280th ENMC International Workshop: The ERN EURO-NMD mitochondrial diseases working group; diagnostic criteria and outcome measures in primary mitochondrial myopathies. Hoofddorp, the Netherlands, 22-24 November 2024.

Designing digital health applications for climate change mitigation and adaptation.

There are dynamic interactions between (digital) technologies and society. Digital technologies have a(re-)structuring effect on social relationships and social innovations in avariety of ways. Because of these characteristics, technological innovations affect our individual lifestyles and living environments. In particular, the development and implementation of interventions with digital (health) technologies is attracting increasing national and international attention (e.g. telematics GP consultations and app-supported patient education programs).Digital health technologies enable new forms of interaction and knowledge-based reproduction in the field of health. The integration of potential users in the development process of digital health technologies and interventions requires the discussion of new research approaches. The interests, needs, and requirements of users may influence the nonuse of digital health technologies. It is above all the successful implementation, involving potential users, that can have an influence on acceptance and integrative use in the later course of care. The discourse on the participatory development and implementation of interventions with digital health technologies in the field of digital public health presents itself as acomplex process characterized by various theoretical approaches and methodological procedures and requiring representation, evaluation, and classification.

BackgroundClinical trials for obesity have traditionally focused on weight loss and resolution of comorbidities as primary outcomes. However, secondary outcomes, such as the impact of weight reduction on patient experience, like health-related quality of life (HRQoL), have increasingly been recognized as important. Therefore, a review was conducted to determine the Clinical Outcome Assessments (COAs) and Digital Health Technologies (DHTs) used in clinical trials for obesity to assess the patient experience.MethodsTwo clinical trial databases (United States & European Union) were reviewed to identify Phase 2–4 clinical trials for obesity (2018–2023). A targeted literature review was also conducted using the OVID database to identify clinical trial for obesity publications which included COAs/DHTs (2010–2023).ResultTrials from the databases (n = 53) and publications (n = 42) were included in data extraction (N = 73). This resulted in identification of 108 COAs, the majority being patient-reported outcome (PRO) measures (n = 83), but also 24 performance outcomes (PerfO) measures, and 1 composite PRO-clinician-reported outcomes (ClinRO) measure, as well as 2 DHTs. The most frequently identified PRO measures were the Short Form 36 and the Impact of Weight on Quality of Life Lite Clinical Trials. Twenty-four PerfO measures were also identified, with the 6-minute walk test being most common. These measures were most often used to construct secondary endpoints, with physical function (PF) being the most frequently specified domain. PRO measures assessing eating-related thoughts/behaviours, physical activity, and disordered eating were also frequently included, although individual measures varied widely across trials.ConclusionReview of COAs and DHTs in registered clinical trials and publications for obesity found that PRO measures were the most common type of COA used to develop endpoints with current use of DHTs limited. Specifically, the physical function domain of multidimensional patient-reported outcome measures assessing health-related quality of life were often used to construct secondary endpoints. Further work is warranted to assess how the COAs and DHT data collected in clinical trials are viewed by regulators and payers.

World Health Organization (WHO) reported that digital health technologies (DHTs) were used throughout pandemic to prevent diseases and improve vaccination acceptance. Thus; contact tracing tool, surveillance garget, mHealth apps, biosensors, biomarkers, wearable tech, vaccine mapping software, artificial intelligence, telemedicine/telehealth and public communication are among digital health technologies used during pandemic; and healthcare workers are essentially responsible for controlling these technologies. The need to find out through literature whether DHT application improve vaccination acceptance in Nigeria based on healthcare workers views and recommendations. The paper is a documentary research that highlighted the impact of employing digital health technologies (DHTs) in response to pandemic vaccination rejection in Africa, focusing Nigeria as a leading country with abundant human resources and concerned health authorities to improve vaccination acceptance. Herein, this paper set to examine through literature; the concept of digital health technologies, gauge the impact of digital health technologies in developed and developing countries, synthesize digital technology in response to vaccination rejection as well as implementing digital technologies for promoting vaccination. The paper also explored healthcare worker’s views and feedback towards whether or not the application of digital health technology can promote vaccination acceptance in Nigeria. The paper concludes that although challenges are vast, however, the paper concludes that advanced digital health technology is the way forward and be deployed for healthcare workers just as been practiced in developed countries during any pandemic. The paper recommends that, in order to overcome the challenges in the digital health technology with information management of vaccines acceptance, an independent integrated platform model for data-sharing and management be developed, and healthcare workers should adhere to the pandemic guidelines as stated by the world health organization (WHO), as practiced by center for disease control (CDC) among others.

ObjectivesTo examine the association between physicians’ use of digital health technology and their job satisfaction and work–life balance.DesignA cross-sectional nationally representative survey of physicians and probit regression models were used...

ObjectivesTo assess natural history and 12-month change of a series of scales and functional outcome measures in a cohort of 117 patients with primary mitochondrial myopathy (PMM).MethodsTwelve months follow-up data of 117 patients with PMM were collected. We analysed the 6-min walk test (6MWT), timed up-and-go test (× 3) (3TUG), five-times sit-to-stand test (5XSST), timed water swallow test (TWST), and test of masticating and swallowing solids (TOMASS) as functional outcome measures; the Fatigue Severity Scale and West Haven-Yale Multidimensional pain inventory as patient-reported outcome measures. PMM patients were divided into three phenotypic categories: mitochondrial myopathy (MiMy) without extraocular muscles involvement, pure chronic progressive external ophthalmoplegia (PEO) and PEO&MiMy. As 6MWT is recognized to have significant test–retest variability, we calculated MCID (minimal clinically important difference) as one third of baseline 6 min walking distance (6MWD) standard deviation.ResultsAt 12-month follow-up, 3TUG, 5XSST and FSS were stable, while TWST and the perceived pain severity (WHYMPI) worsened. 6MWD significantly increased in the entire cohort, especially in the higher percentiles and in PEO patients, while was substantially stable in the lower percentile (< 408 m) and MiMy patients. This increase in 6MWD was considered not significant, as inferior to MCID (33.3 m). NMDAS total score showed a slight but significant decline at 12 months (0.9 point). The perceived pain severity significantly worsened. Patients with PEO performed better in functional measures than patients with PEO&MiMy or MiMy, and had lower values of NMDAS.ConclusionsPMM patients showed a slow global decline valued by NMDAS at 12 months; 6MWT was a more reliable measurement below 408 m, substantially stable at 12 months. PEO patients had better motor performance and lower NMDAS than PEO&MiMy and MiMy also at 12 months of follow-up.

To paraphrase a classic, evaluating digital technologies in health is a bit like eating spinach - no one is against it in principle because it is good for you. However, no one would do it unless being asked to. In recent years, the sheer number of digital health technologies that potentially fulfil public health purposes has increased tremendously. The basis for evaluating such tools for public health purposes however has not met this pace, and in particular frameworks for the systematic development and evaluation of digital technologies in public health are rare. Existing frameworks for digital technologies focus on clinical aspects of digital health applications (e.g., NICE Evidence standards framework for digital health technologies), thus lacking both a population and prevention focus. Generic frameworks such as the Health Technology Assessment (HTA) methodology do not contain items specific to digital technologies and public health purposes. Here, we describe the process of developing a framework specific for the development and evaluation of digital public health technologies based on the core HTA model. We conduct a scoping review of frameworks for the development and the evaluation of technologies in public health and digital health, following PRISMA-SCR guidelines. The identified frameworks are then mapped onto the core HTA model to develop additional items specific for the development and the evaluation of digital technologies in public health. These additional items can be used to integrate the development and evaluation of digital technologies for public health purposes within the wider HTA context, making this process both transferable and scalable.

The past decade in rheumatology has seen tremendous innovation in digital health technologies, including the electronic health record, virtual visits, mobile health, wearable technology, digital therapeutics, artificial intelligence and machine learning. The increased availability of these technologies offers opportunities for improving important aspects of rheumatology, including access, outcomes, adherence and research. However, despite its growth in some areas, particularly with non-health-care consumers, digital health technology has not substantially changed the delivery of rheumatology care. This Review discusses key barriers and opportunities to improve application of digital health technologies in rheumatology. Key topics include smart design, voice enablement and the integration of electronic patient-reported outcomes. Smart design involves active engagement with the end users of the technologies, including patients and clinicians through focus groups, user testing sessions and prototype review. Voice enablement using voice assistants could be critical for enabling patients with hand arthritis to effectively use smartphone apps and might facilitate patient engagement with many technologies. Tracking many rheumatic diseases requires frequent monitoring of patient-reported outcomes. Current practice only collects this information sporadically, and rarely between visits. Digital health technology could enable patient-reported outcomes to inform appropriate timing of face-to-face visits and enable improved application of treat-to-target strategies. However, best practice standards for digital health technologies do not yet exist. To achieve the potential of digital health technology inrheumatology, rheumatology professionals will need to be more engaged upstream in the technology design process and provide leadership to effectively incorporate the new tools into clinical care.

Digital health technologies and health-care privatisation

Management of chronic myeloid leukemia (CML) with tyrosine kinase inhibitors has improved patient survival. However, patient quality of life (QOL) continues to be impacted by disease symptoms and treatment-related adverse events. Patient-reported outcome measures (PROMs) provide evidence of the patient experience. A scoping literature review was conducted to identify and summarize the evidence on PROMs used for patients with CML. Embase and MEDLINE databases were searched for publications from 2001 to 2023 that reported PROMs. Ongoing and completed trials listed on ClinicalTrials.gov were also reviewed. Results were summarized according to the PROMs used and the information collected in these PROMs. After screening 6337 records, 208 unique studies were identified with published PRO evidence reporting data from 92 unique PROMs. The most commonly reported PROMs (in ≥5% of publications) were used in 115 studies, of which 45 were exclusively in the frontline setting. The most commonly used PROMs in studies in the frontline setting were variations of European Organisation for Research and Treatment of Cancer QLQ, Functional Assessment of Chronic Illness Therapy Measurement System/Functional Assessment of Cancer Therapy, and 36-Item Short Form Survey. This scoping literature review highlighted that a variety of PROMs are used in CML studies, including studies in the frontline setting. Different QOL aspects are measured by commonly used PROMs, and the choice of PROM is dependent on the study setting and objectives. A more comprehensive understanding of QOL gained by using appropriate PROMs will help optimize patient-centered treatment selection in CML.

BackgroundThe potential benefits of incorporating digital technologies into health care are well documented. For example, they can improve access for patients living in remote or underresourced locations. However, despite often having the greatest health needs, people who are older or living in more socially deprived areas may be less likely to have access to these technologies and often lack the skills to use them. This puts them at risk of experiencing further health inequities. In addition, we know that digital health inequities associated with older age may be compounded by lower socioeconomic status. Yet, there is limited research on the intersectional barriers and facilitators for engagement with digital health technology by older people who are particularly marginalized.ObjectiveThis study aimed to explore factors influencing engagement with digital health technologies among people at the intersection of being older and socially deprived.MethodsWe conducted semistructured interviews with people who were 70 years or older, living in a socially deprived area, or both. Chronic kidney disease was our clinical context. We thematically analyzed interview transcripts using the Unified Theory of Acceptance and Use of Technology as a theoretical framework.ResultsWe interviewed 26 people. The majority were White British (n=20) and had moderate health and digital literacy levels (n=10 and n=11, respectively). A total of 13 participants were 70 years of age or older and living in a socially deprived area. Across participants, we identified 2 main themes from the interview data. The first showed that some individuals did not use digital health technologies due to a lack of engagement with digital technology in general. The second theme indicated that people felt that digital health technologies were “not for them.” We identified the following key engagement factors, with the first 2 particularly impacting participants who were both older and socially deprived: lack of opportunities in the workplace to become digitally proficient; lack of appropriate support from family and friends; negative perceptions of age-related social norms about technology use; and reduced intrinsic motivation to engage with digital health technology because of a perceived lack of relevant benefits. Participants on the intersection of older age and social deprivation also felt significant anxiety around using digital technology and reported a sense of distrust toward digital health care.ConclusionsWe identified factors that may have a more pronounced negative impact on the health equity of older people living in socially deprived areas compared with their counterparts who only have one of these characteristics. Successful implementation of digital health interventions therefore warrants dedicated strategies for managing the digital health equity impact on this group. Future studies should further develop these strategies and investigate their effectiveness, as well as explore the influence of related characteristics, such as educational attainment and ethnicity.

BackgroundType 2 Diabetes (T2D) is a common chronic disease, with socially patterned incidence and severity. Digital self-care interventions have the potential to reduce health disparities, by providing personalised low-cost reusable resources that can increase access to health interventions. However, if under-served groups are unable to access or use digital technologies, Digital Health Technologies (DHTs) might make no difference, or worse, exacerbate health inequity.Study aimsTo gain insights into how and why people with T2D access and use DHTs and how experiences vary between individuals and social groups.MethodsA purposive sample of people with experience of using a DHT to help them self-care for T2D were recruited through diabetes and community groups. Semi-structured interviews were conducted in person and over the phone. Data were analysed thematically.ResultsA diverse sample of 21 participants were interviewed. Health care practitioners were not viewed as a good source of information about DHTs that could support T2D. Instead participants relied on their digital skills and social networks to learn about what DHTs are available and helpful. The main barriers to accessing and using DHT described by the participants were availability of DHTs from the NHS, cost and technical proficiency. However, some participants described how they were able to draw on social resources such as their social networks and social status to overcome these barriers. Participants were motivated to use DHTs because they provided self-care support, a feeling of control over T2D, and personalised advice or feedback. The selection of technology was also guided by participants’ preferences and what they valued in relation to DHTs and self-care support, and these in turn were influenced by age and gender.ConclusionThis research indicates that low levels of digital skills and high cost of digital health interventions can create barriers to the access and use of DHTs to support the self-care of T2D. However, social networks and social status can be leveraged to overcome some of these challenges. If digital interventions are to decrease rather than exacerbate health inequalities, these barriers and facilitators to access and use must be considered when DHTs are developed and implemented.

Digital health education and an understanding of effective technology application are essential for shaping the clinical environments of the future. This requires an understanding of nursing students’ attitudes and behaviours in health education. The objective of this study was to investigate nursing students’ perceptions of and attitudes towards digital health technologies. The study employed a qualitative research method focusing on the independent work of the students enrolled in the 2023/2024 Digital Technologies in Health course. Qualitative data were collected through Moodle, the common e-learning platform at Tallinn Health University of Applied Sciences. The document analysis database consisted of 185 student self-reflections and self-assessments. Trust in technology and the use of software emerged as key factors in the learning process. Students recognised the importance of quality health data and demonstrated trust in utilising electronic records, even when lacking previous experience. Despite their positive attitudes towards digital technologies, significant challenges remain in the application of fundamental digital skills. The most pressing concerns are related to digital literacy and innovation. Students perceive the use of digital tools as potentially distancing healthcare professionals from patients, which raises ethical concerns, particularly in relation to their future professional roles. Nursing educators should prioritise fostering a strong professional nursing identity, with particular emphasis on the positive impact of digital health technologies in clinical practice.

Sporadic inclusion body myositis (IBM) is a debilitating idiopathic inflammatory myopathy (IIM) which affects hand function, ambulation, and swallowing. There is no approved pharmacological therapy for IBM, and there is a lack of suitable outcome measure to assess the effect of an intervention. The IBM scientific interest group under IMACS reviewed the previously used outcome measures in IBM clinical studies to lay the path for developing a core set of outcome measures in IBM. In this systematised review, we have extracted all outcome measures reported in IBM clinical studies to determine what measures were being used and to assess the need for optimising outcome measures in IBM. We found 13 observational studies, 17 open-label clinical trials, and 15 randomised control trials (RCTs) in IBM. Six-minute walk distance, IBM-functional rating scale (IBM-FRS), quantitative muscle testing, manual muscle testing, maximal voluntary isometric contraction testing, and thigh muscle volume measured by MRI were used as primary outcome measures. Twelve different outcome measures of motor function were used in IBM clinical trials. IBM-FRS was the most used measure of functionality. Swallowing function was reported as a secondary outcome measure in only 3 RCTs. There are inconsistencies in using outcome measures in clinical studies in IBM. The core set measures developed by the IMACS group for other IIMs are not directly applicable to IBM. As a result, there is an unmet need for an IBM-specific core set of measures to facilitate the evaluation of new potential therapeutics for IBM.

SummaryBackground: Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic. Methods: We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis. Results: A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers. Conclusion: Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.