The Impact of Assistive Technology on Quality of Life in Individuals with Low Vision: A Systematic Review
Bibliographic record
Abstract
Background: Low vision significantly impairs daily functioning and well-being.Assistive Technology (AT) has emerged as a promising intervention, but its impact on Quality of Life (QoL) remains inconsistently synthesized.Objective: To evaluate the efficacy of AT in improving QoL for individuals with low vision through a systematic review of peer-reviewed evidence.Methods: A PRISMA-compliant search was conducted in PubMed, Embase, Scopus, and Cochrane Library (inception to [date]).Studies were included if they (1) assessed AT interventions (e.g., magnifiers, digital apps, smart glasses), (2) measured QoL outcomes (e.g., NEI-VFQ, EQ-5D), and (3) involved adults with low vision (WHO criteria).Risk of bias was assessed via ROB-2 (RCTs) and Newcastle-Ottawa Scale (observational studies).Results: From [X] screened records, [Y] studies met inclusion criteria.Meta-analysis, revealed a standardized mean difference (SMD) of [Z] in QoL scores favoring AT (95% CI: [ ]).Subgroup analyses highlighted greater benefits in mobility (p=0.XX) and mental health (p=0.XX). Conclusion:AT significantly enhances QoL in low vision, particularly for functional independence.Future research should standardize outcome measures and address long-term adherence.
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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 | 0.040 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.004 | 0.021 |
| Science and technology studies | 0.000 | 0.008 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".