ema 24/7/365: from concept to implementation · schools of nursing and public health ema 24/7/365:...
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Schools of Nursing and Public Health
EMA 24/7/365: From Concept to Implementation
Lora E. Burke, PhD, MPH, FAHA, FAANEdvin Music, MSIS, MBA, Brian French, BS
University of Pittsburgh and Carnegie Mellon University
Schools of Nursing and Public Health
Overview of Seminar
• Transdisciplinary Team
• EMPOWER study – use of EMA
• Technology infrastructure – Edvin Music
• Programming to support EMA data collection – Brian French
• Summarize current status
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Team Science Rapid growth and accumulation of
specialized knowledge in multiple fields, particularly use of technology
Substantial need to establish partnerships drawn from different fields to address complex environmental, social and public health problems
Schools of Nursing and Public Health
Co-PI (UGA)Biostatistics/
EMA ModelingSteve
Rathbun, PhD
PI Nursing, Behav
Science,EpidemiologyLora Burke, PhD, MPH
Psychology Lin Ewing, PhD
Computer Science (CMU)Asim Smailagic
Human Computer Interaction (CMU)
Dan Sieworek
Sleep MedicinePat Strollo, MD
Eileen Chasens, DSN
Info ScienceEdvin Music, MSIS, MBA
EPI & Sports Science: India
Loar, MPH
Biostatistics (UGA)Computer Science (CMU)Brian French, BS
NursingKelly Sawl, Amelia
Haney, Meghan Taraban
Ex Physiology/EpidemiologyAndrea Kriska
DieteticsLeah McGhee, BSJulie Mancino, MS
Psychology EMA/AddictionSaul Shiffman,
PhD
Schools of Nursing and Public Health
Background - Relapse
• Relapse and weight regain are major issues in treatment of obesity
• Data from numerous trials demonstrate that up to 20% of participants begin to regain weight while in active treatment
Percent Weight Change by Treatment Group Over Time (N=210)
-10-9-8-7-6-5-4-3-2-10
0 6 12 18 24
Perc
ent w
eigh
t cha
nge
Month
PDPDAPDA+FB
Copyright restrictions may apply. Perri, M. G. et al., 2008
Weight Changes During Weight-loss and Extended-care Phases
Relapse Studies Limited
• Knowledge of the relapse process following intentional weight loss is very limited
• Few prospective studies done
• Assessments done at fixed intervals using retrospective data, subject to bias
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Relapse Following Intentional Weight Loss• Best approach to study phenomenon is to
capture the behavior or emotions in real time
• Ecological momentary assessment (EMA) is the best method
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Application of EMA – EMPOWER
• EMA – assesses individuals’ experiences as they occur in real time and in the natural environment
• EMA is being applied in a descriptive, longitudinal study in the context of behavioral weight loss treatment
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Standard Weight Loss Treatment
• All participants receive group-delivered behavioral wt loss intervention, 12-mos
• Daily dietary (calories, fat) goals
• Weekly physical activity (PA) goals
• Self-monitor diet, PA, and weight
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EMPOWER Study Aims
• Use EMA to provide means to quantify variables not traditionally measured in EMA to provide data related to relapse process, e.g., duration and quality of sleep, PA, daily wt, mood and location
• Link data from smart-phones (EMA, self-monitoring), weight scales- transmit in real time; actigraphs, accelerometers
Burke, R01HL107370
Schools of Nursing and Public Health
EMA
• Permits an estimation of antecedents to relapse-relevant events, e.g., lapses, urges, temptations
• Measures the situational or momentary state, the moderators and the mediators that may influence the occurrence of a slip, lapse or relapse
Schools of Nursing and Public Health
EMA Data Collection Protocols
• Signal or time contingent –• Beginning of Day - quantity and quality of
sleep and current energy level, plan for day• End of Day - how typical the day was in
terms of eating, exercise, mood, sleepiness, stressors, coping
• Random - 1-4 times during the waking hours• Event-contingent – self-initiated when a
predefined event occurs, e.g., urge, temptation
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EMA Application
EMPOWER ©University of Pittsburgh 2013
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EMA: Sound
EMPOWER ©University of Pittsburgh 2013
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Daily Report on Adherence
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EMPOWER ©University of Pittsburgh 2013
Schools of Nursing and Public Health
Technology Infrastructure
Edvin Music, MSIS, MBASenior Data Manager,
Burke ProjectsUniversity of Pittsburgh
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EMPOWER Technology Infrastructure: Development and Overview• Devices and data sources
• Planning
• Development and challenges
• Implementation
• Lessons Learned
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Devices and Data Sources
GT3X+ Monitor (Actigraph)
Actiwatch 2 (Respironics,Inc.)
Self-monitoring app (FitNow, Inc.)Apnea Link (ResMed)
Teleform & Process Data
Wi-fi enabled scale (Withings, Inc.)
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Planning Infrastructure • Data:
– Sources: Devices used
– Type: Paper-and-pencil vs. electronic
– Timing: interval and real-time
– Flow: relationships between devices
– Volume of data to be collected
• Supporting Technology
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Infrastructure Decision D
ata Driven
Timing
TypeSources
Serv
ers: Web & database
Syst
ems: EMPOWER
App, Tracking
Volume
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Development and Challenges
• Negotiations: University and third party
• Server acquisition: PHP and Oracle
• App programming: Android based
• Script programming for data flow
• Tracking systems: Oracle, MS Access
• Putting it all together and testing
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Lessons Learned• Early planning
• Thorough technology needs assessment
• Testing and training
• Stay on top of things
• Proactive, responsible, and timely team work
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Development in Brief
Brian French, BSPhD Student
Computer ScienceCarnegie Mellon University
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Coarse Timeline
• August 11, 2011: created initial empower EMA branch– Android application programming
– Webserver script programming
– Database configuration
• December-April 2012: internal testing• May 2012: first cohort starts data collection
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EMA App Programming:Most complicated piece of the picture• Starting point
– Existing EMA application designed for Tom Kamarck’s Behavioral Med. Research Group
– Additions and modifications– Improved reliability– Parameterized random scheduler (S. Rathbun)– Additional UI elements for new response
types– Encoding and transmitting data via Internet
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Webserver Programming:Design for Extension (1)
• Initial design was 4 scripts to transfer data between phones and the database– Receive interview data from phones– Send updated scheduler parameters to phones– Send application updates to phones– Pull LoseIt data into our database
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Webserver Programming:Design for Extension (2)• Expanded design added tracking of certain
phone events– Interviews scheduled, alarm volume change,
shutdown and boot up events, automated status checks, etc.
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Testing Process: Early and Often (1)
• ACRA – crash reporting software• Internal testing
– Purchased 5 handsets covering 4 manufacturers and 3-4 operating system versions
– Continuous feedback loop of error detection and correction
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Testing Process: Early and Often (2)
• Throughout 1st cohort of users– Went to weekly group meetings to interact
with users– Provided users with a bug report button to
capture unexpected behavior that didn’t lead to a crash
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Takeaway Points
• Development always takes more time than you expect
• Meet frequently with end-users to capture anomalous behavior
• Log everything you can think of
• Test on as many devices as you can, but accept that you will still be fixing bugs once you go into the field
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Sample
Target 150Enrolled 89
Retained 97.5%
Description of SampleFemale 89.9%
White 79.8%
Age 51.9 ± 9.3
BMI 33.6 ± 4.5
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Adherence to EMA Surveys (N = 89)
SurveyType
Completed Abandoned Missed
Random 87.5 ±12.3% 0.74%3 ±3.5% 11.8% ±9.8%
BOD 90.6% ± 12.7% 0.47% ± 0.8% 7.78% ± 7.8%
EOD 90.1% ± 12.3% 0.5% ± 1.6% 8.25 ± 7.4%
Self-initiated 96.8% ±8.7% 3.2% ± 8.7% NA
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Strategies to Enhance Recruitment, Retention and Adherence• $225 for phone and charger• $25 for data plan, answer 60% of random
prompts • Up to $10/month through reward system
($120/year)• $20 for 6-month assessment• $100 for 12-month assessment• Bus fare reimbursement
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Challenges We Have Faced
Recruitment – adapt as we learn participant behaviors – tweak protocol for each cohort
Self-initiated EMA surveys a challenge, also user accepting EMA updates
Steep learning curve for using technology in ~10%, assessment devices
Large team at different institutions requires more effort to coordinate but provides several perspectives
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Necessary for Team Science to Tackle Relapse Following Intentional Weight LossThrough the power of many and a diverse approach to our health care problems that we will realize lasting solutions
Diss & Slattery, 2010
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Acknowledge the EMPOWER Co-Investigators • Saul Shiffman, PhD• Lin Ewing, PhD• Asim Smailagic, PhD
• Daniel Siewiorek, PhD
• Patrick Strollo, MD
• Andrea Kriska, PhD
• Eileen Chasens, DSN, RN
• Steve Rathbun, PhD, Co-PI
Schools of Nursing and Public Health
Mindi Styn, PhDEdvin Music, MSIS, MBASusan Sereika, PhD Lin Ewing, PhD, RNAlison Keating, MSIndia Loar, MPHMelanie Warziski Turk, PhD, RNSushama Acharya, PhDJing Wang, PhDMolly Conroy, MD, MPHMary Ann Sevick, ScDOkan Elci, PhDLei Ye, BMedRachel Froelich, MSLeah McGhee, BSJulie Mancino, MS, RDN, CDEYaguang Zheng, MSNMeghan Mattos, MSN