| dc.description.abstract | Health and wellbeing is a rapidly growing area within Human-Computer Interaction (HCI). In recent years, many have developed systems, theoretical stances, and methodologies that aim at positioning information and communication technologies as an important component of strategies for diagnosis, treatment, prevention, or self-management for people facing health or mental health difficulties. Technology has opened the door to a multitude of opportunities for improving health and mental health interventions. Particularly, recent innovations such as wearable devices, embedded sensors, and machine learning show great potential for enhancing the effectiveness, tailoring, and convenience of traditional interventions.
Designing technologies for health and wellbeing requires negotiating a complex design space, for which various design philosophies building on User-Centred Design, and validation methods have been proposed, all associated with a range of challenges and opportunities.
While clinical effectiveness is critical to the success of technology-supported health and wellbeing interventions, a lack of user acceptance will impede individuals' uptake and long-term use of the technology. Due to the longitudinal nature of healthcare, non-use or discontinued use of technology in this context risks exacerbating the issues of lack of adherence to treatment and drop-out. User acceptance is therefore a core challenge for successful health and mental health technologies. In addition, this question requires consideration as early as design stage, and throughout the user journey with technology. The theoretical landscape of user acceptance is rich, and includes a wide range of definitions, models, and measurement methods. However, the ambiguous use of terminology, and large number of both validated and ad-hoc models available make the application of acceptance theory into design and research practices a complex task.
This thesis examines how to support design for, and measurement of user acceptance of health and mental health care technologies. It asks how to guide designers of digital health and wellbeing technologies in leveraging the theory of user acceptance in design practice, and how to capture a rich picture of the evolution of user acceptance in clinical settings. Through collaboration with digital health researchers, designers, patients, and mental health professionals, this thesis attempts to bridge the gap between theory and practice.
This thesis first contributes to our knowledge on how user acceptance is addressed in research practice, including how the concept is defined, discussed, designed for, and measured. It identifies differences that exist between research practices and existing theoretical frameworks, and contributes to the clarification of the process of technology acceptance by (i) defining user acceptance and the user acceptance journey, and (ii) proposing the Technology Acceptance Lifecycle (TAL), a temporal, theory-based model consolidating existing definitions, articulating the different stages of technology acceptance - namely pre-use acceptability, initial use acceptance, and sustained use acceptance - and providing an explicit terminology.
This thesis then proposes and examines a novel, theory-based approach to designing for user acceptance of health and mental health care technologies. This approach consists of a design tool and method, the TAC toolkit, which supports designers in leveraging user acceptance theory in the design of health and wellbeing technologies. The TAC toolkit method was evaluated with 21 digital health designers. Findings indicated that the method (i) revised and extended designers' knowledge of user acceptance, (ii) fostered their appreciation, empathy, and ethical values while designing for acceptance, and (iii) motivated the future use of the toolkit in their design practice.
This thesis finally investigates a novel, theory-based longitudinal approach to evaluating user acceptance in the context of mental health care, following the TAL timeline. As a practical example to implement such a longitudinal theory-based approach, we developed the Mood Monitor - an application for smartwatch intended to facilitate mood and lifestyle habits self-monitoring in depression treatment. This practical case study took the form of an 8-week randomised controlled clinical trial (n=69), examining patient acceptance of the Mood Monitor watch app in a routine internet-delivered therapy for depression. This thesis shows how the results of the case study exploring the longitudinal measurement method can inform the technology design, and identifies challenges of conducting user acceptance research in clinical settings.
This thesis enriches the HCI field with a better understanding of user acceptance, and how the concept can be designed for and measured, in the context of digital health and wellbeing. | en |
