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Abstract

Background:

Diabetes prevalence is rising and projected to affect 783 million globally by 2045. Effective diabetes self-management relies on diabetes knowledge, lifestyle modifications, and health care support; yet global health care workforce shortages hinder the provision of adequate care. Socially assistive technologies, such as robots or artificial intelligence, are proposed as potential solutions to meet rising demands.

Aim and Methods:

To map the current literature on Socially Assistive Robots for diabetes care, identifying robotic types, barriers and enablers to use, and impact on health-related outcomes. A scoping review using Arskey and O’Malley’s Framework was conducted, screening studies published between 2013 and 2025 across key databases and extracting data using COVIDENCE.

Results:

Twenty-two studies met the inclusion criteria, mostly focused on children with type 1 diabetes. Studies were largely conducted in Europe, cross-sectional, and with small sample sizes. Socially assistive robots demonstrated high acceptability, especially among younger children, positively affecting knowledge acquisition, self-management, and self-efficacy. Personalized interactions, gamified features, and emotional responsiveness were key enablers of engagement. However, engagement waned over time, particularly when participants’ practical and emotional expectations were unmet. Barriers included usability challenges, privacy concerns, and lack of customization. Economic and sustainability evaluations were absent.

Conclusions:

Despite growing evidence for robotics in diabetes care, research remains methodologically limited and focused primarily on younger populations. Future studies should include adults, employ multi-faceted robotics designs, and be adequately powered to assess acceptability and efficacy across diverse groups, facilitating broader application in diabetes care.

Keywords

artificial intelligence diabetes self-management e-Health diabetes review social robot

Introduction

In 2021, 10.5% (540 million) of the global adult population were living with diabetes, a figure projected to reach 783 million by 2045.¹ Poorly managed diabetes can cause serious complications, including cardiovascular disease, neuropathy, end-stage renal disease, visual impairment, and cognitive decline, placing a heavy burden on global health care systems.² Effective management consists of education, regular health care follow-up, and self-care activities such as blood glucose monitoring, lifestyle adjustments, and medication optimization.³ These demands often present significant challenges, leading to a high disease burden and distress.⁴

Growing global health care workforce shortages have intensified calls for innovation, driving the adoption of technology and artificial intelligence (AI) in care delivery.^5-7 Robotic technologies, particularly socially assistive robots (SARs), have the potential to improve health outcomes for people with chronic conditions.⁸ In this review, social robots are defined as physical systems with autonomous or semi-autonomous interaction capabilities, often featuring mobility, sensing, and social responsiveness. They are distinct from software-only systems (eg, mobile apps and voice-assistants like Alexa or Google Home) and from diabetes-specific technologies, such as automated insulin pumps and continuous glucose monitors.

Research shows that SARs with facial expressions and social features improve perceived social intelligence, user acceptance, and engagement among older adults, compared with virtual characters on tablets or mobile apps.^9-11 SARs offer companionship, enhance safety, and support complex self-management tasks.¹² Their feasibility is increasingly evident in managing conditions like diabetes, chronic obstructive pulmonary disease (COPD), and dementia,^13-15 through functions such as health data collection (eg, blood glucose, blood pressure, and medication tracking), reminders, adherence monitoring, and health education.^13-16

However, research on robotic applications for diabetes management is nascent, with limited focus across different age groups and diabetes types. Two earlier reviews examined SARs in children with type 1 diabetes, reporting improved diabetes knowledge and adherence to self-management,^13,17 but no reviews have investigated their use in the broader diabetes population.

Our review fills this gap by exploring robotic technologies in diabetes care across the lifespan. It examines their impact on diabetes-related health outcomes and identifies facilitators and barriers to implementation. These insights aim to guide future research and inform the development of tailored, effective robotic interventions. The review follows PRISMA-ScR (Preferred Reporting Items for systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines¹⁸ and the protocol was registered on the Open Science Framework (DOI: 10.17605/OSF.IO/5CZ37).

Methods

Study Design and Search Strategy

We conducted a scoping review using Arksey and O’Malley’s five-stage framework to map the literature, identify key concepts, and clarify knowledge gaps.¹⁹ The search strategy was underpinned by the Population Concept and Context (PCC) framework to enable replication, strength and methodological rigor. In collaboration with a subject-matter librarian, CINAHL, Medline (via EBSCO), PubMed Central, Web of Science, Ovid Emcare, Ovid Nursing, Proquest SciTech Collection, IEEE Xplore, ACM Digital Library, Proquest Social Science Premium Collection, and gray literature databases were searched (Table 1). Supplementary searching was undertaken following the CLUSTER method²⁰ in Google Scholar, checking publications of lead authors, lists of included studies in similar reviews, and backwards and forwards citation searching of papers selected for inclusion via all other search methods. The inclusion and exclusion criteria are presented in Table 2.

Table 1.

Search Strategy.

	Search line	Search terms
Diabetes	1	TI (diabetes or diabetic or T2D or T1D) OR AB (diabetes or diabetic or T2D or T1D)
	2	(MH “Diabetes Mellitus”) OR (MH “Diabetes Mellitus, Type 1”) OR (MH “Diabetes Mellitus, Type 2”) OR (MH “Diabetes, Gestational”)
	3	S1 or S2
Robotics	4	TI (Robot* or Telerobot* or Humanoid* or Robot-assisted or Robot-enhanced) OR AB (Robot* or Telerobot* or Humanoid* or Robot-assisted or Robot-enhanced)
	5	(MH “Robotics”)
	6	S4 or S5
Community	7	TI (Community or Home or Outpatient or Self N2 care or Self N2 manage* or Self N2 regulat* or Patient N2 directed or Self N2 monitor* or Personal* N2 care or Individual* N2 care or Independ) OR AB (Community or Home or Outpatient or Self N2 care or Self N2 manage or Self N2 regulat* or Patient N2 directed or Self N2 monitor* or Personal* N2 care or Individual* N2 care or Independ*)
	8	(MH “Community Health Workers”) OR (MH “Community Health Services”) OR (MH “Nurses, Community Health”) OR (MH “Community Health Nursing”) OR (MH “Home Nursing”) OR (MH “Home Health Aides”) OR (MH “Patient-Centered Care”) OR (MH “Outpatients”) OR (MH “Self Care”) OR (MH “Self-Testing”) OR (MH “Blood Glucose Self-Monitoring”) OR (MH “Self Medication”) OR (MH “Self Administration”) OR (MH “Self-Management”) OR (MH “Self-Assessment”)
	9	S7 or S8
	10	S3 AND S6 AND S9

Table 2.

Inclusion and Exclusion Criteria.

	Inclusion	Exclusion
Study Types	Primary research.	Reviews (eg, systematic reviews, meta-analyses, narrative reviews).Editorial articles (eg, perspective pieces, position statements).
Focus	Interventions or evaluations involving robotic impacts on diabetes health outcomes.	Interventions not related to robotics.
Setting	Any community-based setting, with no geographic restrictions.
Publication Date	Published between January 2013 and January 2025 to ensure relevance in the context of rapid technological and clinical advancements.
Language and Access	Full-text articles available in English.

The review aimed to answer the following research questions:

Research Question 1 (RQ1): What are the types of robotic devices and interventions used with people living with diabetes mellitus?

Research Question 2 (RQ2): What are the enablers and barriers to the use of robotics in the care and management of people living with diabetes mellitus?

Research Question 3 (RQ3): What impact does the use of robotic devices have on patient- and family-related health outcomes in people living with diabetes mellitus?

Data Extraction and Synthesis

Potential papers identified through electronic searches were imported into Covidence, and duplicates removed. Two reviewers (AAB and RP) independently screened titles and abstracts, followed by full-text reviews. Discrepancies were resolved through discussion or consultation with a third reviewer (GW). A standardized data extraction form was piloted and revised (Tables 3 and 4). Consistent with scoping review methodology,¹⁹ no formal quality appraisal was conducted, as the aim is to map literature breadth and depth without excluding studies based on quality. However, to ensure rigor and reliability in data extraction, the third reviewer (GW) performed a quality assurance check on all included studies, identifying one inaccuracy that was resolved through reviewer comparison, reference to original papers, and team consensus. Thematic content analysis, guided by Arksey and O’Malley’s framework,¹⁹ was conducted in alignment with the research questions. Identified themes were organized and classified by the authors without predefined categories to minimize bias. Descriptive methods were used for quantitative data analysis. Results are presented using PRISMA (Figure 1), with the completed PRISMA-ScR checklist provided as Supplementary Material.

Table 3.

Study Characteristics and Data Collection.

Study (author, year) and country	Sample/setting	Robot name	Type of study, data collection and analysis
Al-Taee et al²¹	Children with type 1 diabetes (T1D).Sample size and setting not specified.	NAO humanoid robot	Prototype evaluation and pilot study. Data collected through observations of human-robot interactions. Results based on observations on the accuracy of data transfer and navigation flexibility between dialogue nodes.
Al-Taee et al²²	37 children (ages 6–16) with T1D, in clinic setting visitsMean HbA1c (9.0%); seven children used insulin pumps. Mean diabetes duration (3.1 years). Gender was evenly distributed.	NAOhumanoid robot	Cross-sectional quantitative study. Surveys assessed attitudes and acceptance of the robot, analysis of acceptability levels across age groups and genders. Descriptive statistics and inferential analyses used.
Al-Taee et al²³	22 children (ages 8-15) with T1D in a home setting; equal gender distribution; also seven health care professionals (HCPs: clinicians, nurses, dieticians).	NAOhumanoid robot	Pilot study evaluated the platform’s acceptability and functionality through surveys, clinician assessments, and digital data from robot interactions, focusing on communication, engagement, and diabetes self-management. Digital data collected from robot-DMH interactions to track system performance in data transmission, patient profile matching, and real-time feedback. Qualitative observations and descriptive analysis described platform interactions and acceptability.
Alhmeidat et al²⁴	Five children (three males and two females) aged 5-9 years, at a diabetes clinic.	SARA—small semi-humanoid (wheeled) robot	Pilot study using cross-sectional quantitative design. Data collected through quantitative questionnaires and observations.
Alhmiedat et al²⁵	30 children (aged 5–15) with T1D at a diabetes clinic. 15-intervention group and 15-control education group. No gender or age distribution differences between groups. Mean HbA1c (control: 9.00 ± 0.50; intervention: 8.75 ± 0.25).	PEPPER semi-humanoid (wheeled) robot	Randomized controlled trial. Data collected through knowledge assessments (12-item questionnaire), home blood glucose measurements, and HbA1c levels before and after a six-week intervention. Statistical analysis included t tests, chi-square tests, and repeated-measures ANOVA.
Baroni et al²⁶	22 families of children with T1D, involving 32 children (aged 5-13) and 38 adults. 22 of the children had diabetes (aged 9-13) and the 10 healthy siblings. Brainstorming sessions at a science museum	NAOhumanoid robot	Qualitative study using brainstorming sessions (six small groups of 5-6 children, and 6-7 adults) and interactions with the robot in a creative, playful environment, followed by interviews and thematic analysis.
Blanson Henkemans et al²⁷	5 children with T1D (aged 8–12), from pediatric clinic. Mean diabetes duration (68 ± 16.58 months). The minimum HbA1c was 7.1 and the maximum 8.2 (M = 7.76, SD = 0.43).	NAOhumanoid robot	A randomized pilot study compared personal and neutral robots using questionnaires and interaction observations. Measures included quality of life, fun, diabetes knowledge, and motivation (child’s choice to play a fourth round). Data were analyzed using descriptive and inferential statistics.
Blanson Henkemans et al²⁸	27 children (13 boys, 14 girls; age 7–14, M=11.04, SD=1.71) with T1D, at a diabetes clinic in the Netherlands. Mean diabetes duration (57 ± 27.6 months). Mean HbA1c 8.36% ± 0.96%).	NAOhumanoid robot	Randomized controlled trial; data collected through surveys, video/audio recordings for observations of engagement with the robot and motivation to play.
Cañamero and Lewis²⁹	17 children with T1D (aged 7-12), parents, and health care professionals, at a hospital clinic and camp.	NAOhumanoid robot	Pilot study using qualitative observational methods from unscripted child-robot interactions.
Chiu et al³⁰	30 adults with diabetes (mean age 69.2; 15 males) and 10 pharmacists from community pharmacies. 15 patients had diabetes >10 years; eight for <5 years; seven for 6–10 years. 15 had no internet experience; 18 were familiar with robots.	Not specified	Mixed-methods study. Twelve patients and 10 pharmacists were interviewed. Data collection included quantitative measures (pre- and posttests on diabetes knowledge, self-efficacy, and robot feasibility) and qualitative insights (semi-structured interviews).
Kaptein et al³¹	19 children with T1D (12 males, aged 8-11) and 19 adults (parents of the children, eight males, aged 35-48) from a diabetes camp.	NAOhumanoid robot	Cross-sectional study. Data collected through forced-choice scenarios; analyzed using Wilcoxon signed rank test and Mann-Whitney test.
Kaptein et al³²	Not applicable	NAO humanoid robot plus avatar	Descriptive study on development; no data collection or empirical analysis involved.
Kruijff-Korbayová et al³³	24 children with T1D (aged 11–14) 10 females and 14 males, during a summer camp in Italy.	NAO humanoid robot	Quantitative between-subjects design with questionnaires, randomly assigned to (off-activity talk, OAT) vs non-OAT vs control group. t tests used for data analysis.
Ligthart et al³⁴	Study 1: 21 children with T1D (eight girls, 13 boys, aged 8–11) at a diabetes camp.Study 2: 13 children (four girls, nine boys, aged 7–12) with T1D in a home setting through the avatar.	NAO humanoid robot (myPAL avatar)	Study 1: Co-design session, questionnaires, semi-structured interviews. Coding is done using grounded theory.Study 2: Mixed-methods approach, with a quasi-experiment quantitative design (three-week use of myPAL diary), interviews, questionnaires.
Looije et al³⁵	17 children with T1D aged 6–10 years (M = 8.24, SD = 1.25). All were diagnosed >18 months prior (range 23–108 months, M = 51, SD = 29.64). Eleven used insulin pumps, six used injections.	NAOhumanoid robot	Mixed-methods study; questionnaires (fun questionnaire, self-determination/ autonomy), tests (knowledge, self-efficacy, memory) and observations (game preference, video and logging analysis) conducted over three sessions. t tests and nonparametric Wilcoxon and Friedman tests done for analysis. Qualitative analysis of observations.
Mall et al³⁶	N/A—System design and proposed framework, no actual patient sample.	Not specified	Conceptual framework, no empirical data collection. The system is based on simulation and theoretical design.
Neerincx et al³⁷	Conceptual development of PAL robotic system, conducted in hospital, home, and camp settings. The system was tested with clinicians and children (aged 7-14 years) over several projects.	PAL	Iterative methodology using self-determination theory and socio-cognitive engineering, with qualitative analysis of prototype testing sessions and feedback.
Neggers et al³⁸	8 children with T1D (4 males) aged 7–11 mean age 8.9 ± 1.7 years, diagnosed with T1D within the past three years; conducted in their homes.	NAO humanoid robot	A mixed-methods study using video-recorded interactions, child questionnaires on comfort and engagement, and parent interviews on robot characteristics. Data analyzed thematically.
Robinson et al³⁹	Four adolescents with T1D (aged 13–16) one male nine females, conducted at a hospital outpatient clinic.	NAO (Andy) humanoid robot	Small scale mixed-methods feasibility study. Quantitative scales (eg, confidence, motivation) and qualitative interviews analyzed thematically.
Sinoo et al⁴⁰	21 children with T1D (aged 8–11), 13 boys and eight girls; at a diabetes camp.	PAL	Mixed-methods using quantitative questionnaires on friendship, similarity, motivation, and usability, and qualitative responses on perceptions.
van der Drift et al⁴¹	Six children (four boys) with T1D, aged 9–12 (M, 10.8 ± 1.3). Mean diabetes duration six years; using a home-based setting with remote robot interaction.	NAO humanoid robot	Cross-sectional study design with diary logs, observation, and questionnaires analyzed for adherence, engagement, and bonding.
Yagi et al⁴²	28 patients with T2D (67.9 ± 14.8 years old, 23 men) in an internal medicine clinic.	RoBoHoN (small humanoid phone robot) by Sharp Corporation	Retrospective cross-sectional observational study. Data collected via robot-assisted remote interviews, questionnaires, and HbA1c measurements at baseline and after two months. Statistical analysis included chi-square tests, multiple regression analysis, and Kruskal-Wallis tests.

Abbreviations: T1D, type 1 diabetes; OAT, off-activity talk; AI, artificial intelligence; T2D, type 2 diabetes; M, median; SD, standard deviation.

Table 4.

Study Findings.

Study (author, year) and country	Aim(s)	Outcome measurement	Key findings
Al-Taee et al²¹	To develop a human-robot sub-dialogue structure using the XML Document Object Model to enhance the interactivity between children with diabetes and their health care providers.	Seamless and accurate data transfer between robot and remote Web-based health portal; high navigation flexibility between different dialogue nodes.	The aim of the robot: (1) Remote collection and monitoring of patient data, such as physical activity, diet, medications and well-being, including short audio messages from the user; (2) Automatic recognition of blood glucose monitoring and provision of timely feedback; (3) Provide diabetes self-management education models, tailored to patient’s individual needs.The proposed system improved bidirectional information flow between patients and health care providers, aiding diabetes self-management with interactive dialogue and gesture system.
Al-Taee et al²²	To assess the acceptability of a humanoid robot in assisting the diabetes management in children.	Patient acceptability of the robot’s assistance, interaction, and feature-specific evaluation.	The robot was well received (acceptability 86.76%). Younger children (ages 6-9) had the highest acceptability. Valued features included the robot’s ability to provide advice on blood glucose management and relay pattern advice. Lower-ranked features included companionship and insulin dose calculation, especially by children over 10 years. 98% of patients under 10 expressed interests in taking the robot home compared with 84% in the older age group.
Al-Taee et al²³	To develop an Internet of Things (IoT)-based e-Health platform incorporating humanoid robots to assist in diabetes management for children.	Acceptability of robot-assisted therapy, effectiveness on perceived self-efficacy and emotional well-being.	High acceptability of the platform by both patients and clinicians, especially in improving patient-health care professionals’ interaction and providing real-time feedback. Robot offered:(1) Verbal information on diet, insulin bolus, physical exercise; (2) Dashboard summary; (3) Diabetes diary, including carbohydrate intake, physical exercise and well-being index; (4) Education and motivation dialogues to patients; (5) Decision support. Greater than 80% acceptability reported across all domains.
Alhmeidat et al²⁴	To analyze the efficiency of a diabetic management system: the Saudi Arabian Robotic Assistant (SARA) in managing diabetes among children.	Quantitative evaluation of interaction time, educational effectiveness, quiz performance, and storytelling engagement.	The participants reported a high acceptance rate (88.2%), indicating the feasibility of using social robots for diabetes management in children.SARA efficiently collects and shares children’s blood glucose levels with health care professionals using MySignals HW, requiring no client intervention.
Alhmiedat et al²⁵	To evaluate the impact of robot-assisted education on knowledge acquisition and metabolic control in children with T1D compared with traditional education methods.	Knowledge improvement (pre and posttest scores), changes in weekly mean blood glucose levels, and HbA1c reduction after three months.	The intervention group showed significantly greater improvement in knowledge scores (P < .05) and better glycemic control, with lower weekly mean blood glucose levels and a greater reduction in HbA1c levels (−1.00% vs. −0.75% in the control group, P = .0330). Children in the robot-assisted group were more engaged compared with the traditional education group.
Baroni et al²⁶	To explore how a robotic companion could support children with T1D, focusing on educational and emotional support.	Children’s/ caregivers’ perceptions of how a SAR can assist with diabetes management, emotional support and diabetes education.	The robot could assist with self-management tasks, like measuring glycemia, insulin reminders, and carbohydrate counting, and could boost children’s motivation and self-confidence in managing their diabetes. Viewed as a tool to teach children about diabetes in a fun way and educate others (eg, teachers). The robot was valued for providing emotional support, particularly during stressful moments (eg, hospital visits).
Blanson Henkemans et al²⁷	To assess the effects of personalized robot behaviors on children’s enjoyment, motivation, and knowledge acquisition related to diabetes self-management.	Enjoyment and motivation levels, increase in diabetes knowledge, and children’s interaction, behavior with personal and neutral robots.	Personalized robot led to more engagement, verbal interaction, and mimicking behaviors compared with the neutral robot. Children enjoyed interacting with the robot, although their fun and motivation levels decreased slightly over time; initial enjoyment (4.8/5) dropped to (4.0/5) by the third session. The children significantly improved their diabetes knowledge, with correct answers increasing from 13/20 at baseline to 18.2/20 by the third session. While physical engagement declined over time, verbal engagement remained strong.
Blanson Henkemans et al²⁸	To evaluate the effects of a SAR on children’s engagement, motivation, and knowledge acquisition of diabetes self-management.	Motivation to play a diabetes quiz, engagement with the robot, pleasure, and diabetes knowledge measured over three sessions.	Children interacting with the personal robot showed higher engagement, motivation to play a diabetes quiz, and pleasure compared with a neutral robot and control (usual care) group. Knowledge about diabetes improved significantly in both robot groups as compared with the control group.
Cañamero and Lewis²⁹	To develop and assess a social robot to support diabetes management in children through positive mastery experiences and emotional well-being.	Engagement levels, emotional responses, and perceived self-efficacy during child-robot interaction.	Stakeholder feedback highlighted children’s need for emotional support, engagement, and empathy; parents’ focus on monitoring, education, and safety; and health care professionals’ priority for personalized care, data feedback, and sustained engagement.Children formed positive bonds with Robin, exhibiting care, empathy, and receiving emotional support. The robot was effective in providing a playful, engaging context for learning diabetes self-management. Additional trialing in a clinical context would be helpful.
Chiu et al³⁰	To evaluate the feasibility and effectiveness of a robot in supporting diabetes management for middle-aged/ older adults in the community.	Changes in diabetes knowledge, self-efficacy, and feasibility of robot use.	The robot improved diabetes knowledge and usability, especially among adults with higher educational attainment, but did not significantly impact self-efficacy. Participants found it engaging and enjoyable to interact with, useful for self-care, and supportive of diabetes management, though practical functionality challenges were noted. Pharmacists emphasized aligning the robot with real-life patient needs.
Kaptein et al³¹	To test hypothesis that adults have a stronger preference than children for goal based over belief-based explanations.	Percentage of scenarios where participants preferred goal-based explanations.	The study presents the user-preference for goal-based and belief-based explanations with a higher preference to goal based for adults and children. Adults preferred more goal-based explanations than children. Adults, as goal-oriented learners, value instructions that enhance existing abilities, whereas children assume all instructions will benefit them in the future.
Kaptein et al³²	To describe the design principles, architecture, and development of a cloud-based robot system (PAL) for supporting diabetes management in children.	Not applicable—no empirical outcomes measured.	The PAL system was developed with a cloud-based architecture for real-time processing and scalability, enabling adaptive interactions. Its modular design allowed for easy updates and integration of new features. A common knowledge base is created with HCPs to ensure accurate, personalized diabetes education. The hybrid AI approach combined machine learning for customization and symbolic AI for structured decision-making. Development challenges included module integration and data privacy.
Kruijff-Korbayová et al³³	To explore the impact of including off-activity talk (OAT) in robot interactions on children’s interest in interactions and adherence to a nutritional diary.	Perception of the robot (engagement and relationship to the robot), interest in future interactions, adherence to filling a nutritional diary.	OAT increased interest in future interactions with the robot but had no significant effect on the perception of the robot or adherence to diary tasks.Children in the OAT group found the robot engaging, suggesting OAT could support long-term interaction.
Ligthart et al³⁴	To identify children’s expectations of robots (Study 1) and explore how misaligned expectations affect the effectiveness of SARs (Study 2).	Study 1: Children’s expectations of robots.Study 2: Diary adherence, motivation, and perception of robot	Study 1: Children expected practical and emotional support from robots. They expected more from the avatar in terms of interaction. The study recommended that a tailored expectation management strategy is put in place.Study 2: Misaligned expectations negatively impacted motivation, diary adherence, and perception of the robot’s sociability.
Looije et al³⁵	To evaluate a social robot’s role in supporting children with T1D in self-management during hospital visits.	Self-management knowledge, engagement, self-efficacy, and social relatedness with the robot.	The children formed positive connections with the robot, with increased autonomy and engagement. There was a significant increase in knowledge, with no difference in memory. The robot’s emotional adaptability fostered openness and engagement, with children even bringing gifts to the robot, indicating strong bonds. Feedback from parents and health care staff suggested improved self-management and reduced anxiety during hospital visits.
Mall et al³⁶	To propose a system using IoT-based robots to improve diabetes care through real-time monitoring of health and diet.	Blood glucose monitoring, dietary management, and patient-health feedback.	The proposed system integrates medical and dietary sensors (to detect chew rate per second) with a robot assistant. It allows real-time monitoring of blood glucose and dietary habits, providing feedback to both patients and health care professionals through IoT connections. The system aims to improve disease management efficiency by offering personalized health advice.
Neerincx et al³⁷	To develop a socio-cognitive engineered robotic partner to support children with T1D in self-management.	Diabetes knowledge, emotional support, self-management efficacy.	The four-year evaluation of the PAL system demonstrated its ability to support long-term engagement and learning through personalized feedback. The robot increased children’s diabetes knowledge and self-management. Emotional bonding with the robot enriched the learning experience. The hybrid approach, combined symbolic reasoning and machine learning to personalize interactions and adapt to the needs and emotional states of the children.
Neggers et al³⁸	To explore how robot characteristics affect children’s experiences in diabetes self-management learning.	Children’s engagement, emotional bonding, perceptions of robot characteristics like eye contact and gestures.	Children were highly engaged by the robot’s gestures and emotional expressions, which improved rapport and interaction quality. Eye contact received mixed reactions, emphasizing the need for personalization. The trivia game increased engagement and encouraged competitive, enjoyable interactions. Parents noted that the robot’s emotional expressions fostered bonding, with children viewing it as a friend and showing empathy.
Robinson et al³⁹	To evaluate the feasibility and effects of a robot-delivered motivational intervention for dietary management in adolescents with T1D.	Frequency of high-energy food intake, dietary treatment index, self-efficacy, motivation to change, robot interaction, treatment usefulness and HbA1c.	The intervention reduced sugary food/drink intake by 70% for some participants and improved self-efficacy and motivation.Qualitative feedback was positive, with participants describing the robot as friendly and nonjudgmental.
Sinoo et al⁴⁰	To explore how children’s perceived similarity between a robot’s physical and virtual forms influences their sense of friendship, motivation to engage in a diabetes game, and the usability of a self-management application featuring the avatar.	Friendship with avatar, perceived similarity of the physical robot with its avatar, motivation to use the app, and app usability.	Children reported stronger feelings of friendship toward the physical robot compared with the avatar. Perceived similarity between the robot and avatar correlated with higher motivation and usability ratings for the app. Qualitative feedback indicated that children found the physical robot more engaging due to its capabilities, spoken feedback, movements and social presence.
van der Drift et al⁴¹	To evaluate the effectiveness of a remote SAR in motivating children with T1D to maintain a diary and foster self-disclosure.	Adherence to diary, engagement with the robot, and emotional bonding.	Children had increased diary adherence and shared more personal experiences with the robot. Participants wrote more when interacting with the robot. They rated the robot as friendly, trustworthy, and human-like, often viewing it as a ‘friend’. Social robots can enhance engagement with diabetes self-management.
Yagi et al⁴²	To evaluate the impact of a robot-assisted diabetes self-management monitoring system on glycemic control.	Robots were limited to data collection without providing educational instruction. Change in HbA1c, classification of glycemic control as Adequate or Inadequate, questionnaire on robot effectiveness.	Significant improvement in glycemic control for seven participants, with an average reduction in HbA1c by 0.19%. Structured inquiries by the robot may have triggered increased awareness, resulting in improved glycemic control. Patients who found the robot’s responses unnatural or unclear showed improvement. Future developments should focus on enhancing conversation content rather than response naturalness.

Abbreviations: AI, artificial intelligence; HCPs, health care professionals; T1D, type 1 diabetes; T2D, type 2 diabetes; OAT, off-activity talk; SAR, socially assistive robot.

Figure 1.

PRISMA diagram of study identifications, screening, and selection.

Results

Study Characteristics

A total of 212 papers were identified from the systematic literature search, and 22 met the inclusion criteria (Figure 1). Tables 3 and 4 provide an overview of the studies’ characteristics and findings.

Sixteen studies were conducted in Europe,^{21-23,26-29,31-35,37,38,40,41} five in Asia,^{24,25,30,36,42} and one in Australia.³⁹ The majority (n=18) of studies focused on children and young people with T1D, of which four included feedback from parents/caregivers^26,29,35,38 and three from health care providers.^23,29,35 Two studies focused on middle-aged and older adults with diabetes,^30,42 and one did not include a specified population.³⁶ All except one paper²¹ reported the setting of the study. Eleven studies^{22,24,25,27-29,33,35,37,39,42} were conducted in clinical settings, five took place in diabetes camps,^{26,29,34,37,40} and five focused on the use of robots in home environments.^{23,34,37,38,41} One study was conducted in community pharmacies,³⁰ and another in a science museum.²⁶

Study Designs and Aims

Eight studies^{22,23,30,31,34,35,38,41} used cross-sectional mixed-methods design and explored multiple aspects of robotic interventions, including emotional responses, engagement, and practical usability. Only one study used a randomized control study design,²⁵ comparing human-led versus robot-led interventions. Three studies^27,28,33 employed quasi-experimental designs, two cross-sectional,^24,42 and six^{21,26,29,37,39,40} qualitative. Two studies^32,36 provided descriptive/conceptual frameworks without empirical data, evaluating software architectures for integrating SARs into e-Health platforms, and their potential in long-term diabetes management.

Twelve papers stemmed from two EU projects: ALIZ-E^{26-29,33,35,41} and Personal Assistant for a healthy Lifestyle (PAL).^{31,32,34,37,40} They describe the design of different facets of the respective systems and evaluate them in various roles, such as educators, motivators and friends. The ALIZ-E project focused on the AI-driven social robots, capable of effective and engaging interaction to support children with T1D, while PAL tested a hybrid e-Health platform combining the robot with an Embodied Conversational Agent (ECA) and mobile health apps for diabetes support in children.

Robotic Devices and Interventions

Fifteen studies used the Aldebaran NAO humanoid robot,²¹-^{23,26-29,31,32,35,37-41} a popular choice for STEM education and research in robot-child interactive systems due to its compact size, friendly humanoid-like appearance, and interactive capabilities (Figure 2). Three studies used other small humanoid robots: RoBoHoN, a small phone mobile robot by Sharp Corporation,⁴² a wheeled semi-humanoid prototype robot called SARA,²⁴ and a small humanoid robot alongside other robot evaluations.³⁰ Only one study²⁵ used a larger robot—Pepper, a wheeled semi-humanoid with a chest-mounted tablet (Figure 2). Study³⁶ describes a robot-agnostic software framework and tested remote interaction with a NAO robot through videoconferencing.³⁸

Figure 2.

Humanoid robots: NAO (left) and Pepper (right), licensed under the creative commons attribution license 4.0.

Across all studies, interventions were socially interactive, promoting behavior change. Eleven studies detailed robots delivering behavior change, and support interventions aimed at building confidence and fostering empathetic interactions.^{23-26,29,30,35,37-39,41} Educational interventions were reported in nine studies, which included educational quizzes and sorting games.^{23-25,28-30,35,37,38} In six studies, self-management was also a feature and included diabetes diaries, carbohydrate counting, controlling high energy drink intake, and monitoring of glucose levels and food intake.^{21,23,26,33,34,39}

Most of the SAR interventions were tested in scripted/semi-scripted experimental scenarios, requiring various degrees of supervision and control by researchers in short experiments through “Wizard of Oz” interfaces. Noticeable exceptions include the PAL system, whose cognitive architecture enabled autonomous interaction with children over several months,³² and the system in,³⁹ which used a simpler rule-based approach to deliver two 60-minute couching sessions and two 15-minutes videos with a NAO robot over eight weeks. Table 4 provides a summary of the study findings.

Barriers, Enablers, and Health Outcomes Associated With SAR Use

The analysis revealed distinct themes related to barriers, enablers, and patient- and family-centered health outcomes associated with SAR use, as illustrated in Figure 3.

Figure 3.

Barriers, enablers, and health outcomes associated with SAR use.

Barriers to SAR use: Practical challenges were cited as discomfort with eye contact,³⁸ limited customization, and usability concerns.³⁰ Engagement with the robots declined over time, particularly among older children, reducing motivation to interact with the robots consistently.²⁷ Misaligned practical and emotional expectations between what children hoped the robot would offer and what it provided, negatively affected their motivation and engagement.³⁴ Concerns regarding system integration, data privacy, and the technical scalability of robotic systems were reported as significant barriers to development.³²

Enablers to SAR use: Hybrid AI approaches enabled robots to tailor interactions based on user preferences and emotional states, enhancing their relevance and effectiveness.^{21,32,37,38,40} Interactive features, such as verbal communication, gestures, real-time feedback, and gamified components, enhanced user engagement, particularly for younger users.^{21,27-29,33,35,38} Emotional responsiveness through eye contact, empathy gestures, and expressions fostered rapport and emotional bonding.^{26,27,29,35,38,40} Robots also offered multi-functional capabilities, such as diabetes diaries, educational tools, decision support, and real-time monitoring, supporting cohesive diabetes self-management. Hybrid approaches combining avatars with real robots^{31,32,34,37,40} and using tele-conference software to access a real robot⁴¹ were designed to increase access to expensive robot platforms, usually confined to research labs. Robots with modular designs, cloud connectivity, and integration with e-Health platforms, supported real-time updates, remote monitoring, and adaptability across diverse settings, including homes, clinics, and camps.^{23,26,30,32,36,37}

Patient- and family-related health outcomes: Robots enhanced participants’ understanding of diabetes and improved their self-management skills by delivering tailored education on insulin use, diet, and physical activity.^{23,25,27-29,35,37} One study³⁰ showed that middle-aged and older adults benefited from self-directed learning using robots, while another⁴² reported that robot-led interviews raised awareness of diabetes management, in adults with diabetes, positively affecting glycemic control. Robots also provided emotional support and companionship, reducing stress during hospital visits,^26,29 and fostering engagement and motivation through emotional bonding.^{27,28,35,38,40,41} They enhanced communication among patients, families, and health care professionals through real-time feedback and streamlined data sharing,^{21,23,24,29,42} thus helping clinicians better address patient needs.^21,23,32 Robots also reduced the burden on caregivers by encouraging patient autonomy, particularly among children, and by offering educational tools and real-time updates.^{26,29,35,37,41} Features like quizzes increased children’s interest and participation in self-management.^27,28 Off-activity talk (OAT) sustained motivation for robot interactions,³³ while trivia games, verbal feedback, and physical interactions further boosted enjoyment and engagement.^24,25,38,40

Discussion

To our knowledge, this is the first review to explore the effect of SARs on diabetes care across the lifespan, offering valuable insights into their application. The review emphasizes how participants interact with SARs and demonstrates their contribution to diabetes self-management. Findings indicate that SARs positively impact health by promoting confidence, knowledge acquisition, and effective lifestyle monitoring. Our findings suggest that future SAR designs should prioritize multi-modal capabilities to support behavior change, enhance self-management, and foster confidence and knowledge acquisition. Integrating cloud-based technology and AI allows real-time interactions and access to current information, though care must be taken to address potential risks associated with misinformation and disinformation during the design and decision-making process.

Many of the studies reviewed focused on children with T1D, reflecting a strong interest in this population. This focus likely stems from two key factors. Children are generally more receptive to playful, interactive technologies and benefit from emotionally engaging educational tools. In addition, early childhood is a critical period for developing lifelong self-management habits, making it a strategic time for intervention.⁴³ The studies demonstrate how SARs can enhance education, motivation and self‑management through gamification, storytelling and emotionally responsive interactions. These findings align with research involving children undergoing cancer treatment, where SARs were found to reduce anxiety and improve treatment‑related behaviors.⁴⁴ Similarly, SARs enhanced social communication and emotional engagement in children with autism⁴⁵ and promoted behavior changes like increased physical activity and improved inhaler technique in children with obesity and asthma.^46,47 However, as with diabetes‑focused interventions, these studies report declining engagement over time due to the novelty effect and limitations in robot capabilities. This common challenge reflects a mismatch between user expectations and SAR functionality across conditions, highlighting the need for age‑appropriate, co‑designed interventions.⁴⁸

Whilst SARs appear effective in pediatric diabetes care, there remains limited evidence supporting their efficacy among adults. This discrepancy may stem from adult reluctance toward robotic assistance in favor of traditional human interaction,⁴⁹ caregiver resistance,⁵⁰ or a growing focus on AI in diabetes self-management.^51,52 Despite the limited research on SARs in adults with diabetes, their successful application in other health contexts suggests strong potential for integration. For instance, SARs have improved adherence to physical therapy in cardiac rehabilitation,⁵³ COPD,¹⁴ and poststroke care,⁵⁴ approaches that could be adapted to promote regular exercise—a key factor in glycemic control. Similarly, in elderly care, SARs have enhanced social interactions, reduced loneliness, and improved mood and cognitive engagement in older adults, including those with dementia.^55,56 They have facilitated therapeutic activities and interactive conversations, contributing to better emotional well-being and overall quality of life.⁵⁷ Given the link between diabetes, cognitive decline, and emotional distress,⁵⁸ these combined benefits could enhance self-management in older adults with diabetes. However, unlike children who value SARs’ playfulness, older adults tend to prioritize practicality, reliability, cost-effectiveness, and maintaining genuine human connections.⁵⁹

While this review incorporates studies included in previous reviews on SARs in pediatric diabetes care,^13,17 it extends existing findings by exploring barriers and enablers influencing their use. Identified barriers include usability challenges, such as limited customization, misaligned user expectations, declining engagement over time, and concerns about system integration, privacy, and scalability. In contrast to the two prior reviews on SAR use in older adults, which highlighted technological challenges as a barrier,^60,61 our review did not observe this issue. Interestingly, prolonged use improved SARs use in older adults with dementia,⁶⁰ contrasting with the “declining engagement” theme observed here. These differences reflect the distinct needs of different demographic groups, emphasizing the importance of personalized interventions. Enablers, such as personalized interactions and multi-functional designs were consistently identified in our review and others,^60,61 while emotionally responsive features showed mixed effects, facilitating or hindering use depending on context.⁶¹ Addressing barriers, while leveraging enablers, is crucial to optimizing SARs to meet diverse user needs across demographics and settings.

A key strength of this review is its comprehensive literature search combined with a thorough data synthesis approach. However, the evidence included in this review is subject to some methodological limitations. Many of the studies included are cross-sectional, offering a snapshot of robotics potential benefits in diabetes care but lacking the ability to capture trends, variation, or longitudinal effects. Cross-sectional designs are also susceptible to selection and recall bias reducing the generalizability of findings.⁶² Furthermore, comparative studies included were not sufficiently powered to draw definitive conclusions. Consequently, current evidence remains insufficient to support widespread implementation of robotic technology in health care. Another key limitation is the geographical concentration of studies in high-income countries (eg, Netherlands and the United Kingdom), with 16 out of 22 studies conducted in European settings. This limits the generalizability of findings to other cultural and health care contexts.

Furthermore, excluding non-English studies may have introduced language bias by omitting research from countries with advanced robotics, thereby limiting global relevance. However, this decision was necessary due to limited translation resources and the need to ensure consistent data extraction. Many studies were laboratory-based, possibly overestimating the acceptability and feasibility of robotic technologies compared with real-world environments. In addition, most studies were led by robotics experts with limited evidence of collaboration with health professionals, highlighting the need for interdisciplinary partnerships. Moreover, none of the reviewed studies assessed the environmental impact of their intervention. Advancements in health technology must prioritize environmental and social sustainability. Digital pollution is likely to escalate as increased efficiency and cost reductions drive higher consumption and demand.⁶³ Therefore, the immediate effects of such technologies may appear positive, long-term implications may prove detrimental.

Finally, it remains unclear how the cost of robotic technology compares to that of traditional health care provider visits aimed at coaching to optimize self-management. In the field of surgical robots, cost-benefit aspects have been explored, noting reductions in open surgery rates.⁶⁴ However, such interventions remain prohibitively expensive due to high material and depreciation costs.⁶⁵ In the context of AI, these applications may be more economically viable for treatment rather than diagnostic purposes.⁶⁶ Some reviewed papers explored ways to facilitate access to expensive robotic hardware by exploiting virtual avatars and/or video calls, as seen in the PAL project. While these approaches and nonembodied conversational agents like chatbots show promise, further research is needed to validate their cost-effectiveness and impact.⁶⁷

Recommendations for Future Research

To address the identified gaps, the following recommendations for future research are proposed:

Conduct robust, adequately powered randomized controlled trials involving both children and adults with diabetes, featuring at least 6 months of follow-up, to evaluate the effects of SARs on key clinical outcomes such as HbA1c, quality of life, and self-management behaviors in real-world clinical and home environments.

Incorporate cost-effectiveness analyses using tools, such as cost diaries and health service logs, comparing SARs with standard professional coaching from national health care payers’ perspectives.

Foster interdisciplinary collaboration among robotics engineers, health care professionals, behavioral scientists, and end users through participatory design to co-create and refine SAR prototypes.

Expand SAR research to low- and middle-income countries through partnerships with local institutions, adapting content to local languages and cultural norms using equity-focused frameworks.

Collaborate with environmental scientists to assess SAR interventions environmental impact, measuring lifecycle carbon footprint, energy use, and e-waste generation.

Conclusions

This review has provided a comprehensive and novel insight into the use of robotics in diabetes care across the lifespan. However, methodological limitations constrain the ability to draw definitive conclusions around widespread implementation in health care. Future robotic design for adults with diabetes should prioritize self-management support, confidence building, and knowledge acquisition. Future studies should adopt an interdisciplinary approach, ensure methodological rigor, and place greater emphasis on cost-benefit analysis, sustainability and patient-centered outcomes.

Supplemental Material

sj-docx-1-dst-10.1177_19322968251356298 – Supplemental material for Robotics Use in the Care and Management of People Living With Diabetes Mellitus: A Scoping Review

Supplemental material, sj-docx-1-dst-10.1177_19322968251356298 for Robotics Use in the Care and Management of People Living With Diabetes Mellitus: A Scoping Review by Amani Al Bayrakdar, Mauro Dragone, Gosha Wojcik, Alistair McConnell, Maria King and Ruth Paterson in Journal of Diabetes Science and Technology

Footnotes

Acknowledgements

AI, artificial intelligence; ECA, embodied conversational agent; IDF, International Diabetes Federation; OAT, off-activity talk; PAL, personal assistant for a healthy lifestyle; PCC, population concept and context; SARs, socially assistive robots; T1D, type 1 diabetes; T2D, type 2 diabetes; WHO, World Health Organization.

Acknowledgements

None.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Amani Al Bayrakdar

Supplemental Material

Supplemental material for this article is available online.

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