Using an Interactive App for Symptom Reporting and Management Following Pancreatic Cancer Surgery to Facilitate Person-Centered Care: Descriptive Study
Notice bibliographique
Résumé
BACKGROUND: Pancreatic and periampullary cancers are rare but have high mortality rates. The only hope for cure is surgical removal of the tumor. Following pancreatic surgery, the patients have a great deal of responsibility for managing their symptoms. Patients report a lack of sufficient knowledge of self-care and unmet supportive care needs. This necessitates a health care system responsive to these needs and health care professionals who pay close attention to symptoms. Person-centered care is widely encouraged and means a shift from a model in which the patient is the passive object of care to a model involving the patient as an active participant in their own care. To address the challenges in care following pancreatic cancer surgery, an interactive app (Interaktor) was developed in which patients regularly report symptoms and receive support for self-care. The app has been shown to reduce patients' symptom burden and to increase their self-care activity levels following pancreaticoduodenectomy due to cancer. OBJECTIVE: The aim of the study was to describe how patients used the Interaktor app following pancreaticoduodenectomy due to cancer and their experience with doing so. METHODS: A total of 115 patients were invited to use Interaktor for 6 months following pancreaticoduodenectomy. Of those, 35 declined, 8 dropped out, and 46 did not meet the inclusion criteria after surgery, leaving 26 patients for inclusion in the analysis. The patients were instructed to report symptoms daily through the app for up to 6 months following surgery. In case of alerting symptoms, they were contacted by their nurse. Data on reported symptoms, alerts, and viewed self-care advice were logged and analyzed with descriptive statistics. Also, the patients were interviewed about their experiences, and the data were analyzed using thematic analysis. RESULTS: The patients' median adherence to symptom reporting was 82%. Fatigue and pain were the most reported symptoms. Alerting symptoms were reported by 24 patients, and the most common alert was fever. There were variations in how many times the patients viewed the self-care advice (range 3-181 times). The most commonly viewed advice concerned pancreatic enzyme supplements. Through the interviews, the overarching theme was "Being seen as a person," with the following 3 sub-themes: "Getting your voice heard," "Having access to an extended arm of health care," and "Learning about own health." CONCLUSIONS: Interaktor proved to be well accepted. It made patients feel reassured at home and offered support for self-care. The app facilitated person-centered care by its multiple features targeting individual supportive care needs and enabled participation in their own care. This supports our recent studies showing that patients using the app had less symptom burden and higher self-care activity levels than patients receiving only standard care.
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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».