mHealth Use, Preferences, Barriers, and eHealth Literacy Among Patients With Inflammatory Bowel Disease: Survey Study
Notice bibliographique
Résumé
BACKGROUND: Mobile health (mHealth), defined as health care facilitated by mobile devices, offers a promising strategy for enhancing disease management and treatment for patients with chronic conditions. However, there is limited information about how patients with inflammatory bowel disease (IBD) use mHealth and their digital preferences. OBJECTIVE: The aim of the study was to investigate the use of mHealth as well as the preferences, obstacles, and eHealth literacy reported by patients with IBD in Germany. METHODS: In April and May 2023, we sequentially enrolled patients diagnosed with IBD, including Crohn disease and ulcerative colitis, to participate in a paper-based survey. The survey included questions on sociodemographic details, health characteristics, mHealth use, internet use, eHealth literacy (measured with the eHealth Literacy Scale), and preferences regarding communication and information. RESULTS: Of the 200 surveyed participants, almost all (197/200, 98.5%) reported regular smartphone use, and more than two-thirds (139/200, 69.5%) indicated regular engagement with social media. Most of the respondents (168/200, 84%) expressed the belief that incorporating medical apps into their routine could positively impact their health. However, only 25 (12.5%) of the 200 patients acknowledged using medical apps, of which just 2 apps were IBD specific, used by only a few (n=3, 12%). Furthermore, awareness of useful websites or mobile apps tailored for IBD was limited (45/200, 22.5%). Nearly all participants (196/200, 98%) expressed willingness to share app data for research purposes, and most (171/200, 85.5%) consented to transmit app data to their treating physicians. A large majority (175/200, 87.5%) indicated readiness to regularly input data into an app, with a preferred duration of up to 5 minutes (109/200, 54.5%) and weekly input frequency (76/200, 38%). For an IBD-specific app, the most frequently requested functions were electronic prescriptions (110/200, 55%) and a newsletter about new scientific work and clinical studies (94/200, 47%). Usability and security were identified as key app attributes. The internet was the predominant source of health-related information (180/200, 90%). The average eHealth literacy score, measured with the eHealth Literacy Scale, was high (mean 28.9, SD 5.4; range 8-40), with a positive correlation observed between higher eHealth literacy and factors such as younger age and more frequent internet use for health information. CONCLUSIONS: Patients with IBD are well prepared and motivated to use mHealth technologies to better understand their chronic condition and optimize treatment. However, their enthusiasm is tempered by the currently low adoption of mHealth. To fully harness the potential of mHealth in IBD treatment, effective and tailored mHealth solutions, guidance for their implementation, and patient education are needed.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».