Factors in Sustained Compliance to a Symptom-Reporting Mobile Application: Implications for Clinical Implementation
Bibliographic record
Abstract
Background: The Internet-based Computerized Patient Assessment System (iComPAsS), a remote pain- and symptom-reporting application was developed to optimize pain monitoring and management. This subanalysis sought to examine factors influencing compliance, to gauge the sustainability of its effects and to guide further development and implementation as part of usual care. Aim: This analysis sought to examine factors influencing compliance, to gauge the sustainability of its effects and to guide further development and implementation as part of usual care. Methods: Patients ≥ 18 years old, with cancer and moderate-severe pain were randomized to standard pain management with pain diary or iComPAsS. Pain and symptom severity (using Edmonton Symptom Assessment Scale) and compliance (to iComPAsS or diary) were evaluated at week 0, 3, 6, 12 and 20. The Treatment Self-regulation Questionnaire (TSRQ), used to assess patient motivation, was administered at week 0, 6, 12 and 20. Pain levels and compliance were compared between the groups using the Student t-test. The Pearson correlation coefficient was used to examine the relationship between compliance and pain control, perceived competence in pain self-care, and relative autonomy index. Results: Out of 100 patients enrolled, 76 were included in the analysis (control; 37; iComPAsS, 39). Baseline pain levels and TSRQ characteristics were similar between the groups. Initial compliance and pain control at week 3 were significantly higher in the iComPAsS group. For the iCompAsS group, compliance directly correlated with uncontrolled pain and intrinsic motivation, and was more sustained compared with the control group. Conclusion: The iComPAsS elicited rapid uptake and sustained compliance through intrinsic motivation. When adopting the iComPAsS for clinical use, patient baseline motivation levels may be assessed using the TSRQ, and depressive symptoms and other barriers to engagement must be identified.
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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 | 0.038 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".