Evidence map›Paper›PMID 35672268›Full record

ReviewJMIR aging2022

The Effect of Cognitive Function Health Care Using Artificial Intelligence Robots for Older Adults: Systematic Review and Meta-analysis.

Hocheol Lee, Min Ah Chung, Hyeji Kim, Eun Woo Nam

Erratum issuedOpen access · goldAbstract readReview
In one paragraph

Review in JMIR aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
5.2field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 42 citations in OpenAlex.

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  6. AI in geriatric psychiatry: precision meets human experience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Hocheol LeeHealthy City Research Center, Yonsei University, Wonju, Republic of Korea.ORCID https://orcid.org/0000-0003-1467-8843
Min Ah ChungHealthy City Research Center, Yonsei University, Wonju, Republic of Korea.ORCID https://orcid.org/0000-0003-1354-9847
Hyeji KimHealthy City Research Center, Yonsei University, Wonju, Republic of Korea.ORCID https://orcid.org/0000-0001-7269-2438
Eun Woo NamHealthy City Research Center, Yonsei University, Wonju, Republic of Korea.ORCID https://orcid.org/0000-0002-3517-6247
Yonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith rapidly aging populations in most parts of the world, it is only natural that the need for caregivers for older adults is going to increase in the near future. Therefore, most technologically proficient countries are in the process of using artificial intelligence (AI) to build socially assistive robots (SAR) to play the role of caregivers in enhancing interaction and social participation among older adults.

objectiveThis study aimed to examine the effect of intervention through AI SAR on the cognitive function of older adults through a systematic literature review.

methodsWe conducted a meta-analysis of the various existing studies on the effect of AI SAR on the cognitive function of older adults to standardize the results and clarify the effect of each method and indicator. Cochrane collaboration and the systematic literature review flow of PRISMA (Preferred Reporting Item Systematic Reviews and Meta-Analyses) were used on original, peer-reviewed studies published from January 2010 to March 2022. The search words were derived by combining keywords including Population, Intervention, and Outcome-according to the Population, Intervention, Comparison, Outcome, Time, Setting, and Study Design principle-for the question "What is the effect of AI SAR on the cognitive function of older adults in comparison with a control group?" (Population: adults aged ≥65 years; Intervention: AI SAR; Comparison: comparison group; Outcome: popular function; and Study Design: prospective study). For any study, if one condition among subjects, intervention, comparison, or study design was different from those indicated, the study was excluded from the literature review.

resultsIn total, 9 studies were selected (6 randomized controlled trials and 3 quasi-experimental design studies) for the meta-analysis. Publication bias was examined using the contour-enhanced funnel plot method to confirm the reliability and validity of the 9 studies. The meta-analysis revealed that the average effect size of AI SAR was shown to be Hedges g=0.43 (95% CI -0.04 to 0.90), indicating that AI SAR are effective in reducing the Mini Mental State Examination scale, which reflects cognitive function.

conclusionsThe 9 studies that were analyzed used SAR in the form of animals, robots, and humans. Among them, AI SAR in anthropomorphic form were able to improve cognitive function more effectively. The development and expansion of AI SAR programs to various functions including health notification, play therapy, counseling service, conversation, and dementia prevention programs are expected to improve the quality of care for older adults and prevent the overload of caregivers. AI SAR can be considered a representative, digital, and social prescription program and a nonpharmacological intervention program that communicates with older adults 24 hours a day. Despite its effectiveness, ethical issues, the digital literacy needs of older adults, social awareness and reliability, and technological advancement pose challenges in implementing AI SAR. Future research should include bigger sample sizes, pre-post studies, as well as studies using an older adult control group.

Indexed as

agingAI SARartificial intelligenceassistive robotassistive technologycaregivercaregivingCochrane collaborationcognitioncognitive functiondementiameta-analysisolder adult populationolder adultsreviewsocially assistive robotssocial prescriptionsocial support

Identifiers

PMID35672268
PMCPMC9277531
OpenAlexW4281980206

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.