This document discusses how large aging-related data sets can help make technology more accessible for older people. It outlines how surveys that collect data on functional impairments, disabilities, activities of daily living, and socioeconomic factors can identify accessibility issues. A case study examines how reanalyzing wealth and shopping difficulty data informed research on online security challenges for older adults. The document argues that comprehensive population-level data is needed to understand the diversity of older adults and ensure inclusive design that meets the needs of all users.

1. How Large Aging-Related Data
Sets Can Help Make Technology
More Accessible for Older People
Maria Wolters
CCACE Cognitive Ageing Journal Club

2. Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion

3. http://www3.eng.cam.ac.uk/inclusivedesign/index.php?section=approaches&page=idc2
Inclusive Design
(John Clarkson et al.,
Cambridge)
Severe
Moderate
Light/No
Impairment
Three dimensions of function:
- perceptual
- cognitive
- motor
The default should be that
technology is accessible
for most people

4. Self-Reports Matter
• Impairment: objective measure of ability
• Disability: restriction of function due to
impairment
• Handicap: problem with functioning and
participating in society due to disability
World Health Organization. International classification of impairments, disabilities and handicaps. Geneva: WHO, 1980.

5. The diversity of older people makes them a
good canary in the coal mine.
Technology that can adapt to the diversity of
older people can also adapt to the diversity of
people in general.
Ford Focus, from Wikipedia http://www.viewpoints.com/expert-reviews/2014/01/31/oxo-good-grips-reviews-helpful-new-kitchen-tools/

6. Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion

7. Role of Large
(Epidemiological) Surveys
• What is the range of function of most people?
• How widespread are clinically / functionally
relevant levels of impairment?
• How often do impairments cooccur?
• How many people are excluded by requiring
certain levels of ability?

8. Example: Hearing
• Definitions of levels of impairment can vary
• Objective measure:
division into mild / moderate / severe according
to pure-tone thresholds
• Somewhat more subjective & more relevant:
Ability to understand speech in noise

9. Example: Blue Mountains Study
• Sydney, Australia
• sensory loss (mostly visual) in n=3594 people,
tested between 1992-1994
• hearing loss in n=2956 people, tested between
1997-2000
• detailed survey of socioeconomic factors and
self-reported hearing difficulties
Sindhusake, D, Mitchell, P, Smith, W, Golding, M, Newall, P, Hartley, D, Rubin, G. 2001. Validation of self-reported hearing
loss. The Blue Mountains Hearing Study. Int. J. Epidemiol. 30: 1371–1378.
Gopinath, B, Rochtchina, E, Wang, JJ, Schneider, J, Leeder, SR, Mitchell, P. 2009. Prevalence of age-related hearing loss
in older adults: Blue Mountains Study. Arch. Intern. Med. 169: 415–416.

10. Key Findings
• incidence of hearing loss grows exponentially
after 50
• some hearing loss is preventable (noise at work)
• while overall trends are in line with other
countries (US/NHANES), prevalence in Australia
is lower than in the US

11. Let’s just use visual interfaces!

12. Visual acuity also declines.
Vision aids are socially more acceptable than
hearing aids.
Visual interfaces need to be in your line of sight,
auditory interfaces can be in your range of
hearing.
Chia, E-M, Mitchell, P, Rochtchina, E, Foran, S, Golding, M, Wang, J-J. 2006. Association between vision and hearing impairments and their
combined effects on quality of life. Arch Ophthalmol 124: 1465–1470.

13. The Gold Mine: Activities of Daily
Living and Socioeconomic Data
• (i)ADL and self-reports show the extent of
perceived disability and handicap
• socioeconomic data show
• resources people have access to (or lack
thereof)
• web of stakeholders and responsibilities

14. Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion

15. Case Study:
Online Shopping
• new Edinburgh cybersecurity network CeSaR
• What types of older people benefit from online
services?
• How can we ensure security?

16. http://www.cartoonstock.com/directory/d/door-to-door_salesmen.asp?addtocart=yes&catref=mba0322&artistadd=baldwin,%20mike

17. Method
• Reanalysis of ELSA Wave 5 data (~2010)
• Include wealth groups as additional variable of
analysis (wealth groups: quintiles of total family
wealth)
• Main target group are those who find it difficult to
get to a supermarket (easiest of the shopping
questions)

18. Difficulty Getting to Supermarket
Wealth Group
PercentofWealthGroup
Lowest 2nd 3rd 4th Highest
0510152025
m
m m
m m
f
f
f
f
f
Difficulty Getting to Supermarket
Wealth Group
2nd 3rd 4th Highest
m m
m m
f
f
f
f

19. But those people are not
online!
• Both ELSA Waves and Ofcom reports show
Internet / email use spreading among older
people
• Of the target group (difficulty getting to a
supermarket), 61.1% have a mobile phone,
42.2% have a PC, 34.8% have a digital TV, and
30.1% are online.

20. Key Accessibility Issues
• Self-reported eyesight rated as fair or poor (OR 3.5)
• problems with CAPTCHAs
• Arthritis (OR 2.6), which is likely to affect dexterity in using
keyboard or mouse
• problems with typing long and complicated passwords
• Accessibility solutions need to work on low-end devices
• problems with using behavioral measures or fingerprinting
that require specific (or high quality) sensors

21. Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion

22. How Useful is this
Information?
• Highlighting potential issues for further research
• e.g., focus on dexterity
• plan sampling frame for more in-depth studies
• e.g., observing technology use of older people with
mobility problems
• push to engage with difficult-to-reach populations
• long-term preparation, need to build relationship

23. CCACE data (Lothian Birth Cohort, 36-Day
Sample) can gives us a better idea of the kinds
of cognitive impairments and personality factors
we need to look at.

24. Taking the Information
Forward
• To what extent do the observed impairments
translate into disability (loss of function) and
handicap (loss of ability to participate in digital
society)?
• How much of that disability / handicap can be
mitigated or eliminated through Inclusive
Design?

25. Questions?
• Summary:
large-scale data (both specialized and general)
shows us what issues we need to address in
order to make sure nobody is left behind
• maria.wolters@ed.ac.uk; @mariawolters

26. Self-Reports in ELSA
• ELSA = English Longitudinal Survey of Ageing
• self-reported change in memory abilities (not reliable)
• hearing self-reports:
• overall level of ability
• phone calls
• ability to understand speech in noise