Humanizing Health and Social Care Support for People With Intellectual and Developmental Disabilities: Protocol for a Scoping Review

\ua9 Madison Milne-Ives, Rohit Shankar, Dan Goodley, Kirsten Lamb, Richard Laugharne, Tracey Harding, Edward Meinert. Background: Health care is shifting toward a more person-centered model; however, people with intellectual and developmental disabilities can still experience difficulties in accessing equitable health care. Given these difficulties, it is important to consider how humanizing principles, such as empathy and respect, can be best incorporated into health and social care practices for people with intellectual and developmental disabilities to ensure that they are receiving equitable treatment and support. Objective: The purpose of our scoping review is to provide an overview of the current research landscape and knowledge gaps regarding the development and implementation of interventions based on humanizing principles that aim to improve health and social care practices for people with intellectual and developmental disabilities. Methods: The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and PICOS (Population, Intervention, Comparator, Outcome, and Study) frameworks will be used to structure the review. A total of 6 databases (PubMed, MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science) will be searched for English articles published in the previous 10 years that describe or evaluate health and social care practice interventions underpinned by the humanizing principles of empathy, compassion, dignity, and respect. Two reviewers will screen and select references based on the eligibility criteria and extract the data into a predetermined form. A descriptive analysis will be conducted to summarize the results and provide an overview of interventions in the following three main care areas: health care, social care, and informal social support. Results: The results will be included in the scoping review, which is expected to begin in October 2022 and be completed and submitted for publication by January 2023. Conclusions: Our scoping review will summarize the state of the field of interventions that are using humanizing principles to improve health and social care for adults with intellectual and developmental disabilities

The Impact of a Digital Artificial Intelligence System on the Monitoring and Self-management of Nonmotor Symptoms in People With Parkinson Disease: Proposal for a Phase 1 Implementation Study

Copyright 2022 The Authors. Background: Nonmotor symptoms of Parkinson disease are a major factor of disease burden but are often underreported in clinical appointments. A digital tool has been developed to support the monitoring and management of nonmotor symptoms. Objective: The aim of this study is to establish evidence of the impact of the system on patient confidence, knowledge, and skills for self-management of nonmotor symptoms, symptom burden, and quality of life of people with Parkinson and their care partners. It will also evaluate the usability, acceptability, and potential for adoption of the system for people with Parkinson, care partners, and health care professionals. Methods: A mixed methods implementation and feasibility study based on the nonadoption, abandonment, scale-up, spread, and sustainability framework will be conducted with 60 person with Parkinson-care partner dyads and their associated health care professionals. Participants will be recruited from outpatient clinics at the University Hospitals Plymouth NHS Trust Parkinson service. The primary outcome, patient activation, will be measured over the 12-month intervention period; secondary outcomes include the system\u27s impact on health and well-being outcomes, safety, usability, acceptability, engagement, and costs. Semistructured interviews with a subset of participants will gather a more in-depth understanding of user perspectives and experiences with the system. Repeated measures analysis of variance will analyze change over time and thematic analysis will be conducted on qualitative data. The study was peer reviewed by the Parkinson\u27s UK Non-Drug Approaches grant board and is pending ethical approval. Results: The study won funding in August 2021; data collection is expected to begin in December 2022. Conclusions: The study\u27s success criteria will be affirming evidence regarding the system\u27s feasibility, usability and acceptability, no serious safety risks identified, and an observed positive impact on patient activation. Results will be disseminated in academic peer-reviewed journals and in platforms and formats that are accessible to the general public, guided by patient and public collaborators

Massive Open Online Courses (MOOC) evaluation methods: protocol for a systematic review

BACKGROUND: Massive open online courses (MOOCs) have increased in popularity in recent years. They target a wide variety of learners and use novel teaching approaches, yet often exhibit low completion rates (10%). It is important to evaluate MOOCs to determine their impact and effectiveness, but little is known at this point about the methodologies that should be used for evaluation. OBJECTIVE: The purpose of this paper is to provide a protocol for a systematic review on MOOC evaluation methods. METHODS: We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines for reporting this protocol. We developed a population, intervention, comparator, and outcome (PICO) framework to guide the search strategy, based on the overarching question, "What methods have been used to evaluate MOOCs?" The review will follow six stages: 1) literature search, 2) article selection, 3) data extraction, 4) quality appraisal, 5) data analysis, and 6) data synthesis. RESULTS: The systematic review is ongoing. We completed the data searches and data abstraction in October and November 2018. We are now analyzing the data and expect to complete the systematic review by March 2019. CONCLUSIONS: This systematic review will provide a useful summary of the methods used for evaluation of MOOCs and the strengths and limitations of each approach. It will also identify gaps in the literature and areas for future work. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12087

Life Course Digital Twins–Intelligent Monitoring for Early and Continuous Intervention and Prevention (LifeTIME): Proposal for a Retrospective Cohort Study

\ua9 Madison Milne-Ives, Lorna K Fraser, Asiya Khan, David Walker, Michelle Helena van Velthoven, Jon May, Ingrid Wolfe, Tracey Harding, Edward Meinert. Originally published in JMIR Research Protocols (https://www.researchprotocols.org),26.05.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included. Background: Multimorbidity, which is associated with significant negative outcomes for individuals and health care systems, is increasing in the United Kingdom. However, there is a lack of knowledge about the risk factors (including health, behavior, and environment) for multimorbidity over time. An interdisciplinary approach is essential, as data science, artificial intelligence, and engineering concepts (digital twins) can identify key risk factors throughout the life course, potentially enabling personalized simulation of life-course risk for the development of multimorbidity. Predicting the risk of developing clusters of health conditions before they occur would add clinical value by enabling targeted early preventive interventions, advancing personalized care to improve outcomes, and reducing the burden on health care systems. Objective: This study aims to identify key risk factors that predict multimorbidity throughout the life course by developing an intelligent agent using digital twins so that early interventions can be delivered to improve health outcomes. The objectives of this study are to identify key predictors of lifetime risk of multimorbidity, create a series of simulated computational digital twins that predict risk levels for specific clusters of factors, and test the feasibility of the system. Methods: This study will use machine learning to develop digital twins by identifying key risk factors throughout the life course that predict the risk of later multimorbidity. The first stage of the development will be the training of a base predictive model. Data from the National Child Development Study, the North West London Integrated Care Record, the Clinical Practice Research Datalink, and Cerner’s Real World Data will be split into subsets for training and validation, which will be done following the k-fold cross-validation procedure and assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). In addition, 2 data sets—the Early-Life Data Cross-linkage in Research study and the Children and Young People’s Health Partnership randomized controlled trial—will be used to develop a series of digital twin personas that simulate clusters of factors to predict different risk levels of developing multimorbidity. Results: The expected results are a validated model, a series of digital twin personas, and a proof-of-concept assessment. Conclusions: Digital twins could provide an individualized early warning system that predicts the risk of future health conditions and recommends the most effective intervention to minimize that risk. These insights could significantly improve an individual’s quality of life and healthy life expectancy and reduce population-level health burdens

Resonance Effects in the Nonadiabatic Nonlinear Quantum Dimer

The quantum nonlinear dimer consisting of an electron shuttling between the two sites and in weak interaction with vibrations, is studied numerically under the application of a DC electric field. A field-induced resonance phenomenon between the vibrations and the electronic oscillations is found to influence the electronic transport greatly. For initially delocalization of the electron, the resonance has the effect of a dramatic increase in the transport. Nonlinear frequency mixing is identified as the main mechanism that influences transport. A characterization of the frequency spectrum is also presented.Comment: 7 pages, 6 figure

Factors associated with compliance and non-compliance by physicians in a large-scale randomized clinical trial

BACKGROUND: In order to minimize the amount of incomplete follow-up data, reducing the non-compliance of participating physicians is one of the key issues for the data coordinating center in a multi-center trial. Identifying the physicians' non-compliance in advance is considered to be an important strategy for more efficient conduct of trials. In this study, we identified physicians' characteristics and factors associated with the need for individual visits to institutions to collect data or to complete information during two years of follow-up in a large Japanese investigator-initiated trial related to cardiovascular disease. METHODS: We categorized the physicians into two groups, "complier" and "non-complier". Odds ratios and corresponding 95% confidence intervals were calculated for 11 factors related to the characteristics of and compliance by physicians. Multiple logistic regression analysis was also performed. In addition, we evaluated the incremental cost for obtaining additional information of the non-compliant physicians. RESULTS: Three factors were identified in multiple logistic regression analysis as being significantly associated with compliance status: 1) prior participation in clinical trials (OR = 0.40 95%CI = 0.21–0.74); 2) physician opinion that the support system for case registration and follow-up was well organized (OR = 0.41 95%CI = 0.22–0.75); and 3) number of patients recruited (OR = 2.25 95%CI = 1.01–5.02). The actual incremental cost was about US $112,000 (14.4$570 per patient. CONCLUSION: Investigator-initiated clinical trials have recently attracted great interest, but they often suffer from insufficient funding. If trial networks are to be well organized, it is important that trials are conducted more efficiently. We believe that our findings will be useful for reducing the additional burden associated with incomplete follow-up data and data lost to follow-up when planning future trials

Internet of things–Enabled technologies as an intervention for childhood obesity: A systematic review

Childhood obesity is one of the most serious public health challenges of the 21st century, with consequences lasting into adulthood. Internet of Things (IoT)-enabled devices have been studied and deployed for monitoring and tracking diet and physical activity of children and adolescents as well as a means of providing remote, ongoing support to children and their families. This review aimed to identify and understand current advances in the feasibility, system designs, and effectiveness of IoT-enabled devices to support weight management in children. We searched Medline, PubMed, Web of Science, Scopus, ProQuest Central and the IEEE Xplore Digital Library for studies published after 2010 using a combination of keywords and subject headings related to health activity tracking, weight management, youth and Internet of Things. The screening process and risk of bias assessment were conducted in accordance with a previously published protocol. Quantitative analysis was conducted for IoT-architecture related findings and qualitative analysis was conducted for effectiveness-related measures. Twenty-three full studies are included in this systematic review. The most used devices were smartphone/mobile apps (78.3%) and physical activity data (65.2%) from accelerometers (56.5%) were the most commonly tracked data. Only one study embarked on machine learning and deep learning methods in the service layer. Adherence to IoT-based approaches was low but game-based IoT solutions have shown better effectiveness and could play a pivotal role in childhood obesity interventions. Researcher-reported effectiveness measures vary greatly amongst studies, highlighting the importance for improved development and use of standardised digital health evaluation frameworks.</jats:p

Probing the Excitations of a Lieb-Liniger Gas from Weak to Strong Coupling

We probe the excitation spectrum of an ultracold one-dimensional Bose gas of Cesium atoms with repulsive contact interaction that we tune from the weakly to the strongly interacting regime via a magnetic Feshbach resonance. The dynamical structure factor, experimentally obtained using Bragg spectroscopy, is compared to integrability-based calculations valid at arbitrary interactions and finite temperatures. Our results unequivocally underly the fact that hole-like excitations, which have no counterpart in higher dimensions, actively shape the dynamical response of the gas.Comment: 9 pages, 10 figure

Bose-Einstein correlations of same-sign charged pions in the forward region in pp collisions at √s=7 TeV

Bose-Einstein correlations of same-sign charged pions, produced in protonproton collisions at a 7 TeV centre-of-mass energy, are studied using a data sample collected by the LHCb experiment. The signature for Bose-Einstein correlations is observed in the form of an enhancement of pairs of like-sign charged pions with small four-momentum difference squared. The charged-particle multiplicity dependence of the Bose-Einstein correlation parameters describing the correlation strength and the size of the emitting source is investigated, determining both the correlation radius and the chaoticity parameter. The measured correlation radius is found to increase as a function of increasing charged-particle multiplicity, while the chaoticity parameter is seen to decreas