Cavallo Francesca Romana, Toumazou Christofer
Centre for Bio-inspired Technology, Electrical and Electronic Engineering Department, Imperial College, London, United Kingdom.
PLOS Digit Health. 2023 Aug 30;2(8):e0000333. doi: 10.1371/journal.pdig.0000333. eCollection 2023 Aug.
Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.
移动健康应用程序利用无线技术进行医疗保健，有助于改变行为，进而改善健康状况。移动健康应用程序旨在增加身体活动，但很少基于行为理论开发，且采用的个性化技术简单，而事实证明这种个性化技术在促进行为改变方面是有效的。在这项工作中，我们提出了一种通过基于知识的自适应系统提供的、理论驱动的个性化行为干预措施。行为系统设计以行为改变轮和能力-机会-动机行为模型为指导。该系统利用可穿戴设备、即时检测和消费者基因检测所产生的日益丰富的健康数据，发布高度个性化的身体活动和久坐行为建议。为了提供个性化建议，该系统首先根据用户的心脏代谢状况将其分为四个糖尿病群组之一。其次，根据用户的基因型和过去的活动历史推荐活动水平，最后，向用户展示其当前患心血管疾病的风险。此外，瘦素（一种参与新陈代谢的激素）作为反馈生物信号被纳入，以进一步使建议个性化。作为一个案例研究，我们针对2型糖尿病患者设计并展示了该系统，因为2型糖尿病是一种通常通过改变生活方式（如增加身体活动和减少久坐行为）来控制的慢性病。我们使用来自大型临床数据库英国生物银行的糖尿病参与者的数据对该系统进行了训练和模拟，并证明该系统可以随着时间的推移帮助增加活动量。这些结果为该系统的实际应用提供了依据，我们旨在通过人体干预对其进行评估。
