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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".