Technological interventions to reduce loneliness and social isolation among community-living older adults: A scoping review
Bibliographic record
Abstract
Background: Loneliness and social isolation are recognized as significant challenges affecting older adults living in the community.In recent years, there has been an increase in the use of technological interventions to address these issues.Objective: The primary objective of this study is to provide a comprehensive overview of the types of technological interventions that have been developed to reduce loneliness and/or social isolation for community-dwelling older adults.The secondary objective is to compare via mapping the technological intervention article characteristics in terms of date of publication, country of publication, study design, sample characteristics, loneliness/ social isolation measure, and efficacy/support for the intervention.Method: Using the Joanna Briggs Institute (JBI) methodology (Joanna Briggs Institute, 2011) and the PRISMA extension for systematic/scoping reviews, a scoping review of seven academic databases (Academic Search Premier, Ageline, Global Health, MEDLINE, PsycIN-FO, Web of Science and ClinicalTrials.gov)was conducted.Results: This scoping review identified 26 technological approaches to reducing loneliness and social isolation that met our criteria.We found three groupings; (1) computer and tablet-based competence training; (2) health-oriented technical interventions; and 3) Video games and animatronic pets interventions.The majority of technological interventions (15 out of 26) were effective in demonstrating support for reductions in loneliness and/or social isolation among older adults.Issues of accessibility, technology literacy, and complexity of the intervention were found to act as barriers to uptake.Conclusion: A variety of technologies have been employed to reduce social and loneliness amongst older adults, with a growing body of evidence in support of technologydriven interventions.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".