Background: This study examined the social well-being of single older adults through the companionship of a social robot, LOVOT (Love+Robot; Groove X). It is designed as a companion for older adults, providing love and affection through verbal and physical interaction. We investigated older adults’ perceptions of the technology and how they benefitted from interacting with LOVOT, to guide the future development of social robots. Objective: This study aimed to use a phenomenological research design to understand the participants’ experiences of companionship provided by the social robot. Our research focused on (1) examining the social well-being of single older adults through the companionship of social robots and (2) understanding the perceptions of single older adults when interacting with social robots. Given the prevalence of technology use to support aging, understanding single older adults’ social well-being and their perceptions of social robots is essential to guide future research on and design of social robots. Methods: A total of 5 single women, aged 60 to 75 years, participated in the study. The participants interacted independently with the robot for a week in their own homes and then participated in a poststudy interview to share their experiences. Results: In total, 4 main themes emerged from the participants’ interactions with LOVOT, such as caring for a social robot, comforting presence of the social robot, meaningful connections with the social robot, and preference for LOVOT over pets. Conclusions: The results indicate that single older adults can obtain psychosocial support by interacting with LOVOT. LOVOT is easily accepted as a companion and makes single older adults feel like they have a greater sense of purpose and someone to connect with. This study suggests that social robots can provide companionship to older adults who live alone. Social robots can help alleviate loneliness by allowing single older adults to form social connections with robots as companions. These findings are particularly important given the rapid aging of the population and the increasing number of single-person households in Singapore.

Background: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. Objective: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. Methods: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. Results: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). Conclusions: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

Background: Given the rapidly aging nature of our global population, policy makers around the world are now emphatically promoting active aging. To address the psychosocial needs of older persons and support active aging, researchers are exploring the use of assistive technologies, specifically social robots as companions. However, there is limited evidence on the efficacy of social robots in promoting active aging for older people in the Hong Kong and Singapore contexts. Objective: This study presents the protocol of a study that investigates the acceptance and quality of interaction between a Japanese social robot, LOVOT, and single older adults in Hong Kong and Singapore. Methods: We used a baseline assessment to measure the primary outcome, participants’ acceptance of technology, and a sense of loneliness, namely, the participants’ differences in responses to LOVOT before and following their interaction with the social robot in this multimethod study design. The baseline assessment consisted of the Qualtrics survey, which measures senior technology acceptance, loneliness, older people’s quality of life, subjective happiness, cultural values, willingness to pay, and demographic characteristics, along with the LOVOT’s sociability and system usability. In the study, participants interacted with LOVOT in 3 sessions before being surveyed to measure the older people’s acceptance and attitudes toward LOVOT. A pre–social robot intervention also occurred in the first session. The study was conducted in both Hong Kong and Singapore. A total of 15 single older adults (ie, individuals who live alone) from Hong Kong and another 15 from Singapore were recruited. Participants were 60-75 years of age, lived by themselves, and had no known cognitive or mental issues. Results: The study began recruiting in March 2022, and recruitment was completed at the end of October 2022. Data collection and data set construction were completed at the end of January 2023. Analysis of the data is currently being conducted, and we plan to publish the results by mid-2023. Conclusions: At an individual level, the study will clarify if LOVOT influences single older adults’ psychosocial well-being by reducing their loneliness. At a community level, the study’s findings will illustrate whether LOVOT can provide increased social connectedness while decreasing individual loneliness. Last, this study’s conclusions can inform policy makers to provide social robots to older people to improve their quality of life. Findings can also inform gerontechnology developers on which aspects and cultural considerations to take into account for future inventions. International Registered Report Identifier (IRRID): DERR1-10.2196/48618

Background: Social robots integrated with serious games hold promise as innovative nonpharmacological strategies in dementia care. However, limited studies have adopted quantitative, platform-level comparisons from the perspective of formal caregivers, who are key stakeholders in technology implementation in dementia care settings. Objective: This study aimed to evaluate the feasibility, usability, and overall user experience of a serious game–based interaction model delivered via a screen-equipped social robot, compared to a tablet-based version of the same model, from the perspective of formal dementia caregivers. Methods: A cross-sectional comparative study was conducted with 120 formal dementia caregivers. Each caregiver individually interacted with both a screen-equipped social robot and a touchscreen tablet, delivering identical serious game content incorporating cognitive exercises, music therapy, and reminiscence. The robot featured multimodal interaction capabilities, including voice, gestures, movement, and facial expression display, while the tablet relied on standard touchscreen functions. Caregivers evaluated both platforms using the User Experience Questionnaire (UEQ), System Usability Scale (SUS), and a customized Technology Acceptance Model (TAM). Group comparisons were performed using t tests, with post hoc Benjamini-Hochberg correction applied to control for multiple comparisons. Results: Caregivers generally favored the social robot over the tablet. The robot received higher total UEQ scores (mean 1.29, SD 1.14, vs mean 0.99, SD 1.08; P=.004), particularly in enjoyment (P=.002), friendliness (P=.006), clarity (P=.002), organization (P=.02), interest (P=.01), and innovation (P=.002). In the SUS, caregivers rated the robot higher for quick learning (mean 2.71, SD 0.79 vs mean 2.44, SD 0.81; P=.002), while overall SUS scores were comparable. TAM results indicated higher total scores for the robot (mean 4.03, SD 0.47 vs mean 3.67, SD 0.58; P=.002), with stronger ratings in perceived usefulness (P=.002), ease of use (P=.002), attitudes (P=.002), and behavioral intentions (P=.002). All P values are from 2-tailed t tests and were adjusted using the Benjamini–Hochberg procedure. Conclusions: The social robot used in this study was perceived by formal dementia caregivers as providing a more favorable user experience and eliciting a stronger intention to use compared to a tablet-based platform. These findings support the feasibility of social robots as a platform for delivering technology-supported activities in dementia care and provide a foundation for future research on their implementation and outcomes in dementia care.

This chapter explores the adaptation of technology acceptance model (TAM) to senior technology acceptance model (STAM). The extension provides more focus on the perception and acceptance of technology by older persons for researchers to have a better understanding of the multidimensional facets of ageing needs. In the studies conducted in Hong Kong and Singapore, we have used the assessment of two different social robots. For the Hong Kong study, Humanoid social robot, Kabochan, was deployed as an intervention in seven nursing homes to understand the acceptance by residents living with dementia, on the other hand, in a separate joint study between Hong Kong and Singapore, a Japanese affectionate robot, LOVOT, was chosen. Both studies used STAM and a number of social well-being measures to assess the benefits of social robots in the health and care of older persons. STAM has demonstrated its usefulness and relevance in building evidence and correlations to elucidate the varied research objectives. Moving forward, as gerontechnology is populated with advancement in wearables, Internet of Things (IoT), artificial intelligence (AI), and robotics, STAM is likely have a stronger place in the research and evaluation scene. The collection of evidence is to appreciate the complexity of factors in the adoption of gerontechnology.