Humanoid robot-assisted interventions among children with diabetes: A systematic scoping review

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Social robots in research on social and cognitive development in infants and toddlers: A scoping review

There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.

Exploring the impact of socially assistive robots on health and wellbeing across the lifespan: An umbrella review and meta-analysis

BACKGROUND

Socially assistive robots offer an alternate source of connection for interventions within health and social care amidst a landscape of technological advancement and reduced staff capacity. There is a need to summarise the available systematic reviews on the health and wellbeing impacts to evaluate effectiveness, explore potential moderators and mediators, and identify recommendations for future research and practice.

OBJECTIVE

To explore the effect of socially assistive robots within health and social care on psychosocial, behavioural, and physiological health and wellbeing outcomes across the lifespan (PROSPERO registration number: CRD42023423862).

DESIGN

An umbrella review utilising meta-analysis, narrative synthesis, and vote counting by direction of effect.

METHODS

14 databases were searched (ProQuest Health Research Premium collection, Scopus, PubMed, Web of Science, ASM Digital Library, IEEE Xplore, Cochrane Reviews, and EPISTEMONIKOS) from 2005 to May 4, 2023. Systematic reviews including the effects of socially assistive robots on health outcomes were included and a pooled meta-analysis, vote counting by direction of effect, and narrative synthesis were applied. The second version of A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) was applied to assess quality of included reviews.

RESULTS

35 reviews were identified, most focusing on older adults with or without dementia (n = 24). Pooled meta-analysis indicated no effect of socially assistive robots on quality of life (standard mean difference (SMD) = 0.43), anxiety (SMD = -0.02), or depression (SMD = 0.21), although vote counting identified significant improvements in social interaction, mood, positive affect, loneliness, stress, and pain across the lifespan, and narrative synthesis identified an improvement in anxiety in children. However, some reviews reported no significant difference between the effects of socially assistive robots and a plush toy, and there was no effect of socially assistive robots on psychiatric outcomes including agitation, neuropsychiatric symptoms, and medication use.

DISCUSSION

Socially assistive robots show promise for improving non-psychiatric outcomes such as loneliness, positive affect, stress, and pain, but exert no effect on psychiatric outcomes such as depression and agitation. The main mechanism of effect within group settings appeared to be the stimulation of social interaction with other humans. Limitations include the low quality and high amount of overlap between included reviews.

CONCLUSION

Socially assistive robots may help to improve loneliness, social interaction, and positive affect in older adults, decrease anxiety and distress in children, and improve mood, stress, and reduce pain across the lifespan. However, before recommendations for socially assistive robots can be made, a cost-effectiveness analysis of socially assistive robots to improve mood across the lifespan, and a quantitative analysis of the effects on pain, anxiety, and distress in children are required.

Improved Glycemic Control through Robot-Assisted Remote Interview for Outpatients with Type 2 Diabetes: A Pilot Study

Background and Objectives: Our research group developed a robot-assisted diabetes self-management monitoring system to support Certified Diabetes Care and Education Specialists (CDCESs) in tracking the health status of patients with type 2 diabetes (T2D). This study aimed to evaluate the impact of this system on glycemic control and to identify suitable candidates for its use. Materials and Methods: After obtaining written informed consent from all participants with T2D, the CDCESs conducted remote interviews with the patients using RoBoHoN. All participants completed a questionnaire immediately after the experiment. HbA1c was assessed at the time of the interview and two months later, and glycemic control status was categorized as either "Adequate" or "Inadequate" based on the target HbA1c levels outlined in the guidelines for adult and elderly patients with type 2 diabetes by the Japan Diabetes Society. Patients who changed their medication regimens within the two months following the interview were excluded from the study. Results: The clinical characteristics of the 28 eligible patients were as follows: 67.9 ± 14.8 years old, 23 men (69%), body mass index (24.7 ± 4.9 kg/m2), and HbA1c levels 7.16 ± 1.11% at interview and two months later. Glycemic control status (GCS) was Adequate (A) to Inadequate (I): 1 case; I to A: 7 cases; A to A good: 14 cases; I to I: 6 cases (p-value = 0.02862 by Chi-square test). Multiple regression analyses showed that Q1 (Did RoBoHoN speak clearly?) and Q7 (Was RoBoHoN's response natural?) significantly contributed to GCS, indicating that the naturalness of the responses did not impair the robot-assisted interviews. The results suggest that to improve the system in the future, it is more beneficial to focus on the content of the conversation rather than pursuing superficial naturalness in the responses. Conclusions: This study demonstrated the efficacy of a robot-assisted diabetes management system that can contribute to improved glycemic control.