Feasibility of intradialytic Exercise in a Rural Community Hemodialysis Unit : Mixed Methods Analysis of Implementation
Bibliographic record
Abstract
Background: The use of leg cycle ergometers (LCEs) during hemodialysis (HD) has been shown to have a number of benefits including improved adequacy of HD and blood pressure reduction while also improving exercise capacity, physical function and quality of life in patients with end stage kidney disease. LCEs have been implemented at several dialysis centres in major Canadian cities, however no known location in British Columbia. Furthermore, a gap exists in the literature with respect to the feasibility of implementing such programs in small or rural HD centres. We examined the feasibility of implementing LCEs concurrent with HD in a rural community hemodialysis unit.Methods: Study participants included patients and clinical care providers (CCPs) recruited from an independent community HD unit in Prince George, British Columbia. Descriptive statistics were used to indicate frequency, intensity and length of cycle utilization. Barriers and facilitators to participation were captured in individual semi-structured interviews conducted with patient and CCPs at the end of the study period. Transcribed interviews were coded and analyzed using a theoretical framework scaffolded by constructs of acceptability and feasibility of implementation (Ou2019Cathian et al. 2015). Results: Of 14 eligible patients, 9 enrolled of which 6 actively participated in the study. Over 1 to 4 months patients used LCEs an average of 85% (63%-100%) of their dialysis sessions. Duration of LCEs use increased over time from a low of 15 minutes to an average of 1 hour (15-120 minutes) at an u201ceasyu201d rate on the Perceived Exertion Scale with no adverse events reported. Thematic analysis of 14 interviews (9 patients; 5 CCPs) identified key elements for successful implementation: patient and CCP orientation and a structured support process aided in acceptability, resulting in minimal disruption to workflow and generating motivation to participate and a positive patient and CPP experience. Conclusion: Exercising during HD is acceptable, feasible, and safe when implemented in a rural/small community HD unit using a structured team-based approach. It does not add workload for clinical staff and can be incorporated in the workflow. Patients reported an overall positive experience and recommended the creation of an education tool to aid in recruitment.
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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 | 0.023 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".