A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain
Abstract
:1. Introduction
- Investing in the development of social robots for rehabilitation and assistance support, such as devices designed to assist elderly individuals, promoting motor, cognitive, and emotional recovery [2].
- Addressing the theme of empathy in social robots and seeking solutions to make interactions with these devices more natural (for example, using pet-like robots) and effective from a human perspective [5].
- Analyze Overall Bibliometric Trends:Provide a comprehensive bibliometric analysis of research output in the field of social and assistance robotics, focusing on trends, key publications, and developments over time.
- Identify Established Themes and Categories:Map out the key areas of focus within the systematic reviews, such as technological innovations, application domains (e.g., rehabilitation, assistance, and caregiving), and evaluation methodologies.
- Examine Opportunities and limitations:Investigate the potential benefits and limitations of integrating robots into healthcare and caregiving settings. This includes exploring advancements in artificial intelligence and workflow efficiency, as well as identifying barriers related to infrastructure, regulatory frameworks, and professional training.
2. Approach to Study Selection
Algorithm 1: Framework for Review Selection |
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2.1. Assessment Process
2.2. Managing Bias in the Review
- Diverse Assessors: Each study was reviewed by two assessors with different academic backgrounds and expertise in both assistive robotics and healthcare. This diversity ensured a broad range of perspectives and minimized the risk of individual biases influencing the evaluation.
- Clear Assessment Criteria: The studies were analyzed using predefined criteria, including Clarity of Rationale, Study Design Appropriateness, methodological rigor, Result Presentation, Justification of Conclusions, and disclosure of conflicts of interest. Furthermore, data were presented based on a standardized checklist, reducing the risk of subjective interpretation.
- Scoring System: Each parameter was rated on a scale from 1 to 5, while the disclosure of conflicts of interest was assessed using a binary evaluation (Yes/No). This quantitative approach ensured consistent evaluations across studies and provided a transparent mechanism for comparing study quality.
- Independent Review: The primary assessors reviewed the studies independently, assigning scores without prior discussion. This independence ensured that individual judgments were based solely on the study’s merit and predefined criteria, minimizing groupthink or shared biases.
- Dispute Resolution: In cases where the two assessors disagreed on scores or study inclusion, a third assessor was consulted to provide an impartial judgment. This adjudication helped resolve conflicts fairly and ensured balanced decision making.
- Structured Mechanism for Disagreements: The process for resolving disagreements was formalized and structured. The third assessor reviewed the initial evaluations and provided a reasoned judgment to reconcile differences. This structured approach ensured that conflicts were systematically addressed and that final decisions were based on a comprehensive evaluation.
- Transparency: The use of a standardized checklist for data presentation and a clear scoring system enhanced transparency in the assessment process. By documenting the criteria and scoring rationale, the review process became more traceable and reproducible, reducing the potential for undisclosed biases.
2.3. Selected Studies
3. Results
3.1. Trends
- Historical and Temporal Trends:The earliest studies in this domain date back to 2006, and since then, a total of 400 studies have been published (see Figure 1). Notably, in the last 10 years, 379 studies have been produced, accounting for approximately 95% of the total publications in this field. This remarkable surge indicates a growing research interest and rapid development in social and assistance robotics. Moreover, in the past five years—especially following the onset of the COVID-19 pandemic—305 studies have been published, representing around 76% of the overall output (see Figure 1). This recent spike suggests that the current global challenges and advancements in digital health have significantly accelerated research activity.
- Review Studies:Among the 400 publications (Figure 2), there are the following:A total of 18 reviews, which account for approximately 4.5% of the total.A total of 12 systematic reviews and/or methanalysys, representing about 3% of total.Collectively, these 30 review-type studies constitute around 8% of the overall publication output (see Figure 2). Review-type studies began to emerge in 2017, and in the last five years, 22 out of the total 30 review publications have been developed, which accounts for roughly 73% of the review literature. This concentration of review studies in recent years reflects a maturing field where researchers are beginning to synthesize the existing knowledge and critically evaluate emerging trends and methodologies.
- Comparative Scope within Robotics Literature:When broadening the search using the keyword (robot[Title/Abstract]), a total of 36,977 results were retrieved. This extensive number illustrates that while the broader field of robotics is vast, studies specifically focused on social and assistance robotics represent only about 1.1% of the overall literature. This comparatively small percentage highlights that social and assistance robotics, despite their potential, remain a relatively niche and emerging area within the larger robotics research landscape.
3.2. Themes and Categorization
3.3. Emerging Opportunities and Limitations/Barriers
4. Discussion
4.1. Synoptic Diagrams
4.1.1. First Diagram (Figure 3): Linking Objectives to Analysis
- Block 1: This block represents the bibliometric trends reported in Figure 1 and Figure 2 (Section 3.1). These trends were analyzed to provide an overview of the scientific production also in relation to robotics in the health domain in general.
- Block 2: This block corresponds to the identification of thematic areas, as presented in Table 2 and Table 3 (Section 3.2). This categorization allowed for the organization of the reviewed studies according to key themes, focus, and contribution, facilitating a structured analysis.
- Block 3: Building upon the thematic categorization, this block highlights the comparative side-by-side analysis of the studies. The classification was refined, with categorization as reported in Table 4 (Section 3.2), enabling a deeper understanding of the different ways SARSs are applied in the health domain.
- Block 4: This block synthesizes the opportunities and barriers/limitations identified in the reviewed studies, as reported in Table 5 (Section 3.3). These findings highlight both the potential benefits of SARSs’ applications in the health domain and the barriers/limitations.
4.1.2. Second Diagram (Figure 4): Connecting Discussion to Findings
- Block 5 identifies the emerging recommendations from the overviewed studies as reported in Table 6 (Section 4.3).
- Connected to Block 5, Block 6 links to a comparison with the findings of recent Cutting Edge Research moving in the direction of the recommendations as reported in Table 7 (Section 4.4).
4.2. Highlight from the Overview
4.3. Emerging Recommendations
4.4. Comparison of the Overview with the Contribution of the Cutting Edge Research
4.5. Limitations
- ○
- Expand Data Sources: Incorporate conference proceedings, preprints, and gray literature to capture emerging trends and novel SASR applications.
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- Enhance Cross-Cultural Insights: Conduct comparative studies across different linguistic and cultural contexts to understand the region-specific adaptations of SASRs.
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- Integrate Primary Research: Combine systematic reviews with qualitative and mixed-methods studies to capture user experiences and implementation barriers.
- ○
- Develop Standardized Evaluation Frameworks: Establish universal benchmarking criteria for assessing SASR effectiveness, usability, and ethical compliance.
- ○
- Explore Long-Term Social and Psychological Effects: Investigate the evolving role of SASRs in human–machine relationships, trust formation, and ethical concerns in caregiving contexts.
- ○
- Promote Interdisciplinary Collaboration: Encourage joint research initiatives among engineers, healthcare professionals, ethicists, and policymakers to ensure holistic SASR development.
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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Keyword | Justification |
---|---|
“Social Robots” | A broad term for robots designed to engage in social interactions with humans, including communication, emotional expression, and companionship. They are used in healthcare, education, and customer service. |
“Assistance Robots” | Robots designed to aid humans in various tasks, including personal assistance, healthcare support, household help, and workplace automation. They can assist individuals with disabilities, elderly people, or workers in logistics and service industries. |
“Humanoid Robots” | Robots with human-like appearance and behavior, designed to facilitate natural interaction through gestures, speech, and facial expressions. They are frequently used in research on human–robot interactions. |
“Assistive Robotics” | Robots that support individuals with disabilities, elderly populations, or patients in rehabilitation. These robots provide physical or cognitive assistance and improve quality of life. |
“Human–Robot Interaction” | The study of how humans and robots communicate, collaborate, and build relationships. A crucial field in assessing usability, acceptance, and ethical concerns of robotic systems. |
“Companion Robots” | Robots designed to provide emotional and social support, often used to alleviate loneliness in elderly individuals or offer companionship in therapeutic settings. |
“Healthcare Robotics” | Application of robots in medical environments, including patient care, surgical assistance, rehabilitation, and mental health support. It improves healthcare efficiency and accessibility. |
“Therapeutic Robots” | Robots used in psychological and medical therapy, such as reducing anxiety in children with autism or improving cognitive engagement in dementia patients. |
“Educational Robotics” | The use of robots as teaching tools in classrooms or for personalized learning, especially in STEM education and early childhood development. |
“Trust in Robots” | The study of human confidence in robotic systems, influenced by reliability, transparency, and social behaviors of robots. It is critical for integration into daily life. |
Reference | Study Focus | Objective | Population | Technological Aspects | Main Effects | Limitations in the Studies of the Systematic Review |
---|---|---|---|---|---|---|
[11] | SASRs for depression and loneliness in the elderly | Assess the effectiveness of SASRs in reducing depression and loneliness | Elderly individuals in assisted living facilities | SASRs with physical embodiment, voice interaction, and motion-based engagement | Significant reduction in depression and loneliness, especially in group settings | Small sample size; short follow-up period |
[12] | Robotic interventions for sleep management in adults | Evaluate whether robots improve sleep quality | Adults with sleep disorders | Companion robots with tactile interaction and programmed routines | No significant improvement over traditional sleep aids | No substantial benefit compared to usual care |
[13] | Empathy in human–robot interaction | Investigate how SASRs can foster empathy in human users | Healthy adults interacting with AI-based robots | Emotion recognition, multimodal interaction (voice, gestures, and expressions), and adaptive response systems | Defined empathy framework for SASRs | Limited generalizability beyond controlled settings |
[14] | SASRs for autism therapy and care | Examine how robots assist in autism therapy | Children with Autism Spectrum Disorder (ASD) | Humanoid and non-humanoid models, speech synthesis, and interactive games | Moderate improvement in social interactions | High variability in individual responses |
[15] | Reducing anxiety in children in healthcare settings | Explore SASRs’ role in alleviating anxiety in pediatric healthcare | Children undergoing medical procedures | Interactive robots with speech, movement, and music | Lower anxiety levels during medical interventions | Short-term study; long-term effects unknown |
[16] | SASRs in dementia care | Examine the impact of SASRs on emotional and cognitive well-being in dementia patients | Dementia patients in long-term care facilities | Pet-like robots (e.g., PARO) with life-like tactile responses and emotional AI | Improved behavioral and emotional outcomes; limited cognitive benefits | Inconsistent cognitive improvements; ethical concerns regarding deception |
[17] | Trust in AI-based SASRs | Analyze design factors that enhance trust in SASRs | Studies on human–robot trust | Facial anthropomorphism, dynamic expressions, real-time gaze adaptation | Identified key facial features enhancing trust | Theoretical framework; lacks empirical validation |
[18] | Effects of non-facilitated meaningful activities for dementia patients | Evaluate the effectiveness of non-facilitated robotic activities in dementia care | Dementia patients in residential care | Animal-like robots (e.g., PARO), life-like dolls with programmed behavioral responses | Positive effects on agitation, well-being, and sleep | Heterogeneity in study designs; limited long-term data |
[19] | Cognitive support for the elderly with early-stage dementia | Investigate how robots enhance cognitive function in early dementia | Early-stage dementia patients | Assistive robots for memory and task completion | Modest cognitive benefits observed | Short study duration; no control group |
[20] | SASRs to support children’s well-being under medical treatment | Assess how SASRs reduce anxiety in hospitalized children | Pediatric patients undergoing medical procedures | Companion robots with interactive storytelling, play-based interactions | Reduced stress and improved coping mechanisms | Variability in engagement levels across children |
[21] | SASRs for depression in older adults | Investigate how SASRs aid in depression management | Older adults diagnosed with depression | AI-driven emotional interaction and adaptive dialog systems | Moderate reduction in depressive symptoms | No long-term data on sustained benefits |
[22] | The effectiveness of SASRs for older adults | Summarize evidence on the efficacy of SASRs for elderly care | Elderly individuals in residential care | Various types of SASRs, including companion and therapeutic robots | Positive impact on emotional well-being, engagement, and quality of life | Variability in the effectiveness depending on robot design and implementation |
[23] | SASRs in adult psychiatry | Overview of the use, effects, and acceptability of SASRs in adult psychiatry | Adult psychiatric patients | Social robots equipped with AI-driven interaction, speech recognition, and emotional response capabilities, used in psychiatric therapy. | Symptom reduction, functional improvements, high patient acceptance and enjoyment. | Small sample sizes, limited generalizability, need for larger randomized controlled trials (RCTs). |
[24] | AI SAR for cognitive function in older adults | Examine the effect of AI SAR on cognitive function in older adults | Adults aged ≥65 years | AI-powered socially assistive robots (SAR) with adaptive learning algorithms, sensor-based interaction, and anthropomorphic design for cognitive engagement. | Improved cognitive function, with anthropomorphic SAR proving more effective. | Ethical concerns, challenges related to low digital literacy among older adults. |
[25] | Non-pharmacological pain management for dementia patients | Synthesize evidence on non-pharmacological pain interventions for dementia | People with dementia | Personal assistive robots with multimodal sensory inputs (voice, touch, motion tracking) integrated into non-pharmacological pain management strategies for dementia patients. | Pain reduction through non-pharmacological interventions, including assistive robots. | Variable methodological quality, studies focused only on mild to moderate pain. |
[26] | SASRs in dementia care | Measure the effects of socially assistive robots in dementia care | Older adults with dementia | Pet-type socially assistive robots with AI-driven behavior simulation, tactile feedback, and emotional interaction capabilities for dementia care. | Significant reduction in agitation and depression, effect dependent on exposure duration. | No significant effect on quality of life, need for further research on long-term benefits. |
[27] | Rehabilitation and Assistive Robots Assessment | To classify outcome measures for assessing rehabilitation or assistive robots. | Users of rehabilitation/assistive robots | Rehabilitation and assistive robots with motion tracking, haptic feedback, and AI-based user adaptation, evaluated with PYTHEIA and other measurement tools. | Identification of validated tools for subjective assessment of assistive technologies. | Lack of standardized and validated outcome measures to compare device performance. |
Study | Brief Description | Contribution to the Health Domain |
---|---|---|
[11] | This study is a systematic review and meta-analysis that explores the impact of SASRs on depression and loneliness among older residents in long-term care facilities. The research synthesizes randomized controlled trials to assess the effectiveness of SASRs as non-pharmacological interventions for improving mental health in elderly populations. | SASRs offer a promising alternative to traditional interventions for reducing depression and loneliness in elderly care settings. By encouraging social engagement and interactions, these robots contribute to the psychosocial well-being of older adults, suggesting their potential for widespread use in long-term care facilities. |
[12] | This systematic review and network meta-analysis compare the effects of robots, plush toys, and usual treatments on sleep in adults, particularly older adults with or without dementia living in nursing homes. Despite mixed results, the study evaluates sleep quality and total sleep time as common sleep measures in trials involving robotic and non-robotic interventions. | While the analysis found no significant improvement in sleep quality due to robot interventions, it suggests that sleep-specific robots may offer benefits. The findings highlight the need for targeted robotic designs aimed at improving sleep quality and the importance of excluding participants with adequate sleep to avoid bias. |
[13] | This review examines the concept of empathy in human–robot interactions, focusing on the ability of robots to recognize human emotions and respond appropriately to foster positive human perceptions. It also defines empathy in the context of SASRs and suggests a conceptual framework for designing robots that can empathize with their users. | Empathy is a critical design factor for robots intended for healthcare and daily interactions. The study highlights how robots’ ability to understand and respond to emotional cues could improve human–robot relationships, which is essential for their integration in therapeutic and caregiving environments. |
[14] | This systematic review evaluates randomized controlled trials assessing the use of SASRs in autism therapy. The study examines various robots used in therapy for individuals with autism, with a focus on how robots serve as engagement tools and their ability to improve various behavioral and cognitive outcomes. | The research shows that SASRs have the potential to improve engagement and behavior in people with autism, particularly in therapeutic settings. While the robots are still in the early stages of development for therapeutic use, the study underlines their promise in enhancing interaction and providing emotional support for individuals with autism. |
[15] | This review assesses the role of SASRs in reducing anxiety and distress in children visiting hospitals or clinical environments. The study investigates different types of robots and their effectiveness in managing children’s emotional responses during medical visits. | The evidence supports the potential of SASRs to reduce anxiety and distress in children during hospital visits. These robots engage children through interaction, music, and movement, showing promise in improving emotional well-being in clinical settings. However, more extensive studies are needed to strengthen the evidence and refine their usage. |
[16] | This systematic review investigates the effects of SASR interventions on individuals with dementia, focusing on behavioral, emotional, and functional outcomes. The study analyzes various robot types, including pet robots, to evaluate their impact on dementia patients’ well-being. | SASRs offer non-pharmacological interventions for people with dementia, promoting engagement and emotional support. However, the results are mixed, and the study calls for further research on the optimal use of robots based on the severity of dementia and the specific characteristics of the intervention. |
[17] | This review focuses on facial anthropomorphic trustworthiness in SASRs, specifically examining static and dynamic facial features and emotional expressions that could improve trust in robots. The study draws from the human facial perception and robot design literature. | The research highlights the importance of facial features in designing SASRs that are trustworthy and relatable. Trust is essential for the acceptance and effectiveness of robots in healthcare, as users need to feel comfortable interacting with robots, especially in sensitive environments like healthcare. |
[18] | This review evaluates the effectiveness of non-facilitated meaningful activities for people with dementia in long-term care facilities, including the use of SASRs, toys, and music. The study investigates the psychological and physiological impacts of such activities on dementia patients. | Non-facilitated activities involving SASRs, such as the PARO robot, have been shown to improve engagement, reduce agitation, and enhance emotional well-being in people with dementia. While the evidence is promising, more robust studies are needed to confirm the long-term benefits of such interventions in dementia care. |
[19] | This systematic review synthesizes evidence from randomized controlled trials (RCTs) on psychosocial interventions by SASRs. It focuses on the effects of SASR interventions on health and well-being outcomes, covering a range of health domains where SASRs have been tested. | While controlled research on SASRs is still in its early stages, the study emphasizes the need for large-scale RCTs with sophisticated methodologies to increase confidence in the efficacy of SASRs. It highlights the potential for robots to improve healthcare access and outcomes but underscores the importance of further studies to establish their clinical effectiveness. |
[20] | This systematic review examines the use of socially assistive robots (SARs) to support children’s well-being during hospitalization. The study reviews ten publications on SARs used for emotional support, distraction during medical procedures, and overall well-being improvement. | The review suggests that SARs can have a positive effect on children’s well-being by reducing stress, pain, and anxiety during medical treatments. It highlights the potential benefits of integrating SARs into hospital routines, though further research is necessary to refine their application and improve outcomes. |
[21] | This systematic review investigates the effectiveness of SASR interventions for depression in older adults. It assesses the impact of companion, communication, and health-monitoring robots in reducing depressive symptoms in older adults. | The study suggests that SASRs have potential in alleviating depressive symptoms among older adults, particularly through companionship and communication. However, the evidence is not yet strong enough to make definitive clinical recommendations, and more research is needed to assess long-term effects and effectiveness. |
[22] | This systematic review and meta-analysis examines the effectiveness of SASRs on older adults’ psychological, physiological, and quality-of-life outcomes from randomized controlled trials. | The review indicates that SASRs may improve emotional support, reduce agitation, anxiety, and loneliness, and enhance the overall quality of life in older adults. However, the lack of high-quality studies calls for further RCTs to better understand the impact of SASRs on older adults’ health and well-being. |
[23] | This scoping review explores the use of SASRs in adult psychiatry, focusing on their impact on mental health conditions like schizophrenia, autism spectrum disorder, and intellectual disability. The study suggests that SASRs can reduce symptoms, improve functioning, and provide insights into these conditions, with positive user feedback on their acceptance. | This study highlights the potential of SASRs to improve mental healthcare, particularly in adult psychiatry, where their use is currently limited. It suggests that further research with larger, randomized trials could establish more concrete evidence of their benefits. |
[24] | This meta-analysis examines the effect of AI socially assistive robots (SAR) on the cognitive function of older adults. Nine studies were included, and the results suggest that AI SAR in anthropomorphic form can improve cognitive function. | The study shows that AI SAR can effectively support cognitive function in older adults, offering a non-pharmacological intervention with the potential to enhance the quality of care and reduce caregiver burden, although challenges in implementation remain. |
[25] | This systematic review investigates non-pharmacological interventions for pain management in people living with dementia. It highlights interventions like ear acupressure, music therapy, and personal assistive robots as effective options for managing pain. | The findings suggest that non-pharmacological interventions, including SASRs, can play a role in pain management for people with dementia, offering safer alternatives to pharmacological treatments. However, more rigorous studies are needed for validation. |
[26] | This study evaluates the effect of exposure duration to socially assistive robots on older adults with dementia, focusing on outcomes like agitation, depression, and quality of life. It found that pet-type robots helped reduce agitation and depression. | The study supports the use of pet-type robots in dementia care, especially in long-term care facilities, showing benefits in reducing agitation and depression. The findings call for further research to develop comprehensive intervention plans for their use. |
[27] | This systematic review identifies and classifies outcome measures used to assess rehabilitation or assistive robot devices. The study finds a lack of standardized measures, which complicates comparisons across studies. | The research highlights the need for standardized, validated scales for evaluating rehabilitation or assistive robots, improving the ability to assess their effectiveness and user satisfaction. The identification of PYTHEIA offers a potential solution for bridging this gap. |
Reference | Cluster | Justification of Cluster |
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[11] | Emotional and Social Support | The study focuses on SASRs designed to offer companionship and emotional relief to elderly individuals, addressing issues like loneliness and depression. |
[16] | Emotional and Social Support | This study explores robots that engage patients emotionally, providing companionship and supporting cognitive functions, which help alleviate emotional distress in dementia patients. |
[18] | Emotional and Social Support | The review examines the impact of non-facilitated interactions by SASRs on dementia patients, focusing on emotional support through unmediated social engagement. |
[13] | Emotional and Social Support | This study focuses on creating robots capable of empathetic interactions, helping establish emotional bonds between robots and users, which enhances social and emotional support. |
[17] | Emotional and Social Support | This study explores how SASRs can build trust through facial and emotional expressions, strengthening the emotional bonds between AI and human users. |
[22] | Emotional and Social Support | This study consolidates evidence on the emotional benefits of SASRs for elderly individuals, emphasizing their role in enhancing emotional well-being and social interaction. |
[21] | Emotional and Social Support | This study focuses on the use of robots to provide emotional support to older adults suffering from depression, aiming to improve their quality of life through social interaction. |
[20] | Therapeutic Robots for Special Needs | This study discusses robots that support children’s emotional well-being in healthcare settings, offering comfort and reducing anxiety during medical treatments. |
[15] | Therapeutic Robots for Special Needs | The study investigates robots designed to alleviate anxiety and provide emotional comfort to children undergoing medical procedures, which serves as therapeutic support. |
[12] | Therapeutic Robots for Special Needs | This study focuses on robots aimed at supporting sleep hygiene for adults, which addresses a therapeutic need, although with limited effectiveness compared to traditional methods. |
[14] | Cognitive Support | The study evaluates robots used in autism therapy, focusing on structured and repetitive tasks designed to support social engagement and cognitive development in children with ASD. |
[19] | Cognitive Support | This study investigates how robots can stimulate cognitive functions in elderly individuals with early-stage dementia, aiming to enhance memory and task completion capabilities. |
[23] | Emotional and Social Support | The study explores the use of SASRs in adult psychiatry, highlighting their potential to reduce symptoms, improve functioning, and gain insights into mental health conditions like schizophrenia and autism spectrum disorder. |
[24] | Emotional and Social Support | This study focuses on the use of AI SAR to enhance social interaction and cognitive function in older adults, emphasizing the promise of socially assistive robots as caregivers to improve older adults’ quality of life. |
[25] | Therapeutic Robots for Special Needs | This study reviews non-pharmacological interventions for managing pain in people with dementia, including the use of assistive robots as a form of therapeutic support. |
[26] | Emotional and Social Support | This study examines the effect of socially assistive robots on the mental state of older adults with dementia, specifically looking at the reduction in agitation and depression, supporting their role in emotional well-being. |
[27] | Therapeutic Ro-bots for Special Needs | This study identifies and classifies outcome measures used to assess rehabilitation or assistive robots, focusing on the importance of standardized assessment tools to improve rehabilitation outcomes for users. |
Study | Opportunities | Limitations/Barriers |
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[11] |
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[12] |
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[13] |
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[14] |
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[15] |
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[16] |
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[17] |
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[18] |
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[19] |
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[20] |
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[21] |
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[22] |
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[23] |
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[24] |
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[25] |
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[26] |
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[27] |
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Source | Recommendation | Description | Importance |
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Yen et al. [11], Park and Whang [13] | Empathy-driven Design | SASRs (SRs) should not only be functional but also designed to understand and engage with patients emotionally. This means incorporating advanced sensors, algorithms, and AI that can interpret human emotions through facial expressions, voice tone, body language, and even physiological cues. This empathetic capability is essential for creating meaningful interactions, especially for patients with cognitive impairments, dementia, or mental health concerns. By offering emotionally intelligent responses, SASRs can help mitigate feelings of loneliness, provide comfort, and strengthen the emotional bond between robot and patient, improving overall well-being. | Empathy in SASRs promotes emotional well-being by providing personalized and comforting interactions that address patients’ emotional needs. SASRs designed with empathy foster trust and a sense of companionship, making them effective for patients who require emotional or social support, particularly the elderly and those with special needs. |
Song and Luximon [17], Robinson et al. [19] | User-Centered Design | SRs should be tailored to meet the diverse needs of various patient populations. This includes considering the cognitive, emotional, and physical abilities of users, such as children with autism, elderly individuals with dementia, and people undergoing medical treatments that affect their mental or physical states. The design should allow for easy customization, ensuring that SASRs are adaptable to different users’ needs, preferences, and specific medical conditions. Personalization can involve adjusting the robot’s behavior, language, and communication methods, ensuring the robot is effective and comfortable to interact with. | Tailoring SASRs to specific patient populations ensures that the robots can provide relevant support that addresses individual needs, thus enhancing their effectiveness in clinical settings. A user-centered design approach makes SASRs more accessible, reducing the likelihood of frustration or disengagement and improving their therapeutic outcomes. |
Giansanti and Gulino [28], Monoscalco et al. [29] | Cybersecurity and Data Privacy | SRs are increasingly used in clinical settings, meaning they handle sensitive patient data such as personal information, health records, and behavioral data. To ensure the trust of both patients and healthcare providers, SASRs must be equipped with robust cybersecurity features. This involves adopting encryption techniques, secure data storage, and secure communication protocols to protect patient data from unauthorized access or cyber-attacks. In addition to protecting patient privacy, SASRs should be designed to comply with healthcare regulations such as GDPR or HIPAA. Ensuring cybersecurity also involves providing regular updates and monitoring the robots for potential vulnerabilities to safeguard against emerging threats. | Cybersecurity is critical for building trust and ensuring the adoption of SASRs in healthcare. Without strong data protection measures, the use of SASRs could lead to privacy violations, data breaches, and loss of patient confidence. Effective cybersecurity is essential not only for protecting sensitive information but also for ensuring that SASRs remain functional and reliable in healthcare environments. |
Lastrucci et al. [30] | Algorethics (AI Ethics) | As SASRs are powered by AI, their development must be guided by ethical principles to ensure they act in ways that are beneficial to patients. These robots need to make decisions or interact with patients in ways that promote fairness, transparency, and accountability. Algorethics involves ensuring that algorithms powering SASRs are free from biases and are designed with mechanisms for accountability so that the robots’ actions are understandable and can be questioned if necessary. Developers must also consider the long-term societal impacts of deploying AI-powered robots in sensitive healthcare settings, where mistakes or unethical decisions could have serious consequences. | Algorethics is essential for ensuring that SASRs act in a fair, transparent, and accountable manner. Incorporating ethical principles into AI systems minimizes the risk of bias, ensures that robots are working for the benefit of all patients, and builds trust in the technology. By addressing ethical concerns, the development of SASRs can align more closely with societal values and patient rights, fostering better acceptance and use of these technologies. |
Various studies [11,12,13,14,15,16,17,18,19,20,21,22] | Interdisciplinary Collaboration | The development of SASRs requires input from a diverse range of disciplines, including healthcare, engineering, psychology, ethics, and sociology. By bringing together experts from these fields, the design process can more comprehensively address the challenges and opportunities presented by SASRs. Engineers may focus on functionality and design, while healthcare providers bring practical insights into how SASRs can be used effectively in patient care. Psychologists can guide the development of empathetic interaction models, and ethicists can ensure the technology adheres to ethical standards. This holistic, interdisciplinary approach ensures that SASRs are well rounded and suitable for real-world applications. | Collaboration across different disciplines is vital for the success of SASRs. It ensures that SASRs are not just technologically advanced but also socially, ethically, and practically viable. Such collaboration maximizes the potential of SASRs to serve patients effectively while addressing potential issues in the robot’s design, use, and impact on human health and well-being. |
Maccioni et al. [31], Monoscalco et al. [29] | Standardization and Training | For SASRs to be integrated successfully into clinical settings, it is necessary to establish clear guidelines, standardized processes, and training programs. Healthcare professionals, including nurses, physiotherapists, and rehabilitation specialists, should be trained not only in the technical operation of SASRs but also in how to manage interactions with patients effectively. These training programs should cover areas such as ensuring patient comfort during interactions with robots, understanding their capabilities and limitations, and addressing cybersecurity risks. By providing healthcare workers with the tools and knowledge to use SASRs appropriately, we can ensure smoother integration and a more positive impact on patient care. | Standardization and training are critical to the successful integration of SASRs into healthcare. With clear guidelines and thorough training, healthcare providers can use SASRs more effectively and safely. These measures improve the overall user experience, enhance patient outcomes, and help ensure that SASRs are used in a way that aligns with clinical goals and patient needs. |
Source | Recommendation | Contribution to Recommendation |
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Figliano et al. [32] | Empathy-driven Design and Interdisciplinary Collaboration | Highlights the importance of empathy and collaborative approaches to SASR design. The study emphasizes the need for robots that are not only emotionally responsive but also adaptable to the cognitive and social development of diverse user groups, enhancing their ability to engage with and support individuals in various contexts. |
Baxter [33] | User-Centered Design and Empathy-driven Design | Examines child-robot interactions, particularly in educational environments. The findings underline how SASRs must be designed to accommodate children’s developmental needs, advocating for emotional engagement and interactive designs that encourage learning and social skill development. |
Paluch et al. [34] | Interdisciplinary Collaboration and Algorethics (AI Ethics) | Focuses on innovative robotic systems, emphasizing the need for collaboration between engineers, healthcare providers, and ethicists. Ethical considerations such as fairness, bias, and transparency are critical in developing socially aware and responsible SASRs, ensuring that their actions align with moral standards. |
Sørensen et al. [35] | User-Centered Design and Empathy-driven Design | Investigates the acceptance of SASRs by individuals with physical disabilities. The study stresses the importance of personalized and empathetic design in creating robots that meet the specific needs of disabled individuals, ensuring that robots can effectively support their users’ daily activities. |
Winslow et al. [36] | Cybersecurity and Data Privacy | Discusses the integration of advanced medical devices with SASRs, highlighting the critical need for robust cybersecurity measures to protect sensitive patient data. SASRs in healthcare must ensure data privacy and security to build trust with users and healthcare providers. |
Salem and Sumi [37] | Algorethics (AI Ethics) | Addresses ethical challenges related to deception in AI interactions, particularly in educational contexts. It emphasizes the necessity for SASRs to operate transparently, ensuring fairness and ethical standards in their behavior to prevent potential harm and maintain trust in AI-driven systems. |
Tozadore and Romero [38] | User-Centered Design and Empathy-driven Design | Examines SASR design in education, stressing the importance of considering diverse perspectives from teachers, students, and researchers. The research highlights that effective SASRs must be designed with empathy to engage and support learning, considering the emotional and cognitive needs of both students and educators. |
Elgarf et al. [39] | Empathy-driven Design and User-Centered Design | Explores how SASRs can foster creativity, particularly in children. It underscores the significance of creating emotionally engaging robots that can interact with children in ways that encourage creativity, imagination, and learning, while also adapting to their individual emotional and developmental needs. |
García-Martínez et al. [40] | User-Centered Design and Empathy-driven Design | Focuses on the concept of joint attention in human–robot interaction, showing that empathetic robot design can improve user perceptions. By aligning the robot’s actions with the user’s attention and emotional cues, the SASR can foster a more engaging and supportive interaction. |
Mizuho et al. [41] | User-Centered Design | Investigates how customers interact with SASRs in commercial environments, highlighting the need for adaptable design. The study emphasizes that SASRs should be designed to cater to the diverse needs and preferences of customers, providing personalized experiences and adapting to various contexts and interactions. |
Ahlin and Mann [42] | Empathy-driven Design and Interdisciplinary Collaboration | Examines resistance to SASRs in healthcare, emphasizing that empathy and interdisciplinary collaboration are vital to overcoming challenges in SASR adoption. By addressing emotional concerns and working across fields, SASRs can be better integrated into healthcare settings, fostering trust and acceptance among healthcare providers and patients. |
Lee et al. [43] | User-Centered Design and Empathy-driven Design | Analyzes the experiences of older adults using companion robots, focusing on emotional and cultural factors that influence their perceptions. The study suggests that empathetic design is essential for developing SASRs that align with the emotional and social needs of aging populations, promoting long-term use and satisfaction. |
Zhou and Dong [44] | User-Centered Design, Empathy-driven Design | Investigates how older adults respond to SASRs with offspring-like voices, showing that emotional responses are key to how these robots are perceived. This research supports the idea that SASRs should be personalized, particularly in terms of emotional cues and voice characteristics, to foster positive interactions with older adults. |
Rosenberg et al. [45] | Interdisciplinary Collaboration, Cybersecurity, and Data Privacy | Discusses a multimodal lifestyle program for older adults, where SASRs are used to support brain health. It highlights the need for collaboration across disciplines and the importance of integrating strong cybersecurity measures to ensure the safe and effective use of SASRs in healthcare, particularly when dealing with vulnerable populations like older adults. |
Fournier et al. [46] | User-Centered Design and Empathy-driven Design | Evaluates the impact of SASRs on autistic children, emphasizing the necessity for robots that are responsive to their emotional and communication needs. This research emphasizes the importance of designing SASRs that are tailored to the unique sensory and communication profiles of children with autism, improving interaction quality. |
Shankar et al. [47] | User-Centered Design and Empathy-driven Design | Provides insights into child-robot interactions, emphasizing the importance of adapting SASRs to children’s learning and communication styles. The study advocates for robots that can respond emotionally and provide educational support based on the child’s individual needs and preferences. |
Haresamudram et al. [48] | Empathy-driven Design and User-Centered Design | Explores the impact of anthropomorphic features in robots on user perception, demonstrating that empathy-driven design can significantly improve user acceptance and trust. SASRs that exhibit human-like features, such as voice and body movement, can foster more natural and positive interactions, especially for those requiring emotional support. |
Han et al. [49] | Interdisciplinary Collaboration, Cybersecurity, and Data Privacy | Reviews the use of SASRs in healthcare, highlighting the importance of interdisciplinary collaboration and robust cybersecurity measures to ensure safe and effective care. Emphasizing collaboration across fields and ensuring data privacy are essential for successful SASR implementation in sensitive healthcare settings. |
Rosero et al. [50] | Algorethics (AI Ethics) | Explores human perceptions of SASR deception, advocating for the adoption of ethical standards and transparency in robot interactions. The study stresses that SASRs should operate with honesty and fairness, ensuring that deceptive behaviors are avoided in all forms of human–robot interaction to maintain trust. |
Yashinski [51] | Cybersecurity and Data Privacy, User-Centered Design | Discusses cognitive monitoring in home settings, emphasizing the importance of secure data handling and the personalization of SASRs for in-home use. It highlights that in-home SASRs must be designed to meet individual needs while ensuring that sensitive data are protected, addressing both security and user-centered requirements. |
Maroto-Gómez et al. [52] | Algorethics (AI Ethics), Empathy-driven Design | Investigates bio-inspired decision making in SASRs, stressing the importance of transparency in decision-making processes. The study advocates for designing SASRs with ethical frameworks that ensure fairness and empathetic responses in their interactions, thus enhancing user trust and experience. |
Tan et al. [53] | Empathy-driven Design | Investigates how the LOVOT SASR enhances emotional well-being in older adults by fostering empathy-driven interactions that reduce loneliness and promote companionship. |
Lym et al. [54] | User-Centered Design | Focuses on designing family companion robots for children, tailoring the robot’s functions to provide emotional and practical support in home settings, especially for children with autism. |
Kok et al. [55] | User-Centered Design | Introduces a novel SASR model designed to sustain long-term engagement in home care services for aging populations, emphasizing the importance of adapting to the evolving needs of elderly users. |
Komariyah et al. [56] | User-Centered Design | Highlights how occupational therapists in Indonesia view SASRs for children with autism, stressing the need for SASRs to be designed with both therapeutic and emotional support in mind. |
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Giansanti, D.; Lastrucci, A.; Iannone, A.; Pirrera, A. A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain. Appl. Sci. 2025, 15, 3793. https://doi.org/10.3390/app15073793
Giansanti D, Lastrucci A, Iannone A, Pirrera A. A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain. Applied Sciences. 2025; 15(7):3793. https://doi.org/10.3390/app15073793
Chicago/Turabian StyleGiansanti, Daniele, Andrea Lastrucci, Antonio Iannone, and Antonia Pirrera. 2025. "A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain" Applied Sciences 15, no. 7: 3793. https://doi.org/10.3390/app15073793
APA StyleGiansanti, D., Lastrucci, A., Iannone, A., & Pirrera, A. (2025). A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain. Applied Sciences, 15(7), 3793. https://doi.org/10.3390/app15073793