Information Deficits, Information Needs, and Preferences Regarding eHealth in a Dutch Population With Metabolic Dysfunction–Associated Steatotic Liver Disease: Cross-Sectional Survey Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".