Depression is an affective disorder with a range of cognitive biases and distortions,
which drives depression onset, development and maintenance. This PhD research
aims to support end users with non-clinical depression, by exploring the possibility of mitigating a range of depression-associated impairments in autobiographical memory processing (D-ABMs) through mobile applications.

Emerging psychological interventions targeting these disrupted D-ABMs issues
hold enormous potential of mitigating depression symptoms and thus been widely explored in the field of psychology. However, they have received less support from HCI research in depression . Current HCI work on digital interventions mainly support the digitization of mainstream psychological interventions such as Cognitive Behavioural Therapy (CBT) as it is acknowledged as the most evidence-based interventions, and its pre-structured nature makes it easier to be transferred into digital app design. However, the pre-defined nature of CBT related interventions can also bring various limitations. Different to the pre-structured interventions such as CBT, D-ABMs interventions hold promises in bringing more person-centric training content that are more flexible to app users’ needs.

This thesis aims to explore the design space of mobile apps for D-ABMs. For
this purpose, first, I explored the key effective components in current depression
interventions while addressing D-ABMs, and analysed how they can inform the design of apps for supporting these interventions. Then, I explored the combination of app features to be included in the design of D-ABM apps, which can support these therapeutic components. Finally, I investigated into an effective design method for helping future designers of D-ABM apps to utilise the empirical findings gained from this thesis work.

Overall, this thesis provides empirical exploration and design perspective that
demonstrate ways of adapting memory assistive technologies to support the mitigation of depression associated cognitive dysfunctions and consequently alleviating depressive symptoms. The work aims to draw attention to depression-associated cognitive impairments as a less explored space in the filed of HCI, and to inspire HCI researchers to develop novel classes of mobile-based technologies for addressing a wide range of cognitive impairments that are associated depression. The contribution of this thesis opens up new design opportunities for both memory assistive and depression management technologies. The work aims to broaden the awareness of HCI researchers
of mental conditions that involve autobiographical memory impairments besides
episodic memory loss, such as depression, PTSD, or anxiety, which can be benefited from memory technologies that tailored for each specific conditions.