Background: Augmented reality (AR), mixed reality (MR), and virtual reality (VR), realized as head-mounted devices (HMDs), may open up new ways of teaching medical content for low-resource settings. The advantages are that HMDs enable repeated practice without adverse effects on the patient in various medical disciplines; may introduce new ways to learn complex medical content; and may alleviate financial, ethical, and supervisory constraints on the use of traditional medical learning materials, like cadavers and other skills lab equipment. Objective: We examine the effectiveness of AR, MR, and VR HMDs for medical education, whereby we aim to incorporate a global health perspective comprising low- and middle-income countries (LMICs). Methods: We conducted a systematic review according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) and Cochrane guidelines. Seven medical databases (PubMed, Cochrane Library, Web of Science, Science Direct, PsycINFO, Education Resources Information Centre, and Google Scholar) were searched for peer-reviewed publications from January 1, 2014, to May 31, 2019. An extensive search was carried out to examine relevant literature guided by three concepts of extended reality (XR), which comprises the concepts of AR, MR, and VR, and the concepts of medicine and education. It included health professionals who took part in an HMD intervention that was compared to another teaching or learning method and evaluated with regard to its effectiveness. Quality and risk of bias were assessed with the Medical Education Research Study Quality Instrument, the Newcastle-Ottawa Scale-Education, and A Cochrane Risk of Bias Assessment Tool for Non-Randomized Studies of Interventions. We extracted relevant data and aggregated the data according to the main outcomes of this review (knowledge, skills, and XR HMD). Results: A total of 27 studies comprising 956 study participants were included. The participants included all types of health care professionals, especially medical students (n=573, 59.9%) and residents (n=289, 30.2%). AR and VR implemented with HMDs were most often used for training in the fields of surgery (n=13, 48%) and anatomy (n=4, 15%). A range of study designs were used, and quantitative methods were clearly dominant (n=21, 78%). Training with AR- and VR-based HMDs was perceived as salient, motivating, and engaging. In the majority of studies (n=17, 63%), HMD-based interventions were found to be effective. A small number of included studies (n=4, 15%) indicated that HMDs were effective for certain aspects of medical skills and knowledge learning and training, while other studies suggested that HMDs were only viable as an additional teaching tool (n=4, 15%). Only 2 (7%) studies found no effectiveness in the use of HMDs. Conclusions: The majority of included studies suggested that XR-based HMDs have beneficial effects for medical education, whereby only a minority of studies were from LMICs. Nevertheless, as most studies showed at least noninferior results when compared to conventional teaching and training, the results of this review suggest applicability and potential effectiveness in LMICs. Overall, users demonstrated greater enthusiasm and enjoyment in learning with XR-based HMDs. It has to be noted that many HMD-based interventions were small-scale and conducted as short-term pilots. To generate relevant evidence in the future, it is key to rigorously evaluate XR-based HMDs with AR and VR implementations, particularly in LMICs, to better understand the strengths and shortcomings of HMDs for medical education.

Background: Wearable devices hold great promise, particularly for data generation for cutting-edge health research, and their demand has risen substantially in recent years. However, there is a shortage of aggregated insights into how wearables have been used in health research. Objective: In this review, we aim to broadly overview and categorize the current research conducted with affordable wearable devices for health research. Methods: We performed a scoping review to understand the use of affordable, consumer-grade wearables for health research from a population health perspective using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A total of 7499 articles were found in 4 medical databases (PubMed, Ovid, Web of Science, and CINAHL). Studies were eligible if they used noninvasive wearables: worn on the wrist, arm, hip, and chest; measured vital signs; and analyzed the collected data quantitatively. We excluded studies that did not use wearables for outcome assessment and prototype studies, devices that cost >€500 (US $570), or obtrusive smart clothing. Results: We included 179 studies using 189 wearable devices covering 10,835,733 participants. Most studies were observational (128/179, 71.5%), conducted in 2020 (56/179, 31.3%) and in North America (94/179, 52.5%), and 93% (10,104,217/10,835,733) of the participants were part of global health studies. The most popular wearables were fitness trackers (86/189, 45.5%) and accelerometer wearables, which primarily measure movement (49/189, 25.9%). Typical measurements included steps (95/179, 53.1%), heart rate (HR; 55/179, 30.7%), and sleep duration (51/179, 28.5%). Other devices measured blood pressure (3/179, 1.7%), skin temperature (3/179, 1.7%), oximetry (3/179, 1.7%), or respiratory rate (2/179, 1.1%). The wearables were mostly worn on the wrist (138/189, 73%) and cost <€200 (US $228; 120/189, 63.5%). The aims and approaches of all 179 studies revealed six prominent uses for wearables, comprising correlations—wearable and other physiological data (40/179, 22.3%), method evaluations (with subgroups; 40/179, 22.3%), population-based research (31/179, 17.3%), experimental outcome assessment (30/179, 16.8%), prognostic forecasting (28/179, 15.6%), and explorative analysis of big data sets (10/179, 5.6%). The most frequent strengths of affordable wearables were validation, accuracy, and clinical certification (104/179, 58.1%). Conclusions: Wearables showed an increasingly diverse field of application such as COVID-19 prediction, fertility tracking, heat-related illness, drug effects, and psychological interventions; they also included underrepresented populations, such as individuals with rare diseases. There is a lack of research on wearable devices in low-resource contexts. Fueled by the COVID-19 pandemic, we see a shift toward more large-sized, web-based studies where wearables increased insights into the developing pandemic, including forecasting models and the effects of the pandemic. Some studies have indicated that big data extracted from wearables may potentially transform the understanding of population health dynamics and the ability to forecast health trends.

In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.

In low- and middle-income countries (LMICs), e-learning for medical education may alleviate the burden of severe health worker shortages and deliver affordable access to high quality medical education. However, diverse challenges in infrastructure and adoption are encountered when implementing e-learning within medical education in particular. Understanding what constitutes successful e-learning is an important first step for determining its effectiveness. The objective of this study was to systematically review e-learning interventions for medical education in LMICs, focusing on their evaluation and assessment methods.

Background: COVID-19 has impacted the capacity of healthcare systems worldwide, particularly in low- and middle-income countries (LMICs), which are already under strain due to population growth and insufficient resources. Since the COVID-19 pandemic's emergence, there has been an urgent need for a rapid and adequate reaction to the pandemic's disruption of healthcare systems. To this end, telemedicine has been shown in prior research to be a feasible approach. The overarching objective of this scoping review was to determine the extent and acceptance of telemedicine in healthcare in low- and middle-income countries (LMICs) during the COVID-19 pandemic.Methods: This scoping review followed PRISMA guidelines and Arksey and O'Malley's five-stage framework to identify available evidence. We systematically searched four academic databases for peer-reviewed literature published between January 2020 and April 2021: Medline, PubMed, Web of Science, and Scopus, as well as Google Scholar as a source for grey literature.Results: The search identified 54 articles with 45,843 participants, including 6,966 healthcare professionals and 36,877 healthcare users. We identified a range of reasons for introducing telemedicine in LMICs during COVID-19, most notably to maintain non-emergency healthcare, enhance access to healthcare providers, and reduce the risk of infection among health users and providers. Overall, healthcare providers and users have shown a high level of acceptance for telemedicine services. During the COVID-19 pandemic, telemedicine provided access to healthcare in the majority of included articles. Nonetheless, some challenges to accepting telemedicine as a method of healthcare delivery have been reported, including technological, regulatory, and economical challenges.Conclusion: Telemedicine was found to improve access to high-quality healthcare and decrease infection risk in LMICs during COVID-19. In general, infrastructure and regulatory barriers found to be the most significant barriers to wider telemedicine use, and should be considered when implementing telemedicine more broadly. There appears to be a need to prioritize patient data safety, as many healthcare practitioners utilized commercial apps and services as telemedicine systems. Additionally, it appears as though there is a need to increase capacity, skill, and transparency, as well as to educate patients about telemedicine.

Disability and mortality burden of non-communicable diseases (NCDs) have risen worldwide; however, the NCD burden among adolescents remains poorly described in the EU. Estimates were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. Causes of NCDs were analysed at three different levels of the GBD 2019 hierarchy, for which mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) were extracted. Estimates, with the 95% uncertainty intervals (UI), were retrieved for EU Member States from 1990 to 2019, three age subgroups (10–14 years, 15–19 years, and 20–24 years), and by sex. Spearman's correlation was conducted between DALY rates for NCDs and the Socio-demographic Index (SDI) of each EU Member State. In 2019, NCDs accounted for 86·4% (95% uncertainty interval 83·5–88·8) of all YLDs and 38·8% (37·4–39·8) of total deaths in adolescents aged 10–24 years. For NCDs in this age group, neoplasms were the leading causes of both mortality (4·01 [95% uncertainty interval 3·62–4·25] per 100 000 population) and YLLs (281·78 [254·25–298·92] per 100 000 population), whereas mental disorders were the leading cause for YLDs (2039·36 [1432·56–2773·47] per 100 000 population) and DALYs (2040·59 [1433·96–2774·62] per 100 000 population) in all EU Member States, and in all studied age groups. In 2019, among adolescents aged 10–24 years, males had a higher mortality rate per 100 000 population due to NCDs than females (11·66 [11·04–12·28] vs 7·89 [7·53–8·23]), whereas females presented a higher DALY rate per 100 000 population due to NCDs (8003·25 [5812·78–10 701·59] vs 6083·91 [4576·63–7857·92]). From 1990 to 2019, mortality rate due to NCDs in adolescents aged 10–24 years substantially decreased (–40·41% [–43·00 to –37·61), and also the YLL rate considerably decreased (–40·56% [–43·16 to –37·74]), except for mental disorders (which increased by 32·18% [1·67 to 66·49]), whereas the YLD rate increased slightly (1·44% [0·09 to 2·79]). Positive correlations were observed between DALY rates and SDIs for substance use disorders (r_s=0·58, p=0·0012) and skin and subcutaneous diseases (r_s=0·45, p=0·017), whereas negative correlations were found between DALY rates and SDIs for cardiovascular diseases (r_s=–0·46, p=0·015), neoplasms (r_s=–0·57, p=0·0015), and sense organ diseases (r_s=–0·61, p=0·0005). NCD-related mortality has substantially declined among adolescents in the EU between 1990 and 2019, but the rising trend of YLL attributed to mental disorders and their YLD burden are concerning. Differences by sex, age group, and across EU Member States highlight the importance of preventive interventions and scaling up adolescent-responsive health-care systems, which should prioritise specific needs by sex, age, and location. Bill & Melinda Gates Foundation.