Health-Related Internet Use Among Outpatients Undergoing Cancer Treatment During the COVID-19 Pandemic: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Health care professionals and patients frequently use the internet to access medical information. However, health-related mobile apps have yet to become fully integrated into routine clinical practice. Although the demand for eHealth apps has grown only modestly, studies suggest that such tools hold significant potential to enhance medical care and improve the quality of life, particularly for patients with cancer. To successfully implement these technologies in everyday practice, it is essential to understand the specific needs and preferences of the target population. Objective: The aim of this study was to assess internet and eHealth use among patients with cancer receiving chemotherapy at an outpatient ward and to evaluate how the COVID-19 pandemic influenced internet and eHealth app use. Methods: Between May 2021 and September 2021, a total of 303 patients receiving outpatient care at the hemato-oncology and gynecology departments of a Comprehensive Cancer Center were surveyed using a 21-item paper-based questionnaire, adapted from a validated information and communication technology-use survey. The questionnaire provided patient-reported information related to general internet abilities and use rates including health-related apps and changes during the COVID-19 pandemic. Data analysis was conducted using descriptive statistics, chi-square tests, Mann-Whitney U tests, and Spearman correlations. Results: In total, 98.7% (299/303) of participants reported regular internet use, 72.6% (217/299) reported using the internet to search for health-related information, and 79.1% (235/297) expressed readiness to communicate digitally with health care providers. Decreasing age and higher internet literacy correlated with a more frequent use of eHealth apps (P<.001). A total of 24.7% (68/275) reported increased internet use during the pandemic. Conclusions: The majority of patients were regular internet users and expressed an openness to eHealth apps. Factors such as internet literacy and average age are important to consider when implementing new eHealth apps in a clinical setting. Despite the positive influence of the pandemic on internet use, there remains a gap between self-reported readiness and real use of eHealth apps.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".