Identifying Mobile Applications Aimed at Self-Management in People With Chronic Kidney Disease
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,106 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,020 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».