Systematic Development of the ReWin Application: A Digital Therapeutic Rehabilitation Innovation for People With Stroke-related Disabilities in India

This is a viewpoint paper that aims to describe the systematic approach to the development of a technology-driven stroke rehabilitation innovation to manage disabilities following a stroke at home in India. This paper intends to sensitize public health innovators and intervention development experts about the important aspects that need to be considered to develop a culturally sensitive, patient-centered, scalable solution for stroke care using technology. Stroke has been the second-leading cause of death and the third-leading cause of disability globally for the past 3 decades. The emerging technological innovations for stroke care were predominantly designed and developed by digital technology experts as stand-alone products with very minimal efforts to explore their feasibility, acceptability, and, more importantly, scalability. Hence, a digital therapeutic rehabilitation innovation for people with stroke-related disabilities in India was systematically developed and is being evaluated. ReWin is an innovation that is technologically driven and envisions digital therapeutics as a medium for the provision of rehabilitation to persons with disabilities. It is conceptualized and developed based on the International Classification of Functioning, Disability and Health. ReWin encompasses specific technological aspects to enable its scientific framework and conceptualization to suit the context and needs of stroke care providers and consumers. The framework is built with 2 separate applications, one for the providers and one for the patients and caregivers. Each of these applications has a specific inbuilt design to add data about the demographic details of the user, stroke severity using the National Institute of Health Stroke Scale, and self-assessment of disability measured by the modified Barthel Index. Users can communicate with each other and decide on their therapeutic goals, therapy training information, and progress remotely from where they are. The ultimate outcome expected from the ReWin innovation is a continuum of care for stroke survivors that is effective, safe, and of good quality. Systematic development cannot make the intervention scalable. The intervention needs to be evaluated for its feasibility, acceptability, and effectiveness. Currently, ReWin is being evaluated for its feasibility and acceptability. The evaluation of ReWin will provide an opportunity to develop a scalable solution for empowering therapists and persons with disabilities, in general, to objectively self-manage their treatment. Findings from this study will also provide valuable information about the resources required to deliver such interventions in resource-constrained settings like India

Detection of Obstructive Sleep Apnea from ECG Signal using SVM based Grid Search

Obstructive Sleep Apnea is one common form of sleep apnea and is now tested by means of a process called Polysomnography which is time-consuming, expensive and also requires a human observer throughout the study of the subject which makes it inconvenient and new detection techniques are now being developed to overcome these difficulties. Heart rate variability has proven to be related to sleep apnea episodes and thus the features from the ECG signal can be used in the detection of sleep apnea. The proposed detection technique uses Support Vector Machines using Grid search algorithm and the classifier is trained using features based on heart rate variability derived from the ECG signal. The developed system is tested using the dataset and the results show that this classification system can recognize the disorder with an accuracy rate of 89%. Further, the use of the grid search algorithm has made this system a reliable and an accurate means for the classification of sleep apnea and can serve as a basis for the future development of its screening

Evaluation of the feasibility and acceptability of ReWin—A digital therapeutic rehabilitation innovation for people with stroke-related disabilities in India

Background: Developing culturally appropriate, scalable interventions to meet the growing needs for stroke rehabilitation is a significant problem of public health concern. Therefore, systematic development and evaluation of a scalable, inclusive, technology-driven solution for community-based stroke care are of immense public health importance in India. ReWin is a digital therapeutics platform that was developed systematically. This study aimed to evaluate its feasibility and acceptability in an Indian context. Objectives: Phase–1: To pilot the intervention for identifying operational issues and finalize the intervention. Phase–2: To assess the feasibility and acceptability of ReWin intervention in an Indian context. Methods: Design: Mixed-methods research design. Setting: Participant's home and rehabilitation centers. Participants were selected from rehabilitation centers in South India. Participants: Ten stroke survivors and their caregivers, as well as four rehabilitation service providers were recruited for phase 1. Thirty stroke survivors who were treated and discharged from the hospital, and their caregivers as well as 10 rehabilitation service providers were recruited for Phase 2. Intervention: ReWin a digital therapeutic platform with the provider and patient app for the rehabilitation of physical disabilities following stroke was piloted. Process: Evaluation of the intervention was completed in two phases. In the first phase, the preliminary intervention was field-tested with 10 stroke survivors and four rehabilitation service providers for 2 weeks. In the second phase, the finalized intervention was provided to a further 30 stroke survivors to be used in their homes with support from their carers as well as to 10 rehabilitation service providers for 4 weeks. Outcome measures: Primary outcomes: (1) operational difficulties in using the ReWin intervention; (2) feasibility and acceptability of the ReWin intervention in an Indian setting. Results: Field-testing identified operational difficulties related to 1. Therapeutic content; 2. Format; 3. Navigation; 4. Connectivity, 5. Video-streaming, 6. Language; and 7. Comprehensibility of the animated content. The intervention was reviewed, revised and finalized before pilot testing. Findings from the pilot testing showed that the ReWin intervention was feasible and acceptable. About 76% of the participants had used ReWin for more than half of the intervention period of 4 weeks. Ninety percentage of the stroke care providers and about 60% of the stroke survivors and caregivers felt that the content of ReWin was very relevant to the needs of the stroke survivors. Forty percentage of the stroke survivors and caregivers rated ReWin intervention as excellent. Another 45% of the stroke survivors and caregivers as well as 90% of the stroke care providers rated ReWin intervention as very good based on its overall credibility, usability, and user-friendliness. Conclusions: ReWin has all the essential components to connect care providers and consumers not just for stroke rehabilitation but for several other health conditions with the use of several other technological features that support rehabilitation of persons with disabilities and strengthen rehabilitation in health systems worldwide. It is critical to amalgamate ReWin and other evidence-based interventions for rehabilitation to innovate scalable solutions and promote universal health coverage for stroke care worldwide

Cardiovascular diseases prediction by machine learning incorporation with deep learning

It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures

4D printing of smart polymer nanocomposites: integrating graphene and acrylate based shape memory polymers

The ever-increasing demand for materials to have superior properties and satisfy functions in the field of soft robotics and beyond has resulted in the advent of the new field of four-dimensional (4D) printing. The ability of these materials to respond to various stimuli inspires novel applications and opens several research possibilities. In this work, we report on the 4D printing of one such Shape Memory Polymer (SMP) tBA-co-DEGDA (tert-Butyl Acrylate with diethylene glycol diacrylate). The novelty lies in establishing the relationship between the various characteristic properties (tensile stress, surface roughness, recovery time, strain fixity, and glass transition temperature) concerning the fact that the print parameters of the laser pulse frequency and print speed are governed in the micro-stereolithography (Micro SLA) method. It is found that the sample printed with a speed of 90 mm/s and 110 pulses/s possessed the best batch of properties, with shape fixity percentages of about 86.3% and recovery times as low as 6.95 s. The samples built using the optimal parameters are further subjected to the addition of graphene nanoparticles, which further enhances all the mechanical and surface properties. It has been observed that the addition of 0.3 wt.% of graphene nanoparticles provides the best results

4D printing of materials for the future: Opportunities and challenges

The concept of 4D printing is its formation of complex three-dimensional structures that have the ability to adopt different shapes and forms when subjected to different environmental stimuli. A few researchers simply view 4D printing as an extended technique of 3D printing or additive manufacturing with the added constraint of time. However, the unique shape change mechanism exhibited in this process is a combination of shape programming and the usage of smart active materials mostly polymers. This review article highlights the various smart materials, activation mechanisms and the shape-changing techniques employed in the 4D printing process. The potential of the shape-changing structures and their current applications in various biomedical and engineering fields is also explored. The article aims to emphasize the potential and viability of 4D printing and focused on providing an in-depth insight into the 4D printing process

Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy

Background A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets. Methods Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis. Results A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001). Conclusion We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty

Population‐based cohort study of outcomes following cholecystectomy for benign gallbladder diseases

Background The aim was to describe the management of benign gallbladder disease and identify characteristics associated with all‐cause 30‐day readmissions and complications in a prospective population‐based cohort. Methods Data were collected on consecutive patients undergoing cholecystectomy in acute UK and Irish hospitals between 1 March and 1 May 2014. Potential explanatory variables influencing all‐cause 30‐day readmissions and complications were analysed by means of multilevel, multivariable logistic regression modelling using a two‐level hierarchical structure with patients (level 1) nested within hospitals (level 2). Results Data were collected on 8909 patients undergoing cholecystectomy from 167 hospitals. Some 1451 cholecystectomies (16·3 per cent) were performed as an emergency, 4165 (46·8 per cent) as elective operations, and 3293 patients (37·0 per cent) had had at least one previous emergency admission, but had surgery on a delayed basis. The readmission and complication rates at 30 days were 7·1 per cent (633 of 8909) and 10·8 per cent (962 of 8909) respectively. Both readmissions and complications were independently associated with increasing ASA fitness grade, duration of surgery, and increasing numbers of emergency admissions with gallbladder disease before cholecystectomy. No identifiable hospital characteristics were linked to readmissions and complications. Conclusion Readmissions and complications following cholecystectomy are common and associated with patient and disease characteristics

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

The Cholecystectomy As A Day Case (CAAD) Score: A Validated Score of Preoperative Predictors of Successful Day-Case Cholecystectomy Using the CholeS Data Set

Background Day-case surgery is associated with significant patient and cost benefits. However, only 43% of cholecystectomy patients are discharged home the same day. One hypothesis is day-case cholecystectomy rates, defined as patients discharged the same day as their operation, may be improved by better assessment of patients using standard preoperative variables. Methods Data were extracted from a prospectively collected data set of cholecystectomy patients from 166 UK and Irish hospitals (CholeS). Cholecystectomies performed as elective procedures were divided into main (75%) and validation (25%) data sets. Preoperative predictors were identified, and a risk score of failed day case was devised using multivariate logistic regression. Receiver operating curve analysis was used to validate the score in the validation data set. Results Of the 7426 elective cholecystectomies performed, 49% of these were discharged home the same day. Same-day discharge following cholecystectomy was less likely with older patients (OR 0.18, 95% CI 0.15–0.23), higher ASA scores (OR 0.19, 95% CI 0.15–0.23), complicated cholelithiasis (OR 0.38, 95% CI 0.31 to 0.48), male gender (OR 0.66, 95% CI 0.58–0.74), previous acute gallstone-related admissions (OR 0.54, 95% CI 0.48–0.60) and preoperative endoscopic intervention (OR 0.40, 95% CI 0.34–0.47). The CAAD score was developed using these variables. When applied to the validation subgroup, a CAAD score of ≤5 was associated with 80.8% successful day-case cholecystectomy compared with 19.2% associated with a CAAD score >5 (p < 0.001). Conclusions The CAAD score which utilises data readily available from clinic letters and electronic sources can predict same-day discharges following cholecystectomy