Evaluating a Wearable-Based Pain Monitoring System in Palliative Cancer Care: Usability and Feasibility Study
Bibliographic record
Abstract
Background: Effective pain management is a cornerstone of cancer palliative care, yet it remains challenging in low- and middle-income countries due to limited resources, regulatory constraints, and a lack of objective tools. While wearable technologies offer promise for augmenting pain-related patient-reported outcomes with physiological data, their usability in palliative settings in low- and middle-income countries is underexplored. Objective: This study aimed to evaluate the technology usability and implementation feasibility of the NEST (Non-intrusive Devices for Telemedicine) system, a low-cost, smartwatch-based pain monitoring solution for palliative cancer care co-designed with health care staff from a cancer hospital in Ecuador. Methods: An observational usability study was conducted with 7 patients with cancer receiving palliative care treatment, combining hospital- and home-based monitoring phases. We used a qualitative and quantitative approach to assess the usability of the NEST system and to identify sociotechnical factors affecting feasibility using the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Results: Quantitative results showed a strong preference for the smartwatch over the mobile phone for submitting patient-reported outcomes (246/296, 83%), with wear-time adherence of the smartwatch ranging from 36% to 92% of the time. Qualitative feedback from patients and health care staff indicated good usability and perceived clinical value, though technical and organizational challenges, such as charging habits, training needs, and dashboard integration into the daily workflow of health care staff, were noted. As for feasibility, most of the complexity was found in the dynamics of the health condition, while the technology shows clear promising signs of having value to patients and health care staff. Conclusions: Our findings suggest that the commonly reported usability hurdles of a smartwatch-based sociotechnical health solution are surmountable given fluid communication between stakeholders during all stages of design and deployment. The primary threats to feasibility in our context seem to lie in the highly complex and dynamic environment of palliative cancer care, regulatory ambiguity regarding the use of medical devices, and the workload burden on health care staff.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".