Samenvatting
Summary Digital Health Communication
- Instelling
- Tilburg University (UVT)
Samenvatting voor het vak Digital Health Communication over alle hoorcolleges, werkcolleges en artikelen die zijn besproken
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Voorbeeld 4 van de 80 pagina's
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Voorbeeld van de inhoud
SUMMARY DIGITAL HEALTH COMMUNICATIONWeek 1 3
T1 Lecture 3
T1 Literature 7
Article: A theory-based online health behavior intervention for new university students: study
protocol. (Epton et al., 2013) 7
Article: A theory-based online health behavior intervention for new university students
(U@Uni:LifeGuide): results from a repeat randomized controlled trial (Cameron et al., 2015) 10
D1 Lecture 14
References 15
Week 2 16
T2 Lecture 16
T2 Literature 22
Article: Using feedback through digital technology to disrupt and change habitual behavior: A critical
review of current literature (Hermsen et al., 2016) 22
Article: Effects of eating with an augmented fork with vibrotactile feedback on eating rate and body
weight: a randomized controlled trial (Hermsen et al., 2019) 25
D2 Lecture 28
References 29
Week 3 31
T3 Lecture 31
T3 Literature 35
Article: Theoretical perspectives of adherence to web-based interventions: a scoping review (Ryan,
Bergin & Wells, 2018) 35
Article: From self-reliers to expert-dependents: identifying classes based on health-related need for
autonomy and need for external control among mobile users (Smit & Bol, 2020) 39
D3 Lecture 41
References 42
Week 4 43
T4 Lecture 43
T4 Literature 49
Article: A cross-cultural comparison of the processes underlying the associations between sharing of and
exposure to alcohol references and drinking intentions (Geusens et al., 2019) 49
Article: A comparison of physical activity mobile apps with and without existing web-based social
networking platforms: systematic review (Petersen et al., 2019) 52
D4 Lecture 55
References 55
Week 5 56
T5 Guest lecture 56
D5 Lecture 61
References 63
,Week 6 63
T6 Lecture 63
T6 Literature 66
Article: The effectiveness of health animations in audiences with different health literacy levels: an
experimental study (Meppelink et al., 2015) 66
Article: A systematic review of tailored eHealth interventions for weight loss (Ryan, Dockray & Linehan,
2019) 68
D6 Lecture 70
References 72
Week 7 73
T7 Lecture 73
T7 Literature 75
Article: Skype or skip? Causes and consequences of intimate self-disclosure in computer-mediated
doctor-patient communication (Bol & Antheunis, 2022) 75
Article: The effect of screen-to-screen versus face-to-face consultation on doctor-patient communication:
an experimental study with simulated patients (Tates et al., 2017) 78
References 80
, WEEK 1
T1 LECTURE
Learning goal: understand the main factors increasing and decreasing the impact of digital
health applications
How impactful are digital health applications?
Impact – what is the evidence? – physical activity (Romeo et al., 2019)
• Aim: this systematic review and meta-analysis aimed to determine the effectiveness of
smartphone apps for increasing objectively measured physical activity in adults
• Current status:
o Which health behavior theories are employed most often?
o Which features are employed most often?
Theory use and implemented features in apps targeting physical activity
• App based on recognized behavior-change theory
o Social cognitive theory (e.g., theory of planned behavior)
o Principles of reinforcement
o Social influencers’ perspective
o Taxonomy of behavior change
• App features
o Visible display of steps
o Physical activity performance summary
o Goal setting
o Visual display of goal achievement
o Motivational prompts
Results
• No significant effect of app-based physical activity interventions – steps per day and
moderate-to-vigorous PA
• But: interventions were effective (significant) when the intervention duration was 3
months or less (compared with longer interventions)
• And: physical activity apps that targeted physical activity in isolation were more
effective than apps that targeted physical activity in combination with diet
Impact – what is the evidence? – healthy eating behavior (Villinger et al., 2019)
• Aim: this systematic review and meta-analysis aimed to determine the effectiveness of
smartphone apps for changing nutrition behavior and nutrition-related health outcomes
• Current status:
o Which health behavior change techniques are employed most often?
, Most implemented features in apps targeting eating behavior
• Goals and planning
• Feedback and monitoring
• Social support
• Shaping knowledge
• Comparison of behavior
• Associations
• Reward and threat
• Antecedent
• Self-belief
Results
• Overall, a small significant of app-based mobile interventions on nutrition behaviors
and nutrition-related outcomes
• Only studies targeting short term (<3 months) and/or intermediate-term (3-6 months)
follow-up intervals yielded significant (small) effect sizes; effects of long term (>6
months) follow-up not significant
Other meta-analyses/reviews
• Medicine adherence: 7/11 studies mobile app increased medicine adherence (Pere-
Jover et al., 2019)
• Alcohol intake: brief web-based interventions decreased the number of alcoholic
drinks consumed (Oosterveen et al., 2017)
• Digital (web) school-based behavior change interventions increased fruit and
vegetable intake and physical activity and reduced screen time in adolescents
immediately after the intervention; effects not sustained at follow-up and no effects
for alcohol intake and smoking (Champion et al., 2019)
o Qualities of studies/evidence low – very low
Maximizing the impact of digital health applications: two main factors
• The effective components should be evidence-based (effective ingredient)
o The application employs features that target determinants from health
behavior theories / features that are based on established behavioral change
techniques
• The uptake of the application should be sufficient (effective dose)
o 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 (Van
Gemert-Pijnen et al., 2011)
• “High-tech with a low impact”
• “Low impact” not because technology does not work
o Low support of health care professionals
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