Assistive technologies that support social interaction in long-term care homes: a scoping review
Bibliographic record
Abstract
ABSTRACT Objective: The objective of this review was to chart the literature on assistive technologies (excluding robots) that support social interaction of older adults in long-term care homes, and to advance a definition of socially assistive technologies. Introduction: Loneliness and social isolation have adverse effects on the health and well-being of older adults. Many long-term care homes provide recreational programming intended to entertain or distract residents, yet the evidence of their effectiveness is limited. Absent from the literature are comprehensive reviews of assistive technologies (other than robots) that are used to support social interaction in long-term care homes. Inclusion criteria: The review considered research studies as well as gray literature that included older adults (≥65 years) living in long-term care homes. The concept of interest was the use of assistive technologies (excluding robots) that support social interaction in long-term care homes. Methods: The databases were searched on June 26, 2019, and included CINAHL Full Text (EBSCO), MEDLINE (Ovid), PsycINFO (EBSCO), Sociological Abstracts (ProQuest), Embase (Elsevier), and Web of Science (Clarivate). The search for gray literature was conducted in ProQuest Dissertations and Theses Databases and across 11 websites during September and October 2019. The recommended JBI approach to study selection, data extraction, and data synthesis was used. Results: Twenty-five articles were included in this review, with comparable numbers of quantitative (n = 6), qualitative (n = 9), and mixed methods (n = 7) studies, with the remaining articles employing non-empirical designs (n = 3). Technologies were categorized as low (easily recognizable to everyone), medium (more electronics), or high (involves internet). Two studies reported on low-assistive technologies, including videotapes and the telephone. Medium-assistive technologies were identified in nine studies and included videophones; Nintendo Wii; tablet-based games; picture- and video-viewing tools; and CRDL (pronounced “cradle”), a special instrument that translates touch into sound. More than half (n = 14) of the included articles utilized high-assistive technologies, such as computer labs/kiosks, tablet-based applications, social media (eg, Facebook), videoconferencing, and multi-functional systems. Five studies measured whether assistive technologies had an impact on the quantity of long-term care residents’ social interaction levels. Qualitative themes were related to residents’ social connections and experiences after using various technologies. Four studies systematically incorporated a framework/model, and Social Structuration Theory was considered the most comprehensive. In the absence of a definition of socially assistive technologies, the definition advanced from this review is as follows: Socially assistive technologies are user-appropriate devices and tools that enable real-time connectivity to enhance social interaction. Conclusions: Included literature reported the benefits of technology use, with considerable variability in engagement and no cost estimates. We recommend that future research continue to advance our definition of socially assistive technologies, make promising assistive technologies available in long-term care homes after studies are completed, report the costs of assistive technologies, and include participants with dementia and culturally and linguistically diverse backgrounds.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".