Week 4 Flashcards

1
Q

Definition Platformization

A

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

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2
Q

E-health European Commission (2012)

A

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.

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3
Q

Purpose health platforms

A

To solicit and collect all kinds of health information from users

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4
Q

Benefits health platforms personal gain

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

Benefits health platform public gain

A
  1. contribute to the common good
  2. data accessible for us and thus helping medical research
  3. improved public health
  4. educate general audience
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6
Q

Cons health platforms

A

Risks to privacy, control and power over data

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7
Q

Three platform mechanisms

A
  1. datafication
  2. commodification
  3. selection
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8
Q

Definition Datafication

A

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)

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9
Q

Definition Commodification

A

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

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10
Q

Definition Selection

A

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

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11
Q

Digital inequality in selection

A
  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
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12
Q

What types of persuasive messages do health apps use?

A
  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
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13
Q

Conclusion Zhou et al. (2018)

A
  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
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14
Q

Definition tailored health communication

A

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

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15
Q

Pros and cons tailored health communication

A

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

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16
Q

Tailored health communication traditionally vs new

A
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
17
Q

Recommender systems in digital health

A
  1. tracks interactions of individuals with the system

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

18
Q

Conclusion Cheung et al. (2019)

A
  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
19
Q

Knowledge-based filtering

A

Predicting items based on explicit knowledge about user

20
Q

User-based collaborative filtering

A

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

21
Q

Three approaches to select messages

A
  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
22
Q

Conclusion Kim et al. (2019)

A
  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