leveraging large data sets to make technology more accessible for older people
TRANSCRIPT
How Large Aging-Related Data Sets Can Help Make Technology
More Accessible for Older PeopleMaria Wolters
CCACE Cognitive Ageing Journal Club
Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion
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
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.
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/
Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion
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?
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
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.
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
Let’s just use visual interfaces!
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.
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
Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion
Case Study: Online Shopping
• new Edinburgh cybersecurity network CeSaR
• What types of older people benefit from online services?
• How can we ensure security?
http://www.cartoonstock.com/directory/d/door-to-door_salesmen.asp?addtocart=yes&catref=mba0322&artistadd=baldwin,%20mike
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)
Difficulty Getting to Supermarket
Wealth Group
Perc
ent o
f Wea
lth G
roup
Lowest 2nd 3rd 4th Highest
05
1015
2025
m
m mm m
f
ff
f
f
Difficulty Getting to Supermarket
Wealth Group
Perc
ent o
f Wea
lth G
roup
Lowest 2nd 3rd 4th Highest
05
1015
2025
m
m mm m
f
ff
f
f
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.
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
Overview
• What is Inclusive Design?
• What Can Large Surveys Contribute?
• Case Study: Online Security
• Discussion
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
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.
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?
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
• [email protected]; @mariawolters
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