E-health Tools in Irritable Bowel Syndrome Management
Notice bibliographique
Résumé
Background: Electronic health (e-health) technologies, including mobile apps, encourage patient engagement and empowerment in patients’ clinical journeys and facilitate self-management. THe commercialization of irritable bowel syndrome-focused (IBS) mobile apps proliferated in recent years, offering IBS patients a myriad of options in incorporating e-health technology and self-management strategies to alleviate symptoms. As IBS is complicated by the lack of known etiologies and their systemic and downstream effects, mobile apps could be an invaluable tool for patients in navigating varying strategies and developing a tailored and personalized management plan. However, there is limited research on understanding and evaluating e-health tools and their perceived utility and value by patients. Objective: To identify and evaluate digital interventions designed for self-management of IBS-related symptoms, and to explore IBS patients’ experiences using these tools to manage or reduce their symptoms. Methods: 1) A systematic review of the literature was conducted using Medline (Ovid), Embase, Web of Science, and CINAHL. Results from the search strategy were retrieved between database inception and May 2023. Data from the study designs, intervention, and associated effectiveness and feasibility outcomes were extracted. 2) A scoping review of commercially available IBS mobile e-health apps is currently underway. Eligible apps will be collated from the iOS App Store and the Android Google Play Store to evaluate their features and functions, areas of focus, and financial burden. 3) Qualitative focus groups will be conducted with adult IBS patients in Canada who are currently using or have previously used a mobile app to manage their IBS symptoms. Data will be coded and assessed using inductive thematic analysis, and descriptive statistics will be utilized to evaluate demographic variables. Focus groups are expected to commence in January 2024. Results: The implemented search strategy for the systematic review yielded 1164 records, of which 11 were eligible and included. Eight studies developed the intervention, and three assessed existing interventions accessible to the public; most were developed for the mobile platform. Intervention features were focused on education, dietary modification, psychological-based therapies and programs, and health tracking and were largely self-directed. The interventions effectively reduced symptom severity and improved quality of life and mental health, while demonstrating feasibility in the form of adherence, compliance, and usability. More in-depth analysis and results for the scoping review and focus groups will be presented. Conclusion: The evidence suggests e-health interventions may be beneficial tools for patients to manage their IBS. However, despite the influx and saturated market of commercialized apps, their effectiveness is yet to be determined. Thus, additional research is warranted for continued digital intervention assessments in this population to help inform researchers and developers in advancing the quality and accessibility of current and future e-health resources for the IBS community. Furthermore, the evaluation of the mobile apps from the systematic and scoping reviews will support the development of a web-based platform to compile detailed information and user feedback of IBS-focused mobile apps to enable patients to efficiently and effectively find the most suitable apps to support their unique needs and circumstances.
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 | 0,008 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 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 ».