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Gerado a 2024-11-21 20:34:46 (Europe/Lisbon).
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Titulo Estágio

EndoME - Manifesting the Lived Experience of Endometriosis

Local do Estágio

Porto or Remote Work

Enquadramento

Project Abstract:
Endometriosis affects 1 in 10 women and undetermined numbers of transgender, genderfluid, and non-binary people (1). Common symptoms of endometriosis include pain: pelvic pain, painful menstrual cramps, pain or discomfort during sexual intercourse (dyspareunia), painful bowel movements or urination. Endometriosis can also sometimes cause gastrointestinal symptoms such as diarrhea, constipation, or bloating, especially during menstruation. Furthermore, some women with endometriosis may have difficulty becoming pregnant and it is suggested that 47% of infertile women have endometriosis (2). Finally, chronic pain and the emotional toll of the condition can lead to fatigue and reduced quality of life. These are the common symptoms reported in the literature, but there is a lack of agreement on the signs and symptoms of the disease, which are generally limited to painful periods and infertility (3).
Despite recent developments, endometriosis remains difficult to diagnose and treat: it has no biomarker for diagnosis, no cure, no standard treatment guidelines, and patients experience unpredictable responses to treatments (4). The average time from the onset of symptoms to diagnosis is typically between 6 and 10 years (5).
This project aims to give visibility on the significant impact on the quality of life, psychological well-being and intimacy of those suffering from endometriosis—as well as explore possible approaches to support and manage the condition, through the design of speculative and exploratory artefacts that enable the personal expression, and autoethnographical analysis of the lived-experience of endometrioses.

Objetivo

Objectives:
The main objective of this thesis is to co-design and prototype speculative artefacts (physical and/or digital) addressing the experience of those suffering with endometriosis. These artefacts should provoke and stimulate reflection on the lived experience of those suffering with endometriosis.

Innovative aspects:
The MSc’s contribution to knowledge will be on the design and prototyping of a proof of concept which can serve as future research, inspiration as well as a basis for future AICOS projects

Plano de Trabalhos - Semestre 1

Workplan:
1. Literature review and SotA on speculative and research through design for health and the issues of pain and intimacy.

Plano de Trabalhos - Semestre 2

Workplan:
2. Analysis of previous studies concerning endometriosis.
3. Participatory and Research through Design experiments.
4. Dissertation and publication writing.

Condições

Student profile:
- Inquisitive, curious and open mind;
- Interest – and, ideally, experience – in human-centred design processes;
- Willingness to perform field work in user research- co-creation and prototype assessment;
- Proficiency in English and good domain of Portuguese for field work.

Observações

References:
(1) Shim, J. Y., Laufer, M. R., & Grimstad, F. W. (2020). Dysmenorrhea and endometriosis in transgender adolescents. Journal of Pediatric and Adolescent Gynecology, 33(5), 524–528. https://doi.org/10.1016/j.jpag.2020.06.001
(2) Meuleman, C., Vandenabeele, B., Fieuws, S., Spiessens, C., Timmerman, D., & D’Hooghe, T. (2009). High prevalence of endometriosis in infertile women with normal ovulation and normospermic partners. Fertility and sterility, 92(1), 68-74.
(3) McKillop, M., Mamykina, L., & Elhadad, N. (2018). Designing in the Dark: Eliciting Self-tracking Dimensions for Understanding Enigmatic Disease. Proceedings of the ACM Conference on Designing Interactive Systems (DIS ‘21) (pp. 907–925). https://doi.org/10.1145/3461778.3461995
(4) Agarwal, S. K., Chapron, C., Giudice, L. C., Laufer, M. R., Leyland, N., Missmer, S. A., … & Taylor, H. S. (2019). Clinical diagnosis of endometriosis: A call to action. American Journal of Obstetrics and Gynecology.
(5) Goldstein, A., & Cohen, S. (2023). Self-report symptom-based endometriosis prediction using machine learning. Scientific Reports, 13, 5499. https://doi.org/10.1038/s41598-023-32761- 8

Orientador

Ricardo Manuel Coelho de Melo
ricardo.melo@fraunhofer.pt 📩