Following Up Patients With Chronic Pain Using a Mobile App With a Support Center: Unicenter Prospective Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is one of the most common diseases in the world and requires a multidisciplinary treatment approach. Spinal cord stimulation is a possible treatment option, but these patients require close follow-up, which is not always feasible. Introduction: eHealth apps also offer the possibility of closer patient follow-up, although adherence tends to decrease over time, dropping to around 60%. To improve adherence to remote follow-up, we developed a remote follow-up system consisting of a mobile phone app for patients, a website for professionals and a remote support center. OBJECTIVE: To evaluate adherence to remote follow-up using a system with mobile phone app and a remote support center. METHODS: After review of the literature and approval by a multidisciplinary committee, a team of experts designed a follow-up system based on a mobile phone app, a website for professionals, and a remote support center. The system was developed with the collaboration of healthcare professionals and uses validated scales to capture patients' clinical data at each stage of treatment (pre-treatment phase, trial phase and implantation phase). Data was collected prospectively from January 2020 to August 2023, including total surveys sent and surveys answered, in addition to notifications sent. RESULTS: A total of 64 patients were included (40 female, 62.5%). At the end of the study, 19 patients were in the pre-treatment phase (29'7%), 8 had reached the trial phase (12.5%) and 37 reached the implantation phase (57.8%). The follow-up period was 15.30 ± 9.43 months (mean ± SD). A total of 1574 surveys were sent out, along with 488 SMS reminders and 53 reminder calls. The adherence rate decreased from 94.53% in the pre-treatment phase to 65.68% in the implantation phase, with an overall adherence rate for the app of 87.37%. ANOVA analysis showed that adherence was higher in the earlier phases of treatment (p < 0.001). CONCLUSIONS: Our remote follow-up system, supported by a remote support center, improves adherence to follow-up, although adherence tends to decrease over time. Further studies are needed to investigate the correlation between adherence to the app and pain management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".