Endometriosis Support and Development of Digital Technology–Based Interventions: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Endometriosis is a chronic disease that affects 1 in 10 women worldwide. The disease affects patients' daily life at physical, psychological, and social levels. In recent years, the management of this disease has evolved, thanks in particular to the emergence of digital technologies and associated interventions. However, despite their growing use, there seems to be no systematic review of their development, design, and efficacy. OBJECTIVE: A systematic review was conducted with the aim of characterizing the development process, design, and effectiveness of interventions using a digital tool for endometriosis. METHODS: A total of 7 databases (MEDLINE, APA PsycArticles, Academic Search Premier, Psychology and Behavioral Sciences Collection, APA PsycInfo, SocINDEX, and SPORTDiscus) were searched to identify relevant articles published between 2010 and 2024. The articles selected were analyzed using a methodological framework specific to the development of digital health interventions (Design and Evaluation of Digital Health Interventions [DEDHI]), consisting of 4 phases: preparation (phase 1, specific to application development), optimization (phase 2, dedicated to identifying the best intervention configurations), evaluation (phase 3, aiming to confirm the effectiveness of the intervention), and implementation (phase 4, implementing and updating the intervention on a large scale). RESULTS: A selection of 10 articles was made from the 381 studies retrieved from the databases. Among these 10 studies, 6 distinct digital health interventions were identified. The interventions based on digital devices produced physical and psychological benefits. Analysis using the DEDHI framework showed (1) a disparity in the responses to the different phases (ie, 9/10, 90% of studies responding to phase 1; 3/10, 30% to phase 2; 4/10, 40% to phase 3; and 2/10, 20% to phase 4) and (2) a variability in the completion of the evaluation criteria ranging from 10% (1/10) to 80% (8/10) in phase 1, 0% (0/13) to 77% (10/13) in phase 2, 0% (0/10) to 80% (8/10) in phase 3, and finally 0% (0/13) to 77% (10/13) in phase 4. The objectives of these digital interventions were to support pain management (5/6, 83%), to provide information about the disease and strategies for managing it (4/6, 67%), and to provide psychosocial support (2/6, 33%). CONCLUSIONS: This systematic review highlights an emerging literature, limited regarding the use of digital technology in the management of endometriosis, and heterogeneous concerning the methodologies used. This variability limits the generalizability of the results and requires a nuanced interpretation of the available data. However, the results of this review have demonstrated the value of digital technology-based interventions to support endometriosis, while highlighting the importance of a methodological framework to structure their development to optimize patient support.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".