The Effectiveness of e-Health Solutions for Aging With Cognitive Impairment: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: e-Health solutions are an innovative approach to support aging with cognitive impairment. Because technology is developing at a fast pace, the aim of this review was to present an overview of the research regarding the effectiveness of these solutions. Moreover, the availability of these solutions was examined. RESEARCH DESIGN AND METHODS: Systematic searches were conducted in 7 databases. Full texts of potentially relevant references were assessed by 2 reviewers, and discrepancies were solved through discussion. Data on study characteristics, technology type, application domain, availability, outcomes, and effects were extracted. A categorization exercise and narrative synthesis were conducted. RESULTS: In total, 72 studies describing 70 e-Health solutions were identified. The majority of solutions comprised cognitive training for older adults, followed by educational and supportive web platforms for caregivers. Outcomes included mainly measures of cognition, psychosocial functioning, caregiving processes, caregiver-care receiver relationship, and activities of daily living. Positive effects of cognitive training technologies were observed on cognitive functioning of older adults, as well as those of supportive web platforms on behavioral and psychological symptoms of dementia and caregiver self-efficacy. The effects of these solutions on depression in both target groups were inconclusive. The methodological quality of the studies was moderate to good. However, some important limitations were observed. DISCUSSION AND IMPLICATIONS: The review identified cognitive training solutions and supportive web platforms as the most effective on a limited number of outcomes. Although other solutions seem promising, further research has to overcome methodological issues. Furthermore, solutions for leisure and reminiscence and outcomes specifically related to independent living deserve more attention.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".