Reluctance to Use a Psycho-Oncology Mobile App Among Patients With Primary Breast Cancer: Retrospective Cross-Sectional Survey
Bibliographic record
Abstract
Background: eHealth is an increasingly used method of health care in the field of psycho-oncology. While many reports highlight the positive impact of psychological eHealth tools, some patients refuse to use them. Objective: This study aimed to expand knowledge of the motivation and psychoemotional functioning of patients who consciously refuse to use eHealth technology in the form of a mobile psycho-oncology app offered as part of a clinical trial. To our knowledge, this is the first study to address this topic. Methods: A retrospective cross-sectional study was conducted between December 2022 and February 2023 to investigate the reasons why 56 patients with breast cancer refused to use the psycho-oncology mobile app offered as part of a clinical trial by the Breast Cancer Unit. The primary aim of the study was to analyze patients' self-reported reasons for not engaging with the app, while also exploring their psychoemotional functioning, including stress levels (measured using the distress thermometer), personality traits (measured using the Ten-Item Personality Inventory), coping strategies (measured using the Coping Orientation to Problems Experienced Questionnaire), and Self-efficacy (measured using the General Self-Efficacy Scale). Participants in this study declined the app intervention but agreed to participate in this separate observational study, indicating that their refusal was related to the app itself rather than to participation in clinical research in general. Results: The patients experienced a clinically meaningful elevation in stress levels (mean 5, SD 2.1 points) and Self-efficacy (mean 32.1, SD 5.1 points). Among 5 dimensions of personality traits, patients scored highest in Agreeableness (mean 6.5, SD 0.8 stens) and Conscientiousness (mean 6.4, SD 0.9) and lowest in Neuroticism (mean 3.4, SD 1.8) (other dimensions: Extraversion [mean 5.8, SD 1.6 stens] and Openness to Experiences [mean 4.4, SD 1.5 stens]). In terms of coping with stress, patients most frequently used the strategies of Active Coping (mean 2.6, SD 0.5 points), Acceptance (mean 2.6, SD 0.6 points), and Seeking Emotional Support (mean 2.6, SD 0.6 points), and least frequently used the strategies of Psychoactive Substance Use (mean 0.2, SD 0.6 points) and Restraint (mean 0.5, SD 0.7 points). Patient responses regarding refusal to participate in app testing were divided into four categories: (1) Focus on Life Outside the Disease, (2) Focus on Disease and Treatment, (3) Denial Mechanism, and (4) Technical Issues. Statistically significant differences were found between the groups. The Focus on Life Outside the Disease group of patients had higher levels of Self-efficacy, lower Neuroticism, and more frequent use of the Positive reevaluation strategy compared to the other groups. Conclusions: Our patients' decision not to use the eHealth psycho-oncology app was mainly influenced by characteristics suggesting better emotional coping with the disease and treatment. These factors were significantly more influential than other factors studied, particularly those related to technology. Assessing reasons for opting out of eHealth and associated psychomotional functioning may be important for improving patients' adoption of eHealth solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".