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6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention
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6.1 – Decreasing Sedentary Behavior in Overweight Youth Using a Real-Time Mobile Intervention

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Wednesday, October 24, 2012 …

Wednesday, October 24, 2012
Technical Session #6

Donna Spruijt-Metz (University of Southern California, US), Gillian O’Reilly (USC, US), Shrikanth Narayanan (USC, US), Murali Annavaram (USC, US), Ming Li (USC, US), Sangwon Lee (USC, US), Cheng Kun Wen (USC, US)

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  • Psychosocial Concerns: cheat, lie, sloppy, naughty, mean, ugly, stupidLeptin is a satiety signalInsulin resistance is where normal amounts of insulin are inadequate to produce a normal insulin response. Insulin resistance in muscle cells reduces glucose uptake (and so local storage of glucose as glycogen), and insulin resistance in liver cells results in impaired glycogen synthesis and a failure to suppress glucose production.
  • Visceral fat is an active endocrine organ, secreting hormones and proteins that speak to body and brainPsychosocial Concerns: cheat, lie, sloppy, naughty, mean, ugly, stupidLeptin is a satiety signalInsulin resistance is where normal amounts of insulin are inadequate to produce a normal insulin response. Insulin resistance in muscle cells reduces glucose uptake (and so local storage of glucose as glycogen), and insulin resistance in liver cells results in impaired glycogen synthesis and a failure to suppress glucose production.
  • However, youth aren’t meeting this recommendations. This is all eth combined & see same trends across race/ethnic groupsHard to intervene onSo, this dissertation focuses on some of the factors that may be related to low levels of activity in youth.
  • Frustration with having data and not using itFrustration with current theories of behaviorFrustration with the slow progress of changing people’s behavior and/or healthExplain mHealth – measurement, also intervention. Understand behavior in real time, in context. Intervene in real time, in context.
  • Bring devices to show?
  • Unstructure & identifiableJustification for Wii & fidgeting (NEAT and active gaming)
  • Collapse diagram? Decisions for all subjects
  • Transcript

    • 1. Decreasing Sedentary Behavior in Overweight Youth:A Real-Time Mobile Intervention Donna Spruijt-Metz, Gillian OReilly, Shrikanth Narayanan, Murali Annavaram, Ming Li, Sangwon Lee, Cheng Kun Wen dmetz@usc.edu www.metzlab.net
    • 2. BMI ≥ 85th Percentile (age 4-19) Data from the National Longitudinal Survey of Youth 1986-1998, NHANES 1998-2010
    • 3. Childhood Obesity:Psychosocial Consequences • Proximal consequences: – Negative stereotyping – Teasing – Fewer friends – Poor body image • Distal consequences: – Lower educational attainment – Discrimination (apartment rentals, college admissions) – Higher poverty
    • 4. Childhood Obesity:Metabolic Consequences Insulin Sensitivity Diabetes Visceral CVD Leptin Fat Resistance Some Cancers Inflammation
    • 5. Physical activity• Decreases adiposity (Lazaar, 2007; Baxter-Jones, 2008)• Improves insulin sensitivity (Imperatore, 2006; Carrel, 2009)• Protects against breast, colon and other cancers (Bernstein, 2004; Percik, 2009)• Improves lipid and cholesterol profiles (Tolfrey, 2000; Pam, 2008)• Alleviates stress and depression (Ortega, 2008; Dockray, 2009)• May improve cognitive function (van Praag, 2009; Li, 2008) and academic performance (Trudeau, 2008; Datar, 2008)
    • 6. Sedentary Behavior• Increases risk for metabolic syndrome (Edwardson, Gorely, Davies, et al, 2012)• Increases risk for cardiovascular disease (Saunders, 2011)• Increases risk for obesity (Saunders, 2011)• Increases risk for hyperglycemia & type II diabetes (Dunstan, Salmon, Owen, et al, 2007)• Related to premature mortality (Healy, Matthews, Dunstan, Winkler, Owen, 2003)• Increases likelihood of participating in risky behaviors in adolescents (Nelson, Gordon-Larsen, 2006)
    • 7. 30 minutes a day is only 50%Minutes/day of recommended MVPA 7
    • 8. mHealth: Measure, Understand, Intervene• Bypass bias – measure ubiquitously and well• Real or near-time data• Understand behavior in time & in context• Share with participants, clinicians […]• Real- time, personalized, tailore d & adaptive interventions• Communication exchange Annavaram, et al 2008, Thatte et al 2009, 2009b,Lee et al 2009, Emken et al
    • 9. KNOWME Networks• A suite of mobile, Bluetooth- enabled, wireless, wearable sensors• That interface with a mobile phone and secure server• To process data in real time,• Designed specifically for use in overweight minority youth
    • 10. KNOWME System Check right to use system Filter noisy updates End-to-end encryption of Web enable data access sensitive data 3G GSM SQL with Wi-Fi Encrypted Data Web Server Nokia N95 Decryption Keys 2 Alive Heart RateMonitor/Accelerometers Database Server Data with Secure (ECG/ACC) connection Doctor
    • 11. KNOWME NETWORKSIn-lab Development: Behavioral Pattern Recognition
    • 12. In-Lab Physical Activity Detection  Developing and testing algorithms to accurately classify types of PA in 20 overweight Hispanic youth  10 F/10M; 14.6 ± 1.8 years old; BMI %tile 96 ± 4  Protocol: 9 activities, 7 minutes/activity F L G R I E V E I N Lie Sit Sit Stand Stand Stand Slow Fast Run Down Still & Still & & Walk Walk Fidget Fidget Play Wii  3 sessions develop algorithm/1 session test algorithm Li et al 2010
    • 13. Predicted by the Model (84-94% accuracy) % Normalized Across Each Row
    • 14. Accuracy of State Detection• Overall accuracy: – 84% with all 9 activities – 94% when collapsed to 7 activities• IMPORTANT FACT: If you are going to ‘share’ data with your participants it needs to be ACCURATE (How accurate??? And what does this mean in the eyes of the participant?)
    • 15. End-to-end Encryption of Sensitive DataECG/ACC Client Application with GUI Local Socket or IPCACC Service Manager Analyzer Local Storage [Plug-in [User Configuration] Transmitter [Analyzed Data] [Encrypt/Decrypt] modules] [Raw Data] Data Collector Device Manager GPS ACC ECG Structure of Data Collecting Software
    • 16. KNOWME NETWORKSFree-Living Out-of-Lab Feasibility Study
    • 17. Will/Can youth use this system in the real? Subjects: 12 overweight Hispanic youth  5F/7M; 14.8 ± 1.9 years old; BMI %tile 97 ± 3 Protocol: Sat Sun In-home Wear KNOWME Remote Trouble-shooting Exit training for 2 days monitoring via text Interview Results/day  Wore KNOWME for 11.4 ± 2.0 hours  Phone battery life 9.2 ± 2.6 hours  8 SMS sent to us / 9 SMS received from us
    • 18. KNOWME Knows you…
    • 19. KNOWME NETWORKSReal-Time Intervention todecrease Sedentary Time
    • 20. Your Activity Meter Active Time in the Last 60 Minutes Each bar = 30 seconds 20 bars = 10 minutes Sedentary Time (since the last reset) Total Active Battery Indicator Time for Each Device Total Elapsed TimeSedentary = lying down, sitting, sitting & fidgeting, standing, standing & fidgetingActive = standing playing Wii, slow walking, brisk walking, running
    • 21. Sedentary InterventionThe intervention:1) When the gauge reaches 120 minutes ofsedentary time, the phone will automaticallybegin delivering “Move!!” messages.2) The sedentary gauge will automaticallyreset to 0 minutes: 1) Following 10 minutes of active time within a 60 minute period; or 2) One hour after 140 minutes of sedentary time is reached if participant doesn’t respond (time out) 3) Researchers are notified at each reset.3) Personalized text messages can be sentfrom the website monitoring team
    • 22. Design & Participant Demographics ~3-day baseline ActigraphBaseline Accelerometer wear ~3-day KNOWME+Intervention Accelerometer wearSample Size 10Mean Age 16.3 ±1.7 yearsMean BMI Percentile 97.2 ± 4.4Sex 50% FemaleEthnicity 100% Hispanic
    • 23. Baseline accelerometer versus KNOWME Wear Time Mean Minutes Mean Hours (±SD) (±SD)Accelerometer 2035.5 (214.7) 33.9 (3.6)KNOWME System 1417.6 (332.8) 23.6 (5.6)There was no significant difference in the duration ofbaseline accelerometer and KNOWME system wear
    • 24. Intervention Activity Measurement Accelerometer vs. KNOWME System Mean Minutes Mean Minutes by Actigraph* by KNOWME t-value p-value* (±SD) (±SD)Sedentary 1594.7 (208.3) 987.2 (272.2) 9.27 <0.0002Light 413.4 (163.4) 309.4 (102.2) 2.54 0.04MVPA 1.8 (3.2) 129.6 (53.9) -7.72 <0.0002*Evansen (J Sports Sci. 2008) cutpoints used to reduce accelerometry data
    • 25. Baseline vs. Intervention Activity Levels Measured by Accelerometer Mean Minutes Mean Minutes Intervention t-value p-value* Baseline (±SD) (±SD)Sedentary 1765.5 (357.7) 1594.7 (208.3) 1.28 0.1Light 436.6 (222.3) 413.4 (163.4) 0.75 0.2MVPA 0.3 (0.6) 1.8 (3.2) -1.45 0.09*1-tailed, significance level set at 0.1
    • 26. SMS Messaging During Intervention Mean # SMS Messages (±SD)Sent by Participants 33 (15)Sent by Research Staff 43 (16)
    • 27. KNOWME: Conclusions and Next Steps• Research & Technology: Tortoise and Hare?• Hopeful pilot results for a tough chore• “Its like having a Doctor in your pocket!”• Did SMS prompts lead to physical activity behavior change responses ? How long did it it take? How long did it last?• Next steps: Redevelop system with new hardware, new software, geared for long-term wear• Full clinical trial
    • 28. THANKS TO• Participants• Research Team• Funders  Qualcomm  NIMHD P60 002564
    • 29. Eat Well and Be Active!Thank you! dmetz@usc.edu

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