Decreasing Sedentary Behavior in Overweight Youth:

A Real-Time Mobile Intervention

Donna Spruijt-Metz, Gillian O'Reilly, Shrikanth Narayanan, Murali Annavaram, Ming Li,

Sangwon Lee, Cheng Kun Wendmetz@usc.edu

www.metzlab.net

BMI ≥ 85th Percentile (age 4-19)

Overweight has doubled in 30 years, and

Obesity has TRIPLED

Data from the National Longitudinal Survey of Youth 1986-1998, NHANES 1998-2010

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

Childhood Obesity: Metabolic Consequences

Visceral Fat

Insulin Sensitivity

Leptin Resistance

Inflammation

DiabetesCVD

Some Cancers

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)

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)

Decline in Moderate to Vigorous Physical Activity (MVPA) by Age

(Males + Females, n = 4867)Troiano, Berrigan, et al. MSSE 20087

Min

utes

/day

30 minutes a day is only 50% of recommended MVPA

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,

tailored & adaptive interventions

• Communication exchangeAnnavaram, et al 2008, Thatte et al 2009, 2009b,Lee et al 2009, Emken et al 2012

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

KNOWME System

Database Server

Web Server

SQL with Encrypted Data

End-to-end encryption of sensitive data

Check right to use systemFilter noisy updates

Web enable data access

3GGSMWi-Fi

Doctor

Data with Secure connection

Decryption KeysNokia N95

2 Alive Heart Rate Monitor/Accelerometers

(ECG/ACC)

KNOWME NETWORKS

In-lab Development: Behavioral Pattern

Recognition

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

3 sessions develop algorithm/1 session test algorithm

Lie Down

Sit Still

Sit &

Fidget

Stand Still

Stand &

Fidget

Stand &

Play Wii

Slow Walk

Fast Walk

Run

FREE LIVING

Li et al 2010

Predicted by the Model (84-94% accuracy)

Actu

al

% Normalized

Across Each Row

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?)

KNOWME: Great idea!

BUT WILL THEY WEAR IT????

ECG/ACC

ACC

Structure of Data Collecting Software

End-to-end Encryption of Sensitive Data

Device Manager

Local Storage[User Configuration]

[Analyzed Data][Raw Data]

Transmitter[Encrypt/Decrypt]

Analyzer[Plug-in

modules]

GPS ACC ECG

Data Collector

Service Manager

Client Application with GUI

Local Socket or IPC

KNOWME NETWORKS

Free-Living Out-of-Lab Feasibility Study

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:

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

In-home training

Wear KNOWME for 2 days

Remote monitoring

Trouble-shooting via text

Exit Interview

SunSat

SMS

KNOWME Knows you…

KNOWME NETWORKS

Real-Time Intervention to decrease Sedentary Time

Your Activity Meter

Sedentary = lying down, sitting, sitting & fidgeting, standing, standing & fidgeting Active = standing playing Wii, slow walking, brisk walking, running

Battery Indicator for Each Device

Sedentary Time (since the last reset)

Active Time in the Last 60 Minutes

Total Active Time

Each bar = 30 seconds20 bars = 10 minutes

Total Elapsed Time

The intervention: 1) When the gauge reaches 120 minutes of sedentary time, the phone will automatically begin delivering “Move!!” messages.

2) The sedentary gauge will automatically reset 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 sent from the website monitoring team

Sedentary Intervention

Design & Participant Demographics

Baseline ~3-day baseline Actigraph Accelerometer wear

Intervention ~3-day KNOWME+ Accelerometer wear

Sample Size 10

Mean Age 16.3 ±1.7 years

Mean BMI Percentile 97.2 ± 4.4

Sex 50% Female

Ethnicity 100% Hispanic

Baseline accelerometer versus KNOWME Wear Time

There was no significant difference in the duration of baseline accelerometer and KNOWME system wear

Mean Minutes (±SD)

Mean Hours (±SD)

Accelerometer 2035.5 (214.7) 33.9 (3.6)

KNOWME System 1417.6 (332.8) 23.6 (5.6)

Intervention Activity Measurement Accelerometer vs. KNOWME System

Mean Minutes by Actigraph*

(±SD)

Mean Minutes by KNOWME

(±SD)t-value p-value*

Sedentary 1594.7 (208.3) 987.2 (272.2) 9.27 <0.0002

Light 413.4 (163.4) 309.4 (102.2) 2.54 0.04

MVPA 1.8 (3.2) 129.6 (53.9) -7.72 <0.0002

*Evansen (J Sports Sci. 2008) cutpoints used to reduce accelerometry data

Baseline vs. Intervention Activity Levels Measured by Accelerometer

Mean Minutes Baseline (±SD)

Mean Minutes Intervention

(±SD)t-value p-value*

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

SMS Messaging During Intervention

Mean # SMS Messages (±SD)

Sent by Participants 33 (15)

Sent by Research Staff 43 (16)

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

• Participants• Research Team

• Funders Qualcomm NIMHD P60

002564

THANKS TO

Eat Well and Be Active!

Thank you! dmetz@usc.edu