Usefulness of Smartphone Apps for Improving Nutritional Status of Pancreatic Cancer Patients: Randomized Controlled Trial
Notice bibliographique
Résumé
Background Approximately 80% of pancreatic ductal adenocarcinoma (PDAC) patients suffer from anorexia, weight loss, and asthenia. Most PDAC patients receive chemotherapy, which often worsens their nutritional status owing to the adverse effects of chemotherapy. Malnutrition of PDAC patients is known to be associated with poor prognosis; therefore, nutritional management during chemotherapy is a key factor influencing the outcome of the treatment. Mobile apps have the potential to provide readily accessible nutritional support for patients with PDAC. Objective We aimed to evaluate the efficacy of a mobile app–based program, Noom, in patients receiving chemotherapy for PDAC. Methods We prospectively enrolled 40 patients who were newly diagnosed with unresectable PDAC from a single university-affiliated hospital in South Korea, and randomly assigned them into a Noom user group (n=20) and a non-Noom user group (n=20). The 12-week in-app interventions included meal and physical activity logging as well as nutritional education feedback from dietitians. The non-Noom user group did not receive any nutrition intervention. The primary outcomes were the changes in the nutritional status and quality of life (QoL) from the baseline to 12 weeks. The secondary outcomes included the changes in the skeletal muscle index (SMI) from the baseline to 12 weeks. The European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Core Questionnaire (QLQ-C30) and the Patient-Generated Subjective Global Assessment (PG-SGA) were used as paper questionnaires to assess the QoL and nutritional status of the patients. Intention-to-treat and per-protocol analyses were conducted. Regarding the study data collection time points, we assessed the nutritional status and QoL at the baseline (T0), and at 4 (T1), 8 (T2), and 12 (T3) weeks. Abdominal computed tomography (CT) imaging was conducted at the baseline and after 8 weeks for tumor response and SMI evaluation. The skeletal muscle area (cm2) was calculated using routine CT images. The cross-sectional areas (cm2) of the L3 skeletal muscles were analyzed. Results Between February 2017 and January 2018, 48 patients were assessed for eligibility. Totally 40 patients with pancreatic cancer were included by random allocation. Only 17 participants in the Noom user group and 16 in the non-Noom user group completed all follow-ups. All the study participants showed a significant improvement in the nutritional status according to the PG-SGA score regardless of Noom app usage. Noom users showed statistically significant improvements on the global health status (GHS) and QoL scales compared to non-Noom users, based on the EORTC QLQ (P=.004). The SMI decreased in both groups during chemotherapy (Noom users, 49.08±12.27 cm2/m2 to 46.08±10.55 cm2/m2; non-Noom users, 50.60±9.05 cm2/m2 to 42.97±8.12 cm2/m2). The decrement was higher in the non-Noom user group than in the Noom user group, but it was not statistically significant (-13.96% vs. -3.27%; P=.11). Conclusions This pilot study demonstrates that a mobile app–based approach is beneficial for nutritional and psychological support for PDAC patients receiving chemotherapy. Trial Registration ClinicalTrials.gov NCT04109495; https://clinicaltrials.gov/ct2/show/NCT04109495.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».