Lieder Falk, Chen Pin-Zhen, Prentice Mike, Amo Victoria, Tošić Mateo
Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.
Max Planck Institute for Intelligent Systems, Tübingen, Germany.
JMIR Serious Games. 2024 Mar 22;12:e43078. doi: 10.2196/43078.
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.
许多人希望养成良好习惯以变得更健康、长寿或更快乐，但在改变行为方面却面临困难。游戏化可以通过对期望行为给予积分并对其遗漏行为扣除积分，使行为改变更容易。
在本研究中，我们引入了一种有原则的数学方法，用于确定根据个人过去成功或未能实施期望行为的时间和频率，对期望行为的实施或遗漏应给予或扣除多少积分。我们将这种方法称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来帮助人们养成健康的饮水习惯。我们在一项为期40天的现场实验中评估了这种游戏化干预的有效性，该实验有1个实验组（n = 43）使用具有优化游戏化功能的聊天机器人，以及2个主动对照组，聊天机器人的优化游戏化功能被禁用。对于第一个对照组（n = 48），所有其他功能都可用，包括言语反馈。第二个对照组（n = 51）没有收到反馈或提醒。我们在干预前、干预期间和干预后使用自我报告习惯指数并询问参与者在前一周有多少天实施了期望行为，来测量所有参与者饮水习惯的强度。此外，所有参与者每天报告他们当天是否实施了饮水意图。
泊松回归分析显示，在干预期间，接受基于优化游戏化反馈的用户比主动对照组（平均11.64次，标准差6.38次；P <.001；发病率比 = 0.80，95%可信区间0.71 - 0.91）或被动对照组（平均11.64次，标准差5.43次；P =.001；发病率比 = 0.78，95%可信区间0.69 - 0.89）更频繁地实施期望行为（平均14.71次，标准差6.57次）。自我报告习惯指数得分在所有情况下均显著增加（所有情况下P <.001），但实验组和对照组之间没有差异（所有情况下P >.11）。干预后，实验组实施期望行为的频率与2个对照组相同（所有情况下P≥.17）。
我们的研究结果表明，优化游戏化可用于使数字行为改变干预更有效。
开放科学框架（OSF）H7JN8；https://osf.io/h7jn8。
