Advanc e s in Me asur ing Ca sc a de o f me a sure me nt Gra - PDF document

9/25/2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Booth ar tic le Advanc e s in Me asur ing • Ca sc a de o f me a sure me nt • Gra phic me tho d o f re c o rding da ta Be havior • Cle ve r thinking a b o ut wha t to me a sure PHT H 5228 – “Co unte r pre ssure whic h wo uld b e ne c e ssa ry 2 nd Class to c a use the pulsa tio n in a n a rte ry to c e a se ” • Co mple xity impo rta nt – simplic ity o f Pro f. Ste phe n I ntille instrume nt pa ra mo unt Offic e : 450 WVH s.intille @ ne u.e du • Surg e o ns – no tic e thing s re se a rc he rs miss • Va lue in e sta b lishing a no rma l ra ng e No rthe a ste rn Unive rsity 1 No rthe a ste rn Unive rsity 2 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Booth ar tic le Stone ar tic le • As to o l g e ts g o o d e no ug h, sta rt se e ing • Re po rte d c o mplia nc e : 90% pa tte rns • Ac tua l c o mplia nc e 11% (20% 90 min) • Co mpre sse d o n a ll side s a nd width: “sma ll • Ho a rding : 32% o f da ys no o pe ning s b ut c ha ng e ” le a ds to b ig pe rfo rma nc e g a in re po rte d c o mplia nc e 92% • Sma ll o b se rva tio ns • 75% o f 40 pe o ple ha d 1+ da y o f ho a rding (“pa rking lo t c o mplia nc e ”) No rthe a ste rn Unive rsity 3 No rthe a ste rn Unive rsity 4 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Smyth ar tic le Smyth ar tic le • Be ha vio ra l me dic ine : inte g ra tive a nd wide • Re c a ll o f pa st e ve nts a rra y o f o utc o me type s a nd mo de ls – Be lie fs a b o ut b e ha vio r o r the wa y the wo rld func tio ns (“e ffo rt a fte r me a ning ”) • T ime ! Ne e d to unde rsta nd inte ra c tio ns – Outc o me o f the e ve nt (re tro a c tive o ve r time re c o nstruc tio n) • E xpe rie nc e sa mpling vs E MA – Curre nt sta te , pa rtic ula rly mo o d (c o ng rue nt mo o d sta te ) – Sa lie nt e ve nts No rthe a ste rn Unive rsity 5 No rthe a ste rn Unive rsity 6 1

9/25/2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Smyth ar tic le Smyth ar tic le • E xte rna l va lidity/ g e ne ra liza b ility o f finding s • E xte rna l va lidity/ g e ne ra liza b ility o f finding s – L a b vs. re a l wo rld – L a b vs. re a l wo rld – “White -c o a t hype rte nsio n” – “White -c o a t hype rte nsio n” – Missing so c ia l e nviro nme nt – Missing so c ia l e nviro nme nt – Surprising ly we a k c o rre spo nde nc e (e .g . HRV) – Surprising ly we a k c o rre spo nde nc e (e .g . HRV) • Dyna mic pro c e sse s – Re pe a te d me a sure s – Sug g e stive o f c a usa l a sso c ia tio ns No rthe a ste rn Unive rsity 7 No rthe a ste rn Unive rsity 8 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Smyth ar tic le Smyth ar tic le • Dyna mic pro c e sse s • E MA – T hink a b o ut yo ur o wn life ! (mo o d/ stre ss) – Multiple time s pe r da y – Va ria tio ns in time o f da y (e .g ., diurna l pa tte rns – We e ks o r mo nths o f mo o d; “o b sc ure d a t b e st, a nd c o ntrib ute – Re duc e pe rio d o f re c a ll to e rro r o r b ia s a t wo rst”) – I n the mo me nt (re duc e summa ry) – Re pe a te d me a sure s – Na tura l e nviro nme nt – Sug g e stive o f c a usa l a sso c ia tio ns – Na tura l e ve nts – Da te / time sta mpe d (+ timing info ) – Pro mpt o r “e ve nt drive n” a c tio ns No rthe a ste rn Unive rsity 9 No rthe a ste rn Unive rsity 10 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Smyth ar tic le Smyth ar tic le • “Re c a ll the mo st stre ssful e ve nt o f the la st • E MA c ha lle ng e s mo nth” – Que stio nna ire de sig n • Co ping : muc h o f wha t wa s re po rte d in – T ra ining o f pa rtic ipa nts re a l time wa s fo rg o tte n a t re c a ll a nd tha t – E xtra de vic e c o ping e ffo rts tha t we re no t re po rte d in – F ie ld mo nito ring re a l time we re re po rte d a t re c a ll – Mo tiva tio n to fo llo w pro to c o l (e .g ., stylus) – T e c h g litc he s – E xpe nsive – Re a c tivity No rthe a ste rn Unive rsity 11 No rthe a ste rn Unive rsity 12 2

9/25/2011 Adva nc e s in Me a sur ing Be ha vior F a ll 2011 Smyth ar tic le • E MA c ha lle ng e s (c o ntinue d) – “Ma ssive ” a mo unts o f da ta Technological innovations – Sta ts c ha lle ng e s • Ag g re g a tio n Circa 2003 • Multi-le ve l • Missing da ta (unb a la nc e d) No rthe a ste rn Unive rsity 13 Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Take away: 4 new opportunities Your task…  Continuous, rich recording from a variety of sensors  Algorithms to process data to reduce coding time What are the possibilities for your research?  Context-sensitive data collection to collect data and prompt for self-report at desired times and places  Context-sensitive , personalized interventions Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Relevance to health research (1) Relevance to health research (2)  Ability to better study how context  Ability to create and measure impact of (people, places, things) “just-in-time” interventions impacts behavior  Example: physical activity  Examples  Measurement is important, but we already know people don’t get enough physical activity!  Measurement of moderate intensity or greater physical activity  Just-in-time detection of activity for positive reinforcement  Dietary decision making  Making every interruption count 3

9/25/2011 Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Overview  New developments  Examples New developments  Context-sensitive experience sampling  Portable kit of “tape on” environmental sensors - New developments  PlaceLab - Examples  Emerging opportunities - Emerging opportunities  Challenges - Challenges Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Data collection in the (not-so-distant) future Sensors in the (not-so-distant) future  Record and save everything from subjects:  Example:  24/7 video stream (160x120 resolution,10fps,MPEG-4) [1.56 GB/day]  Video/photos from miniature pocket/cap camera  24/7 audio stream (24kHz mp3) [.57 GB/day]  Continuous audio recording, keyword detection  24/7 1 photo per minute or other data [.57 GB/day]  Real-time HR data  16/7 One 3MB data file per hour [72MB/day]  Real-time motion data all limbs, hip  Real-time indoor/outdoor position  Real-time position relative to other people  A year of data: 990MB  Real-time data from home: objects touched/used  2007: Terabyte of data < $300  Data on use of communication devices  No encumbering or nerdy-looking devices  Context-sensitive self report Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Technology I nnovations for Real-Time Data Capture (S. I ntille – MI T) Data analysis in the (not-so-distant) future Personalized mobile computing device  Computers pre-process data:  Translate noisy sensor data into meaningful labels E.G. Cooking, socializing, running, smoking, …  Computer helps researcher search data:  “find all the moments when the subject might have been cooking”  “query the subject whenever the subject is near another subject” Take your pick…  “show me video clips of moments when the subject was with other people” Powerful, inexpensive, sensor-enabled  “indicate where the subject spent the most time” mobile computing device carried nearly everywhere 4