Using codesign to develop a mobile application for pelvic floor muscle training with an intravaginal device (femfit®)
Bibliographic record
Abstract
AIMS: The aim of this project was to use codesign to develop a mobile application (app) for pelvic floor muscle training, with an intravaginal device (femfit®). The objective was to obtain user feedback to guide the design and development of a mobile app, consistent with the Mobile Application Rating Scale (MARS) framework. METHODS: Twenty-six women (22-62 years) provided mobile app feedback using a Design Thinking framework and grounded theory approach. Four focus groups (2 h each) and two sets of one-to-one interviews (1 h each) were held from May 2018 to October 2019. The researchers debriefed the focus groups and interviews, and undertook analysis based on project objectives and key questions. RESULTS: Recurring themes throughout the study aligned with sections of the MARS: (A) engagement (e.g., progress tracking), (B) functionality (e.g., intuitive interface), (C) aesthetics (e.g., smart graphics and colors), (D) information (e.g., clear, concise information). An internal preliminary assessment determined a MARS Quality Mean Score of 4.1 of 5 (engagement: 3.6 of 5; functionality: 4 of 5; aesthetics: 4.3 of 5: information: 4.4 of 5). CONCLUSIONS: The development of the mobile app is on track to meet MARS requirements, and to be a fun, motivating app for women. Future work is required to investigate its efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".