Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Using Smartphones to Study Vaccination Decisions in the Wild

Nicolò Alessandro Girardini, Arkadiusz Stopczynski, Olga Baranov, Cornelia Betsch, Dirk Brockmann, Sune Lehmann, Robert Böhm

TL;DR

This work proposes integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread, and finds that participants strongly responded to some of the information provided to them during or after each decision round.

Abstract

One of the most important tools available to limit the spread and impact of infectious diseases is vaccination. It is therefore important to understand what factors determine people's vaccination decisions. To this end, previous behavioural research made use of, (i) controlled but often abstract or hypothetical studies (e.g., vignettes) or, (ii) realistic but typically less flexible studies that make it difficult to understand individual decision processes (e.g., clinical trials). Combining the best of these approaches, we propose integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread. In our 12-week proof-of-concept study conducted with $N$ = 494 students, we found that participants strongly responded to some of the information provided to them during or after each decision round, particularly those related to their individual health outcomes. In contrast, information related to others' decisions and outcomes (e.g., the number of vaccinated or infected individuals) appeared to be less important. We discuss the potential of this novel method and point to fruitful areas for future research.

Using Smartphones to Study Vaccination Decisions in the Wild

TL;DR

This work proposes integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread, and finds that participants strongly responded to some of the information provided to them during or after each decision round.

Abstract

One of the most important tools available to limit the spread and impact of infectious diseases is vaccination. It is therefore important to understand what factors determine people's vaccination decisions. To this end, previous behavioural research made use of, (i) controlled but often abstract or hypothetical studies (e.g., vignettes) or, (ii) realistic but typically less flexible studies that make it difficult to understand individual decision processes (e.g., clinical trials). Combining the best of these approaches, we propose integrating real-world Bluetooth contacts via smartphones in several rounds of a game scenario, as a novel methodology to study vaccination decisions and disease spread. In our 12-week proof-of-concept study conducted with = 494 students, we found that participants strongly responded to some of the information provided to them during or after each decision round, particularly those related to their individual health outcomes. In contrast, information related to others' decisions and outcomes (e.g., the number of vaccinated or infected individuals) appeared to be less important. We discuss the potential of this novel method and point to fruitful areas for future research.
Paper Structure (23 sections, 3 equations, 3 figures, 3 tables)

This paper contains 23 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Spatial and Temporal Dynamics in Vaccination Decisions and Their Consequences
  3. Using Smartphones to Study Vaccination Decisions and Infection Spread
  4. Study Set-Up and Data Handling
  5. Game Mechanics.
  6. Manipulations.
  7. Analytical Approach.
  8. Results
  9. Players' Behaviour given Individual Outcome in Previous Round
  10. Players' Behaviour Given Environmental Feedback
  11. Players' Behaviour Given Daily Feedback
  12. Discussion
  13. Limitations
  14. Conclusion
  15. Methods
  16. ...and 8 more sections

Figures (3)

  • Figure 1: Vaccination Probability given Point Loss in Former Round and Previous Decision. Effects of point loss in the former round and the previous decision on the probability to get vaccinated in the subsequent round ($k$ = 3,934 observations by $n$ = 469 players). Dots represent the estimated probability and error bars the standard error. We see that a point loss implies a lower vaccination probability, but only for players that did not vaccinate in the previous round.
  • Figure 2: Likelihood of Vaccination given Daily Susceptible Feedback. Hazard Ratio (i.e., likelihood of vaccinating) of daily feedback about the share of susceptible others in the local vs. global feedback condition ($k$ = 2,003 observations by $n$ = 382 players). We see that, for the Global Feedback condition, the likelihood of vaccinating exponentially increases when the number of susceptible individuals is high.
  • Figure 3: Finite State Automaton of the Experiment. All players start from the susceptible state and they can remain so, decide to get vaccinated or get infected and then recover. Labels on the transitions show what event is triggering them in the partly probabilistic-causal game framework.