smartphone apps talk given at the international conference for behavioral medicine
DESCRIPTION
TRANSCRIPT
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
A N
et
Inc
reas
e M
inu
tes
/Day
- S
ma
rtph
one
Acc
ele
rom
ete
r
MV
PA
Ne
t In
cre
ase
Min
ute
s/D
ay -
Sm
art
pho
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
n s
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
Eric Hekler
desiginghealth.lab.asu.edu
Twitter: @ehekler