mobile hci 2014 workshop: reimagining commonly user interfaces for the eldlerly
DESCRIPTION
A slide deck presented at Mobile HCI 2014 on using the lifecourse perspective in the mobile HCI.TRANSCRIPT
The Value of theLife Course Perspectivefor the Design of Mobile Technology Foong Pin Sym, PhD CandidateNUS Graduate School of Integrated Sciences and EngineeringNUS HCI [email protected]: @interfaceaddict
Mobile HCI 2014 Workshop: Re-imagining commonly used mobile interfaces for older adults
FOONG (NUS)
Introduction and Goals
•Interests: Ageing, health, technology, persuasion, end-of-life decision making
•Goals:▫Feedback and discussion on Life Course
perspective as a research frame
9/18/2014
FOONG (NUS)
The Life Course Perspective
•Experience of Aging is shaped by ...unique personal biographies + location in the social system + historical period
• ‘transitions’ • ‘choice points’
9/18/2014
Image credit: luma photography @ flickr CC
Stoller and Gibson (2000)
FOONG (NUS)
Uses of the life course perspective
In Gerontology In Sociology
9/18/2014
Image credit: osteoporosis_female @ flickr CC
Image credit: Bev Norton @ flickr CC
• In HCI: As predictive factors of elderly technology acceptance – education, work-usage, cognitive ability, socio-economic status
Image credit: Barbara Krawcowlcz @ flickr CC
Older
Younger
65 years old and above
GENERAL POPULATION HCI ACCESS/DISABILITY HCI
ACTIVE AGEING TECHNOLOGIES
Well Unwell
Goal: MAINTENANCE-Work-Physical Health-Screening
Goal: COPING I- normal age-related impairment- reminder systems- accessibility barrier reduction- social/health maintenance
Goal: COPING II- pathological age-related conditions- cognitive orthotics- accessibility barrier reduction- Occupational Therapy- Physical Therapy-Caregiver aids-Telecare
Loss o
f in
dep
en
den
ce
Goal? - Quality of life?- pleasures?- End-of-life care?- CAREGIVER HELP
HCI & Ageing Clock
9/18/2014FOONG (NUS)
Older
Younger
65 years old and above
GENERAL POPULATION HCI ACCESS/DISABILITY HCI
Well Unwell
Loss o
f in
dep
en
den
ce
Transitions #1-Technology ageing with us?-Changes in social networking, relationships, activity, retirement
Transitions #2-Characterized by loss
- Of independence- of technology
habits?
caregivers
caregivers
COPING I COPING II
MAINTENANCE CARING
Transitions #3- How can access technologies age with us?
9/18/2014FOONG (NUS)
HCI & Ageing Clock
Year of birth
1910’s: Colonial
1920’s: Colonial
1930’s: Colonial
1940’s: WWII
1950’s: WWII
1960’s: Ec Boom
1970’s: Ec Boom
1980’s: ICT
1990’s: ICT
2000’s: Millenials
Older
Younger
Well Unwell
COPING I COPING II
MAINTENANCE CARING
Singapore Population Demographics & Implications for Design Goals and Technological Acceptance
2050: Projected population 35% > 60 Parental support ratio from 10: 1 (current) to 2: 1
Q: What sort of tool can accompany us through the life span?
Q: Can this tool help navigate the vagaries of the ageing process, including illness, disability and death?
FOONG (NUS)
Thank You & Questions
•Is it useful to study the transitions?▫Dis-continuity: the challenge of age-related
limitations and ongoing technology renewal▫Whose work should I be looking at?
•Nursing home studies▫Finding the balance between caregiver and
elderly users’ needs?▫‘Toys’ for elderly with Mild Cognitive
Impairment /Dementia?
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FOONG (NUS)
Extra Slides
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FOONG (NUS)
Maintenance
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FOONG (NUS)
Coping I
•Acessibility
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http://app.mot.gov.sg/page_land.aspx?p=/Land_Transport/Meeting_Diverse_Needs/Enhancing_Accessibility.aspx
FOONG (NUS)
Coping II
•http://www.myhappystroke.com/2011/03/modified-constraint-induced-movement.html
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FOONG (NUS)
Coping II• Making Family Care Work: Dependence, Privacy and Remote
Home Monitoring Telecare Systems John Vines, Stephen Lindsay, Gary W. Pritchard, Mabel Lie, David Greathead, Patrick Olivier and Katie Brittain, Culture 2 Fit Lab, Newcastle University Lab, Swansea University
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FOONG (NUS)
Multi-Level Linear Modelling to study mobile phone usage
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Variance of
intercepts
ss1
ss2
ss3
ss4
Variance of slopes Tech ability
Freq of mob usage
Time-varying variables:1. Dependent Variable: mob0-mob3
(frequency of mobile phone usage, measured at time 0,1,2, and 3)
2. Independent Variables: tech0-tech3 (technological ability score, measured at time 0,1,2 and 3)
Time-invariant variables:3. Gender: male or female4. Tech support (received from
family/friends)
Research Question:What are the growth patterns of mobile
phone usage over time? (intra individual differences)
What predicts the growth patterns of mobile phone usage? (inter-individual differences)
Issue: Change over time
FOONG (NUS)
Level-1 model:mobij = β0j + β 1jTimeij + rij
Level-2 model:Intercept: β0j = γ00 + γ 01Genderj + γ 02Supportj + u0j
Slope:
β1j = γ10 + γ 11Genderj + γ 12Supportj + u1j
Variance of
intercepts
ss1
ss2
ss3
ss4
Variance of slopes Tech ability
Freq of mob usage
Multi-Level Linear Modelling to study mobile phone usage
Issue: Change over time9/18/2014
FOONG (NUS)
Speed of population ageing
9/18/2014