Bibliographic record
Abstract
Millions of cancer patients and survivors all around the globe suffer from cancer-related fatigue and experience a reduced quality of life due to their cancer and cancer treatment. With our large-scale international waiting-list RCT, including participants from four English-speaking countries (i.e., Australia, Canada, the United Kingdom, and the United States), we demonstrated that fatigue could be reduced and QoL improved by means of a self-management mHealth app (chapter 5). From March till October 2018, we recruited via 76 Facebook Ads and included 755 participants, of which 355 completed the follow-up measure 12 weeks later, at the expense of €22.42 and €47.69 per participant, respectively (chapter 4). We saw that the most interested participants were female, middle-aged, and came from the UK. We think that reaching participants for international mHealth studies via Facebook Ads has potential but can be very costly, especially when more balanced sub-samples are desired. However, we believe that constant optimization and testing of ads can make an essential difference in reducing recruitment costs. Regarding the app’s effectiveness, we learned that participants do not need to engage excessively with the intervention since medium app use (3-8 days) was already significantly associated with fatigue reduction (chapter 5). Our findings on fatigue reduction were statistically significant and clinically relevant since more people recovered in the intervention group than the control group. We explored whether the effect of the intervention was related to specific age groups and saw that the intervention effect was significant across all age groups but even more pronounced in younger individuals (<56 years). Individuals with different education levels and both cancer patients and survivors seemed to benefit significantly from the app. We do not have enough data to compare outcomes between gender, cancer types, and treatment types and must acknowledge that our study sample is limited in its representativeness to Facebook users. We also explored several processes targeted by the app and their effect on fatigue reduction (chapter 6). We found that app access was significantly associated with reduced fatigue severity and interference via the mechanism of reduced fatigue catastrophizing, depression, sleep disruption, and increased mindfulness and physical activity. Besides, we described the experiences we had with applying for ethical approval in different countries (chapter 3). We believe that research guidelines could support scientists aiming to conduct international internet-based studies regarding whether these should be considered single or multi-center trials. We describe where researchers can apply for ethical approval across different countries.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".