App-based intervention among adolescents with persistent pain: a pilot feasibility randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Persistent pain in adolescence adversely affects everyday life and is an important public health problem. The primary aim was to determine the feasibility of an 8-week app-based self-management intervention to reduce pain and improve health-related quality of life in a community-based population of adolescents with persistent pain. A secondary aim was to explore differences in health outcomes between the intervention and control groups. METHODS: app, which includes symptom tracking, goal setting, self-management strategies, and social support. The attention control group received a symptom tracking app. Feasibility was assessed as attrition rates and level of engagement (interactions with the app). The secondary outcomes included pain intensity, health-related quality of life, self-efficacy, pain self-efficacy, perceived social support from friends, anxiety and depression, and patient global impression. Statistical analyses were conducted using SPSS. RESULTS: Demographic and baseline outcome variables did not differ between the 2 groups. No differences were found between the participants completing the study and those who withdrew. Twenty-eight adolescents completed the intervention as planned (62% attrition). Both groups had a low level of app engagement. Intention-to-treat analysis (n = 19 + 14) showed no significant differences in outcomes between groups. However, the large effect size (Cohen's d = .9) for depression suggested a lower depression score in the intervention group. CONCLUSIONS: High treatment attrition and low engagement indicate the need for changes in trial design in a full-scale randomized controlled trial to improve participant retention. TRIAL REGISTRATION: The iCanCope with Pain Norway trial was retrospectively registered in Clinical Trials.gov (ID: NCT03551977 ). Registered 6 June 2018.
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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 | 0.013 | 0.003 |
| 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 | 0.001 |
| 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 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".