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

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

Ethnography of Older Adults with Cognitive Impairment 45 Households in US Range from normal aging to advanced Alzheimer’s A variety of needs - elderhousehold communi ty normal aging and mild impairment moderate and severe impairment

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 Perceived functioning Actual functioning Foresight Denial

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

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

Concept Feedback

Context Aware Medication Prompting A pilot study on the effectiveness of intelligent medication tracking and reminding Sensors 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. 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 iMed Tracker Detects when pills are taken Health Spot Wrist watch that detects location of subject 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 Prompters iMed Tracker LED, beep, and text display Health Spot Beep, text display Activity Beacon LED, beep, and voice reminder Phone Sensor Detects phone calls Inference Engine 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

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

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