Development and Implementation of an eHealth Oncohematonootric Program: Descriptive, Observational, Prospective Cohort Pilot Study
Notice bibliographique
Résumé
BACKGROUND: In oncohematology, both the development of the disease and the side effects of antineoplastic treatment often take a toll on patients' physical and nutritional well-being. In this era of digital transformation, we launched a pioneering project for oncohematologic patients to promote adherence to a healthy lifestyle and improve their physical and nutritional well-being. We aim to achieve this goal by involving doctors and nutritionists through the Nootric app. OBJECTIVE: This study aims to assess the impact of the use of eHealth tools to facilitate nutrition and well-being in oncohematologic patients. We also aim to determine the usefulness of physical-nutritional management in improving tolerance to chemotherapy treatments within routine clinical practice. METHODS: We designed a descriptive, observational, longitudinal, prospective cohort pilot study that included a total of 22 patients from March to May 2022 in the Vinalopó University Hospital. The inclusion criteria were adults over 18 years of age diagnosed with oncohematological pathology in active chemotherapy treatment. An action plan was created to generate alerts between the doctor and the nutritionist. In the beginning, the patients were trained to use the app and received education highlighting the importance of nutrition and physical exercise. Sociodemographic, clinical-biological-analytical (eg, malnutrition index), health care impact, usability, and patient adherence data were collected. Tolerance to chemotherapy treatment and its health care impact were evaluated. RESULTS: We included 22 patients, 11 (50%) female and 11 (50%) male, ranging between 42 and 84 years of age. Among them, 13 (59%) were adherents to the program. The most frequent diseases were lymphoproliferative syndromes (13/22, 59%) and multiple myeloma (4/22, 18%). Moreover, 15 (68%) out of 22 patients received immunochemotherapy, while 7 (32%) out of 22 patients received biological treatment. No worsening of clinical-biological parameters was observed. Excluding dropouts and abandonments (n=9/22, 41%), the adherence rate was 81%, established by calculating the arithmetic mean of the adherence rates of 13 patients. No admission was observed due to gastrointestinal toxicity or discontinuation of treatment related to alterations in physical and nutritional well-being. In addition, only 5.5% of unscheduled consultations were increased due to incidents in well-being, mostly telematic (n=6/103 consultation are unscheduled). Additionally, 92% of patients reported an improvement in their nutritional habits (n=12/13), and up to 45% required adjustment of medical supportive treatment (n=5/11). There were no cases of grade 3 or greater gastrointestinal toxicity. All of this reflects improved tolerance to treatments. Patients reported a satisfaction score of 4.3 out of 5, while professionals rated their satisfaction at 4.8 out of 5. CONCLUSIONS: We demonstrated the usefulness of integrating new technologies through a multidisciplinary approach. The Nootric app facilitated collaboration among the medical team, nutritionists, and patients. It enabled us to detect health issues related to physical-nutritional well-being, anticipate major complications, and mitigate potentially avoidable risks. Consequently, there was a decrease in unscheduled visits and admissions related to this condition.
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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».