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While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Background: Rapid population aging occurring in developing nations necessitates innovation to ensure we continue to gain ground on aging research despite pandemic threats. While developed nations have resorted to virtual communications, this is challenging in developing nations due to poor internet connectivity and digital literacy. Objectives: The aim of this study was to determine the feasibility of virtual data collection for a longitudinal study of aging assessing cognitive frailty in a middle-income Southeast Asian country. Methods: The Transforming Cognitive Frailty into Later-Life Self-Sufficiency (AGELESS) longitudinal study of aging involved community-dwelling participants aged 60 years and above. A semi-structured focus group discussion was conducted via videoconferencing with selected representatives from existing participants. The survey instrument was compiled during a hybrid meeting and refined using a virtual Delphi process involving 51 AGELESS investigators. The final draft survey and recruitment strategy were then piloted among selected participants. Results: Twelve individuals participated in the virtual focus group interview. Smartphone, tablet computer, laptops, and desktop personal computers were used for information gathering, communication, banking, shopping, leisure, religion, and education, within this group. The survey instrument was redacted from 362 items in 18 sections to 141 items in 12 sections through 3 virtual Delphi rounds facilitated by email, social media messaging, and videoconferencing which attracted 213 comments. Of 45 participants selected for the pilot survey, 30 were successfully contacted after one attempt and 18 completed the survey. Cognitive frailty was present in 13%, cognitive impairment in 20%, frailty in 20%, and 47% were robust. Conclusion: A virtual survey instrument was developed for the AGELESS longitudinal survey of aging which was vital for determining the effects of the COVID-19 pandemic on our older population as well as sustaining research into aging despite barriers posed by the pandemic.

Background Prolonged life expectancy is associated with increased prevalence of chronic diseases. The aim of this study was to determine the different disability trajectories for the top ten leading causes of death in Taiwan . Methods A total of 2,431 participants aged 50–96 in 1996 from the Taiwan longitudinal study on aging (TLSA) who died from 1996 to 2016 were analyzed. Integration of Cause of Death Data and TLSA helped sort out participants who had died from the ten leading causes of death. The level of physical disability was evaluated with the Activities of Daily Living Scale (ADLs), ranging from 0 to 6 points, in 1996, 1999, 2003, 2007, and 2011. A multilevel model was used to investigate the levels and rates of change in disability development before death. Results The outcome of the research showed that the earliest group to experience physical limitation was individuals living with diabetes. The groups with the highest ADL scores were participants with diabetes, cerebrovascular disease, and hypertension-related diseases. Most groups reach ADL scores ≥ 1 (mild-level) during 4–6 years before death except chronic hepatitis and cirrhosis and injury. Conclusions People who had died from the ten leading causes of death experienced different disability trajectories before death. The trajectory of the participants who had died from diabetes showed a unique pattern with the earliest occurrence and more severe deterioration in terms of development of disabilities. Disability trajectories provide a prediction of survival status for middle-aged and older adults associated with the ten leading causes of death.

Introduction Osteoarthritis (OA) is a major cause of physical impairment in older persons. Few studies have determined the relationship between the presence of OA and frailty. We evaluated the prospective relationship between knee pain and frailty in a Malaysian longitudinal cohort. Method Data from Malaysian Elders Longitudinal Research (MELoR) study were utilised. Baseline data were obtained from home-based computer-assisted questionnaires and hospital-based health-checks from 2013–2015. The presence of knee pain was determined with the single question, ‘do you have pain in your knee(s)?’. Frailty status was determined at follow-up in 2019 using SARC-F. Results Data from 1,226 individuals, mean age (SD) = 68.97 (7.48), range = 54–97 years and 56.6% women at baseline, were included. 408 (33.3%) had knee pain. Individuals with knee pain were significantly more likely to be female (66.2% vs 51.8%, p Conclusion Knee pain was associated with an increased risk of frailty with 5-years follow-up in an urban population in Kuala Lumpur. More detailed evaluation using imaging and clinical diagnosis of osteoarthritis is now indicated. Future studies should also seek to identify modifiable risk factors for the development of frailty in individuals with knee OA and develop strategies to prevent frailty.

Bacterial chemotaxis in unicellular Escherichia coli, the simplest biological creature, enables it to perform effective searching behaviour even with a single sensor, achieved via a sequence of “tumbling” and “swimming” behaviours guided by gradient information. Recent studies show that suitable random walk strategies may guide the behaviour in the absence of gradient information. This article presents a novel and minimalistic biologically inspired search strategy inspired by bacterial chemotaxis and embodied intelligence concept: a concept stating that intelligent behaviour is a result of the interaction among the “brain,” body morphology including the sensory sensitivity tuned by the morphology, and the environment. Specifically, we present bacterial chemotaxis inspired searching behaviour with and without gradient information based on biological fluctuation framework: a mathematical framework that explains how biological creatures utilize noises in their behaviour. Via extensive simulation of a single sensor mobile robot that searches for a moving target, we will demonstrate how the effectiveness of the search depends on the sensory sensitivity and the inherent random walk strategies produced by the brain of the robot, comprising Ballistic, Levy, Brownian, and Stationary search. The result demonstrates the importance of embodied intelligence even in a behaviour inspired by the simplest creature.

Introduction: This review will systematically examine the effectiveness of educational interventions on dementia health literacy (DHL) among informal caregivers (ICs) in 42 Asia Pacific countries and territories as more than half of people with dementia worldwide are living in this region. This study will examine the effectiveness of interventions on ICs’ capacity to access, understand, evaluate, and apply dementia-related information as the primary outcome and ICs’ beliefs and attitudes toward dementia and people living with dementia as secondary outcomes.Methods: A systematic literature search will be performed in EMBASE, PubMed, PsycINFO, CINAHL, Cochrane Library, ProQuest Central, Web of Science, OpenGrey, and Google Scholar. No date nor language restriction will be applied. The population of interest are ICs in 42 Asia Pacific countries and territories. The quality of studies will be assessed using existing appraisal tools (i.e. Joanna Briggs Institute’s Checklists). An inductive narrative synthesis technique will be used to analyse the extracted data. Randomized Controlled Trial (RCT) and non-RCT interventions will be full-reviewed but only RCT studies will be pooled in a meta-analysis. Heterogeneity, publication bias, and sensitivity will also be assessed.Results: The results will be reported following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement. The standardized procedure and reporting will ensure the review’s quality and could be used by the stakeholders in the region for planning DHL-related programs. This comprehensive review will also significantly contribute to dementia caregiving and caregiver literature and programs.Conclusion: This protocol of the planned systematic review and meta-analysis of the effectiveness of educational interventions on DHL for ICs in the Asia Pacific countries and territories will set the foundation for its further progress.Systematic review registration: PROSPERO CRD42022355424

Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment.The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated.This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol.The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Aerial grasping empowers unmanned aerial vehicles to find applications beyond structured logistics. However, it brings a number of challenges including inaccurate positioning of the end effector and limited energy sources. Moreover, solutions so far have difficulty in handling a variety of objects. A novel closed structure compliant gripper was developed to address the challenges above. The gripper has a large self-centering work envelope and is normally-closed for passive grasping. Introduction of compliance as a form of morphological computation was also considered to enhance grasping capabilities, where multi-material 3D printing would facilitate rapid design changes based on target application. To grasp different objects, the gripper has hot-swappable 3D printed fingertips which are optimized with a multi-objective Bayesian Optimization process using physical bench experiments mimicking drone grasping and ascent on a common object set. The morphology of the fingertip including tip width, curvature and distribution of soft and hard material on contact surface, are optimized with a bench test that mimics quadcopter takeoff and landing. The best design from optimization shows an improvement of more than 10% from the initial design in successful grasp operations, demonstrated by field tests with a quadcopter.

The mechanical compliance of soft robots comes at a cost of higher uncertainty in their sensing and perception, which deteriorates the accuracy of predictive models. Predictive uncertainty, which expresses the confidence behind model predictions, is necessary to compensate for the loss of accuracy in soft robot perceptive models. Nevertheless, developing a general framework to capture uncertainties is further challenged by the complex dynamics of soft robots and the difficulties in sensorizing them. In this work, we present a predictive uncertainty estimation framework based on deep learning for soft robot multimodal sensing. We show that the framework can learn to quantify uncertainty and thus is able to express the confidence associated with the predictions during inference. Being data-driven, it is scalable to different types of soft robots and sensor modalities. We demonstrate the framework on a complex multimodal sensing task where a single flex sensor is used to predict the full-body configuration of a soft actuator, as well as the magnitude and location of external contact force. We also discuss how predictive uncertainties are critical to achieve safe learning and model interpretability in soft robotics.

Connected and automated vehicle (CAV) technology, along with advanced traffic control systems, cannot ensure congestion-free traffic when the number of vehicles exceeds the road capacity. To address this problem, in this paper, we propose a dynamic ride-sharing system based on incentives (for both passengers and drivers) that incorporates travelers of similar routes and time schedules on short notice. The objective is to reduce the number of private vehicles on urban roads by utilizing the available seats properly. We develop a mobile-cloud architecture-based system that enables real-time ride-sharing. The effectiveness of the proposed system is evaluated through microscopic traffic simulation using Simulation of Urban Mobility (SUMO) considering the traffic flow behavior of a real smart city. Moreover, we develop a lab-scale experimental prototype in the form of Internet of Things (IoT) network. The simulation results show that the proposed system reduces fuel consumption, CO2 and CO emissions, and average waiting time of vehicles significantly, while increasing the vehicle’s average speed. Remarkably, it is found that only 2–10% ride-sharing can improve the overall traffic performance.

Background Falls incidence rate and comprehensive data on factors that predict occasional and repeated falls from large population-based studies are scarce. In this study, we aimed to determine the incidence of falls and identify predictors of occasional and recurrent falls. This was done in the social, medical, physical, nutritional, biochemical, cognitive dimensions among community-dwelling older Malaysians. Methods Data from 1,763 Malaysian community-dwelling older persons aged ≥ 60 years were obtained from the LRGS-TUA longitudinal study. Participants were categorized into three groups according to the presence of a single fall (occasional fallers), ≥two falls (recurrent fallers), or absence of falls (non-fallers) at an 18-month follow-up. Results Three hundred and nine (17.53 %) participants reported fall occurrences at an 18-month follow-up, of whom 85 (27.51 %) had two or more falls. The incidence rate for occasional and recurrent falls was 8.47 and 3.21 per 100 person-years, respectively. Following multifactorial adjustments, being female (OR: 1.57; 95 % CI: 1.04–2.36), being single (OR: 5.31; 95 % CI: 3.36–37.48), having history of fall (OR: 1.86; 95 % CI: 1.19–2.92) higher depression scale score (OR: 1.10; 95 % CI: 1.02–1.20), lower hemoglobin levels (OR: 0.90; 95 % CI: 0.81-1.00) and lower chair stand test score (OR: 0.93; 95 % CI: 0.87-1.00) remained independent predictors of occasional falls. While, having history of falls (OR: 2.74; 95 % CI: 1.45–5.19), being a stroke survivor (OR: 8.57; 95 % CI: 2.12–34.65), higher percentage of body fat (OR: 1.04; 95 % CI: 1.01–1.08) and lower chair stand test score (OR: 0.87; 95 % CI: 0.77–0.97) appeared as recurrent falls predictors. Conclusions Having history of falls and lower muscle strength were predictors for both occasional and recurrent falls among Malaysian community-dwelling older persons. Modifying these predictors may be beneficial in falls prevention and management strategies among older persons.

Introduction: Supportive care models considering inclusivity and community services to improve integrated care for cancer survivors are limited. In this case study, we described the implementation of a multidisciplinary care model employing routine distress screening and embedded integrated care pathways to integrate care across disciplines and care sectors, while remaining inclusive of the multi-ethnic and multilingual population in Singapore. We reported implementation outcomes after 18 months of implementation. Description: We reviewed the model’s process indicators from September 2019 to February 2021 at the largest public ambulatory cancer centre. Outcomes assessed included penetration, fidelity to screening protocol, and feasibility in three aspects – inclusiveness of different ethnic and language groups, responsiveness to survivors reporting high distress, and types of community service referrals. Discussion/conclusion: We elucidated opportunities to promote access to community services and inclusivity. Integration of community services from tertiary settings should be systematic through mutually beneficial educational and outreach initiatives, complemented by their inclusion in integrated care pathways to encourage systematic referrals and care coordination. A hybrid approach to service delivery is crucial in ensuring inclusivity while providing flexibility towards external changes such as the COVID-19 pandemic. Future work should explore using telehealth to bolster inclusiveness and advance community care integration.

To determine the association between visual acuity and contrast sensitivity with frailty measures Older adults with poor contrast sensitivity are significantly more likely to be frail (OR: 5.34, p = 0.004). No association was identified between visual acuity and frailty (p = 0.241). Poor contrast sensitivity is significantly associated with frailty. Using data from the Malaysian Elders Longitudinal Research (MELoR), this study investigated the association between visual function (visual acuity and contrast sensitivity) and frailty in a group of urban-dwelling older adults. This was a cross-sectional study. 1332 participants aged ≥ 55 years were selected by random sampling from the parliamentary electoral register. Only 1274 participants completed the frailty assessment and 1278 participants completed the contrast sensitivity assessment. Impaired vision was defined as a Snellen visual acuity of worse than 6/12 in the better eye. Poor contrast sensitivity was defined as a score on the Pelli Robson chart of lower than 1.65. Frailty was defined with the Fried’s phenotype criteria. Inter-group comparisons were determined with the independent T-test for continuous variables and the Pearson’s Chi-squared test for categorical variables. The odds ratio (OR) with 95% confidence interval (CI) was used to evaluate the cross-sectional association between frailty and visual function. The mean age of participants was 68.8 ± 7.5 years, of which 58.1% (774) were women. Impaired vision and poor contrast sensitivity were present in 187 (14%) and 271 (21.2%) subjects respectively. 73 (5.8%) individuals were classified as frail, 1161 (91.0.%) pre-frail, and 40 (2.8%) non-frail. There was no significant difference in frailty phenotypes between those with good and impaired vision (p = 0.241). Fried’s component of handgrip strength, gait speed and exhaustion were significantly better in those with good visual function (p

This article introduces a nonlinear observer to reconstruct faults in a class of nonlinear descriptor systems affected by disturbances, in both the state and output equations. The fault enters the system through nonlinear functions in both its state and output equations. The original system is first transformed such that design freedom in its structure is easier to exploit, and then a nonlinear observer is designed based on the transformed system to reconstruct the fault. Linear matrix inequalities are used to determine the gains of the observer such that the effect of the disturbances on the fault reconstruction is bounded. Finally, three simulation examples are carried out to verify the effectiveness of the scheme.

The rise of model-based and machine learning methods have created increasingly realistic opportunities to implement personalized, patient-specific mechanical ventilation (MV) in the ICU. These methods require monitoring of real-time patient ventilation waveform data (VWD) during MV treatment. However, there are relatively few non-invasive and/or non-proprietary systems to monitor and record patient-specific lung condition in real-time. In this paper, we present a CARE network data acquisition and monitoring system (CARENet) to automate data collection and to remotely monitor patient-specific lung condition and ventilation parameters. The automated system acquires VWD from a mechanical ventilator using a data acquisition device (DAQ), stores data in network-attached storage (NAS), and processes VWDs via a data management platform (DMP) web application. The web application enables real-time and retrospective model-based monitoring and analysis of clinical MV data. In particular, CARENet provides detailed breath-by-breath patient-specific respiratory mechanics, as well as the overall trends assessing changes in patient condition. Validation testing with a retrospective data set illustrated how breath-to-breath and time-varying patient-ventilator interaction during MV can be monitored, and, in turn, can be used to guide MV treatment. The network data acquisition system design presented is low-cost, open, and enables continuous, automated, scalable, real-time collection and analysis of waveform data, to help improve decision making, care, and outcomes in MV.

Background: We studied humoral and cellular responses against SARS-CoV-2 longitudinally in a homogeneous population of healthy young/middle-aged men of South Asian ethnicity with mild COVID-19. Met...

Objective: Alzheimer’s disease (AD), the commonest form of dementia which is characterized by progressive decline in cognitive function, can only be definitively diagnosed after death. Although bio...

Context Advanced colorectal cancer and its treatment can bring about challenges associated with psychological distress. Objectives The primary aims of this study were to examine the feasibility and acceptability of a cognitive behavioral therapy (CBT)-based intervention to improve coping with the disease. The secondary aim is to evaluate preliminary intervention efficacy. Methods Patients with advanced colorectal cancer in Singapore (N = 60) were randomized to either receive a four-session CBT intervention immediately or be waitlisted. Intervention feasibility (i.e., recruitment and intervention adherence) and acceptability (i.e., participant satisfaction and cultural sensitivity) were assessed. Changes in psychological distress and self-efficacy were examined. Results The study successfully recruited the intended sample (mean age 61; 62% men). A proportion (12%) reported Hospital Anxiety and Depression Scale scores indicative of distress at baseline. Most (88%) completed all sessions. Participants reported high rates of satisfaction (97%), helpfulness (96%), and cultural sensitivity (95%) of the intervention. The intervention group did not show decrease in psychological distress; however, self-efficacy in cancer-related coping (information seeking: effect size [ES] = 0.64; 95% CI = 0.17, 0.85; coping with side effects: ES = 0.69; 95% CI = 0.33, 0.82; and maintaining positive attitude: ES = 0.45; 95% CI = 0.19, 0.79) increased in the intervention group compared with the waitlisted group. Conclusion The CBT-based intervention was feasible and acceptable to patients in Singapore. There is no sufficient evidence to warrant a larger trial in this sample with low baseline distress. Future work should identify and target those who are most in need of support.

To conduct a scoping review of articles which have evaluated BPV and cognitive function. Articles with keywords, titles or abstracts containing the terms 'cognitive' OR 'cognition' OR 'dementia' AND 'blood pressure variability' were identified from CINAHL, Medline, PMC and Web of Science.Methods of acquisition and analysis of BPV and cognitive measurements and their relationship were extracted from selected articles. Of 656 studies identified, 53 articles were selected. Twenty-five evaluated long-term (LTBPV), nine mid-term (MTBPV), 12 short-term (STBPV) and nine very short-term BPV (VSTBPV) with conflicting findings on the relationship between BPV and cognition. Variations existed in devices, period and procedure for acquisition. The studies also utilized a wide range of methods of BPV calculation. Thirteen cognitive assessment tools were used to measure global cognition or domain functions which were influenced by the population of interest. The interpretation of available studies was hence limited by heterogeneity. There is an urgent need for standardization of BPV assessments to streamline research on BPV and cognition. Future studies should also establish whether BPV could be a potential modifiable risk factor for cognitive decline, as well as a marker for treatment response.

The use of medications with anticholinergic (ACh) properties is associated with numerous adverse effects especially in older adults. Emerging evidence suggests the presence of long-term effects with ACh use.Our article presents an overview of ACh effects and falls in older individuals including examination of emerging evidence on ACh use and cumulative exposure on short-term and long-term falls risk. The databases CINAHL, MEDLINE, EMBASE, and Web of Science were searched for articles published from January 2002 to December 2021.Anticholinergic side effects include muscle weakness, blurred vision, and mental confusion which are likely to lead to increased falls risk. Many commonly used medications such as beta-blockers, calcium-channel blockers, and antihistamines are now known to have mild ACh properties. With polypharmacy now considered unavoidable in older patients, the cumulative effects of the use of multiple drugs with mild ACh properties may also lead to increased falls risk. The relationship between ACh drugs and falls may also be irreversible as ACh effects may extend beyond the period of drug use, due to cognitive and physical deconditioning following the initial exposure to ACh drugs. Future long-term studies with accurate quantification of cumulative ACh exposure and measurement of actual falls outcomes are urgently required.