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Daihua Yu, MS 1,3 , Bambang Parmanto , PhD 1 , 3 & Brad Dicianno , MD 2,3

The Accessibility Needs of Patients with Dexterity Impairments to Use mHealth Apps on Smartphone. 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

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Daihua Yu, MS 1,3 , Bambang Parmanto , PhD 1 , 3 & Brad Dicianno , MD 2,3

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  1. The Accessibility Needs of Patients with Dexterity Impairments to Use mHealth Apps on Smartphone Daihua Yu, MS1,3, BambangParmanto, PhD1, 3 & Brad Dicianno, MD2,3 1Department of Health Information Management 2Department of Physical Medicine and Rehabilitation 3Rehabilitation Engineering Research Center (RERC) on Telerehabilitation

  2. 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

  3. Motivation • Market penetration (US) reached 55% in early 2013 (comScore Incorporation, 2013). • 4.04 million dexterity impairments in US (Pleis et. al., 2010) . • The smartphone is an ideal tool for implementing wellness programs for PwDs(Holman, 2004). • Smartphones poses accessibility challenges: • Lack of screen space (Brewster, 2002); • Small form factors, low contrast and tiny text, and undifferentiated keys (Abascal & Civit, 2000; Kane et. al., 2009); • Unnecessary steps (Kurniawan et. al., 2006).

  4. Method – Introduction to iMHere • iMHere(iMobileHealth 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

  5. Method • Dexterity impairments: Purdue Pegboard Assessment (Lafayette Instrument, 2002) • Face-to-face orientation • One-week field trial • Lab test with in-depth interview • Task 1: scheduling a new medication alert; • Task 2: modifying a medication reminder; • Task 3: scheduling a skin check up alert; • Task 4: responding to a skincare reminder.

  6. Methods – Measurements • Error Ratio • 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. • TelehealthUsability Questionnaire (TUQ) • Structured Open-ended Questions

  7. 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)

  8. Results – Error Ratio • Pearson Correlation: • A moderately negative correlation was identified between subjects’ dexterity levels and their error ratios, r = -0.434, n=36, p= 0.004 ANOVA: F (2, 33) = 3.604, p=0.038, significant

  9. 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

  10. Results – TelehealthUsability Questionnaire • Average TUQ score: 5.9 out of 7 (84.29%)

  11. 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. • Several of them are not very comfortable using the in-screen camera button. The use of colors: • Suggested to extended to application level. • Contrast: • They might be more comfortable with dark text on a white background or try different pictures. Needs Preferences

  12. Preferences • Shortcuts Conclusion • Users want to have simpler apps with easier processes • Approach to accessible and personalized smartphone apps: Needs for Personalization

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

  14. 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.

  15. Questions?Thanks! • Contacts: • Daihua Yu, dxy1@pitt.edu • BambangParmanto, parmanto@pitt.edu • Brad Dicianno, dicianno@pitt.edu

  16. Result: Dexterity Levels Male & Female General factory Work (n=282) Average = 46.76, -1S.D.= 42.72, -2S.D.= 38.68, -3S.D.= 34.64. • 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.

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