THE ACCESSIBILITY NEEDS OF

jiggerbarnacleMobile - Wireless

Nov 24, 2013 (3 years and 11 months ago)

110 views

Daihua Yu, MS
1,3
,
Bambang

Parmanto
, PhD
1
,
3


& Brad
Dicianno
, MD
2,3

1
Department
of Health Information Management

2
Department
of Physical Medicine and Rehabilitation

3
Rehabilitation
Engineering Research Center (RERC) on
Telerehabilitation


THE ACCESSIBILITY NEEDS OF
PATIENTS WITH DEXTERITY
IMPAIRMENTS

TO USE
MHEALTH

APPS ON
SMARTPHONE

OBJECTIVE & TARGET POPULATION

Goal
:
to explore and to identify the
accessibility needs and preferences for
Persons
with disabilities (
PwDs
) to use
mobile health
smartphone apps.

Target Population
:
Persons with
dexterity
impairments

MOTIVATION



M
arket
penetration
(US)
reached 55% in early 2013 (
comScore

Incorporation, 2013).



4.04
million
dexterity impairments in US (
Pleis

et. al., 2010)
.



The
s
martphone
is an ideal tool for implementing wellness
programs for
PwDs

(Holman, 2004
).


Smartphones poses
accessibility
challenges:

1)
Lack
of screen space (Brewster, 2002);

2)
Small
form factors, low contrast and tiny text, and
undifferentiated keys (
Abascal

&
Civit
, 2000; Kane et.
al., 2009);


3)
U
nnecessary
steps (
Kurniawan

et. al., 2006).



METHOD


INTRODUCTION TO
IMHERE

iMHere

(

iMobile

Health and
Rehabilitation)
,
a novel
mHealth

platform that
has been developed to support self
-
care in the
management of chronic and complex conditions (
Parmanto

et.
al., 2013).


Two
-
way
Communication

METHOD


Dexterity impairments: Purdue
Pegboard

Assessment
(Lafayette Instrument, 2002
)


Face
-
to
-
face
orientation


One
-
week field trial


Lab
test with in
-
depth
interview

1)
Task 1: scheduling
a new medication
alert;

2)
Task 2: modifying
a medication reminder;

3)
Task 3: scheduling
a skin check up alert;

4)
Task 4: responding
to a skincare reminder.


METHODS


MEASUREMENTS

1.
Error Ratio

2.
Difficulty
-
on
-
Performance (DP):
the sum of weighted scores are divided by
the total steps to
complete a task.



Weighted
scores have been added to all errors:


1


solve the problem without any help,


2


need help in one sentence,


3


need help in two to four sentences,


4
-

unable to solve the problem.


3.
Telehealth

Usability Questionnaire (TUQ
)

4.
Structured Open
-
ended
Q
uestions

RESULT


BACKGROUND



N
= 9 subjects with dexterity
impairments



4 tasks



Ages ranged:
18


55



4
women, 5
men



8
spina

bifida patients, & 1 patients with spinal
cord injury (SCI)


RESULTS


ERROR RATIO

ANOVA
: F (2,
33)
=
3.604,
p=
0.038, significant

Pearson Correlation:


A moderately negative correlation was identified between subjects’ dexterity levels
and
their error
ratios, r =
-
0.434, n=36, p= 0.004



Sub

Task 1

Task 2

Task
3

Task
4

Average

Group
Avg

Group 1:
Mild

5

6.25%

12.50%

0.00%

10.00%

7.19%

8.83%

6

12.50%

12.50%

0.00%

0.00%

6.25%

7

12.50%

25.00%

0.00%

12.50%

12.50%

9

0.00%

25.00%

0.00%

12.50%

9.38%

Group 2:
Moderate

1

13.33%

12.50%

0.00%

10.00%

8.96%

9.69%

3

0.00%

0.00%

0.00%

0.00%

0.00%

4

6.25%

37.50%

16.67%

20.00%

20.10%

Group 3:
Severe

2

17.65%

25.00%

16.67%

37.50%

24.20%

19.65%

8

6.25%

25.00%

16.67%

12.50%

15.10%

Total Avg

8.30%

19.44%

5.56%

12.78%

11.52%

12.72%

RESULTS


DIFFICULTY
-
ON
-
PERFORMANCE


Pearson Correlation
:


An
increasing in error ratio might significantly increase the difficulty
-
on
-
performance for user
in completing tasks (r=0.724, n=36, p<0.001).

ANOVA
: F(2, 33), p=0.983



Sub

Taks 1

Task 2

Task 4

Task 5

Average

Group
Avg

Group 1:
Mild

5

25.00%

50.00%

0.00%

40.00%

28.75%

20.47%

6

43.75%

37.50%

0.00%

0.00%

20.31%

7

25.00%

25.00%

0.00%

37.50%

21.88%

9

6.25%

25.00%

0.00%

12.50%

10.94%

Group 2:
Moderate

1

33.33%

25.00%

0.00%

40.00%

24.58%

20.63%

3

0.00%

0.00%

0.00%

0.00%

0.00%

4

25.00%

87.50%

16.67%

20.00%

37.29%

Group 3:
Severe

2

23.53%

25.00%

16.67%

37.50%

25.67%

21.69%

8

16.67%

25.00%

16.67%

12.50%

17.71%

Total Average:

22.06%

33.33%

5.56%

22.22%

20.79%

20.93%

RESULTS


TELEHEALTH

USABILITY
QUESTIONNAIRE

Average TUQ score: 5.9 out of 7 (84.29%)

DISCUSSION

Instructive Guidance:


About 51% of errors were self
-
corrected without any help, but other errors called for resolution
from a researcher and received higher
-
weighted scores for difficulty
-
on
-
performance.

Personalized target size:


User frustrations were identified regarding text entry and accessing buttons.

Functional button:


Subjects with severe dexterity impairments needed help from a family member or clinical staff
to take a
photo.


S
everal
of them are not very comfortable using the in
-
screen camera
button.


The use
of colors:


Suggested to extended to application level.

Contrast
:


T
hey
might be more comfortable with dark text on a white
background or try
different
pictures.


Needs

Preferences

CONCLUSION



Users want
to have simpler apps with easier processes


Approach to accessible and personalized smartphone apps:



Accessible
Smartphone
App

Physical
Presentation

(User Interface)

Navigation

(Streamlined
procedures)

Preferences

Shortcuts

ACKNOWLEDGEMENT


This
study is funded by Grant #1R21HD071810
-
01
-
A1 from
the National Institute of Child Health and Human
Development (NICHD), USA.


REFERENCES

Pleis
, J. R., Ward, B. W., & Lucas, J. W. (2010). Summary health statistics for U.S. adults: National Health Interview
Survey, 2009.
Vital and health statistics. Series 10, Data from the National Health Survey
(249), 1
-
207.

Abascal
, J., &
Civit
, A. (2000).
Mobile Communication for Older People: New Opportunities for Autonomous Life.

Paper presented at the The 6th ERCIM Workshop.

Boyer, EW,
Smelson
, D., Fletcher, R.,
Ziedonis
, D., & Picard, RW (2010). Wireless Technologies, Ubiquitous
Computing and Mobile Health: Application to Drug Abuse Treatment and Compliance with HIV Therapies.
J Med
Toxicol
, 6
(2), 212
-
216.

Brewster, S. (2002). Overcoming the Lack of Screen Space on Mobile Computers.
Personal Ubiquitous Computing.
6
(3), 188
-
205.


Cipresso
, P.,
Serino
, S.,
Villani
, D.,
Repetto
, C.,
Selitti
, L., &
Albani
, G. (2012). Is your phone so smart to affect your
state? An exploratory study based on psychophysiological measures.
Neurocomputing
, 84
(23
-
30).

comScore

Incorporation. (2013).
comScore

Reports January 2013 U.S. Smartphone Subscriber Market Share
.

Han, D., Lee, M., & Park, S. (2010). THE
-
MUSS: Mobile u
-
health service system.
Comput

Methods Programs Biomed.
97
(2), 178
-
188.

Holman, H. (2004). Chronic disease
--
the need for a new clinical education.
JAMA : the journal of the American
Medical Association. 292
(9), 1057
-
1059.


Kane, SK,
Jayant
, C.,
Wobbrock
, JO, & Ladner, RE. (2009).
Freedom to roam: a study of mobile device adoption and
accessibility for people with visual and motor disabilities.

Paper presented at the 11th international ACM
SIGACCESS conference on Computers and accessibility.


QUESTIONS?


THANKS!



Contacts:

Daihua Yu,
dxy1@pitt.edu

Bambang

Parmanto
,
parmanto@pitt.edu

Brad
Dicianno
,
dicianno
@
pitt.edu



RESULT: DEXTERITY LEVELS

S
u
b
je
c
ts

R
i
gh
t
H
an
d

Le
ft
H
an
d

Both

H
an
d
s

R+L

+
Both

A
s
s
e
mb
l
y

#1

8.67

9.00

15.33

33.00

14.33

#2

0.00

0.00

0.00

0.00

0.00

#3

10.67

6.33

10.00

27.00

13.33

#4

8.67

5.00

10.00

23.67

13.33

#5

10.00

10.33

16.00

36.33

17.33

#6

9.33

9.67

16.00

35.00

15.33

#7

9.67

10.67

16.67

37.00

18.00

#8

0.00

0.00

0.00

0.00

0.00

#9

12.00

12.67

13.67

38.33

17.67



Group
1) Mild: From
-
3 S.D. to
-
2 S.D including subject #5, #6, #7 and #9
;


Group
2) Moderate: below
-
3 S.D. including subject #1, #3, #4
;


Group
3) Severe: Not able to complete Purdue Pegboard tests, including
subject #2 and #8.


Male & Female

General factory

Work (n=282)

Average = 46.76,

-
1S.D.
= 42.72,

-
2S.D.
= 38.68
,

-
3S.D.
= 34.64
.