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Summary: Digital Health Communication Summary 2023 (Master: CIS/CIW/NMD/BDM)
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Digital Health Communication Summary T & D Lectures 2023 with learning goals
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eHealth Development in Context
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eHealth Research, Theory and Development
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Summary Digital Health CommunicationDigital Health Communication T lectures
T1: Persuasive technology
Mandatory literature:
- A1) A theory-based online health behavior intervention for new university students: study protocol ~
Epton et al., 2013
- A2) A theory-based online health behavior intervention for new university students: Results from a
repeat randomized controlled trial ~ Cameron et al., 2015
Digital health communication:
- Persuasive health technology
o Information provision & beyond
o Change attitudes towards health behaviors
o Support initiation and maintain health behaviors that require self-control
- Digital applications that support the health-care setting
o Doctor-patient communication / shared decision-making
o Information provision
o Self-management
eHealth: Refers to the use of information communication technologies (ICTs) to deliver, or support the
delivery of, health services
mHealth: The use of mobile wireless technologies for public health
Learning goal 1: Understand the main factors increasing and decreasing the impact of digital health
applications
Potential:
Health apps in app stores
2021: 350k health apps in the app stores
2020: 90k being released (250 per day)
➔ Fitness, nutrition, self-care, vitals, sleep, mindfulness, reproductive health, wearables etc.
Use and reach of digital health apps
- Almost 1 in 3 use health apps
- Health apps users on average have 3 health apps on their phone of which 2 actively used
o Mobile health users were younger and more highly educated than non-users ~
Krebs & Duncan (2015)
▪ Is this reach sufficient?
Confidence in the potential impact
- Increasing implementation of digital health solutions in hospitals
- Insurance companies develop their own apps
Impact – what is the evidence? – Physical activity
Study 1: Can smartphone apps increase physical activity? Systematic review and meta-analysis ~ 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 → (summary of existing studies to
see if there are some general trends / to see if the findings are robust)
• Current status:
o Which health behavior theories are employed most often?
, o Which features are employed most often?
➔ A lot of ‘weak’ studies were excluded and they went from 169 to 7 studies (a lot of
studies were not of very high quality).
➔ App based on recognized behavior-change theory (only 4 studies were backed up
with science)
▪ Social cognitive theory (3) (e.g. theory of planned behavior)
▪ Principles of reinforcement (1)
▪ Social influencers perspective (1)
▪ Taxonomy of behavior change (1)
➔ App features
▪ Visible display of steps (all apps)
▪ Physical activity performance summary (4)
▪ Goal setting (4)
▪ Visual display of goal achievement (4)
▪ Motivational prompts (3)
• Results: did people actually take more steps when they used these applications → 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.
Study 2: The effectiveness of app-based mobile intervention on nutrition behaviors and nutrition-related
health outcomes: a systematic review and meta-analysis ~ Villinger et al., 2019
• Aim: This systematic review and meta-analysis aimed to determine the effectiveness of
smartphone apps for changing nutrition behaviors and nutrition-related health outcomes
• Current status: Which health behavior change techniques are employed most often?
o Most implemented features in apps targeting eating behavior: feedback and
monitoring. Most apps included 4 different behavior change techniques not only
focusing on 1 single technique
• Results: overall, a small significant of app-base mobile interventions on nutrition
behaviors and nutrition-related outcomes
o 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
o Medicine adherence: 7/11 studies mobile app medicine adherence ~ Perez-Jover
et al., 2019
o Alcohol intake: brief web-based interventions decreased the number of
alcoholic drinks consumed ~ Oosterveen et al., 2017
o Digital (web) school-based behavior change interventions increased fruit and
vegetable intake and physical activity and reduces 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
→ Quality of studies/evidence low- very low
,A holistic framework to improve the uptake and impact of eHealth technologies
• “High-tech with a low impact” (currently, but lots of potential)
• “Low impact” not because technology does not work, but:
o Low support of health care professionals
o Not designed with user in mind, resulting in low uptake
• Real impact of eHealth technologies unknown
~ Van Gemert-Pijnen et al, 2011
Learning goal 2: Understand and explain the importance of theory use in the development of digital health
applications
Employing theory in digital health applications:
Study 1: Reported theory use in electronic health weight management interventions targeting young adults: a
systematic review ~ Willmott et al, 2019
→ In non-digital health interventions: effectiveness of intervention is higher when theory-based and when it
employs one or more behavioral change technique (Michie et al., 2009)
→Aims
- Provide overview of extent of theory use in digital health applications
- Determine importance of the extent of theory use
Extent of theory use in digital apps targeting weight management (from low level of theory use to high)
• Mentioning/referencing a theory:
o Just mentioning: 18/24
o Explicitly describing the relationship between the theoretical constructs and
behavior of interest: 9/24 (not a lot of studies)
• Application of the theory in intervention development
o Used theoretical predictors to select/develop intervention techniques: 17/24
o Using theory/predictors to tailor intervention techniques to recipients: 4/24 (e.g.
based on age an intervention is tailored)
• Measurement and testing of the theory
o Measuring theory-relevant constructs before and after the intervention: 6/24
o Reporting reliability and validity of the scales to measure theory-relevant
constructs: 3/24
• Building and refining the theory
o Discussing findings in relation to the theory: 5/24
o Provided support for the theory (theory-relevant constructs were reported to
significantly mediate the relationship between the intervention and observed
behavioral change: 1/24
o Reported using intervention results or build and/or refine the theory upon which
the intervention was based, or formulate suggestions for future refinement: 0/24
(highest level of all)
➔ Extend of theory use is quite low and the quality of research is not that high.
, 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.
➔ GGD Appstore: apps that are scientifically backed up.
Learning goal 3: Understand the main characteristics and assumptions of rational models of health behavior.
Know the main behavioral determinants of these models and how to influence these by digital health
communication
Rational models of (health) behavior – assumptions
- Human behavior is under the voluntary control of the individual
o Behavior is planned
- If you successfully change the determinants of behavior, you will be successful in
changing behavior
- Rational models: Theory of planned behavior (Ajzen & Madden, 1986), Theory of
reasoned action (Fishbein, 1967), etc.
The theory of planned behavior
- Behavior: the desired health behavior e.g. go to bed at 10h on
weekdays
- Intention: the motivational state to engage in the behavior
- Attitude: General evaluation of the behavior (good versus bad,
wise versus unwise, pleasant versus unpleasant)
o Beliefs: e.g. When I go to bed at 10 on weekdays, I
will perform better at my work (I need to believe this
in order to have the intention and the behavior)
o Intervention example: “Too much salt can lead to high blood pressure and
damage to heart and kidneys”
- Subjective norm: Do other perform the behavior? Do others approve of me performing
the behavior?
o Beliefs: e.g. my colleagues go to ed early when lecturing the next day.
- Perceived behavioral control: performing the behavior is (not) under my control
o Self-efficacy: performing the behavior is easy/difficult
▪ Belief: it is feasible for me to finish all household-related chores before
9 and go to bed at 10.
▪ Intervention example: “remind yourself that it gets better with time...”
How to employ TPB in a digital health intervention? 3 steps:
1) Which determinants and underlying beliefs are most predictive of (intention to perform)
the target behavior?
a. Think about target behavior (e.g. alcohol intake is most of the time a social
behavior, so subjective norm can be important), population, context (in a work
environment other determinants can be important than at home e.g. subjective
norm more important when you’re eating with other people at work)
2) There should be sufficient people who do not already hold the belief
3) Develop message strategies and application features that change the beliefs (and impact
on determinants)
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