Spain Vs Italy Spain to be banned from participating in Euro 2024.docx
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Leveraging DXA for use in Sports Performance
1. Leveraging DXA to Understand
Body Types And Add Context To
How Athletes Move In Their
Sport
Sports Biometric Conference 2018
San Francisco, CA
Tyler Bosch PhD
sports-biometrics-conference.com 1
2. Disclosures
ā¢ Have consulted for Hologic Inc.
ā¢ Co-founder of Dexalytics a body composition analyses software
ā¢ Recovering Early Career Academic
sports-biometrics-conference.com 2
3. What is body composition?
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4. Do totals tell us everything we need to know?
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5. Lesson from other data:
Player 1 Player 2
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Players have the same jump height but much different strategies
Digging deeper into the data allows for a deeper comparison of these players
6. Lesson from other data:
Player 1 Player 2
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7. So, how can we think
differently about body
composition?
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8. DXA provides an opportunity
to rethink body composition
Advantages 3-component model
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ā¢ Accurate
ā¢ Reliable (both w/in and
btw people)
ā¢ Regional measurements
9. Breaking down the components
To add more context to thisā¦
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How can we use thisā¦
11. A quick intro:
Each red point and line
represents a
measurement
Green shaded area
represents an āideal
rangeā that is based on a
performance context set
up by the team
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16. sports-biometrics-conference.com 16
Black = Total Lean Mass
Red = Trunk Lean Mass
Green = Leg Lean Mass
+0.17 kg (+0.3 lbs.) from baseline
+1.57 kg (3.45 lbs.) from baseline
-1.4 kg (-3 lbs.) from baseline
19. sports-biometrics-conference.com 19
Total Upper Mass (lean + fat + bone)
----------------------------------------------------
Leg Lean Mass
Lean Upper (Trunk + Arms)
----------------------------------------------------
Leg Lean Mass
Variance in shifts
between the ratios
suggests a role of fat
mass
21. Looking at the data with context
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Body type and distribution when performing at his
best
Body type and distribution with decreased
performance
23. Our process for adding context to the data:
Step 1: Assess Health
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Healthy
Yes
Great let's keep them
where they are at
Rehab
Where are they at
now? (relative to
before the injury)
Chronic injury
Is there anything about
their type or
distribution that may
be contributing?
24. Step 2: Assess Performance
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Are they performing
the way they want to
Yes: Great let's keep
them where they are
at
No: Is there anything
about their type or
distribution that may
be contributing?
No: Great let's keep
them where they are
at
Yes: What areas can we
change
25. Step 3: How does body type relate to
movement patterns
When we seeā¦.
1) Asymmetry
2) Abnormal distribution
3) High/low ratios
4) Shifts in mass
5) Low BMD
6) Frame Size
We evaluateā¦.
1) Strength, power or movement
asymmetries
2) Nutrition, stress, training strategies
3) Speed and power production
4) Nutrition and training strategies
5) Other risk factors
6) Relative force and power
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Three different kinds of
asymmetry:
- 1 measure,
- Change in asym
- increase in asym
26. Step 3: How does body type relate to
movement patterns
When we seeā¦.
1) Asymmetry
2) Abnormal distribution
3) High/low ratios
4) Shifts in mass
5) Low BMD
6) Frame Size
We evaluateā¦.
1) Strength, power or movement
asymmetries
2) Nutrition, stress, training strategies
3) Speed and power production
4) Nutrition and training strategies
5) Other risk factors
6) Relative force and power
sports-biometrics-conference.com 26
27. Step 3: How does body type relate to
movement patterns
When we seeā¦.
1) Asymmetry
2) Abnormal distribution
3) High/low ratios
4) Shifts in mass
5) Low BMD
6) Frame Size
We evaluateā¦.
1) Strength, power or movement
asymmetries
2) Nutrition, stress, training strategies
3) Speed and power production
4) Nutrition and training strategies
5) Other risk factors
6) Relative force and power
sports-biometrics-conference.com 27
28. Step 3: How does body type relate to
movement patterns
When we seeā¦.
1) Asymmetry
2) Abnormal distribution
3) High/low ratios
4) Shifts in mass
5) Low BMD
6) Frame Size
We evaluateā¦.
sports-biometrics-conference.com 28
29. Step 3: How does body type relate to
movement patterns
When we seeā¦.
1) Asymmetry
2) Abnormal distribution
3) High/low ratios
4) Shifts in mass
5) Low BMD
6) Frame Size
We evaluateā¦.
1) Strength, power or movement
asymmetries
2) Nutrition, stress, training strategies
3) Speed and power production
4) Nutrition and training strategies
5) Other risk factors
6) Relative force and power
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30. Case 1
Assessment
Step 1: Healthy ā but, has an
anatomical abnormality in one ankle
Step 2: Performing well, needs to
improve overall technique
Step 3: Large asymmetry, likely
influenced by the anatomical issue.
Does it affect function?
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31. How did we evaluate?
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32. Case 1 Conclusion:
ā¢ Does not appear to be any functional asymmetry associated with the
mass asymmetry.
ā¢ This pattern is likely who this athlete is and influenced by an
anatomical issue.
ā¢ Will monitor for compensation or complications
ā¢ Evaluate again in a few months
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33. Case 2: Optimal body types
vs traditional ānormsā
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ā¢ He has to be xxx weight to
play xxx position
ā¢ We need to add xxx amount
of weight to this player
34. Case 2: Optimal body types
vs traditional ānormsā
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ā¢ He has to be xxx weight to
play xxx position
ā¢ We need to add xxx amount
of weight to this player
ā¢ Optimal weight is player
dependent
35. How does mass distribution change?
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20
40
60
80
100
120
140
200 250 300 350
Weight (lbs)
TotalFat(lbs)
ā¢ Fat mass proportionally
36. How does mass distribution change?
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20
40
60
80
100
120
140
200 250 300 350
Weight (lbs)
TotalFat(lbs)
ā¢ Fat mass proportionally
ā¢ Lean mass begins to plateau
160
180
200
220
240
200 250 300 350
Weight (lbs)
TotalLean(lbs)
37. How does mass distribution change?
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20
40
60
80
100
120
140
200 250 300 350
Weight (lbs)
TotalFat(lbs)
ā¢ Fat mass proportionally
ā¢ Lean mass begins to plateau
ā¢ Abdominal/Visceral Fat
exponentially
160
180
200
220
240
200 250 300 350
Weight (lbs)
TotalLean(lbs)
0
2
4
6
200 250 300 350
Weight (lbs)
VisceralFat(lbs)
38. How does mass distribution change?
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In-season weight changes are associated with abdominal fat increases.
39. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
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40. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
sports-biometrics-conference.com 40
41. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
sports-biometrics-conference.com 41
42. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
sports-biometrics-conference.com 42
43. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
sports-biometrics-conference.com 43
44. Linking mass with function
ā¢ Is a mass asymmetry a
functional asymmetry?
ā¢ How does mass relate to
force production?
ā¢ How do mass ratios
affect power?
sports-biometrics-conference.com 44
45. Future directions: evaluation of on field
movement patterns with different body types
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ā¢ How do distribution patterns
affect movement strategies?
ā¢ Do distribution patterns
affect the ability to handle
force?
Iād like to thank the conference organizers for the opportunity to present this research.
First off what is body composition, itās a measure of what our body is made of, and generally is thought of as percent body fat. Percent body fat is the ratio of total body fat mass to total body mass. The problem with this is that it focuses on totals and misses an important aspect of composition which is the distribution of mass.
Letās compare some force plate data for two athletes. Looking at jump height, we see both players are relatively equal. However, as we dig deeper into the data we identify that that different components of the data begin to tell a different story. There are clear differences in how each athlete achieves this jump height. Comparing the relative propulsive (power) accounting for weight, (which in reality is accounting for weight twice). And braking rate of force development player 1 starts to separate himself from player 2 a bit. These factors are important when looking at training strategies or tracking players over time and they may be missed by just looking at a total outcome.
Letās compare some force plate data for two athletes. Looking at jump height, we see both players are relatively equal. However, as we dig deeper into the data we identify that that different components of the data begin to tell a different story. There are clear differences in how each athlete achieves this jump height. Comparing the relative propulsive (power) accounting for weight, (which in reality is accounting for weight twice). And braking rate of force development player 1 starts to separate himself from player 2 a bit. These factors are important when looking at training strategies or tracking players over time and they may be missed by just looking at a total outcome.
DXA (or dual x-ray absorptiometry) provides an option for looking at the distribution of masses in athletes. DXA is a 3 component method that measure fat, lean soft tissue and, bone masses. Following standard procedures for measurement, DXA is precide, reliable both within and between people. (A critical component for comparing people) because the assumptions are applicable across populations)
Finally regional measures are also accurate and reliable. Regional measures allow for comparison and distribution patterns to be examined.
So how can we use the lessons from force plate data and apply them to body types and composition. We break the totals into different parts and look at how we get to those totals.
We all know that data without context is really just numbers. Knowing any specific value without underlying context of how it was measured/collected/and relates to performance doesnāt add a lot. Context is king so lets look at a retrospective example of a player.
What does this plot mean what do the lines and colored area represtent. A way to quantify body types in relation to performance in a sport and positions specific way
We see the weight dropping (only about 1 pound from the scan closest to the injury and after the injury), but the lean mass dropped nearly 10 pounds during that time. That is a significant amount that canāt be seen by just looking at weight and means that mass must have increased from somewhere
Total fat increased 9 pounds during this period thus the 1 pound of weight drop. This isnāt necessarily a bad thing during the the rehab process, but its important to know as you start training again.
So we saw a large drop in total lean mass. Where did that come from? Legs Trunk mass both? We see that the legs and Trunk mass decreased during this time. The important information is shown in that Trunk lean mass recovers and increases after the injury. Whereas leg mass recovers initially but than steadily decreases at one point going lower than the injured measurement. So essentially this athlete recovered nicely prior to RTS, but then shifted slowly after that point.
Again what does this look like
Talk about how we can use our retrospective example to understand current data.
We want to identify are these issues resulting in compensatory patterns or reduced function i.e. asymmetry in strength, power
We want to identify are these issues resulting in compensatory patterns or reduced function i.e. asymmetry in strength, power
We want to identify are these issues resulting in compensatory patterns or reduced function i.e. asymmetry in strength, power
We want to identify are these issues resulting in compensatory patterns or reduced function i.e. asymmetry in strength, power
We want to identify are these issues resulting in compensatory patterns or reduced function i.e. asymmetry in strength, power
Allows us to evaluate the movement patterns in the sport
Rowers will perform hundreds more reps in their boat compared to the weight room so it made sense to use Athos in order to get an estimate of activation asymmetry between legs
