Smartphone apps for menstrual pain and symptom management: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The past decade marks a surge in the development of mobile apps used to digitally track and monitor aspects of personal health, including menstruation. Despite a plethora of menstruation-related apps, pain and symptom management content available in apps has not been systematically examined. The objective of this study was to evaluate app characteristics, overall quality (i.e., engagement, functionality, design aesthetics, and information), nature and quality of pain and symptom tracking features, and availability and quality of pain-related intervention content. A scoping review of apps targeting facets of the menstrual experience was conducted by searching the Apple App Store. After removal of duplicates and screening, 119 apps targeting menstrual experiences were retained. Pain and menstrual symptoms tracking were available in 64 % of apps. Checkboxes or dichotomous (present/absent) reporting was the most common method of tracking symptoms and was available in 75 % of apps. Only a small subset (n = 13) of apps allowed for charting/graphing of pain symptoms across cycles. Fourteen percent of apps included healthcare professionals or researchers in their development and one app reported use of end-users. Overall app quality measured through the Mobile App Rating Scale (MARS) was found to be acceptable; however, the apps ability to impact pain and symptom management (e.g., impact on knowledge, awareness, behaviour change, etc.) was rated as low. Only 10 % of apps (n = 12) had interventions designed to manage pain. The findings suggest that despite pain and symptom management content being present in apps, this content is largely not evidence-based in nature. More research is needed to understand how pain and symptom management content can be integrated into apps to improve user experiences.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it