RESUMEN

This article explores the phenomenon of Internet Derived Information Obstruction Treatment (IDIOT) syndrome, highlighting the impact of internet-derived health information on individuals' treatment decisions. Drawing on recent studies, including the rise of IDIOT syndrome due to increased internet use and the potential risks associated with self-medication based on online information, the editorial emphasizes the importance of critically evaluating health information. Insights from research conducted in the last few years highlight the complexity of health conditions and the necessity of seeking professional medical guidance to address the various clinical conditions and their consequences. This article sets the stage for a detailed examination of the IDIOT syndrome and its implications for healthcare decision-making in the digital era.

RESUMEN

The growing prominence of artificial intelligence (AI) in mobile health (mHealth) has given rise to a distinct subset of apps that provide users with diagnostic information using their inputted health status and symptom information-AI-powered symptom checker apps (AISympCheck). While these apps may potentially increase access to health care, they raise consequential ethical and legal questions. This paper will highlight notable concerns with AI usage in the health care system, further entrenchment of preexisting biases in the health care system and issues with professional accountability. To provide an in-depth analysis of the issues of bias and complications of professional obligations and liability, we focus on 2 mHealth apps as examples-Babylon and Ada. We selected these 2 apps as they were both widely distributed during the COVID-19 pandemic and make prominent claims about their use of AI for the purpose of assessing user symptoms. First, bias entrenchment often originates from the data used to train AI systems, causing the AI to replicate these inequalities through a "garbage in, garbage out" phenomenon. Users of these apps are also unlikely to be demographically representative of the larger population, leading to distorted results. Second, professional accountability poses a substantial challenge given the vast diversity and lack of regulation surrounding the reliability of AISympCheck apps. It is unclear whether these apps should be subject to safety reviews, who is responsible for app-mediated misdiagnosis, and whether these apps ought to be recommended by physicians. With the rapidly increasing number of apps, there remains little guidance available for health professionals. Professional bodies and advocacy organizations have a particularly important role to play in addressing these ethical and legal gaps. Implementing technical safeguards within these apps could mitigate bias, AIs could be trained with primarily neutral data, and apps could be subject to a system of regulation to allow users to make informed decisions. In our view, it is critical that these legal concerns are considered throughout the design and implementation of these potentially disruptive technologies. Entrenched bias and professional responsibility, while operating in different ways, are ultimately exacerbated by the unregulated nature of mHealth.

Asunto(s)

RESUMEN

Testing of ChatGPT has recently been performed over a diverse range of topics. However, most of these assessments have been based on broad domains of knowledge. Here, we test ChatGPT's knowledge of tinnitus, an important but specialized aspect of audiology and otolaryngology. Testing involved evaluating ChatGPT's answers to a defined set of 10 questions on tinnitus. Furthermore, given the technology is advancing quickly, we re-evaluated the responses to the same 10 questions 3 and 6 months later. The accuracy of the responses was rated by 6 experts (the authors) using a Likert scale ranging from 1 to 5. Most of ChatGPT's responses were rated as satisfactory or better. However, we did detect a few instances where the responses were not accurate and might be considered somewhat misleading. Over the first 3 months, the ratings generally improved, but there was no more significant improvement at 6 months. In our judgment, ChatGPT provided unexpectedly good responses, given that the questions were quite specific. Although no potentially harmful errors were identified, some mistakes could be seen as somewhat misleading. ChatGPT shows great potential if further developed by experts in specific areas, but for now, it is not yet ready for serious application.

RESUMEN

Background: Symptom checker apps (SCAs) offer symptom classification and low-threshold self-triage for laypeople. They are already in use despite their poor accuracy and concerns that they may negatively affect primary care. This study assesses the extent to which SCAs are used by medical laypeople in Germany and which software is most popular. We examined associations between satisfaction with the general practitioner (GP) and SCA use as well as the number of GP visits and SCA use. Furthermore, we assessed the reasons for intentional non-use. Methods: We conducted a survey comprising standardised and open-ended questions. Quantitative data were weighted, and open-ended responses were examined using thematic analysis. Results: This study included 850 participants. The SCA usage rate was 8%, and approximately 50% of SCA non-users were uninterested in trying SCAs. The most commonly used SCAs were NetDoktor and Ada. Surprisingly, SCAs were most frequently used in the age group of 51-55 years. No significant associations were found between SCA usage and satisfaction with the GP or the number of GP visits and SCA usage. Thematic analysis revealed skepticism regarding the results and recommendations of SCAs and discrepancies between users' requirements and the features of apps. Conclusion: SCAs are still widely unknown in the German population and have been sparsely used so far. Many participants were not interested in trying SCAs, and we found no positive or negative associations of SCAs and primary care.

RESUMEN

PURPOSE: TikTok is increasingly becoming a source of health information, peer support, and validation regarding mental health. The goal of this study was to analyze the content of TikTok videos related to depression and anxiety. METHODS: The sample included 100 videos, each with at least a million views, discussing either depression, anxiety, or both. The videos were retrieved from hashtag searches. The videos were coded for the type of mental health condition; specific content being discussed (e.g., symptoms or treatment); video presentation (i.e., personal experience, expert information, or general discussion), and more subcategories. Engagement statistics (i.e., likes, views, reposts, and number of comments) for each video were also recorded. RESULTS: The engagement statistics were higher for personal experience videos than for videos from healthcare professionals. Anxiety was the subject of 57% of the videos irrespective of the search hashtags, and over two-thirds of the videos were created by females. The most discussed topics included the description or enactment of depression/anxiety symptoms (e.g., emotional displays); mention of being diagnosed by a healthcare professional was the least prevalent. DISCUSSION: These results suggest that depression and anxiety videos featuring personal experiences are prevalent on TikTok with higher engagement compared to similar videos by health professionals. The attribution of generic symptoms to these mental health conditions may result in self-diagnosis. There is a need for more strategic efforts to ensure quality of health content on TikTok and increased focus on digital health literacy to make young social media users critical consumers of online content.

Asunto(s)

RESUMEN

The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.

Asunto(s)

RESUMEN

BACKGROUND: Symptom checker applications (SCAs) may help laypeople classify their symptoms and receive recommendations on medically appropriate actions. Further research is necessary to estimate the influence of user characteristics, attitudes and (e)health-related competencies. OBJECTIVE: The objective of this study is to identify meaningful predictors for SCA use considering user characteristics. METHODS: An explorative cross-sectional survey was conducted to investigate German citizens' demographics, eHealth literacy, hypochondria, self-efficacy, and affinity for technology using German language-validated questionnaires. A total of 869 participants were eligible for inclusion in the study. As n = 67 SCA users were assessed and matched 1:1 with non-users, a sample of n = 134 participants were assessed in the main analysis. A four-step analysis was conducted involving explorative predictor selection, model comparisons, and parameter estimates for selected predictors, including sensitivity and post hoc analyses. RESULTS: Hypochondria and self-efficacy were identified as meaningful predictors of SCA use. Hypochondria showed a consistent and significant effect across all analyses OR: 1.24-1.26 (95% CI: 1.1-1.4). Self-efficacy OR: 0.64-0.93 (95% CI: 0.3-1.4) showed inconsistent and nonsignificant results, leaving its role in SCA use unclear. Over half of the SCA users in our sample met the classification for hypochondria (cut-off on the WI of 5). CONCLUSIONS: Hypochondria has emerged as a significant predictor of SCA use with a consistently stable effect, yet according to the literature, individuals with this trait may be less likely to benefit from SCA despite their greater likelihood of using it. These users could be further unsettled by risk-averse triage and unlikely but serious diagnosis suggestions. TRIAL REGISTRATION: The study was registered in the German Clinical Trials Register (DRKS) DRKS00022465, DERR1- https://doi.org/10.2196/34026 .

Asunto(s)

RESUMEN

Since OpenAI (San Francisco, CA) released its generative AI chatbot, ChatGPT, we are on the cusp of technological transformation. The tool is capable of generating text according to the input that the user adds to it. Due to its ability to imitate human speech tone while extracting encyclopedic knowledge, ChatGPT can be a platform for personalized patient interaction. Thus, it has the potential to revolutionize the healthcare system. Our study aims to evaluate how ChatGPT can answer the queries of patients suffering from obstructive sleep apnea and aid in self-diagnosis. By analyzing symptoms and guiding patients' behavior toward prevention, ChatGPT can play a major role in avoiding serious health repercussions that develop in the later course of obstructive sleep apnea.

Asunto(s)

RESUMEN

This qualitative study conducted between November 2020 and March 2021 in the US state of Mississippi examines the experiences of 25 people who obtained medication abortion at the state's only abortion facility. We conducted in-depth interviews with participants after their abortions until concept saturation was reached, and then analysed the content using inductive and deductive analysis. We assessed how people use embodied knowledge about their individual physical experiences such as pregnancy symptoms, a missed period, bleeding, and visual examinations of pregnancy tissue to identify the beginning and end of pregnancy. We compared this to how people use biomedical knowledge such as pregnancy tests, ultrasounds, and clinical examinations to confirm their self-diagnoses. We found that most people felt confident that they could identify the beginning and end of pregnancy through embodied knowledge, especially when combined with the use of home pregnancy tests that confirmed their symptoms, experiences, and visual evidence. All participants concerned about symptoms sought follow-up care at a medical facility, whereas people who felt confident of the successful end of the pregnancy did so less often. These findings have implications for settings of restricted abortion access that have limited options for follow-up care after medication abortion.

Asunto(s)

RESUMEN

BACKGROUND: Diagnosis is a core component of effective health care, but misdiagnosis is common and can put patients at risk. Diagnostic decision support systems can play a role in improving diagnosis by physicians and other health care workers. Symptom checkers (SCs) have been designed to improve diagnosis and triage (ie, which level of care to seek) by patients. OBJECTIVE: The aim of this study was to evaluate the performance of the new large language model ChatGPT (versions 3.5 and 4.0), the widely used WebMD SC, and an SC developed by Ada Health in the diagnosis and triage of patients with urgent or emergent clinical problems compared with the final emergency department (ED) diagnoses and physician reviews. METHODS: We used previously collected, deidentified, self-report data from 40 patients presenting to an ED for care who used the Ada SC to record their symptoms prior to seeing the ED physician. Deidentified data were entered into ChatGPT versions 3.5 and 4.0 and WebMD by a research assistant blinded to diagnoses and triage. Diagnoses from all 4 systems were compared with the previously abstracted final diagnoses in the ED as well as with diagnoses and triage recommendations from three independent board-certified ED physicians who had blindly reviewed the self-report clinical data from Ada. Diagnostic accuracy was calculated as the proportion of the diagnoses from ChatGPT, Ada SC, WebMD SC, and the independent physicians that matched at least one ED diagnosis (stratified as top 1 or top 3). Triage accuracy was calculated as the number of recommendations from ChatGPT, WebMD, or Ada that agreed with at least 2 of the independent physicians or were rated "unsafe" or "too cautious." RESULTS: Overall, 30 and 37 cases had sufficient data for diagnostic and triage analysis, respectively. The rate of top-1 diagnosis matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 9 (30%), 12 (40%), 10 (33%), and 12 (40%), respectively, with a mean rate of 47% for the physicians. The rate of top-3 diagnostic matches for Ada, ChatGPT 3.5, ChatGPT 4.0, and WebMD was 19 (63%), 19 (63%), 15 (50%), and 17 (57%), respectively, with a mean rate of 69% for physicians. The distribution of triage results for Ada was 62% (n=23) agree, 14% unsafe (n=5), and 24% (n=9) too cautious; that for ChatGPT 3.5 was 59% (n=22) agree, 41% (n=15) unsafe, and 0% (n=0) too cautious; that for ChatGPT 4.0 was 76% (n=28) agree, 22% (n=8) unsafe, and 3% (n=1) too cautious; and that for WebMD was 70% (n=26) agree, 19% (n=7) unsafe, and 11% (n=4) too cautious. The unsafe triage rate for ChatGPT 3.5 (41%) was significantly higher (P=.009) than that of Ada (14%). CONCLUSIONS: ChatGPT 3.5 had high diagnostic accuracy but a high unsafe triage rate. ChatGPT 4.0 had the poorest diagnostic accuracy, but a lower unsafe triage rate and the highest triage agreement with the physicians. The Ada and WebMD SCs performed better overall than ChatGPT. Unsupervised patient use of ChatGPT for diagnosis and triage is not recommended without improvements to triage accuracy and extensive clinical evaluation.

Asunto(s)

RESUMEN

OBJECTIVE: To investigate the prevalence of cyberchondria among university students, and to explore their self diagnosis behaviour. METHODS: The cross-sectional study was conducted in different cities of Pakistan from September 2021 to July 2022. Participants were approached through purposive sampling at different institutions of higher education and were asked about access to internet. Data was collected using a demographic proforma and through the self-reporting Cyberchondria Severity Scale-Short Version. Data was analysed using SPSS 26. RESULTS: Of the 500 subjects, 248(49.6%) were male and 252(50.4%) were female. The overall mean age of the sample was 24.14±3.68 years (range: 18-45 years). Of the total, 286(57.2%) subjects were diagnosed with some medical condition, 214(42.8%) self-diagnosed themselves, 302(60.4%) rated their health status as fair, 123(24.6%) rated their health status as good, and 320(64%) said they did not check the accuracy of health-related information. The prevalence of cyberchondria was moderate 252(50.4%) to high 119(23.80%) which indicates the severe severity level of cyberchondria among students. The prevalence of cyberchondria was moderate in women 151(60%) compared to men 101(40.7%). Mean scores of women on cyberchondria severity scale were higher than men (p<0.01). Cyberchondria was more prevalent among individuals with diagnosed medical condition (p<0.01) and those who self-diagnose their symptoms via the internet (p<0.001). CONCLUSIONS: Cyberchondria must be seen as a serious public health concern in Pakistan. Since it is associated with distress and worry, measures need to be adopted to evaluate, prevent, and treat it at the population level.

Asunto(s)

RESUMEN

BACKGROUND: Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. OBJECTIVE: The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. METHODS: Over a 5-day course, each of the study authors submitted the same questions to ChatGPT. The conditions evaluated were carpal tunnel syndrome (CTS), cervical myelopathy (CM), lumbar spinal stenosis (LSS), knee osteoarthritis (KOA), and hip osteoarthritis (HOA). Answers were categorized as either correct, partially correct, incorrect, or a differential diagnosis. The percentage of correct answers and reproducibility were calculated. The reproducibility between days and raters were calculated using the Fleiss κ coefficient. Answers that recommended that the patient seek medical attention were recategorized according to the strength of the recommendation as defined by the study. RESULTS: The ratios of correct answers were 25/25, 1/25, 24/25, 16/25, and 17/25 for CTS, CM, LSS, KOA, and HOA, respectively. The ratios of incorrect answers were 23/25 for CM and 0/25 for all other conditions. The reproducibility between days was 1.0, 0.15, 0.7, 0.6, and 0.6 for CTS, CM, LSS, KOA, and HOA, respectively. The reproducibility between raters was 1.0, 0.1, 0.64, -0.12, and 0.04 for CTS, CM, LSS, KOA, and HOA, respectively. Among the answers recommending medical attention, the phrases "essential," "recommended," "best," and "important" were used. Specifically, "essential" occurred in 4 out of 125, "recommended" in 12 out of 125, "best" in 6 out of 125, and "important" in 94 out of 125 answers. Additionally, 7 out of the 125 answers did not include a recommendation to seek medical attention. CONCLUSIONS: The accuracy and reproducibility of ChatGPT to self-diagnose five common orthopedic conditions were inconsistent. The accuracy could potentially be improved by adding symptoms that could easily identify a specific location. Only a few answers were accompanied by a strong recommendation to seek medical attention according to our study standards. Although ChatGPT could serve as a potential first step in accessing care, we found variability in accurate self-diagnosis. Given the risk of harm with self-diagnosis without medical follow-up, it would be prudent for an NLP to include clear language alerting patients to seek expert medical opinions. We hope to shed further light on the use of AI in a future clinical study.

Asunto(s)

RESUMEN

BACKGROUND: With the rapid advancement of artificial intelligence (AI) technologies, AI-powered chatbots, such as Chat Generative Pretrained Transformer (ChatGPT), have emerged as potential tools for various applications, including health care. However, ChatGPT is not specifically designed for health care purposes, and its use for self-diagnosis raises concerns regarding its adoption's potential risks and benefits. Users are increasingly inclined to use ChatGPT for self-diagnosis, necessitating a deeper understanding of the factors driving this trend. OBJECTIVE: This study aims to investigate the factors influencing users' perception of decision-making processes and intentions to use ChatGPT for self-diagnosis and to explore the implications of these findings for the safe and effective integration of AI chatbots in health care. METHODS: A cross-sectional survey design was used, and data were collected from 607 participants. The relationships between performance expectancy, risk-reward appraisal, decision-making, and intention to use ChatGPT for self-diagnosis were analyzed using partial least squares structural equation modeling (PLS-SEM). RESULTS: Most respondents were willing to use ChatGPT for self-diagnosis (n=476, 78.4%). The model demonstrated satisfactory explanatory power, accounting for 52.4% of the variance in decision-making and 38.1% in the intent to use ChatGPT for self-diagnosis. The results supported all 3 hypotheses: The higher performance expectancy of ChatGPT (ß=.547, 95% CI 0.474-0.620) and positive risk-reward appraisals (ß=.245, 95% CI 0.161-0.325) were positively associated with the improved perception of decision-making outcomes among users, and enhanced perception of decision-making processes involving ChatGPT positively impacted users' intentions to use the technology for self-diagnosis (ß=.565, 95% CI 0.498-0.628). CONCLUSIONS: Our research investigated factors influencing users' intentions to use ChatGPT for self-diagnosis and health-related purposes. Even though the technology is not specifically designed for health care, people are inclined to use ChatGPT in health care contexts. Instead of solely focusing on discouraging its use for health care purposes, we advocate for improving the technology and adapting it for suitable health care applications. Our study highlights the importance of collaboration among AI developers, health care providers, and policy makers in ensuring AI chatbots' safe and responsible use in health care. By understanding users' expectations and decision-making processes, we can develop AI chatbots, such as ChatGPT, that are tailored to human needs, providing reliable and verified health information sources. This approach not only enhances health care accessibility but also improves health literacy and awareness. As the field of AI chatbots in health care continues to evolve, future research should explore the long-term effects of using AI chatbots for self-diagnosis and investigate their potential integration with other digital health interventions to optimize patient care and outcomes. In doing so, we can ensure that AI chatbots, including ChatGPT, are designed and implemented to safeguard users' well-being and support positive health outcomes in health care settings.

RESUMEN

BACKGROUND: Symptom checkers (SCs) for laypersons' self-assessment and preliminary self-diagnosis are widely used by the public. Little is known about the impact of these tools on health care professionals (HCPs) in primary care and their work. This is relevant to understanding how technological changes might affect the working world and how this is linked to work-related psychosocial demands and resources for HCPs. OBJECTIVE: This scoping review aimed to systematically explore the existing publications on the impacts of SCs on HCPs in primary care and to identify knowledge gaps. METHODS: We used the Arksey and O'Malley framework. We based our search string on the participant, concept, and context scheme and searched PubMed (MEDLINE) and CINAHL in January and June 2021. We performed a reference search in August 2021 and a manual search in November 2021. We included publications of peer-reviewed journals that focused on artificial intelligence- or algorithm-based self-diagnosing apps and tools for laypersons and had primary care or nonclinical settings as a relevant context. The characteristics of these studies were described numerically. We used thematic analysis to identify core themes. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist to report the study. RESULTS: Of the 2729 publications identified through initial and follow-up database searches, 43 full texts were screened for eligibility, of which 9 were included. Further 8 publications were included through manual search. Two publications were excluded after receiving feedback in the peer-review process. Fifteen publications were included in the final sample, which comprised 5 (33%) commentaries or nonresearch publications, 3 (20%) literature reviews, and 7 (47%) research publications. The earliest publications stemmed from 2015. We identified 5 themes. The theme finding prediagnosis comprised the comparison between SCs and physicians. We identified the performance of the diagnosis and the relevance of human factors as topics. In the theme layperson-technology relationship, we identified potentials for laypersons' empowerment and harm through SCs. Our analysis showed potential disruptions of the physician-patient relationship and uncontested roles of HCPs in the theme (impacts on) physician-patient relationship. In the theme impacts on HCPs' tasks, we described the reduction or increase in HCPs' workload. We identified potential transformations of HCPs' work and impacts on the health care system in the theme future role of SCs in health care. CONCLUSIONS: The scoping review approach was suitable for this new field of research. The heterogeneity of technologies and wordings was challenging. We identified research gaps in the literature regarding the impact of artificial intelligence- or algorithm-based self-diagnosing apps or tools on the work of HCPs in primary care. Further empirical studies on HCPs' lived experiences are needed, as the current literature depicts expectations rather than empirical findings.

Asunto(s)

RESUMEN

Essential tremor (ET) is one of the most common neurological disorders, and its mainly clinical symptoms, including patient hand's kinetic tremor, dystonia, ataxia, etc., would influence the daily life of patients inordinately. Current ET diagnosis highly replies on the clinical evaluation and neurological examination, so the objective measurement indicators are particularly important in the auxiliary diagnosis of ET. In this research, the Archimedes spiral line freehand sketching samples without template assistance is collected and the Convolutional Neural Network (CNN) model of optimized structure is adopted to fully analyze the tremor, spacing of turns, shape, etc. shown in the handwriting samples of patients with ET, including the following main process: characteristics extraction, model visualization and subregional relevance evaluation. Dropout is used as a regularization technique in the network structure. The test group consisted of 50 patients with confirmed ET and the control group consisted of 40 healthy individuals. The main research objectives of this paper comprise two points: on the one hand, to achieve effective automatic classification of patients with ET and healthy controls using a scheme combining deep learning and simple hand mapping for the purpose of primary disease screening; on the other hand, to design sub-regional automatic classification experiments to demonstrate that Archimedean spiral hand drawings of patients with ET do have distinct local features, and to lay the experimental foundation for future hand drawing-based automatic aid for the identification of a variety of neurodegenerative diseases. Our model's average accuracy rate in test set reaches 89.3%, and average AUC is 0.972, with favorable stability and generalization performance. Besides, subregional characteristics recognition proofs that the spiral line samples of most of the patients with ET show more category-related characteristics in the local area of upper right, which provides evidences and theory update for predecessors' medical research.

Asunto(s)

RESUMEN

FUNDAMENTOS: La infección por SARS-CoV-2 ha constituido una pandemia con un impacto sanitario y socioeconómico global sin precedentes. Con más de trece millones de casos confirmados en España hasta agosto de 2022, la realización de pruebas diagnósticas para detectar los casos de infección ha permitido atenuar parcialmente la expansión del virus. Durante 2021 se comercializaronlos primeros test de antígenos para autodiagnóstico, de dispensación en farmacias comunitarias, y desde julio de ese año se permitió su dispensación sin receta médica. La red de farmacias comunitarias jugó un papel fundamental, no solo por la dispensación informada de dichos test, sino participando activamente en la realización, en la supervisión de su realización y en la notificación de resultadosa las autoridades sanitarias, e incluso en la emisión de certificados digitales.Se ha realizado una recopilación de todos los datos disponibles al respecto, fijando como límite temporal la semana del 13 de febrero de 2022, por considerarse como el final de la sexta ola de la epidemia en España. El presente artículo revela los resultados derivados de la actuación de las farmacias de doce comunidades autónomas, que participaron de una forma u otra en dichas iniciativas mediante la realización o supervisión de un total de 1.043.800 pruebas, a partir de las cuales se detectaron 109.570 casos positivos (un 10,5% del total), que fueron comunicados al Sistema Nacional de Salud. Los resultados son provisionales, pues muchos de los programas continúan vigentes, pero son una muestra inequívoca del potencial que las farmacias comunitarias pueden desempeñar en tareas de Salud Pública.(AU)

BACKGROUND: SARS-CoV-2 infection was an unprecedented pandemic with unprecedented global health and socio-economic impact. More than 13 million cases had been confirmed in Spain by August 2022, and diagnostic testing to detect cases of infection in the country has helped to partially mitigate the spread of the virus. In 2021, the first self-testing antigen tests were marketed for dispensing in community pharmacies, and over-the-counter dispensing was allowed from July of that year. The network of communitypharmacies played a key role, not only in the informed dispensing of these tests, but also in actively participating in the performance, supervision and reporting of results to the health authorities, and even in the issuing of digital certificates. A compilation has been made of all the available data on the subject, with a deadline of 13 February 2022, which is considered to be the end of the sixth wave of the epidemic in Spain. The results of the action taken by community pharmacies in twelve Autonomous Communities, which somehow participated in these initiatives by carrying out or supervising a total of 1,043,800 tests, from which 109,570 positive cases (10.5% of the total) were detected and reported to the National Health System, are presented in this article. Although the results are provisional, because many of the programmes are still ongoing, they are a clear demonstration of the potential that community pharmacies can play in Public Health work.(AU)

Asunto(s)

RESUMEN

Wearable biosensors (WB) are currently attracting considerable interest for rapid detection and monitoring of biomarkers including metabolites, protein, and pathogen in bodily fluids (e.g., sweat, saliva, tears, and interstitial fluid). Another branch of WB termed wearable nucleic acid testing (NAT) is blossoming thanks to the development of microfluidic technology and isothermal nucleic acid amplification technique (iNAAT); however, there are only few reports on this. The wearable NAT is an emerging field of point-of-care (POC) diagnostics, and holds the promise for time-saving self-diagnosis, and evidence-based surveillance of infectious diseases in remote or low-resource settings. The use of wearable NAT can also be advanced to include molecular diagnosis, the identification of cancer biomarkers, genetic abnormalities, and other aspects. The wearable NAT provides the potential for evidence-based surveillance of infectious diseases when combined with internet connectivity and App software. To make the wearable NAT accessible to the end users, however, improvements must be made to the fabrication, cost, speed, sensitivity, specificity, sampling, iNAAT, analyzer, and a few other features. So, in this paper, we looked at the wearable NAT's most recent development, identified its difficulties, and defined its potential for managing infectious diseases quickly in the future. This is the wearable NAT review's first effort. We expect that this article will provide the concise resources needed to develop and deploy an efficient wearable NAT system.

Asunto(s)

RESUMEN

SARS-CoV-2 infection was an unprecedented pandemic with unprecedented global health and socio-economic impact. More than 13 million cases had been confirmed in Spain by August 2022, and diagnostic testing to detect cases of infection in the country has helped to partially mitigate the spread of the virus. In 2021, the first self-testing antigen tests were marketed for dispensing in community pharmacies, and over-the-counter dispensing was allowed from July of that year. The network of community pharmacies played a key role, not only in the informed dispensing of these tests, but also in actively participating in the performance, supervision and reporting of results to the health authorities, and even in the issuing of digital certificates. A compilation has been made of all the available data on the subject, with a deadline of 13 February 2022, which is considered to be the end of the sixth wave of the epidemic in Spain. The results of the action taken by community pharmacies in twelve Autonomous Communities, which somehow participated in these initiatives by carrying out or supervising a total of 1,043,800 tests, from which 109,570 positive cases (10.5% of the total) were detected and reported to the National Health System, are presented in this article. Although the results are provisional, because many of the programmes are still ongoing, they are a clear demonstration of the potential that community pharmacies can play in Public Health work.

La infección por SARS-CoV-2 ha constituido una pandemia con un impacto sanitario y socioeconómico global sin precedentes. Con más de trece millones de casos confirmados en España hasta agosto de 2022, la realización de pruebas diagnósticas para detectar los casos de infección ha permitido atenuar parcialmente la expansión del virus. Durante 2021 se comercializaron los primeros test de antígenos para autodiagnóstico, de dispensación en farmacias comunitarias, y desde julio de ese año se permitió su dispensación sin receta médica. La red de farmacias comunitarias jugó un papel fundamental, no solo por la dispensación informada de dichos test, sino participando activamente en la realización, en la supervisión de su realización y en la notificación de resultados a las autoridades sanitarias, e incluso en la emisión de certificados digitales. Se ha realizado una recopilación de todos los datos disponibles al respecto, fijando como límite temporal la semana del 13 de febrero de 2022, por considerarse como el final de la sexta ola de la epidemia en España. El presente artículo revela los resultados derivados de la actuación de las farmacias de doce comunidades autónomas, que participaron de una forma u otra en dichas iniciativas mediante la realización o supervisión de un total de 1.043.800 pruebas, a partir de las cuales se detectaron 109.570 casos positivos (un 10,5% del total), que fueron comunicados al Sistema Nacional de Salud. Los resultados son provisionales, pues muchos de los programas continúan vigentes, pero son una muestra inequívoca del potencial que las farmacias comunitarias pueden desempeñar en tareas de Salud Pública.

Asunto(s)

RESUMEN

Background: Prediction of type 2 diabetes mellitus (DM) has been studied widely. However, a hospital visit was necessary to apply previous prediction models for the evaluation of DM. This study was conducted to develop and validate a hospital visit-free self-diagnosis tool for DM. Methods: Participants who underwent health screening between 2017-2018 (n=7,519; training cohort) and 2019-2020 (n=7,564; validation cohort) were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES). DM was defined as doctor-diagnosed DM in a questionnaire. Logistic regression was used to determine independent predictors for DM, and a multivariable logistic regression-based nomogram was developed for the prediction of DM, which was validated in a cohort consisting of an independent population. The presence of nonalcoholic fatty liver disease (NAFLD) was operationally defined using the KNHANES-NAFLD score. Results: Age, sex, waist circumference, systolic blood pressure, total cholesterol, triglyceride, aspartate aminotransferase, blood urea nitrogen, urinary protein, urinary glucose, and NAFLD were identified as independent predictors for DM. After excluding laboratory variables that require laboratory tests, a simplified multivariable model was conducted based on hospital visit-free variables, including age, sex, waist circumference, systolic blood pressure, and NAFLD. The full and simplified prediction models for DM were presented as nomograms. In the independent validation cohort, the full and simplified DM prediction models were validated with an area under the curve values of 0.903 and 0.824 from the receiver operating characteristic curves, respectively. Conclusions: Involvement of NAFLD has allowed satisfactory prediction of DM without laboratory tests that require a hospital visit. The developed model may be promising in terms of early diagnosis of DM among individuals without hospital visits and may reduce the socioeconomic burden of DM in the real-world, which awaits future prospective trials to confirm.

RESUMEN

Triboelectric nanogenerators (TENGs) were initially invented as an innovative energy-harvesting technology for scavenging mechanical energy from our bodies or the ambient environment. Through adaptive customization design, TENGs have also become a promising player in the self-powered wearable medical market for improving physical fitness and sustaining a healthy lifestyle. In addition to simultaneously harvesting our body's mechanical energy and actively detecting our physiological parameters and metabolic status, TENGs can also provide personalized medical treatment solutions in a self-powered modality. This review aims to cover the recent advances in TENG-based electronics in clinical applications, beginning from the basic working principles of TENGs and their general operation modes, continuing to the harvesting of bioenergy from the human body, and arriving at their adaptive design toward applications in chronic disease diagnosis and long-term clinical treatment. Considering the highly personalized usage scenarios, special attention is paid to customized modules that are based on TENGs and support complex medical treatments, where sustainability, biodegradability, compliance, and bio-friendliness may be critical for the operation of clinical systems. While this review provides a comprehensive understanding of TENG-based clinical devices that aims to reach a high level of technological readiness, the challenges and shortcomings of TENG-based clinical devices are also highlighted, with the expectation of providing a useful reference for the further development of such customized healthcare systems and the transfer of their technical capabilities into real-life patient care.

Asunto(s)