Perspectives on adapting a mobile application for pain self-management in neurofibromatosis type 1: results of online focus group discussions with individuals living with neurofibromatosis type 1 and pain management experts
Bibliographic record
Abstract
OBJECTIVE: Neurofibromatosis type 1 (NF1) is a genetic disorder in which chronic pain commonly occurs. The study sought to understand the needs of individuals with NF1 and pain management experts when adapting a pain self-management mobile health application (app) for individuals with NF1. DESIGN: We conducted a series of online, audio-recorded focus groups that were then thematically analysed. SETTING: Online focus groups with adults currently residing in the USA. PARTICIPANTS: Two types of participants were included: individuals with NF1 (n=32 across six focus groups) and pain management experts (n=10 across three focus groups). RESULTS: Six themes across two levels were identified. The individual level included lifestyle, reasons for using the mobile app and concerns regarding its use. The app level included desired content, desired features and format considerations. Findings included recommendations to grant free access to the app and include a community support feature for individuals to relate and validate one another's experience with pain from NF1. In addition, participants noted the importance of providing clear instructions on navigating the app, the use of an upbeat, hopeful tone and appropriate visuals. CONCLUSIONS: Both participant groups endorsed the use of iCanCope (iCC) as an NF1 pain self-management mobile app. Differences between groups were noted, however. The NF1 group appeared interested in detailed and nuanced pain tracking capabilities; the expert group prioritised tracking information such as mood, nutrition and activity to identify potential associations with pain. In tailoring the existing iCC app for individuals with NF1, attention should be paid to creating a community support group feature and to tailoring content, features and format to potential users' specific needs.
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 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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".