Using Computer Tablets to Improve Moods for Older Adults With Dementia and Interactions With Their Caregivers: Pilot Intervention Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".