Intergenerational Reminiscence Approach in Improving Emotional Well-Being of Older Asian Americans in Early-Stage Dementia Using Virtual Reality: Protocol for an Explanatory Sequential Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: After a dementia diagnosis, Asian Americans experience anxiety, feelings of shame, and other negative effects. Emotional well-being is not only an important aspect of mental health, but also a quality of resilience that helps people bounce back faster from difficulties. However, few studies have addressed issues in developing, implementing, and testing intervention strategies to promote emotional well-being among older adults. Intergenerational solidarity between grandparents and grandchildren has been emphasized in Asian families and is beneficial for the health of persons with dementia. Reminiscence and life review have been identified as potentially effective intervention strategies for helping depression and emotional well-being for older adults. OBJECTIVE: This proposed study aims to develop and implement an intergenerational reminiscence approach and evaluate its potential feasibility and effectiveness in improving the emotional well-being of older Asian American adults who have a recent dementia diagnosis. METHODS: An explanatory sequential mixed methods design will be used in which quantitative data will first be collected and analyzed to identify subsamples of participants who report the greatest and least change in emotional well-being; then, these subsamples will be interviewed to further understand why or why not this intervention works for them. Older adults will receive 6 sessions of life review with grandchildren in virtual reality (VR; 1-1.5 hours each week for 6 weeks), aided by pictures and virtually traveling to important places in their life using Google Earth to look around at those places and remember important times. Quantitative survey data will be collected pre- and postintervention and at a 3-month follow-up. Qualitative interviews with selected participants will also be integrated into the study design. The quantitative data from the surveys will be entered into SPSS (IBM Corp) and analyzed using descriptive analyses, Pearson chi-square tests, nonparametric Friedman tests, or nonparametric Wilcox signed-rank tests (2-tailed). The qualitative data will be transcribed by research assistants, coded by the investigators independently, and analyzed with guidance from content analysis software (Atlas.ti; Atlas.ti Scientific Software Development GmbH). RESULTS: The project was delayed due to the COVID-19 pandemic. Data collection started in late 2021, and 26 participants were recruited as of December 2022. While we are still cleaning and analyzing the quantitative data, the qualitative interviews showed promising results of this intergenerational reminiscence approach in improving emotional well-being among older Asian American adults who have cognitive impairment. CONCLUSIONS: Intergenerational reminiscence provided by grandchildren is promising in improving the emotional well-being of grandparents. VR technology is likely to be accepted by older adults. Future research may consider scaling up this pilot into a trackable, replicable model that includes more participants and develops a more rigorous study design with control groups to test the effectiveness of this intervention for older adults with dementia. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48927.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".