Kaveladze Benjamin T, Young Sean D, Schueller Stephen M
Department of Psychological Science, University of California, Irvine, CA, United States.
Department of Emergency Medicine, University of California, Irvine, CA, United States.
JMIR Form Res. 2022 Jun 3;6(6):e32571. doi: 10.2196/32571.
Digital health behavior change interventions (DHBCIs) offer users accessible support, yet their promise to improve health behaviors at scale has not been met. One reason for this unmet potential may be a failure to offer users support that is tailored to their personal characteristics and goals. We apply the concept of antifragility to propose how DHBCIs could be better designed to support diverse users' behavior change journeys. We first define antifragility as a feature of an individual's relationship to a particular challenge such that if one is antifragile to a challenge, one is well positioned to benefit from facing that challenge. Second, we introduce antifragile behavior change to describe behavior change processes that leverage person-specific antifragilities to maximize benefits and minimize risk in the behavior change process. While most existing behavior change models focus on improving one's motivation and ability to face challenges, antifragile behavior change complements these models by helping to select challenges that are most likely to produce desired outcomes. Next, we propose three principles by which DHBCIs can help users to develop antifragile behavior change strategies: providing personalized guidance, embracing variance and exploration in choosing behaviors, and prioritizing user agency. Finally, we offer an example of how a DHBCI could be designed to support antifragile behavior change.
数字健康行为改变干预措施（DHBCIs）为用户提供了便捷的支持，但其在大规模改善健康行为方面的承诺尚未实现。这一未实现潜力的一个原因可能是未能为用户提供根据其个人特征和目标量身定制的支持。我们应用反脆弱性的概念，提出如何更好地设计DHBCIs，以支持不同用户的行为改变历程。我们首先将反脆弱性定义为个体与特定挑战的关系的一个特征，即如果一个人对某一挑战具有反脆弱性，那么他就处于从面对该挑战中受益的有利位置。其次，我们引入反脆弱行为改变来描述利用特定于人的反脆弱性来在行为改变过程中最大化收益并最小化风险的行为改变过程。虽然大多数现有的行为改变模型侧重于提高一个人面对挑战的动机和能力，但反脆弱行为改变通过帮助选择最有可能产生预期结果的挑战来补充这些模型。接下来，我们提出三条原则，通过这些原则，DHBCIs可以帮助用户制定反脆弱行为改变策略：提供个性化指导、在选择行为时接受多样性和探索，以及优先考虑用户自主性。最后，我们提供一个示例，说明如何设计一个DHBCI来支持反脆弱行为改变。
