The Role of Self- Monitoring in Changing Behavior Focus on Weight Loss Treatment Lora E. Burke, PhD, MPH, FAHA, FAAN School of Nursing and Graduate School of Public Health University of Pittsburgh Industry Nutrition Advisory Panel American Heart Association March 12, 2012
Overview Self-monitoring process and underlying theory Brief review of self-monitoring studies Limitations of paper diaries Use of technology current/future Implications for food industry
Self-Monitoring in Weight Loss Is the systematic recording of one s eating or physical activity behaviors Purpose is to increase one s awareness of eating behaviors Should occur in real time Traditionally, food and physical activity, recently weight self-monitoring (SM)
Self-Regulation Theory Self-regulation has 3 distinct stages: self-monitoring self-evaluation self-reinforcement SM precedes self-evaluation and selfreinforcement in an individual s learning process
Self-Regulation Theory SM is central to self-regulation process Successful self-regulation depends on the fidelity, consistency & proximity of SM
Self-Monitoring and Weight Change: Clinical Study 56 adults in clinical treatment for wt loss Given booklet weekly to record foods/calories Results: Those in the highest level of SM lost significantly more wt than ones at lower levels Consistency/quality of SM in initial wks predicted weight loss over 6-mos period Baker & Kirschenbaum, 1993
Recommendations Important to view SM not only as a process that mediates weight control but also an important outcome Need to develop means of sustaining SM Baker & Kirschenbaum, 1993
Why Study Self-Monitoring? Anecdotal evidence - ongoing Observations of waiting room adherence
Limitations of Paper Diaries
Use of Instrumented Paper Diary Purpose of study: To examine and describe the actual patterns of self-monitoring among participants in a weight loss intervention study, using an instrumented paper diary (IPD) Conducted as an ancillary study to a 12-mon Behavioral Wt Loss Program (BWLP), n = 35, 13, 16 across 3 phases Burke, Sereika, Choo et al., 2006
Instrumented Paper Diary Photosensor detects opening & closing of binder Unobtrusive instrumentation records time/date -circuit board in right pocket Plastic ties lock rings closed invivodata, inc., Pittsburgh, PA
Adherent: IPD Report Time Day of Monitoring Period
Adherent: IPD Report and Self-Report of Eating Time Day of Monitoring Period
Adherent: IPD Report and Self- Report of Eating and Recording Time Day of Monitoring Period Burke et al., 2006; 2008
Sample Diary Page - Adherence Burke et al., 2006; 2008
Sample Diary Page - Adherence Burke et al., 2006; 2008
Hoarding: IPD Report Time Day of Recording
Hoarding: IPD Report and Self-Report of Eating Time Day of Recording
Hoarding: IPD Report and Self- Report of Eating and Recording Time Day of Recording Burke et al., 2006; 2008
Sample Diary Page - Hoarding Burke et al., 2006; 2008
Summary of Diary and IPD Use Variable M ± SD Mean # IPD openings/day 2.7 ± 1.8 Mean # of diary entries/day 4.1 ± 2.2 Mean time btw eating & recording (hrs) 6.4 ± 17.1 % of days IPD was used 64.9 ± 27.4 % eating entries w/in 2 hrs of recording 51.5 ± 29.9 % eating entries w/in 6 hrs of recording 66.8 ± 30.2 % of recording w/in 15 min. IPD opening 39.5 ± 34.0 % of eating w/in 15 min. IPD opening 20.5 ± 22.1 Burke et al., 2006; 2008
Summary of Diary and IPD Use Variable M ± SD Mean # IPD openings/day 2.7 ± 1.8 Mean # of diary entries/day 4.1 ± 2.2 Mean time btw eating & recording (hrs) 6.4 ± 17.1 % of days IPD was used 64.9 ± 27.4 % eating entries w/in 2 hrs of recording 51.5 ± 29.9 % eating entries w/in 6 hrs of recording 66.8 ± 30.2 % of recording w/in 15 min. IPD opening 39.5 ± 34.0 % of eating w/in 15 min. IPD opening 20.5 ± 22.1 Burke et al., 2006; 2008
Correlations of Recording and Weight Change Baseline 6 Months Variable r value % wt 0-6 mos. p value % days IPD used -0.52.001 % recordings w/in 15 min IPD opening -0.36.04 % diary recordings of eating w/in 15 min IPD opening -0.45.007 Burke et al., 2006; 2008
Summary of IPD Study Findings Little concordance between SR and electronically documented data First evidence of actual patterns of SM Recording within 15 min. of eating significantly correlated with weight loss Confirms adherence to SM results in improved weight loss; findings replicated in later study (Sereika et al, 2011) Burke et al., 2006; 2008
Qualitative Study of SM Experiences Post completion of above trial - conducted in-depth interview of 15 Ss to explore their reflections on SM, use of PD Qualitative analysis procedures were followed to analyze the data
Qualitative Study Results 3 categories of SM experiences identified: Well-Disciplined high SM adherence high wt loss, can do positive approach; 4 of 5 male, all 1 st time in BWLP Missing the Connection mod. adherence, mod to low wt loss, unable to sustain the connection between SM and wt loss Diminished Support poor adherence, poor wt control, adversely affected by co-existing negative factors Burke, Swigart, Warziski Turk et al., 2009
% Adherence Trajectory of Mean Adherence to Self-Monitoring (N = 15) 100 80 60 40 Well Disciplined Missing the Connection Diminished Support 20 0 1-3 mo 4-6 mo 7-9 mo 10-12 mo Time Burke et al., 2009
% Weight Loss Trajectory of Mean Percent Weight Loss (N = 15) 25 20 15 10 Well Disciplined Missing the Connection Diminished Support 5 0 6 mo 12 mo 18 mo Time Burke et al., 2009
Studies Using Technology
Self-Monitoring in Internet Studies (1) Compared structured Internet BWLP to a weight loss education, 6-mo study # of diaries submitted related to wt loss, r =.50, p =.001 Tate, Wing & Winett, 2001 Compared Internet wt loss program alone to Internet wt loss + behavioral counseling via e- mail for 12 mos. Logins correlated with wt loss, r = -0.47, p =.003 Tate, Jackvony & Wing, 2003
SMART Trial Aim: compare 3 approaches to SM: 1) paper diary 2) PDA with diet & PA software 3) PDA with tailored feedback message All participants received the same standard behavioral treatment intervention; group sessions weekly with gradual decline in frequency, one session in last 6 months Burke, Styn, Glanz et al., 2009
SMART Trial - Participant Flow 704 Screened for eligibility 210 randomized PD (n= 72) PDA (n= 68) PDA+FB (n= 70) 24-month Assessment n = 62, 86.1% n = 59, 86.8% n = 59, 84.3% Burke et al., 2012
Comparison of 3 SM Approaches Group 1: Traditional paper diary (PD) Group 2: PDA with dietary and exercise software (PDA) Group 3: Same as Group 2 + daily diet feedback message (PDA+FB), PA every other day
Proportion of Ps Adherent to Dietary SM 0-6 Mos. by Tx Group Burke, Elci, Wang et al., 2009
Percent weight change Percent Weight Change by Treatment Group, Baseline to 18 Months (N=210) 0-1 PD PDA PDA+FB -2-3 -4-5 -6-7 -8-9 -10 0 6 12 18 Month Burke et al., 2012
Percent of time Percent of Time Adherent to SM 100 80 60 40 20 0 0-6 7-12 13-18 Months PR PDA PDA+FB Burke, Styn, Sereika et al., 2012, in press, AJPM
Percent Weight Change by Adherence to Self-Monitoring 0 Months 6 12 18-5 -10-15 <30% adherent 30%-59% adherent >=60% adherent Burke et al., 2012 in press
Summary of Self-Monitoring Studies IPD study revealed that self-reported times of SM may not be accurate Qualitative study revealed variations in how individuals integrated the process of SM SMART trial data showed improved adherence to SM with PDA use, which mediated weight change receiving feedback messages improved adherence and weight loss
Summary of Self-Monitoring Studies Many find SM burdensome, difficult to sustain adherence to SM Systematic review showed consistent positive association between SM and weight loss SMART data would suggest that delivering tailored feedback messages in real time improves adherence and weight loss
Tools Available to Self-Monitor Paper diaries can use own personal appt. book or diary; food diaries available Internet has programs for self-monitoring diet, exercise, weight Smartphones increasingly more apps available, lacking an evidence base
Smartphone Self-Monitoring Programs
Smartphone Self-Monitoring Programs Features Can scan food package bar code for direct entry Can store recipes and get estimate of calories Can create/save meals
Option to Scan Bar Code for Food Entry
Entry from Bar Code Scan
Pros & Cons of Electronic Diaries Pros: Extensive database Calculates subtotals Monitors several nutrients Provides feedback related to goals May create & store meals Electronic diary socially acceptable Cons: Literacy required for all diaries May lack ethnic foods Need to learn use of hardware/software Burke et al., 2005; 2011
Smart Phones Use to collect data in real time Include a date stamp Can assess SR diet and PA Accelerometer contained in most provides gross estimate of PA Camera for bar code scan May also provide context data (GPS)
Current Application of Mobile Technology EMPOWER Ecological Momentary Assessment (EMA) assesses individuals experiences as they occur in real time and in the natural environment Primary purpose of study: describe the micro-processes of relapse following intentional weight loss Burke, R01HL107370
EMPOWER Study Smartphones are ideal for EMA data collection Queries a participant 1-5 times/day to ask about current mood, hunger, activity, energy, self-efficacy, etc. Participant initiates a query related to urge/temptations Query at beginning and end of day Burke, 2011, R01HL107370
Smartphone Screen with EMA Questions Burke et al., 2009
Scale Sends Weight to Phone
Future Directions for Monitoring of Food Intake and other Behaviors
ebutton
The ebutton Mingui Sun, PhD, University of Pittsburgh, patents pending, funding support U01 HL091736 under the NIH Gene, Environment, and Health Initiative (GEI)
ebutton - Typical Pictures of What the Camera Captures Mingui Sun, PhD University of Pittsburgh, Department of Neurosurgery ebutton development supported by NIH grant U01HL091736 under the NIH Gene, Environment, and Health Initiative (GEI)
Eating
Cooking
Shopping
Yard Work
ebutton Assessment of Portion Size Sun, Mingui 2011
Current Status of ebutton Undergoing extensive validation testing Refinement of the data reports and the hardware are in progress
Future Directions Use technology to reduce burden of SM and improve adherence Technology can facilitate use of realtime feedback to reinforce behavior changes and improve adherence Need to establish evidence base for SM software programs (apps) that are rapidly proliferating
Why is this Important to Food Industry? Individuals will become increasingly more aware of food content Will compare across brands at their fingertips Will enable consumer to make informed choices/shopping list prior to supermarket visit
Acknowledgements Funding sources: NIH, NIDDK R01 DK5863 and DK5863-04S1, R01 DK071817 and the Center for Research in Chronic Disorders, P30 NR03924 NIH, School of Nursing at the University of Pittsburgh K24NR010742 R01HL107370