Cognitive Bias Modification Training Targeting Fatigue in Patients With Kidney Disease: Usability Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».