using a smartphone application to capture sedentary behavior and multitasking among adolescents
TRANSCRIPT
Yue Liao, MPHEldin Dzubur, MSGenevieve Dunton, PhD, MPH
University of Southern CaliforniaInstitute for Health Promotion & Disease Prevention Research
USING A SMARTPHONE APPLICATION TO CAPTURE SEDENTARY BEHAVIOR AND MULTITASKING AMONG ADOLESCENTS
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Sedentary Behavior in Adolescents
An average U.S. adolescent spent 2.4 hours/day in TV viewing
1.5 hours/day in computer use
1.3 hours/day in video gamingIannotti & Wang, 2013
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Sedentary Behavior as a Risk Factor
www.activelivingresearch.org/sedentaryreview
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Multiple Behaviors and Multitasking
Most studies examined effects of one sedentary behavior or sitting time in general
Little is known about whether multiple sedentary behaviors might pose a greater health risk
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Measurement of Sedentary Behavior
Hardy et al., 2013; http://www.acaorn.org.au/streams/activity/method-selection/sedentary.php
Objective Methods Subjective Methods
Observation
Activity Monitor
Screen Monitoring Device
SenseCam
Questionnaire• Proxy/self-report• Usual/recall
Time Use Diary
Ecological Momentary Assessment (EMA)
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Aims of Current Study
• To demonstrate the use of EMA via a smartphone app
to capture multiple behaviors in adolescents’
daily lives• To describe the prevalence and type of multiple behaviors in a sample of adolescents
• To preliminarily test whether the engagement of multiple behaviors differed by demographic
factors (i.e., age, gender, ethnicity, and weight
category)
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Data Source
• The current study analyzed signal-contingent (i.e.,
random) EMA survey prompts from Mobile TEEN
An app that designed for Android-based mobile phone
• EMA survey was prompted up to 3 times per day on
weekdays (3 pm – 9 pm) and 7 times per day on
weekend days (7 am – 9 pm) for 14 consecutive
days
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Mobile Phone EMA Questions
• Each EMA survey assessed
type
body position
duration
of all activities performed in the past 30
minutes
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Sample Screenshots
Activity Type Body Position Length
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Analytical Plan
• A selection of “sitting” or “lying down” was defined
as a sedentary behavior for that activity
• A selection of more than one activity occurring during the same 30-min period indicatesengagement of multiple behaviors
• Analysis only included prompts with at least one
self-reported sedentary activity
• Multilevel logistic regression was conducted to test
whether the probability of multiple behaviors
(yes/no) differs by demographic variables
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Sample Characteristics
• 51 high school students from Los Angeles
Ages 14 – 19 years
54.9% female
56.9% Hispanic
39.2% overweight/obese
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Results
• On average, each participant answered 67 random
survey prompts across the 14 days
ranged 20 – 143 answered prompts each
• 90% of these prompts had at least one sedentary
activity reported
ranged 68% - 100%
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Sedentary Behavior by Type of Activity
0%
30%
60%
90%
98% 95% 92%68%
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Multiple Behaviors and Multitasking• Of all the sedentary prompts, 85% reported
one activity and 15% reported multiple
activities
• Of all the multiple activity prompts
75% reported engaging in 2 activities during the past 30 minutes
22% reported engaging in 3 activities 3% reported engaging in 4 or more activities
• The average combined length of all activity during
the past 30 minutes was 59.3 minutes for multiple activity prompts, implying some
degree of multitasking
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Multiple Behaviors – Using Technology• Of all the sedentary prompts where using
technology was chosen, 73% reported one activity and
27% reported multiple activities
• Of these multiple activity prompts reporting technology use:
69% reported engaging in 2 activities 34% with reading/doing homework 29% with eating/drinking 14% with hanging out
27% reported engaging in 3 activities 26% with eating/drinking and hanging out 17% with hanging out and reading/doing
homework 14% with eating/drinking and reading/doing
homework
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Multiple Behaviors – Using Technology by Gender
Female Male0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Using Technology and Other Behav-iors
Pre
dic
ted
Marg
inal P
rob
ab
il-
ity
Adj. Wald F = 4.64, p = .04
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Multiple Behaviors – Using Technology by Weight Category
Normal Weight Overweight Obese0%
10%20%30%40%50%60%70%80%90%
100%
Using Technology and Other Behav-iors
Pre
dic
ted
Marg
inal P
rob
-ab
ilit
y
Adj. Wald F = 2.66, p = .08
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Discussion
• EMA via smartphone app could be used as a self-
report tool to assess multiple sedentary behaviors among adolescents in their daily
lives
• Future research could explore the predictors and
health outcomes of multiple sedentary behaviors
• Protocol design needs to consider keeping the balance between capturing multiple
behaviors/multitasking and participant burden
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Acknowledgements
• Funding agencies NHLBI (1R21HL108018) (Dunton, Intille PIs) NHLBI (1R01HL119255-01) (Dunton, PI) NIEHS(5 P30 ES07048-16) (Dunton, PI on
pilot)
• Participants
• Project staff Bin Bo (App Developer) Keito Kawabata (Project Manager) Student interns