using a smartphone application to capture sedentary behavior and multitasking among adolescents

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Yue Liao, MPH Eldin Dzubur, MS Genevieve Dunton, PhD, MPH University of Southern California Institute for Health Promotion & Disease Prevention Research USING A SMARTPHONE APPLICATION TO CAPTURE SEDENTARY BEHAVIOR AND MULTITASKING AMONG ADOLESCENTS

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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