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Design a Treadmill Profile for Diabetes Patients (2022-US-30MP-1098)

This presentation demonstrates how to design a HIIT (High-Intensity Interval Training) profile to help a type 2 diabetes patient avoid insulin glargine injections. In addition to meal control and taking metformin and/or insulin, diabetes patients should exercise at a higher heart rate to burn sugar faster.

 

A full factorial DOE of treadmill settings (incline and speed) was conducted to build a heart rate RSM model to design the optimal HIIT profile. Based on the RSM model, interaction effects were all very small, which may indicate the treadmill heart rate model is not coupling (complicate). Heart rate is linearly proportional to incline level (potential energy when incline angle is small) and in quadratic form with speed (kinetic energy). To avoid injury to the knee/foot and ACL (anterior cruciate ligament), jumping patterns were studied using 3D motion biomechanics modeling. The fatigued muscles could not hold the knee stable or provide sufficient knee cushion during the shorter soft landing, which could increase the risk of an ACL injury during the second hard landing period.

 

By using the Model Driven SPC, the injury mechanism was studied to determine the treadmill's highest speed limit for this diabetes patient. Through these ACL risk studies, the HIIT profile has been further optimized to consider these ACL design constraints. By following the HIIT profile that was designed with JMP, this diabetic patient has seen significant reduction of blood glucose levels and serum readings (falling from over 200 mg/dL to near 75 mg/dL) in four months.

 

 

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.

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.