A New Mobile App to Train Attention Processes in People With Traumatic Brain Injury: Logical and Ecological Content Validation Study
Notice bibliographique
Résumé
BACKGROUND: Attention is at the base of more complex cognitive processes, and its deficits can significantly impact safety and health. Attention can be impaired by neurodevelopmental and acquired disorders. One validated theoretical model to explain attention processes and their deficits is the hierarchical model of Sohlberg and Mateer. This model guides intervention development to improve attention following an acquired disorder. Another way to stimulate attention functions is to engage in the daily practice of mindfulness, a multicomponent concept that can be explained by the theoretical model of Baer and colleagues. Mobile apps offer great potential for practicing mindfulness daily as they can easily be used during daily routines, thus facilitating transfer. Laverdière and colleagues have developed such a mobile app called Focusing, which is aimed at attention training using mindfulness-inspired attentional exercises. However, this app has not been scientifically validated. OBJECTIVE: This research aims to analyze the logical content validity and ecological content validity of the Focusing app. METHODS: Logical content validation was performed by 7 experts in neuropsychology and mindfulness. Using an online questionnaire, they determined whether the content of the attention training app exercises is representative of selected constructs, namely the theoretical model of attention by Sohlberg and Mateer and the theoretical model of mindfulness by Baer and colleagues. A focus group was subsequently held with the experts to discuss items that did not reach consensus in order to change or remove them. Ecological content validation was performed with 10 healthy adults. Participants had to explore all sections of the app and assess the usability, relevance, satisfaction, quality, attractiveness, and cognitive load associated with each section of the app, using online questionnaires. RESULTS: Logical content validation results demonstrated a high content validity index (CVI) of the attention training app. Excellent scores (CVI ≥0.78) in both the attention and mindfulness models were obtained for all exercises in the app, except 2 exercises. One of these exercises was subsequently modified to include expert feedback, and one was removed. Regarding ecological content validation, the results showed that workload, quality, user experience, satisfaction, and relevance of the app were adequate. The Mobile Application Rating Scale questionnaire showed an average quality rating between 3.75/5 (SD 0.41) (objective quality) and 3.65/5 (SD 0.36) (subjective quality), indicating acceptable quality. The mean global attractiveness rating from the AttrakDiff questionnaire was 2.36/3 (SD 0.57), which represents one of the strengths of the app. CONCLUSIONS: Logical and ecological content validation showed that Focusing is theoretically valid, with a high level of agreement among experts and healthy participants. This tool can be tested to train attention processes after a neurological insult such as traumatic brain injury.
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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 | 0,010 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».