Exploring an Energy Management Approach to Improve Fatigue and Life Participation in Adults on Chronic Dialysis
Bibliographic record
Abstract
Abstract Background Fatigue and its negative impact on life participation are top research priorities of people on chronic dialysis therapy. Energy management education is a fatigue management approach that teaches people to use practical strategies (e.g. prioritizing, using efficient body postures, organizing home environments) to manage their energy expenditure during everyday life. The study objective was to explore if energy management education is associated with improvements in fatigue and life participation in adults on chronic dialysis. Methods This study consisted of five single-case, interrupted time-series AB studies, and follow-up qualitative interviews. Participants on chronic dialysis therapy at an academic hospital in Toronto, Canada were purposively selected to represent diversity in age, gender and modality. All 5 participants underwent “The PEP Program”, a personalized, web-supported energy management education (EME) program designed to meet the needs of people on dialysis. During the program, participants complete two brief web modules about energy management, and then use energy management principles and a problem-solving framework to work on 3 life participation goals during sessions with a trained program administrator. Fatigue and life participation were measured weekly using short questionnaires during the baseline and intervention periods, and additional validated questionnaires (the Fatigue Impact Scale, SF36 Vitality Scale, & Canadian Occupational Performance Measure) were administered pre- and post-intervention. Data were analyzed using visual analysis and the Tau-U statistic for the weekly time-series data, and thematic analysis for the qualitative interviews. Results Three of five participants displayed a consistently positive response to the PEP program across multiple measures of fatigue and life participation. Tau-U effect size estimates ranged from small to moderate, according to the time-series data. All five participants expressed that the program had benefitted them in qualitative follow-up interviews, with the most common reported benefit being that the program made day-to-day activities easier. The format of the program was also said to be feasible and convenient. Conclusions The PEP program has potential to improve fatigue-related outcomes in people on chronic dialysis. Larger, controlled studies of the program are now warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".