Integrating a Smartphone–Based Self–Management System into Usual Care of Advanced CKD
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patient self-management has been shown to improve health outcomes. We developed a smartphone-based system to boost self-care by patients with CKD and integrated its use into usual CKD care. We determined its acceptability and examined changes in several clinical parameters. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We recruited patients with stage 4 or 5 CKD attending outpatient renal clinics who responded to a general information newsletter about this 6-month proof-of-principle study. The smartphone application targeted four behavioral elements: monitoring BP, medication management, symptom assessment, and tracking laboratory results. Prebuilt customizable algorithms provided real-time personalized patient feedback and alerts to providers when predefined treatment thresholds were crossed or critical changes occurred. Those who died or started RRT within the first 2 months were replaced. Only participants followed for 6 months after recruitment were included in assessing changes in clinical measures. RESULTS: In total, 47 patients (26 men; mean age =59 years old; 33% were ≥65 years old) were enrolled; 60% had never used a smartphone. User adherence was high (>80% performed ≥80% of recommended assessments) and sustained. The mean reductions in home BP readings between baseline and exit were statistically significant (systolic BP, -3.4 mmHg; 95% confidence interval, -5.0 to -1.8 and diastolic BP, -2.1 mmHg; 95% confidence interval, -2.9 to -1.2); 27% with normal clinic BP readings had newly identified masked hypertension. One hundred twenty-seven medication discrepancies were identified; 59% were medication errors that required an intervention to prevent harm. In exit interviews, patients indicated feeling more confident and in control of their condition; clinicians perceived patients to be better informed and more engaged. CONCLUSIONS: Integrating a smartphone-based self-management system into usual care of patients with advanced CKD proved feasible and acceptable, and it appeared to be clinically useful. The results provide a strong rationale for a randomized, controlled trial.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".