Background: As Canada’s population ages, delivering high-quality, personalized care to older adults, particularly in home and community settings, has become increasingly important. The interRAI Home Care (RAI-HC) assessment is widely used to evaluate older adults’ health, functional status, and ability to live safely at home. However, its effectiveness can be limited by infrequent updates, administrative burden, and reliance on episodic data collection. Integrating sensor technologies into a digital version of the RAI-HC may help address these challenges. Research aim/question(s): This study examines the technical and conceptual feasibility of using sensor-generated data to support selected components of the interRAI Home Care (RAI-HC) assessment, as a foundation for developing a Digital Comprehensive Geriatric Assessment (D-CGA) system. Methods: A five-stage feasibility framework was applied. First, commercially available technologies were identified through structured searches of major Canadian consumer-facing online platforms, alongside AGE-WELL–affiliated technologies. Second, a structured extraction matrix was developed to document sensor characteristics, data types, interoperability features, and validation evidence. Third, sensorderived data were mapped to geriatric health domains based on existing literature. Fourth, these literature-informed domains and data types were analytically aligned with RAI-HC assessment sub-domains and questions using the interRAI Version 10.0 Standard Edition. Finally, initial clinical validation was conducted through consultation with a clinician experienced in RAI-HC implementation. Results: Eighty-three sensor technologies (49 commercial and 34 AGE-WELL) were retained. Mapping identified 15 RAI-HC assessment questions for which sensor-derived data could potentially contribute. Clinical validation indicated that five questions could be fully supported by sensor data, five partially supported, and five required clinical judgment. Conclusions: Sensor technologies show selective potential to support components of the interRAI Home Care assessment, while many RAI-HC items remain dependent on clinical interpretation. These findings support the feasibility of partial digitization, rather than full automation, of comprehensive geriatric assessment within a future D-CGA system.
Background: Bowel incontinence is a persistent challenge among bedridden and disabled senior patients, often leading to adverse clinical outcomes such as skin infections and significant caregiver burden. Conventional adult diapers generate substantial environmental waste, and existing diaper-based monitoring systems are often seen with delayed response times and high costs. Therefore, there is a critical need for more advanced and sustainable alternatives. Research aim: This study aims to develop and evaluate an "Intelligent Gas-Sensing Fecal Collection System". The goal is to provide an eco-friendly solution that utilizes biodegradable materials, real-time biosensing, and Bluetooth communication to improve caregiving efficiency, user hygiene, and patient dignity. Methods: The system integrates a reusable sensor module featuring a semiconductor for detection of fecal gases, Bluetooth Low Energy (BLE) transmission, and a biodegradable collection bag composed of pineapple leaf fibers and polylactic acid (PLA). The proposed system's performance was evaluated through laboratory gas exposure tests and a usability trial involving 30 caregivers. Results: Laboratory tests confirmed that the sensor detected 1 ppm of ammonia within 60 seconds, triggering an immediate wireless notification. In the usability trial, the proposed system demonstrated considerable potential for caregiving efficiency improvement, achieving an 87% reduction in caregiver workload and a 75% decrease in cleaning time. Furthermore, 83% of caregivers reported enhanced patient dignity and comfort. Conclusion: The smart fecal collection system offers an effective and sustainable solution for senior care. By reducing labor intensity, the proposed system demonstrates high clinical potential. Future work will focus on scaling production, optimizing in IT infrastructure, and integrating AI-based predictive analytics.
Background: Nowadays, sarcopenia is widely affecting numerous people in the world, which directly leads to devastating clinical outcomes like catastrophic falls, functional disability in mobility, and increased mortality. Ultrasound (US) plays an important role in sarcopenia assessment for quantitatively and qualitatively detecting muscle performance by muscle thickness (MT) and cross-sectional area (CSA). However, it has been hindered by operator dependency and a lack of standardization, making manual measurements impractical for large-scale screening. Research Aims Our study addresses this clinical bottleneck by adopting image segmentation models to enable deep-learning-based automated ultrasound system that standardizes the assessment of multiple muscle groups across the body. Methods: We trained the deep learning models (U-Net and nnU-Net) for segmenting five key muscle groups (biceps, triceps, rectus abdominis (RA), rectus femoris(RF), and peroneal longus and brevis (PLPB) from US images acquired from 94 adult participants (young (n=22), middle-aged (n=22), and elder (n=50)). The models’ segmentation performance was evaluated against expert manual annotations using the Dice Similarity Coefficient (Dice). For the predicted dataset, the results are correlated with body composition results and physical performance data. Results: The deep learning segmentation achieved excellent performance across all muscle groups. The mean Dice were: RF (0.8260), RA (0.8810), biceps (0.8370), triceps (0.8080), and PLPB (0.9200), indicating a high accuracy compared to the ground truth. Furthermore, these AI-derived metrics demonstrated strong clinical validity through high correlations with body composition data. The appendicular muscles has the highest associations found between triceps CSA and MM (r=0.8212), PLPB MT and ASM (r=0.7462), and RA CSA and BMR (r=0.6398). Conclusions: By applying a deep learning-powered framework, the ultrasound provides accurate, highly efficient segmentation of multiple muscles for sarcopenia assessment, making US a practical method for sarcopenia assessment and enabling its adoption in community-based screening and the longitudinal management of sarcopenia.
Objective: To address challenges faced by older adults in smart home interface operations, such as high cognitive load and symbol recognition difficulties, this study attempts to propose a preliminary age-friendly interaction design method based on the "Long-term Memory Mapping (LMM) model" and explore the potential value of nostalgic symbols in interface design. Method: Through literature analysis and in-depth interviews, nostalgic symbols familiar to older adults were identified, and a five-layer LMM structural model was constructed. Retro-style icons with a 1980s aesthetic were designed. A within-subject controlled experiment was conducted with 15 older adults aged 60-75 to compare their performance between the nostalgic icon group and the modern flat icon group in terms of first-time recognition accuracy, task completion time, and error frequency. Results: The nostalgic icon group performed better than the control group in first-time recognition accuracy, task completion time, and error frequency. Conclusion: The preliminary findings of this exploratory study suggest that the use of nostalgic symbols is associated with the activation of long-term memory in older adults and may contribute to reduced cognitive load and improved operational efficiency. The LMM model offers some insights for the design of age-friendly interfaces in smart homes; however, the specific contribution of the “nostalgia effect” and its causal mechanisms still require further testing in future studies that strictly control for confounding variables.
Background: Singapore’s population is rapidly aging. By 2026, the nation will be “super-aged” with over 1 in 5 citizens ≥65 years old, with this ratio increasing to 1 in 4 (approximately 1 million residents) by 2030. Loneliness and social isolation are prevalent among older adults, and are linked to depression, cognitive decline, and poorer physical health. A 2021 survey found nearly 1 in 3 Singapore older persons (aged ≥60) feel lonely most of the time and global statistics indicate an estimated one in four older persons experience loneliness. Reminiscence therapy – the guided recall of past events and experiences – is a popular psychosocial intervention that can improve older adults’ well-being. Traditional reminiscence utilizes stimuli like photographs, music, or personal mementos. Emerging technology such as immersive virtual reality (VR) offers a novel medium for reminiscence by vividly recreating places and experiences, potentially enhancing engagement and emotional impact. VR-based reminiscence has shown promise in improving mood, reducing isolation, and stimulating memories in older adults (Syed-Abdul et al., 2019). However, the role of fully immersive VR in reminiscence interventions remains nascent; few rigorous studies have evaluated its effects on healthy older persons’ psychosocial outcomes. To date, no structured RCT research in Singapore has examined the use of VR in reminiscence, making this the first local study to do so. Objective: This protocol aims to evaluate the efficacy of immersive VR reminiscence therapy in enhancing psychosocial well-being among community-dwelling older adults in Singapore, compared to traditional reminiscence using photos/videos. The primary objectives are to evaluate the impact of VR on quality of life, subjective happiness, and loneliness., and to examine older adults’ technology acceptance of VR using the Senior Technology Acceptance Model (STAM). A secondary objective aims to explore participants’ experiences in the use of VR and any practical or ethical considerations in implementing VR for older persons. Methods: The study will involve a two-arm randomized controlled trial with 60 older adults (≥60 years) recruited from elder care centers. Participants will be randomly assigned (1:1) to an intervention group (VR reminiscence therapy) or a control group (traditional reminiscence with equivalent content in 2D). Both groups will undergo four weekly reminiscence sessions guided by a trained facilitator, each built around a specific theme and content module. VR sessions will include the use of an immersive headset to present 360° video or virtual environments tailored to reminiscence themes (e.g., visiting a local heritage site or a past travel destination), followed by guided group discussions. Control sessions will involve similar themes and facilitator prompts but use photos, videos or music on a TV/tablet (2D format) instead. Outcome measures will be administered at baseline (pre-intervention) and post-intervention. The key measurements include the Older People’s Quality of Life questionnaire (OPQOL-brief) for quality of life, the Subjective Happiness Scale, and the UCLA Loneliness Scale. Technology acceptance will be measured via a 14-item Senior Technology Acceptance Model (STAM) questionnaire adapted for VR (Shin et al., 2023). Additional measures include demographic and health information, and session feedback. All participants will provide informed consent, and ethical approval has been obtained from the institutional review board. Quantitative data will be collected via interviewer-administered surveys, and qualitative observations or interviews will be conducted to capture participants’ experiences with the VR intervention. Results: The primary outcomes (quality of life, happiness, loneliness) and technology acceptance scores will be compared between the VR and control groups after the intervention. We hypothesize that the VR group will show greater improvements in psychosocial well-being than the control group, and that older adults will report high acceptance of the VR technology (e.g. perceiving it as useful and not overly difficult to use). No interim analyses are planned. As this is a protocol paper, the results are not presented at this stage. Conclusions: This study will inform if immersive VR reminiscence can enhance older adults’ well-being beyond conventional reminiscence approaches, and if VR could become a valuable tool for active aging initiatives, reducing loneliness and improving quality of life for older persons in Singapore (Syed-Abdul et al., 2019). The findings will also inform gerontechnology acceptance theory by applying the STAM to immersive VR: identifying factors that facilitate or hinder older persons’ adoption of VR for therapy. By outlining detailed session designs and ethical safeguards, this protocol aims to guide safe and meaningful implementation of VR reminiscence in eldercare settings. Ultimately, this pioneering local study can provide evidence to policymakers and practitioners on integrating immersive media into psychosocial care for the aging population and pave the way for larger-scale trials and innovation in reminiscence therapy.
