This paper is in the following e-collection/theme issue:

Reviews on Aging (38) Dementia and Cognitive Decline (429) Artificial Intelligence (1214) Loneliness and Social Isolation (80) Preventing Social Isolation and Fostering Social Interactions in Old Age (56)

Published on in Vol 5, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38896, first published .
The Effect of Cognitive Function Health Care Using Artificial Intelligence Robots for Older Adults: Systematic Review and Meta-analysis

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

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

Authors of this article:

Hocheol Lee1, 2 Author Orcid Image ;   Min Ah Chung1, 2 Author Orcid Image ;   Hyeji Kim1, 2 Author Orcid Image ;   Eun Woo Nam1, 2, 3, 4 Author Orcid Image
Article Authors Cited by (7) Tweetations (3) Metrics

    Review

    1Healthy City Research Center, Yonsei University, Wonju, Republic of Korea

    2Yonsei Global Health Center, Yonsei University, Wonju, Republic of Korea

    3Center of Evidence Based Medicine, Institute of Convergence Science, Yonsei University, Wonju, Republic of Korea

    4Department of Health Administration, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, Republic of Korea

    Corresponding Author:

    Eun Woo Nam, MPH, PhD

    Department of Health Administration

    College of Software and Digital Healthcare Convergence

    Yonsei University

    Unit 412, Chang-jo gwan

    1 Yonseidae-gil

    Wonju, 26493

    Republic of Korea

    Phone: 82 33 760 2413

    Fax:82 33 762 9562

    Email: ewnam@yonsei.ac.kr


    Related ArticleThis is a corrected version. See correction statement in: https://aging.jmir.org/2022/3/e42312

    Background: With 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.

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

    Methods: We 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.

    Results: In 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.

    Conclusions: The 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.

    JMIR Aging 2022;5(2):e38896

    doi:10.2196/38896

    Keywords

    older adult population; older adults; cognition; cognitive function; artificial intelligence; socially assistive robots; AI SAR; social prescription; dementia; social support; aging; caregiver; caregiving; meta-analysis; review; Cochrane collaboration; assistive robot; assistive technology 


    Population aging is progressing worldwide due to the development of medical technology, and it is predicted that the number of older adults aged ≥65 years will increase from 730 million in 2019 to 1.5 billion in 2050 [1]. The World Health Organization has marked dementia and the mental health of older adults as public health problems due to an increase in the older adult population [2]. Dementia is a disease that occurs mainly in older adults aged ≥65 years and causes cognitive dysfunction, hyperactivity, sleep disturbance, violence, and depression, weakening daily life activities and making social activities difficult [3,4]. Currently, most patients with dementia are receiving treatment through drug therapy, but the medication rate is low since the symptoms of dementia impair the patients’ ability to recognize the need to take medication [5]. To overcome these problems, treatment methods that combine nonpharmacological treatment with drug treatment are increasing. Psychosocial therapy is being used as a representative nonpharmacological treatment for the improvement of cognitive function of older adults around the world. The United Kingdom’s National Health Service is implementing social prescribing, a nonpharmacological intervention program that connects patients with mental health conditions including dementia with nonmedical support sources in the community. Representative social prescribing programs include line dance, gardening, art therapy, music therapy, counseling therapy, and caring therapy [6]. According to previous studies, treatment methods based on interaction and conversation, rather than medication, for older adults with weakening cognitive function provide a sense of relief and stability, which in turn increases emotional support and social communication and thereby helps them recover their cognitive function [7]. As the older adult population increases, so does the population of older adults with cognitive impairment, and as a result, human resources and various nonpharmacological treatment programs are required. However, due to the rapidly aging global population, there is a shortage of caregivers; caregivers are particularly reluctant to take care of older adults with dementia due to mental stress, and the number of caregivers for patients with dementia is decreasing. As an alternative solution to this problem, technologically proficient countries such as the United States, Korea, Japan, and Australia are prioritizing the development of artificial intelligence (AI) socially assistive robots (SAR) as a part of digital health care [8]. According to previous studies, AI SAR have been found to be effective in preventing the overwork of caregivers for older adults, increasing work efficiency, and performing 24-hour monitoring [9,10].

    AI SAR are robots designed to interact with humans (eg, older adults) using AI. As a method of promoting interaction and social participation among older adults, the development and research of AI SAR are actively being conducted [11,12]. AI SAR started in the form of an animal-type pet robot in early development and have been developed into various forms such as human- and doll-like robots. Regardless of the form, AI SAR were found to effectively increase the frequency of independent communication by making older adults initiate conversations [13]. Due to the development of various technologies, AI SAR have developed to the extent in which they can interpret and express not only verbal expressions, gestures, eye contact, and emotional expressions but also nonverbal communication methods, and their ability to communicate with older adults is also developing at an increasing rate. The role of AI becomes more important particularly when an infectious disease such as COVID-19 becomes prevalent, which limits the visiting service of nursing personnel.

    AI SAR have been proven to be effective in enhancing interaction [14,15], improving the quality of life [16], improving depression and anxiety [17], and improving the quality of life of patients with dementia [18] for older adults aged ≥65 years. In addition, there has been a meta-analysis study published on the effect of the use of robots on older adults aged ≥65 years [19]. However, in an effectiveness study through a meta-analysis of AI SAR, it was confirmed that the study results including agitation, depression, and quality of life [19-21], etc, were inconsistent depending on the intervention method, SAR method, and characteristics of the older adults. A meta-analysis is necessary to standardize these various results, methods, and indicators. In other words, although the intervention using AI SAR has various effects on older adults, which has been proven through various studies, a meta-analysis based on the results of existing studies is necessary to clarify what kind of effect each indicator has. Currently, there is a lack of meta-analysis studies that analyze the effect of robots on cognitive function by setting a control group.

    Therefore, the purpose of this study was to understand the effect of intervention using AI SAR on the cognitive function of older adults through a systematic literature review. To this end, the detailed goals were as follows: (1) to search and review the existing literature on the effect of AI personal care on cognitive function; (2) to objectively identify the feasibility of the effect of nursing care service through AI SAR and the effect of AI SAR on cognitive function based on the results of the collected theses; and (3) to provide the basis for supporting policies and research on providing AI SAR to older adults aged ≥65 years.


    Study Design

    This systematic literature review and meta-analysis study identified the intervention effect of AI SAR to understand its effect on the cognitive function of older adults aged ≥65 years.

    Search Strategy

    This study was conducted according to the systematic literature review method by the Cochrane collaboration and the systematic literature review flow of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) [22,23]. The target data included original, peer-reviewed studies published from January 2010 to March 2022. The databases used for the search included PubMed and Google Scholar.

    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 (PICOTS-SD) principle (Multimedia Appendix 1).

    • Population: “Elderly” OR “Elderly People” OR “older adults” OR “older people” OR “senior” OR “Dementia” OR “Alzheimer” OR “Cognitive impairment”
    • Intervention: “Robot” OR “AI robot” OR “social assistive robot” OR “social interactive robot” OR “assistive robot” OR “companion robot” OR “robot interaction” OR “health care robot”
    • Outcome: “MMSE” OR “Mini-Mental State Examination” OR “cognitive function” OR “cognitive” OR “cognitive impairment” OR “cognitive disorder” OR “mental health”

    Eligibility

    This study used the PICOTS-SD selection and constituted the question “If older adults aged ≥65 years are provided with AI SAR, what would be the effect on cognitive function in comparison with a control group?” The PICOTS-SD criteria for this question includes older adults aged ≥65 years (Population), AI SAR (Intervention), comparison group (Comparison), popular function (Outcome), and prospective study (Study Design). Subsequently, a systematic literature review was conducted, focusing on the core research.

    From the above PICOTS-SD criteria, studies in which even one condition among subjects, intervention, comparison, and study design was different than those indicated were excluded from the literature review.

    Quality Assessment

    To minimize the deviation that occurs in literature search, 2 researchers searched and collected the data and then confirmed whether the same results were obtained. In addition, only peer-reviewed studies were included to increase the validity of the literature selection.

    A risk of bias (ROB) assessment was performed to evaluate the quality of the literature selected in this study. Both subjective and objective evaluations were performed in the ROB assessment. For subjective evaluation, Cochrane ROB assessment was used [22]. Cochrane ROB assessment consisted of (1) Random Sequence Generation, (2) Allocation Concealment, (3) Blinding of Outcome Assessment, (4) Incomplete Outcome Data, (5) Selective Reporting, and (6) Other Bias, and the researchers confirmed that the studies were selected according to the guidelines. Subjective evaluation was conducted using a funnel plot.

    All studies were reviewed by 3 researchers and selected based on a consensus of opinions to confirm the validity and consistency of the study.

    Data Extraction and Data Synthesis

    In this study, data were extracted and processed for the analysis of the selected studies. Data were synthesized by entering into Excel the (1) characteristics of the literature (year, journal, author, country, and study design), (2) research method (intervention, number of experimental groups, and number of control groups), and (3) research results (mean and SD of the experimental group and control group).

    Data Analysis

    This study calculated the effect size from 9 studies to analyze the effect of AI SAR on the cognitive function of older adults. To calculate the effect size, a normal distribution of the mean of each study was applied using a random effects model. For assigning weights in the random effects model, the DerSimonian and Laird method was used, including between-study variance [24]. For the effect size, the Standardized Mean Difference was used as an analysis value, and 95% CI and inverse of variance were used for weights [25].

    To analyze the heterogeneity of the 9 studies investigated in this study, a visual review was conducted using a Forest plot and a Galbraith plot. The effect size, direction, and CI of each study were analyzed using the Forest plot, and they were listed by year, effect size, and sample size. In the Galbraith plot, the effect size divided by the SE was plotted on the y-axis, and the reciprocal of the SE was plotted on the x-axis. If a data point was plotted within 2 SEs on the regression line, then it was interpreted as having no heterogeneity.

    To identify the reporting bias of this meta-analysis study, publication bias was classified by analyzing the contour-enhanced funnel plot and determining whether it was symmetrical.


    Search Result

    In total, 275,970 studies from PubMed and 10,800 studies from Google Scholar were searched using keywords to select the suitable literature for this study. Titles and abstracts were reviewed for 152 studies, excluding duplicate studies (36,017 cases), those marked ineligible by automation tools (250,386 cases), and those removed for other reasons (215 cases). A total of 30 studies were selected as a result, and among them, 9 studies were included in the meta-analysis, excluding those that were not retrieved (13 cases), lacked statistics (3 cases), lacked a control group (1 case), were in a non-English language (2 cases), and had an insufficient sample size (2 cases; Figure 1).

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow chart.
    View this figure

    Characteristics of Studies Included in the Meta-analysis

    The characteristics of the 9 studies selected through the PRISMA procedure are shown in Table 1. The selected studies were conducted between January 2010 and March 2022, and they evaluated the effectiveness of AI SAR on cognitive function improvement in older adults aged ≥65 years, using Mini Mental State Examination (MMSE) evaluation and comparison with a control group. A total of 575 individuals aged ≥65 years participated in the selected studies, including 273 in the experimental group and 302 in the control group. Among the selected studies, there were 6 randomized controlled trials and 3 quasi-experimental design studies.

    All selected studies used MMSE to measure cognitive function, and other indices were used, including the Global Deterioration Scale (GDS), Neuropsychiatric Inventory (NPI), Apathy Scale for Institutionalized Patients with Dementia Nursing Home version (APADEM-NH), Quality of Life in Late-stage Dementia (QUALID) scale, Apparent Emotion Rating (AER) Instrument, Korean version of the Cohen-Mansfield Agitation Inventory (K-CMAI), Subjective Memory Complaint Questionnaire (SMCQ), Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K), Geriatric Depression Scale Short Form: Korean Version (GDSSF-K), Japanese version of the Montreal Cognitive Assessment (MOCA-J), Tokyo Metropolitan Institute of Gerontology-Index of Competence (TMIG-IC), Functional Independence Measure (FIM), Duke Older Americans Resources and Services (OARS) Procedures, Mobility subsection of Dysfunction section of Sickness Impact Profile (SIP), and Craig Handicap Assessment and Reporting Technique (CHART).

    Table 1. Characteristics of the studies included in the meta-analysis.
    Author, yearStudy designSample size (intervention group; control group)InterventionOutcome indicator
    Tanaka et al, 2012 [26]Randomized controlled trial18; 16Community robot resembling a 3-year-old boyMMSEa and BMI
    Yoshii et al, 2021 [27]Quasi-experimental design47; 47Humanoid robotMMSE
    Valentí Soler et al, 2015 [28]Randomized controlled trial33; 38PARO robotMMSE, GDSb, NPIc, APADEM-NHd, and QUALIDe
    Valentí Soler et al, 2015 [28]Randomized controlled trial30; 38NAO robotMMSE, GDS, NPI, APADEM-NH, and QUALID
    Koh and Kang, 2018 [29]Quasi-experimental design17; 16PARO robotMMSE, AERf, and K-CMAIg
    Park et al, 2021 [30]Randomized controlled trial45; 45Humanoid robot (Sil-bot)MMSE, SMCQh, CERAD-Ki, and GDSSF-Kj
    Otake-Matsuura et al, 2021 [31]Randomized controlled trial32; 33Photo-integrated conversation moderated by robotsMMSE-Jk, MOCA-Jl, GDS-15-Jm, and TMIG-ICn
    Oh et al, 2015 [32]Quasi-experimental design17; 25Silver-care robotMMSE and GDS
    Tomita et al, 2007 [33]Randomized controlled trial34; 44X10 ActiveHome kitMMSE, FIMo, OARSp, SIPq, and CHARTr

    aMMSE: Mini Mental State Examination.

    bGDS: Global Deterioration Scale.

    cNPI: Neuropsychiatric Inventory.

    dAPADEM-NH: Apathy Scale for Institutionalized Patients with Dementia Nursing Home version.

    eQUALID: Quality of Life in Late-stage Dementia.

    fAER: Apparent Emotion Rating.

    gK-CMAI: Korean version of the Cohen-Mansfield Agitation Inventory.

    hSMCQ: Subjective Memory Complaint Questionnaire.

    iCERAD-K: Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease.

    jGDSSF-K: Geriatric Depression Scale Short Form: Korean Version.

    kMMSE-J: Japanese version of the Mini Mental State Examination.

    lMOCA-J: Japanese version of the Montreal Cognitive Assessment.

    mGDS-15-J: Japanese version of the 15-item Geriatric Depression Scale.

    nTMIG-IC: Tokyo Metropolitan Institute of Gerontology-Index of Competence.

    oFIM: Functional Independence Measure.

    pOARS: Duke Older Americans Resources and Services Procedures.

    qSIP: Mobility subsection of Dysfunction section of Sickness Impact Profile.

    rCHART: Craig Handicap Assessment and Reporting Technique.

    Assessment of Publication Bias

    To secure the reliability and validity of the 9 studies that were selected, publication bias was examined using the contour-enhanced funnel plot method. As a result, it was confirmed that the selected literature in this study represents a well-behaved data set, showing general symmetry (Figure 2).

    ‎
    Figure 2. Adjusted funnel plot to examine publication bias.
    View this figure

    Effect Size of AI SAR

    For the 9 studies included in the systemic literature review, the standardized mean differences were calculated by the Hedges g formula using the mean, SD, and sample size of the pre-post change of the MMSE indices of the experimental and control groups. This was visualized as a Forest plot (Figure 3). As a result of the meta-analysis, 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 MMSE scale, which reflects cognitive function. The overall size heterogeneity was confirmed according to the ratio of the interstudy variance to the total variance (I2=86%; P<.001). Furthermore, as a result of confirming the heterogeneity between studies using the Galbraith plot, it was confirmed that all studies had no heterogeneity within the 95% CI as the SEs were within 2 (Figure 4).

    ‎
    Figure 3. Forest plot results. SMD: standardized mean difference.
    View this figure
    ‎
    Figure 4. Galbraith plot to identify heterogeneity.
    View this figure

    Principal Findings

    Due to the aging global population and technological developments, AI SAR for the care of older adults are continuously being developed. The purpose of this study, therefore, was to objectively identify the effect of AI SAR on the cognitive function of older adults through a systematic literature review and prepare and provide recommendations regarding AI SAR policy. The effectiveness of care services using robots in aging populations is socially recognized through continuous research and publications, but many experts agree that more objective evidence is needed. To this end, this study conducted a systematic review and meta-analysis on 9 studies that analyzed the effect of AI SAR on cognitive function improvement in older adults aged ≥65 years since 2010. As a result, it was found that AI SAR showed a significant effect in improving the cognitive function of older adults aged ≥65 years (Hedges g=0.43, 95% CI: –0.04 to 0.90). This is consistent with the results of a systematic review article, which states that robots are effective in improving cognitive function in older adults [34]. The difference between previous studies and this study is that the effects of various indicators were confirmed (GDS, NPI, APADEM-NH, QUALID, K-CMAI, SMCQ, CERAD-K, GDSSF-K, TMIG-IC, MOCA-J, FIM, OARS, SIP, and CHART).

    In all 9 studies, a control group was designed to analyze the effects. With these results, we will mainly discuss (1) interactive robots, (2) the prospect of resolving the shortage of caregivers, (3) the possibility of expanding the digital social prescription program, and (4) what needs to be overcome for the application of AI SAR.

    First, it is essential for AI SAR to be an interactive robot. The 9 studies that have been analyzed have in common that AI SAR could interact with older adults through dialogue. According to previous studies, the cognitive function of older adults aged ≥65 years was shown to be more effective in two-way communication than one-way communication [35]. In this case, the form of the robot greatly affects the formation of rapport. The 9 studies that have been analyzed made use of SAR in the form of animals, robots, and humans. Among them, AI SAR in anthropomorphic form were able to improve cognitive function more effectively. According to a literature review on AI SAR marketing, it is necessary to develop a robot that resembles a human being as much as possible, and it emphasizes the need to develop customized robots for customers by customer segmentation [36]. In this study, it was also found that human-shaped dolls and humanoid forms increased cognitive function more effectively than nonhuman, doll-shaped robots.

    Second, as AI SAR have recently been developed to the extent that they can communicate with each other, they have been loaded with various functions including health notification, play therapy, counseling service, conversation, and dementia prevention programs. The development and expansion of AI SAR programs are expected to improve the quality of care for older adults and prevent an overload of caregivers. By conducting a meta-analysis of 9 studies, this study was able to objectively confirm that AI SAR are effective in improving cognitive function. This is evidence that AI SAR can relieve some of the work of caregivers looking after older adult patients with cognitive impairment, including patients with dementia. Older adults living alone with cognitive function impairment particularly require continuous monitoring due to the risk of various incidents when they are alone at home, which demands that caregivers be on-call 24 hours a day. However, since technological advancements have allowed AI SAR to continuously monitor older adults for 24 hours a day and contact facilities in the case of an emergency, it is expected to partially replace the work of caregivers in the future.

    Third, AI SAR can be expanded to digital social prescription programs as a nonpharmacological intervention that improves the cognitive function of older adults aged ≥65 years. Social prescription began based on an idea conceived in the 1990s, in which patients were encouraged to exercise as part of their treatment. In the United Kingdom, the National Health Service defines social prescribing as a general practitioner prescribing a nonpharmacological intervention community program to a patient using community resources [37]. Recently, due to the shortage of mental health counselors and caregivers, digital social prescriptions, which convert existing social prescription programs to programs using digital technology, are expanding [38]. Social prescription has conflicted with existing prescription methods for the past 10 years, and there has been a lot of controversy. The main argument is that it is difficult to prove the effectiveness of social prescription, which is a nonpharmacological treatment, unlike existing pharmacological treatments. However, AI SAR are a representative digital social prescription program and a nonpharmacological intervention program in the form of a care service that communicates with the older adults aged ≥65 years 24 hours a day. The data collected through this 24-hour monitoring will be an important stepping-stone in proving that AI SAR are effective as a digital social prescription program. However, more objective research and development is necessary to support this.

    Fourth, despite the effectiveness of AI SAR, there are currently problems to be overcome, including (1) ethical issues, (2) the digital literacy needs of older adults, (3) social awareness and reliability, and (4) technological advancements, etc.

    The ethical and social issues of AI SAR should be addressed first. The development of AI SAR has replaced some of the existing caregivers, and AI SAR have been developed to a level that can provide care for older adults. However, as they enter the daily life of older adults, personal information is highly likely to be exposed. This is because AI SAR generate and transmit various real-time data using a camera, microphone, and voice tool.

    AI SAR are a digital device, and basic digital literacy is required, particularly for charging and the user manual of the device. However, older adults have low digital literacy and limited access to devices, especially in low-income countries, rural areas, and in higher age groups [39]. The low digital literacy of older adults will cause problems in the use of AI SAR. In other words, the digital literacy of older adults is a basic requirement for the application of AI SAR. Therefore, to improve the digital literacy of older adults at a social level, it is necessary to provide a pre-education service to expand the AI SAR service.

    Socially, there is a negative view on robots managing various tasks in daily life. Robots took on many tasks as they became gradually more developed and interactive. In the case of AI SAR, they live together with older adults and carry out 24-hour monitoring. Due to this, if a systemic defect causes AI SAR to make a mistake when concerning the older adults, who are a vulnerable group, it is possible that a negative view on the introduction of AI SAR in society might spread. To prevent this, systematic and continuous algorithm development and cognitive training of AI SAR is suggested, including the need to develop an internal algorithm that makes AI SAR apologize for their mistakes [40].

    AI SAR still require further technological advancement and have challenges that need to be addressed. Currently, they perform limited word selection and dialogue based on algorithms, and functions such as dementia prevention programs are provided with limited technology. It is clear that the role of AI SAR should gradually expand at a time when the global population is aging, the number of caregivers is decreasing, and technological advancement is becoming essential for solving these issues. To improve the cognitive function of older adults, more development is needed to provide physical care, and technological advancement is necessary to indirectly help them engage in social activities through various communications.

    This study possesses some limitations. First, the number of sampled studies that investigated the improvement of cognitive function through SAR was insufficient. It is necessary to conduct future research by including single pre-post studies as well as studies conducted by selecting an older adult control group. Second, only studies using MMSE to measure cognitive function improvement were selected, but various indices such as GDS and NPI also exist. A meta-analysis including all the different indices is recommended for obtaining more objective results in the future. Third, the types of AI SAR used in the 9 selected studies were all different. This is a limitation as it is difficult to measure the nonsampling error that occurs due to the different types of AI SAR. Lastly, we searched using the PubMed and Google Scholar databases. Therefore, we may be missing articles from another database such as IEEE, Embase, and Cochrane Library. In future, we will consider searching using the IEEE, Embase, and Cochrane Library databases.

    Conclusion

    In this study, a meta-analysis was performed on 9 studies to examine the effect of AI SAR on improving cognitive function in older adults. As a result, AI SAR were found to be effective in improving cognitive function, suggesting that it is possible to (1) socially expand interactive robots, (2) solve the shortage of caregivers, and (3) expand AI SAR use into a digital social prescription program. Furthermore, the challenges of ethical issues, the digital literacy needs of older adults, social cognition and reliability, and technological development must be solved for the commercialization and expansion of AI SAR. Nonetheless, in times of pandemics such as COVID-19, the need for AI-assisted care is likely to further increase due to its safety.

    Acknowledgments

    This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1C1C2005464).

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Search keywords.

    DOCX File , 12 KB
    1. UN. Population Division. World Population Ageing 1950-2050. New York, NY: United Nations Digital Library; 2002.
    2. Prince M, Comas-Herrera A, Knapp M, Guerchet M, Karagiannidou M. World Alzheimer Report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future. Alzheimer's Disease International. 2016.   URL: https://www.alzint.org/u/WorldAlzheimerReport2016.pdf [accessed 2022-06-17]
    3. Peters ME, Schwartz S, Han DD, Rabins PV, Steinberg M, Tschanz JT, et al. Neuropsychiatric symptoms as predictors of progression to severe Alzheimer's dementia and death: the Cache County Dementia Progression Study. Am J Psychiatry 2015 May;172(5):460-465 [FREE Full text] [CrossRef] [Medline]
    4. Moyle W, Jones C, Murfield J, Thalib L, Beattie E, Shum D, et al. Effect of a robotic seal on the motor activity and sleep patterns of older people with dementia, as measured by wearable technology: a cluster-randomised controlled trial. Maturitas 2018 Apr;110:10-17. [CrossRef] [Medline]
    5. Kovich H, DeJong A. Common questions about the pharmacologic management of depression in adults. Am Fam Physician 2015 Jul 15;92(2):94-100 [FREE Full text] [Medline]
    6. Husk K, Blockley K, Lovell R, Bethel A, Bloomfield D, Warber S, et al. What approaches to social prescribing work, for whom, and in what circumstances? a protocol for a realist review. Syst Rev 2016 Jun 03;5(1):93 [FREE Full text] [CrossRef] [Medline]
    7. de Vries K. Communicating with older people with dementia. Nurs Older People 2013 May;25(4):30-37. [CrossRef] [Medline]
    8. Tanioka T. Nursing and rehabilitative care of the elderly using humanoid robots. J Med Invest 2019;66(1.2):19-23 [FREE Full text] [CrossRef] [Medline]
    9. Kachouie R, Sedighadeli S, Khosla R, Chu MT. Socially assistive robots in elderly care: a mixed-method systematic literature review. Int J Hum Comput Interact 2014 Apr;30(5):369-393. [CrossRef]
    10. Mordoch E, Osterreicher A, Guse L, Roger K, Thompson G. Use of social commitment robots in the care of elderly people with dementia: a literature review. Maturitas 2013 Jan;74(1):14-20. [CrossRef] [Medline]
    11. Bemelmans R, Gelderblom GJ, Jonker P, de Witte L. Socially assistive robots in elderly care: a systematic review into effects and effectiveness. J Am Med Dir Assoc 2012 Feb;13(2):114-120.e1. [CrossRef] [Medline]
    12. Huber A, Lammer L, Weiss A, Vincze M. Designing adaptive roles for socially assistive robots: a new method to reduce technological determinism and role stereotypes. J Hum Robot Interact 2014 Jul;3(2):100-115. [CrossRef]
    13. Robinson H, Broadbent E, MacDonald B. Group sessions with Paro in a nursing home: structure, observations and interviews. Australas J Ageing 2016 Jun 09;35(2):106-112. [CrossRef] [Medline]
    14. Jøranson N, Pedersen I, Rokstad AMM, Ihlebæk C. Effects on symptoms of agitation and depression in persons with dementia participating in robot-assisted activity: a cluster-randomized controlled trial. J Am Med Dir Assoc 2015 Oct 01;16(10):867-873. [CrossRef] [Medline]
    15. Moyle W, Bramble M, Jones CJ, Murfield JE. "She had a smile on her face as wide as the Great Australian Bite": a qualitative examination of family perceptions of a therapeutic robot and a plush toy. Gerontologist 2019 Jan 09;59(1):177-185. [CrossRef] [Medline]
    16. Moyle W, Cooke M, Beattie E, Jones C, Klein B, Cook G, et al. Exploring the effect of companion robots on emotional expression in older adults with dementia: a pilot randomized controlled trial. J Gerontol Nurs 2013 May;39(5):46-53. [CrossRef] [Medline]
    17. Petersen S, Houston S, Qin H, Tague C, Studley J. The utilization of robotic pets in dementia care. J Alzheimers Dis 2017;55(2):569-574 [FREE Full text] [CrossRef] [Medline]
    18. Jøranson N, Pedersen I, Rokstad AMM, Ihlebaek C. Change in quality of life in older people with dementia participating in Paro-activity: a cluster-randomized controlled trial. J Adv Nurs 2016 Dec 16;72(12):3020-3033. [CrossRef] [Medline]
    19. Pu L, Moyle W, Jones C, Todorovic M. The effectiveness of social robots for older adults: a systematic review and meta-analysis of randomized controlled studies. Gerontologist 2019 Jan 09;59(1):e37-e51. [CrossRef] [Medline]
    20. Abbott R, Orr N, McGill P, Whear R, Bethel A, Garside R, et al. How do "robopets" impact the health and well-being of residents in care homes? a systematic review of qualitative and quantitative evidence. Int J Older People Nurs 2019 Sep 09;14(3):e12239 [FREE Full text] [CrossRef] [Medline]
    21. Leng M, Liu P, Zhang P, Hu M, Zhou H, Li G, et al. Pet robot intervention for people with dementia: a systematic review and meta-analysis of randomized controlled trials. Psychiatry Res 2019 Jan;271:516-525. [CrossRef] [Medline]
    22. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al, editors. Cochrane Handbook for Systematic Reviews of Interventions. 2nd ed. Chichester, United Kingdom: John Wiley & Sons; 2019.
    23. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol 2009 Oct;62(10):e1-34 [FREE Full text] [CrossRef] [Medline]
    24. DerSimonian R, Kacker R. Random-effects model for meta-analysis of clinical trials: an update. Contemp Clin Trials 2007 Feb;28(2):105-114. [CrossRef] [Medline]
    25. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. 2nd ed. Hoboken, New Jersey: John Wiley & Sons; Apr 2021.
    26. Tanaka M, Ishii A, Yamano E, Ogikubo H, Okazaki M, Kamimura K, et al. Effect of a human-type communication robot on cognitive function in elderly women living alone. Med Sci Monit 2012 Sep;18(9):CR550-CR557 [FREE Full text] [CrossRef] [Medline]
    27. Yoshii K, Nishimura M, Kimura D, Kosugi A, Shinkawa K, Takase T, et al. A study for detecting mild cognitive impairment by analyzing conversations with humanoid robots. 2021 Presented at: 2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech); March 9-11, 2021; Nara, Japan p. 347-350. [CrossRef]
    28. Valentí Soler M, Agüera-Ortiz L, Olazarán Rodríguez J, Mendoza Rebolledo C, Pérez Muñoz A, Rodríguez Pérez I, et al. Social robots in advanced dementia. Front Aging Neurosci 2015 Sep 03;7:133 [FREE Full text] [CrossRef] [Medline]
    29. Koh IS, Kang HS. Effects of intervention using PARO on the cognition, emotion, problem behavior, and social interaction of elderly people with dementia. J Korean Acad Community Health Nurs 2018 Sep 28;29(3):300. [CrossRef]
    30. Park EA, Jung AR, Lee KA. The humanoid robot Sil-Bot in a cognitive training program for community-dwelling elderly people with mild cognitive impairment during the COVID-19 pandemic: a randomized controlled trial. Int J Environ Res Public Health 2021 Aug 03;18(15):8198 [FREE Full text] [CrossRef] [Medline]
    31. Otake-Matsuura M, Tokunaga S, Watanabe K, Abe MS, Sekiguchi T, Sugimoto H, et al. Cognitive intervention through Photo-Integrated Conversation Moderated by Robots (PICMOR) program: a randomized controlled trial. Front Robot AI 2021 Apr 12;8:633076 [FREE Full text] [CrossRef] [Medline]
    32. Oh J, Yi Y, Shin C, Park C, Kang S, Kim J, et al. [Effects of Silver-Care-Robot Program on cognitive function, depression, and activities of daily living for institutionalized elderly people]. J Korean Acad Nurs 2015 Jun;45(3):388-396. [CrossRef] [Medline]
    33. Tomita MR, Mann WC, Stanton K, Tomita AD, Sundar V. Use of currently available smart home technology by frail elders. Top Geriatr Rehabil 2007;23(1):24-34. [CrossRef]
    34. Góngora Alonso S, Hamrioui S, de la Torre Díez I, Motta Cruz E, López-Coronado M, Franco M. Social robots for people with aging and dementia: a systematic review of literature. Telemed J E Health 2019 Jul;25(7):533-540. [CrossRef] [Medline]
    35. Kemper S, Marquis J, Thompson M. Longitudinal change in language production: effects of aging and dementia on grammatical complexity and propositional content. Psychol Aging 2001 Dec;16(4):600-614. [CrossRef] [Medline]
    36. Blut M, Wang C, Wünderlich NV, Brock C. Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI. J Acad Mark Sci 2021 Jan 06;49(4):632-658. [CrossRef]
    37. Drinkwater C, Wildman J, Moffatt S. Social prescribing. BMJ 2019 Mar 28;364:l1285. [CrossRef] [Medline]
    38. Patel S, Craigen G, Pinto da Costa M, Inkster B. Opportunities and challenges for digital social prescribing in mental health: questionnaire study. J Med Internet Res 2021 Mar 09;23(3):e17438 [FREE Full text] [CrossRef] [Medline]
    39. Rossiter-Base S. Adults' media use and attitudes report 2019. Ofcom. 2019 May 30.   URL: https:/​/www.​ofcom.org.uk/​__data/​assets/​pdf_file/​0031/​149872/​Adults-media-use-and-attitudes-report-2019-chart-pack.​pdf [accessed 2022-06-17]
    40. Robinette P, Howard AM, Wagner AR. Timing is key for robot trust repair. Cham, Switzerland: Springer; 2015 Oct 28 Presented at: ICSR 2015: International Conference on Social Robotics; October 26-30, 2015; Paris, France p. 574-583. [CrossRef]

    ‎
    AER: Apparent Emotion Rating
    AI: artificial intelligence
    APADEM: Apathy Scale for Institutionalized Patients with Dementia Nursing Home version
    CERAD-K: Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease
    CHART: Craig Handicap Assessment and Reporting Technique
    FIM: Functional Independence Measure
    GDS: Global Deterioration Scale
    GDSSF-K: Geriatric Depression Scale Short Form: Korean Version
    K-CMAI: Korean version of the Cohen-Mansfield Agitation Inventory
    MMSE: Mini Mental State Examination
    MOCA-J: Japanese version of the Montreal Cognitive Assessment
    NPI: Neuropsychiatric Inventory
    OARS: Older Americans Resources and Services
    PICOTS-SD: Population, Intervention, Comparison, Outcome, Time, Setting, and Study Design
    PRISMA: Preferred Reporting Items for Systemic Reviews and Meta-Analyses
    QUALID: Quality of Life in Late-stage Dementia
    ROB: risk of bias
    SAR: socially assistive robots
    SIP: Sickness Impact Profile
    SMCQ: Subjective Memory Complaint Questionnaire
    TMIG-IC: Tokyo Metropolitan Institute of Gerontology-Index of Competence

    Edited by J Wang; submitted 20.04.22; peer-reviewed by S Schüssler; comments to author 24.05.22; revised version received 02.06.22; accepted 04.06.22; published 28.06.22

    Copyright

    ©Hocheol Lee, Min Ah Chung, Hyeji Kim, Eun Woo Nam. Originally published in JMIR Aging (https://aging.jmir.org), 28.06.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included.