Testing the Feasibility and Usability of a Novel Smartphone-Based Self-Management Support System for Dialysis Patients: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Diet and fluid restrictions that need continuous self-management are among the most difficult aspects of dialysis treatment. Smartphone applications may be useful for supporting self-management. OBJECTIVE: Our objective is to investigate the feasibility and usability of a novel smartphone-based self-management support system for dialysis patients. METHODS: We developed the Self-Management and Recording System for Dialysis (SMART-D), which supports self-monitoring of three mortality-related factors that can be modified by lifestyle: interdialytic weight gain and predialysis serum potassium and phosphorus concentrations. Data is displayed graphically, with all data evaluated automatically to determine whether they achieve the values suggested by the Japanese Society for Dialysis Therapy guidelines. In a pilot study, 9 dialysis patients used SMART-D system for 2 weeks. A total of 7 of them completed questionnaires rating their assessment of SMART-D's usability and their satisfaction with the system. In addition, the Kidney Disease Quality of Life scale was compared before and after the study period. RESULTS: All 9 participants were able to use SMART-D with no major problems. Completion rates for body weight, pre- and postdialysis weight, and serum potassium and phosphorus concentrations were, respectively, 89% (SD 23), 95% (SD 7), and 78% (SD 44). Of the 7 participants who completed the usability survey, all were motivated by the sense of security derived from using the system, and 6 of the 7 (86%) reported that using SMART-D helped improve their lifestyle and self-management. CONCLUSIONS: Using SMART-D was feasible, and the system was well regarded by patients. Further study with larger scale cohorts and longer study and follow-up periods is needed to evaluate the effects of SMART-D on clinical outcomes and quality of life.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".