Self-management Interventions for People With Parkinson Disease: Scoping Review.

BACKGROUND: Parkinson disease can impose substantial distress and costs on patients, their families and caregivers, and health care systems. To address these burdens for families and health care systems, there is a need to better support patient self-management. To achieve this, an overview of the current state of the literature on self-management is needed to identify what is being done, how well it is working, and what might be missing. OBJECTIVE: The aim of this scoping review was to provide an overview of the current body of research on self-management interventions for people with Parkinson disease and identify any knowledge gaps. METHODS: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) and Population, Intervention, Comparator, Outcome, and Study type frameworks were used to structure the methodology of the review. Due to time and resource constraints, 1 reviewer systematically searched 4 databases (PubMed, Ovid, Scopus, and Web of Science) for the evaluations of self-management interventions for Parkinson disease published in English. The references were screened using the EndNote X9 citation management software, titles and abstracts were manually reviewed, and studies were selected for inclusion based on the eligibility criteria. Data were extracted into a pre-established form and synthesized in a descriptive analysis. RESULTS: There was variation among the studies on study design, sample size, intervention type, and outcomes measured. The randomized controlled trials had the strongest evidence of effectiveness: 5 out of 8 randomized controlled trials found a significant difference between groups favoring the intervention on their primary outcome, and the remaining 3 had significant effects on at least some of the secondary outcomes. The 2 interventions included in the review that targeted mental health outcomes both found significant changes over time, and the 3 algorithms evaluated performed well. The remaining studies examined patient perceptions, acceptability, and cost-effectiveness and found generally positive results. CONCLUSIONS: This scoping review identified a wide variety of interventions designed to support various aspects of self-management for people with Parkinson disease. The studies all generally reported positive results, and although the strength of the evidence varied, it suggests that self-management interventions are promising for improving the care and outcomes of people with Parkinson disease. However, the research tended to focus on the motor aspects of Parkinson disease, with few nonmotor or holistic interventions, and there was a lack of evaluation of cost-effectiveness. This research will be important to providing self-management interventions that meet the varied and diverse needs of people with Parkinson disease and determining which interventions are worth promoting for widespread adoption.

Comparison between centralized and decentralized supply chains of autologous chimeric antigen receptor T-cell therapies: a UK case study based on discrete event simulation.

BACKGROUND AIMS: Decentralized, or distributed, manufacturing that takes place close to the point of care has been a manufacturing paradigm of heightened interest within the cell therapy domain because of the product's being living cell material as well as the need for a highly monitored and temperature-controlled supply chain that has the potential to benefit from close proximity between manufacturing and application. METHODS: To compare the operational feasibility and cost implications of manufacturing autologous chimeric antigen receptor T (CAR T)-cell products between centralized and decentralized schemes, a discrete event simulation model was built using ExtendSIM 9 for simulating the patient-to-patient supply chain, from the collection of patient cells to the final administration of CAR T therapy in hospitals. Simulations were carried out for hypothetical systems in the UK using three demand levels-low (100 patients per annum), anticipated (200 patients per annum) and high (500 patients per annum)-to assess resource allocation, cost per treatment and system resilience to demand changes and to quantify the risks of mix-ups within the supply chain for the delivery of CAR T treatments. RESULTS: The simulation results show that although centralized manufacturing offers better economies of scale, individual facilities in a decentralized system can spread facility costs across a greater number of treatments and better utilize resources at high demand levels (annual demand of 500 patients), allowing for an overall more comparable cost per treatment. In general, raw material and consumable costs have been shown to be one of the greatest cost drivers, and genetic modification-associated costs have been shown to account for over one third of raw material and consumable costs. Turnaround time per treatment for the decentralized scheme is shown to be consistently lower than its centralized counterpart, as there is no need for product freeze-thaw, packaging and transportation, although the time savings is shown to be insignificant in the UK case study because of its rather compact geographical setting with well-established transportation networks. In both schemes, sterility testing lies on the critical path for treatment delivery and is shown to be critical for treatment turnaround time reduction. CONCLUSIONS: Considering both cost and treatment turnaround time, point-of-care manufacturing within the UK does not show great advantages over centralized manufacturing. However, further simulations using this model can be used to understand the feasibility of decentralized manufacturing in a larger geographical setting.

The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review.

BACKGROUND: The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. OBJECTIVE: This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. METHODS: PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. RESULTS: A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. CONCLUSIONS: The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/16934.

Massive Open Online Course Evaluation Methods: Systematic Review.

BACKGROUND: Massive open online courses (MOOCs) have the potential to make a broader educational impact because many learners undertake these courses. Despite their reach, there is a lack of knowledge about which methods are used for evaluating these courses. OBJECTIVE: The aim of this review was to identify current MOOC evaluation methods to inform future study designs. METHODS: We systematically searched the following databases for studies published from January 2008 to October 2018: (1) Scopus, (2) Education Resources Information Center, (3) IEEE (Institute of Electrical and Electronic Engineers) Xplore, (4) PubMed, (5) Web of Science, (6) British Education Index, and (7) Google Scholar search engine. Two reviewers independently screened the abstracts and titles of the studies. Published studies in the English language that evaluated MOOCs were included. The study design of the evaluations, the underlying motivation for the evaluation studies, data collection, and data analysis methods were quantitatively and qualitatively analyzed. The quality of the included studies was appraised using the Cochrane Collaboration Risk of Bias Tool for randomized controlled trials (RCTs) and the National Institutes of Health-National Heart, Lung, and Blood Institute quality assessment tool for cohort observational studies and for before-after (pre-post) studies with no control group. RESULTS: The initial search resulted in 3275 studies, and 33 eligible studies were included in this review. In total, 16 studies used a quantitative study design, 11 used a qualitative design, and 6 used a mixed methods study design. In all, 16 studies evaluated learner characteristics and behavior, and 20 studies evaluated learning outcomes and experiences. A total of 12 studies used 1 data source, 11 used 2 data sources, 7 used 3 data sources, 4 used 2 data sources, and 1 used 5 data sources. Overall, 3 studies used more than 3 data sources in their evaluation. In terms of the data analysis methods, quantitative methods were most prominent with descriptive and inferential statistics, which were the top 2 preferred methods. In all, 26 studies with a cross-sectional design had a low-quality assessment, whereas RCTs and quasi-experimental studies received a high-quality assessment. CONCLUSIONS: The MOOC evaluation data collection and data analysis methods should be determined carefully on the basis of the aim of the evaluation. The MOOC evaluations are subject to bias, which could be reduced using pre-MOOC measures for comparison or by controlling for confounding variables. Future MOOC evaluations should consider using more diverse data sources and data analysis methods. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/12087.

Examining Cost Measurements in Production and Delivery of Three Case Studies Using E-Learning for Applied Health Sciences: Cross-Case Synthesis.

BACKGROUND: The World Health Report (2006) by the World Health Organization conveys that a significant increase is needed in global health care resourcing to meet the current and future demand for health professionals. Electronic learning (e-Learning) presents a possible opportunity to change and optimize training by providing a scalable means for instruction, thus reducing the costs for training health professionals and providing patient education. Research literature often suggests that a benefit of e-Learning is its cost-effectiveness compared with face-to-face instruction, yet there is limited evidence with respect to the comparison of design and production costs with other forms of instruction or the establishment of standards pertaining to budgeting for these costs. OBJECTIVE: To determine the potential cost favorability of e-Learning in contrast to other forms of learning, there must first be an understanding of the components and elements for building an e-Learning course. Without first taking this step, studies lack the essential financial accounting rigor for course planning and have an inconsistent basis for comparison. This study aimed to (1) establish standard ingredients for the cost of e-Learning course production and (2) determine the variance instructional design has on the production costs of e-Learning courses. METHODS: This study made use of a cross-case method among 3 case studies using mixed methods, including horizontal budget variance calculation and qualitative interpretation of responses from course designers for budget variance using total quality management themes. The different implementation-specific aspects of these cases were used to establish common principles in the composition of budgets in the production and delivery of an applied health professional e-Learning course. RESULTS: A total of 2 case studies reported significant negative budget variances caused by issues surrounding underreporting of personnel costs, inaccurate resource task estimation, lack of contingency planning, challenges in third-party resource management, and the need to update health-related materials that became outdated during course production. The third study reported a positive budget variance because of the cost efficiency derived from previous implementation, the strong working relationship of the course project team, and the use of iterative project management methods. CONCLUSIONS: This research suggests that the delivery costs of an e-Learning course could be underestimated or underreported and identifies factors that could be used to better control budgets. Through consistent management of factors affecting the cost of course production, further research could be undertaken using standard economic evaluation methods to evaluate the advantages of using e-Learning.

Design Choices and Trade-Offs in Health Care Blockchain Implementations: Systematic Review.

BACKGROUND: A blockchain is a list of records that uses cryptography to make stored data immutable; their use has recently been proposed for electronic medical record (EMR) systems. This paper details a systematic review of trade-offs in blockchain technologies that are relevant to EMRs. Trade-offs are defined as "a compromise between two desirable but incompatible features." OBJECTIVE: This review's primary research question was: "What are the trade-offs involved in different blockchain designs that are relevant to the creation of blockchain-based electronic medical records systems?" METHODS: Seven databases were systematically searched for relevant articles using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Papers published from January 1, 2017 to June 15, 2018 were selected. Quality assessments of papers were performed using the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I) tool and the Critical Assessment Skills Programme (CASP) tool. Database searches identified 2885 articles, of which 15 were ultimately included for analysis. RESULTS: A total of 17 trade-offs were identified impacting the design, development, and implementation of blockchain systems; these trade-offs are organized into themes, including business, application, data, and technology architecture. CONCLUSIONS: The key findings concluded the following: (1) multiple trade-offs can be managed adaptively to improve EMR utility; (2) multiple trade-offs involve improving the security of blockchain systems at the cost of other features, meaning EMR efficacy highly depends on data protection standards; and (3) multiple trade-offs result in improved blockchain scalability. Consideration of these trade-offs will be important to the specific environment in which electronic medical records are being developed. This review also uses its findings to suggest useful design choices for a hypothetical National Health Service blockchain. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10994.

Implementing Blockchains for Efficient Health Care: Systematic Review.

BACKGROUND: The decentralized nature of sensitive health information can bring about situations where timely information is unavailable, worsening health outcomes. Furthermore, as patient involvement in health care increases, there is a growing need for patients to access and control their data. Blockchain is a secure, decentralized online ledger that could be used to manage electronic health records (EHRs) efficiently, therefore with the potential to improve health outcomes by creating a conduit for interoperability. OBJECTIVE: This study aimed to perform a systematic review to assess the feasibility of blockchain as a method of managing health care records efficiently. METHODS: Reviewers identified studies via systematic searches of databases including PubMed, MEDLINE, Scopus, EMBASE, ProQuest, and Cochrane Library. Suitability for inclusion of each was assessed independently. RESULTS: Of the 71 included studies, the majority discuss potential benefits and limitations without evaluation of their effectiveness, although some systems were tested on live data. CONCLUSIONS: Blockchain could create a mechanism to manage access to EHRs stored on the cloud. Using a blockchain can increase interoperability while maintaining privacy and security of data. It contains inherent integrity and conforms to strict legal regulations. Increased interoperability would be beneficial for health outcomes. Although this technology is currently unfamiliar to most, investments into creating a sufficiently user-friendly interface and educating users on how best to take advantage of it would lead to improved health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/10994.

Decision Support Tools for Regenerative Medicine: Systematic Review.

BACKGROUND: Decisional tools have demonstrated their importance in informing manufacturing and commercial decisions in the monoclonal antibody domain. Recent approved therapies in regenerative medicine have shown great clinical benefits to patients. OBJECTIVE: The objective of this review was to investigate what decisional tools are available and what issues and gaps have been raised for their use in regenerative medicine. METHODS: We systematically searched MEDLINE to identify articles on decision support tools relevant to tissue engineering, and cell and gene therapy, with the aim of identifying gaps for future decisional tool development. We included published studies in English including a description of decisional tools in regenerative medicines. We extracted data using a predesigned Excel table and assessed the data both quantitatively and qualitatively. RESULTS: We identified 9 articles addressing key decisions in manufacturing and product development challenges in cell therapies. The decision objectives, parameters, assumptions, and solution methods were analyzed in detail. We found that all decisional tools focused on cell therapies, and 6 of the 9 reviews focused on allogeneic cell therapy products. We identified no available tools on tissue-engineering and gene therapy products. These studies addressed key decisions in manufacturing and product development challenges in cell therapies, such as choice of technology, through modeling. CONCLUSIONS: Our review identified a limited number of decisional tools. While the monoclonal antibodies and biologics decisional tool domain has been well developed and has shown great importance in driving more cost-effective manufacturing processes and better investment decisions, there is a lot to be learned in the regenerative medicine domain. There is ample space for expansion, especially with regard to autologous cell therapies, tissue engineering, and gene therapies. To consider the problem more comprehensively, the full needle-to-needle process should be modeled and evaluated.

Weighing benefits and risks in aspects of security, privacy and adoption of technology in a value-based healthcare system.

BACKGROUND: Technology can potentially enable the implementation of a value-based healthcare system, where the impact of quality of care is offered at optimised cost for maximised patient benefit. Technology can deliver value by aiding in data collection to evaluate outcomes and measure costs on a patient and population level. Healthcare organisations, however, face several challenges and risks that result almost exclusively from the use of these technologies. DISCUSSION: Some challenges associated with healthcare technology include their unsustainability, due to lack of scale-up plans and timely evaluations. Other risks include noncompliance with data protection policies, inadequate data governance, and overestimated expectations resulting from the rapid introduction of new technologies. CONCLUSION: Organisations need to consider the risks and challenges associated with the use of technology and develop comprehensive strategies that mitigate factors leading to non-adoption and to realise benefits for achieving a value-based healthcare system.

Impact of Responsible AI on the Occurrence and Resolution of Ethical Issues: Protocol for a Scoping Review.

BACKGROUND: Responsible artificial intelligence (RAI) emphasizes the use of ethical frameworks implementing accountability, responsibility, and transparency to address concerns in the deployment and use of artificial intelligence (AI) technologies, including privacy, autonomy, self-determination, bias, and transparency. Standards are under development to guide the support and implementation of AI given these considerations. OBJECTIVE: The purpose of this review is to provide an overview of current research evidence and knowledge gaps regarding the implementation of RAI principles and the occurrence and resolution of ethical issues within AI systems. METHODS: A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines was proposed. PubMed, ERIC, Scopus, IEEE Xplore, EBSCO, Web of Science, ACM Digital Library, and ProQuest (Arts and Humanities) will be systematically searched for articles published since 2013 that examine RAI principles and ethical concerns within AI. Eligibility assessment will be conducted independently and coded data will be analyzed along themes and stratified across discipline-specific literature. RESULTS: The results will be included in the full scoping review, which is expected to start in June 2024 and completed for the submission of publication by the end of 2024. CONCLUSIONS: This scoping review will summarize the state of evidence and provide an overview of its impact, as well as strengths, weaknesses, and gaps in research implementing RAI principles. The review may also reveal discipline-specific concerns, priorities, and proposed solutions to the concerns. It will thereby identify priority areas that should be the focus of future regulatory options available, connecting theoretical aspects of ethical requirements for principles with practical solutions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/52349.

The conceptualisation and measurement of engagement in digital health.

Digital tools are an increasingly important component of healthcare, but their potential impact is commonly limited by a lack of user engagement. Digital health evaluations of engagement are often restricted to system usage metrics, which cannot capture a full understanding of how and why users engage with an intervention. This study aimed to examine how theory-based, multifaceted measures of engagement with digital health interventions capture different components of engagement (affective, cognitive, behavioural, micro, and macro) and to consider areas that are unclear or missing in their measurement. We identified and compared two recently developed measures that met these criteria (the Digital Behaviour Change Intervention Engagement Scale and the TWente Engagement with Ehealth Technologies Scale). Despite having similar theoretical bases and being relatively strongly correlated, there are key differences in how these scales aim to capture engagement. We discuss the implications of our analysis for how affective, cognitive, and behavioural components of engagement can be conceptualised and whether there is value in distinguishing between them. We conclude with recommendations for the circumstances in which each scale may be most useful and for how future measure development could supplement existing scales.

Development of a Mental Health Apps Recommender Platform.

INTRODUCTION: The aim of the paper is to establish the requirements and methodology for the development and implementation of a recommender system for mental health apps to support patients in self-managing their mental health while awaiting formal treatment. METHODS: The system was developed using an algorithm-based approach, including: (1) user needs assessment through literature review and interviews with various stakeholders, (2) software modelling and prototype creation, and (3) bench testing of the prototype with health experts and users. RESULTS: Based on initial exploration of users' requirements, relevant standards and regulations, a library of trusted mental health apps was compiled and a recommendation engine was built to generate accurate user profiles and deliver personalised health recommendations, which will be further tested to ensure quality. CONCLUSION: Developing a constructive mental health recommendation system requires the establishment of clear and comprehensive requirements, as well as a robust methodology adressing concerns related to data security, confidentiality, safety, and reliability. Subsequent research may compare various indicators of mental health outcomes at the start and end of patients' waiting period to gain more insights into how the recommender system could be further improved to enhance user experience and their overall well-being.

Accuracy and safety of an autonomous artificial intelligence clinical assistant conducting telemedicine follow-up assessment for cataract surgery.

Background: Artificial intelligence deployed to triage patients post-cataract surgery could help to identify and prioritise individuals who need clinical input and to expand clinical capacity. This study investigated the accuracy and safety of an autonomous telemedicine call (Dora, version R1) in detecting cataract surgery patients who need further management and compared its performance against ophthalmic specialists. Methods: 225 participants were recruited from two UK public teaching hospitals after routine cataract surgery between 17 September 2021 and 31 January 2022. Eligible patients received a call from Dora R1 to conduct a follow-up assessment approximately 3 weeks post cataract surgery, which was supervised in real-time by an ophthalmologist. The primary analysis compared decisions made independently by Dora R1 and the supervising ophthalmologist about the clinical significance of five symptoms and whether the patient required further review. Secondary analyses used mixed methods to examine Dora R1's usability and acceptability and to assess cost impact compared to standard care. This study is registered with ClinicalTrials.gov (NCT05213390) and ISRCTN (16038063). Findings: 202 patients were included in the analysis, with data collection completed on 23 March 2022. Dora R1 demonstrated an overall outcome sensitivity of 94% and specificity of 86% and showed moderate to strong agreement (kappa: 0.758-0.970) with clinicians in all parameters. Safety was validated by assessing subsequent outcomes: 11 of the 117 patients (9%) recommended for discharge by Dora R1 had unexpected management changes, but all were also recommended for discharge by the supervising clinician. Four patients were recommended for discharge by Dora R1 but not the clinician; none required further review on callback. Acceptability, from interviews with 20 participants, was generally good in routine circumstances but patients were concerned about the lack of a 'human element' in cases with complications. Feasibility was demonstrated by the high proportion of calls completed autonomously (195/202, 96.5%). Staff cost benefits for Dora R1 compared to standard care were £35.18 per patient. Interpretation: The composite of mixed methods analysis provides preliminary evidence for the safety, acceptability, feasibility, and cost benefits for clinical adoption of an artificial intelligence conversational agent, Dora R1, to conduct follow-up assessment post-cataract surgery. Further evaluation in real-world implementation should be conducted to provide additional evidence around safety and effectiveness in a larger sample from a more diverse set of Trusts. Funding: This manuscript is independent research funded by the National Institute for Health Research and NHSX (Artificial Intelligence in Health and Care Award, AI_AWARD01852).

Barriers and facilitators to parents' engagement with and perceived impact of a childhood obesity app: A mixed-methods study.

Childhood obesity is a growing global health concern. Although mobile health apps have the potential to deliver behavioural interventions, their impact is commonly limited by a lack of sufficient engagement. The purpose of this study was to explore barriers and facilitators to engagement with a family-focused app and its perceived impact on motivation, self-efficacy, and behaviour. Parents with at least one child under 18 and healthcare professionals working with children were recruited; all participants were allocated to use the NoObesity app over a 6-month period. The mixed-methods design was based on the Non-adoption, Abandonment, Scale-Up, Spread, and Sustainability and Reach, Effectiveness, Adoption, Implementation, and Maintenance frameworks. Qualitative and quantitative data were gathered through semi-structured interviews, questionnaires, and app use data (logins and in-app self-reported data). 35 parents were included in the final analysis; quantitative results were analysed descriptively and thematic analysis was conducted on the qualitative data. Key barriers to engagement were boredom, forgetting, and usability issues and key barriers to potential impact on behaviours were accessibility, lack of motivation, and family characteristics. Novelty, gamification features, reminders, goal setting, progress monitoring and feedback, and suggestions for healthy foods and activities were key facilitators to engagement with the app and behaviours. A key observation was that intervention strategies could help address many motivation and capability barriers, but there was a gap in strategies addressing opportunity barriers. Without incorporating strategies that successfully mitigate barriers in all three determinants of behaviour, an intervention is unlikely to be successful. We highlight key recommendations for developers to consider when designing the features and implementation of digital health interventions. Trial Registration: ClinicalTrials.gov (NCT05261555).

Impact of digital technologies on self-efficacy in people with Parkinson's: a scoping review protocol.

INTRODUCTION: Parkinson's disease (PD) is the second most common neurological disease globally, for which currently no one definitive cause or cure exists. Estimates suggest that 145 000 people with Parkinson's (PwP) live in the UK. PD presents with motor and non-motor symptoms fluctuating significantly in and between individuals continually throughout the day. PD adversely affects activities of daily living, quality of life and well-being. Self-efficacy is an important belief to improve for PwP as it enables the individual to develop confidence in their ability to exert control over their own motivation, behaviour and social environment. This scoping review aims to identify digital technologies which have been shown to positively impact on promoting self-efficacy in PwP. METHODS AND ANALYSES: Six bibliographic databases MEDLINE, PsycINFO, Web of Science, CINAHL, EMBASE and IEEE Xplore will be searched from the date of their inception to the May 2023. The primary outcome will be to identify interventions which are associated with a change in self-efficacy in PwP to enable positive and negative outcomes, as well as safety to be evaluated. The secondary outcomes of this review will focus on the intervention's proposed mechanisms for success, particularly looking at the impact they had on positive behaviour change(s) or modification(s) on study participants. ETHICS AND DISSEMINATION: This scoping review will not require ethical approval as it will use data collected from previously published primary studies. The findings of this review will be published in peer-reviewed journals and widely disseminated.

Potential associations between behavior change techniques and engagement with mobile health apps: a systematic review.

Introduction: Lack of engagement is a common challenge for digital health interventions. To achieve their potential, it is necessary to understand how best to support users' engagement with interventions and target health behaviors. The aim of this systematic review was to identify the behavioral theories and behavior change techniques being incorporated into mobile health apps and how they are associated with the different components of engagement. Methods: The review was structured using the PRISMA and PICOS frameworks and searched six databases in July 2022: PubMed, Embase, CINAHL, APA PsycArticles, ScienceDirect, and Web of Science. Risk of bias was evaluated using the Cochrane Collaboration Risk of Bias 2 and the Mixed Methods Appraisal Tools. Analysis: A descriptive analysis provided an overview of study and app characteristics and evidence for potential associations between Behavior Change Techniques (BCTs) and engagement was examined. Results: The final analysis included 28 studies. Six BCTs were repeatedly associated with user engagement: goal setting, self-monitoring of behavior, feedback on behavior, prompts/cues, rewards, and social support. There was insufficient data reported to examine associations with specific components of engagement, but the analysis indicated that the different components were being captured by various measures. Conclusion: This review provides further evidence supporting the use of common BCTs in mobile health apps. To enable developers to leverage BCTs and other app features to optimize engagement in specific contexts and individual characteristics, we need a better understanding of how BCTs are associated with different components of engagement. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42022312596.

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review.

BACKGROUND: Parkinson disease (PD) is the second most prevalent neurodegenerative disease, with around 10 million people with PD worldwide. Current assessments of PD symptoms are conducted by questionnaires and clinician assessments and have many limitations, including unreliable reporting of symptoms, little autonomy for patients over their disease management, and standard clinical review intervals regardless of disease status or clinical need. To address these limitations, digital technologies including wearable sensors, smartphone apps, and artificial intelligence (AI) methods have been implemented for this population. Many reviews have explored the use of AI in the diagnosis of PD and management of specific symptoms; however, there is limited research on the application of AI to the monitoring and management of the range of PD symptoms. A comprehensive review of the application of AI methods is necessary to address the gap of high-quality reviews and highlight the developments of the use of AI within PD care. OBJECTIVE: The purpose of this protocol is to guide a systematic review to identify and summarize the current applications of AI applied to the assessment, monitoring, and management of PD symptoms. METHODS: This review protocol was structured using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) and the Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks. The following 5 databases will be systematically searched: PubMed, IEEE Xplore, Institute for Scientific Information's Web of Science, Scopus, and the Cochrane Library. Title and abstract screening, full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a predetermined form, and any disagreements in screening or extraction will be discussed. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias 2 tool for randomized trials and the Mixed Methods Appraisal Tool for nonrandomized trials. RESULTS: As of April 2023, this systematic review has not yet been started. It is expected to begin in May 2023, with the aim to complete by September 2023. CONCLUSIONS: The systematic review subsequently conducted as a product of this protocol will provide an overview of the AI methods being used for the assessment, monitoring, and management of PD symptoms. This will identify areas for further research in which AI methods can be applied to the assessment or management of PD symptoms and could support the future implementation of AI-based tools for the effective management of PD. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/46581.

At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review.

BACKGROUND: Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research. METHODS: The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations. RESULTS: 34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID. CONCLUSIONS: Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.

Incorporating usability evaluation into iterative development of an online platform to support research participation in Parkinson's disease: a mixed methods protocol.

INTRODUCTION: Many people with Parkinson's (PwP) are not given the opportunity or do not have adequate access to participate in clinical research. To address this, we have codeveloped with users an online platform that connects PwP to clinical studies in their local area. It enables site staff to communicate with potential participants and aims to increase the participation of the Parkinson's community in research. This protocol outlines the mixed methods study protocol for the usability testing of the platform. METHODS AND ANALYSIS: We will seek user input to finalise the platform's design, which will then be deployed in a limited launch for beta testing. The beta version will be used as a recruitment tool for up to three studies with multiple UK sites. Usability data will be collected from the three intended user groups: PwP, care partners acting on their behalf and site study coordinators. Usability questionnaires and website analytics will be used to capture user experience quantitatively, and a purposive sample of users will be invited to provide further feedback via semistructured interviews. Quantitative data will be analysed using descriptive statistics, and a thematic analysis undertaken for interview data. Data from this study will inform future platform iterations. ETHICS AND DISSEMINATION: Ethical approval was obtained from the University of Plymouth (3291; 3 May 2022). We will share our findings via a 'Latest News' section within the platform, presentations, conference meetings and national PwP networks.

Impact of fast-track regulatory designations on strategic commercialization decisions for autologous cell therapies.

Background: Regulatory authorities around the world have introduced incentives to improve the speed-to-market of innovative therapies. Aim & methods: To better understand the capacity and portfolio planning decisions of autologous cell therapies and particularly the impact of fast-tracking designations, this paper describes a mixed-integer linear programming approach for the optimization of capacity investment and portfolio selection decisions to maximize the net present value of a candidate portfolio of therapies under different regulatory programs. Results: The illustrative example shows that fast-track designations allow a 25% earlier breakeven, 42-86% higher net present value over a 20-year horizon with earlier upfront capital and reduce the portfolio's sensitivity to uncertainties. Conclusion: Fast-track designations are effective in providing commercialization incentives, but high capital risks given the compressed timeline should be better considered.