UNASSIGNED: The use of mobile health (mHealth, wireless communication devices, and/or software technologies) in health care delivery has increased rapidly in recent years. Their integration into disease management programs (DMPs) has tremendous potential to improve outcomes for patients with coronary artery disease (CAD), yet a more robust evaluation of the evidence is required.
UNASSIGNED: The purpose of this study was to undertake a systematic review and meta-analysis of mHealth-enabled DMPs to determine their effectiveness in reducing readmissions and mortality in patients with CAD.
UNASSIGNED: We systematically searched English language studies from January 1, 2007, to August 3, 2021, in multiple databases. Studies comparing mHealth-enabled DMPs with standard DMPs without mHealth were included if they had a minimum 30-day follow-up for at least one of all-cause or cardiovascular-related mortality, readmissions, or major adverse cardiovascular events.
UNASSIGNED: Of the 3,411 references from our search, 155 full-text studies were assessed for eligibility, and data were extracted from 18 publications. Pooled findings for all-cause readmissions (10 studies, n = 1,514) and cardiac-related readmissions (9 studies, n = 1,009) indicated that mHealth-enabled DMPs reduced all-cause (RR: 0.68; 95% CI: 0.50-0.91) and cardiac-related hospitalizations (RR: 0.55; 95% CI: 0.44-0.68) and emergency department visits (RR: 0.37; 95% CI: 0.26-0.54) compared to DMPs without mHealth. There was no significant reduction for mortality outcomes (RR: 1.72; 95% CI: 0.64-4.64) or major adverse cardiovascular events (RR: 0.68; 95% CI: 0.40-1.15).
UNASSIGNED: DMPs integrated with mHealth should be considered an effective intervention for better outcomes in patients with CAD.

UNASSIGNED: Diabetes in the 21st century presents one of the greatest burdens of disease on the global population. Digitally mediated interventions have become imperative in alleviating this disease epidemic. We aimed to systematically review randomized controlled trials (RCTs) on different health technologies for preventing Type 2 diabetes mellitus, and their efficacy in decreasing diabetes risk-related outcomes in at-risk patients in comparison to standard care.
UNASSIGNED: Five electronic databases were searched between October 2021 and December 2022. Studies including digital health technology interventions used for preventing diabetes development by reducing diabetes risk-related outcomes in at-risk adults (⩾18 years) were identified. Data on glycemic levels, incidence of T2DM, weight, and intervention descriptions were extracted, and the risk of bias (ROB) was assessed.
UNASSIGNED: Nine studies met the inclusion criteria and 5 studies (56%) achieved clinically significant outcomes in at least one of the following: decreased weight (22%), glycemic levels (22%), or incidence of T2DM (11%). Two of the 3 (67%) computer-based interventions effectively reduced the HbA1c levels and mean weight of their study population, and 3 of 6 (50%) mobile based interventions (text messages, mobile app, and telehealth) decreased the incidence of T2DM and HbA1c levels. Four studies each had an overall low ROB and one had a high ROB due to attrition.
UNASSIGNED: Preliminary evidence identified in our review demonstrated that health technologies for diabetes prevention are effective for improving diabetes risk-related outcomes. Future research into digital technology protocol and studies of longer duration and more diverse populations are needed for clinical feasibility.
Role of Digital technology in Diabetes prevention In the 21st century, diabetes has become a major health challenge globally. To address this, we conducted a systematic review, looking at studies that used digital interventions to prevent Type 2 diabetes mellitus (T2DM) and how effective they are compared to standard care. In this study, we searched electronic databases from October 2021 to December 2022 and identified 9 studies that met our criteria. These studies focused on adults aged 18 and above who were at risk of developing diabetes. We looked at outcomes like glucose concentrations, T2DM incidence, and weight, and assessed the risk of bias in each study. The results showed that more than half of the studies showed significant outcomes. For instance, some interventions led to decreased weight, lower glucose concentrations, or reduced incidence of T2DM. Computer-based interventions and mobile-based interventions (including text messages, mobile apps, and telehealth) were particularly effective in improving these outcomes. In conclusion, our review suggests that digital health technologies can be effective in preventing diabetes and improving related outcomes. However, we note that more research is needed, especially looking at diverse populations and longer study durations, to confirm the clinical feasibility of these digital interventions for diabetes prevention. This is a promising step forward in using technology to tackle the growing diabetes epidemic, offering new ways to support individuals at risk and improve their health outcomes.

BACKGROUND: Equity-deserving groups face well-known health disparities that are exacerbated by rural residence. Health technologies have shown promise in reducing disparities among these groups, but there has been no comprehensive evidence synthesis of outcomes.
OBJECTIVE: The purpose of this systematic review was to examine the patient, healthcare, and economic outcomes of health technology applications with rural living equity-deserving groups.
METHODS: The databases searched included Medline and Embase. Articles were assessed for bias using the McGill mixed methods appraisal tool.
METHODS: Data were synthesized narratively using a convergent integrated approach for qualitative and quantitative findings.
RESULTS: This evidence synthesis includes papers (n = 21) that reported on health technologies targeting rural equity-deserving groups. Overall, patient outcomes - knowledge, self-efficacy, weight loss, and clinical indicators - improved. Healthcare access improved with greater convenience, flexibility, time and travel savings, though travel was still occasionally necessary. All studies reported satisfaction with health technologies. Technology challenges reported related to connectivity and infrastructure issues influencing appointment quality and modality options. While some studies reported additional costs, overall, studies indicated cost savings for patients.
CONCLUSIONS: There is a paucity of research on health technologies targeting rural equity-deserving groups, and the available research has primarily focused on women. While current evidence was primarily of high quality, research is needed inclusive of equity-deserving groups and interventions co-designed with users that integrate culturally sensitive approaches. Review registered with Prospero ID = CRD42021285994.

BACKGROUND: In recent years, healthcare systems have progressively adopted several technologies enhancing access to healthcare for older adults and support the delivery of efficient and effective care for this specific population. These technologies include both assistive technologies designed to maintain or improve the independence, social participation and functionality of older people at home, as well as health information technology developed to manage long-term conditions. Examples of such technologies include telehealth, wearable devices and mobile health. However, despite the great promise that health technology holds for promoting independent living among older people, its actual implementation remains challenging.
METHODS: This study aimed to conduct an integrative systematic review of the research evidence on the factors that facilitate or hinder the adoption of different types of technology by older individuals with chronic diseases. For this purpose, four electronic databases (PsycArticles, Scopus, Web of Science and PubMed) were queried to search for indexed published studies. The methodological quality of the selected papers has been assessed using the Mixed Methods Appraisal Tool (MMAT).
RESULTS: Twenty-nine articles were selected, including 6.213 adults aged 60 or older. The studies have been synthesised considering the types of technological interventions and chronic diseases, as well as the main barriers and facilitators in technology acceptance. The results revealed that the majority of the selected articles focused on comorbid conditions and the utilisation of telemedicine tools. With regard to hindering and facilitating factors, five main domains were identified: demographic and socioeconomic, health-related, dispositional, technology-related and social factors.
CONCLUSIONS: The study results have practical implications not only for technology developers but also for all the social actors involved in the design and implementation of healthcare technologies, including formal and informal caregivers and policy stakeholders. These actors could use this work to enhance their understanding of the utilisation of technology by the ageing population. This review emphasises the factors that facilitate technology adoption and identifies barriers that impede it, with the ultimate goal of promoting health and independent living.

Digital health interventions (DHIs) are increasingly used to address the health of migrants and ethnic minorities, some of whom have reduced access to health services and worse health outcomes than majority populations. This study aims to give an overview of digital health interventions developed for ethnic or cultural minority and migrant populations, the health problems they address, their effectiveness at the individual level and the degree of participation of target populations during development. We used the methodological approach of the scoping review outlined by Tricco. We found a total of 2248 studies, of which 57 were included, mostly using mobile health technologies, followed by websites, informational videos, text messages and telehealth. Most interventions focused on illness self-management, mental health and wellbeing, followed by pregnancy and overall lifestyle habits. About half did not involve the target population in development and only a minority involved them consistently. The studies we found indicate that the increased involvement of the target population in the development of digital health tools leads to a greater acceptance of their use.

BACKGROUND: Many children today are growing up in environments that predispose them to develop noncommunicable diseases. While no single preventive solution exists, evidence supports interventions in childcare settings for establishing good nutrition and physical activity behaviors as a \"critical window\" that could reduce the risk of developing noncommunicable diseases later in life. Emerging eHealth tools have shown potential in promoting best practices for nutrition and physical activity environments in early childhood education and care (ECEC) settings.
OBJECTIVE: The primary objective of this review is to map the breadth of available evidence on eHealth tools currently available to assess and support best practices for nutrition, physical activity, or both in ECEC settings and to highlight potential research directions.
METHODS: This scoping review will be conducted in accordance with the Joanna Briggs Institute Manual for Scoping Reviews with adherence to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist guidelines. Eligibility is based on the Population, Concept, and Context criteria as follows: (1) early childhood educators (population); (2) eHealth (digital) technology, such as websites, smartphone apps, email, and social media (concept); and (3) measurement and intervention tools to support best practices for nutrition, physical activity, or both in ECEC settings (context). The information sources for this review are the bibliographic databases PubMed, Scopus, CINAHL Plus, ERIC, and Embase in English and French with no date restrictions. Following this, a scan of gray literature will be undertaken. The electronic search strategy was developed in collaboration with two librarians. Two independent reviewers will screen the titles and abstracts of all relevant publications against inclusion criteria, followed by a full-text review using a data extraction tool developed by the reviewers. A synthesis of included papers will describe the publication, assessment, and intervention tool details. A summary of the findings will describe the types of eHealth assessment tools available, psychometric properties, eHealth intervention components, and theoretical frameworks used for development.
RESULTS: Preliminary searches of bibliographic databases to test and calibrate the search were carried out in May 2023. Study selection based on titles and abstracts was started in August 2023. The developed search strategy will guide our search for gray literature. The findings will be presented in visualized data map format, waffle chart, or tabular format accompanied by a narrative discussion. The scoping review is planned for completion in 2024.
CONCLUSIONS: A structured review of the literature will provide a summary of the range and type of eHealth tools available for ECEC programs to assess and improve nutrition environments, physical activity environments, or both in order to identify gaps in the current evidence base and provide insights to guide future intervention research.
BACKGROUND: Open Science Framework XTRNZ; https://osf.io/xtrnz.
UNASSIGNED: DERR1-10.2196/52252.

BACKGROUND: Antimicrobial resistance is a global threat, which requires novel intervention strategies, for which priority pathogens and settings need to be determined.
OBJECTIVE: We evaluated pathogen-specific excess health burden of drug-resistant bloodstream infections (BSIs) in Europe.
METHODS: A systematic review and meta-analysis.
METHODS: MEDLINE, Embase, and grey literature for the period January 1990 to May 2022.
METHODS: Studies that reported burden data for six key drug-resistant pathogens: carbapenem-resistant (CR) Pseudomonas aeruginosa and Acinetobacter baumannii, third-generation cephalosporin or CR Escherichia coli and Klebsiella pneumoniae, methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecium. Excess health outcomes compared with drug-susceptible BSIs or uninfected patients. For MRSA and third-generation cephalosporin E. coli and K. pneumoniae BSIs, five or more European studies were identified. For all others, the search was extended to high-income countries.
METHODS: Paediatric and adult patients diagnosed with drug-resistant BSI.
METHODS: Not applicable.
UNASSIGNED: An adapted version of the Joanna-Briggs Institute assessment tool.
UNASSIGNED: Random-effect models were used to pool pathogen-specific burden estimates.
RESULTS: We screened 7154 titles, 1078 full-texts and found 56 studies on BSIs. Most studies compared outcomes of drug-resistant to drug-susceptible BSIs (46/56, 82.1%), and reported mortality (55/56 studies, 98.6%). The pooled crude estimate for excess all-cause mortality of drug-resistant versus drug-susceptible BSIs ranged from OR 1.31 (95% CI 1.03-1.68) for CR P. aeruginosa to OR 3.44 (95% CI 1.62-7.32) for CR K. pneumoniae. Pooled crude estimates comparing mortality to uninfected patients were available for vancomycin-resistant Enterococcus and MRSA BSIs (OR of 11.19 [95% CI 6.92-18.09] and OR 6.18 [95% CI 2.10-18.17], respectively).
CONCLUSIONS: Drug-resistant BSIs are associated with increased mortality, with the magnitude of the effect influenced by pathogen type and comparator. Future research should address crucial knowledge gaps in pathogen- and infection-specific burdens to guide development of novel interventions.

BACKGROUND: This is the first scoping review to focus broadly on the topics of machine learning and medication adherence.
OBJECTIVE: This review aims to categorize, summarize, and analyze literature focused on using machine learning for actions related to medication adherence.
METHODS: PubMed, Scopus, ACM Digital Library, IEEE, and Web of Science were searched to find works that meet the inclusion criteria. After full-text review, 43 works were included in the final analysis. Information of interest was systematically charted before inclusion in the final draft. Studies were placed into natural categories for additional analysis dependent upon the combination of actions related to medication adherence. The protocol for this scoping review was created using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines.
RESULTS: Publications focused on predicting medication adherence have uncovered 20 strong predictors that were significant in two or more studies. A total of 13 studies that predicted medication adherence used either self-reported questionnaires or pharmacy claims data to determine medication adherence status. In addition, 13 studies that predicted medication adherence did so using either logistic regression, artificial neural networks, random forest, or support vector machines. Of the 15 studies that predicted medication adherence, 6 reported predictor accuracy, the lowest of which was 77.6%. Of 13 monitoring systems, 12 determined medication administration using medication container sensors or sensors in consumer electronics, like smartwatches or smartphones. A total of 11 monitoring systems used logistic regression, artificial neural networks, support vector machines, or random forest algorithms to determine medication administration. The 4 systems that monitored inhaler administration reported a classification accuracy of 93.75% or higher. The 2 systems that monitored medication status in patients with Parkinson disease reported a classification accuracy of 78% or higher. A total of 3 studies monitored medication administration using only smartwatch sensors and reported a classification accuracy of 78.6% or higher. Two systems that provided context-aware medication reminders helped patients to achieve an adherence level of 92% or higher. Two conversational artificial intelligence reminder systems significantly improved adherence rates when compared against traditional reminder systems.
CONCLUSIONS: Creation of systems that accurately predict medication adherence across multiple data sets may be possible due to predictors remaining strong across multiple studies. Higher quality measures of adherence should be adopted when possible so that prediction algorithms are based on accurate information. Currently, medication adherence can be predicted with a good level of accuracy, potentially allowing for the development of interventions aimed at preventing nonadherence. Monitoring systems that track inhaler use currently classify inhaler-related actions with an excellent level of accuracy, allowing for tracking of adherence and potentially proper inhaler technique. Systems that monitor medication states in patients with Parkinson disease can currently achieve a good level of classification accuracy and have the potential to inform medication therapy changes in the future. Medication administration monitoring systems that only use motion sensors in smartwatches can currently achieve a good level of classification accuracy but only when differentiating between a small number of possible activities. Context-aware reminder systems can help patients achieve high levels of medication adherence but are also intrusive, which may not be acceptable to users. Conversational artificial intelligence reminder systems can significantly improve adherence.

BACKGROUND: Nurses comprise over half of the global health care workforce, and the nursing care they provide is critical for the global population\'s health. High patient volumes and increased medical complexity have increased the workload and stress of nurses. As a result, the health of nurses is often negatively impacted. Wearables are used within the health care setting to assess patient outcomes; however, efforts to synthesize the use of wearable devices focusing on nurses\' health are limited.
OBJECTIVE: The primary objective of our integrative review is to synthesize available data concerning the utility of wearable devices for evaluating or improving (or both) the health of nurses.
METHODS: We are conducting an integrative review synthesizing data specific to wearable devices and nurses\' health. The research question for this review aims to answer how wearable devices are used to evaluate health outcomes among nurses. We searched the following electronic databases from inception until July 2022: PubMed, Embase, CINAHL, Web of Science, IEEE Explore, and AS&T. Titles and abstracts were imported into Covidence software, where citations were screened and duplicates removed. Title and abstract screening has been completed; however, full-text screening has not been started. Further screening is being conducted independently and in duplicate by 2 teams of 2 reviewers each. These reviewers will extract data independently.
RESULTS: Search strategies have been developed, and data were extracted from 6 databases. After the removal of duplicates, we collected 8603 studies for title and abstract screening. Two independent reviewers conducted the title and abstract review, and after resolving conflicts, 277 full-text articles are available for review to determine whether they meet the inclusion criteria.
CONCLUSIONS: This integrative review will provide synthesized data to inform nurses and other stakeholders about the extent of wearable device-related work done with nurses and provide direction for future research.
UNASSIGNED: DERR1-10.2196/48178.

Frailty, neurodegeneration and geriatric syndromes cause a significant impact at the clinical, social, and economic level, mainly in the context of the aging world. Recently, Information and Communication Technologies (ICTs), virtual reality tools, and machine learning models have been increasingly applied to the care of older patients to improve diagnosis, prognosis, and interventions. However, so far, the methodological limitations of studies in this field have prevented to generalize data to real-word. This review systematically overviews the research designs used by studies applying technologies for the assessment and treatment of aging-related syndromes in older people.
Following the PRISMA guidelines, records from PubMed, EMBASE, and Web of Science were systematically screened to select original articles in which interventional or observational designs were used to study technologies\' applications in samples of frail, comorbid, or multimorbid patients.
Thirty-four articles met the inclusion criteria. Most of the studies used diagnostic accuracy designs to test assessment procedures or retrospective cohort designs to build predictive models. A minority were randomized or non-randomized interventional studies. Quality evaluation revealed a high risk of bias for observational studies, while a low risk of bias for interventional studies.
The majority of the reviewed articles use an observational design mainly to study diagnostic procedures and suffer from a high risk of bias. The scarce presence of methodologically robust interventional studies may suggest that the field is in its infancy. Methodological considerations will be presented on how to standardize procedures and research quality in this field.