Evaluating the Clinical Use and Utility of a Digital Support App for Employees With Chronic Pain Returning to Work (SWEPPE): Observational Study
Bibliographic record
Abstract
BACKGROUND: The digital app SWEPPE (sustainable worker, a digital support for persons with chronic pain and their employers) was developed to improve the support of people with chronic pain in their return-to-work process after sick leave and includes functions such as the action plan, daily self-rating, self-monitoring graphs, the coach, the library, and shared information with the employer. OBJECTIVE: This study aims to describe the use of the smartphone app SWEPPE among people with chronic pain who have participated in an interdisciplinary pain rehabilitation program. METHODS: This is a case study including 16 people participating in a feasibility study. The analyses were based on user data collected for 3 months. Quantitative data regarding used functions were analyzed with descriptive statistics, and qualitative data of identified needs of support from the employer were grouped into 8 categories. RESULTS: Self-monitoring was used by all participants (median 26, IQR 8-87 daily registrations). A total of 11 (N=16, 69%) participants set a work-related goal and performed weekly evaluations of goal fulfillment and ratings of their work ability. In total, 9 (56%) participants shared information with their employer and 2 contacted the coach. A total of 15 (94%) participants identified a total of 51 support interventions from their employer. Support to adapt to work assignments and support to adapt to work posture were the 2 biggest categories. The most common type of support identified by 53% (8/15) of the participants was the opportunity to take breaks and short rests. CONCLUSIONS: Participants used multiple SWEPPE functions, such as daily self-registration, goal setting, self-monitoring, and employer support identification. This shows the flexible nature of SWEPPE, enabling individuals to select functions that align with their needs. Additional research is required to investigate the extended use of SWEPPE and how employers use shared employee information.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".