A New Mobile App to Train Attention Processes in People With Traumatic Brain Injury: Logical and Ecological Content Validation Study
Bibliographic record
Abstract
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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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".