PROMOTING PHYSICAL ACTIVITY THROUGH MOTIVATIONALLY DISTINCT MOBILE PHONES APPS: THE MILES STUDY

Eric B. Hekler, PhDSchool of Nutrition and Health

Promotion

Arizona State University

Presenting on behalf of:

Abby C. King, PhDStanford Prevention Research Center

Stanford University

Collaborators: Abby King Tom Robinson Matt Buman Lauren Grieco Frank Chen Jesse Cirimele Beth Mezias Banny Banerjee Martin Alonso

Health promotion interventionsEvidence-based

Cost-effective

Tailored

Easy to disseminate

Promote maintenance

Introduction Mobile Interventions for Lifestyle Exercise and

Eating at Stanford (MILES)

NHLBI-funded Challenge Grant (10/09 – 08/12) PI- King, 1RC1HL099340-01

Status: Ran wave 1 with 36 older adults; iterated on design and almost complete with second wave of data collection for final sample of 80.

Purpose

Develop theoretically meaningful smartphone apps for midlife & older adults

Physical activity & sedentary behavior

Passively assess PA & SB

Provide just-in-time feedback for behavior change

Activity Algorithm Validation

Hekler, Buman, et al, 2010, November

N=15, Men & Women, Mean Age=55 12 laboratory-based activities 3-4 min each Hip- and pocket-worn Android phones Compared to Actigraph & Zephyr Bioharness

Validation Results

Hekler, Buman, et al, 2010, November

0 2000 4000 6000 8000 10000 120000

200

400

600

800

1000

f(x) = 0.0896939917730109 x + 55.0754613923821R² = 0.825752545985109

Comparison of Phone to Actigraph "Counts"

Minute-level "counts" Linear (Minute-level "counts")

Actigraph "counts"

Ph

on

e A

UC

m/s

3

The “Apps”

mTrack mSmiles mConnect

King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011

Control: Calofiric

Components study armsmTrack mSmiles mConnect Calorific

Push component X X X XPull component X X X X"Glance-able" display X X X XPassive activity assessment X X X XReal-time feedback X X X XSelf-monitoring X X X X“Help” tab X X X X

King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011

Components study armsmTrack mSmiles mConnect Calorific

Push component X X X XPull component X X X X"Glance-able" display X X X XPassive activity assessment X X X XReal-time feedback X X X XSelf-monitoring X X X X“Help” tab X X X XGoal-setting X XFeedback about goals X X

Problem-solving X XReinforcement X X XVariable reinforcement schedule X XAttachment X"Play" X"Jack pot" random reinforcement XSocial norm comparison XCompetition/collaboration X

King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011

MILES Study Design

Activity Assessment, Continuous

Ecological Momentary Assessment, Daily

Real-time use of phone features

Assess:

ModeratorsSelf-reportPA, Sed Beh

Assess:

AcceptabilitySelf-reportPA, Sed Beh

Randomize

mTrack (Cognitive App, n=20)

mSmiles (Affect App, n=20)

mConnect (Social App, n=20)

Diet Tracker Control App (n=20)

Week8Week2Week1

Visit1 Visit2, check in Visit3

Pre-study

Baseline Feedback Follow up

King, Hekler, et al. April, 2012, Hekler, et al. 2012, Hekler et al. 2011

(n = 30 inactive, smartphone-naive adults ages > 45 yrs)2-mos Daily Increases in MVPA vs. Control (Calorific)

5

10

15

20

??? ??? ???

P = .39

P < .01

P < .01

King, Hekler, Grieco, Winter, Buman, et al., Ann Behav Med, 2012 (abstract)

Preliminary Activity ResultsM

VP

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et

Inc

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e M

inu

tes

/Day

- S

ma

rtph

one

Acc

ele

rom

ete

r

MV

PA

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ase

Min

ute

s/D

ay -

Sm

art

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ne A

cce

lero

me

ter

5

10

15

20

Cognitive Affect Social

P = .39

P < .01

P < .01

King, Hekler, Grieco, Winter, Buman, et al., Ann Behav Med, 2012 (abstract) Which App for WHOM?

(n = 30 inactive, smartphone-naive adults ages > 45 yrs)2-mos Daily Increases in MVPA vs. Control (Calorific)

Preliminary Activity Results

Preliminary Eating Results

Hekler, King, et al. April, 2012 (N=30)

Veget

ables

Fruits

Proce

ssed

Foo

ds

Sweets

Fatty

Mea

ts

Fatty

Dair

y

-4

-2

0

2

4

6

∆C

on

su

mp

tio

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erv

ing

s/w

k

Food-tracking App

Average of ActivityApps

Conclusions & Next Steps

Game dynamics/operant conditioning and social comparison appear more influential than goal-setting and feedbackMay be due to specificity of data

Redesigned apps, running a second wave now

Exploring the use of other research methods for testing (e.g., Multiphase Optimization Strategy, Linda Collins et al., 2010).

Thank you!

Abby King

king@stanford.edu

Eric Hekler

desiginghealth.lab.asu.edu

Twitter: @ehekler

ehekler@asu.edu