Using Computer Tablets to Improve Moods for Older Adults With Dementia and Interactions With Their Caregivers: Pilot Intervention Study
Notice bibliographique
Résumé
Background Persons living with dementia represent a significant and growing segment of the older adult (aged 65 years and older) population. They are often challenged expressively and may experience difficulties with sharing their feelings or moods. Availability of, and easy access to, tablets facilitates the use of information and communication technologies (ICTs) as a delivery mechanism for nonpharmacological interventions, especially for persons living with dementia. Evidence of the impact of ICTs in different community settings on mood with older adults and the impact of engagement on their caregivers is needed to promote broader adoption and sustainment of these technologies in the United States. Objective This study aimed to determine the extent of the effects of tablets on positive mood change and examine the effects of study variables on care recipients’ mood changes and caregivers’ daily interactions. Methods The tablet intervention was developed and evaluated in five programs. The primary outcome was caregivers’ assessment of care recipients’ mood (n=1089) before and after a tablet engagement session using an eight-point mood visual analog scale. Session influence on caregivers’ daily activities was captured for a subsample of participants (n=542). Frequency distributions were computed for each study variables. Chi-square tests of association were calculated to determine the association of the variables on mood changes for all care recipients, as well as those being treated in skilled nursing facilities and in-home, and then for those that affected caregivers’ daily activities. Results The study sample comprised 1089 care recipient and caregiver engagement sessions. Cumulatively, 50.78% (553/1089) of care recipients showed a transition from negative to positive moods, whereas another 41.78% (455/1089) maintained an already-positive mood after the caregiver engagement session. Chi-square analyses demonstrated that positive mood changes resulted from using music (χ210=72.9; P<.001), using YouTube as the sole app (χ212=64.5; P<.001), using multiple engagement strategies (χ22=42.8; P<.001), and when cared for in a skilled nursing facility (χ24=236.8; P<.001) across the entire care recipient sample. In addition, although many features of the engagement session positively influenced the caregivers’ day, the largest effect was observed when care recipients’ mood was considered to have improved following the session (χ24=234.7; P<.001). Conclusions The study is one of the first in the United States to explore the impact of ICTs, in particular managed tablets and Web-based video services that can be used on a tablet through an app, on improving mood in persons living with dementia, and enhancing caregivers’ perceptions about their care recipient interactions. Importantly, these pilot data substantiate ICTs as part of a personalized engagement approach, as beneficial alternatives to pharmaceutical interventions for mood enhancement. However, a more comprehensive study that explores the ICT’s impact on additional clinical outcomes is needed to confirm these preliminary findings.
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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 | 0,002 | 0,003 |
| 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 | 0,001 | 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 ».