scaling self-experimentation

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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