Digital physical activity intervention via the Kidney BEAM platform in patients with polycystic kidney disease: a randomized controlled trial
Bibliographic record
Abstract
ABSTRACT Background In people living with polycystic kidney disease (PKD), physical inactivity may contribute to poor health-related quality of life (HRQoL). To date, no research has elucidated the impact of a PKD-specific physical activity programme on HRQoL and physical health. This substudy of the Kidney BEAM Trial evaluated the impact of a PKD-specific 12-week educational and physical activity digital health intervention for people living with PKD. Methods This study was a mixed-methods, single-blind, randomized waitlist-controlled trial. Sixty adults with a diagnosis of PKD were randomized 1:1 to the intervention or a waitlist control group. Primary outcome was difference in the Kidney Disease QoL Short Form 1.3 Mental Component Summary (KDQoL-SF1.3 MCS) between baseline and 12 weeks. Six participants completed individualized semi-structured interviews. Results All 60 individuals (mean 53 years, 37% male) were included in the intention-to-treat analysis. At 12 weeks, there was a significant difference in mean adjusted change in KDQoL MCS score between the intervention group and waitlist control [4.2 (95% confidence interval 1.0–7.4) arbitrary units, P = .012]. Significant between-group differences in KDQoL subscales—burden of kidney disease (P = .034), emotional wellbeing (P = .001) and energy/fatigue (P = .001)—were also achieved. There was no significant between-group difference in KDQoL PCS scores (P = .505). Per-protocol analyses revealed significant between group differences in the PAM-13 patient activation score (P = .010) and body mass (P = .027). Mixed-methods analyses revealed key influences of the programme, including opportunities for peer support and to build on new skills and knowledge, as well as the empowerment and self-management. Conclusion A PKD-specific digital health educational and physical activity intervention is acceptable and has the potential to improve HRQoL. Further research is needed to better understand how specific education and lifestyle management may help to support self-management behaviour.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".