Assistive technologies in reducing caregiver burden among informal caregivers of older adults: a systematic review
Bibliographic record
Abstract
AIM: The world population is rapidly ageing. As population age, the incidence of functional limitations increases, demanding higher levels of care from caregivers. Assistive technologies improve individuals' functioning, independence, well-being and quality of life. By increasing independence of older adults, assistive technologies decrease workloads required from informal caregivers. This review investigates, evaluates, and synthesises existing findings to examine whether and how assistive technologies reduce caregiver burden. METHODS: Databases searched included MEDLINE, EMBASE, Scopus, and Cochrane Library. Three groups of keywords were combined: those relating to assistive technology, caregiver burden, and older adults. RESULTS: Two theories emerged from the analysis of study results. Caregivers reported that assistive technologies decrease caregiver burden. However, caregivers had concerns that assistive technologies could add to caregiver burden, highlighting the limitations of assistive technology. CONCLUSIONS: As suggested by a majority of the studies in this review, assistive technologies contribute to reducing caregiver burden among caregivers of older adults. Assistive technologies assisted caregivers by reducing time, levels of assistance and energy put towards caregiving, anxiety and fear, task difficulty, safety risk particularly for activities requiring physical assistance and increasing the independence of the users. Further research is required to better understand limitations of assistive technologies. Implications for Rehabilitation Support for informal caregivers of older adults need more attention and recognition. Assistive technologies can reduce caregiver burden among informal caregivers of older adults. Further research is required to better understand the effectiveness of assistive technologies in reducing caregiver burden as well as limitations and barriers associated with using assistive technologies.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".