Summary of all lecture content and parts of the e-book of the course Digital Health Communication, part of the master Communication and Information Sciences at Tilburg University
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SUMMARY DIGITAL HEALTH COMMUNICATION
Master Communication & Information Sciences, Tilburg University
March, 2022
T-lectures 1 to 6
D-lectures 1 to 6
,Digital Health Communication T lectures
Mandatory literature:
A theory-based online health behavior intervention for new university students: study
protocol. BMC Public Health. 2013 Feb 5;13:107. doi: 10.1186/1471-2458-13-107.
Assignment: answer the following questions
▪ What added value is expected from the use of digital technologies, according to the
authors?
▪ Which theories are used in the development of the intervention?
▪ What argumentation do the authors use for selecting these specific theories?
▪ How are these theories translated into intervention components?
▪ Do you think the findings of this planned study potentially contribute to identifying
effective components of the intervention?
A theory-based online health behavior intervention for new university students (U@ Uni:
LifeGuide): Results from a repeat randomized controlled trial. Trials, 16(1), 555.
doi:10.1186/s13063-015-1092-4
Assignment: answer the following questions
▪ In the introduction it is described that the current study is a repeat trial. What were the
main findings of the first study, according to the introduction? What reasons do the
authors mention for repeating the original trial?
▪ Did they find any effects for the primary outcomes?
▪ Explain what a per-protocol analysis is and why do you think the authors perform that
analysis. Why do you think the authors mention that the results of the per-protocol
should be treated with caution?
▪ What explanation do the authors give for the null findings for the other primary
outcomes?
▪ What explanation do the authors give for the small effect sizes?
Digital health communication T1 – persuasive technology
Learning goals:
▪ Understand the main factors increasing and decreasing the impact of digital health
applications
Theories used in the development of digital health applications:
Social cognitive theory (e.g., theory of planned behavior)
Principles of reinforcement
Social influencers’ perspective
Taxonomy of behavior change
Maximizing the impact of digital health applications: two main factors
1. The effective components should be evidence-based
The application employs features that target determinants from health behavior
theories / features that are based on established behavioral change techniques
2. The uptake of the application should be sufficient.
The app should be designed based on scientific theories of technology acceptance and
engagement. The app should be human-centered.
, A holistic framework to improve the uptake and impact of eHealth technologies
▪ “High-tech with a low impact”
▪ “Low impact” not because technology does not work
▪ Low support of health care professionals
▪ Not designed with user in mind
▪ Real impact of eHealth technologies unknown
▪ Understand and explain the importance of theory use in the development of digital
health applications
Importance of theory use in digital health applications
More likely to find a significant positive effect of the digital health intervention on weight-
related outcomes when extent of theory use was higher, namely:
▪ at least one or more theoretical constructs are explicitly linked to an intervention
technique
▪ when theoretical constructs are included in evaluations (measured pre and post
intervention)
eHealth-based interventions provide vast potential for testing and advancing behavior change
theories, generating large amounts of ecologically valid, real-time, and objective data.
Per-protocol analysis is a comparison of treatment groups that includes only those patients
who completed the treatment originally allocated
▪ True
▪ Not true
Intention-to-treat versus per protocol analysis
▪ Intention-to-treat analysis (‘normal’ analysis):
▪ A comparison of treatment groups that includes all randomized participants in
the groups to which they were randomly assigned, regardless of adherence or
withdrawal
▪ Per protocol analysis:
▪ a comparison of treatment groups that includes only those participants who
completed the treatment originally allocated
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