Week 4

Question 1

Definition Platformization

Answer 1

The penetration of infrastructures, economic processes and governmental frameworks of Digital frameworks in different economic sectors and spheres of life, as well as the reorganization of cultural practices and imaginations around these platforms

Question 2

E-health European Commission (2012)

Answer 2

E-health - when applied effectively - delivers more personalized ‘citizen-centric’ healthcare, which is more targeted, effective and efficient and helps reduce errors, as well as the length of hospitalization.

It facilitates socio-economic inclusion and equality, quality of life and patient empowerment through greater transparency, access to services and information and the use of social media for health.

Question 3

Purpose health platforms

Answer 3

To solicit and collect all kinds of health information from users

Question 4

Benefits health platforms personal gain

Answer 4

1. personalized, data-driven service
2. quick diagnosis
3. healthier lifestyle
4. proper monitoring device
5. speedy updates

Question 5

Benefits health platform public gain

Answer 5

1. contribute to the common good
2. data accessible for us and thus helping medical research
3. improved public health
4. educate general audience

Question 6

Cons health platforms

Answer 6

Risks to privacy, control and power over data

Question 7

Three platform mechanisms

Answer 7

1. datafication
2. commodification
3. selection

Question 8

Definition Datafication

Answer 8

Refers to the ability of networked platforms to render into data many aspects of the world that have never been quantified before: not just demographic, or profiling data volunteered by customers or solicited from them in (online) surveys but behavioral meta-data automatically derived from smartphones such as timestamps and GPS-inferred locations.
(Sleepcycle)

Question 9

Definition Commodification

Answer 9

The ideals of collectivity where patients were asked to donate their data for the greater good of research turns out to be an investment in connectivity that helps companies like 23andMe accrue value because they turn data into tradeable goods

Question 10

Definition Selection

Answer 10

The ability of platforms to trigger and filter user activity through interfaces and algorithms, while users, through their interaction with these coded environments, influence the online visibility and availability of particular content, services and people

Question 11

Digital inequality in selection

Answer 11

1. Mobile health app users are generally younger, higher educated, and have higher levels of e-health literacy skills than non-users
2. Men are more likely to use fitness apps, whereas women are more likely to use nutrition, self-care and reproductive health apps

Question 12

What types of persuasive messages do health apps use?

Answer 12

1. Providing feedback on performance
2. Prompt self-monitoring of behavior
3. Plan social support or social change
4. Prompt specific goal setting
5. Provide contingent rewards

Question 13

Conclusion Zhou et al. (2018)

Answer 13

1. The personalized daily step goal seemed to be more effective in engaging participants and maintaining daily step counts compared with constant step goals
2. Adaptively personalized step goals computed by an algorithm are capable of creating challenging yet attainable goals, and these goals effectively promoted physical activity

Question 14

Definition tailored health communication

Answer 14

The opportunity to use computer tailoring to deliver personalized interventions to users via the internet, motivating users to adopt health behaviors without face-to-face counseling

Question 15

Pros and cons tailored health communication

Answer 15

Pro: effective way to motivate individuals to adopt healthy behaviors
Cons:
1. high drop-out rates
2. intervention not used correctly or as recommended

Question 16

Tailored health communication traditionally vs new

Answer 16

Traditionally = select and target messages based on demographic and other individual characteristics that are relevant to the targeted behavior
New = tailoring via machine learning recommendation algorithms / recommender systems

Question 17

Recommender systems in digital health

Answer 17

1. tracks interactions of individuals with the system

2. using algorithm to predict and select messages that are relevant to user

Question 18

Conclusion Cheung et al. (2019)

Answer 18

1. most recommender systems recommended messages
2. some also made recommendations regarding the medical provider
3. Recommender systems have the potential to improve efficiency, effectiveness, enjoyment, and trustworthiness of messages

Question 19

Knowledge-based filtering

Answer 19

Predicting items based on explicit knowledge about user

Question 20

User-based collaborative filtering

Answer 20

Users rate previously recommended items. Predict top-ranked items (based on user similarity and ratings for each message)

Question 21

Three approaches to select messages

Answer 21

1. Off the shelf = random selection of eligible ads, less directed by scientific principles and data
2. Best-in-show = pretested in targeted audience
3. Tailoring using algorithms = targeting message based on individual characteristics

Question 22

Conclusion Kim et al. (2019)

Answer 22

1. recommending algorithm that tailor message selection can be useful in health communication
2. The algorithm outperforms the Best-in-show with regard to accuracy
3. The algorithm led to more perceived effectiveness compared to the (random) Off-the-shelf selection and ultimately more quitting behavior