Internet-Based Supportive Interventions for Family Caregivers of People With Dementia: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: As dementia progresses, patients exhibit various psychological and behavioral symptoms, imposing a significant burden on families and society, including behavioral and psychological symptoms of dementia. However, caregivers lack professional care knowledge and skills, making it difficult for them to effectively cope with the diverse challenges of caregiving. Therefore, it is necessary to provide caregivers with professional knowledge and skills guidance. Objective: This study aimed to analyze the impact of internet-based training on behavioral and psychological symptoms of dementia in patients, and explore how this training model affects the caregiving abilities and caregiving burden of the family caregivers of patients with dementia. Methods: Using a consecutive enrollment method, the Department of Geriatrics at Zhejiang Hospital (Zhejiang, China) recruited 72 informal caregivers of patients with dementia. These caregivers were randomly divided into an intervention group and a control group, with 36 participants in each group. The intervention group underwent caregiver skill training via a web-based platform, whereas the control group initially received face-to-face follow-up guidance and was subsequently offered web-based training after 6 months. To assess the effectiveness of the intervention program, we used the Neuropsychiatric Inventory Questionnaire (NPI-Q), the Chinese version of the Zarit Burden Interview (CZBI), and the Sense of Competence in Dementia Care Staff Scale (SCIDS) for evaluations conducted before the intervention, 3 months after the intervention, and 6 months after the intervention. Results: Between July 2019 and December 2020, a total of 66 patients successfully completed the intervention and follow-up. After 6 months of intervention, the NPI-Q score of the intervention group was 3.18 (SD 3.81), the CZBI score was 10.97 (SD 5.43), and the SCIDS score was 71.88 (SD 4.78). The NPI-Q score of the control group was 8.09 (SD 8.52), the CZBI score was 30.30 (SD 13.05), and the SCIDS score was 50.12 (SD 9.10). There were statistically significant differences in NPI-Q (P=.004), CZBI (P<.001), and SCIDS scores (P<.001) between the intervention group and the control group. Repeated measures analysis of variance showed that compared with before the intervention, there were statistically significant differences in CZBI (P<.001) and SCIDS (P<.001) scores 3 months after the intervention, while the difference in NPI-Q (P=.11) scores was not significant. The total scores of NPI-Q (P<.001), CZBI (P<.001), and SCIDS (P<.001) were significantly improved 6 months after the intervention. In addition, the results of the covariance analysis showed that after excluding the time effect, the web-based training intervention significantly reduced the NPI-Q score (-2.79, 95% CI -4.38 to -1.19; P<.001) of patients with dementia and the CZBI score (-13.52, 95% CI -15.87 to -11.16; P<.001) of caregivers, while increasing the SCIDS score (12.24, 95% CI 9.02-15.47; P<.001). Conclusions: Internet-based training could significantly reduce the level of behavioral symptoms in older patients with dementia and alleviate the burden on caregivers, enhancing their caregiving abilities. Our results confirmed the effectiveness and feasibility of web-based training, which was of great significance in providing caregiving knowledge training for informal caregivers of persons with dementia.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».