Digital interventions to reduce social isolation and loneliness in older adults: An evidence and gap map
Bibliographic record
Abstract
Background: Social isolation and loneliness are more common in older adults and are associated with a serious impact on their well-being, mental health, physical health, and longevity. They are a public health concern highlighted by the COVID-19 pandemic restrictions, hence the need for digital technology tools to enable remotely delivered interventions to alleviate the impact of social isolation and loneliness during the COVID-19 restrictions. Objectives: To map available evidence on the effects of digital interventions to mitigate social isolation and/or loneliness in older adults in all settings except hospital settings. Search Methods: We searched the following databases from inception to May 16, 2021, with no language restrictions. Ovid MEDLINE, Embase, APA PsycInfo via Ovid, CINAHL via EBSCO, Web of Science via Clarivate, ProQuest (all databases), International Bibliography of the Social Sciences (IBSS) via ProQuest, EBSCO (all databases except CINAHL), Global Index Medicus, and Epistemonikos. Selection Criteria: Titles and abstracts and full text of potentially eligible articles were independently screened in duplicate following the eligibility criteria. Data Collection and Analysis: We developed and pilot tested a data extraction code set in Eppi-Reviewer and data were individually extracted and coded based on an intervention-outcome framework which was also used to define the dimensions of the evidence and gap map. Main Results: We included 200 articles (103 primary studies and 97 systematic reviews) that assessed the effects of digital interventions to reduce social isolation and/or loneliness in older adults. Most of the systematic reviews (72%) were classified as critically low quality, only 2% as high quality and 25% were published since the COVID-19 pandemic. The evidence is unevenly distributed with clusters predominantly in high-income countries and none in low-income countries. The most common interventions identified are digital interventions to enhance social interactions with family and friends and the community via videoconferencing and telephone calls. Digital interventions to enhance social support, particularly socially assistive robots, and virtual pets were also common. Most interventions focused on reducing loneliness and depression and improving quality of life of older adults. Major gaps were identified in community level outcomes and process indicators. No included studies or reviews assessed affordability or digital divide although the value of accessibility and barriers caused by digital divide were discussed in three primary studies and three reviews. Adverse effects were reported in only two studies and six reviews. No study or review included participants from the LGBTQIA2S+ community and only one study restricted participants to 80 years and older. Very few described how at-risk populations were recruited or conducted any equity analysis to assess differences in effects for populations experiencing inequities across PROGRESS-Plus categories. Authors' Conclusions: The restrictions placed on people during the pandemic have shone a spotlight onto social isolation and loneliness, particularly for older adults. This evidence and gap map shows available evidence on the effectiveness of digital interventions for reducing social isolation or loneliness in older adults. Although the evidence is relatively large and recent, it is unevenly distributed and there is need for more high-quality research. This map can guide researchers and funders to consider areas of major gaps as priorities for further research.
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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 | 0.033 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".