The Role of Health in the Technology Acceptance Model Among Low-Income Asian American Older Adults: Cross-Sectional Survey Analysis
Bibliographic record
Abstract
BACKGROUND: Self-rated health is associated with information and communications technology (ICT) use among older adults. Non-US born, older Asian American individuals are more inclined to rate their health as fair or poor compared to individuals from other racial and ethnic backgrounds. This population is also less likely to use ICTs as compared to White older Americans. Furthermore, cognitive decline may impact technology acceptance. In a previous adaptation of the technology acceptance model for low-income, Asian American older adults, perceived usefulness (PU), perceived ease of use (PEOU), age, educational attainment, ethnicity, and English proficiency were significant predictors of ICT use. However, the association between health and technology acceptance has not been explored among Asian American older adults. OBJECTIVE: This study examined the role of self-rated health and subjective cognitive decline in the acceptance and use of ICTs among low-income, Asian American older adults. METHODS: This cross-sectional survey included Asian American individuals aged ≥62 years living in affordable housing for older adults (N=392). Using hierarchical multiple regression, we explored the association between self-rated health and ICT use and technology acceptance model mediators (PU and PEOU) while adjusting for demographics, English proficiency, and subjective cognitive decline. Contrast statements were used to estimate contrasts of interest. To further examine the separate and joint association between age and subjective cognitive decline and the dependent variables, we examined scatterplots with locally estimated scatterplot smoothing lines, revealing that the relationship between subjective cognitive decline and ICT use varied in 3 age segments, which led to updating our analysis to estimate differences in ICT use among age categories with and without subjective cognitive decline. RESULTS: =0.011; P=.04). In reviewing scatterplots, we determined that, in the youngest age group (62-74 years), ICT use increased with subjective cognitive decline, whereas in the older age groups (75-84 and ≥85 years), ICT use decreased with subjective cognitive decline, more so in the oldest age category. Through regression analysis, among participants with subjective cognitive decline, ICT use significantly decreased in the middle and older age groups as compared to the youngest age group. However, among participants without subjective cognitive decline, the difference in use among age groups was not significant. CONCLUSIONS: This study contributes to the understanding of the complex relationship between health and ICT acceptance among low-income, Asian American older adults and suggests the need for tailored interventions to promote digital engagement and quality of life for this population.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.012 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".