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Creating the Evidence
  Base for mHealth
  Txt4Health: Using Mobile Technology in Public
 Health Communication and Education Campaigns

       American Public Health Association
        San Francisco, 30 October 2012
Presenter Disclosures
      R. Craig Lefebvre, aka chiefmaven

The following personal financial relationships with
commercial interests relevant to this presentation
existed during the past 12 months:
          “No relationships to disclose”
Mobile Thoughts
When the expectations of
wireless experts are
realized everyone will
have his own pocket
telephone and may be
called wherever he
happens to be…When
that invention is
perfected, we shall have
a new series of daily
miracles (circa 1908).
It Took About 100 Years, But Then
     It Went Mainstream - Fast.
The 7th Mass Media Channel

1 - first personal mass media
2 - permanently carried media
3 - always-on mass media
4 - built-in response
mechanisms
5 - at the point of creative
inspiration
6 - accurate audience
measurement
7 - captures the social context
                                  Tomi T Ahomen. Communities
of people’s lives                 Dominate Brands blog, 2 May 2008.
mHealth Evidence
            Challenges
• Create and evaluate scalable systems capable of
  collecting unprecedented amounts of data

• Analyze and integrate that data with other health
  information

• Field interventions—some in real time—while at
  the same time providing value and protecting the
  safety of participants.
mHealth Opportunities

• Contributes novel measurement methods and
  processes

• Enable the design and delivery of novel
  interventions that can be delivered remotely and in
  real time

• Offers new methods of data collection and
  analysis that can improve the speed and efficiency
  of health research and evidence of an intervention
  or treatment effect.
mHealth Research
            Challenges
• Difficult to create controlled and reproducible
  environments
• mHealth devices are frequently used by
  individuals with little training that may affect their
  reliability and validity
•    Few gold standard measurements exist in the
    mobile environment, self-report measures may be
    the most accepted existing assessment
• Risks to privacy and security
Evidence Requirements

• Statistical Conclusion Validity - evidence of a
  meaningful, causal effect
• Internal Validity - rule out confounders
• Construct Validity - validity of outcome measures
• External Validity - generalizability across
  persons, settings, and times
• Ecological Validity – can be implemented in real-
  world settings and integrated into life and work
  flow
When are Randomized
    Clinical Trials Indicated?
• Interventions have been shown to be feasible and
  acceptable

• Whose efficacy has been demonstrated in quasi-
  experimental designs

• There is the desire to demonstrate superiority to
  exiting approaches to the same problem
Quasi-Experimental Designs
       for mHealth
• Pretest-posttest (with and without comparison
  conditions)

• N of 1 (with multiple crossover – ABABAB)

• Interrupted Times Series Design – (AAAAB, with
  and without comparison groups)

• Stepped Wedge (or delayed treatment)
Other Research
            Considerations
• Continuous Evaluation of Evolving Interventions
  (CCEI) – new versions are added to original
  protocol

• Adaptive Interventions - personal tailoring
Quality Considerations for
    mHealth Interventions
• Selection of              • Timing of
  appropriate                 communication
  technology
                            • Understanding of
• Privacy assurances          priority group
• Linguistic and literacy   • Long-term evaluation
  competency
                            Gurman et al (2012). Effectiveness of
  • 2-way                   mHealth behavior change
    communication           communication interventions in
                            developing countries: A systematic
  • Targeting & tailoring
                            review of the literature. Journal of
    content                 Health Communication; 17 (suppl 1):
                            82-104.
Marketing and
mHealth


How do I add
mHealth features
to my behavior
change
products, service
s and
programs??
Marketing and
mHealth
•   How do I use these
    technologies to
    overcome
    psychological and
    social barriers
    (costs)
•   develop new
    incentives and
    reinforcers
•   create new ways of
    providing social
    support to people
    who are trying to
    change behaviors?
    social support to people who are trying to
    change behaviors?
Marketing and
mHealth


How can I place-
shift; use SNS, co-
presence and
virtual worlds; and
add GPS to create
scalable behavior
change
programs??
Marketing and
mHealth

How do I facilitate
conversations
among
people, not aim
messages at
them?
Are we available when, where
and how people want us to be?
R. Craig Lefebvre, PhD
     Lead Change Designer, RTI International
University of South Florida College of Public Health

              socialShift, Sarasota, FL
        social|design, marketing and media

         On Social Marketing and Social Change
            http://socialmarketing.blogs.com
                http://twitter.com/chiefmaven

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Creating the Evidence Base for mHealth

  • 1. Creating the Evidence Base for mHealth Txt4Health: Using Mobile Technology in Public Health Communication and Education Campaigns American Public Health Association San Francisco, 30 October 2012
  • 2. Presenter Disclosures R. Craig Lefebvre, aka chiefmaven The following personal financial relationships with commercial interests relevant to this presentation existed during the past 12 months: “No relationships to disclose”
  • 3. Mobile Thoughts When the expectations of wireless experts are realized everyone will have his own pocket telephone and may be called wherever he happens to be…When that invention is perfected, we shall have a new series of daily miracles (circa 1908).
  • 4. It Took About 100 Years, But Then It Went Mainstream - Fast.
  • 5. The 7th Mass Media Channel 1 - first personal mass media 2 - permanently carried media 3 - always-on mass media 4 - built-in response mechanisms 5 - at the point of creative inspiration 6 - accurate audience measurement 7 - captures the social context Tomi T Ahomen. Communities of people’s lives Dominate Brands blog, 2 May 2008.
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  • 14. mHealth Evidence Challenges • Create and evaluate scalable systems capable of collecting unprecedented amounts of data • Analyze and integrate that data with other health information • Field interventions—some in real time—while at the same time providing value and protecting the safety of participants.
  • 15. mHealth Opportunities • Contributes novel measurement methods and processes • Enable the design and delivery of novel interventions that can be delivered remotely and in real time • Offers new methods of data collection and analysis that can improve the speed and efficiency of health research and evidence of an intervention or treatment effect.
  • 16. mHealth Research Challenges • Difficult to create controlled and reproducible environments • mHealth devices are frequently used by individuals with little training that may affect their reliability and validity • Few gold standard measurements exist in the mobile environment, self-report measures may be the most accepted existing assessment • Risks to privacy and security
  • 17. Evidence Requirements • Statistical Conclusion Validity - evidence of a meaningful, causal effect • Internal Validity - rule out confounders • Construct Validity - validity of outcome measures • External Validity - generalizability across persons, settings, and times • Ecological Validity – can be implemented in real- world settings and integrated into life and work flow
  • 18. When are Randomized Clinical Trials Indicated? • Interventions have been shown to be feasible and acceptable • Whose efficacy has been demonstrated in quasi- experimental designs • There is the desire to demonstrate superiority to exiting approaches to the same problem
  • 19. Quasi-Experimental Designs for mHealth • Pretest-posttest (with and without comparison conditions) • N of 1 (with multiple crossover – ABABAB) • Interrupted Times Series Design – (AAAAB, with and without comparison groups) • Stepped Wedge (or delayed treatment)
  • 20. Other Research Considerations • Continuous Evaluation of Evolving Interventions (CCEI) – new versions are added to original protocol • Adaptive Interventions - personal tailoring
  • 21. Quality Considerations for mHealth Interventions • Selection of • Timing of appropriate communication technology • Understanding of • Privacy assurances priority group • Linguistic and literacy • Long-term evaluation competency Gurman et al (2012). Effectiveness of • 2-way mHealth behavior change communication communication interventions in developing countries: A systematic • Targeting & tailoring review of the literature. Journal of content Health Communication; 17 (suppl 1): 82-104.
  • 22. Marketing and mHealth How do I add mHealth features to my behavior change products, service s and programs??
  • 23. Marketing and mHealth • How do I use these technologies to overcome psychological and social barriers (costs) • develop new incentives and reinforcers • create new ways of providing social support to people who are trying to change behaviors? social support to people who are trying to change behaviors?
  • 24. Marketing and mHealth How can I place- shift; use SNS, co- presence and virtual worlds; and add GPS to create scalable behavior change programs??
  • 25. Marketing and mHealth How do I facilitate conversations among people, not aim messages at them?
  • 26. Are we available when, where and how people want us to be?
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  • 28. R. Craig Lefebvre, PhD Lead Change Designer, RTI International University of South Florida College of Public Health socialShift, Sarasota, FL social|design, marketing and media On Social Marketing and Social Change http://socialmarketing.blogs.com http://twitter.com/chiefmaven

Notes de l'éditeur

  1. Unisys study – 26 hours to report a loss wallet; 68 minutes for a phone2. Replaces the wrist watch3. 80% physically take it to bed – alarm, txt, phone callsMobile health (mHealth) has the potential to reduce the cost of health care and improve health by providing continuous remote sensing of biological and psychological status at both the individual and population level, encouraging healthy behaviors to prevent or reduce health problems, reducing health care visits, and providing personalized, localized and on-demand interventions in the mobile environment
  2. Recent developments have begun addressing tailoring and optimizing interventions. In Multiphase Optimization Strategy (MOST), promising components of an intervention are identified in a screening phase (say, via factorial or fractional factorial analysis of variance design), the promising components are then refined, and a confirmatory trial such as RCT or Stepped Wedge is conducted on the final intervention. For refining the intervention, Sequential Multiple Assignment Randomized Trial (SMART) can be used where individuals are randomly assigned to various intervention choices several times; in SMARTs scientists decide which treatment decisions require investigation and then use SMARTs to randomize individuals among feasible/ethical options at each treatment decision [Collins’07].
  3. mobile communications are changing our expectations about when and how others are available to us
  4. Mobile phones are not the next ‘magic bullet’ – we need to think of them as part of the personalized media space our people formerly know as ‘audiences’ are creating for themselves. Ubiquity in this increasingly mobile environment will be a key factor for our future successes in public health.