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Enregistrement W2155081065 · doi:10.2196/mhealth.2391

iMHere: A Novel mHealth System for Supporting Self-Care in Management of Complex and Chronic Conditions

2013· article· en· W2155081065 sur OpenAlexvenueno aff
Bambang Parmanto, Gede Pramana, Daihua X. Yu, Andrea D. Fairman, Brad E. Dicianno, Michael McCue

Notice bibliographique

RevueJMIR mhealth and uhealth · 2013
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Disability and Rehabilitation ResearchNational Institutes of HealthVerizon Foundation
Mots-clésmHealthTelehealthTelemedicineUsabilityPopulationMedicineHealth careService (business)Service delivery frameworkeHealthSelf-managementMedical emergencyNursingPsychological interventionComputer scienceBusinessHuman–computer interaction

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Individuals with chronic conditions are vulnerable to secondary complications that can be prevented with adherence to self-care routines. They benefit most from receiving effective treatments beyond acute care, usually in the form of regular follow-up and self-care support in their living environments. One such population is individuals with spina bifida (SB), the most common permanently disabling birth defect in the United States. A Wellness Program at the University of Pittsburgh in which wellness coordinators supervise the care of individuals with chronic disease has produced remarkably improved outcomes. However, time constraints and travel costs have limited its scale. Mobile telehealth service delivery is a potential solution for improving access to care for a larger population. OBJECTIVE: The project's goal was to develop and implement a novel mHealth system to support complex self-care tasks, continuous adherence to regimens, monitoring of adherence, and secure two-way communications between patients and clinicians. METHODS: We developed and implemented a novel architecture of mHealth system called iMHere (iMobile Health and Rehabilitation) consisting of smartphone apps, a clinician portal, and a two-way communication protocol connecting the two. The process of implementing iMHere consisted of: (1) requirement analysis to identify clinically important functions that need to be supported, (2) design and development of the apps and the clinician portal, (3) development of efficient real-time bi-directional data exchange between the apps and the clinician portal, (4) usability studies on patients, and (5) implementation of the mHealth system in a clinical service delivery. RESULTS: There were 9 app features identified as relevant, and 5 apps were considered priority. There were 5 app features designed and developed to address the following issues: medication, skin care, bladder self-catheterization, bowel management, and mental health. The apps were designed to support a patient's self-care tasks, send adherence data to the clinician portal, and receive personalized regimens from the portal. The Web-based portal was designed for clinicians to monitor patients' conditions and to support self-care regimens. The two-way communication protocol was developed to facilitate secure and efficient data exchange between the apps and the portal. The 3 phases of usability study discovered usability issues in the areas of self-care workflow, navigation and interface, and communications between the apps and the portal. The system was used by 14 patients in the first 6 months of the clinical implementation, with 1 drop out due to having a poor wireless connection. The apps have been highly utilized consistently by patients, even those addressing complex issues such as medication and skincare. The patterns of utilization showed an increase in use in the first month, followed by a plateau. CONCLUSIONS: The system was capable of supporting self-care and adherence to regimen, monitoring adherence, supporting clinician engagement with patients, and has been highly utilized.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,821
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,077
Tête enseignante GPT0,462
Écart entre enseignants0,385 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations129
Publié2013
Routes d'admission1
Résumé présentoui

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