29/5/13 6:01 PMMobile phones as a source of data from everyday lifeReal time (always on)Real place (always carried)Real context (historical, environmental, spatial, social)Real application (environment, education, community, health)Choose applications that scaled downas well as up

Mobile phone application covers a large population6.8B phone subscription1.1B in China, 860M India, 650M in Africa

Why chronic disease management?3 lifestyle behaviors (poor diet, lack of exercise, smoking) 1/3rd of US deaths, 50% Americans (real population, not only among those with disease) have 1 or more chronic diseases, age of onset getting youngerNon communicable diseases will cost > $30 trillion; mental health > $16.1 trillionEquip individuals, families w/ tools for measurement, management, understanding outside clinical settingTarget demographic: - ages 16-65 w/ no interfering impairments own smartphone (43% and growing in US)

Third IT pillar of personalized, precision, medicineBig data (web mining)+ Omics + small data (mHealth, digital traces)Participant self-care, clinical care, research evidence (essential data driven feedback loops)

The promise of mobile health (mHealth)Transform previously unmeasured behaviors and practices into personalized, evidence-based, and evidence-producing care

Example 1: A pre-diabetic women with hypertension tracks diet, physical activity, weight, fatigue, blood pressure, dizziness, to inform Rx dosageKeep tolerable dosage to prevent dropout due to adverse events

Example 2: A young man with ADHD tracks medication dose/adherence, sleep, cognitive-control, physical activity, daily patterns (e.g. arrival tie at work/school), to inform Rx dosage and timing and catch lapses daily

How do we inform those processes now?PatientInformal, ephemeral records of they feel and behave day to day/hour to hourCausal and flawed hypothesis formation on what works for themPrescribed behaviors challenging to achieve and sustainCliniciansUnder-samples the process they are trying to manageHas few to offer patients outside of clinical encountersHas little information about individuals phenotype

Mobile apps also generate relevant data (e.g. medical related application)

Some mobile apps are passive run in the background (can link with GPS, wifi, compass, activity level; etc)

Digital traces: usage data from diverse digital servicesInternet search, maps, mobile carriers, cable TV, social media, social media, online commerce, entertainment digital behavior can tell a lot personal health

The challenge: transform and fuse these data streams into personalized, precision measures of chronic disease

Data reduction, selective sharing, privacyTMI (too much information) filter

mHealth and behavior change

Innovative infrastructure will fuel a learning health system

Open architecture and communitySo mHealth solutions can integrate best available apps and techniques

http://smalldata.tech.cornell.edumHealth: Leveraging everyday mobile phones to personalize healthcare

29/5/13 6:01 PM