Hi everyone. I'm Mason.
Today I'll be presenting
a project on designing a treadmill exercise plan for diabetes patients.
Just to give a bit of a background as to why did this project.
In the spring of 21,
one of my family members was told that he had type two diabetes.
A follow-up report in the summer of 21
showed that his glucose level was higher than 200 milligram per deciliter,
which is much higher than the normal glucose range
of 65 to 99 milli grams per deciliter.
A t the same time, I wanted to conduct a project
to analyze exercise data,
especially because diabetes is so common across human race.
We did this project on designing a treadmill program for my family member.
A fter following this plan for a few months,
his glucose level went back to the normal range in the fall of 21.
To define our project we want to listen to the voice of the customer,
which is our doctor,
who provides his advice on what my family member should do.
The doctor suggests to see how mammals take metformin and insulin
and also exercise more intensely to burn sugar.
In this project will focus on the last piece of advice because
the other three are quite easy to follow.
But we don't quite know you know how to exercise the most efficiently yet.
We need to translate this advice to what we will do,
which is critical to quality.
Our goal is to design a treadmill program specifically
focusing on the legs, to strengthen the lower body muscle,
prevent injury and also help cure diabetes.
In more quantitative terms, we want to lower live glucose levels
to below 100 mg per deciliter and also reduce the resting heart rate.
A s healthier individuals,
usually have a lower heart rate,
since it takes more rigorous exercise for them
to lack the same amount of oxygen and have a higher heart rate.
Just introduce the team. The project leader is me.
We will have a 52.5 year old diabetes patient as the experimental subject,
who will monitor his daily blood glucose level.
Our family doctor is also in the team
who will follow up with the diabetes patient every three months.
We will also have two advisors,
a Six Sigma advisor to assist in the DIMAC framework,
and a JMP advisor who will help in the physical analysis.
To design our treadmill program,
we wanted to know how intense we should exercise.
So doing exercise for normal individuals,
it's recommended to reach 50 to 85 % of your maximum heart rate
for the exercise to be effective.
However, our patient is at a moderate to higher risk of having a heart attack.
So and that's because his calcium score was 131,
which is from the coronary artery heart attack risk assessment,
which is at the 72nd percentile for his age.
The family doctor advised to limit
the upper bound of the target heart rate to just 80 %
because a too vigorous and intense exercise can lead to heart attacks.
But to accommodate that drop in the upper limit of the target heart rate,
we also increased the lower limit from 50 % to 65 %.
Now for the exercise, specifically,
we chose to do brisk walking,
since one leg will always be on the ground and [inaudible 00:03:52]
so brisk walking helps to protect the knee and lower injury risk.
Also choosing brisk walking over running helps prevent heart attacks,
because if we do run, we may accidentally go over 80 % of the maximum heart rate.
So to determine the upper and lower balance of our target heart rate,
we have to first calculate the maximum heart rate, which is 220 minus the years.
For a 52.5 year old, the maximum heart rate would be 167.5 beats per minute.
And the upper limits for the target heart rate would be 134 beats per minute
and a lower limit would be 109 beats per minute.
As you might recall, one of our goals is to reduce the resting heart rate.
We want to lower the heart resting heart
because doing so makes the heart muscles stronger,
and as a result helps prevent heart attacks.
When we strengthen the heart muscles,
the heart pumps more blood and more oxygen is available.
So now that we've set our goals and what we'll be levering,
we can design our treadmill program.
We'll be considering three control variables.
The first is walking uphill, so whether we want to add incline or speed.
The second is HIIT or High Intensity Interval Training,
which involves a short period of intense exercise followed by a recovery period.
And we need to design this HIIT workout so that
the heart rate does not go below 65 %, but maximum high rate,
or above 80 % of the maximum heart rate.
The third variable is frequency, which is,
how many times we will conduct this exercise every week,
and also how long for each time.
To set up our experimental design,
we chose to alter two variables, incline and speed.
An i ncline has two levels, zero or five degrees,
and speed has nine levels, from zero to 3.6 mph.
Now the most vigorous level would be
at an incline of five degrees and a speed of 3.6 mph.
We don't want to increase the speed cast 3.6 mph
because that we transitioning to running, which we do not want,
since we want to focus on brisk walking,
and also don't want to exceed the 80 % the maximum heart rate.
I also want to add rests after each exercise, so that the patient returns
to resting heart rate before undergoing another treatment.
We ran stepwise regression on the response for surface design.
Our model has a pretty higher R- square of 97 % and a p-value less than 0.05.
For the studentized residuals,
which are the residuals that underground [inaudible 00:06:39] ,
only about one point goes over the green line, which is two 2 cm to the mean
and the red line represents three studentized deviations.
So jump towards the most significant variables, which include
the two main effects, both incline and speed,
the interaction term between incline speed and the quadratic term for speed.
Why did the model include the quadratic term for speed, but not inclined?
Well, if we look at the interaction profiles on the right,
we can see that heart rate has a linear correlation with incline
and occur relationship with speed.
We can explain the linear relationship between heart rate and inclined
as due to potential energy,
which is mgh, mass X gravity X height.
So height is a linear term.
When the angle of the incline is small enough, we can use the mgh approximation.
So the relationship is linear based on physics.
On the other hand, speed is connected to kinetic energy,
which is 100 squared or one half X mass X Velocity squared .
So kinetic energy has a quadratic speed term.
From the bottom two propellers,
we see that we can reach the lower bound of the target heart rate
at 109 beats per minute
at an incline of zero degrees and a speed of 2.9 mph.
In the improved [inaudible 00:08:06] ,
we won't need to include easier settings than these levels,
since the heart rate will then be too low.
So we don't want to go under the 60%.
Also, the upper bound of the target heart rate is reached at an incline of
five degrees and a speed of 3.5 mph.
So 2.6 mph is a good maximum level for speed.
We also want to prevent injury risk
in addition to managing diabetes, which is our second objective.
More than 80 % of runs are injured each year,
and some of the most common ones include
Achilles tendonitis, G splits and hamstringing injuries.
We also wanted to avoid injuries,
so we made sure that the patient was using correct form while brisk walking
by keeping their head up neck relaxed and back straights.
In addition to posture, muscle coordination is also really important
to preventing lower body injuries.
So three motion around mechanics studies
the correct angle of joints relative to each other in order to lower injury risk.
Centers allow us to measure and monitor the angles of joints
relative to each other.
We can also conduct exercise on [inaudible 00:09:19] ,
to look at which places on the feet hit the ground [inaudible 00:09:] ,
Based on the acces,
whether the runner is using correct form or not.
Just to take a quick detour in, I guess, a greater study of injury risk.
The first thing that we did to study injury risk,
is we conduct variable clustering,
which groups the different sports together.
You can see that every sport has different injury areas.
For example, cluster one and cluster three have different pattern.
So one targets the lower body,
which makes sense as it consists of basketball, soccer, foot skating,
tennis, which I'll use the lower blood extensively.
Three as more upper body injuries,
as it consists of golf, volleyball , weightlifting.
You can see that these clusters are differentiated quite well from
the principal component analysis.
Any, I guess, exercise plan that is used for running, for example,
can be modified for the other sports.
It's an efficient way of both designing access to plan
as well as studying injury risk.
The specific injury risk that we looked at
as a result of running was anterior cruciate ligament,
because it is a common injury in a lot of sports
that used to lower body muscle, such as basketball, for example.
And ACL is located at the center of the knee joint,
from the backside of the thigh bone or the femur,
to the front the shinbone or the tibia.
The image shows the three othe important ligaments of the knee,
the LCL, the MCL and the PCL.
These four ligaments are crucial to protecting the ACL from the injury,
especially the lateral collateral ligament, as well as,
the lateral and medial
which are pieces of cartilage that further cushions the ACL.
ACL injuries occur when the tibia, or the shinbone moves two foot forward
and is hyper extended, so, in other words, straining too much.
That causes the ACL to tear.
This can be caused by a variety of ways, such as sudden desolation
or pivoting in places,
or when the foot is planted and the body changes direction suddenly.
These movements are common, in basketball, I said,
but also football, soccer, downhill skiing,
and mostly this sports, of course, use a lot of running.
So we want to understand how ACL injury can be altered
before an act fatigue, specifically in the context of running,
as part of this project that focuses on running and injury risk.
To understand the connection between fatigue and ACL injury,
we wanted to conduct an experiment to measure how fatigue
and ACL injury risk are related.
We need to choose an exercise that can compare
before and after fatigue flexion and forces.
And choosing the right exercise that can accurately measure ACL injury risk
is really important because,
after we consulted with the local physical therapist,
we found that the countermovement jump
can assess the ACL injury risk quite well
through force and flexion of different body parts.
Before I go into what exactly is a countermovement jump,
let me tell you why we chose this exercise specifically.
The countermovement jump is a jump.
So it can assess how much force your knee puts on the ground.
AO nce again, Newton's third law comes into play here physics,
as the same amount of force from your knee to the ground
is experienced by the knee from the ground.
Too much force onto the ground can increase ACL injury risk
as your knee experiences too much force.
And this is how it can land awkwardly and [inaudible 00:13:33] the ACL.
In addition to force,
self coordination between flexion and extension of hip sneezes and ankles
are really important when doing this exercise.
Both force and joint flexion are connected as how well the test subject
effectively transitions from flexion extension during the exercise
is reflected in the amount of force they've put on the ground.
This is why we chose the countermovement jump,
because it enables us to compare
the before and after fatigue state for both flexion and force,
which are the two most important factors related to ACL injury.
How does the countermovement jump work?
There's five main exercise,
as you can see here, the unleaded, breaking, propulsive flights and landing.
Five images on the top,
or an example of where the test subject is at each of these phases in the exercise.
The bottom graph shows the time versus force exerted on the ground.
For the graph on the bottom, I'll focus on the top curve.
So the darkest blue curve.
As that is the total force,
whereas the two curves below it are the left and right forces.
The first phase of a countermovement jump is the unrated phase.
When the person is standing upright,
and is currently the orange portion of the graph.
Now the force briefly decreases before coming back up
as the person continues bending their knees.
When they reach maximum knee and hip flexion
at the bottom of their prejump, which is the breaking phase,
they start extending their body, which is propulsive fit.
A smooth transition from breaking to propulsive
is reflected in a smooth curve over here.
The smoother the curve, the more the knee and hip are coordinated well.
Now the flight time is when the force is zero, before the ending,
before ending the landing phase.
As you can see, the huge spike in the amount of force in the landing phase,
that is when the subject lands.
The first major peak is the soft landing.
So it's the light blue dots.
When the person lands on their toe first is a soft landing
before hitting the hard landing,
which is when the soles of the feet touch the ground.
That's the light grey dot.
Doing the soft landing period is when hip and knee
flexion can help balance the force across different body parts so that
the knee isn't the only one experiencing all of the force.
That can help reduce ACL injuries.
But if the hard landing or the second peak has too much force,
that's when there can be a greater risk of ACL injury,
as that's when the whole foot runs on the ground.
In addition to the general flexion and force patterns,
we'll be looking to see if there's any difference
in the soft and hard landings before and after fatigue.
This brings me to experimental design.
We wanted to measure the flexion of your different joints,
such as ankle, hips and knees, to study them in detail further,
as they reflect how fatigued the muscles are.
The more the muscles are fatigued, the greater the ACL injuries .
To measure those joints,
we used several different sensors that can measure all of these joints together,
and we attach them to the test subject, as seen on the right.
Two on the bilateral thigh, two on the bilateral shank ,
and two on the bilateral dorsum.
Four on the front side, and one on the pelvis for the backside.
A fter calibrating our sensors,
the test subject did ten runs of countermovement jumps.
He jump ten times on force weight to measure the force.
A fterwards he ran, squatted, played basketball jumps, did some cone drills
anything to get fatigued for an hour.
We decided 1 h would be enough fatigue
because it was pretty hot outside, when we did this experiment.
After fatigue, we put back the sensors
and he conducted the ten trials of the counter movement jump once again.
We collected our data
through a biomedical software called Meloxicam
that enabled us to simulate the different degrees
and angles of bending for several different joints,
as well as the forces on the ground.
When we look into the individual force profiles,
comparing before and after fatigue ,
we can observe even more differences in the two behaviors.
The prejump, which is the transition from the breaking simple to the propulsive,
is a lot smoother for before than after fatigue.
We can see a minor plateau during the after fatigue
which could indicate that the different body parts are not oriented as well
after fatigue.
The also for the landing period, the heart landing and the soft landing,
while our contrast is quite huge for before fatigue,
but the contrast isn't as large for active fatigue.
The soft landing is important, once again,
because only the toe touches the ground.
So it doesn't increase ACL injury risk
as much as compared to the greater force during the hard landing.
The hard and soft landing contrast isn't as great for active fatigue,
which may increase the ACl injury risk during the [inaudible 00:18:59] .
This may have been due to the muscles
not being able to hold the knee as stable during after fatigue.
So the force for the soft landing wasn't too much greater than the hard landing.
We want to know if there are any other platforms
besides a multivariate control that
we can use to help us find
at what time is the difference between before and after fatigue the most.
The multivariate SBC control chart helps us visualize the differences.
The top right corner is a screenshot of the different trials.
A ll of the six variables we study are considered in that graph.
We use the T- square chart
because it can help us detect the relationship
between the six variables that we chose.
So hip, ankle and knee flexion for both the right and left side.
The red line is the T- squared on the limit.
And outliers are points that do exceed this upper control limit.
Is a good thing,
because it means tha there's more contrast
between the jumping and the lining behavior.
If you look more in-depth in one of these specific,
I guess, [inaudible 00:20:13] spikes,
which each represent one trial for before and after fatigue
You can see that we outline five main points for one trial,
before fatigue and after fatigue,
To help visualize the differences.
The biggest difference should be in points two and four,
since two is right before the chest center leaves it there.
So it should have one of the highest flexion
because the knee are bent the most there.
Four, it should also be similarly as high as two because,
it's when the [inaudible 00:20:45] the ground
and lands on the ground, and the knees are bent the most.
You can see that before fatigue,
points two and four are way above the upper controller
and quite different from one, three and five.
But for after fatigue, the contrast is much less obvious.
We'll try to understand why that is
and connect us back to our research on ACL injury.
Discussed previously by looking at the specific contributions
of each of these flexions and joints in each of these five points.
But the multivariate control chart for now tells us specifically
that points two and four are when
the before and after fatigue defer the most.
As I said, we are going to look into the specific flexion components.
So the top portion is the before fatigue, top row.
The bottom is the active fatigue.
Now these three joints can really detect the difference
between the before and after fatigue,
because during the countermovement jump, the lower body is fatigue.
So the muscle fatigue and the different angle flexion
for the different joints is evident when we compare the contributions.
If you look at the graphs starting at one,
you can see that ankle has the greatest contribution,
where hip and knee are not so much for after fatigue.
This may be because some of the muscles are already fatigue
and only some muscles contribute to the overall flexion.
Now if we move from one to two for before fatigue ,
we see a very clear transition, permit even distribution
across all the different joints to focusing on just ankle joint flexion .
But if we two, the other knee and hip components
still somewhat flexed and haven't been able to reach full extension.
We have these 4 bar still providing some contribution.
You can see that in three, the atrophy contrast between
the knee and hip and ankle
is also not as large for after fatigue.
Again, there's not a full extension of the hips and knee.
Then for the first distribution, isn't as good for after fatigue,
as the knee and hips are already bending at the same time as ankle.
So the soft landing, which is at point 4, is in as effective.
And finally, in five, the ankle is still flexed.
It seems that the knee hip aren't able to support the body now
and rely only on ankle.
This may indicate the lower body in general is really fatigued,
and hip and knee are mostly fatigued.
As we don't see much contribution from them the fifth point.
There's less flexion for these two joints, causing a greater reliance on the ankle,
which increases ACL injury risk.
Now back to our treadmill program.
With information from injury risk, as well as the previous research on HIIT,
we can set up a HIIT workout plan.
We designed a 15 minute workout with,
the first 2 minute for warm up,
the next 12 minutes for three cycles of exercises,
consisting of 2 minute at the lower bound of the target heart rate
at zero inclined and 2.9 speed,
and 2 minute at the upper bound of the heart target rate
at five inclined and 3.5 speed.
So total of 12 minutes and then one minute cool down.
We chose relatively short time period for each exercise
so that the patient can work out for a longer period of time
without getting tired too quickly, which may have happened if,
the exercise at the upper bound of the target heart rate was done for too long.
To prove that our project is successful, we will need to validate our results.
We want to see if the workout plan helped lower the diabetes risk,
which can be seen through the glucose reading, and the resting heart rate.
Heart disease risk as well, which can be measured reducing the calcium score.
All these values to decrease if the treadmill exercise helps,
we may also want to revise the treadmill settings every three to six months,
because the resting heart rate may have decreased due to stronger heart muscles.
In that case, we may want to increase incline and speed
to achieve the same target heart rate,
since the resting heart rate is now lower due to a stronger heart.
So in conclusion, we utilize the DMAIC approach
and [inaudible 00:25:36] methods
to help the patient with type two diabetes reduce their glucose levels
while preventing them from getting a heart attack or getting injured.
We also designed an experimental plan to study injury risk,
but looking at joint flexion as well as force.
We used the DOE to designed as transplant
and from the model results,
we selected the settings at 109 beats per minute
and 134 beats per minute to be included
in a 15 minute High Intensity Interval Training workout.
So we're currently finishing the improvement control phases and which
we hope to present at a future conference.
Yeah, that's all I have for today.
Thanks for tuning in.