EMA 24/7/365: From Concept to Implementation · Schools of Nursing and Public Health EMA 24/7/365:...

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


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


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


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


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

Weight Loss/Regain over 24 Months

Jakicic et al., 2008

Copyright restrictions may apply. Perri, M. G. et al., 2008

Weight Changes During Weight-loss and Extended-care Phases

Svetkey, et al., 2008

Weight Change in the Maintenance Study

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


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


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


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


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


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


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


EMA Application

EMPOWER ©University of Pittsburgh 2013

Presenter

Presentation Notes

The home screen of the EMA application is where you will see event interview which is where you will self initiate a prompt, change your BOD & EOD times, change alarm sounds, and break a delayed survey.


EMA: Sound


Presenter

Presentation Notes

By selecting change alarm you can choose a specific ring for the surveys, change the volume, and enable vibrate.


Examples of BOD Questions


Examples of EOD Questions


Daily Report on Adherence

Presenter

Presentation Notes

Lesson learned from the first cohort: participants wanted to know how well they did at completing the random surveys. They were concerned that they were missing alerts. This report appears at the end of the EOD survey.


Examples of Random Questions


Examples of Self-Initiated Questions



Presenter

Presentation Notes

We will download Lose It! application on your phone tonight if you have not already done so. The Lose It! application is where you will record all of your food, drink, and exercise. Tonight we will give you your individual calorie and fat gram goal for the study. How to use the application and examples of EMA surveys so you can practice recording your foods and answering the surveys. Open application you will see 5 tabs at the top of the page. My Day Tab: Today’s calories (sliding bar which will increase as you add food) This weeks calories, Today’s Nutrients (track your percent fat) Note: each one can be selected to show you more information and graphs.


Screen Shot of LoseIt for SM Diet


Option to Scan Bar Code for Food Entry


Scale Sends Weight to Phone

Withings wifi scale, withings.com


Technology Infrastructure

Edvin Music, MSIS, MBASenior Data Manager,

Burke ProjectsUniversity of Pittsburgh


EMPOWER Technology Infrastructure: Development and Overview• Devices and data sources

• Planning

• Development and challenges

• Implementation

• Lessons Learned


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.)


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


Infrastructure Decision D

ata Driven

Timing

TypeSources

Serv

ers: Web & database

Syst

ems: EMPOWER

App, Tracking

Volume


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


Lessons Learned• Early planning

• Thorough technology needs assessment

• Testing and training

• Stay on top of things

• Proactive, responsible, and timely team work


Development in Brief

Brian French, BSPhD Student

Computer ScienceCarnegie Mellon University


10,000 Foot view of the System


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


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


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


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.


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


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


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


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


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


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


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


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


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


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


Funding Source

nR01HL07370 and UL1RR024153

Real Time Data Collection with Adaptive Sampling and Innovative Technologiesologies

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