scaling self-experimentation
DESCRIPTION
Presented at Medicine X, September 2012TRANSCRIPT
IDA SIM, CO-‐FOUNDER September 28, 2012
A project of the Tides Center
and Professor of Medicine, University of California San Francisco
Scaling Self-Experimentation
n = 1
(n = 1).n
(n = 1).n Σ
data driven feedback loops
2
without better sensemaking to drive these feedback loops…
Plateau of Diminished Promise
open architecture for mobile health
a small set of common principles/practices by which these modules are described and interface to one another
activity classification
graphing mobility data over time
enabling reuse, integration, and innovation
getting further together faster…
(n = 1).n Σ
‘does caffeine affect my sleep? N-‐of-‐1 study design
sleep caffeine
no caffeine
no caffeine
caffeine
caffeine
no caffeine
sleep
scaling (n = 1) n
Outcome Variables • a caffeine definition module • a sleep definition module, with APIs for getting sleep data from
various monitors • new variables that take advantage of mobile (e.g., reality mining)
Scripting study protocols • e.g., modules for setting up an n-‐of-‐1 study
scaling (n=1) n
Make the findings comparable for aggregation • libraries of standard measures (e.g., PHQ-‐9, PROMIS) • indexing of variables and results and to standard vocabularies
Σ
scaling (n=1) n Need to describe context to combine apples with apples • who is “n”: demographics, important clinical features • study approach: ad hoc, n-‐of-‐1, etc. • activity context: walking? running? • social context: … • technical context: device, operating system, app, version, sampling
rate… • etc.
Σ
2
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