Maintaining Engagement in Adults with Neurofibromatosis Type 1 to Use the iCanCope Mobile Application (iCanCope-NF)
Bibliographic record
Abstract
Introduction: Neurofibromatosis Type 1 (NF1) is an autosomal dominant genetic condition in which chronic pain is a predominant issue. Given the rarity of the disease, there are limited psychosocial treatments for individuals with NF1 suffering with chronic pain. Using mobile applications can facilitate psychosocial treatments; however, there are consistent issues with engagement. Utilizing a mixed methodology, the current study evaluated the customized iCanCope mobile application for NF1 on increasing engagement through the usage of contingency management. Methods: A mixed methods study from a subset of data coming from a randomized clinical trial that occurred from January 2021 to August 2022 was undertaken. Two groups (iCC and iCC + CM) were exposed to the customized iCanCope mobile application in which engagement data were captured in real-time with daily check-ins for interference, sleep, mood, physical activity, energy levels, goal setting, and accessing article content (coping strategies). Additionally, semi-structured interviews were conducted to gain insight into the participants’ experience at the end of the trial. Results: Adults (N = 72) were recruited via NF patient advocacy groups. Significant differences were noted between the groups in total articles read (p = 0.002), goals achieved (p = 0.017), and goals created (p = 008). Additionally, there were significant differences observed between user-generated goals and those that were app recommended (p < 0.001). Both groups qualitatively reported positive feedback on the customized mobile application, indicating that continued usage and engagement of the mobile application were acceptable. Conclusions: Employing customized mobile applications for adults with NF1 along with contingency management can leverage self-managed pain treatments while providing auxiliary resources to this population.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".