Safety and adherence to medications and self-care advice in oncology (SAMSON): pilot randomised controlled trial protocol.

INTRODUCTION: With the increasing use of oral anti-cancer medicines (OAMs), research demonstrating the magnitude of the medication non-adherence problem and its consequences on treatments' efficacy and toxicity is drawing more attention. Mobile phone interventions may be a practical solution to support patients taking OAMs at home, yet evidence to inform the efficacy of these interventions is lacking. The safety and adherence to medications and self-care advice in oncology (SAMSON) pilot randomised control trial (RCT) aims to evaluate the acceptability, feasibility and potential efficacy of a novel digital solution to improve medication adherence (MA) among people with cancer. METHODS AND ANALYSIS: This is a two-arm, 12-week, pilot RCT aiming to enrol 50 adults with haematological, lung or melanoma cancers at an Australian metropolitan specialised oncology hospital, who are taking oral anti-cancer medicines. Participants will be randomised (1:1 allocation ratio) to either the intervention group (SAMSON solution) or the control group (usual care). The primary outcomes are the acceptability and feasibility of SAMSON. The secondary outcomes are MA, toxicity self-management, anxiety and depressive symptoms, health-related quality of life, and parameters relating to optimal intervention strategy. Quantitative data will be analysed on a modified intention-to-treat basis. SUMMARY: While multicomponent interventions are increasingly introduced, SAMSON incorporates novel approaches to the solution. SAMSON provides a comprehensive, patient-centred, digital MA intervention solution with seamless integration of a mobile platform with clinical consultations that are evidence-based, theory-based, co-designed and rigorously tested. The pilot trial will determine whether this type of intervention is feasible and acceptable in oncology and will provide a foundation for a future full-scale RCT. ETHICS AND DISSEMINATION: Primary ethics approvals were received from Peter MacCallum Cancer Centre and Swinburne University of Technology Human Research Ethics Committees (HREC/95332/PMCC and 20237273-15836). Results will be disseminated via peer-reviewed publications and presentations at international and national conferences. TRIAL REGISTRATION NUMBER: The protocol has been prospectively registered on the Australian New Zealand Clinical Trials Registry with trial registration number (ACTRN12623000472673).

A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing.

Co-Design, Development, and Evaluation of a Mobile Solution to Improve Medication Adherence in Cancer: Design Science Research Approach.

BACKGROUND: Medication nonadherence negatively impacts the health outcomes of people with cancer as well as health care costs. Digital technologies present opportunities to address this health issue. However, there is limited evidence on how to develop digital interventions that meet the needs of people with cancer, are perceived as useful, and are potentially effective in improving medication adherence. OBJECTIVE: The objective of this study was to co-design, develop, and preliminarily evaluate an innovative mobile health solution called Safety and Adherence to Medication and Self-Care Advice in Oncology (SAMSON) to improve medication adherence among people with cancer. METHODS: Using the 4 cycles and 6 processes of design science research methodology, we co-designed and developed a medication adherence solution for people with cancer. First, we conducted a literature review on medication adherence in cancer and a systematic review of current interventions to address this issue. Behavioral science research was used to conceptualize the design features of SAMSON. Second, we conducted 2 design phases: prototype design and final feature design. Last, we conducted a mixed methods study on patients with hematological cancer over 6 weeks to evaluate the mobile solution. RESULTS: The developed mobile solution, consisting of a mobile app, a web portal, and a cloud-based database, includes 5 modules: medication reminder and acknowledgment, symptom assessment and management, reinforcement, patient profile, and reporting. The quantitative study (n=30) showed that SAMSON was easy to use (21/27, 78%). The app was engaging (18/27, 67%), informative, increased user interactions, and well organized (19/27, 70%). Most of the participants (21/27, 78%) commented that SAMSON's activities could help to improve their adherence to cancer treatments, and more than half of them (17/27, 63%) would recommend the app to their peers. The qualitative study (n=25) revealed that SAMSON was perceived as helpful in terms of reminding, supporting, and informing patients. Possible barriers to using SAMSON include the app glitches and users' technical inexperience. Further needs to refine the solution were also identified. Technical improvements and design enhancements will be incorporated into the subsequent iteration. CONCLUSIONS: This study demonstrates the successful application of behavioral science research and design science research methodology to design and develop a mobile solution for patients with cancer to be more adherent. The study also highlights the importance of applying rigorous methodologies in developing effective and patient-centered digital intervention solutions.

"Take the tablet or don't take the tablet?"-A qualitative study of patients' experiences of self-administering anti-cancer medications related to adherence and managing side effects.

PURPOSE: Medication non-adherence is a well-recognised problem in cancer care, negatively impacting health outcomes and healthcare resources. Patient-related factors influencing medication adherence (MA) are complicated and interrelated. There is a need for qualitative research to better understand their underlying interaction processes and patients' needs to facilitate the development of effective patient-tailored complex interventions. This study aimed to explore experiences, perceptions, and needs relating to MA and side effect management of patients who are self-administering anti-cancer treatment. METHODS: Semi-structured audio-recorded interviews with patients who have haematological cancer were conducted. A comparative, iterative, and predominantly inductive thematic analysis approach was employed. RESULTS: Twenty-five patients from a specialist cancer hospital were interviewed. While self-administering cancer medications at home, patients' motivation to adhere was affected by cancer-related physical reactions, fears, cancer literacy and beliefs, and healthcare professional (HCP) and informal support. Patients desired need for regular follow-ups from respectful, encouraging, informative, responsive, and consistent HCPs as part of routine care. Motivated patients can develop high adherence and side effect self-management over time, especially when being supported by HCPs and informal networks. CONCLUSION: Patients with cancer need varied support to medically adhere to and manage side effects at home. HCPs should adapt their practices to meet the patients' expectations to further support them during treatment. We propose a multi-dimensional and technology- and theory-based intervention, which incorporates regular HCP consultations providing tailored education and support to facilitate and maintain patient MA and side effect self-management.

Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

One of the research directions in Internet of Things (IoT) is the field of Context Management Platforms (CMPs) which is a specific type of IoT middleware. CMPs provide horizontal connectivity between vertically oriented IoT silos resulting in a noticeable difference in how IoT data streams are processed. As these context data exchanges can be monetised, there is a need to model and predict the context metrics and operational costs of this exchange to provide relevant and timely context in a large-scale IoT ecosystem. In this paper, we argue that caching all transient context information to satisfy this necessity requires large amounts of computational and network resources, resulting in tremendous operational costs. Using Service Level Agreements (SLAs) between the context providers, CMP, and context consumers, where the level of service imperfection is quantified and linked to the associated costs, we show that it is possible to find efficient caching and prefetching strategies to minimize the context management cost. So, this paper proposes a novel method to find the optimal rate of IoT data prefetching and caching. We show the main context caching strategies and the proposed mathematical models, then discuss how a correctly chosen proactive caching strategy and configurations can help to maximise the profit of CMP operation when multiple SLAs are defined. Our model is accurate up to 0.0016 in Root Mean Square Percentage Error against our simulation results when estimating the profits to the system. We also show our model is valid using the t-test value tending to 0 for all the experimental scenarios.

Development of a person-centred digital platform for the long-term support of people living with an adult-onset genetic disease predisposition: a mixed-methods study protocol.

INTRODUCTION: Individuals at an inherited high-risk of developing adult-onset disease, such as breast cancer, are rare in the population. These individuals require lifelong clinical, psychological and reproductive assistance. After a positive germline test result, clinical genetic services provide support and care coordination. However, ongoing systematic clinical follow-up programmes are uncommon. Digital health solutions offer efficient and sustainable ways to deliver affordable and equitable care. This paper outlines the codesign and development of a digital health platform to facilitate long-term clinical and psychological care, and foster self-efficacy in individuals with a genetic disease predisposition. METHODS AND ANALYSIS: We adopt a mixed-methods approach for data gathering and analysis. Data collection is in two phases. In phase 1, 300 individuals with a high-risk genetic predisposition to adult disease will undertake an online survey to assess their use of digital health applications (apps). In phase 2, we will conduct focus groups with 40 individuals with a genetic predisposition to cardiac or cancer syndromes, and 30 clinicians from diverse specialities involved in their care. These focus groups will inform the platform's content, functionality and user interface design, as well as identify the barriers and enablers to the adoption and retention of the platform by all endusers. The focus groups will be audiorecorded and transcribed, and thematic and content data analysis will be undertaken by adopting the Unified Theory of Acceptance and Use of Technology. Descriptive statistics will be calculated from the survey data. Phase 3 will identify the core skillsets for a novel digital health coordinator role. Outcomes from phases 1 and 2 will inform development of the digital platform, which will be user-tested and optimised in phase 4. ETHICS AND DISSEMINATION: This study was approved by the Peter MacCallum Human Research Ethics Committee (HREC/88892/PMCC). Results will be disseminated in academic forums, peer-reviewed publications and used to optimise clinical care.

Weed Detection Using Deep Learning: A Systematic Literature Review.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes.

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes.

Investigation of Intervention Solutions to Enhance Adherence to Oral Anticancer Medicines in Adults: Overview of Reviews.

BACKGROUND: Adherence to anticancer medicines is critical for the success of cancer treatments; however, nonadherence remains challenging, and there is limited evidence of interventions to improve adherence to medicines in patients with cancer. OBJECTIVE: This overview of reviews aimed to identify and summarize available reviews of interventions to improve adherence to oral anticancer medicines in adult cancer survivors. METHODS: A comprehensive search of 7 electronic databases was conducted by 2 reviewers who independently conducted the study selection, quality assessment using the A Measurement Tool to Assess Systematic Reviews 2, and data extraction. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist was adapted to report the results. RESULTS: A total of 29 reviews were included in the narrative synthesis. The overall quality of the systematic reviews was low. The 4 main strategies to promote adherence were focused on education, reminders, behavior and monitoring, and multicomponent approaches. Digital technology-based interventions were reported in most reviews (27/29, 93%). A few interventions applied theories (10/29, 34%), design frameworks (2/29, 7%), or engaged stakeholders (1/29, 3%) in the development processes. The effectiveness of interventions was inconsistent between and within reviews. However, interventions using multiple strategies to promote adherence were more likely to be effective than single-strategy interventions (12/29, 41% reviews). Unidirectional communication (7/29, 24% reviews) and technology alone (11/29, 38% reviews) were not sufficient to demonstrate improvement in adherence outcomes. Nurses and pharmacists played a critical role in promoting patient adherence to oral cancer therapies, especially with the support of digital technologies (7/29, 24% reviews). CONCLUSIONS: Multicomponent interventions are potentially effective in promoting patient adherence to oral anticancer medicines. The seamless integration of digital solutions with direct clinical contacts is likely to be effective in promoting adherence. Future research for developing comprehensive digital adherence interventions should be evidence-based, theory-based, and rigorously evaluated.

IoT Device Integration and Payment via an Autonomic Blockchain-Based Service for IoT Device Sharing.

The Internet of Things (IoT) incorporates billions of IoT devices (e.g., sensors, cameras, wearables, smart phones, as well as other internet-connected machines in homes, vehicles, and industrial plants), and the number of such connected IoT devices is currently growing rapidly. This paper proposes a novel Autonomic Global IoT Device Discovery and Integration Service (which we refer to as aGIDDI) that permits IoT applications to find IoT devices that are owned and managed by other parties in IoT (which we refer to as IoT device providers), integrate them, and pay for using their data observations. aGIDDI incorporates a suite of interacting sub-services supporting IoT device description, query, integration, payment (via a pay-as-you-go payment model), and access control that utilise a special-purpose blockchain to manage all information needed for IoT applications to find, pay and use the IoT devices they need. The paper describes aGIDDI's novel protocol that allows any IoT application to discover and automatically integrate and pay for IoT devices and their data that are provided by other parties. The paper also presents aGIDDI's architecture and proof-of-concept implementation, as well as an experimental evaluation of the performance and scalability of aGIDDI in variety of IoT device integration and payment scenarios.

Situation-Aware IoT Data Generation towards Performance Evaluation of IoT Middleware Platforms.

With the increasing growth of IoT applications in various sectors (e.g., manufacturing, healthcare, etc.), we are witnessing a rising demand of IoT middleware platform that host such IoT applications. Hence, there arises a need for new methods to assess the performance of IoT middleware platforms hosting IoT applications. While there are well established methods for performance analysis and testing of databases, and some for the Big data domain, such methods are still lacking support for IoT due to the complexity, heterogeneity of IoT application and their data. To overcome these limitations, in this paper, we present a novel situation-aware IoT data generation framework, namely, SA-IoTDG. Given a majority of IoT applications are event or situation driven, we leverage a situation-based approach in SA-IoTDG for generating situation-specific data relevant to the requirements of the IoT applications. SA-IoTDG includes a situation description system, a SySML model to capture IoT application requirements and a novel Markov chain-based approach that supports transition of IoT data generation based on the corresponding situations. The proposed framework will be beneficial for both researchers and IoT application developers to generate IoT data for their application and enable them to perform initial testing before the actual deployment. We demonstrate the proposed framework using a real-world example from IoT traffic monitoring. We conduct experimental evaluations to validate the ability of SA-IoTDG to generate IoT data similar to real-world data as well as enable conducting performance evaluations of IoT applications deployed on different IoT middleware platforms using the generated data. Experimental results present some promising outcomes that validate the efficacy of SA-IoTDG. Learning and lessons learnt from the results of experiments conclude the paper.

Digital Twins Supporting Efficient Digital Industrial Transformation.

Industry 4.0 applications help digital industrial transformation to be achieved through smart, data-driven solutions that improve production efficiency, product consistency, preventive maintenance, and the logistics of industrial applications and related supply chains. To enable and accelerate digital industrial transformation, it is vital to support cost-efficient Industry 4.0 application development. However, the development of such Industry 4.0 applications is currently expensive due to the limitations of existing IoT platforms in representing complex industrial machines, the support of only production line-based application testing, and the lack of cost models for application cost/benefit analysis. In this paper, we propose the use of Cyber Twins (CTs), an extension of Digital Twins, to support cost-efficient Industry 4.0 application development. CTs provide semantic descriptions of the machines they represent and incorporate machine simulators that enable application testing without any production line risk and cost. This paper focuses on CT-based Industry 4.0 application development and the related cost models. Via a case study of a CT-based Industry 4.0 application from the dairy industry, the paper shows that CT-based Industry 4.0 applications can be developed with approximately 60% of the cost of IoT platform-based application development.

Efficient Execution of Complex Context Queries to Enable Near Real-Time Smart IoT Applications.

As the Internet of Things (IoT) is evolving at a fast pace, the need for contextual intelligence has become more crucial for delivering IoT intelligence, efficiency, effectiveness, performance, and sustainability. Contextual intelligence enables interactions between IoT devices such as sensors/actuators, smartphones and connected vehicles, to name but a few. Context management platforms (CMP) are emerging as a promising solution to deliver contextual intelligence for IoT. However, the development of a generic solution that allows IoT devices and services to publish, consume, monitor, and share context is still in its infancy. In this paper, we propose, validate and explain the details of a novel mechanism called Context Query Engine (CQE), which is an integral part of a pioneering CMP called Context-as-a-Service (CoaaS). CQE is responsible for efficient execution of context queries in near real-time. We present the architecture of CQE and illuminate its workflows. We also conduct extensive experimental performance and scalability evaluation of the proposed CQE. Results of experimental evaluation convincingly demonstrate that CoaaS outperforms its competitors in executing complex context queries. Moreover, the advanced functionality of the embedded query language makes CoaaS a decent candidate for real-life deployments.

Internet of Things Platform for Smart Farming: Experiences and Lessons Learnt.

Improving farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fuelled by rapid population growth across the world. Farm productivity can be increased by understanding and forecasting crop performance in a variety of environmental conditions. Crop recommendation is currently based on data collected in field-based agricultural studies that capture crop performance under a variety of conditions (e.g., soil quality and environmental conditions). However, crop performance data collection is currently slow, as such crop studies are often undertaken in remote and distributed locations, and such data are typically collected manually. Furthermore, the quality of manually collected crop performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators but is essential to filter out collected data that will lead to invalid conclusions (e.g., solar radiation readings in the afternoon after even a short rain or overcast in the morning are invalid, and should not be used in assessing crop performance). Emerging Internet of Things (IoT) technologies, such as IoT devices (e.g., wireless sensor networks, network-connected weather stations, cameras, and smart phones) can be used to collate vast amount of environmental and crop performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via mobile smart phone applications. Such data can then be analysed to filter out invalid data and compute personalised crop recommendations for any specific farm. In this paper, we present the design of SmartFarmNet, an IoT-based platform that can automate the collection of environmental, soil, fertilisation, and irrigation data; automatically correlate such data and filter-out invalid data from the perspective of assessing crop performance; and compute crop forecasts and personalised crop recommendations for any particular farm. SmartFarmNet can integrate virtually any IoT device, including commercially available sensors, cameras, weather stations, etc., and store their data in the cloud for performance analysis and recommendations. An evaluation of the SmartFarmNet platform and our experiences and lessons learnt in developing this system concludes the paper. SmartFarmNet is the first and currently largest system in the world (in terms of the number of sensors attached, crops assessed, and users it supports) that provides crop performance analysis and recommendations.

Constructing Pairing-Friendly Elliptic Curves under Embedding Degree 1 for Securing Critical Infrastructures.

Information confidentiality is an essential requirement for cyber security in critical infrastructure. Identity-based cryptography, an increasingly popular branch of cryptography, is widely used to protect the information confidentiality in the critical infrastructure sector due to the ability to directly compute the user's public key based on the user's identity. However, computational requirements complicate the practical application of Identity-based cryptography. In order to improve the efficiency of identity-based cryptography, this paper presents an effective method to construct pairing-friendly elliptic curves with low hamming weight 4 under embedding degree 1. Based on the analysis of the Complex Multiplication(CM) method, the soundness of our method to calculate the characteristic of the finite field is proved. And then, three relative algorithms to construct pairing-friendly elliptic curve are put forward. 10 elliptic curves with low hamming weight 4 under 160 bits are presented to demonstrate the utility of our approach. Finally, the evaluation also indicates that it is more efficient to compute Tate pairing with our curves, than that of Bertoni et al.