Personalized Integrated Care Promoting Quality of Life for Older People: Protocol for a Multicenter Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Alzheimer disease (AD) and Parkinson disease (PD) are the 2 most common neurodegenerative diseases affecting millions of people worldwide. The Personalized Integrated Care Promoting Quality of Life for Older People (PC4L) project proposes an integrated, scalable, and interactive care ecosystem that can be easily adapted to the needs of several neurodegenerative and chronic diseases, care institutions, and end user requirements. OBJECTIVE: The study protocol developed within the framework of the PC4L project aims to iteratively test the integrated platform and its modules, and focuses primarily on assessing the impact of the proposed solution (ie, the PC4L platform) on patients' quality of life, as well as its usability and feasibility on a large-scale sample size in 3 different scenarios (home, neurorehabilitation, and day care centers). METHODS: A prospective multicenter clinical study is conducted in 5 European countries (Germany, Italy, Portugal, Romania, and Spain) at 6 different pilot centers, for 3 months, in patients with PD, Parkinsonism, AD, and other dementias (ODs). Patients were randomized in a ratio of 1:1 to the intervention group (use of the PC4L system) or the control group (no intervention). The PC4L system consists mainly of a wristband for monitoring parameters such as steps and levels of physical activity, and the PC4L app, which includes different engaging functionalities. Both groups are assessed through baseline and end-of-study clinical evaluations, including assessment of quality of life through the EQ-5D-3L scale. RESULTS: The study protocol is part of a project approved and funded by the European Commission Horizon 2020 (grant agreement number 875221). The ethics committees of all involved centers reviewed and approved the study protocol. The study began with the recruitment phase in September 2022, and enrollment ended in February 2023. Recruitment is now closed (April 2023). The results of this study are expected to be published in summer 2023. A total of 558 patients, 279 per study group, were recruited. The results will allow to clarify the impact of PC4L on quality of life, will assess the empowerment of patients and the medical resources use, as well as the usability of the final version of the PC4L system. It will also provide information on the support of the system as a tool to facilitate the decision-making process. CONCLUSIONS: The PC4L project intends to test a technology-based, integrated, scalable, and interactive care platform on patients with neurodegenerative diseases and proposes a good coordinated care model between all involved actors. Future developments of the PC4L solution may involve caregivers and socio-health professionals in the decision-making process in order to facilitate efficient communication between all stakeholders and ensure reliable and protected access to data within Europe. TRIAL REGISTRATION: ClinicalTrials.gov NCT05538455; https://clinicaltrials.gov/study/NCT05538455. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47916.
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 | 0,035 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,002 |
| Méta-épidémiologie (sens large) | 0,012 | 0,006 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,007 |
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 ».