts whenever i try to do a situp or raise my left leg. The pain is not going into the leg or buttocks so i think might not be a disc issue. I did warmup for the set, although i think i could have done it better. What do you think this is and what do you suggest to heal it? Its been two days and the pain is still there.

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