EHealth Acceptance and New Media Preferences for Therapy Assistance Among Breast Cancer Patients
Bibliographic record
Abstract
BACKGROUND: Electronic health (eHealth) and mobile communication-based health care (mHealth) applications have been increasingly utilized in medicine over the last decade, and have facilitated improved adherence to therapy regimens in patients with chronic conditions. Due to the long duration of breast cancer therapy, and the long course of disease in metastatic breast cancer, a need for more intensified physician-patient communication has emerged. Various support mechanisms, including new media such as mHealth and eHealth, have been proposed for this purpose. OBJECTIVE: The aim of this study was to analyze the correlation between sociodemographic factors, as well as health status of breast cancer patients, and their current utilization of new media, or their willingness to use Internet and mobile phone apps for improvement of therapy management. METHODS: The survey for this study was conducted anonymously during the 2012 Mamazone Projekt Diplompatient meeting (Augsburg, Germany), which hosted approximately 375 participants per day. A total of 168 questionnaires were completed. The questionnaire aimed to assess sociodemographic status, disease patterns, and current use of new media (ie, Internet, mobile phone, and mobile phone apps) in breast cancer patients. Habits and frequency of use for these new technologies, as well as patients' affinity towards eHealth and mHealth tools for therapy management improvement, were investigated. RESULTS: Almost all participants used the Internet (95.8%, 161/168), with 91.5% (151/165) also utilizing this technology for health-related issues. Approximately 23% (38/168) of respondents owned a mobile phone. When asked about their preferences for therapy assistance, 67.3% (113/168) of respondents were interested in assistance via the Internet, 25.0% (42/168) via mobile phone, and 73.2% (123/168) via call center. Patients diagnosed with breast cancer <5 years before the survey were significantly more interested in a call center than patients diagnosed >5 years before survey participation. CONCLUSIONS: The vast majority of breast cancer patients accept the Internet for therapy assistance, which indicates that eHealth is a promising medium to improve patient-physician communication. Such technologies may improve individual disease management and ultimately lead to an enhanced adherence to therapy regimens.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.005 | 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".