Improvement in Quality of Life With the Use of a Technological System Among Patients With Chronic Disease Followed Up in Primary Care (TeNDER Project): Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Among chronic diseases, cognitive, neurological, and cardiovascular impairments are becoming increasingly prevalent, generating a shift in health and social needs. Technology can create an ecosystem of care integrated with microtools based on biosensors for motion, location, voice, and expression detection that can help people with chronic diseases. A technological system capable of identifying symptoms, signs, or behavioral patterns could provide notification of the development of complications of disease. This would help the self-care of patients with chronic disease and save health care costs, promoting the autonomy and empowerment of patients and their caregivers, improving their quality of life (QoL), and providing health professionals with monitoring tools. OBJECTIVE: The main objective of this study is to evaluate the effectiveness of a technological system (the TeNDER system) to improve quality of life in patients with chronic diseases: Alzheimer disease, Parkinson disease, and cardiovascular disease. METHODS: A multicenter, randomized, parallel-group clinical trial will be conducted with a follow-up of 2 months. The scope of the study will be the primary care health centers of the Community of Madrid belonging to the Spanish public health system. The study population will be patients diagnosed with Parkinson disease, Alzheimer disease, and cardiovascular disease; their caregivers; and health professionals. The sample size will be 534 patients (380 in the intervention group). The intervention will consist of the use of the TeNDER system. The system will monitor the patients by means of biosensors, and their data will be integrated into the TeNDER app. With the information provided, the TeNDER system will generate health reports that can be consulted by patients, caregivers, and health professionals. Sociodemographic variables and technological affinity will be measured, as will views on the usability of and satisfaction with the TeNDER system. The dependent variable will be the mean difference in QoL score between the intervention and control groups at 2 months. To study the effectiveness of the TeNDER system in improving QoL in patients, an explanatory linear regression model will be constructed. All analyses will be performed with the 95% CI and robust estimators. RESULTS: Ethics approval for this project was received on September 11, 2019. The trial was registered on August 14, 2020. Recruitment commenced in April 2021, and the expected results will be available during 2023 or 2024. CONCLUSIONS: This clinical trial among patients with highly prevalent chronic illnesses and the people most involved in their care will provide a more realistic view of the situation experienced by people with long-term illness and their support networks. The TeNDER system is in continuous development based on a study of the needs of the target population and on feedback during its use from the users: patients, caregivers, and primary care health professionals. TRIAL REGISTRATION: ClinicalTrials.gov NCT05681065; https://clinicaltrials.gov/ct2/show/NCT05681065. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47331.
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 | 0,033 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,016 | 0,007 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,008 | 0,006 |
| 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 ».