Research: Are We Missing Risk Factors in Rehabilitation and Performance?

How can we keep people healthy and participating in the activity they love? How do we ensure that those going back to sport or going back to work are ready, and how do we make sure they don’t reinjure?  These questions have fascinated me for much of my professional career.  Whether this has been through pre-participation physicals or return to sport/work testing, it has been my personal quest to make our current system better.

Part of the problem with researching this area is that injury risk is multifaceted and most research looks at a single variable in isolation.  It is difficult to determine injury risk in complex human beings in unpredictable environments.  That is why I am so excited to talk about the latest injury risk factor study that was published. I am excited because it is a major study from a research line that we have been working on for the past 10 years. 

So, if we all accept that injury risk is multifactorial and that multiple factors interplay to increase someone’s injury risk, then we need to be able to investigate as many of those factors as we can simultaneously. But studies that examine multiple risk factors are extremely hard to do and require large numbers of subjects with extensive follow up. 

Just doing a single factor (balance) prospective cohort study in high school basketball players across 8 high schools almost caused me to quit research altogether! The amount of time it takes to design the research, get IRB approval, get each institution’s approval, subjects’ consent, and then testing for days/weeks is massive; and you haven’t even started following the people for a year.  

But the biggest hurdle is testing enough subjects and then performing long enough follow up to have an adequately powered study to test multiple risk factors. Most studies are limited by this.  It’s the main reason there are so few studies that look at the multiple variables of injury risk, and it is one of the reasons I am so excited about the research this team was able to accomplish.   

The group includes some of the most amazing people that I have worked with. There are so many people to name so please see the author list, the acknowledgements section, and beyond that there are even so many more who helped make this possible. So here is the background…

It all started when researchers from Army Baylor (Drs. Teyhen and Shafer) came to Evansville and saw how we were categorizing large numbers of athletes based on risk factors using our research and Move2Perform algorithm. That is where this 10-year journey began.

This core team of 5 PhDs and me scoured the literature for as many risk factors for military injury as possible. The only criteria was that the risk factor needed to be tested in a field expedient and reliable manner. We really cast a wide net. 

Here are the factors that we included:

86 Survey Questions

Demographic (e.g. age, sex, education, income, smoking)

Military Specific Job (e.g. deployment, load carriage)

Fitness level (e.g. overall, running, military specific)

Current & prior injuries (e.g. number, body areas, SANE (% recovered from previous injuries))

Biopsychosocial (e.g. satisfaction, depression, anxiety, catastrophizing, fear of pain)

Physical Factors

Arch Height Index (AHI)

Half Kneeling Ankle Dorsiflexion (DF)

Functional Movement Screen (FMS)

Lower Quarter Y-Balance Test (YBT-LQ)

Upper Quarter Y-Balance Test (YBT-UQ) 

Triple and 6-meter hop tests

Pain with Any Tests

Population

We tested 1466 soldiers and then followed them for a year tracking their injuries. We separately analyzed 211 special forces (Army Rangers) and published that risk factor study here. So we were left with 966 combat, combat service, and combat service support members. 

What is interesting to me is that when people think of a military study, they think of those directly in combat (front line soldiers). Our study included those folks but also included all of the combat service and combat service support personnel — everyone from mechanics to cooks to office workers. It really represents the average population more than you would think.

We followed this group of soldiers for a year and tracked injury using direct monthly follow up with the soldiers, medical record review, and profile data. We analyzed these data to identify the most robust combination of risk factors for injury. We can break the factors into two groups: 6 factors that you can just ask the person and the other 6 require physical testing. The logistic regression identified the following risk factors:

Risk Factors From Survey Data

• Age > 26
• Sex: Female
• Prior Injury
• Perceived Recovery
• Length of Profile
• Army Run ≥ 15.3 min

Risk Factors From Physical Testing

• DF Asymmetry ≥ 4.5°
• YBT-LQ: Anterior Reach ≤ 72% Limb Length
• YBT-UQ: Superolateral Reach ≤ 80.1% Limb Length
• YBT-UQ: Inferolateral Reach Asymmetry ≥ 7.75
• Pain present with Movement

While it is interesting that each of these factors is predictive of injury, what is more important is that the number of risk factors you possess dramatically increased your risk for injury. Look at this table, the greater number of risk factors, the greater risk of injury.

The implications of the findings can’t be underestimated. Regardless of whether you are doing a wellness or pre-season physical or discharging someone from rehab, you need to check the number of risk factors present and do everything you can do reduce that number. These risk factors may or may not be related to the original injury. But we need to check them as part of our standard operating procedure for discharge and pre-participation.

If you have any questions about the study, don’t hesitate to email me here. In the next video, I will talk about our next study and some of the preliminary results we presented at CSM.

Studies Resulting from the MP3 Trial

1. Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.
2. Automation to improve efficiency of field expedient injury prediction screening.
3. Normative data and the influence of age and gender on power, balance, flexibility, and functional movement in healthy service members.
4. Association of Physical Inactivity, Weight, Smoking, and Prior Injury on Physical Performance in a Military Setting.
5. Application of Athletic Movement Tests that Predict Injury Risk in a Military Population: Development of Normative Data.
6. Incidence of Musculoskeletal Injury in US Army Unit Types: A Prospective Cohort Study.
7. What Risk Factors Are Associated With Musculoskeletal Injury in US Army Rangers? A Prospective Prognostic Study.
8. Identification of Risk Factors Prospectively Associated With Musculoskeletal Injury in a Warrior Athlete Population.