Changes in Wheelchair Cushions Over Time

Stephen Sprigle, PhD PT

Study Principals (alphabetically)

• Laura Cohen PhD, PT• Kim Davis, PT• Cami Godsey• Michelle Nemeth, PT• David Rivard

Wheelchair seat cushion degradation study

Wheelchair seat cushion degradation study

• To document the state of wheelchair cushions after everyday use

• To determine changes in state and performance over time

Data sources

• Interview• Physical Examination

• Dimensioning• Visual Inspection

Data Sources

• Human pressure mapping

• Rigid model pressure mapping

202 Subjects• 65:35 Male: female• Age

– 46 (20-78)

• Weight– 173 (82-334)

• 44% paraplegia• 35% tetraplegia• 7% Multiple sclerosis• 6% Spina Bifida• 7% Other

Cushion Descriptions

• Avg Age: 32 months– ‘New’→ 196 months (16 yrs)

• Hours sat upon per day– Mean= 12– 1 → 22 hrs

Cushion Descriptions

Repeat visits

• 10 cushions were measured 4 times• 30 cushions were measured 3 times • 51 cushions were measured twice

345 visits in total

Tons of variables collected

Self report and Inspection variables

• User information– Height, weight, age,

gender– Diagnosis– Skin hx– Pelvis asymmetry– Wheelchair type

• Equipment usage– Transfer technique and

frequency– Environmental exposure– Stressful activities

• Cushion information– Model and manufacturer– Material construction– Age and daily use– Condition of cover– Condition of cushion

components

Stressors and Exposures

• age of cushion• hours sitting on cushion• # transfers/day • exposure to hot/cold

temperatures• exposure to rain/sun weather• exposure to moisture; shower• is exposed to sparks from

matches, ashes, smoking, machinery, grinding, or welding

• incontinence/week

• High impact activities such as rough transfers

• Full body dropping onto cushion verses sliding smoothly onto the seat

• Transfers from different heights • Curb jumping • Vibration from rolling over rough • Sports • Cushion is used as a seat in a car, plane,

restaurant, movie theater, etc• Cushion exposed to pets sitting upon and

being clawed. Do they shed their fur or have they relieved themselves on the cushion?

Model and Human IPM

• Total Pressure• Mean Pressure• Contact Area• Peak Pressure Index (PPI)• Total Pressure_IT region• Dispersion Index (DI)• Seat Pressure Index• Asymmetry-Total Pressure • Asymmetry- PPI

Cushion Inspection

Cover• condition of seams• condition of fabric• condition of zipper• condition of pocket• condition of attachment

Cushion• condition bladder• condition valve• condition of internal seams,

sewn or welded structure• visible tears, breaks, or

fractures of any component• amount of interior

component discoloration

cushion's cleanliness• 0 = Like New • 1 = Very Clean• 2 = Clean• 3 = Moderately Unclean• 4= Very unclean

<12 months

12 to 36 months

>36 months total

clean 81% 73% 72% 75%not clean 19% 27% 28% 25%

Clean

Not clean

Each category , n>80

Cover inspections

damage to the seams 0.227

damage to fabric 0.425

damage to the pocket 0.073

damage to the attachment 0.032

damage to the zipper 0.077

• 247 inspections were done on cushions with a cover, • 168 of those inspections were on covers with zippers. • The probability of damage to certain components is

shown below

Data allows calculation of the prevalence of different signs of wear

Gives insight into what wears out and how often

153 foam cushion inspections

• 21% foam cushions with visible damage• 56% foam cushions showing discoloration• 21% foam showing granulation• 36% foam showing brittleness

87 inspections of cushions using viscous fluid

• 9% damage to seams or welded components• 20% showing visible tears or breaks

114 inspections of cushions with air bladders

• 16% air cushions with damage to bladder• 6% air cushions with damage to valve• 4% air cushions showing damage to seams or

welded components

Material Comparison155 inspections of foam components % showing damageVisible Tears, Breaks, or Fractures of any Component .21Interior Component Discoloration .56Foam granulation .21Foam brittleness .36

114 inspections of air % showing damageair cushions with damage to bladder .16air cushions with damage to valve 0.06air cushions showing damage to seams or welded components

0.04

87 inspections of cushions using viscous fluid % showing damagedamage to seams or welded components 0.09showing visible tears or breaks .20

• Cushion age calculated in hours– Age (days) * Daily use (hrs)

Dichotomized 2 manners• 12 months: defined as 4320 hrs of use

– 12 hrs/day for 12 months with 30 days/mo• 36 months: defined as 12960 hrs of use

– 12 hrs/day for 36 months with 30 days/mo

Foam cushion damage

Compared to cushions ≤12 mo old, those in use

>12 months were• 7.2 times more likely to

show Visible Tears, Breaks, or Fractures of any Component

Compared to cushions ≤ 36 mo old, those in use >36 months were

• 1.6 times more likely to show Visible Tears, Breaks, or Fractures of any Component

This means that significant degradation occurs between 1 and 3 years

That was inspection

Now, look at performance

Model and Human IPM• Total Pressure• Mean Pressure• Contact Area• Peak Pressure Index (PPI)• Total Pressure_IT region• Dispersion Index (DI)• Seat Pressure Index• Asymmetry-Total Pressure • Asymmetry- PPI

IPM and Age: the relationship is not simple

≈3 years

≈3 years

Usehrs = age(days) * daily use (hrs/day)

Examples illustrate complexity

60 months old

80 months old

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025400162290002418000

1535150133034231401043221200

30432203349674841141850301120

4968511324496455508850512070

53725072635404022111345412272

43387174102623312228472719124

3557416310766495054734734352097

4934555444453737465752372226223

29456044285730305334393831152312

45505659533139353637343530402012

3947505157312536373834362948299

46404142546722443234384038302220

46404139444934472735384527233318

28343638462931343234312332323120

36453331272830412521153347302133

Minimum

200

mmHg

0secs 1-1-2-3-4-5-6-7-8-9

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

Regional distribution (%)

Comfort index (%)

0.00

106.80

29.93

349.92

18.71

62.51

6.99

6.80

256.00

100.00

74.99

0

20

40

60

80

100

120

140

160

180

200

54 months old

0000000000000000

0300000000000000

00183034193000005000

01880555855503224191741572030

060575169504943274426436044260

049367068585571615338575250489

1516681886871625450314455504324

0546075828663453559445142514423

0535562645359428253553752382619

825525559425140843644675636250

78353638423949181344636334130

11112192947332822234727282290

5715161720171214112223262770

17121580000018161400

25880000001116321960

18400000031929584120

Minimum

200

mmHg

0secs 1-1-2-3-4-5-6-7-8-9

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

Regional distribution (%)

Comfort index (%)

0.00

88.05

25.79

611.44

24.73

95.89

7.78

8.23

256.00

100.00

8.23

0

20

40

60

80

100

120

140

160

180

200

12 months old

000232600005110000

07323146391502839361410000

52945504550531652544552441600

323835545165612256625756391880

3849475364705238616260474327110

4146537469633934465351476139240

4143536669564344636869615438240

2840456450465449606153605756300

243551505346464753504949673670

2332313848453635493544403236270

2531372842198682242373437200

133123362432232127333745373170

61620212114102313202032323500

015201913100126121724251400

1512920070491619800

018300010001214000

Minimum

200

mmHg

0secs 1-1-2-3-4-5-6-7-8-9

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

Regional distribution (%)

Comfort index (%)

69.15

69.15

69.15

0.00

0.00

0.00

10.50

9.50

1.00

0.96

100.00

69.04

69.04

69.04

0.00

0.00

0.00

4.50

10.50

1.00

0.96

100.00

0

20

40

60

80

100

120

140

160

180

200

196 months old

007015401330101114160

1013354038341516474354473260

65164257864540486482755444220

1518364037453848297286825338260

4629454443334344445767805749300

4534455651383456583648645048372

3842413743393544333046444768509

37323531364140362540383034485314

2930302932393230363632334134313

2320282730333024262629233754216

18132925262514321720222934381614

282415151914532181822314450

192024168710041115402200

24162324143000122440361700

111123226000072129361700

758110000011817161000

Minimum

200

mmHg

0secs 1-1-2-3-4-5-6-7-8-9

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

Regional distribution (%)

Comfort index (%)

0.00

85.93

26.43

378.13

19.45

73.58

8.12

9.32

256.00

100.00

52.84

0

20

40

60

80

100

120

140

160

180

200

48 months old

08193242534557514853545339330

12122151526249454745535151312214

442528342425203141130304440130

584143364768248285055282014210

54594760807433312885686053385312

7574424775491542559535756256131

5849353652261512255494456206042

434120151270035192636224127

40283725344132174031323938584724

32362228243122244438244355333839

34212425242334404426373252501417

2115262322171420203539392153200

101219191513121025342338352550

0581210711128302328241700

004643003118292322400

000550002919302415000

Minimum

200

mmHg

0secs 1-1-2-3-4-5-6-7-8-9

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

Regional distribution (%)

Comfort index (%)

0.00

85.21

28.89

367.03

19.16

66.31

8.04

9.46

256.00

100.00

67.38

0

20

40

60

80

100

120

140

160

180

200

60 months old

Sometimes, inspection reflects performance

Sometimes, it doesn’t

Model IPM data designed to provide consistency

0000102112000000000

000327393783202270123702100

0005199801308215951401801295520

0068110711714811650128202142717310

00085106145143131271211981929974100

000898118126127291121281171144600

0000270699571191078634000

0000000000000000

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0000000000000000

0000000000000000

0000000000000000

Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

0.00

201.99

23.06

2127.67

46.13

200.07

8.68

12.04

256.00

0

20

40

60

80

100

120

140

160

180

200

0000000000000000

0009221700014120000

004871101857405689857444000

0041119110182801311111816917052000

0020102174137476721592009520000

002491149200115121021881046247000

000591151201431912816574828000

00004202903026710000

0000000000000000

0000000000000000

0000000000000000

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0000000000000000

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Minimum (mmHg)

Maximum (mmHg)

Average (mmHg)

Variance (mmHg²)

Standard deviation (mmHg)

Coefficient of variation (%)

Horizontal center (in)

Vertical center (in)

Sensing area (in²)

0.00

200.00

19.97

1986.00

44.56

223.13

8.02

12.13

289.27

0

20

40

60

80

100

120

140

160

180

200

27 months 43 months

MeanPress ContactArea PPI PPIasymm27 months 76.4 0.049 166 0.7043 months 73.1 0.052 160 14.1

Model IPM

A lot of variables, a lot of variance

• In principal components analysis (PCA), one wishes to extract from a set of p variables a reduced set of m components that accounts for most of the variance in the p variables.

• Goal: to reduce a set of p variables to m components prior to further analyses

Karl L. Wuensch

Principal Component Analysis

Data subset of Repeat Visits only (232 assessments)

• Model IPM data• Information about user• Information about cushion use

pipiii XWXWXWComp 2211

Weights

variables

TotPressureMeanPressContactAreaPPITotPres_ITDISPITotPress-asymmPPI-asymm

Daily cush use (hr)WC typeDaily xfersSUMFactorsSUMExposures

genderageWeightHeightAsymmParaTetraother

3 “Performance”components

3 “Demo”components

2 “Stressor” components

Regression: use to identify predictors of performance

Cush_Perf= β0 + β1(Stress1) + β2(Stress 2)

+ β3(Demo1) + β4(Demo2) + β5(Demo3) + β6(AgeHrs)

Y= mx + b

Regression model: can the Components predict Performance?

),3,2,1,2,1(_ AgeHrsDemoDemoDemoStressorStressorfComponentPerf

Standardized

Sig.Beta(Constant)   .163

Stress1 .287 .000Demo1 -.226 .000Demo2 -.354 .000Demo3 -.114 .038AgeHrs .108 .048

R R Square.611 .373

Positive Beta

Skipping over some stuff…

• Users who impart stress on the cushion resulted in cushions with greater IPM component.

• Stressful factors included the number of daily transfers , factors resulting from user activities such as curb jumps, sports, high vibration activities, etc and environmental exposures such as moisture, and high or low temperatures.

• Manual wheelchair users appear to induce these stressors more than power wheelchair users.

• The overall amount of cushion use, was also predictive of higher IPM Performance Measurements.

Summary

• Take the cover off and look at the cushion• Foam components show damage relatively early and

often – High odds of damage occurring between YR1 and 3

• Overall, the results indicate that the manner in which the cushion is used has a greater influence in the IPM Performance Component than the age of the cushion or the individual’s demographic variables.

• In addition, stressful activities and exposures are more associated with users of manual wheelchairs compared to powered wheelchairs.

Acknowledgements• This study was funded from two grants

supported by the National Institute on Disability and Rehabilitation Research within the Department of Education.

• The study design and data collection was funded as part of the Spinal Cord Injury Model Systems (SCIMS): Georgia Regional Spinal Cord Injury System grant #H133N060009.

• Secondary data analysis was supported by the Wheeled Mobility RERC- #H133030035.

State of the Science Conference:Wheeled Mobility in Everyday Life

In conjunction with RESNA, July 1-2, 2012

Please Visit:www.catea.gatech.edu/SOSC.php

For More Information

• cushion shape– 0=original shape 1=slight deformation

2=permanent deformation

• cushion's cleanliness• 0 = Like New 1 = Very Clean 2 = Clean 3

= Moderately Unclean 4= Very unclean