Mobile Rapid Response Tobacco Surveillance

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Thomas Kirchner presented on using mobile technologies for rapid response tobacco surveillance. Some key points include: 1) Mobile phone penetration in the US is over 96% providing an opportunity for large scale public health surveillance. 2) Ecological momentary assessment (EMA) using mobile devices can provide dynamic, real-time surveillance but challenges include privacy, reliability of data, and data processing. 3) Rapid learning studies using mobile technologies may help answer public health questions more quickly through prospective cohorts, iterative design, and rapid analysis cycles.

Health & Medicine

Mobile Rapid Response Tobacco
Surveillance

Thomas R. Kirchner, PhD

Research Investigator
The Steven A. Schroeder Institute for
Tobacco Research & Policy Studies

Adjunct Assistant Professor
Johns Hopkins Bloomberg School of Public Health

Adjunct Assistant Professor
Georgetown Lombardi Comprehensive Cancer Center
PRESENTED AT:
CDC OSH EXPERT PANEL ON RAPID RESPONSE METHODOLOGY
FOR TOBACCO CONTROL SURVEILLANCE
CDC OFFICE ON SMOKING AND HEALTH, ATLANTA, GA
JUNE 2, 2011

Wireless Penetration: US

CTIA, 2010 Year-end Data

302.9 million active mobile devices

~308.7 million people (+territories)

= ~96% US Market Penetration

Average wireless bill = $47.21
Down 95 cents from 2009

Source:
Wireless
Substitution:
International Assoc. for the Wireless
Telecommunications Industry (CTIA), 2011

Minority Smartphone
Preferences

Nielsen, 2011

Minority Feature
Preferences

Pew Internet & American Life

Mobile Access, 2010

Underserved SMS

PricewaterhouseCoopers

HRI Consumer Survey, 2010

Wireless Substitution

%
Adults
living
in
wireless-‐only
households,
January
2007-‐June
2010

Source:
Wireless
Substitution:
Early
Release
of
Estimates
From
the
National
Health
Interview
Survey,
January
-‐
June
2010

by
Stephen
J.
Blumberg,
Ph.D.,
and
Julian
V.
Luke,
Division
of
Health
Interview
Statistics,
National
Center
for
Health
Statistics

Ecological Momentary
Assessment

•  ~ 2 decades mobile health survey research

EMA 2.0

•  Participant provided device

•  Dynamic Remote Content Management

•  Mixed-mode Integration

•  Ecological (i.e., GIS) Integration

•  Remote Sensing (e.g., accelerometer)

Ecological Momentary
Intervention

Legacy: Become and EX Mobile (iPhone)

Ecological Momentary
“Surveillance”

IVR
MMS
SMS
Email
GPS

Prospective Targets

•  Industry?

•  Policy?

•  Advocacy?

•  Smoker Attitudes / Beliefs?

•  Tobacco Product Use Patterns?

•  Smoking Initiation and/or Cessation Milestones?

Rapid Learning Studies

•  Prospective Cohort

•  Large, Representative Samples

•  Longitudinal (but not “long”)

•  Iterative
•  c.f., Multiphase optimization strategy (MOST)

•  Adaptive
•  c.f., Sequential Multiple Assignment Randomized Trial (SMART)

Opportunities

•  Methodological
–  Reach among Priority Populations
–  Prospective, Longitudinal Research Questions
–  Rapid Learning Studies

•  Logistical
–  Existing systems lower cost
–  Automation lowers staffing overhead
–  Improved retention / follow-up

•  Collaborative
–  Community Outreach
–  Participatory Involvement

Challenges

•  Privacy
•  Platform standards
•  Mobile Usage Fees
•  Mixed-mode effects
•  Reliability / Validity
•  Data Processing
•  Data Analysis

Recommendations

•  National Prospective Cohort

•  Mobile Platform / Device Independent System Development

•  Feature Phone Focus (for now)

•  User Preferences Trump Mode Effects

•  Mobile + GIS Integration

Many of today's mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products' and users' expectations. To address this challenge, we aim at developing an intelligent mobile system that automatically infers moments in which users are open to engage with suggested content. To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire. In this study, however, we analyze whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In addition, the study app logged a wide range of data related to their phone use. Based on 120 Million phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that before delivering a notification predicts whether a participant will click on the notification and subsequently engage with the offered content. When compared to a naïve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user's past behavior, predictions improve 5-fold over the baseline. Based on these findings, we discuss the implications for building an intelligent service that identifies opportune moments for proactive user engagement, while, at the same time, reduces the number of undesirable interruptions.

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Mobile Rapid Response Tobacco Surveillance

1. Mobile Rapid Response Tobacco Surveillance Thomas R. Kirchner, PhD Research Investigator The Steven A. Schroeder Institute for Tobacco Research & Policy Studies Adjunct Assistant Professor Johns Hopkins Bloomberg School of Public Health Adjunct Assistant Professor Georgetown Lombardi Comprehensive Cancer Center PRESENTED AT: CDC OSH EXPERT PANEL ON RAPID RESPONSE METHODOLOGY FOR TOBACCO CONTROL SURVEILLANCE CDC OFFICE ON SMOKING AND HEALTH, ATLANTA, GA JUNE 2, 2011

2. Wireless Penetration: US CTIA, 2010 Year-end Data 302.9 million active mobile devices ~308.7 million people (+territories) = ~96% US Market Penetration Average wireless bill = $47.21 Down 95 cents from 2009 Source: Wireless Substitution: International Assoc. for the Wireless Telecommunications Industry (CTIA), 2011

3. Minority Smartphone Preferences Nielsen, 2011

4. Minority Feature Preferences Pew Internet & American Life Mobile Access, 2010

5. Underserved SMS PricewaterhouseCoopers HRI Consumer Survey, 2010

6. Wireless Substitution % Adults living in wireless-‐only households, January 2007-‐June 2010 Source: Wireless Substitution: Early Release of Estimates From the National Health Interview Survey, January -‐ June 2010 by Stephen J. Blumberg, Ph.D., and Julian V. Luke, Division of Health Interview Statistics, National Center for Health Statistics

7. Ecological Momentary Assessment •  ~ 2 decades mobile health survey research EMA 2.0 •  Participant provided device •  Dynamic Remote Content Management •  Mixed-mode Integration •  Ecological (i.e., GIS) Integration •  Remote Sensing (e.g., accelerometer)

9. Ecological Momentary Intervention Legacy: Become and EX Mobile (iPhone)

10. Ecological Momentary “Surveillance” IVR MMS SMS Email GPS

11. Prospective Targets •  Industry? •  Policy? •  Advocacy? •  Smoker Attitudes / Beliefs? •  Tobacco Product Use Patterns? •  Smoking Initiation and/or Cessation Milestones?

12. Rapid Learning Studies •  Prospective Cohort •  Large, Representative Samples •  Longitudinal (but not “long”) •  Iterative •  c.f., Multiphase optimization strategy (MOST) •  Adaptive •  c.f., Sequential Multiple Assignment Randomized Trial (SMART)

13. Gulf Oil Spill

14. Japan

15.

16. Amazon Mechanical Turk

17. Opportunities •  Methodological –  Reach among Priority Populations –  Prospective, Longitudinal Research Questions –  Rapid Learning Studies •  Logistical –  Existing systems lower cost –  Automation lowers staffing overhead –  Improved retention / follow-up •  Collaborative –  Community Outreach –  Participatory Involvement

18. Challenges •  Privacy •  Platform standards •  Mobile Usage Fees •  Mixed-mode effects •  Reliability / Validity •  Data Processing •  Data Analysis

19. Recommendations •  National Prospective Cohort •  Mobile Platform / Device Independent System Development •  Feature Phone Focus (for now) •  User Preferences Trump Mode Effects •  Mobile + GIS Integration

Mobile Rapid Response Tobacco Surveillance

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