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

IF 4.7 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-03-22 DOI:10.2196/43078
Falk Lieder, Pin-Zhen Chen, Mike Prentice, Victoria Amo, Mateo Tošić
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Abstract

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: 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: 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: 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: Our findings suggest that optimized gamification can be used to make digital behavior change interventions more effective.

Trial registration: Open Science Framework (OSF) H7JN8; https://osf.io/h7jn8.

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行为改变的游戏化:数学原理和实地实验(预印本)
背景介绍许多人都希望养成良好的生活习惯,从而变得更健康、更长寿或更快乐,但却很难改变自己的行为。游戏化可以使行为改变变得更容易,方法是对想要的行为给予积分,对不想要的行为进行扣分:在这项研究中,我们引入了一种原则性的数学方法,根据人们过去成功或失败的时间和频率,来决定做出或不做出期望行为时应获得或扣除多少分数。我们称这种方法为行为改变的优化游戏化:作为概念验证,我们设计了一个聊天机器人,应用我们的优化游戏化方法帮助人们建立健康的饮水习惯。我们在一个为期 40 天的实地实验中评估了这种游戏化干预措施的效果,其中一个实验组(人数=43)使用了优化游戏化的聊天机器人,另外两个活动对照组则禁用了聊天机器人的优化游戏化功能。第一个对照组(人数=48)使用所有其他功能,包括口头反馈。第二个对照组(人数=51)没有收到任何反馈或提醒。我们使用 "自我报告习惯指数"(Self-Report Habit Index)测量了所有参与者在干预前、干预期间和干预后的饮水习惯强度,并询问参与者在上周有多少天养成了所需的习惯。此外,所有参与者每天都会报告当天是否实现了自己的饮水意愿:泊松回归分析表明,在干预期间,收到基于优化游戏化反馈的用户比主动用户(平均 14.71 次,标准差 6.57 次;所有情况下 P.11)更频繁地养成预期行为。干预结束后,实验组与两个对照组一样更频繁地实施期望行为(所有情况下的 P≥.17):我们的研究结果表明,优化的游戏化可以使数字化行为改变干预更加有效:开放科学框架(OSF)H7JN8;https://osf.io/h7jn8。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
期刊介绍: ACS Applied Electronic Materials is an interdisciplinary journal publishing original research covering all aspects of electronic materials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials science, engineering, optics, physics, and chemistry into important applications of electronic materials. Sample research topics that span the journal's scope are inorganic, organic, ionic and polymeric materials with properties that include conducting, semiconducting, superconducting, insulating, dielectric, magnetic, optoelectronic, piezoelectric, ferroelectric and thermoelectric. Indexed/​Abstracted: Web of Science SCIE Scopus CAS INSPEC Portico
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