The Impact of Disability and Assistive Technology Use on Well-Being in Later Life: Findings From the National Health and Aging Trends Study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although assistive technologies have the potential to bridge the gap between personal capabilities and environmental demands, they may not always fully accommodate disability. This study examined the implications of change in the extent of accommodation provided by assistive technology for well-being in older adulthood. RESEARCH DESIGN AND METHODS: Data from 5 waves (2015-2019) of the National Health and Aging Trends Study provided information on disability and assistive technology use among older adults aged 65 and older in the United States (n = 7,057). An eight-level index that jointly characterized the spectrum of disability and assistive technology use was applied to 7 activities of daily living (ADLs). Fixed-effects panel model assessed within-person associations between well-being and the extent of assistive technology accommodation along different levels of the disability spectrum. RESULTS: At baseline, bathing (28.7%; 95% confidence interval [CI]: 27.6, 29.8) and toileting (37.9%; 95% CI: 36.2, 39.6) were the 2 activities in which most older adults successfully accommodated their limitations with assistive technologies. Longitudinally, the level of support provided by assistive technology changed widely across activities and over time. Within-person analyses showed that for all ADLs except for eating, there was a significant decline in well-being when the adopted assistive technology no longer supported users' needs and successfully resolved their disabilities. DISCUSSION AND IMPLICATIONS: Our findings highlight the utility of technology-based interventions and underscore the imperative that assistive technologies attend to the specific needs of older adults and support independence in everyday activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".