Background/Objectives: Clinical trials have investigated the efficacy of rehabilitation robotics for various pathological conditions, but the overall impact on rehabilitation practice remains unclear. We comprehensively examined and analyzed systematic reviews (SRs) of randomized controlled trials (RCTs) investigating rehabilitative interventions with robotic devices. Methods: Four databases were searched using term combinations of keywords related to robotic devices, rehabilitation, and SRs. The SR meta-analyses were categorized into “convincing”, “highly suggestive”, “suggestive”, “weak”, or “non-significant” depending on evidence strength and validity. Results: Overall, 62 SRs of 341 RCTs involving 14,522 participants were identified. Stroke was most frequently reported (40 SRs), followed by spinal cord injury (eight SRs), multiple sclerosis (four SRs), cerebral palsy (four SRs), Parkinson’s disease (three SRs), and neurological disease (any disease causing limited upper- and lower-limb functioning; three SRs). Furthermore, 38, 21, and 3 SRs focused on lower-limb devices, upper-limb devices, and both upper- and lower-limb devices, respectively. Quantitative synthesis of robotic intervention effects was performed by 51 of 62 SRs. Robot-assisted training was effective for various outcome measures per disease. Meta-analyses offering suggestive evidence were limited to studies on stroke. Upper-limb devices were effective for motor control and activities of daily living, and lower-limb devices for walking independence in stroke. Conclusions: Robotic devices are useful for improving impairments and disabilities in several diseases. Further high-quality SRs including RCTs with large sample sizes and meta-analyses of these RCTs, particularly on non-stroke-related diseases, are required. Further research should also ascertain which type of robotic device is the most effective for improving each specific impairment or disability.

Background: Various digital biomarkers have been explored to detect cognitive impairment in community-dwelling older individuals, among which electricity consumption (EC) data obtained from smart meters are novel and promising because they pose no burden to the individuals. Objective: The study aimed to explore the potential of EC as a digital biomarker to screen older individuals with cognitive impairment living alone. Methods: We recruited 40 older individuals living alone and recorded their 1-year daily household EC data. We used the Japanese version of the Montreal Cognitive Assessment to categorize participants into 2 groups: those with and without cognitive impairment. As the pattern of daily household EC is different between lower and higher temperature ranges because of the use of heating and cooling equipment, we divided the daily household EC into 3 temperature ranges. Using a linear mixed model, we evaluated the association between daily household EC, daily outside temperature, and the groups. Results: After excluding 12 participants, they were categorized into 2 groups: those with (10/28, 36%) and without cognitive impairment (18/28, 64%). The daily household EC data consisting of 9391 points showed two characteristics: (1) daily household EC was significantly lower in the group with cognitive impairment than in the group without cognitive impairment in the high temperature range (2.158 kWh at 25 °C, P=.02; 3.712 kWh at 30 °C, P<.001). The increase in EC with rising temperature from 25 °C to 30 °C was less in the group with cognitive impairment (2.387 kWh, P<.001) than in the group without cognitive impairment (3.940 kWh, P<.001); and (2) a tendency for lower daily household EC in the group with cognitive impairment was observed in the moderate temperature range (1.795 kWh at 15 °C, P=.06; 1.582 kWh at 20 °C, P=.08). Conclusions: The group with cognitive impairment may use less cooling equipment in the high temperature range and fewer home appliances in the moderate temperature range. Daily household EC might be useful in screening cognitive impairment in older individuals living alone.

Japan faces a shortage of care workers due to its rapidly aging population. Although nursing care technologies can reduce caregiver burden and improve care quality, their long-term feasibility remains unclear. We investigated the implementation of these technologies and associated challenges in long-term care facilities. A cross-sectional questionnaire survey of all long-term care facilities in Aichi Prefecture, Japan (total population, approximately 7.5 million), was conducted between February and March 2024. Of 2,514 eligible facilities, 497 participated (a 19.8% response rate), and 477 were included in the final analysis. The overall implementation rate was 22.0%. Implementing facilities reported a lack of information on available devices (55.2%) and challenges with subsidy application processes (52.4%). Among non-implementing facilities, 63.2% expressed a willingness to adopt devices, primarily to reduce staff workload (95.7%). Key barriers included financial constraints (91.1%), cost-effectiveness concerns (62.1%), and limited information (55.7%). The results indicate significant interest in nursing-care technologies and identify key implementation difficulties and adoption barriers. Improving information accessibility and simplifying subsidy-application procedures could facilitate the adoption of nursing care technologies. These results may also benefit countries facing similar demographic challenges.