Mobile Health to Maintain Continuity of Patient-Centered Care for Chronic Kidney Disease: Content Analysis of Apps
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a global health problem with a high economic burden, which is particularly prevalent in Taiwan. Mobile health apps have been widely used to maintain continuity of patient care for various chronic diseases. To slow the progression of CKD, continuity of care is vital for patients' self-management and cooperation with health care professionals. However, the literature provides a limited understanding of the use of mobile health apps to maintain continuity of patient-centered care for CKD. OBJECTIVE: This study identified apps related to the continuity of patient-centered care for CKD on the App Store, Google Play, and 360 Mobile Assistant, and explored the information and frequency of changes in these apps available to the public on different platforms. App functionalities, like patient self-management and patient management support for health care professionals, were also examined. METHODS: We used the CKD-related keywords "kidney," "renal," "nephro," "chronic kidney disease," "CKD," and "kidney disease" in traditional Chinese, simplified Chinese, and English to search 3 app platforms: App Store, Google Play, and 360 Mobile Assistant. A total of 2 reviewers reached consensus on coding guidelines and coded the contents and functionalities of the apps through content analysis. After coding, Microsoft Office Excel 2016 was used to calculate Cohen kappa coefficients and analyze the contents and functionalities of the apps. RESULTS: A total of 177 apps related to patient-centered care for CKD in any language were included. On the basis of their functionality and content, 67 apps were recommended for patients. Among them, the most common functionalities were CKD information and CKD self-management (38/67, 57%), e-consultation (17/67, 25%), CKD nutrition education (16/67, 24%), and estimated glomerular filtration rate (eGFR) calculators (13/67, 19%). In addition, 67 apps were recommended for health care professionals. The most common functionalities of these apps were comprehensive clinical calculators (including eGFR; 30/67; 45%), CKD medical professional information (16/67, 24%), stand-alone eGFR calculators (14/67, 21%), and CKD clinical decision support (14/67, 21%). A total of 43 apps with single- or multiple-indicator calculators were found to be suitable for health care professionals and patients. The aspects of patient care apps intended to support self-management of CKD patients were encouraging patients to actively participate in health care (92/110, 83.6%), recognizing and effectively responding to symptoms (56/110, 50.9%), and disease-specific knowledge (53/110, 48.2%). Only 13 apps contained consulting management functions, patient management functions or teleconsultation functions designed to support health care professionals in CKD patient management. CONCLUSIONS: This study revealed that the continuity of patient-centered care for CKD provided by mobile health apps is inadequate for both CKD self-management by patients and patient care support for health care professionals. More comprehensive solutions are required to enhance the continuity of patient-centered care for CKD.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".