A Digital Health Intervention for Concussion: Development and Clinical Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Concussion is a common condition that can lead to a constellation of symptoms that affect quality of life, social integration, and return to work. There are several evidence-based behavioral and psychological interventions that have been found to improve postconcussion symptom burden. However, these are not routinely delivered, and individuals receive limited support during their concussion recovery. OBJECTIVE: This study aimed to develop and test the feasibility of a digital health intervention using a systematic evidence-, theory-, and person-based approach. METHODS: This was a mixed methodology study involving a scoping review (n=21), behavioral analysis, and logic model to inform the intervention design and content. During development, the intervention was optimized with feedback from individuals who had experienced concussions (n=12) and health care professionals (n=11). The intervention was then offered to patients presenting to the emergency department with a concussion (n=50). Participants used the intervention freely and input symptom data as part of the program. A number of outcome measures were obtained, including participant engagement with the intervention, postconcussion symptom burden, and attitudes toward the intervention. A selection of participants (n=15) took part in in-depth qualitative interviews to understand their attitudes toward the intervention and how to improve it. RESULTS: Engagement with the intervention functionality was 90% (45/50) for the symptom diary, 62% (31/50) for sleep time setting, 56% (28/50) for the alcohol tracker, 48% (24/50) for exercise day setting, 34% (17/50) for the thought diary, and 32% (16/50) for the goal setter. Metrics indicated high levels of early engagement that trailed off throughout the course of the intervention, with an average daily completion rate of the symptom diary of 28.23% (494/1750). A quarter of the study participants (13/50, 26%) were classified as high engagers who interacted with all the functionalities within the intervention. Quantitative and qualitative feedback indicated a high level of usability and positive perception of the intervention. Daily symptom diaries (n=494) demonstrated a wide variation in individual participant symptom burden but a decline in average burden over time. For participants with Rivermead scores on completion of HeadOn, there was a strong positive correlation (r=0.86; P<.001) between their average daily HeadOn symptom diary score and their end-of-program Rivermead score. Insights from the interviews were then fed back into development to optimize the intervention and facilitate engagement. CONCLUSIONS: Using this systematic approach, we developed a digital health intervention for individuals who have experienced a concussion that is designed to facilitate positive behavior change. Symptom data input as part of the intervention provided insights into postconcussion symptom burden and recovery trajectories. TRIAL REGISTRATION: ClinicalTrials.gov NCT05069948; https://clinicaltrials.gov/ct2/show/NCT05069948.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».