Identifying Mobile Applications Aimed at Self-Management in People With Chronic Kidney Disease
Bibliographic record
Abstract
BACKGROUND: A growing number of mobile applications (apps) target people with chronic illness as the primary user. There is increasing evidence that digital technology can improve health outcomes for users but the sheer number of apps available is likely to overwhelm many potential users. OBJECTIVE: The purpose of this study was to systematically search for apps aimed at people with chronic kidney disease. An important secondary objective was to develop a search strategy that could be used to identify similar apps in the future. DESIGN: A systematic review of the scientific and gray literature including app stores, clearinghouses, and Google. SETTING/PATIENTS: The focus of this research was the identification of apps that may be of use to people interested in self-management of chronic kidney disease. METHODS: Three reviewers independently searched app stores, websites, and databases to identify apps of potential interest and any information related to the function and efficacy of these. Apps that met the inclusion criteria were short-listed, reviewed in more detail, and cross-referenced with other sources such as clearinghouses, Google, and kidney care organizations. A population, intervention, comparison, outcome, and design framework was used to search selected databases. RESULTS: Of the 1464 apps purporting to be for chronic kidney disease, only 15 were eligible for inclusion. Searching the 2 major app stores (iOS and Android) appeared to be the most productive way of identifying apps of potential interest. An increasing number of public and private clearinghouses have been established to assist users with finding apps. Privacy and security of user information is a particular and valid concern of health care professionals and organizations. LIMITATIONS: The breadth and depth of information relating to each app varied and made it difficult to systematize the evaluation of apps. Due to the large number of health care apps and the challenges to searching app stores and websites, it is possible that some apps were missed during our searches. Similarly, while there are many kidney care-related websites that contain useful information, these were not captured by our study. CONCLUSION: There are very few available apps aimed specifically at people with chronic kidney disease; those that are available are best identified by manually searching the 2 major app stores. Privacy and confidentiality of user information when using the apps is a concern among health care providers in particular.
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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 | 0.021 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".