Exploring Adherence to Pelvic Floor Muscle Training in Women Using Mobile Apps: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Pelvic floor dysfunction is a public health issue, with 1 in 3 women experiencing symptoms at some point in their lifetime. The gold standard of treatment for pelvic floor dysfunction is supervised pelvic floor muscle training (PFMT); however, adherence to PFMT in women is poor. Mobile apps are increasingly being used in the National Health Service to enable equity in the distribution of health care and increase accessibility to services. However, it is unclear how PFMT mobile apps influence PFMT adherence in women. OBJECTIVE: We aimed to identify which behavior change techniques (BCTs) have been used in PFMT mobile apps, to distinguish the core "capability, opportunity, and motivation" (COM) behaviors targeted by the BCTs used in PFMT mobile apps, and to compare the levels of PFMT adherence in women between those using PFMT mobile apps and those receiving usual care. METHODS: We conducted a scoping review of the literature. Published quantitative literature that compared the use of a PFMT mobile app to a control group was included to address the objectives of the study. The electronic bibliographic databases searched included MEDLINE, CINAHL, Scopus, Web of Science, and PEDro, along with CENTRAL. Studies were also identified from reference searching of systematic reviews. Original articles written in English from 2006 onward were included. Nonexperimental quantitative studies, qualitative studies, studies that use male participants, case studies, web-based interventions, and interventions that use vaginal probes were excluded. Narrative synthesis was conducted on eligible articles based on the aims of the study. RESULTS: Of the 114 records retrieved from the search, a total of 6 articles met the eligibility and inclusion criteria. The total number of participants in the studies was 471. All PFMT mobile apps used the BCT "prompts and cues." Opportunity was the core COM behavior targeted by the PFMT mobile apps. Higher levels of adherence to PFMT were observed among women using PFMT mobile apps. CONCLUSIONS: Digital "prompts and cues" are a BCT commonly used in PFMT mobile apps, and further research is required to practically assess whether a future randomized controlled trial that investigates the effectiveness of digital "prompts and cues" on PFMT adherence in women can be conducted.
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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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".