Galliford Natasha, Yin Kathleen, Blandford Ann, Jung Joshua, Lau Annie Y S
UCL Interaction Centre, University College London, London, United Kingdom.
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, North Ryde, NSW, Australia.
Front Digit Health. 2022 Jun 23;4:838651. doi: 10.3389/fdgth.2022.838651. eCollection 2022.
Many have argued that a "one-size-fits-all" approach to designing digital health is not optimal and that personalisation is essential to achieve targeted outcomes. Yet, most digital health practitioners struggle to identify which design aspect require personalisation. Personas are commonly used to communicate patient needs in consumer-oriented digital health design, however there is often a lack of reproducible clarity on development process and few attempts to assess their accuracy against the targeted population. In this study, we present a transparent approach to designing and validating personas, as well as identifying aspects of "patient work," defined as the combined total of work tasks required to manage one's health and the contextual factors influencing such tasks, that are sensitive to an individual's context and may require personalisation.
A data-driven approach was used to develop and validate personas for people with Type 2 diabetes mellitus (T2DM), focusing on patient work. Eight different personas of T2DM patient work were constructed based physical activity, dietary control and contextual influences of 26 elderly Australian participants (median age = 72 years) wearable camera footage, interviews, and self-reported diaries. These personas were validated for accuracy and perceived usefulness for design, both by the original participants and a younger (median age bracket = 45-54 years) independent online cohort f 131 T2DM patients from the United Kingdom and the United States.
Both the original participants and the independent online cohort reported the personas to be accurate representations of their patient work routines. For the independent online cohort, 74% (97/131) indicated personas stratified to their levels of exercise and diet control were similar to their patient work routines. Findings from both cohorts highlight aspects that may require personalisation include , and .
Personas made for a specific purpose can be very accurate if developed from real-life data. Our personas retained their accuracy even when tested against an independent cohort, demonstrating their generalisability. Our data-driven approach clarified the often non-transparent process of persona development and validation, suggesting it is possible to systematically identify whether persona components are accurate or. and which aspects require more personalisation and tailoring.
许多人认为，采用“一刀切”的方法来设计数字健康并不理想，个性化对于实现目标成果至关重要。然而，大多数数字健康从业者难以确定哪些设计方面需要个性化。在以消费者为导向的数字健康设计中，角色通常用于传达患者需求，但是在开发过程中往往缺乏可重复的清晰度，并且很少有人尝试根据目标人群评估其准确性。在本研究中，我们提出了一种设计和验证角色的透明方法，以及识别“患者工作”的各个方面，“患者工作”定义为管理个人健康所需的工作任务总和以及影响此类任务的背景因素，这些方面对个人背景敏感，可能需要个性化。
采用数据驱动的方法为2型糖尿病（T2DM）患者开发和验证角色，重点关注患者工作。基于26名澳大利亚老年参与者（中位年龄 = 72岁）的身体活动、饮食控制和背景影响，通过可穿戴摄像头 footage、访谈和自我报告的日记，构建了8种不同的T2DM患者工作角色。这些角色通过原始参与者以及来自英国和美国的131名T2DM患者组成的较年轻（中位年龄范围 = 45 - 54岁）的独立在线队列，对设计的准确性和感知有用性进行了验证。
原始参与者和独立在线队列都报告说这些角色准确地反映了他们的患者工作日常。对于独立在线队列，74%（97/131）表示根据他们的运动和饮食控制水平分层的角色与他们的患者工作日常相似。两个队列的研究结果都突出了可能需要个性化的方面包括 ，以及 。
如果从现实生活数据中开发，针对特定目的创建的角色可以非常准确。我们的角色即使在针对独立队列进行测试时也保持了准确性，证明了它们的可推广性。我们的数据驱动方法澄清了角色开发和验证过程中通常不透明的情况，表明有可能系统地确定角色组件是否准确，以及哪些方面需要更多的个性化和定制。
