Gamification of Behavior Change: Mathematical Principle and Proof-of-Concept Study.

artificial intelligence behavior change chatbot digital interventions feedback gamification habit formation mobile phone points

Journal

JMIR serious games
ISSN: 2291-9279
Titre abrégé: JMIR Serious Games
Pays: Canada
ID NLM: 101645255

Informations de publication

Date de publication:
22 Mar 2024
Historique:
received: 30 09 2022
accepted: 31 08 2023
revised: 12 06 2023
medline: 22 3 2024
pubmed: 22 3 2024
entrez: 22 3 2024
Statut: epublish

Résumé

Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission. In this study, we introduced a principled mathematical method for determining how many points should be awarded or deducted for the enactment or omission of the desired behavior, depending on when and how often the person has succeeded versus failed to enact it in the past. We called this approach optimized gamification of behavior change. As a proof of concept, we designed a chatbot that applies our optimized gamification method to help people build healthy water-drinking habits. We evaluated the effectiveness of this gamified intervention in a 40-day field experiment with 1 experimental group (n=43) that used the chatbot with optimized gamification and 2 active control groups for which the chatbot's optimized gamification feature was disabled. For the first control group (n=48), all other features were available, including verbal feedback. The second control group (n=51) received no feedback or reminders. We measured the strength of all participants' water-drinking habits before, during, and after the intervention using the Self-Report Habit Index and by asking participants on how many days of the previous week they enacted the desired habit. In addition, all participants provided daily reports on whether they enacted their water-drinking intention that day. A Poisson regression analysis revealed that, during the intervention, users who received feedback based on optimized gamification enacted the desired behavior more often (mean 14.71, SD 6.57 times) than the active (mean 11.64, SD 6.38 times; P<.001; incidence rate ratio=0.80, 95% CI 0.71-0.91) or passive (mean 11.64, SD 5.43 times; P=.001; incidence rate ratio=0.78, 95% CI 0.69-0.89) control groups. The Self-Report Habit Index score significantly increased in all conditions (P<.001 in all cases) but did not differ between the experimental and control conditions (P>.11 in all cases). After the intervention, the experimental group performed the desired behavior as often as the 2 control groups (P≥.17 in all cases). Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective. Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

Sections du résumé

BACKGROUND BACKGROUND
Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission.
OBJECTIVE OBJECTIVE
In this study, we introduced a principled mathematical method for determining how many points should be awarded or deducted for the enactment or omission of the desired behavior, depending on when and how often the person has succeeded versus failed to enact it in the past. We called this approach optimized gamification of behavior change.
METHODS METHODS
As a proof of concept, we designed a chatbot that applies our optimized gamification method to help people build healthy water-drinking habits. We evaluated the effectiveness of this gamified intervention in a 40-day field experiment with 1 experimental group (n=43) that used the chatbot with optimized gamification and 2 active control groups for which the chatbot's optimized gamification feature was disabled. For the first control group (n=48), all other features were available, including verbal feedback. The second control group (n=51) received no feedback or reminders. We measured the strength of all participants' water-drinking habits before, during, and after the intervention using the Self-Report Habit Index and by asking participants on how many days of the previous week they enacted the desired habit. In addition, all participants provided daily reports on whether they enacted their water-drinking intention that day.
RESULTS RESULTS
A Poisson regression analysis revealed that, during the intervention, users who received feedback based on optimized gamification enacted the desired behavior more often (mean 14.71, SD 6.57 times) than the active (mean 11.64, SD 6.38 times; P<.001; incidence rate ratio=0.80, 95% CI 0.71-0.91) or passive (mean 11.64, SD 5.43 times; P=.001; incidence rate ratio=0.78, 95% CI 0.69-0.89) control groups. The Self-Report Habit Index score significantly increased in all conditions (P<.001 in all cases) but did not differ between the experimental and control conditions (P>.11 in all cases). After the intervention, the experimental group performed the desired behavior as often as the 2 control groups (P≥.17 in all cases).
CONCLUSIONS CONCLUSIONS
Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective.
TRIAL REGISTRATION BACKGROUND
Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

Identifiants

pubmed: 38517466
pii: v12i1e43078
doi: 10.2196/43078
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e43078

Informations de copyright

©Falk Lieder, Pin-Zhen Chen, Mike Prentice, Victoria Amo, Mateo Tošić. Originally published in JMIR Serious Games (https://games.jmir.org), 22.03.2024.

Auteurs

Falk Lieder (F)

Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Pin-Zhen Chen (PZ)

Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Mike Prentice (M)

Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Victoria Amo (V)

Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Mateo Tošić (M)

Max Planck Institute for Intelligent Systems, Tübingen, Germany.

Classifications MeSH