Kim Kristoffer Dysthe

• Dysthe, Kim Kristoffer; Røssberg, Jan Ivar; Brandtzæg, Petter Bae; skjuve , Marita; Følstad, Asbjørn & Haavet, Ole Rikard [Show all 7 contributors for this article] (2023). The Depressed Teenage Brain: Chatbot Psychoeducation. Mayo Clinic proceedings. ISSN 0025-6196. 1(3), p. 334–342. doi: 10.1016/j.mcpdig.2023.03.008. Full text in Research Archive
• Dysthe, Kim Kristoffer; Røssberg, Jan Ivar; Brandtzæg, Petter Bae; skjuve , Marita; Haavet, Ole Rikard & Følstad, Asbjørn [Show all 7 contributors for this article] (2023). Analyzing User-Generated Web-Based Posts of Adolescents' Emotional, Behavioral, and Symptom Responses to Beliefs About Depression: Qualitative Thematic Analysis. Journal of Medical Internet Research. ISSN 1438-8871. 25. doi: 10.2196/37289. Full text in Research Archive
• Dysthe, Kim Kristoffer; Haavet, Ole Rikard; Røssberg, Jan Ivar; Brandtzæg, Petter Bae; Følstad, Asbjørn & Klovning, Atle (2021). Finding relevant psychoeducation content for adolescents experiencing symptoms of depression: Content analysis of user-generated online texts. Journal of Medical Internet Research. ISSN 1438-8871. 23(9), p. 1–15. doi: 10.2196/28765. Full text in Research Archive Show summary

  Background: Symptoms of depression are frequent in youth and may develop into more severe mood disorders, suggesting interventions should take place during adolescence. However, young people tend not to share mental problems with friends, family, caregivers, or professionals. Many receive misleading information when searching the internet. Among several attempts to create mental health services for adolescents, technological information platforms based on psychoeducation show promising results. Such development rests on established theories and therapeutic models. To fulfill the therapeutic potential of psychoeducation in health technologies, we lack data-driven research on young peoples’ demand for information about depression. Objective: Our objective is to gain knowledge about what information is relevant to adolescents with symptoms of depression. From this knowledge, we can develop a population-specific psychoeducation for use in different technology platforms. Methods: We conducted a qualitative, constructivist-oriented content analysis of questions submitted by adolescents aged 16-20 years to an online public information service. A sample of 100 posts containing questions on depression were randomly selected from a total of 870. For analysis, we developed an a priori codebook from the main information topics of existing psychoeducational programs on youth depression. The distribution of topic prevalence in the total volume of posts containing questions on depression was calculated. Results: With a 95% confidence level and a ±9.2% margin of error, the distribution analysis revealed the following categories to be the most prevalent among adolescents seeking advice about depression: self-management (33%, 61/180), etiology (20%, 36/180), and therapy (20%, 36/180). Self-management concerned subcategories on coping in general and how to open to friends, family, and caregivers. The therapy topic concerned therapy options, prognosis, where to seek help, and how to open up to a professional. We also found young people dichotomizing therapy and self-management as opposite entities. The etiology topic concerned stressors and risk factors. The diagnosis category was less frequently referred to (9%, 17/180). Conclusions: Self-management, etiology, and therapy are the most prevalent categories among adolescents seeking advice about depression. Young people also dichotomize therapy and self-management as opposite entities. Future research should focus on measures to promote self-management, measures to stimulate expectations of self-efficacy, information about etiology, and information about diagnosis to improve self-monitoring skills, enhancing relapse prevention.

• Uddin, Md Zia; Dysthe, Kim Kristoffer; Følstad, Asbjørn & Brandtzæg, Petter Bae (2021). Deep learning for prediction of depressive symptoms in a large textual dataset. Neural Computing & Applications. ISSN 0941-0643. 34, p. 721–744. doi: 10.1007/s00521-021-06426-4. Full text in Research Archive Show summary

  Depression is a common illness worldwide with potentially severe implications. Early identification of depressive symptoms is a crucial first step towards assessment, intervention, and relapse prevention. With an increase in data sets with relevance for depression, and the advancement of machine learning, there is a potential to develop intelligent systems to detect symptoms of depression in written material. This work proposes an efficient approach using Long Short-Term Memory (LSTM)-based Recurrent Neural Network (RNN) to identify texts describing self-perceived symptoms of depression. The approach is applied on a large dataset from a public online information channel for young people in Norway. The dataset consists of youth’s own text-based questions on this information channel. Features are then provided from a one-hot process on robust features extracted from the reflection of possible symptoms of depression pre-defined by medical and psychological experts. The features are better than conventional approaches, which are mostly based on the word frequencies (i.e., some topmost frequent words are chosen as features from the whole text dataset and applied to model the underlying events in any text message) rather than symptoms. Then, a deep learning approach is applied (i.e., RNN) to train the time-sequential features discriminating texts describing depression symptoms from posts with no such descriptions (non-depression posts). Finally, the trained RNN is used to automatically predict depression posts. The system is compared against conventional approaches where it achieved superior performance than others. The linear discriminant space clearly reveals the robustness of the features by generating better clustering than other traditional features. Besides, since the features are based on the possible symptoms of depression, the system may generate meaningful explanations of the decision from machine learning models using an explainable Artificial Intelligence (XAI) algorithm called Local Interpretable Model-Agnostic Explanations (LIME). The proposed depression symptom feature-based approach shows superior performance compared to the traditional general word frequency-based approaches where frequency of the features gets more importance than the specific symptoms of depression. Although the proposed approach is applied on a Norwegian dataset, a similar robust approach can be applied on other depression datasets developed in other languages with proper annotations and symptom-based feature extraction. Thus, the depression prediction approach can be adopted to contribute to develop better mental health care technologies such as intelligent chatbots.

• Brandtzæg, Petter Bae; Skjuve, Marita; Dysthe, Kim Kristoffer & Følstad, Asbjørn (2021). When the Social Becomes Non-Human: Young People’s Perception of Social Support in Chatbots. In Kitamura, Yoshifumi (Eds.), CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM). ISSN 978-1-4503-8096-6. p. 1–13. doi: 10.1145/3411764.3445318. Full text in Research Archive Show summary

  Although social support is important for health and well-being, many young people are hesitant to reach out for support. The emerging uptake of chatbots for social and emotional purposes entails opportunities and concerns regarding non-human agents as sources of social support. To explore this, we invited 16 participants (16–21 years) to use and reflect on chatbots as sources of social support. Our participants first interacted with a chatbot for mental health (Woebot) for two weeks. Next, they participated in individual in-depth interviews. As part of the interview session, they were presented with a chatbot prototype providing information to young people. Two months later, the participants reported on their continued use of Woebot. Our findings provide in-depth knowledge about how young people may experience various types of social support—appraisal, informational, emotional, and instrumental support—from chatbots. We summarize implications for theory, practice, and future research.

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• Eide, Torunn Bjerve; Guassora, Ann Dorrit; Johansen, May-Lill; Dysthe, Kim Kristoffer & Johansson, Stefan (2022). The Qualitative Toolbox of General Practice Research.
• Dysthe, Kim Kristoffer; Wahl, Alexander; Tjensvoll, Kjell & Rosness, Tor Atle (2022). Fastleger trenger bred kunnskap – samlet på ett sted. Tidsskrift for Den norske legeforening. ISSN 0029-2001. 142(15). doi: 10.4045/tidsskr.22.0557. Full text in Research Archive
• Dysthe, Kim Kristoffer (2020). Artificial intelligence algorithms promoting help-seeking behavior among depressed youth.

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