Cognitive Bias Modification Training Targeting Fatigue in Patients With Kidney Disease: Usability Study
Bibliographic record
Abstract
BACKGROUND: Fatigue is an important symptom for many patients, including patients with kidney disease. Cognitive biases, such as attentional bias and self-identity bias, are thought to influence fatigue. Cognitive bias modification (CBM) training is a promising technique to counter fatigue. OBJECTIVE: We aimed to evaluate a CBM training among patients with kidney disease and health care professionals (HCPs) and assess acceptability and applicability in the clinical setting using an iterative design process to evaluate expectations and experiences with the training. METHODS: This was a longitudinal, qualitative, and multiple stakeholder-perspective usability study in which we interviewed end users and HCPs during the prototyping phase and after the end of training. We conducted semistructured interviews with 29 patients and 16 HCPs. The interviews were transcribed and analyzed thematically. Next to a general evaluation of the training, the acceptability of the training was evaluated using the Theoretical Framework of Acceptability, and applicability was assessed by evaluating obstacles and solutions for implementation in the kidney care setting. RESULTS: Generally, participants were positive about the training and its applicability. The biggest negatives were doubts about effectiveness and annoyance about the repetitive character of CBM. Acceptability was judged with a mixed evaluation, with a negative evaluation of perceived effectiveness; mixed results for burden, intervention coherence, and self-efficacy; and positive results for affective attitude, ethicality, and opportunity costs. Barriers for applicability were patients' varying computer skills, subjectivity of fatigue, and integration with regular treatment (eg, the role of HCPs). Possible solutions included assigning representatives among nurses, offering training on an app, and providing assistance via a help desk. The iterative design process, including repeated waves of testing user expectations and experiences, yielded complementary data. CONCLUSIONS: To the best of our knowledge, this study is the first to introduce a CBM training targeting fatigue. Furthermore, this study provides one of the first user evaluations of a CBM training, both among patients with kidney disease and their care providers. Overall, the training was evaluated positively, although acceptability showed mixed results. Applicability was positive although barriers were identified. The proposed solutions require further testing, preferably following the same frameworks, as the iteration in this study contributed positively to the quality of the training. Therefore, future research should follow the same frameworks and consider stakeholders and end users in eHealth intervention design.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".