A87 LEVERAGING DIGITAL HEALTH: SYMPTOM TRACKING IN PEDIATRIC IBD USING A SMARTPHONE APP FOR ENHANCED PATIENT-REPORTED OUTCOMES
Notice bibliographique
Résumé
Abstract Background Patient-reported outcome measures (PROMs) are essential for managing chronic diseases, as they provide valuable insights into patients’ perspectives on their condition’s impact and help tailor treatments accordingly. However, collecting this information from pediatric patients with inflammatory bowel disease (IBD) presents several challenges, such as the reluctance of patients to discuss sensitive topics such as bowel movements, defecation, and rectal bleeding. Additionally, the subjective nature of physicians’ evaluations compared to the experiences of patients affect the symptom assessment. An IBD app could help teenagers to routinely assess the clinical symptoms. Aims The objectives of the study were twofold: first, to describe the trajectory of symptoms as self-reported by patients through a digital application, and second, to compare these symptoms with those evaluated by physicians during medical visits. Methods We conducted a prospective study involving teenagers with IBD aged 15-18 years old. All participants had a smartphone and an understanding of English. They received an app (Injoy Gut Health tracking by Phyla Technologies Inc.) that allowed for the collection of symptoms at home, which they were asked to use daily. At inclusion, participants completed the IBD Control questionnaire. Additionally, we collected information on disease status such as the physician global assessment (PGA). Results We included 25 children (60% female) with a median age at diagnosis of 14.5 years (interquartile range (IQR): 13.2-14.5) for IBD (64% Crohn’s disease) for a median of 3.6 years (IQR: 2.7-5.4). Most participants (84%) were in complete remission and 16% had mild active disease, according to the PGA. The teenagers completed the app survey for a median of 122 (IQR: 44-200) days. Participants reported gastrointestinal symptoms during daily data collection: 48% experienced rectal pain, 64% experienced nausea, 96% experienced gas, 76% experienced bloating and 80% experienced abdominal pain. Finally, 28% noted blood in their stools representing a median of 7.8% (IQR 8.88-11.9) of their stools. The PROMs filled at baseline depicted a spectrum of disease activity. The median IBD-control-8 subscore was 12 (IQR: 10-15). The PROMs and the data self-collected daily at home were not in agreement with the PGA. Indeed, symptom data showed that participants experienced a median of 7 (IQR: 4-8) symptoms while 92% (23) of patients reported more than two symptoms. Conclusions These results highlight the importance of incorporating PROMs for more precise and personalized management of IBD in children. The use of an app for at-home daily data collection proved to be a valuable tool in capturing real-time symptomatology. Funding Agencies:
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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».