Abstract Introduction Despite digital health tools being popular for supporting self‐management of chronic diseases, little research has been undertaken on stroke. We developed and pilot tested, using a randomized controlled design, a multicomponent digital health programme, known as Inspiring Virtual Enabled Resources following Vascular Events (iVERVE), to improve self‐management after stroke. The 4‐week trial incorporated facilitated person‐centred goal setting, with those in the intervention group receiving electronic messages aligned to their goals, versus limited administrative messages for the control group. In this paper, we describe the participant experience of the various components involved with the iVERVE trial. Methods Mixed method design: satisfaction surveys (control and intervention) and a focus group interview (purposively selected intervention participants). Experiences relating to goal setting and overall trial satisfaction were obtained from intervention and control participants, with feedback on the electronic message component from intervention participants. Inductive thematic analysis was used for interview data and open‐text responses, and closed questions were summarized descriptively. Triangulation of data allowed participants' perceptions to be explored in depth. Results Overall, 27/54 trial participants completed the survey (13 intervention: 52%; 14 control: 48%); and 5/8 invited participants in the intervention group attended the focus group. Goal setting: The approach was considered comprehensive, with the involvement of health professionals in the process helpful in developing realistic, meaningful and person‐centred goals. Electronic messages (intervention): Messages were perceived as easy to understand (92%), and the frequency of receipt was considered appropriate (11/13 survey; 4/5 focus group). The content of messages was considered motivational (62%) and assisted participants to achieve their goals (77%). Some participants described the benefits of receiving messages as a ‘reminder’ to act. Overall trial satisfaction: Messages were acceptable for educating about stroke (77%). Having options for short message services or email to receive messages was considered important. Feedback on the length of the intervention related to specific goals, and benefits of receiving the programme earlier after stroke was expressed. Conclusion The participant experience has indicated acceptance and utility of iVERVE. Feedback from this evaluation is invaluable to inform refinements to future Phase II and III trials, and wider research in the field. Patient or Public Contribution Two consumer representatives sourced from the Stroke Foundation (Australia) actively contributed to the design of the iVERVE programme. In this study, participant experiences directly contributed to the further development of the iVERVE intervention and future trial design.

Background Electronic communication is used in various populations to achieve health goals, but evidence in stroke is lacking. We pilot tested the feasibility and potential effectiveness of a novel personalised electronic self-management intervention to support person-centred goal attainment and secondary prevention after stroke. Methods A phase I, prospective, randomised controlled pilot trial (1:1 allocation) with assessor blinding, intention-to-treat analysis, and a process evaluation. Community-based survivors of stroke were recruited from participants in the Australian Stroke Clinical Registry (AuSCR) who had indicated their willingness to be contacted for research studies. Inclusion criteria include 1–2 years following hospital admission for stroke and living within 50 km of Monash University (Melbourne). Person-centred goals were set with facilitation by a clinician using a standardised template. The intervention group received electronic support messages aligned to their goals over 4 weeks. The control group received only 2–3 electronic administrative messages. Primary outcomes were study retention, goal attainment (assessed using Goal Attainment Scaling method) and satisfaction. Secondary outcomes were self-management (Health Education Impact Questionnaire: 8 domains), quality of life, mood and acceptability. Results Of 340 invitations sent from AuSCR, 73 responded, 68 were eligible and 57 (84%) completed the baseline assessment. At the goal-setting stage, 54/68 (79%) were randomised (median 16 months after stroke): 25 to intervention (median age 69 years; 40% female) and 29 to control (median age 68 years; 38% female). Forty-five (83%) participants completed the outcome follow-up assessment. At follow-up, goal attainment (mean GAS-T score ≥ 50) in the intervention group was achieved for goals related to function, participation and environment (control: environment only). Most intervention participants provided positive feedback and reported that the iVERVE messages were easy to understand (92%) and assisted them in achieving their goals (77%). We found preliminary evidence of non-significant improvements between the groups for most self-management domains (e.g. social integration and support: β coefficient 0.34; 95% CI − 0.14 to 0.83) and several quality-of-life domains in favour of the intervention group. Conclusion These findings support the need for further randomised effectiveness trials of the iVERVE program to be tested in people with new stroke. Trial registration ANZCTR, ACTRN12618001519246 . Registered on 11 September 2018—retrospectively registered.

Cadilhac, Dominique A, Busingye, Doreen, Li, Jonathan C, Andrew, Nadine E, Kilkenny, Monique F, Thrift, Amanda G, Thijs, Vincent, Hackett, Maree ORCID: 0000­0003­1211­9087, Kneebone, Ian et al (2018) Development of an electronic health message system to support recovery after stroke: Inspiring Virtual Enabled Resources following Vascular Events (iVERVE). Patient Preference and Adherence, 2018 (12). pp. 1213­1224. ISSN 1177­889X

The handling of chemicals in the laboratory presents a challenge in instructing large class sizes and when students are relatively new to the laboratory environment. In this work, we describe and demonstrate an augmented reality colorimetric titration tool that operates out of the smartphone or tablet of students. It allows multiple students to conduct the exercise at the same time, respond quickly to actions made, and correctly depict the colors associated with changes in pH values for the indicator used. The tool imbues unparalleled realism in the conduct of the experiment and offers to strongly help students acquire bench skills with minimal use of liquid chemicals, thereby reducing handing risks for them and resulting in lower negative impacts on the environment. The feedback received from undergraduate students that participated in an initial small test exercise with the tool corroborates this.

Purpose: Worldwide, stroke is a leading cause of disease burden. Many survivors have unmet needs after discharge from hospital. Electronic communication technology to support post-discharge care has not been used for patients with stroke. In this paper, we describe the development of a novel electronic messaging system designed for survivors of stroke to support their goals of recovery and secondary prevention after hospital discharge. Participants and methods: This was a formative evaluation study. The design was informed by a literature search, existing data from survivors of stroke, and behavior change theories. We established two working groups; one for developing the electronic infrastructure and the other (comprising researchers, clinical experts and consumer representatives) for establishing the patient-centered program. Following agreement on the categories for the goal-setting menu, we drafted relevant messages to support and educate patients. These messages were then independently reviewed by multiple topic experts. Concurrently, we established an online database to capture participant characteristics and then integrated this database with a purpose-built messaging system. We conducted alpha testing of the approach using the first 60 messages. Results: The initial goal-setting menu comprised 26 subcategories. Following expert review, another 8 goal subcategories were added to the secondary prevention category: managing cholesterol; smoking; physical activity; alcohol consumption; weight management; medication management; access to health professionals, and self-care. Initially, 455 health messages were created by members of working group 2. Following refinement and mapping to different goals by the project team, 980 health messages across the health goals and 69 general motivational messages were formulated. Seventeen independent reviewers assessed the messages and suggested adding 73 messages and removing 16 (2%). Overall, 1,233 messages (18 administrative, 69 general motivation and 1,146 health-related) were created. Conclusion: This novel electronic self-management support system is ready to be pilot tested in a randomized controlled trial in patients with stroke.

Audience Response System (ARS) usage in lectures has grown in the higher education sector over the past decade with the evolution from hardware “clickers” to web and mobile-based systems due to the ubiquity of mobile internet and mobile computing devices. Benefits of ARSs for instructors include the capability to receive real-time feedback on student understanding, breaking up lecture time, increased student engagement and improved learning outcomes. The interest in, and understanding of, the perceived benefits of ARSs are often sufficient motivators for instructors to trial this technology. However, the sustained use of this technology presents a hurdle for some instructors, beyond the difficulties in initial uptake. This paper investigates the motivating factors and barriers for sustained use of Audience Response Systems. Engineering instructors were interviewed in addition to a university-wide survey using an instructor technology adoption framework. This framework was used to study participants' “pedagogical beliefs”, “knowledge”, “self-efficacy”, and the institutional “culture” with respect to ARSs. The findings suggest that culture and pedagogical beliefs were the primary themes with respect to enabling sustained use, while knowledge and self-efficacy were secondary enablers. The major barrier for sustained use was “time”, which was an aspect that appeared in multiple themes. This identification and understanding of the motivating factors and barriers for sustained use for ARSs has helped advance the design and development of departmental support and a bespoke ARS software tool.

Poorly controlled asthma during pregnancy is hazardous for both mother and foetus. Better asthma control may be achieved if patients are involved in regular self‐monitoring of symptoms and self‐management according to a written asthma action plan. Telehealth applications to optimize asthma management and outcomes in pregnant women have not yet been evaluated. This study evaluated the efficacy of a telehealth programme supported by a handheld respiratory device in improving asthma control during pregnancy.

Flexible automation systems provide the needed adaptability to serve shorter-term projects and specialty applications in biochemical analysis. A low-cost selective compliant articulated robotic arm designed for liquid spillage avoidance is developed here. In the vertical-plane robotic arm movement test, the signals from an inertial measurement unit (IMU) and accelerometer were able to sense collisions. In the horizontal movement test, however, only the signals from the IMU enabled collision to be detected. Using a calculation method developed, it was possible to chart the regions where the obstacle was likely to be located when a collision occurred. The low cost of the IMU and its easy incorporation into the robotic arm offer the potential to meet the pressures of lowering operating costs, apply laboratory automation in resource-limited venues, and obviate human intervention in response to sudden disease outbreaks.

Telehealth has the potential to improve asthma management through regular monitoring of lung function and/or asthma symptoms by health professionals in conjunction with feedback to patients. Although the benefits of telehealth for improving asthma management have been extensively studied, the feasibility of telehealth for supporting asthma management in pregnant women has not been investigated. This study aims to evaluate the use of telehealth for remotely monitoring lung function and optimising asthma control during pregnancy.

Background: This study presents the investigation of a web-based audience response system, Monash eLearning Tools System (MeLTS), in a first year engineering unit. The system was designed and built by two engineering students and trailed by approximately 230 students over the course of a semester. Given the increasing size of higher education classes, non-traditional methods are needed in order to engage students and provide an active learning environment. Current research has shown that, while there are some minor challenges to employing audience response systems, when used correctly, they have been effective at improving students' learning and their instructors' assessments in addition to the learning environment itself. Purpose: The purpose of this research was to monitor how first year engineering students respond to, use and benefit from the MeLTS web-based audience response system and compare these findings to other methods of engagement. Design/Method: In order to explore these questions, an online audience response system was developed as a webapp by two fourth year engineering students. This system was used over the course of a first year engineering unit to test students' understanding of lecture content with student answers being recorded over the duration of the semester. This data was analysed in conjunction with tutorial attendance, final unit scores and a student survey amongst other data. Results: There was a positive correlation between the MeLTS audience response system questions attempted and students' final scores for the unit. Survey responses suggested that the audience response system was well received by the students. It was found that smartphones were by far the most popular device students used to access the system, followed by tablets and then laptops. Conclusion: Students tended to use smartphones to access the web-app, they predominantly enjoy using audience response technology such as MeLTS and, given the correlation between audience response system question attempts and final unit scores, it appears that using audience response systems has a positive effect on student learning and increased student engagement. This agrees with current literature on the subject which states that, when used correctly, audience response systems are effective at increasing learning performance and are largely well received by students.

Efficient fault detection and characterization are crucial requirements for automated network diagnosis systems. In this paper, we present normalized statistical signatures (NSSs), a network ‘soft-failure’ characterization technique for user devices (UDs) based upon exhaustive sets of aggregated statistical features extracted from transmission control protocol (TCP) packet streams. TCP streams are collected on-demand (e.g. upon user complaint) with fixed data transfer limits to capture the artifacts resulting from UD faults. NSSs created using live network and testbed data are able to uniquely characterize many faults and offer insight into how various types of features are affected by the faults and networks. We then introduce the link adaptive signature estimation (LASE) technique to reduce the quantities of collected NSSs required for generalized diagnostic systems with variable link parameters. We create feature estimator functions using multivariate regression techniques to generate artificial NSSs, which are subsequently used to train machine learning systems that have robust generalization capabilities. Performance of a prototype fault classifier system based on NSSs shows that an overall detection accuracy of 98% can be achieved for eight types of faults in a live network environment. In this paper, we specifically focus on formulating the basic framework of NSS and LASE, and limit the analysis to wired networks. This work can later be extended to encompass more complex fixed and mobile wireless networking environments. We expect that the combination of NSSs and LASE can serve as the foundation of next-generation automated network diagnosis systems.

We present an automated system for the diagnosis of both known and unknown soft-failures in end-user devices (UDs). Known faults that cause network performance degradation are used to train the classifier-based system in a supervised manner while unknown faults are automatically detected and clustered to identify the existence of new categories of soft-failures. The supervised classifier used in the system can be retrained by including the newly detected faults to enhance its performance. The system uses 460 features to construct Normalized Statistical Signatures (NSSs) for fault characterization. Due to the high dimensionality of NSSs, EigenNSS was proposed to reduce the complexity without losing important information. Because of the natural network inconsistencies that exist in communication links, we propose FisherNSS, a reduced signature that provides improved linear separability between classes to further enhance classification performance. The system is evaluated over a live campus network using 17 emulated UD faults. The results show that the best overall classification accuracy of up to 97% was achieved by using FisherNSS with a dimensionality reduction of 96.74%. In comparison, both EigenNSS and FisherNSS have faster training and diagnosis time compared to NSS, which makes them suitable for on-demand as well as real time diagnostic applications. Furthermore, FisherNSS compared to EigenNSS has a higher diagnostic accuracy and quicker diagnosis time (order of microseconds).

We present a new approach for effective soft-failure characterization in end-user devices (EUDs) on networks that support the TCP/IP. Our method can be employed for creating fully automated, accurate and scalable fault diagnostic systems. First, we describe Normalized Statistical Signatures (NSSs), a technique for characterizing EUD soft-failures. We create the NSSs by using aggregated statistical features extracted from TCP packet streams collected on-demand upon user complaint. We then introduce the Link Adaptive Signature Estimation (LASE) technique to minimize the number of NSSs needed to create diagnostic systems that have generalization capability for coping with communication link variations. To achieve this, we create Feature Estimator Functions (FEFs) using multivariate regression techniques and a minimal number of signatures of emulated EUD faults. We use these FEFs to generate synthetic NSSs which, can be used to train diagnostic systems with robust generalization capabilities. We expect that the combined use of NSSs and LASE technique will serve as the foundation of next-generation fault diagnosis systems.

We present an automated solution for rapid diagnosis of both known and unknown “soft-failures” in network User Devices (UDs). A multiclass classifier is first trained with the known faults and during diagnosis, the unknown faults are clustered to determine the existence of a new fault. Then, in an iterative process, the classifier is re-trained with the newly detected fault. The system relies on 410 features long Normalized Statistical Signature (NSSs) for fault characterization. Since, the high dimensionality of the NSS can create model overfitting, we propose EigenNSS, a transformed signature with lower dimensions and minimum information loss. The system is evaluated with live network data of 17 emulated UD faults. The results show an overall detection accuracy of 97.2%with minimum false positives and dimensionality reduction of 93.9%. Also, compared with the NSS, the EigenNSS has faster training and diagnosis times suitable for on-demand as well as real-time diagnostic applications.

We present the Intelligent Automated Client Diagnostic (IACD) system, which only relies on inference from Transmission Control Protocol (TCP) packet traces for rapid diagnosis of client device problems that cause network performance issues. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems, and (ii) identifies characteristics unique to client faults to report the root cause of the client device problem. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy in healthy links. The system can perform fault diagnosis independent of the client's specific TCP implementation, enabling diagnosis capability on diverse range of client computers.

Traditional network diagnosis methods of Client-Terminal Device (CTD) problems tend to be laborintensive, time consuming, and contribute to increased customer dissatisfaction. In this paper, we propose an automated solution for rapidly diagnose the root causes of network performance issues in CTD. Based on a new intelligent inference technique, we create the Intelligent Automated Client Diagnostic (IACD) system, which only relies on collection of Transmission Control Protocol (TCP) packet traces. Using soft-margin Support Vector Machine (SVM) classifiers, the system (i) distinguishes link problems from client problems and (ii) identifies characteristics unique to the specific fault to report the root cause. The modular design of the system enables support for new access link and fault types. Experimental evaluation demonstrated the capability of the IACD system to distinguish between faulty and healthy links and to diagnose the client faults with 98% accuracy. The system can perform fault diagnosis independent of the user's specific TCP implementation, enabling diagnosis of diverse range of client devices