Harnessing Mobile Technology
for Health Behavior Change
Bonnie Spring
Northwestern University
Center for Behavior and Health
Overview
1. Why bother with technology supported intervention?
2. Hare versus tortoise timelines: technology development
versus public health evidence
2. Can widgets do magic?
3. Behavior change mechanisms in the digital ecology
• Self-monitoring
• Social support
4. Emergence of optimized, adaptive, dynamic interventions
for Precision Behavioral Medicine
Evolution of Technology
/integrated
circuit
Abundant new sensors, apps, and devices
Microsoft High Tech Bra
Czerwinski M, Johns P, Kapoor A, et
al. Food and Mood: Just-in-Time
Support for Emotional Eating.
Psychological Science. 2013
6
>1000 apps for weight loss
>20% U.S. adults using a health tracker
What is the evidence that
these work?
R. Kaplan & A. Stone (2013)
Annu. Rev. Psychol. 64:15.1–15.28
• 21 published RCTs
• Only 6 show significant benefit for mhealth intervention
versus control
************
2015:
38 mHealth RCTs for weight loss, physical activity, smoking
Successful trials:
weight loss: ~50% (but not text alone)
physical activity: 66% (mixed evidence re. texts)
smoking: most (text alone effective, tho’ 90% still
don’t quit)
Results: After 6 months,
weight change was minimal,
with no difference between
groups (mean between-group
difference, 0.30 kg [95% CI,
1.50 to 0.95 kg]; P 0.63)…..
MyFitness Pal in primary care vs. usual care (n=212)
9
Under Armour Buys 2 Fitness
Apps, Including MyFitnessPal
Fitness-tracking apps like MyFitnessPal (80
mill registered) and Endomondo (20 mill reg)
allow users to track their diets and workouts.
Jim Wilson / The New York Times
By MICHAEL J. DE LA MERCED
February 4, 2015
10
Spring et al (2013 JAMA Internal Medicine, 173(2):105-111
Spring et al (2012). Arch Intern Med, 172(10), 789-796
Make Better
Choices Study
Techniques
●Goal setting
●Self-monitoring
●Feedback
●Incentives
●Social support/
Accountability
Low FV = < 5 /day
High Sat => 8% kcal
Low PA =< 60 min/day
mod-vig PA
High Sed = >120
min/day sed
leisure
Inducing
Change in Multiple Health Behaviors
N = 204 adults
Make Better
Choices
Have ALL of:
Hypotheses:
1. Familiarity - ↓Fat↑PA > others
2. Optimal substitution - ↑FV↓Sed>others
Low FV = < 5 /day
High Sat => 8% kcal
Low PA =< 60 min/day mod-vig PA
High Sed = >120 min/day sed leisure
↑Healthy Eat (FV+)
↓Unhealthy Act (Sed-)
↑ Healthy Eat (FV+)
↑ Healthy Act (PA+)
↓ Unhealthy Eat (Fat-)
↓Unhealthy Act (Sed-)
↓Unhealthy Eat (Fat-)
↑ Healthy Act (PA+)
Spring et al (2012) Arch Intern Med, 172(10), 789-796
Make Better Choices Trial
Treatment:
↑Healthy Eat (FV+)
↓ ↓Unhealthy Act (Sed-)
N = 204 adults
Have ALL of:
12.3% sat fat
9.8% SF
Low FV = < 5 fruits/vegetables/day
High Sat => 8% kcal from sat fat
Low PA =< 60 min/day mod-vig PA
High Sed = >120 min/day sed
leisure
Spring, Schneider, McFadden et al (2012) Arch Intern Med, 2012;172:789-796
Supported by HL075451
Change in Self Efficacy by Treatment
Saturated Fat
FV+ PA+
Fat- Sed-
Targeted Sedentary Leisure
FV+ Sed-
Fat- PA+
3.1
FV+ PA+
Fat- Sed-
FV+ Sed-
Fat- PA+
3.3
F(3, 167) =4.307, p < .01
F(3, 167) =1.313, p = .272
**
3.2
3
3.1
3
2.9
2.9
2.8
2.8
*
2.7
2.7
2.6
Screening
Post Rx
Screening
Physical Activity
FV+ PA+
Fat- Sed-
FV+ Sed-
Fat- PA+
3.5
F(3, 167) =3.599, p < .05
3.4
Post Rx
Fruits & Vegetables
*
FV+ PA+
3.4
F(3, 167) =7.491, p < .001
3.3
3.2
3.1
3.3
Fat- Sed-
FV+ Sed-
Fat- PA+
***
*
3
2.9
3.2
*
3.1
2.8
2.7
2.6
*
2.5
3
Screening
Post Rx
Screening
Post Rx
Implications………….
•Not all health behaviors are the same
•Increasing fruit/veg, decreasing sed maximizes
magnitude and maintenance of healthy change
•Decreasing saturated fat intentionally reduces
magnitude and maintenance of healthy change
Appears to undermine self-efficacy
•Decreasing sat fat unintententionally via complementary
and substitute change doesn’t undermine self-efficacy
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Dual Processes of Self-Regulation
Type 1 processes: fast, automatic and non-conscious. Automatically
triggered, cued, primed, defaults, unconscious (habits). E.g., Dual
Process Theory
Type 1 interventions (LOW BURDEN): stimulus control,
conditioning, cues, defaults, environmental, social network
Type 2 processes: slow, deliberative, conscious, self-initiated effortful
(decisions).
E.g., Social Cognitive Theory, Control Systems Theory
Type 2 interventions (HIGH BURDEN): education, self-monitoring,
goal setting with feedback, planning, problem solving
Inhibition of automatic processes very challenging
Sustaining Self-Monitoring Engagement: Tech + Human
+Mobile Study (all new referrals to MOVE!)
Record 2
weeks
R
Randomize
Standard:
MOVE group
[w/ paper
recording]
+Mobile:
MOVE group
w/ connected
mobile
recording
JAMA Int Med,
2013, 173(2):105-111
JAMA Int Med,
2013, 173(2):105111
10
Weight Loss over Time as a function of
Treatment Assignment and MOVE! Adherence*
*Adherent
5
0
Widgets don’t do
magic
-15
-10
-5
Support for hybrid
intervention
-20
Mean weight change (lbs)
= Attended
>80% of
treatment
sessions.
+Mobile non-adherers
Standard non-adherers
Standard adherers
+Mobile adherers
0
3
6
month
9
12
JAMA Int Med,
2013, 173(2):105-111
ENGAGED Trial – Same intervention or different?
Techniques
●Goal setting
●Self-monitoring
●Feedback
●Incentives
●Social support/
Accountability
RC1DK087126
Social Support and Accountability
Message Board Themes
Support:
“Happy Birthday,Philly!
May you eat well, but stay
within the Fan Meter safe
zone…”
App-Related:
“Happy that I got a ‘green
3’ today.”
“Love Shimmer. So
reinforcing!”
“Hey Peppa, Ok. I’m in for
the three-peat flag feat.”
ENGAGED: E-Networking Guiding Adherence to Goals in
Exercise and Diet (RC1DK087126)
Can we cut treatment intensity in half but preserve
weight loss by using mHealth to reconfigure treatment
components?
(1)Technology (app, wireless accelerometer, social media)
(2) Control (traditional paper and pencil records)
(3) Self-Guided(traditional paper and pencil records)
1 & 2 receive same 8 modified DPP group sessions, brief phone coaching
Self-Guided receives DPP group sessions delivered via DVD, no coaching
All receive team weight loss incentives
Demographics
All Participants
(n=96)
39.3 (11.7)
Self-Guided
(n=32)
40.1 (11.1)
Standard
(n=32)
37.3 (13.3)
TechnologySupported
(n=32)
40.4 (10.7)
Female, No. (%)
84.4
84.4
81.3
87.5
Ethnicity, No. (%)
Hispanic or Latino
Not Hispanic or Latino
19.8
80.2
15.6
84.4
31.3
68.8
12.5
87.5
Race, No. (%)
White
Black or African American
57.3
31.3
53.1
34.4
62.5
18.8
56.3
40.6
College Graduate or above (%)
68.8
71.9
65.6
68.8
Weight (kg)
94.8 (12.4)
93.5 (11.0)
96.0 (14.6)
94.7 (11.6)
BMI (kg/m2)
34.6 (3.0)
34.3 (3.2)
34.8 (3.0)
34.8 (2.8)
Waist Circumference (cm)
96.0 (8.7)
95.6 (9.9)
96.2 (8.3)
96.3 (8.1)
Characteristic
Age, mean (SD) (y)
Abbreviations: BMI, body mass index
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Self-monitoring Adherence
Mean ± 1 SE
Self-Guided
Standard
Outcome Variable
P Value
TechnologySupported
TECH and
STND vs.
SELF
TECH vs. STND
(n=32)
(n=32)
(n=32)
Diet
16.0(4.6)
32.9(3.9)
48.0(4.1)
p<.001
p<.05
Exercise
9.3(2.4)
30.1(4.4)
56.8 (4.8)
p<.001
p<.001
Adherence (% days)
STND: Standard Weight Loss Program; TECH: Technology-Supported Weight Loss Program; SELF:
Self-Guided Weight Loss Program
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Weight Change (% of Baseline body weight) at 3, 6, and 12 Months
Bars reflect +/- 1 Standard Error
0
-1
*
-2
% weight loss
-3
-4
-5
-6
n.s.
n.s.
n.s
.
-7
-8
-9
01/01/2001
Baseline
01/10/2001
301/04/2001
months 01/07/2001
6Title
months
1201/01/2002
mos
Axis
12 months
Only 8 (vs. 16-36)
treatment
26
sessions
3/20/2015
ENGAGED POST-STUDY SURVEY: Technology
Percent of Tech Group who
Consider Tech the Most
Important Component:
83%
Percent of Each Group That
Wants More Tech
•Self-Guided:
•Standard:
•Tech:
25%
21%
33%
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ENGAGED POST-STUDY SURVEY: Self-Monitoring vs. Social Support
Per Cent of Responses
90
80
70
60
50
40
30
20
10
0
Self-Monitoring
Most Helpful
Self-Monitoring
Least Helpful
Self-Guided
Social Support
Most Helpful
Standard
Lack of Support a
Barrier
Tech
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Most users reported high
satisfaction with MyFitnessPal,
but logins decreased sharply
after the first month.
At 3 months, self-efficacy for
weight loss increased +0.5
0 for controls but decreased
-0.44 for intervention (p<.05)
32% of intervention group
participants and 19% of
control group participants
were lost to follow-up at 6
months.
SELF-MONITORING BURDEN
29
Team Tab
Like?
• “Liked the ability to chat with team
members”
• “Loved the kind words from
everyone”
• “Liked reading about everyone’s
progress, ideas/recipes,
encouraging comments.”
• “Liked knowing when someone
wasn’t on or doing well so I could
support”
• “Made me feel more accountable
to my team and myself”
• “My team members and I pushed
each other to continue on.”
Dislike? Want to Change?
• “Did not have the full participation of the
group”
• “I found their messages well- meaning, but also
annoying”
• “Prefer to meet people in person so barely used
it.”
• “Wasn’t able to keep up with it. Forgot about it.”
• “I’m not a social media butterfly. I lost interest.”
• “Annoying when carrying 3 phones.”
• “Lack of response discouraged me.”
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Social Networking in an Online Weight Management Program
•Calorie-King data for 2 years (2009 – 2010)
•Sample : 27,382 members of whom 17,765 have >2 weigh-ins
& engaged in system >50 days
•Heckman (1976) econometrics method using probit selection
model to control for selection bias and censored data
•Social Networking:
89% isolated (no online friends)
11% (N=1,935 ) did make friends
• 26% in isolated clusters of 2-5
•64% in giant component
Cumulative distribution of engagement time for
all unique identifiers in the database (a) and for
programme members with at least one friend (b)
Poncela-Casasnovas, Spring, et al.
2015, J. Royal Society Interface
12:20140686
©2015 by The Royal Society
Friending linked to retention
Social network and weight loss in
an online weight management
community - Poncelas, Spring, et al (2015)
J. Royal Society Interface 12: 20140686
Right treatment for right patient at
right time [in right context]
Conclusions
1. Technology supported interventions enable us to expand
the reach and reduce the burden of behavioral
interventions.
2. Although technology evolves rapidly, many of the catalytic
behavior change mechanisms harnessed by mHealth
interventions remain unchanged.
3. mHealth widgets can be very engaging, but they rarely change
behavior in and of themselves. Rather, they afford new,
compelling delivery channels for behavior change interventions.
Conclusions (continued)
4. Potent behavior change mechanisms may
function differently in the digital ecology
• Self-monitoring – may be enhanced by tech, but
(so far) burden not decreased – adverse impact
on self-efficacy?
• Social support – Positive association with retention and
weight change, but more opportunity to “hide” than in
person group. Can we induce sociability?
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Conclusions (end)
5. New research designs extend the RCT
MOST:
best optimized average intervention to
maximize impact at least resource consumption
SMART:
best strategy for treatment sequence and
tactics for addressing treatment nonresponse
JITAI:
best tailoring variables and decision rules
to respond to variations in context and within person
variability
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Acknowledgments
•NIDDK R01 DK097364, RC1 DK087126
•NHLBI R01 HL075451
•NCI (P20CA165592, P20CA165588) (PI Steve
Rosen)
•NIDA DA031147 (PI Terry Bush)
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Thank you!
bspring@northwestern.edu