Information Deficits, Information Needs, and Preferences Regarding eHealth in a Dutch Population With Metabolic Dysfunction–Associated Steatotic Liver Disease: Cross-Sectional Survey Study
Notice bibliographique
Résumé
Background: Globally, metabolic dysfunction-associated steatotic liver disease (MASLD) is a common lifestyle-related disease. Lifestyle interventions focusing on healthy eating habits, physical exercise, and reducing body weight in case of obesity are the primary recommended therapies to reverse or improve MASLD. However, patients often experience difficulties in complying with the required lifestyle changes for several reasons, including a lack of knowledge. Health care professionals express limited time during consultations as one of the barriers to discussing lifestyle behavior change. A potential solution to eliminate these barriers and improve the provision of information to patients with MASLD is the use of eHealth. Objective: This study aimed to explore the information needs and deficits of patients with MASLD regarding a variety of disease-related topics and their preferences regarding a future eHealth intervention. Methods: In a cross-sectional survey study, patients with MASLD were recruited via 2 Dutch patient organizations. The questionnaire included questions on sociodemographics, information provision and needs, and preferences regarding an eHealth intervention. Data were reported using descriptive statistics. Pearson chi-square tests and logistic regression analysis were used to identify differences in outcomes between subgroups. Results: The questionnaire was filled out by 449 respondents (women: 363/449, 81%; age: mean 56, SD 11 y). Fewer than 20% of them indicated that they had received sufficient information on a broad range of disease-related topics. Approximately 72% (325/449) to 90% (405/449) of respondents indicated that they would like to receive additional information. Respondents who did not know their disease stage reported a significantly higher need for information on general topics, compared to respondents who reported their disease stage (P values ranging from <.01 to .03). Respondents with (self-reported) metabolic dysfunction-associated steatohepatitis were more interested in contact with fellow patients than respondents with an early or unknown stage of disease (P=.002). Regarding a future eHealth intervention, respondents were most interested in receiving MASLD-related information, practical examples, and references to relevant websites or apps. Respondents were least interested in contact, collaboration, or competition with other app users. Conclusions: The vast majority of respondents reported a high rate of information deficits on a broad range of MASLD-related topics and expressed a strong need for additional information. Insights into information needs and preferences regarding eHealth can be used to develop an eHealth intervention for patients with MASLD.
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
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 ».