Jay Lundell, PhD Margaret Morris, PhD

1. Design Research Techniques for Elders with Cognitive Decline: Examples from Intel’s Digital Health Group Jay Lundell, PhD Margaret Morris, PhD

2. Techniques Applied Technology Development Late Early Wide Ethnography Concept Feedback Pilot/Probe Studies Breadth of Functionality Clinical Trials Usability Testing Narrow

3. normal aging and mild impairment moderate and severe impairment community elder household Ethnography of Older Adults with Cognitive Impairment • 45 Households in US • Range from normal aging to advanced Alzheimer’s • A variety of needs -

4. Ethnography of Older Adults with Cognitive Impairment • 45 Households in US • Range from normal aging to advanced Alzheimer’s • A variety of needs - Balancing foresight and optimism/denial Denial Perceived functioning Actual functioning Foresight

5. Ethnography of Older Adults with Cognitive Impairment • 45 Households in US • Range from normal aging to advanced Alzheimer’s • A variety of needs - Having an impact Independence and control: the home, finances, relationships Mental stimulation Physical activity Connection to the outside world

6. Concept Feedback • Focus Troupe – dramatic scenarios • Three user groups – Normal aging, Mild cognitive impairment, Care givers/Boomers • Focus on context of use, social implications

7. Concept Feedback

8. Context Aware Medication PromptingA pilot study on the effectiveness of intelligent medication tracking and reminding • Hypotheses There is a predictive relationship between daily patterns of activity (and/or sleep (inferred from bed activity)) and the likelihood of taking medications on time. Automatic, contextual prompting can improve adherence to a medication regimen. • Sensors • Prompters • Inference Engine • Methods • Recruit 25 people over 65 who have difficulty with medication adherence (50-80% adherence) • Six week baseline – sensors in the home track activities, sleep patterns and when medications are taken • Eight week intervention – two types of reminders: • 1. basic “alarm clock” that always goes off at medication time, • 2. context aware prompting that only prompts when user is likely to miss a dose (based on data collected in baseline) • Measures: effectiveness of reminders (as measured by adherence to a pre-determined regimen), subjective preference for reminders, ability of system to predict non-adherence Motion Sensor Detect motion in each room in the house. Also detects front door and refrigerator door opening Bed Sensor Detect movement in bed, sleep quality iMed Tracker Detects when pills are taken Phone Sensor Detects phone calls Health Spot Wrist watch that detects location of subject • Bayesian inference engine uses data collected during baseline to decide when and where to deliver a prompt • Detects activities such as sleep, visitors, on the phone, kitchen movement, taking meds Activity Beacon LED, beep, and voice reminder iMed Tracker LED, beep, and text display Health Spot Beep, text display

9. Usability Testing – Parkinson’s disease • 4 Patients with Parkinson’s Disease and their spouses • Tested for usability, learnability, and livability

10. Summary • Standard design and usability approaches adopted for special users • Downplay technology, emphasize environmental and social context • Pilot technology in extended trials – test for livability, use over time • Include stakeholders and design for their needs as well