Developing a Mood and Menstrual Tracking App for People With Premenstrual Dysphoric Disorder: User-Centered Design Study
Bibliographic record
Abstract
Background: People with premenstrual dysphoric disorder (PMDD) experience a range of symptoms that increase and decline as a result of the natural hormonal fluctuations of the menstrual cycle. For the diagnosis of PMDD, symptom severity needs to be recorded daily for at least two symptomatic cycles. In recent years, the rise in interest in Femtech (tools and technology developed to address women's health issues) has resulted in a large quantity of "period-tracking apps" being developed and downloaded. However, there is not currently a menstrual and mood tracking app that has the full capabilities to accurately capture the symptoms of PMDD to aid with diagnosis. Objective: This study aimed to collect feedback and insights from potential users (ie, people with lived experience of PMDD or severe premenstrual syndrome) to inform the development of a prototype app that could support prospective mood monitoring of PMDD symptoms for research, and to support diagnosis. Methods: We conducted two user-centered design studies. Study 1 consisted of 4 interviews with individual participants who had taken part in our previous web-based mood tracking study for PMDD. During the interviews, participants were encouraged to identify the strengths and weaknesses of the existing web-based mood tracking system. Study 2 consisted of 2 workshops with a total of 8 participants, in which participants were asked to discuss the needs and desirable features they would like in a PMDD-specific tracking app. Interviews and workshops were recorded, and the transcripts were analyzed inductively following a thematic approach. Results: A total of four themes were identified from the interviews and workshops with potential users: (1) ease of use as a key consideration for users with PMDD; (2) avoiding a reductionist approach for a broad range of symptoms; (3) recognizing the importance of correct language; and (4) integrating features for the users' benefits. These suggestions align with the current understanding of the implications of PMDD symptoms on daily activities and with findings from previous research on encouraging long-term engagement with apps. Conclusions: To meet the needs of potential users with PMDD or suspected PMDD, there needs to be a special consideration to how their symptoms impact the way they might interact with the app. In order for users to want to interact with the app daily, particularly during the days where they may not have symptoms to track, the app needs to be simple yet engaging. In addition, if the app provides insights and feedback that can benefit the well-being of the users, it is suggested that this could ensure prolonged use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".