Patients’ Experiences of Using an eHealth Pain Management Intervention Combined With Psychomotor Physiotherapy: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Chronic pain is a major health challenge to those affected. Blended care with psychomotor physiotherapy (PMP) combined with eHealth self-management might be beneficial. OBJECTIVE: This study aims to explore how patients with chronic pain experience the combination of PMP and the use of EPIO, an eHealth self-management intervention for chronic pain. METHODS: Individual semistructured interviews were conducted with 5 adult patients with chronic pain (ie, participants) who used EPIO in combination with PMP over a period of 10 to 15 weeks. Interviews explored participants' experiences using this treatment combination in relation to their pain and analyzed their experiences using systematic text condensation. RESULTS: Participants described having benefited from using EPIO in combination with PMP in terms of increased awareness of bodily signals and how pain was related to stress and activity. They also described changes in the relationship to themselves in terms of increased self-acceptance, self-assertion, and hope and their relationship to their pain in terms of seeing pain as less harmful and engaging in more active coping strategies. CONCLUSIONS: Results indicate that a blended care approach combining eHealth self-management interventions such as EPIO with PMP may be of value to patients living with chronic pain. TRIAL REGISTRATION: ClinicalTrials.gov NCT03705104; https://clinicaltrials.gov/ct2/show/NCT03705104.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".