Robot-Based Cognitive Intervention Effects on Brain Function and Cognition in Patients With Mild Alzheimer’s Disease Dementia
Article information
Abstract
Objective
Cognitive interventions (CIs) are recognized for enhancing cognition and mitigating cognitive decline in dementia patients. Our study assessed the effects of a 12-week, home-based, robot-assisted CI on cognition and brain function in patients with mild Alzheimer’s disease (AD) dementia.
Methods
In this single-blind randomized controlled trial, 51 patients with mild AD dementia were assigned to either a robot-assisted CI group (n=27) or a waitlist control group (n=24). The CI was conducted for 60 minutes per day over 12 weeks. The primary outcome was brain function, measured by resting-state electroencephalogram (EEG) using a 19-channel wireless EEG device and the secondary outcome was cognitive function, measured using the Cambridge Neuropsychological Test Automated Battery. No significant baseline demographic or clinical differences were observed between the groups. Eighteen participants in the robot group and 19 in the control group completed the study.
Results
EEG analysis revealed a decrease in theta band activity in the mid-frontal area for the robot group, while the control group exhibited an increase in this area. In addition, the robot group showed significant cognitive improvements in working memory, visual association memory, and reaction time after the 12-week CIs.
Conclusion
These findings suggest that robot-assisted CIs may be associated with enhanced cognitive and brain function in patients with mild AD dementia.
INTRODUCTION
Approximately 50 million people worldwide have been diagnosed with dementia. Forecasts predict that the number of patients diagnosed with dementia will increase rapidly in the coming years, with estimates of 152 million patients diagnosed with this disease by 2050, a number that triples the current dementia population [1]. Despite efforts to develop pharmacological treatments for dementia, only a few such treatments have been implemented to delay cognitive deterioration in such patients.
In parallel, increasing attention has been directed toward non-pharmacological interventions, such as cognitive training and rehabilitation, as complementary strategies in dementia care [2]. By engaging in cognitive exercises and activities, individuals with dementia can help maintain their memory, attention, and problem-solving skills, thereby enhancing their overall quality of life [3,4]. Importantly, non-pharmacological interventions are generally safe, cost-effective, and sustainable over the long term, highlighting their value as adjunctive approaches in comprehensive dementia management [5-7].
Traditionally, cognitive interventions (CIs) have been performed as group-based or face-to-face programs requiring qualified therapists, sufficient space, and convenient locations for accessibility [8,9]. However, experienced instructors are not always available to perform CIs in small local hospitals and community centers. Additionally, considering that there may be a lack of qualified personnel in the near future because of the dramatic increase in the aging population, the provision of CIs for patients with dementia may be challenging.
In this regard, socially assistive robots (SARs) have been considered for use in home-based care, including cognitive training in patients with dementia, which can be designed to meet the social and psychological needs of older adults through human–robot interactions [10]. Based on this prior research, in the present study, we developed home care robots that can provide CIs for patients with cognitive impairments. Therefore, in this study, we aimed to demonstrate the effects of our newly developed CI using home care robots on brain function and cognitive performance in patients with mild Alzheimer’s disease (AD) dementia.
METHODS
Study design
This was a 12-week, prospective, rater-blinded, controlled trial conducted between November 2019 and December 2020 at the Memory Disorder Clinic of Ewha Womans University Mokdong Hospital. Participants were randomly allocated in a 1:1 ratio to two groups: CI using a personal robot (robot) and no CI (control).
Sample size calculation
This calculation was informed by a prior study involving patients with mild cognitive impairment11 where an electroencephalography (EEG) was used as the primary outcome measure and showed a change in the delta band of μ1–μ0=0.115 over 6 weeks. Given our focus on patients with dementia, we expected similar changes over the 12-week robotic cognitive training period. Using a standard deviation of 0.13 for EEG changes, and aiming for a significance level of 0.05 and a power of 80%, we determined a requirement of 22 participants per group. Adjusting for an expected dropout rate of approximately 15%, we planned to enrol 26 participants in each group, summing up to 52 participants overall.
Participants
Fifty-five volunteers aged 60 years or older diagnosed with AD dementia were recruited from the Gwangyang Dementia Center and the Yangcheon Dementia Center. The participants were diagnosed with probable AD dementia based on the National Institute of Neurological and Communicative Disorders and Stroke of the United States and the Alzheimer’s Disease and Related Disorders Association [12].
The inclusion criteria were as follows: 1) age 60–90 years; 2) global clinical dementia rating score of 0.5 or 1; 3) Korean version of the Mini-Mental State Examination-2 (K-MMSE-2) [13] score ≥18; 4) ability to read and write; 5) no visual or hearing impairment severe enough to interfere with cognitive testing/cognitive training; and 6) presence of a reliable informant who live with them or meet them for more than 6 hours a day, at least 3 times a week. The cut-off score of 18 on the K-MMSE-2 was selected because it is widely used in clinical and research settings to define mild AD dementia, reflecting preserved basic functioning with measurable cognitive impairment [14]. Participants were excluded if they had major psychiatric illnesses, a history of cancer in the past 5 years, serious or unstable symptomatic cardiovascular diseases, or other serious medical conditions. In addition, participants who were uncooperative or unable to participate in intervention programs were excluded. Of the 55 participants initially recruited, three were excluded during the screening process because they met one or more of the exclusion criteria.
All patients underwent neuropsychological testing using a standardised neuropsychological battery, the Seoul Neuropsychological Screening Battery 2nd edition, which encompasses five cognitive domains: attention, memory, visuospatial, language, and frontal executive functions [15]. Additionally, laboratory assessments included a complete blood count, blood chemistry, thyroid function tests, syphilis serology, and vitamin B12/folate levels. Brain MRI was performed to confirm the absence of structural lesions including cerebral infarctions, brain tumours, vascular malformations, and hippocampal sclerosis. All participants were being treated with acetylcholinesterase inhibitors and there was no change in the medication dosage during the intervention.
Of the 52 participants, one was excluded because of the withdrawal of consent before randomization. Therefore, the final sample comprised 51 participants (Figure 1), who were randomly allocated into the following two groups: the robot intervention group (robot) (n=27) and the control group (control) (n=24). The research involving human participants was approved by the Institutional Review Board of the Ewha Womans University Mokdong Hospital (EUMC 2019-08-019-001) and conducted in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. Written informed consent was obtained from all participants and their legal guardians.
Study design and flow of the study. Fifty-one patients diagnosed with Alzheimer’s disease dementia aged 60 years or older were enrolled in this single-blind randomized controlled trial. Enrolled participants were subsequently randomized into the control and robot intervention groups. Five of the 24 participants in the control group and 9 of the 27 participants in the robot group dropped out, and the remaining 19 and 18 participants in these two groups, respectively, were included in the final analyses.
The study was also registered at the Clinical Research Information Service Site (KCT0004473) on 26/11/2019.
Randomization
Following the baseline assessments, 51 participants were randomly allocated to one of two groups using simple randomization: the robot intervention group (n=27) and the control group (n=24). Randomization was performed by a single independent researcher.
Cognitive intervention
All participants and their caregivers received 1 hour of education on dementia after undergoing the baseline assessment. This education included the definition, diagnosis, and current treatments of dementia and lifestyle management for dementia, encompassing the importance of cognitive training, healthy foods for the brain, aerobic exercise, and stretching that can be performed at home [7,16].
A robot named Bomy (#Robocare), capable of administering 20 cognitive training programs, was set up at each patient’s home within a week of the baseline evaluation (Figure 2). Participants in the robot group were instructed to engage in cognitive training programs with the robot targeting specific cognitive domains, such as memory, language, visuospatial function, calculation, and frontal executive function (Supplementary Figure 1). They were further required to engage in these programs for a minimum of 60 minutes per day, 5 days a week, over a period of 12 weeks. Each daily training session included at least two different programs: one focused on memory training and the other on various cognitive domains, including language, visuospatial function, calculation, and frontal executive function. The robot was programmed to provide two cognitive training programs at designated times from Monday to Friday. Therefore, over a 12-week period, patients in the robot group underwent a minimum of 120 training sessions, consisting of 60 sessions for memory and 15 sessions each for language, calculation, visuospatial function, and frontal executive function. If the participants did not adhere to the scheduled programs, the robot delivered encouraging messages to promote participation in the cognitive training. If the participant failed to engage in the program after three encouraging messages, the robot would notify the designated caregiver to directly encourage the patient to participate in the cognitive training program. Although the minimum requirement was 5 days per week (Monday to Friday), participants were free to engage in additional training sessions at any time, including weekends, and there was no upper limit on the number of sessions. As a result, some participants performed far more than the minimum requirement.
The appearance and specifications of the robot (Bomy2; Robocare; https://www.robocare.co.kr/eng/sub_robot/bomy2.php). Reproduced with permission from Robocare.
Outcome measures and blinding
The primary outcome was change in brain function measured by resting-state EEG, which was measured in eyes-open and eyes-closed conditions for 3 minutes each, with a 19-channel wireless EEG device. EEG parameters were chosen as the primary outcomes because they offer a direct, objective measurement of brain functional changes and can be more sensitive than cognitive test scores in detecting subtle neurophysiological alterations related to dementia progression and intervention effects [17,18]. While cognitive assessments remain essential for clinical evaluation, resting-state EEG provides complementary mechanistic insights and reliable biomarker readouts, thereby strengthening the evaluation of intervention efficacy [19,20].
Resting-state EEGs were recorded over a 3-minute period with eyes open, and another 3-minute period with eyes closed using 19 electrodes based on the international 10–20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, P7, P3, Pz, P4, P8, O1, and O2) at baseline and at the end of the study. We selected 3 minutes of eyes-closed and artefact-free data based on visual inspection for further analysis. The EEG signals were digitised after filtering with a bandpass of 0.5–70 Hz, and artefacts were removed in two steps. The first step involved the rejection of non-stationary bad epochs, and the second step involved the removal of stationary bad components related to electromyograms, electrooculograms, cardiac signals such as heartbeat, and slow drift waves such as drowsiness to yield cleaned quantitative EEG data using adaptive mixture independent component analysis [21]. At the sensor level, the absolute power of EEG and the square of the amplitudes was calculated using fast Fourier transform spectral analysis in each of the following eight frequency bands: delta (1–4 Hz); theta (4–8 Hz); alpha1 (8–10 Hz); alpha2 (10–12 Hz); beta1 (12–15 Hz); beta2 (15–20 Hz); beta3 (20–30 Hz); and gamma (30–45 Hz). To calculate relative power, the absolute power of each frequency band was divided by the total power. In source-level analysis, standardised low-resolution brain electromagnetic tomography (sLORETA) was used with 68 regions of interest (ROIs) based on the Desikan-Killiany atlas. The imaginary part of the coherence was calculated as the functional connectivity between the 68 ROIs at eight frequencies [22]. All EEG preprocessing, sensor-level data, source-level data calculations, and extractions were performed using a cloud-based AI-driven auto-analysing platform, iSyncBrain® (iMediSync Inc.; http://isyncbrain.com).
The secondary outcomes included the mean scores on three tests based on the Cambridge Neuropsychological Test Automated Battery (CANTAB), which included the spatial working memory (SWM), the paired associates learning (PAL), and the reaction time (RTI) tests (detailed descriptions of the tests are available on the associated website: http://www.cambridgecognition.com/academic/cantabsuite/tests). In brief, the SWM evaluates the visual working memory function to retain spatial information and manipulate remembered items. The error in the SWM task was defined as the time at which the participant revisited a box in which a token had previously been found. The lower the error of the SWM task, the better the performance. The PAL task measured simple visual memory and visuospatial associative learning. The total number of errors from the PAL task was used to measure performance, with lower scores indicating better performance. The RTI task assessed reaction and processing speeds. The outcome measure for the RTI task was the median movement time of the five circles, which referred to the interval between the release of the starting position button and contact with the yellow circle. The raw scores for each of the three tasks were converted to standardized Z-scores using the baseline mean scores and standard deviations of the control group. For better visualisation, the Z-scores were reversed such that positive Z-scores represented better performance.
All primary and secondary outcomes were evaluated by independent researchers blinded to the participants’ intervention status.
Statistical analyses
Demographic and clinical characteristics were compared using t-tests for continuous variables and chi-square tests for categorical variables. Analysis of covariance was applied to compare baseline neuropsychological tests using age, sex, and years of education as covariates. A generalised linear model was used to estimate group differences in the changes in CANTAB scores before and after the intervention, using age, sex, and years of education as covariates. Significance for all tests was set at α=0.05, two-tailed. All statistical analyses were performed using Stata version 18.0 (Stata Corp).
RESULTS
No statistical differences in baseline demographic characteristics between the control and robot groups, including age, sex, and years of education (Table 1). Baseline cognitive function, including the K-MMSE-2, neuropsychological test scores and CANTAB measures such SWM, PAL, and RTI showed no difference between the two groups (Table 1). Among the 51 participants, 5 in the control group and 9 in the robot group withdrew from the study, with no significant difference in the dropout rate between the two groups (20.8% in the control group vs. 33.3% in the robotic group, p=0.318). Therefore, a total of 19 participants in the control group and 18 in the robot group completed the study. The robot group completed 177.6 (147.9%) of the total of 120 sessions, spending 38.4 minutes a day on average for the cognitive training programs. The shortest session lasted 5.9 minutes, and the longest session lasted 144.5 minutes. The minimum number of sessions completed by a participant was 5, while the maximum reached 724 sessions.
Baseline characteristics of the participants
EEG changes after intervention (primary outcome) The topographical map illustrating the absolute band power revealed significant disparities in the changes in beta 3, specifically in the central area. The mean changes for the control and robot groups were 0.32±2.7 μV2/Hz and 2.3±2.9 μV2/Hz, respectively (p for group=0.035). However, no other significant differences were observed in the changes in absolute band power between the two groups (Figure 3).
Changes in the absolute power spectrum of resting-state electroencephalogram (EEG) (μV2/Hz). The topographical map illustrates the absolute band power, which revealed significant disparities in the changes in Beta 3, specifically in the central area. The mean changes for the control group were 0.32±2.7 μV2/Hz, whereas those for the robot group were 2.3±2.9 μV2/Hz (p for group=0.035). However, no other significant differences were observed in the absolute band power between the two groups. A: Delta band. B: Theta band. C: Alpha1 band. D: Alpha2 band. G1, control group; G2, robot group. Changes in the absolute power spectrum of resting-state electroencephalogram (EEG) (μV2/Hz). The topographical map illustrates the absolute band power, which revealed significant disparities in the changes in Beta 3, specifically in the central area. The mean changes for the control group were 0.32±2.7 μV2/Hz, whereas those for the robot group were 2.3±2.9 μV2/Hz (p for group=0.035). However, no other significant differences were observed in the absolute band power between the two groups. E: Beta1 band. F: Beta2 band. G: Beta3 band. H: Gamma band. G1, control group; G2, robot group.
Analysis of the relative power spectrum revealed that the control group exhibited an increase in the relative power of the theta band (4.8%±5.2%) in the mid-frontal area (Fz), whereas the robot group demonstrated a decrease (-0.1%±4.8%) in this area (p for group=0.006) (Figure 4). Additionally, the relative power of Beta 3 over the central region (Cz) decreased in the control group (-1.2%±7.0%), whereas it increased in the robot group (4.7%±5.4%) over the 12-week period (p for group=0.007). Furthermore, the control group displayed an increase in the relative power of the gamma band (1.9%±3.2%), while the robot group exhibited a decrease (-0.3%±2.5%) in the left frontal area (F7) (p for group=0.029).
Changes in the relative power spectrum (%) of the resting-state electroencephalogram (EEG). The control group exhibited an increase in the relative power of the theta band (4.8%±5.2%) in the mid-frontal area (Fz), whereas the robot group demonstrated a decrease of the same magnitude in the same area (p for group=0.006). Additionally, the relative power of Beta 3 over the central region (Cz) was decreased in the control group (-1.2%±7.0%), whereas it was increased in the robot group (4.7%±5.4%) over the 12-week period (p for group= 0.007). Furthermore, the control group displayed an increase in the relative power of the gamma band (1.9%±3.2%), while the robot group exhibited a decrease (-0.3%±2.5%) in the left frontal area (F7) (p for group=0.029). A: Delta band. B: Theta band. C: Alpha1 band. D: Alpha2 band. G1, control group; G2, robot group. Changes in the relative power spectrum (%) of the resting-state electroencephalogram (EEG). The control group exhibited an increase in the relative power of the theta band (4.8%±5.2%) in the mid-frontal area (Fz), whereas the robot group demonstrated a decrease of the same magnitude in the same area (p for group=0.006). Additionally, the relative power of Beta 3 over the central region (Cz) was decreased in the control group (-1.2%±7.0%), whereas it was increased in the robot group (4.7%±5.4%) over the 12-week period (p for group= 0.007). Furthermore, the control group displayed an increase in the relative power of the gamma band (1.9%±3.2%), while the robot group exhibited a decrease (-0.3%±2.5%) in the left frontal area (F7) (p for group=0.029). E: Beta1 band. F: Beta2 band. G: Beta3 band. H: Gamma band. G1, control group; G2, robot group.
Cognitive performance measured by CANTAB (secondary outcomes)
The robot group showed improved changes in SWM (p=0.039), PAL (p=0.044), and RTI (p=0.033) compared to the control group (Figure 5).
Effects of the cognitive intervention on cognitive function. The robot group showed improved changes in the scores of (A) spatial working memory (p=0.039), (B) paired associates learning (p=0.044), and (C) reaction time (p=0.033) compared to those of the control group. *p<0.05.
Correlations between the number of participating sessions and cognitive changes
Although a positive correlation was identified between the number of participating sessions and changes in the SWM Zscores, the difference did not reach statistical significance (r=0.414, p=0.068) (Supplementary Figure 2). Further, there were no statistically significant correlations between the number of participating sessions and the changes in PAL (r=-0.073, p=0.770) or changes in RTI (r=0.100, p=0.691).
DISCUSSION
The present study explored the efficacy of robotic CIs in patients with AD dementia. Our findings showed that robot-assisted CIs may lead to significant functional brain changes in the frontal areas as well as cognitive benefits.
Overall, we found that the topographical map illustrating the absolute and relative band powers revealed that the robot group showed an increase in beta 3 power in the central area compared with the control group. Notably, previous studies have indicated that AD dementia is associated with a decrease in alpha and beta power [23]. Given that an increase in beta band power is known to reflect top-down attentional modulation between brain areas, promoting feedback interactions across visual areas [24], this observed increase could also be associated with conscious thoughts, logical thinking, and active, focused cognitive processing [25,26]. As such, our study results suggest that CI with a robot facilitates functional restoration, particularly related to top-down attentional processes in patients with AD dementia. These findings are further supported by improvements in cognitive domains related to working memory and RTI observed in the robot group following the CI in our study.
We further found that the robot group demonstrated a decrease in theta bands in the midfrontal area (Fz), whereas the control group exhibited an increase in theta bands in the same area. Given that patients with AD dementia exhibit an increase in theta power [27], which is often linked to cognitive decline in neurodegenerative conditions [28,29], the decrease in theta power in the robot group may reflect a more alert and less drowsy state. These changes in the theta bands may also indicate that robot-assisted CI could induce the potential normalisation of neural activities that are typically disrupted in AD dementia.
In addition, we found that a 12-week home-based multidomain CI with a personal robot improved cognitive functions, including working memory, visual association memory, and RTI, in patients with AD dementia. These findings are consistent with previous findings, which showed that computerised or conventional multi-domain CIs induce benefits in the cognitive domains of patients with AD dementia [2]. Improvement in PAL was a particularly noteworthy finding, because it involves learning and memory recall skills. This suggests that robotic intervention could be particularly beneficial for enhancing associative memory, which is often significantly compromised in patients with AD dementia [30].
It should be noted that the mean number of sessions in the robot programs in the current study (177.6 sessions out of 120 planned) exceeded the target goal. Previous studies have demonstrated that SARs can provide companionship that enhances user engagement in activities [31], a feature which may play a significant role in CIs. Notably, our study observed a trend towards a positive association between more frequent participation in robot-assisted CIs and improvements in working memory. Considering that a high participation rate is crucial for positive cognitive outcomes in CIs, our findings suggest that robotic interfaces may offer more engaging and consistent cognitive stimulation for patients with dementia. This, in turn, can further influence the cognitive improvements related to interventions.
One of the distinctive features of our study is that robots were used as providers of CI for patients with dementia at home. CIs are usually provided by professional instructors. However, experienced instructors of CIs are not always available, a situation which is exacerbated by the dramatic increase in the aging population. As the aging population increases and the number of younger healthcare professionals decreases, additional technological assistance will be strongly desired to support the maintenance of CIs for older adults, specifically for those with cognitive impairment. It should further be noted that robots have fewer requirements for space and time than conventional cognitive training, and there is no requirement for specialised trained personnel [32,33]. From this perspective, robots could be used to assist in implementing home-based CIs for patients with dementia.
Robots can monitor the improvement of users; intelligently adjust the level of difficulty of training if necessary; and assess mood, interest, and engagement based on a variety of sensors. These features expand the possibilities of home-based CIs beyond what conventional face-to-face programs can easily provide [31].
In addition to these advantages over conventional face-to-face cognitive training, it is also important to consider how robot-assisted interventions compare with other technology-based approaches, such as web- or app-based programs. Although these interventions have also demonstrated measurable benefits for individuals with mild cognitive impairment and dementia [34], their real-world uptake is often constrained by self-directed use models, engagement drop-off, and usability barriers in older adults [35-38]. In contrast, Bomy, as a SAR, delivers embodied social cues (verbal/visual prompts), on-device adaptability, and just-in-time feedback with caregiver escalation, mechanisms that are linked to higher engagement and sustained participation in dementia care [39,40]. Recent studies of SARs including an RCT of the PIO robot and pragmatic trials in dementia services report improvements in cognitive or neuropsychiatric outcomes alongside good acceptability and adherence [41-43]. These features likely contributed to the high adherence observed in our study and may differentiate Bomy from typical app-based programs that rely primarily on self-initiation.
Our study had several limitations. First, the small sample size may have affected the generalisability of our findings. Future studies with larger cohorts and longer follow-up periods are required to validate these results and assess the long-term benefits of robot-based CIs. Additionally, although there was no statistical significance in dropout rates (20.8% vs. 33.3%, p=0.997) between the two groups, the higher dropout rate in the robot group suggests that factors such as the user-friendliness and accessibility of robotic systems require further exploration. Another limitation of this study is the single-blind design (rater-blinded), i.e., the participants with AD dementia were aware of the group they belonged to (i.e., robotic or control group). This recognition may have influenced the positive results observed in the robotic group. Implementing mock programmes that do not include targeted cognitive training could help to address this limitation. Furthermore, in the current study, we did not compare the effects of robotic CIs with active controls, such as conventional cognitive training using a paper or pencil. Therefore, the observed results may be attributed to the CI itself, rather than to the use of the robot. Future studies should include an active control group to directly compare the effects of robot CIs with those of traditional methods. Additionally, we measured only three cognitive subtests (working memory, attention, and visual associative memory) as secondary outcomes. Therefore, other cognitive domains that were not investigated in this study, such as language or calculation, could also be improved with robotics-based cognitive training.
Despite these limitations, our study demonstrated that use of a personal robot to administer CI at home can be beneficial for patients with mild AD dementia by improving cognitive function and brain activity. Although only a few studies have explored the effects of CIs using robots in older adults [31,44-46], most studies have focused on mood improvements, such as reducing depression, agitation, and apathy, or enhancing the quality of life in patients with advanced dementia. These studies found that patients with dementia generally enjoyed interacting with robots, which helped reduce their stress and loneliness [44-46].
To the best of our knowledge, this is the first study to examine the effects of home-based CIs using robots on the cognitive and functional changes in patients with mild AD dementia. Our findings provide evidence supporting the positive effects of robot-assisted CIs on cognitive functions, as well as changes in beta and gamma bands, indicative of enhanced cognitive engagement and neural connectivity in AD dementia.
Supplementary Materials
The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0109.
Brief description of cognitive training programs provided by Bomy.
Correlations between the number of participating sessions and cognitive changes. (A) Although a positive correlation was observed between the number of participating sessions and changes in the spatial working memory Z-scores, the difference did not reach statistical significance (r=0.414, p=0.068). There were no statistically significant correlations between the number of participating sessions and (B) changes in paired associates learning (r=-0.073, p=0.770) or (C) changes in reaction time (r=0.100, p=0.691).
Notes
Availability of Data and Material
The data supporting the findings of this study are available from Ewha W. University but are subject to restrictions under the participant agreement approved by the Institutional Review Board (IRB), and therefore are not publicly accessible. However, the datasets used and/or analyzed during this study in anonymized form can be obtained from the corresponding author upon reasonable request.
Conflicts of Interest
GH Kim and JH Jeong hold a patent for a co-developed cognitive intervention program with Robocare, and receive running royalties from Robocare. However, this company had no role in the design, data collection, analysis, interpretation, or writing of this report. BR Kim, SW Chung, SI Moon have nothing to disclose.
Author Contributions
Conceptualization: all authors. Data curation: Sooin Moon, Seungwon Chung, Bori R. Kim. Formal analysis: Bori R. Kim. Investigation: Sooin Moon, Seungwon Chung, Bori R. Kim. Validation: Jee Hyang Jeong. Supervision: Geon Ha Kim. Writing—original draft: Bori R. Kim. Writing—review & editing: Geon Ha Kim.
Funding Statement
This research was supported by the “Dementia Care Robot Service” project of the Intelligent Information Project for Solving Social Issues, funded by the National Information Society Agency, the Ministry of Science and ICT (GR1904-01), and by an Institute of Information & Communications Technology Planning & Evaluation grant funded by the Korean government (MSIT) (No. RS-2022-00155966) and Artificial Intelligence Convergence Innovation Human Resources Development (Ewha Womans University).
Acknowledgments
None