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Due to advances in cardiovascular medicine and preventive cardiology, patients benefit from a better prognosis, even in case of significant disease burden such as acute and chronic coronary syndromes, advanced valvular heart disease and chronic heart failure. These advances have allowed CVD patients to increase their life expectancy, but on the other hand also experience aging-related syndromes such as frailty. Despite being underrecognized, frailty is a critical, common, and co-existent condition among older CVD patients, leading to exercise intolerance and compromised adherence to cardiovascular rehabilitation (CR). Moreover, frail patients need a different approach for CR and are at very high risk for adverse events, but yet are underrepresented in conventional CR. Fortunately, recent advances have been made in technology, allowing remote monitoring, coaching and supervision of CVD patients in secondary prevention programs with promising benefits. Similarly, we hypothesized that such programs should also be implemented to treat frailty in CVD patients. However, considering frail patients' particular needs and challenges, telerehabilitation interventions should thus be appropriately adapted. Our purpose is to provide, for the first time and based on expert opinions, a framework of how such a cardiac telerehabilitation program could be developed and implemented to manage a prevention and rehabilitation program for CVD patients with frailty.

Refereed/Peer-reviewed Introduction: Studies have shown that use of medicines with sedative or anticholinergic properties is associated with a decline in physical function; however, the effects have not been quantified, and it is not known how and which specific physical movements are affected. This prospective study quantified the impact of a change in sedative or anticholinergic load over time on 24-hour activity composition. Methods: This study used data collected from a randomised trial assessing an ongoing pharmacist service in residential aged care. The 24-hour activity composition of sleep, sedentary behaviour, light-intensity physical activity, and moderate to vigorous physical activity was derived from 24-hour accelerometry bands. Mixed effect linear models were used to regress the multivariate outcome of 24-hour activity composition on medication load at baseline and at 12 months. A fixed effect interaction between trial stage and medication load was included to test for differing sedative or anticholinergic load effects at the two trial stages. Results: Data for 183 and 85 participants were available at baseline and 12 months respectively. There was a statistically significant interaction between medication load and time point on the multivariate outcome of 24-hour activity composition (sedative F = 7.2, p < 0.001 and anticholinergic F = 3.2, p = 0.02). A sedative load increase from 2 to 4 over the 12-month period was associated with an average increase in daily sedentary behaviour by an estimated 24 min. Conclusion: As sedative or anticholinergic load increased, there was an increase in sedentary time. Our findings suggest wearable accelerometry bands are a possible tool for monitoring the effects on physical function of sedative and anticholinergic medicines. Trial registration: The ReMInDAR trial was registered on the Australian and New Zealand Trials Registry ACTRN12618000766213.

The RELIEF project’s main aim was to improve pain management of children with cancer at home. To achieve this, three sub-aims were formulated: first, to explore pain prevalence in children with cancer at home (chapter 2); second, to develop and test feasibility (chapter 3) and effectiveness (chapter 4) of a pain monitoring app for the home setting (the KLIK Pijnmonitor app), and third, to assess barriers and facilitators of implementation of digital health tools in clinical care (chapters 5 and 6). This final chapter provides a summary of the main findings of the thesis, followed by implications for clinical practice and future research, strengths, methodological considerations and limitations of the research, and a conclusion. An overview of the studies and main findings presented in this thesis are summarized in Table 1.

Patients with inflammatory bowel disease (IBD) require lifelong and personalized care by a multidisciplinary healthcare team. However, the traditional medical model is not ideal for patients who require continuous close monitoring and whose symptoms may dramatically worsen between regularly scheduled visits. Additionally, close dietary follow-up and monitoring of IBD in a traditional setting are challenging because of the disease complexity, high pressure on outpatient clinics with a small number of IBD specialist dietitians, and rising incidence. Given the significant burden of IBD, there is a need to develop effective dietary management strategies. The coronavirus disease 2019 pandemic caused an unprecedented shift from in-person care to delivering health care via technological remote devices. Traditional nutrition therapy and consultation can be provided by telenutrition through remote electronic communication applications that could greatly benefit patient care. Telenutrition might be useful, safe, and cost-effective compared with standard care. It is likely that virtual care for chronic diseases including IBD will continue in some form into the future. This review article summarizes the evidence about telenutrition applications in the management of IBD patients, and we gave an overview of the acceptance and impact of these interventions on health outcomes.

Smartphone apps for mental health (MH apps) and wellness reach millions of people and have the potential to reduce the public health burden of common mental health problems. Thousands of MH apps are currently available, but real-world consumers generally gravitate toward a very small number of them. Given their widespread use and the lack of empirical data on their effects, understanding the content within MH apps is an important public health priority. An overview of the content within these apps could be an important resource for users, clinicians, researchers, and experts in digital health. Here, we offer summaries of the content within highly popular MH apps. Our aim is not to provide comprehensive coverage of the MH app space. Rather, we sought to describe a small number of highly popular MH apps in three common categories: meditation and mindfulness, journaling and self-monitoring, and AI chatbots. We downloaded the two most popular apps in each of these categories (respectively: Calm, Headspace; Reflectly, Daylio; Replika, Wysa). These six apps accounted for 83% of monthly active users of MH apps. For each app, we summarize information in four domains: intervention content, features that may contribute to engagement, the app’s target audience, and differences between the app’s free version and its premium version. In the years ahead, rigorous evaluations of highly popular MH apps will be needed. Until then, we hope that this overview helps readers stay up-to-date on the content within some of the most widely used digital mental health interventions.

This paper investigates whether or not there is a policy window for making health data ‘Findable’, ‘Accessible’ (under well-defined conditions), ‘Interoperable’ and ‘Reusable’ (FAIR) in Ethiopia. The question is answered by studying the alignment of policies for health data in Ethiopia with the FAIR Guidelines or their ‘FAIR Equivalency’. Policy documents relating to the digitalisation of health systems in Ethiopia were examined to determine their FAIR Equivalency. Although the documents are fragmented and have no overarching governing framework, it was found that they aim to make the disparate health data systems in Ethiopia interoperable and boost the discoverability and (re)usability of data for research and better decision making. Hence, the FAIR Guidelines appear to be aligned with the regulatory frameworks for ICT and digital health in Ethiopia and, under the right conditions, a policy window could open for their adoption and implementation.

Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalised care. A recognised aspect in cancer care is the need for more personalised treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate and Black Box models using systems and mathematical modelling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalised uterine cancer care. This paper presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health and computer science respectively.

Digital Twins constitute a class of innovative data-intensive network-based computing solutions based on real-time interactive simulations of physical systems-Physical Twins-in multiple domains, representing and predicting the states of complex systems (from bacteria to cities). However, the variety of Digital Twins proposed in literature makes difficult to shape a general definition for this class of computational tools (possibly merging with phygital systems). On the other hand, this is a sign of the versatility of Digital Twins, in both their own capability to solve heterogeneous problems in real world and to inspire novel approaches to handle the uncertainty in complex systems. Such advantages are also explored in fields like healthcare, especially considering domains of Digital Health and mHealth. Indeed, major efforts in research are pushing the methodological and technological boundaries of biomedical sciences, translational research, and precision medicine. Such efforts should be pursued for implementing Digital Twins helping, for instance, the humancentric optimization of the resources allocation in healthcare settings (hospitals, homes, and cities). This chapter will present the opportunity and the ethical issues of using these systems for foreseeing the future states of a person or a population (a need that certainly was highlighted during the COVID-19 pandemic) or a system including a medical device and its user.

Digital Medication Review Tools (DMRTs) are increasingly important in pharmacy practice. To ensure that young pharmacists are sufficiently competent to perform medication reviews after graduation, the introduction of DMRTs teaching in academic education is necessary. The aim of our study was to demonstrate the effect of DMRTs use on pharmacy students’ performance when conducting a medication review (MR) in a randomized controlled pre-post design. Forty-one pharmacy students were asked to complete a MR within 60 min, followed by a 10-min consultation with (intervention group) and without a DMRT (control group). The MR performance was subdivided into four categories: communication skills, subjective and objective patient data, assessment, and plan. Performance was assessed using objective structured clinical examinations (OSCEs) and analytical checklists. With the use of DMRTs, the overall performance was improved by 17.0% compared to the control group (p < 0.01). Improvement through DMRTs was seen in the subcategories “Assessment” and “Plan”. Furthermore, pharmacy students liked using DMRTs and felt more confident overall. Our study results demonstrate that DMRTs improve the performance of MRs, hence DMRTs should become an integral part of pharmacy curriculum. Consequently, digitally enabled pharmacists using DMRTs will be better prepared for their professional careers in pharmacy practice.

The use of technology has proven to be a powerful tool in the fight against COVID-19 and its variants of concern (Gamma, Beta, Alpha, Delta, and Omicron). The urgency of responding to this worldwide pandemic has accelerated the development of monitoring systems and contact tracing applications. Without technology’s contribution, the adverse effects on humanity economically, socially, and psychologically would be even more devastating. This study aimed to translate, adapt, and validate the Unified Theory of Acceptance and Use of Technology (UTAUT) model in a Portuguese university population, to evaluate adherence to a mobile application for tracking COVID-19. An observational cross-sectional study was developed using a sample with 1081 participants (71% female, 59.2% with ages between 16 and 24, and 78.2% of the respondent’s university students). The Portuguese version model showed a good reliability (Cronbach’s α = 0.82) and an acceptable overall adjustment to the sample under study (χ2/df = 3.732, CFI = 0.955, TLI = 0.944, RMSEA = 0.05, SRMR = 0.06); however, this model could be improved, as we proved. Since this is a pilot study, more studies are needed. The results indicated that the P-UTAUT can be improved for evaluating adherence to a COVID-19 mobile application.

One of the most exciting trends in digital health is the growing use of wearables and other mobile devices to monitor and track health metrics in real time. From smart watches that track heart rate and activity levels, to glucose monitors that can be worn on the skin, these technologies are enabling patients to take a more active role in managing their own health1. However, we must also address concerns around data privacy and security, as well as the need for more research to establish the efficacy and safety of these tools. I also think it's important to pay attention to the growing use of telemedicine and online platforms for health care. These tools have been especially critical during the COVID-19 pandemic, allowing patients to receive care remotely and reducing the burden on healthcare facilities2. However, we must ensure that these technologies are accessible and affordable for all patients, regardless of their location or socioeconomic status. Additionally, we must continue to refine and improve virtual care delivery models to ensure that they are effective and efficient. Finally, I would like to highlight the role of social media and other communications technologies in digital health. These tools have enormous potential to facilitate health communication and education, and to connect patients with healthcare providers and supportive communities. However, they also pose challenges in terms of ensuring the accuracy and credibility of health information, and preventing the spread of misinformation. As we continue to explore the possibilities and limitations of digital health, I believe that it is essential for researchers, healthcare providers, and policymakers to work together to address these challenges and advance the field. I hope that this letter will inspire further discussion and collaboration around these important issues., {"references":["Rodriguez-León C, Villalonga C, Munoz-Torres M, Ruiz JR, Banos O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR Mhealth Uhealth. 2021 Jun 3;9(6):e25138. doi: 10.2196/25138. PMID: 34081010; PMCID: PMC8212630.","Garfan S, Alamoodi AH, Zaidan BB, Al-Zobbi M, Hamid RA, Alwan JK, Ahmaro IYY, Khalid ET, Jumaah FM, Albahri OS, Zaidan AA, Albahri AS, Al-Qaysi ZT, Ahmed MA, Shuwandy ML, Salih MM, Zughoul O, Mohammed KI, Momani F. Telehealth utilization during the Covid-19 pandemic: A systematic review. Comput Biol Med. 2021 Nov;138:104878. doi: 10.1016/j.compbiomed.2021.104878. Epub 2021 Sep 20. PMID: 34592585; PMCID: PMC8450049."]}

The implementation of artificial intelligence (AI) is driving significant transformation inside the administrative and clinical workflows of healthcare organizations at an accelerated rate. This modification highlights the significant impact that AI has on a variety of tasks, especially in health procedures relating to early detection and diagnosis. Papers done in the past imply that AI has the potential to increase the overall quality of services provided in the healthcare industry. There have been reports that technology based on AI can improve the quality of human existence by making life simpler, safer, and more productive. A comprehensive analysis of previous scholarly research on the use of AI in the health area is provided in this research in the form of a literature review. In order to propose a classification framework, the review took into consideration 132 academic publications sourced from scholarly sources. The presentation covers both the benefits and the issues that AI capabilities provide for individuals, medical professionals, corporations, and the health industry. In addition, the social and ethical implications of AI are examined in the context of the output of value-added medical services for decision-making processes in healthcare, privacy and security measures for patient data, and health monitoring capabilities.

In the document we come up with a first approach to identify synergies amongst Tasks 5.1 ‘Market and stakeholder needs analysis’ (Leader: UKOLN) and WP3 (Leader: KINNO) and all its encompassing Tasks. Our aim is to design a lean scheme for the Service Offer, that its value will be demonstrated with a respective ‘storylines’ that will help build the regional ecosystems. To this we make a start in the document by providing insights of the ecosystems in Porto and Cologne. In the next version we shall include also information on the Thessaloniki ecosystem and elaborate for the other two ecosystems as well.

IntroductionHealthcare information is increasingly internet-based. Standards require websites to be ‘perceivable, operable, understandable and robust’ with relevant content for citizens in appropriate language. This study examined UK and international websites offering public healthcare information on advance care planning (ACP) using current recommendations for website accessibility and content and informed by a public engagement exercise.MethodsGoogle searches identified websites in English from health service providers, governmental or third sector organisations based in the UK and internationally. Target keywords that would be used by a member of the public informed the search terms. Data extraction used criterion-based assessment and web content analysis of the first two pages of each search result. Public patient representatives as key members of the multidisciplinary research team guided the development of the evaluation criteria.ResultsA total of 1158 online searches identified 89 websites, reduced to 29 by inclusion/exclusion criteria. Most sites met international recommendations for knowledge/understanding about ACP. Differences in terminology, lack of information about ACP limitations and non-adherence to recommended reading levels, accessibility standards and translation options were apparent. Sites targeting members of the public used more positive, non-technical language than those for both professional and lay users.ConclusionsSome websites met accepted standards required to facilitate understanding and public engagement in ACP. Others could be improved significantly. Website providers have important roles and responsibilities in increasing people's understanding of their health conditions, future care options and ability to take an active role in planning for their health and care.

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results: We identified two cohort-specific top performing algorithms for GSD and CAD, and a single best for ICD and SL. GSD best algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (PFF). Algorithms’ performances were lower for short WBs; slower gait speeds (Conclusions: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findingsshowed that the choice of algorithm for estimation of GSD and CAD DMOs should be cohort-specific (e.g., slow walkers and with gait impairments). Short WB length and slow walking speed worsened algorithms’ performances. Trial registration: ISRCTN – 12246987.

Introduction: This study focuses on medical students from the University of Medicine and Pharmacy in Cluj-Napoca, Romania, and has three objectives. First, it evaluates the opinions of medical students regarding their previous training as well as their needs for future training in the field of digital health. Second, it assesses their attitudes regarding digital health and their intention to use digital tools as physicians. Lastly, the interrelationship between these issues as well as the socio-demographic factors which influence them are investigated. Materials and methods: A cross-sectional survey was performed during June–August 2021 among fifth and sixth year students of the Faculty of Medicine from the Iuliu Hațieganu University of Medicine and Pharmacy in Cluj-Napoca, Romania. Anonymous online questionnaires were used which were filled in by 306 students. Results: Less than half of the participating students declared that they benefited from training or different practical examples during medical education regarding the use of digital tools in different medical areas, while the majority said that they would like to receive more training in the field of digital health. A total of 58.2% said that they totally agree with the introduction of a formal training in the medical curricula regarding digital health. Many students declared positive attitudes toward the use of digital tools in different domains within the medical field and intention to use digital tools as physicians; several differences were noted, including gender, year of study, type of domain, and previous training with regard to the use of digital tools in those domains. Moreover, the need for future training and the desire for the introduction of a formal training program into the medical curricula with regard to this field were stronger among those with more positive attitudes and higher intentions to use digital tools in their medical activity. Conclusions: To the best of our knowledge, this is the first study from Romania which investigated the training, attitudes, and intentions regarding the use of digital health among Romanian medical students, and offers valuable information to guide the education of medical students.

Introduction: Differences in digital health literacy levels are associated with a lack of access to digital tools, usage patterns, and the ability to effectively use digital technologies. Although some studies have investigated the impact of sociodemographic factors on digital health literacy, a comprehensive evaluation of these factors has not been conducted. Therefore, this study sought to examine the sociodemographic determinants of digital health literacy by conducting a systematic review of the existing literature. Methods: A search of four databases was conducted. Data extraction included information on study characteristics, sociodemographic factors, and the digital health literacy scales used. Meta-analyses for age and sex were conducted using RStudio software with the metaphor package. Results: A total of 3922 articles were retrieved, of which 36 were included in this systematic review. Age had a negative effect on digital health literacy (B=-0.05, 95%CI [-0.06; -0.04]), particularly among older adults, whereas sex appeared to have no statistically significant influence among the included studies (B=0.17, 95%CI [-0.61; 0.30]). Educational level, higher income, and social support also appeared to have a positive influence on digital health literacy. Discussion: This review highlighted the importance of addressing the digital health literacy needs of underprivileged populations, including immigrants and individuals with low socioeconomic status. It also emphasizes the need for more research to better understand the influence of sociodemographic, economic, and cultural differences on digital health literacy. Conclusions: Overall, this review suggests digital health literacy is dependent on sociodemographic, economic, and cultural factors, which may require tailored interventions that consider these nuances. published

Background Patient-facing digital technologies may reduce barriers to and alleviate the burden on genetics services. However, no work has synthesised the evidence for patient-facing digital interventions for genomics/genetics education and empowerment, or to facilitate service engagement more broadly. It is also unclear which groups have been engaged by digital interventions. Aim This systematic review explores which existing patient-facing digital technologies have been used for genomics/genetics education and empowerment, or to facilitate service engagement, and for whom and for which purposes the interventions have been developed. Methods The review adhered to the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Eight databases were searched for literature. Information was extracted into an Excel sheet and analysed in a narrative manner. Quality assessments were conducted using the Mixed Methods Appraisal Tool. Results Twenty-four studies were included, of which 21 were moderate or high quality. The majority (88%) were conducted in the United States of America or within a clinical setting (79%). More than half (63%) of the interventions were web-based tools, and almost all focussed on educating users (92%). There were promising results regarding educating patients and their families and facilitating engagement with genetics services. Few of the studies focussed on empowering patients or were community-based. Conclusion Digital interventions may be used to deliver information about genetics concepts and conditions, and positively impact service engagement. However, there is insufficient evidence related to empowering patients and engaging underserved communities or consanguineous couples. Future work should focus on co-developing content with end users and incorporating interactive features.

Interventions intended to reduce the consumption of dietary sugars among those population groups demonstrating disproportionately greater and more frequent consumption of sugar-sweetened beverages and foods (SSBF) would benefit from intervention strategies that are tailored to population-specific barriers and facilitators. The objective of this study was to develop and evaluate the acceptability of photo-enhanced and theory-based health promotion messages that target the reduction in SSBF among adult residents of public housing developments, a population known for their high rates of chronic disease. Using the message development tool as a framework, we developed a series of 15 SSBF reduction messages, using an iterative process with community member input. We then evaluated the acceptability of the messages and compared three delivery mechanisms: print, text, and social media. We recruited participants who were residents of urban public housing developments, and who spoke either English or Spanish. A majority of participants identified as being of Hispanic ethnicity (73%). The message acceptability scoring did not appear to differ according to the assigned delivery mechanism, despite some imbalances in participants’ characteristics across delivery mechanisms. The messages that targeted motivation were least likely to be accepted. In conclusion, our findings suggest that engaging members of the community at all phases of the development process was a feasible method to develop SSBF reduction messages with a high perceived acceptability.

Electrocardiograms (ECGs) provide crucial information for evaluating a patient's cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson's correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices., Bioengineering, 10 (6), ISSN:2306-5354

In 2030, it is expected that 15% of the country's population will be classified as elderly and there is driving up demand for elderly healthcare services. The evolution of digital health technology has emerged as a solution to this issue. However, there has been a recent decline in the elderly adoption of digital health technologies. This issue is worsened by the emergence of interfaces and interaction styles in newly developed technologies. A systematic review was conducted in this article to investigate the usability measures used to improve the user experience of digital health technology among the elderly. This study includes articles selected from the Web of Science and Scopus databases, both of which are well-established. Using thematic analysis, data from 29 articles were analyzed, yielding four main themes: i) effectiveness; i) efficiency; iii) satisfaction; and iv) learnability. The four main themes generated 12 sub-themes. The appearance, functionality, and structure of new digital health technology are the primary barriers to adoption. User interface (UI) design should take into account the limitations of elderly users. Additionally, elderly users require motivation, support, and training to utilize digital health technologies effectively. This study's findings make significant contributions to digital health and gerontechnology fields.

Currently, more than half of the adults worldwide have at least one chronic condition; however, poor medication adherence and self-management are observed. Tools, such as mHealth apps, may play a crucial role for millions of people in increasing the patients’ involvement in the treatment process. The purpose of this study is to evaluate the characteristics and quality of the existing mHealth apps for patients with various chronic conditions. The study was conducted from February 1 to March 31, 2022. The mHealth applications’ search was performed on the iOS platform, and 29 apps were included in the final analysis. The quality of apps was evaluated using Mobile App Rating Scale. The majority of the apps had symptom and medication tracking functionality. The mean score of apps’ overall quality was 3.45 (SD = 0.61) out of five, ranging from 2.04 to 4.50. The highest-rated category was the functionality of the app—3.75 (0.61); followed by aesthetics—3.67 (0.62); subjective quality—3.47 (0.88); engagement—3.33 (0.78); and information—3.04 (0.61). The study revealed that only a few high-quality apps for chronic condition management exist, and there is still some space for improvements.

The COVID-19 pandemic has changed the world. Today, the use of Information and Communications Technology (ICT) in support of education, medicine, business and administration has become a reality practically everywhere. In particular, the eHealth (digital Health) sector is on the cusp of a revolution, fueled by the worldwide health emergency due to the spread of the new coronavirus. With a view to developing new sixth generation (6G)-oriented architectures, advanced eHealth services like telemonitoring would benefit from the support of technologies that guarantee secure data access, ultra-low latency and very-high reliability targets, which are hardly achievable by the fifth generation (5G). This is the reason why this work proposes an eHealth system architecture, in which low-latency enabling technologies like Device-to-Device (D2D) communications and Multi-access Edge Computing (MEC) are integrated and supported by security mechanisms for an optimal management of sensitive health data collected by Internet of Medical Things (IoMT) devices. A preliminary evaluation of the proposed framework is provided that shows promising results in terms of data security and latency reduction.

IntroductionThe ability to perform psoriasis skin assessments remotely through digital image-based psoriasis area and severity index (DIB-PASI) would be a valuable tool for psoriasis clinical trials. An ideal teledermatological assessment would be robust across patients of diverse skin tones as well as across assessors of varying experience levels. In this pilot study, we evaluated the reliability of face-to-face (FTF) versus DIB-PASI scores determined by trained clinical assessors with a spectrum of experience and with patients of different skin tones.MethodsFourteen subjects of varying skin tones with moderate-to-severe plaque psoriasis were treated with adalimumab. In-person PASI assessments and digital photography were performed in the clinic at weeks 0, 12, and 24. Photographs were reviewed by four independent assessors to derive a digital image-based PASI score. The concordance of face-to-face PASI (FTF-PASI) and DIB-PASI were analyzed across patient and assessor factors.ResultsOverall concordance between FTF-PASI and DIB-PASI was high (ICC 0.82, p

Introduction Over 90% of people living with cancer access information online to inform healthcare decisions. Older adults with cancer are also increasingly adopting electronic healthcare services, or eHealth, particularly with the rapid transition to virtual care amidst the pandemic. Therefore, the purpose of this review is to understand the level of eHealth literacy among older adults with cancer and their caregivers, as well as any barriers and facilitators in terms of accessing, comprehending, and implementing eHealth information. Methods This scoping review was guided by Arksey and O'Malley methodology and PRISMA ScR guidelines. Comprehensive searches for the concepts of “eHealth Literacy” and “cancer” were performed in MEDLINE, Scopus, CINAHL, PsycINFO, AMED and EMBASE, from 2000 to 2021. We used descriptive quantitative and thematic analysis to analyze the literature. Results Of the 6076 articles screened by two reviewers, eleven articles were included. Quantitative findings suggest older adults with cancer and their caregivers have low self-perceived eHealth literacy and less confidence evaluating online health information for cancer decision-making. Low socioeconomic status, lower education levels, rapid expansion of digital applications, broadband access, reduced familiarity, and frequency of use were cited as prominent barriers. eHealth literacy appears to be positively correlated with caregivers seeking a second opinion, awareness of treatment options, shared decision making, and trust in the health care system. Conclusion With the growing reliance on eHealth tools, developing credible digital health applications that require minimal internet navigation skills, patient education, and collaborative efforts to address access and affordability are urgently warranted.

This study examined cancer patients’ (N = 355, 65.4 % female, Mage = 54.35, SDage = 11.25) perceived emotional and informational support from a variety of interpersonal and media sources. We recruited patients from cancer patient association websites and online cancer forums and asked them to report to what extent they received support from interpersonal and media sources. Patients rated professional sources (e. g., oncologists) and personal sources (e. g., family) as nearly equal sources of emotional support; however, professional sources were rated as significantly greater sources of informational support. Although family and oncologists were the most mentioned interpersonal sources of support, they were also the most mentioned disappointing sources. Of the media sources, online interaction sources (e. g., online support groups) were rated as nearly equivalent sources of emotional support as interpersonal sources. That patients perceived emotional support, not only informational support, from various media sources is promising since interpersonal sources can be disappointing to some patients.

The brain is one of the most important and complex organs in humans. On the one hand, it dictates our behavioral outcomes, such as seeing, speaking, and walking, and, through these behaviors, it determines how we interact with and perceive the world. On the other hand, its pathological outcomes, such as the development of neurodegenerative diseases, affect the longevity and quality of millions of people’s lives. Today, the brain’s neural activities can be captured by neuroimaging modalities such as functional magnetic resonance imaging (fMRI), and its behavioral markers can be recorded using smartphone sensors. Through uncovering how patterns of these large-scale data are related to the brain’s behavioral and pathological outputs, one contributes to our understanding of the biological underpinnings of knowledge, consciousness, cognition, and behavior. In parallel, by developing targeted statistical frameworks, one begins to unveil the intrinsic spatial and temporal relationships between neural signatures, behavioral features, and the brain’s pathological outcomes, and to assess and monitor neurodegenerative diseases more effectively and accurately. This thesis consists of four inter-correlated topics aimed at building targeted and efficient biomarkers using large-scale data to inquire into human behavior and brain disorders. The four topics are arranged according to their logic flow (from relatively simple to reasonably complex) and will be present in the following order: assessing outcomes of the brain using (a) static, (b) dynamic, (c) intermediating, and (d) longitudinal biomarkers. 1. Building static biomarkers. Methodological approaches are developed to identify time-averaged biomarkers to assess brain disease status and severity. This is supported by an article on developing static biomarkers to assess the status and severity of Parkinson’s disease. 2. Building dynamic biomarkers. Methodological approaches are developed to identify and isolate dynamic markers or directed information flows in the whole brain. This is supported by an article on extracting dynamic neural information flows and using them to predict cognitive flexibility in humans. 3. Building intermediating biomarkers. In a complex regulative biological system, some markers are on the one hand associated with behavior (e.g., prodromal symptoms), and on the other hand give rise to brain disorders (e.g., psychosis). Methodological approaches are developed to identify, isolate, and quantify these intermediating biomarkers or neural mediators. This is supported by an article on brain-wide functional mediation analysis. 4. Building longitudinal biomarkers. The patterns of biomarkers fluctuate over time. Identifying biomarkers whose temporal patterns are coupled with neurodegenerative disorders longitudinally is useful for assessing the development and trajectory of the diseases. This is supported by an article on developing longitudinal biomarkers to assess multiple sclerosis over time. Taken together, the methodological explorations and neurobiological discoveries in this thesis suggest that biomarker-based predictive models are promising for advancing our understanding of the human brain and behavior, and for facilitating brain illness assessment and monitoring in an automated way.

The rise of the Internet of Things (IoT) has enabled the development of measurement systems dedicated to preventing health issues and monitoring conditions in smart homes and workplaces. IoT systems can support monitoring people doing computer-based work and avoid the insurgence of common musculoskeletal disorders related to the persistence of incorrect sitting postures during work hours. This work proposes a low-cost IoT measurement system for monitoring the sitting posture symmetry and generating a visual alert to warn the worker when an asymmetric position is detected. The system employs four force sensing resistors (FSR) embedded in a cushion and a microcontroller-based read-out circuit for monitoring the pressure exerted on the chair seat. Java-based software performs the real-time monitoring of the sensors’ measurements and implements an uncertainty-driven asymmetry detection algorithm. The shifts from a symmetric to an asymmetric posture and vice versa generate and close a pop-up warning message, respectively. In this way, the user is promptly notified when an asymmetric posture is detected and invited to adjust the sitting position. Every position shift is recorded in a web database for further analysis of the sitting behavior.

Background: The Modular Open Research Platform (MORE) is being developed as an open-source platform for long-term situated digital health (DH) research and observations with the potential to facilitate studies, evaluations of DH interventions, and remote telehealth monitoring. Objectives: To implement an iterative development approach that integrates multi-stakeholder perspectives to support a single platform development process. Method: Capture, fulfil and balance the requirements of a multi-disciplinary group of stakeholders interacting with the system through a Delphi-inspired, iterative and participatory design process encompassing a series of workshops and online surveys. Results: Through interaction with a multi-disciplinary group of key platform stakeholders, diverse feedback and requirements for the design and development process were elicited and integrated. Conclusion: Findings from the initial rounds of stakeholder involvement lay the stepping stone towards further iterations in the process. Experts who participated in the process reported being generally supportive of and feeling involved in the development process.&nbsp

Background Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses. Findings We developed an R package for electronic health data preparation, “eHDPrep,” demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative “meta-variables” according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset. Conclusions eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package=eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

This study aimed at assessing the extent to which the Saudi general population has embraced digital health medical applications to meet their health-related needs so that the Saudi Ministry of Health and government can appropriately be guided on scaling up of digital health across the country. As such, this study was guided by the question of to what extent do the Saudi people use digital health mobile-based applications?. This was a cross-sectional study utilizing snowballing sampling approach. Frequencies, Chi-square, and Spearman rank correlation statistics were used to offer descriptive and inferential analysis of the variables. The majority of the participants were economically able to afford smart devices that have medical apps, had at least an app on such devices, and highly regarded the benefits of the apps. Unfortunately, their understanding of how to use such apps was limited and this posed a barrier to embracing digital health alongside difficulty downloading apps, and medical ethical concerns. Although there is a willingness, extra effort is needed from the Saudi Ministry of Health and government to promote the uptake of digital health in Saudi Arabia.

My capstone research addresses the flaws in food, drug, and cosmetic ingredient legislation in America. To address this issue, I am proposing an app idea that tracks an individual's health statistics using a phone, which will provide people with the proper environment for safe and healthy products. Alongside this app idea will include my STS research in which I will conduct a literature review of papers that capture different parts of the problem, such as food legislation history, legislation review, cigarette and opiates studies, and food labels, I will explore how the variables synergize and affect one another. It is essential to consider the human and social dimensions of this technology because food, drug, and cosmetic legislation impacts millions of citizens of America, and it can have a significant effect on public health. As seen in present-day America, when this legislation is not considered seriously, the prevalence of chronic diseases and obesity is widespread. Theories of STS, such as Actor-Network Theory, apply to analyze my problem-solving approach by creating a framework for understanding how all the different variables such as the government,t legislation, people, and even the product all affect one another.. I plan to conduct my STS research using a qualitative research method, analyzing data from surveys, and observational studies. Through my STS research, I expect to find information that provides a big-picture idea of how the flaws in food legislation arise and how to prevent them in the future. The implications of my capstone project and STS research, when considered in concert, could lead to both a better environment for citizens to buy food, drugs, and cosmetics, while also having the ability to stay informed optimally with their phones. Ultimately, my capstone project and STS research could contribute to a healthier, more informed society.

Immersive virtual reality (IVR) uses customized and advanced software and hardware to create a digital 3D reality in which all of the user’s senses are stimulated with computer-generated sensations and feedback. This technology is a promising tool that has already proven useful in Parkinson’s disease (PD). The risk of falls is very high in people with PD, and reaction times and processing speed may be markers of postural instability and functionality, cognitive impairment and disease progression. An exploratory study was conducted to explore the feasibility of reaction time tests performed in IVR as predictors of falls. A total of 26 volunteers (79.2% male; 69.73 ± 6.32 years) diagnosed with PD (1.54 ± 0.90 H&Y stage; 26.92 ± 2.64 MMSE) took part in the study. IVR intervention was feasible, with no adverse effects (no Simulator Sickness Questionnaire symptoms). IVR reaction times were related (Spearman’s rho) to functionality (timed up and go test (TUG) (rho = 0.537, p = 0.005); TUG-Cognitive (rho = 0.576, p = 0.020); cognitive impairment mini mental state exam (MMSE) (rho = −0.576, p = 0.002)) and the years of the patients (rho = 0.399, p = 0.043) but not with the first PD symptom or disease stage. IVR test is a complementary assessment tool that may contribute to preventing falls in the proposed sample. Additionally, based on the relationship between TUG and reaction times, a cut-off time is suggested that would be effective at predicting the risk of suffering a fall in PD patients using a simple and quick IVR test.

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

Data Availability: Findings from the study will be disseminated at national and international conferences. All baseline data generated or analysed during this study are included in this published article. Electronic supplementary material is available online at https://bmcnephrol.biomedcentral.com/articles/10.1186/s12882-023-03173-7#Sec51 . Copyright © The Author(s) 2023. Background: Physical activity and emotional self-management has the potential to enhance health-related quality of life (HRQoL), but few people with chronic kidney disease (CKD) have access to resources and support. The Kidney BEAM trial aims to evaluate whether an evidence-based physical activity and emotional wellbeing self-management programme (Kidney BEAM) leads to improvements in HRQoL in people with CKD. Methods: This was a prospective, multicentre, randomised waitlist-controlled trial, with health economic analysis and nested qualitative studies. In total, three hundred and four adults with established CKD were recruited from 11 UK kidney units. Participants were randomly assigned to the intervention (Kidney BEAM) or a wait list control group (1:1). The primary outcome was the between-group difference in Kidney Disease Quality of Life (KDQoL) mental component summary score (MCS) at 12 weeks. Secondary outcomes included the KDQoL physical component summary score, kidney-specific scores, fatigue, life participation, depression and anxiety, physical function, clinical chemistry, healthcare utilisation and harms. All outcomes were measured at baseline and 12 weeks, with long-term HRQoL and adherence also collected at six months follow-up. A nested qualitative study explored experience and impact of using Kidney BEAM. Results: 340 participants were randomised to Kidney BEAM (n = 173) and waiting list (n = 167) groups. There were 96 (55%) and 89 (53%) males in the intervention and waiting list groups respectively, and the mean (SD) age was 53 (14) years in both groups. Ethnicity, body mass, CKD stage, and history of diabetes and hypertension were comparable across groups. The mean (SD) of the MCS was similar in both groups, 44.7 (10.8) and 45.9 (10.6) in the intervention and waiting list groups respectively. Conclusion: Results from this trial will establish whether the Kidney BEAM self management programme is a cost-effective method of enhancing mental and physical wellbeing of people with CKD. Trial Registration: NCT04872933. Registered 5th May 2021. This study was funded by a grant from Kidney Research UK. Funding for Kidney Beam is currently supported by the four major UK charities: Kidney Research UK, Kidney Care UK, National Kidney Federation and UK Kidney Association. HMLY is funded by the NIHR [NIHR302926].

Background: An incomplete spinal cord injury (SCI) refers to remaining sensorimotor function below the injury with the possibility for the patient to regain walking abilities. However, these patients often suffer from diverse gait deficits, which are not objectively assessed in the current clinical routine. Wearable inertial sensors are a promising tool to capture gait patterns objectively and started to gain ground for other neurological disorders such as stroke, multiple sclerosis, and Parkinson's disease. In this work, we present a data-driven approach to assess walking for SCI patients based on sensor-derived outcome measures. We aimed to (i) characterize their walking pattern in more depth by identifying groups with similar walking characteristics and (ii) use sensor-derived gait parameters as predictors for future walking capacity. Methods: The dataset analyzed consisted of 66 SCI patients and 20 healthy controls performing a standardized gait test, namely the 6-min walking test (6MWT), while wearing a sparse sensor setup of one sensor attached to each ankle. A data-driven approach has been followed using statistical methods and machine learning models to identify relevant and non-redundant gait parameters. Results: Clustering resulted in 4 groups of patients that were compared to each other and to the healthy controls. The clusters did differ in terms of their average walking speed but also in terms of more qualitative gait parameters such as variability or parameters indicating compensatory movements. Further, using longitudinal data from a subset of patients that performed the 6MWT several times during their rehabilitation, a prediction model has been trained to estimate whether the patient's walking speed will improve significantly in the future. Including sensor-derived gait parameters as inputs for the prediction model resulted in an accuracy of 80%, which is a considerable improvement of 10% compared to using only the days since injury, the present 6MWT distance, and the days until the next 6MWT as predictors. Conclusions: In summary, the work presented proves that sensor-derived gait parameters provide additional information on walking characteristics and thus are beneficial to complement clinical walking assessments of SCI patients. This work is a step towards a more deficit-oriented therapy and paves the way for better rehabilitation outcome predictions., Journal of NeuroEngineering and Rehabilitation, 20 (1), ISSN:1743-0003

Early detection of frailty is one of the main challenges in the world we live in. Being aware of physiologic and behavior changes can predict and prevent the onset of mental complications for older people. By using modern technologies, one can get insights, which may help detection of different pathologies. In this paper, preliminary results for a novel system to detect early signs of frailty are presented. A prototype was developed and tested in laboratory conditions after requirements and functional capabilities were defined. The main advantages of the proposed architecture are the usage of commercial off-the-shelf (COTS) components and custom mechanisms of security to assure a high level of confidentiality and integrity of user-specific data. Other original elements are its easy-to-use and non-intrusive characteristics.

Automation through adaptive and learning systems – be they driven by heuristics or machine learning and artificial intelligence methods – is a firm requirement if the visions of personalized digital health and precision medicine are to be addressed at scale. Manual adjustments based on individual abilities, interests, and needs are not possible at a fine-grained many-dimensional, and high-frequency level in a life-accompanying manner. Humans tend to anthropomorphologize interactive systems; especially those that display or indicate autonomy and agency. This begs the question, whether a possibly shifting sense of perceived agency that will likely be exacerbated with the more widespread release of conversational and agent-like automation interfaces – e.g. as in recent developments in chat-based AI systems, may lead to noteworthy, possibly increased dangers of “not appropriately questioning outputs or actions” of such systems. Given the often sensitive and high-risk application areas, these issues are arguably particularly relevant in digital health, where both more tool-like systems, such as “traffic light status indicators” that simply present coarse data summaries, as well as more agent-like systems, e.g. in conversational decision support, could foster justification practices by conveniently or subversively inviting for the attribution of responsibility to the systems, at the peril of overlooking legal accountability for resulting outcomes and actions. This is the conceptual motivation and foundation of current work-in-progress to empirically investigate the assumptions underpinning this chain of arguments. The work to be discussed aims to provide foundational understandings and to investigate design patterns that can help address these issues.

Google Search occupies a unique space within broader discussions of direct-to-consumer marketing of stem cell treatments in digital spaces. For patients, researchers, regulators, and the wider public, the search platform influences the who, what, where, and why of stem cell treatment information online. Ubiquitous and opaque, Google Search mediates which users are presented what types of content when these stakeholders engage in online searches around health information. The platform also sways the activities of content producers and the characteristics of the content they produce. For those seeking and studying information on digital health, this platform influence raises difficult questions around risk, authority, intervention, and oversight. This thesis addresses a critical gap in digital methodologies used in mapping and characterising that influence as part of wider debates around algorithmic accountability within STS and digital health scholarship. By adopting a novel methodological approach to Blackbox auditing and data collection, I provide a unique evidentiary base for the analysis of ads, organic results, and the platform mechanisms of influence on queries related to stem cell treatments. I explore the question: how does Google Search mediate information that people access online about ‘proven’ and ‘unproven’ stem cell treatments? Here I show that, in spite of a general ban on advertisements of stem cell treatments, users continue to be presented with content promoting unproven treatments. The types, frequency, and commercial intent of results related to stem cell treatments shifted across user groups including geography and, more troublingly, those impacted by Parkinson’s Disease and Multiple Sclerosis. Additionally, I find evidence that the technological structure of Google Search itself enables primary and secondary commercial activities around the mediation and dissemination of health information online. It suggests that Google Search’s algorithmically-mediated rendering of search results – including both commercial and non-commercial activities - has critical implications for the present and future of digital health studies.

Background Adherence to antiretroviral (ARV) treatment for HIV infection is challenging because of many factors. The World Health Organization (WHO) has recommended using digital adherence technologies (DATs). However, there is limited evidence on how DATs improve adherence. Wisepill® is an internet-enabled medication dispenser found feasible and acceptable in several studies. However, limited evidence is available on its effectiveness in improving ART adherence, specifically among children and adolescents. Furthermore, DATs are often developed without involving the target groups. We propose a two-stage project consisting of a formative study to customize an existing Wisepill DAT intervention and a randomized clinical trial to investigate the effectiveness of DAT combined with reminder cues and tailored feedback on adherence to ARV treatment among children and adolescents living with HIV and retention in care among breastfeeding women living with HIV in Kilimanjaro and Arusha Region, Tanzania. Methods We will conduct a formative mixed-methods study and three sub-trials in Kilimanjaro and Arusha Regions among (1) children aged 0–14 years and their caregivers, (2) adolescents aged 15–19 years and (3) breastfeeding women and their HIV-negative infants. In the formative study, we will collect and analyse data on needs and contents for DATs, including the contents of short message service (SMS) texts and tailored feedback. The results will inform the customization of the DAT to be tested in the sub-trials. In the trials, participants will be randomized in the intervention arm, where the DAT will be implemented or the control arm, where standard care will be followed. Participants in the intervention arm will take their medication from the Wisepill box and receive daily reminder texts and tailored feedback during clinic visits. Discussion If the intervention improves adherence to ART and the devices are acceptable, accurate and sustainable, the intervention can be scaled up within the National Aids Control Programmes. Trial registration PACTR202301844164954, date 27 January 2023.

Self-harm and other mental health difficulties are very common amongst university students, but students face numerous barriers in accessing professional support. Support offered via a smartphone app may help to overcome some of the barriers they face, while providing support that is acceptable and helpful. However, there is limited research on supportive apps for students who self-harm. This study aimed to evaluate a self-help app (BlueIce) for helping students manage their self-harm, mental wellbeing and coping ability. This was a pre-post study in which 80 participants completed baseline measures online and were sent a link to download BlueIce. Of these, 27 completed follow-up questionnaires six weeks later assessing anxiety, depression, self-harm, and coping self-efficacy/styles. At follow-up, participants also completed a questionnaire evaluating BlueIce. Self-harm urges and symptoms of anxiety and depression significantly decreased, and coping self-efficacy significantly increased. Around two thirds (64%) said that BlueIce had stopped them from harming themselves an average of 24 times. Feedback showed that BlueIce helped provide a distraction in difficult times and helped them to manage their emotions in a more adaptive way. Following the trial period, participants’ wellbeing had significantly improved, suggesting that BlueIce may be helpful for university students in managing their self-harm urges and general mental health.

Wynand Alkema vertelt over het gebruik van ChatGPT in het ontwikkelen van nieuwe medicijnen. Een samenvatting van het praatje is hieronder weergegeven:The FAIR use of Large Language Models in the drug development pipeline.With the emergence of applications that are based on large language models (LLMs), such as ChatGPT and BARD, large amounts of unstructured scientific texts have become readily available for a wide public, including researchers in drug discovery and development. LLM based applications can now be used for a wide range of drug development tasks such as target finding and evaluation, lead optimisation and biomarker discovery. The stochastic nature of these models and the dependence of the result on correct prompt engineering poses a number of challenges when integrating the use LLMs into the drug discovery pipeline in a reproducible and interpretable way.In this talk we will give examples of how LLMs can be used for fast and highly efficient literature searches to discover novel drug-target combinations. We will also discuss the potential drawbacks of these technologies as well as the opportunities to link the results of LLMs to structured databases with chemical and biological data.

Background Mobile applications can facilitate or improve gastrointestinal surgical care by benefiting patients, healthcare providers, or both. The extent to which applications are currently in use in gastrointestinal surgical care is largely unknown, as reported in literature. This systematic review was conducted to provide an overview of the available gastrointestinal surgical applications and evaluate their prospects for surgical care provision. Methods The PubMed, EMBASE and Cochrane databases were searched for articles up to October 6th 2022. Articles were considered eligible if they assessed or described mobile applications used in a gastrointestinal surgery setting for healthcare purposes. Two authors independently evaluated selected studies and extracted data for analysis. Descriptive data analysis was conducted. The revised Cochrane risk of bias (RoB-2) tool and ROBINS-I assessment tool were used to determine the methodological quality of studies. Results Thirty-eight articles describing twenty-nine applications were included. The applications were classified into seven categories: monitoring, weight loss, postoperative recovery, education, communication, prognosis, and clinical decision-making. Most applications were reported for colorectal surgery, half of which focused on monitoring. Overall, a low-quality evidence was found. Most applications have only been evaluated on their usability or feasibility but not on the proposed clinical benefits. Studies with high quality evidence were identified in the areas of colorectal (2), hepatopancreatobiliary (1) and bariatric surgery (1), reporting significantly positive outcomes in terms of postoperative recovery, complications and weight loss. Conclusions The interest for applications and their use in gastrointestinal surgery is increasing. From our study, it appears that most studies using applications fail to report adequate clinical evaluation, and do not provide evidence on the effectiveness or safety of applications. Clinical evaluation of objective outcomes is much needed to evaluate the efficacy, quality and safety of applications being used as a medical device across user groups and settings.

BACKGROUND: Patient education is a key element in the management of asthma.AIMS: This study aimed to evaluate the popularity and usefulness of YouTube videos on asthma.METHODS: Two authors screened and evaluated the 200 most popular videos. Data on likes, dislikes, views, comment, source of uploader, days since upload, and usefulness were recorded and included for analyses. The usefulness of the videos was categorized as follows: useful, misleading, or neutral. Misleading videos provided at least one scientifically incorrect detail, whereas useful videos contained scientifically correct information.RESULTS: A total of 130 videos were included, and the total number of views was 100,290,242 with a total duration of 29 h and 8 min. While 26.6% of videos were uploaded by TV shows and YouTube channels, only 7.7% were uploaded by lung specialists. 65.4% of the videos contained scientifically correct information, whereas 18.5% contained misleading information. Although videos from medical professionals had a higher quality than videos from YouTube channels and TV shows, the latter were more popular. Misleading videos had numerically, but not statistically significant higher views compared with useful videos.CONCLUSIONS: YouTube videos on asthma are popular in terms of viewer interaction, and the popularity is not restricted to videos uploaded by professional sources. Although more than half of the videos were found to be useful, a non-negligible proportion of videos were assessed as misleading. The usefulness of YouTube videos on asthma is variable and initiatives should be taken to increase the potential of YouTube as an useful source in patient education.

Growing interest is being directed to the health applications of so-called ‘passive data’ collected through wearables and sensors without active input by users. High promises are attached to passive data and their potential to unlock new insights into health and illness, but as researchers and commentators have noted, this mode of data gathering also raises fundamental questions regarding the subject's agency, autonomy and privacy. To explore how these tensions are negotiated in practice, we present and discuss findings from an interview study with 30 members of the public in the UK and Italy, which examined their views and experiences of the coronavirus disease 2019 contact tracing apps as a large-scale, high-impact example of digital health technology using passive data. We argue that, contrary to what the phrasing ‘passive data’ suggests, passivity is not a quality of specific modes of data collection but is contingent on the very practices that the technology is supposed to unobtrusively capture.

BackgroundThe progression into the Digital Age has brought an array of novel skill requirements. Unlike traditional literacy, there are currently few measures that can reliably measure eHealth literacy. The Transactional Model of eHealth Literacy and subsequent Transactional eHealth Literacy Instrument may provide a feasible option for measuring eHealth literacy.ObjectiveThis instrument has yet to be validated, which is the aim of this study. In particular, this article was conducted to validate the TeHLI to see which components of the tool (how many and which components included) would be the best fit statistically and whether the tool applies to groups of different characteristics.MethodsWe conducted an online cross-sectional study among 236 Vietnamese young people. A exploratory factor analysis was used to identify the best fit model of the Transactional eHealth Literacy Instrument. A confirmatory factor analysis tested measurement invariance at four levels: configural, metric, scalar, and strict invariance. Only metric invariance was partially invariant, while the rest tested fully invariant. Even with partial metric invariance, there is reason to assume that functional, communicative, critical, and translational eHealth literacy (the four levels according to the transactional model) are consistently measured when deploying the Transactional eHealth Literacy Instrument across groups.ResultsThe study findings substantiate that the most optimal composition of the TeHLI consists of four factors: functional, communicative, critical, and translational eHealth literacy, with RMSEA = 0.116; CFI = 0.907, and the highest internal consistency (Cronbach's α = 0.91, 0.92, 0.88, and 0.92 for each factor respectively). After using measurement invariance, that gender, education, marital status, age, location, and household economy do not influence the way participants to respond to the TeHLI to the point that would introduce measurement bias. In other word, using TeHLI across population groups should not produce error margins that substantially differ from each other.ConclusionsThis study suggests the instrument can be used for comparisons across groups and has the potential to generate high-quality data usable for informing change agents as to whether a particular population is proficient enough to adopt novel eHealth innovations.

Background Even with the introduction of new genetic techniques that enable accurate genomic characterization, knowledge about the phenotypic spectrum of rare chromosomal disorders is still limited, both in literature and existing databases. Yet this clinical information is of utmost importance for health professionals and the parents of children with rare diseases. Since existing databases are often hampered by the limited time and willingness of health professionals to input new data, we collected phenotype data directly from parents of children with a chromosome 6 disorder. These parents were reached via social media, and the information was collected via the online Chromosome 6 Questionnaire, which includes 115 main questions on congenital abnormalities, medical problems, behaviour, growth and development. Methods Here, we assess data consistency by comparing parent-reported phenotypes to phenotypes based on copies of medical files for the same individual (n = 20) and data availability by comparing the data available on specific characteristics reported by parents (n = 34) to data available in existing literature (n = 39). Results The reported answers to the main questions on phenotype characteristics were 85–95% consistent, and the consistency of answers to subsequent more detailed questions was 77–96%. For all but two main questions, significantly more data was collected from parents via the Chromosome 6 Questionnaire than was currently available in literature. For the topics developmental delay and brain abnormalities, no significant difference in the amount of available data was found. The only feature for which significantly more data was available in literature was a sub-question on the type of brain abnormality present. Conclusion This is the first study to compare phenotype data collected directly from parents to data extracted from medical files on the same individuals. We found that the data was highly consistent, and phenotype data collected via the online Chromosome 6 Questionnaire resulted in more available information on most clinical characteristics when compared to phenotypes reported in literature reports thus far. We encourage active patient participation in rare disease research and have shown that parent-reported phenotypes are reliable and contribute to our knowledge of the phenotypic spectrum of rare chromosomal disorders.

Prescription request applications (apps) have changed the way consumers can obtain prescription-only medications. However, there is a lack of research surrounding such apps and their potential risks to consumers. We conducted an Australian study to (1) identify and characterise prescription request apps available in Australia and (2) assess whether prescription request apps in Australia adhere to guidelines for safe prescribing. Three online platforms (iOS App Store, Google Play store and Google search engine) were searched using 14 different search terms. Prescription request apps were identified based on pre-defined inclusion criteria. To determine whether the prescription request apps adhere to a safe prescribing framework, five medications were selected, and their corresponding consultation questionnaires were assessed against the Australian National Prescribing Service MedicineWise 12 core competencies for safe prescribing. A total of seven prescription request apps were identified. Assessment of the prescription request apps revealed that none of the apps provided prescribers with sufficient information to meet all the competencies required for safe prescribing; rather, they inconsistently adhered to the safe prescribing framework. Thus, consumers and healthcare professionals should consider the implications and safety concerns of obtaining medications via prescription request apps.

Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant's age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model's accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA. Determining the presence and severity of knee osteoarthritis (OA) is a valuable application of inertial measurement units (IMUs) in the remote monitoring of patients. This study aimed to employ the Fourier representation of IMU signals to differentiate between individuals with and without knee OA. We included 27 patients with unilateral knee osteoarthritis (15 females) and 18 healthy controls (11 females). Gait acceleration signals were recorded during overground walking. We obtained the frequency features of the signals using the Fourier transform. The logistic LASSO regression was employed on the frequency domain features as well as the participant’s age, sex, and BMI to distinguish between the acceleration data from individuals with and without knee OA. The model’s accuracy was estimated by 10-fold cross-validation. The frequency contents of the signals were different between the two groups. The average accuracy of the classification model using the frequency features was 0.91 ± 0.01. The distribution of the selected features in the final model differed between patients with different severity of knee OA. In this study, we demonstrated that using logistic LASSO regression on the Fourier representation of acceleration signals can accurately determine the presence of knee OA.