An expandable approach for design and personalization of digital, just-in-time adaptive interventions
An expandable approach for design and personalization of digital, just-in-time adaptive interventions
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DOI：
10.1093/jamia/ocy160
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发表时间：
2019-03-01
影响因子：
6.4
通讯作者：
Cosar, Ahmet
中科院分区：
文献类型：
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作者：
Gonul, Suat;Namli, Tuncay;Cosar, Ahmet
目的：我们的目标是提供一个有两个主要目标的框架：1）促进理论驱动的、适应性的、数字化的干预措施的设计，以解决慢性疾病或健康问题; 2）通过优化各种干预组件，根据人们的个人需求、瞬时环境和心理社会变量，制定个性化的干预措施交付策略，以支持自我管理。材料和方法：我们提出了一个基于模板的数字干预设计机制，使配置基于证据的，及时的，自适应的干预组件。设计机制采用了一种规则定义语言，使专家能够根据瞬时和历史背景/个人数据指定干预措施的触发条件。该框架持续监测和处理个人数据空间，并评估干预触发条件。我们从强化学习方法中受益，可以在干预的时间、频率和类型（内容）方面制定个性化的干预交付策略。为了验证个性化的算法，我们布置了一个模拟测试床与2人物，不同的在他们的各种模拟现实生活conditions.Results：我们评估的设计机制，提出的例子干预定义的基础上的行为变化分类法和临床指南。此外，我们还为针对糖尿病患者的现实护理计划提供了干预定义。最后，我们通过一组假设验证了个性化的交付机制，根据与个人偏好，特质和生活方式模式相关的模拟差异，断言交付策略中的某些适应方式。虽然设计机制可充分扩展以满足理论和临床介入设计要求，个性化算法能够使干预递送策略适应于模拟的现实生活条件。
Objective: We aim to deliver a framework with 2 main objectives: 1) facilitating the design of theory-driven, adaptive, digital interventions addressing chronic illnesses or health problems and 2) producing personalized intervention delivery strategies to support self-management by optimizing various intervention components tailored to people's individual needs, momentary contexts, and psychosocial variables.Materials and Methods: We propose a template-based digital intervention design mechanism enabling the configuration of evidence-based, just-in-time, adaptive intervention components. The design mechanism incorporates a rule definition language enabling experts to specify triggering conditions for interventions based on momentary and historical contextual/personal data. The framework continuously monitors and processes personal data space and evaluates intervention-triggering conditions. We benefit from reinforcement learning methods to develop personalized intervention delivery strategies with respect to timing, frequency, and type (content) of interventions. To validate the personalization algorithm, we lay out a simulation testbed with 2 personas, differing in their various simulated real-life conditions.Results: We evaluate the design mechanism by presenting example intervention definitions based on behavior change taxonomies and clinical guidelines. Furthermore, we provide intervention definitions for a real-world care program targeting diabetes patients. Finally, we validate the personalized delivery mechanism through a set of hypotheses, asserting certain ways of adaptation in the delivery strategy, according to the differences in simulation related to personal preferences, traits, and lifestyle patterns.Conclusion: While the design mechanism is sufficiently expandable to meet the theoretical and clinical intervention design requirements, the personalization algorithm is capable of adapting intervention delivery strategies for simulated real-life conditions.