Professional Support Through a Tailor-Made Mobile App to Reduce Stress and Depressive Symptoms Among Family Caregivers of People With Dementia: Mixed Methods Pilot Study
Notice bibliographique
Résumé
Background: Providing informal care to people with dementia living at home can be challenging and may cause caregiver stress and depression. Interventions delivered through mobile apps provide innovative solutions for community-based social care professionals to address the increasing support needs of family caregivers (FCs) of people with dementia. Objective: This study aimed to examine, among FCs of people with dementia living at home, (1) the potential association between professional support provided through a mobile app and caregiver stress and depressive symptoms, (2) types of support provided through chat interactions between FCs and social care professionals, and (3) how support provided through a mobile app relates to changes in caregiver stress and depressive symptoms. Methods: A mixed methods pilot study integrated quantitative pre- and postintervention data with qualitative logged chat data. FCs of people with dementia living at home (n=35) were recruited to test a tailor-made mobile app over 8 weeks. The primary and secondary outcome measures were caregiver stress and depressive symptoms, respectively. Descriptive statistics were used to summarize sociodemographic factors; inferential statistics were used to analyze mean differences in outcomes pre- and postintervention. FCs were divided into 3 groups based on changes in caregiver stress scores between pre- and postintervention. Generalized linear model analyses determined the association between participation in the intervention and caregiver stress and depressive symptoms, adjusting for age, gender, and relationship to the person with dementia. Logged chat data were analyzed using summative content analysis to identify types of support provided and received. Changes in caregiver stress were integrated with chat data to determine patterns in types of support received. Results: The mean age of FCs was 69.4 (SD 11.9) years, with most being women (28/35, 80%), partners (24/35, 68.6%), and living with the person with dementia (26/35, 74%). The mean score of caregiver stress was marginally higher postintervention (24.1, SD 9.3) than preintervention (23.9, SD 9.2), whereas the mean score of depressive symptoms decreased from pre- (6.5, SD 5.1) to postintervention (6.2, SD 5.2). These differences were not statistically significant. Regression analyses showed that participation in the intervention was not statistically significantly associated with caregiver stress (β=0.171, α=.05; P=.86) or depressive symptoms (β=-0.293, α=.05; P=.75) after adjusting for age, gender, and relationship to the person with dementia. However, mixed methods analysis at the subgroup level suggested that frequent tailored support by social care professionals delivered through a mobile app may reduce caregiver stress among FCs of people with dementia living at home. Conclusions: The study highlights the importance of providing frequent and individualized support to meet the needs of FCs of people with dementia. Findings from this study may help community-based social care providers plan and organize digital support content provided to FCs of people with dementia living at home.
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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 | 0,007 | 0,008 |
| 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) | 0,003 | 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 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 ».