RESUMO
This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients' needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.
RESUMO
BACKGROUND: Persons with diabetes and depression have increased risk of complications and increased mortality. We aimed to investigate the prevalence, clinical characteristics and impact with regard to glycosylated haemoglobin (HbA1c) of depressive disorders in persons with type 1 diabetes at an outpatient specialist diabetes clinic. FINDINGS: A total of 51 persons with type 1 diabetes were diagnosed according to Mini International Neuropsychiatric Interview (M.I.N.I) with regard to dysthymia and previous or ongoing depressive episodes during spring 2005. HbA1c was measured at the day of the interview, and self-reported information on family history of depressive disorders was obtained. Eight persons (16%; 95% CI: 7%, 29%) were in the midst of a major depressive episode, 4 of these also reported a previous episode of depression. Seven of the 8 persons with an ongoing major depressive episode met the criteria for melancholia. Three persons (6%) met the criteria for dysthymia, and 6 persons (12%) had previous episode(s) of depression, without being currently depressed. The 17 (33%; 95% CI: 21%, 48%) persons with ongoing and/or previous depressive disorder had increased HbA1c (8.5%; 95% CI: 7.6%, 9.4%) compared to those without depressive disorders (7.9%; 95% CI: 7.5%, 8.3%), although the difference did not reach statistical significance. CONCLUSIONS: Persons with type 1 diabetes had a high prevalence of depressive disorders, mainly depressive episodes that also met the criteria for melancholia, a subtype often considered a more serious and "biologic" form of depression. We were not able to demonstrate that persons with depressive disorders had poorer regulated diabetes compared to those without depressive disorders.
