Effect of the Information Support Robot on the Daily Activity of Older People Living Alone in Actual Living Environment

Investigating the effectiveness of socially assistive robot on depression and cognitive functions of community dwelling older adults with cognitive impairments

We evaluated a socially assistive robot (SAR) named Hyodol during a six-week intervention. This study enrolled 69 older adults with cognitive decline. To screen the eligibility, we have used the following three criteria, namely Korean-Mini-Mental Status Exam score ≤ 26). Clinical Dementia Rating 0.5-2), and Diagnostics and Statistical Manual V. Participants were divided into three groups based on their cognitive function (i.e. very mild cognitive impairment (vMCI), mild cognitive impairment (MCI), and moderate cognitive impairment (MOCI). The groups were instructed to use Hyodol at home for a six-week period. Baseline and post-intervention surveys were performed after six weeks to examine the changes in perceived health, depression, and cognitive function. The vMCI group showed a reduction in the depression score after the intervention (t = -2.447, p = 0.040), in comparison to their peers in the control group. Further, the MCI group showed an improvement in the cognitive function score after the intervention (t = 2.690, p = 0.021). No significant improvement was found among MOCI participants who used the SARs. The significance of this study was to examine whether participants with different levels of cognitive functioning would diverge after a period of intervention using the Hyodol SARs. Moreover, it presented preliminary data for services and policies for home care treatment targeted to cognitive decline in older adults.

Investigating Older Adults' Use of a Socially Assistive Robot via Time Series Clustering and User Profiling: Descriptive Analysis Study

BACKGROUND

The aging population and the shortage of geriatric care workers are major global concerns. Socially assistive robots (SARs) have the potential to address these issues, but developing SARs for various types of users is still in its infancy.

OBJECTIVE

This study aims to examine the characteristics and use patterns of SARs.

METHODS

This study analyzed log data from 64 older adults who used a SAR called Hyodol for 60 days to understand use patterns and their relationship with user characteristics. Data on user interactions, robot-assisted content use, demographics, physical and mental health, and lifestyle were collected. Time series clustering was used to group users based on use patterns, followed by profiling analysis to relate these patterns to user characteristics.

RESULTS

Overall, 4 time series clusters were created based on use patterns: helpers, friends, short-term users, and long-term users. Time series and profiling analyses revealed distinct patterns for each group. We found that older adults use SARs differently based on factors beyond demographics and health. This study demonstrates a data-driven approach to understanding user needs, and the findings can help tailor SAR interventions for specific user groups.

CONCLUSIONS

This study extends our understanding of the factors associated with the long-term use of SARs for geriatric care and makes methodological contributions.

Provision status of assistive products for cognition to older adults with cognitive impairment among rehabilitation therapists

PURPOSE

This study aimed to investigate the provision and need for assistive products for older adults with cognitive impairment among rehabilitation therapists, identify any unmet needs, and analyze current issues in providing such products.

METHODS

We conducted a questionnaire survey with rehabilitation therapists involved in selecting and providing assistive products for cognitive function in older adults with cognitive impairments. Questionnaires comprising ten items about the experience and need for cognitive assistive products were sent to 300 facilities for older adults in Japan between January and February 2021. One hundred thirty-five responses were received.

RESULTS

The mean age of the participants was 39.5 ± SD7.1. There were 70 females, 64 males, and one unanswered questionnaire. In total, 72 (53.3%) of the 135 therapists had introduced or provided assistive products to their patients. The most frequently provided products were for safety and security; some were provided to support memory and orientation. 86% of therapists did not use the insurance system to provide them. The survey showed that most participants with experience providing assistive products needed information on the available resources.

CONCLUSIONS

Although the need for assistive products for cognition is increasing, they are not sufficiently supplied in caring for older adults with cognitive impairment. Their availability differed depending on the assistive product type. It is necessary to have an effective social insurance system and access to up-to-date information to provide appropriate assistive products.

Sequential model based on human cognitive processing to robot acceptance

Robots have tremendous potential, and have recently been introduced not only for simple operations in factories, but also in workplaces where customer service communication is required. However, communication robots have not always been accepted. This study proposes a three-stage (first contact, interaction, and decision) model for robot acceptance based on the human cognitive process flow to design preferred robots and clarifies the elements of the robot and the processes that affect robot acceptance decision-making. Unlike previous robot acceptance models, the current model focuses on a sequential account of how people decide to accept, considering the interaction (or carry-over) effect between impressions established at each stage. According to the model, this study conducted a scenario-based experiment focusing on the impression of the first contact (a robot's appearance) and that formed during the interaction with robot (politeness of its conversation and behavior) on robot acceptance in both successful and slightly failed situations. The better the appearance of the robot and the more polite its behavior, the greater the acceptance rate. Importantly, there was no interaction between these two factors. The results indicating that the impressions of the first contact and interaction are additively processed suggest that we should accumulate findings that improving the appearance of the robot and making its communication behavior more human-like in politeness will lead to a more acceptable robot design.

My Precious Friend: Human-Robot Interactions in Home Care for Socially Isolated Older Adults

OBJECTIVES

Using a friendship framework, we explored interactions between a multi-functional companion robot and older adults residing in a low-resource community in South Korea.

METHODS

We conducted in-depth interviews with 12 older adults who kept a doll-shaped companion robot called Hyodol for 18 months on average. We applied the Framework Analysis Method to explore three types of friendship (i.e., friendships of utility, pleasure, and the good) that participants cultivated with the robot.

RESULTS

The most common aspect of utility companionship reported by all participants was Hyodol's role as their health coach who reminded them to take medication and to exercise. Participants also found pleasure in playing with Hyodol and reported reduced feelings of loneliness. In the absence of other social supports, all participants also regarded Hyodol as a surrogate family member or human-friend, and interacted with Hyodol as such.

CONCLUSIONS

Findings illustrated high acceptability of Hyodol among these socially isolated older adults especially during the global COVID-19 pandemic, suggesting that a humanoid like Hyodol could be complementary to homecare services for solo-living older adults.

CLINICAL IMPLICATIONS

Well-designed robot interventions, as complements to existing aging service and clinical interventions, have a potential to improve health behaviors among socially isolated older adults.

The leading global health challenges in the artificial intelligence era

Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.

The effects of a sleep robot intervention on sleep, depression and anxiety in adults with insomnia—A randomized waitlist‐controlled trial

The study objective was to assess if a 3-week intervention with the Somnox sleep robot had effects on symptoms of insomnia, somatic arousal, and/or concurrent symptoms of depression and anxiety in adults with insomnia, compared with a waitlist-control group. The participants (n = 44) were randomized to a 3-week intervention with the sleep robot (n = 22), or to a waitlist-control group (n = 22). The primary outcome measure was the Insomnia Severity Index administered at baseline, mid-intervention, post-intervention and at 1-month follow-up. Secondary outcome measures were the Pre-Sleep Arousal Scale, and the Hospital Anxiety and Depression Scale. Additionally, sleep-onset latency, wake time after sleep onset, total sleep time and sleep efficiency were measured the week prior to and the last week of the intervention, both subjectively with the Consensus Sleep Diary and objectively with wrist actigraphy. Mixed-effects models were used to analyse data. The effect of the sleep robot on the participants' insomnia severity was not statistically significant. The differences between the intervention group and the control group on the measures of arousal, anxiety and depression were also not statistically significant, and neither were the sleep diary and actigraphy variables. In conclusion, a 3-week intervention with daily at-home use of the robot was not found to be an effective method to relieve the symptom burden in adults with insomnia.

Artificial intelligence and Psychiatry: An overview

The burden of mental illness both in world and India is increasing at an alarming rate. Adding to it, there has been an increase in mental health challenges during covid-19 pandemic with a rise in suicide, loneliness and substance use. Artificial intelligence can act as a potential solution to address this shortage. The use of artificial intelligence is increasingly being employed in various fields of mental health like affective disorders, psychosis, and geriatric psychiatry. The benefits are various like lower costs, wider reach but at the same time it comes with its own disadvantages. This article reviews the current understanding of artificial intelligence, the types of Artificial intelligence, its current use in various mental health disorders, current status in India, advantages, disadvantages and future potentials. With the passage of time and digitalization of the modern age, there will be an increase in the use of artificial intelligence in psychiatry hence a detailed understanding will be thoughtful. For this, we searched PubMed, Google Scholar, and Science Direct, China national Knowledge Infrastructure (CNKI), Globus Index Medicus search engines by using keywords. Initial searches involved the use of each individual keyword while the later searches involved the use of more than one word in different permutation combinations.