Self-Selection into Health Check-Ups

Last registered on March 24, 2022

Pre-Trial

Trial Information

General Information

Title
Self-Selection into Health Check-Ups
RCT ID
AEARCTR-0009059
Initial registration date
March 23, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 24, 2022, 4:44 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
JGU Mainz

Other Primary Investigator(s)

PI Affiliation
Goethe University Frankfurt

Additional Trial Information

Status
In development
Start date
2022-04-01
End date
2022-04-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Using a unique data set from a German health check-up provider we have compiled a concise and validated questionnaire tool to estimate individuals' risk for high cholesterol levels. We now apply this tool in an online experiment in order to study individual health information avoidance behavior. In particular, we use our tool to classify individuals into ‘healthy’ and ‘unhealthy’ individuals, and ask (1) which group participates more in health check-ups, (2) whether a potential difference in participation rates between the two groups is driven by selection, and (3) whether we can influence selection by increasing the salience of potentially bad news. Hereby, we base our hypotheses on a pilot study in which we find that healthy individuals have higher check-up rates than unhealthy individuals.
External Link(s)

Registration Citation

Citation
Eyting, Markus and Michael Kosfeld. 2022. "Self-Selection into Health Check-Ups." AEA RCT Registry. March 24. https://doi.org/10.1257/rct.9059
Experimental Details

Interventions

Intervention(s)
We randomly divide our sample into a ‘control’ group and an ‘information’ group. In the ‘information’ group, participants receive information about adverse health risks from untreated, high cholesterol levels. Participants in the control treatment do not get this information.
Intervention Start Date
2022-04-01
Intervention End Date
2022-04-30

Primary Outcomes

Primary Outcomes (end points)
- Check-up rates in the past of healthy and unhealthy individuals
- Willingness to see the estimated individual health risk
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
- Planned check-up rates in the future of healthy and unhealthy individuals
- Perceived individual health risk
- Perceived quality of classification algorithm
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
1) Questionnaire: We ask several survey questions, in order to be able to estimate the individual health risk, as well as gather information on whether or not individuals have been to a check-up before, how often, and their likelihood to go to a check-up in the future.
2) Information: Depending on treatment, we provide individuals with different kinds of information about cholesterol levels (see intervention).
3) Willingness: We elicit individuals’ willingness to see the estimated health risk. They are informed that an algorithm was able to predict their individual risk based on answers they have given in the questionnaire at the beginning of the experiment. By paying marginal rates, participants can increase the likelihood to be provided with their calculated risk score: using a slider that is initially set at 0%, participants can increase the likelihood to see their calculated risk score. Each percentage point increase costs 1 cent.
4) Quality: We elicit individuals’ perceived quality of the classification algorithm.
5) Beliefs: We elicit individuals’ beliefs about their own health risk.
6) Depending on their previously elicited willingness, we show individuals their respective estimated individual health risk.
7) Demographics: We ask for socioeconomic information and individuals’ reasoning for their decision in 3).
Experimental Design Details
Randomization Method
The randomization is done by a computer.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1000 individuals
Sample size: planned number of observations
1000 individuals
Sample size (or number of clusters) by treatment arms
500 individuals in the control treatment; 500 individuals in the information treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Gemeinsame Ethikkommission Wirtschaftswissenschaften der Goethe-Universität Frankfurt und der Johannes Gutenberg-Universität Mainz
IRB Approval Date
2021-12-22
IRB Approval Number
N/A
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials