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I argue that the recurring practice of self-diagnosis of psychiatric conditions such as autism spectrum disorder and attention-deficit/hyperactivity disorder on social media platforms poses a threat to personal autonomy understood as self-governance. My main argument is that self-diagnosis conducted without professional expertise is prone to lead to misdiagnosis, which can take the form of a distortion of self-image. This may result in pathologizing normal experiences and behaviors and the adoption of behavioral adjustments that harm those who engage in self-diagnosis. Acknowledging that the surge of self-diagnosis content is a response to inadequacies of mental health care systems in many parts of the world, I argue against prohibiting such content and end with a more modest suggestion of implementing warnings alongside it on social media platforms, as is already common practice for misinformation content on diseases like Covid-19. [ABSTRACT FROM AUTHOR]

Copyright of Technisches Messen is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines, from a psychological perspective, why some young people may diagnose themselves with a mental health condition or neurodevelopmental difference after engaging with TikTok content. The article joins the discourse around TikTok self-diagnosis among adolescents, which has circulated since 2022, and offers alternative considerations and perspectives. The reasons a young person may self-diagnose are complicated and multifaceted, extending beyond any explanation of naivety or attention-seeking. In this paper the phenomenon is approached through an application of social psychological theories relating to identity, belonging, and self-labelling. The broader context is considered against the backdrop of the neurodiversity movement and corresponding cultural shift, with reference to the intersecting systemic barriers faced by young people which impede the availability and accessibility of adequate support. Opportunities for future research and suggestions for how educational psychologists may consider online environments within professional practice are discussed. [ABSTRACT FROM AUTHOR]

» #ActuallyAutistic: Konkurrierende Kulturen der Expertise und des Wissens in Bezug auf die Selbstdiagnose von Autismus und ADHS auf TikTok«. In recent times, the micro-video sharing platform TikTok has become extremely popular globally, especially among young people. Psychological and medical topics are among the diverse array of issues addressed on TikTok, sometimes sparking controversies over how "accurate" or helpful the information is. One such issue concerns TikTok content relating to self-diagnosis of neurodivergent conditions such as autism and ADHD. A dominant portrayal of this phenomenon focuses on the possibilities for self-optimisation such diagnoses can offer. In this article, we discuss these issues from a sociomaterial perspective, recognising the gatherings of humans, digital platforms, content, and the affective and relational connections that comprise TikTok assemblages. Digital sociology is brought together with health sociology and the sociology of diagnosis to explore how TikToks about self-diagnosis of ADHD and autism contribute to broader discourses and practices related to self-optimisation. In particular, the socioeconomic and cultural dimensions of health and identity issues on TikTok are highlighted. We delve into the contestations over power and authority as they receive expression both in Tik- Toks and off the platform in medical/"psy" apparatuses of expertise. In so doing, both the possibilities and the limitations for digitised and algorithmic self-optimisation related to self-diagnosis via digital media are identified. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this article is to analyze the existing literature on the role of identity in mental illness and self-diagnostic behavior among individuals on social media. In this paper, we consolidate existing frameworks for illness identity formation based on principles of social contagion and community formation principles to conceptualize abnormal behaviors related to social media use, including Munchausen’s by Internet, mass social media-induced illness, and mass sociogenic illness. The importance of a diagnosis in personal identity formation and ingroup involvement is an emergent theme in this review. Social media communities represent a cultural antithesis of the medical establishment by rejecting healthcare expertise and creating spaces whose membership is only afforded through a psychiatric diagnosis for individuals to participate in shared experiences. Clinical implications of these findings include tools to identify and dismantle harmful self-pathologizing of normal behavioral variants in young adults who present with specific symptomatology.

Understanding why people identify themselves as having a mental disorder is crucial for making sense of recent rises in self-diagnosis and help-seeking. Previous studies have implicated factors such as levels of distress, mental health literacy, and stigma. Motivated by concept creep research, we tested whether self-diagnosis is also associated with the expansiveness of people's concepts of mental disorder. A nationally representative sample of 474 Americans completed measures of distress, impairment, mental health literacy, stigma, and newly validated concept breadth scales, in addition to current and lifetime mental disorder (both self- and professionally-diagnosed) and help-seeking. Structural equation modeling demonstrated that participants with broader concepts of disorder were more likely to self-diagnose and seek help, independent of distress and impairment, mental health literacy, and low stigma. Holding broader concepts also partially accounted for higher levels of self-diagnosis among younger and more liberal participants and predicted self-diagnosis independently of formal diagnosis. Implications for the surge in self-diagnosis and concerns about pathologization of everyday life are discussed.

In the context of the proliferation of access to online medical information through search engines such as Google, this study aims to examine the impact of this phenomenon on the health decision-making behavior of consumers. Utilizing a digital questionnaire completed by 763 participants, the study employs descriptive and inferential methodology to assess how online medical information influences self-diagnosis and self-treatment. The results indicate a widespread use of the internet for medical searches, with a notable tendency toward self-diagnosis and self-treatment based on found information, frequently leading to anxiety and potentially inadequate medical care decisions. The study highlights the importance of improving digital health literacy and strengthening the doctor-patient relationship to effectively navigate the online informational landscape. The findings underscore the need for a critical and informed approach in using digital resources for health information, highlighting new aspects regarding the behavioral and emotional influence of „Dr. Google“ on patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The purpose of this article is to analyze the existing literature on the role of identity in mental illness and self-diagnostic behavior among individuals on social media. In this paper, we consolidate existing frameworks for illness identity formation based on principles of social contagion and community formation principles to conceptualize abnormal behaviors related to social media use, including Munchausen's by Internet, mass social media-induced illness, and mass sociogenic illness. The importance of a diagnosis in personal identity formation and ingroup involvement is an emergent theme in this review. Social media communities represent a cultural antithesis of the medical establishment by rejecting healthcare expertise and creating spaces whose membership is only afforded through a psychiatric diagnosis for individuals to participate in shared experiences. Clinical implications of these findings include tools to identify and dismantle harmful self-pathologizing of normal behavioral variants in young adults who present with specific symptomatology. [ABSTRACT FROM AUTHOR]

Addiction is a term recently extended to problem sexual behaviours. Proponents of pornography addiction (PA) argue that pornography is comparable to drugs in its impact on brain processes and behaviour, producing effects similar to substance abuse disorders. Critics, however, assert that PA lacks diagnostic validity, that supporting research is methodologically unsound, and that diagnosis obscures the social contexts and discursive practices in which sexual behaviours are embedded. This study investigates how self-identified pornography addicts describe their experiences and explores the implicit motives and meanings at play in this identity construction. Using a psychoanalytically informed discursive methodology, the authors analyzed interviews with 10 self-identified pornography addicts, focusing on the meaning of self-diagnosis, the process of self-labelling, and participants' psychological investment in diagnosis. Participants perceived an enslavement to desire as central to their addiction identities. Underlying this identity work were defences and conflicts about power, gender, sexuality, and perversion, as well as histories of disturbed attachment and deprivation by parental figures. The PA diagnosis counteracted feelings of shame and allowed participants to speak more freely about their difficulties. However, it also precluded more nuanced self-understandings and identity possibilities. The article concludes with a discussion of the mental health and psychotherapeutic implications of those presenting with self-diagnosed PA. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Self-diagnosis in psychiatry is where individuals diagnose themselves rather than rely upon official diagnosticians to supply a psychiatric diagnosis. The accuracy of self-diagnosis is a contested topic. In this paper, I outline what arguments are needed to see self-diagnosis as accurate and how different approaches to self-diagnosis require different arguments. I show how different arguments are required to justify accuracy for an autistic individual judging they are autistic compared to non-autistic individuals judging they are not autistic. Different arguments are required if a self-diagnosing individual accepts or rejects official diagnostic criteria. Finally, different arguments are required depending upon whether diagnoses are seen as objective entities, the product of theoretical virtues or practically useful groupings. All these approaches require unique arguments to justify the accuracy of self-diagnosis. Identifying the required arguments for different approaches is a stepping stone toward establishing whether those arguments and which of those arguments can be successfully justified. [ABSTRACT FROM AUTHOR]

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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 .

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. [ABSTRACT FROM AUTHOR]

Abstract Background Mental illness is still stigmatized and poorly understood in Indonesian society, but university students could be a key target for promoting positive attitudes and increasing awareness. This study aims to explore the knowledge and attitudes of university students in Indonesia toward mental illness and to identify associated factors, emphasizing self-diagnosis. Method An analytical cross-sectional study on university students in Yogyakarta, Indonesia, was conducted from September to November 2022. Participants completed an online questionnaire about their sociodemographic data, mental health knowledge (utilized Mental Health Knowledge Schedule questionnaire), and attitudes toward mental illness (utilized Community Attitude to Mental Illness Inventory questionnaire). Univariate, bivariate, and multivariate logistic regression were performed to analyze collected data. Results A total of 402 university students were included in the study. Results demonstrated that half of the students had good mental illness knowledge (50.7%), and slightly more than half had favorable attitudes (53.0%). Self-diagnosis was significantly associated with poorer knowledge and negative attitudes (adjusted odds ratio [AOR]: 2.31, 95% confidence interval [CI] 1.19 to 4.50 and AOR: 2.12, 95% CI 1.13 to 3.99; P

Background: Mental illness is still stigmatized and poorly understood in Indonesian society, but university students could be a key target for promoting positive attitudes and increasing awareness. This study aims to explore the knowledge and attitudes of university students in Indonesia toward mental illness and to identify associated factors, emphasizing self-diagnosis. Method: An analytical cross-sectional study on university students in Yogyakarta, Indonesia, was conducted from September to November 2022. Participants completed an online questionnaire about their sociodemographic data, mental health knowledge (utilized Mental Health Knowledge Schedule questionnaire), and attitudes toward mental illness (utilized Community Attitude to Mental Illness Inventory questionnaire). Univariate, bivariate, and multivariate logistic regression were performed to analyze collected data. Results: A total of 402 university students were included in the study. Results demonstrated that half of the students had good mental illness knowledge (50.7%), and slightly more than half had favorable attitudes (53.0%). Self-diagnosis was significantly associated with poorer knowledge and negative attitudes (adjusted odds ratio [AOR]: 2.31, 95% confidence interval [CI] 1.19 to 4.50 and AOR: 2.12, 95% CI 1.13 to 3.99; P < 0.05, respectively). Additionally, students who had never studied psychology or psychiatry and had a family mental illness history were at higher risk (P < 0.05) for poor knowledge of mental illness (AOR: 2.24, 95% CI 1.18 to 4.25 and AOR: 1.82, 95% CI 1.00 to 3.30, respectively). Interestingly, students with lower monthly family incomes had more positive attitudes (AOR: 0.39, 95% CI 0.18 to 0.86; P < 0.05). A very weak negative correlation (ρ = − 0.173; P < 0.001) between mental illness knowledge and attitudes was observed. Conclusion: Promoting awareness and knowledge about mental health, as well as preventing self-diagnosis, is crucial in addressing the lack of knowledge and negative attitudes toward mental illness among university students in Indonesia, which is expected to play a role in the wider community ultimately. [ABSTRACT FROM AUTHOR]

To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network's malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%. [ABSTRACT FROM AUTHOR]

The display platform of train provides the train driver with the key data of the running train and receives control command input. Its operation reliability is very important for safe driving. In order to quickly locate and response to the fault of the display platform and reduce the impact of the fault on train operation, the paper proposes a scheme for real-time diagnosis and data synchronization of train display platform operation status based on redundant display platform with two display units. Firstly, the self-diagnosis of the display unit was realized to monitor and diagnose the equipment status in real time based on the application layer, and to quickly locate the problems such as application exit, system crash and anomalies in resource utilization. Secondly, based on the RS422 communication link, a reliable communication protocol was defined to achieve the mutual diagnosis and data synchronization of operation status between multiple display units, and solve the problems of self-diagnosis failure in abnormal state of the unit and failure to obtain key data of faulty unit. A function verification experiment was carried out based on the train redundant display platform to verify the effectiveness of this scheme. The experimental results indicate that on the one hand, this scheme provides comprehensive self-diagnosis and redundant display unit state mutual diagnosis from the application layer, providing a basis for fault analysis and localization. On the other hand, it provides a way for two display units to work coordinately through key data synchronization and storage functions.

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.

Background: Self-diagnosis is the process of determining a disease or disorder without seeking medical care. Advances in technology have made it easier for patients to get medical information online, fostering self-diagnosis and doctor cross-examination. The aim of this study is to estimate the proportion of study respondents who use the internet and other sources for self-diagnosis and explore their attitudes concerning self-diagnosis. Methodology: This cross- sectional study was conducted among 186 participants from hospitals affiliated to Kasturba Medical college, Mangalore (Govt Wenlock hospital and KMC Hospital, Attavar). The data was collected using a semi-structured, internally validated questionnaire- consisting of 3 sections: 1) General information of study participants 2A) Questions on self- diagnosis through different sources 2B) On their attitudes concerning self-diagnosis. Data was analyzed using SPSS version 24. Results were expressed in descriptive statistics. Results: This study on self-diagnosis was conducted among OPD attendees and found a mean age of 31 years, with 66.0 % males and 34.0 % females. Majority (98.4%) had media access, in which 67.7% online media for self-diagnosis. Specific diseases like cancer and infectious disease were common search topics (76.3%). One third of the total participants reported they have done online search for ailments related to their relatives or friends. Around 61.3% considered their sources reliable, while 50.5% intended to continue self-diagnosing. Notably, 61.3% made correct self- diagnoses which were confirmed later after the physical consultation with their treating physician, particularly among participants under 25 with higher education. Conclusion: Due to the development of artificial intelligence and internet access, even non-medical people are now searching the media for medical issues. Although there is a growing dependence on internet resources, most individuals still say they trust in future self-diagnosis but still prefer visiting a doctor rather than self-medicating. To educate the public about the risks associated with self-diagnosis and the purchase of over-the-counter medications, community-based behavioral change communication should be employed.

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. [ABSTRACT FROM AUTHOR]

Aim at the problem that a coal mine safety monitoring system cannot accurately sense the power-off state of a controlled object due to the fact that the output wire of a mine-used contact detection feed breaker is wrong, this paper introduces a self-diagnosis circuit based on fixed frequency DC pulse, gives the circuit principle, designs the fixed frequency DC pulse signal generator and contact pulse fusion signal detection circuit, and introduces the program design of self-diagnosis breaker from two aspects of feed power-off detection process and wiring state self-diagnosis process. The mining breaker with surface self-diagnosis function can effectively identify the output wiring error of the breaker and ensure the reliability of the feed signal.

Objectives: To investigate the prevalence of cyberchondria among university students, and to explore their self-diagnosis behaviour. Method: 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

Copyright of Diversitas Journal is the property of Diversitas Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

There is concern that a growing number of individuals, especially adolescents, are diagnosing themselves with mental disorders. However, there has been limited empirical research into this phenomenon: why it might happen, what the costs and benefits might be, and what the implications are for anyone who is experiencing distress. To address this, this study used reflexive thematic analysis to explore attitudes toward self-diagnosis of mental disorders as expressed on the discussion website Reddit. From 1195 user comments, five themes were generated: (1) There is tension over who is the expert in diagnosis; (2) Self-diagnosis is a route to self-understanding in an inaccessible system; (3) Teenagers on social media are the problem; (4) Self-diagnosis can become self-fulfilling, and (5) Now no one is believed. Together, these themes highlight that there is considerable anger, derision, and criticism targeted toward people who self-diagnose with mental disorders, and that this is particularly targeted toward adolescents who self-diagnose on or as a result of social media. The findings have important implications for understanding how to support and validate people, particularly adolescents, who (sometimes accurately) use diagnostic language to express how they are feeling. [ABSTRACT FROM AUTHOR]

To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-configuration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous configuration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.

To solve the global shortage of land and offshore resources, the development of deep-sea resources has become a popular topic in recent decades. Deep-sea composites are widely used materials in abyssal resources extraction, and corresponding marine exploration vehicles and monitoring devices for deep-sea engineering. This article firstly reviews the existing research results and limitations of marine composites and equipment or devices used for resource extraction. By combining the research progress of smart composites, deep-sea smart composite materials with the three characteristics of self-diagnosis, self-healing, and self-powered are proposed and relevant studies are summarized. Finally, the review summarizes research challenges for the materials, and looks forward to the development of new composites and their practical application in conjunction with the progress of composites disciplines and AI techniques. [ABSTRACT FROM AUTHOR]

CONTROLAR provides Bosch with an intelligent functional testing machine used to test the correct functioning of the car radios produced. During this process, the radios are submitted to several tests, raising the problem of how the machine detects errors in several radios consecutively, making it impossible to know if the device has a problem since it has no module to see if it works correctly. This article arises from the need to find a solution to solve this problem, which was to develop a self-diagnostic system that will ensure the reliability and integrity of the cyber-physical system, passing a detailed state of the art. The development of this system was based on the design of an architecture that combines the KDT methodology with a DSL to manage and configure the tests to integrate the self-diagnostic test system into a CPS. A total of 28 test cases were performed to cover all its functionalities. The results show that all test cases passed. Therefore, the system meets all the proposed objectives. [ABSTRACT FROM AUTHOR]

Background: For a decade, the topic of self-diagnosis and self-medication has gained significant attention due to the widespread availability of information on the internet and over-the-counter medication. This research explores the rational considerations behind individuals' self-diagnosis and self-medication practices. Our main objective is to find out the frequency of self-diagnosis and self-medication in the general population and its associated risks and benefits., Methods: A cross-sectional community-based prospective study was conducted over 7 months and included 160 students from various nonmedical universities in Karachi. A questionnaire regarding baseline characteristics, self-medication, and self-diagnosis was made, and the data was collected from the participants and then analyzed using SPSS statistical software., Results: One-fifth of the participants used the method of diagnosing themselves regularly, whereas 9% of the involved population demonstrated medicating themselves very often. However, most of the population had self-diagnosed (50.6) or self-medicated (61.9) sometimes. The internet was the primary source of searching (75%), and home remedies were the preferred medications (71.7%). The two primary reasons for this were the scarcity of time and resources., Conclusion: Overall, our study points out the significance of self-medication and self-diagnosis among different nonmedical students of Karachi. Teaching people about medicines, enforcing strong prescription policies, and providing medical facilities are vital steps toward preventing this problem. The role of doctors and medical students is significant; therefore, detailed doctor-patient communication needs to be encouraged., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Dissociative Identity Disorders (DIDs) are controversial psychiatric conditions encountered in clinical practice and nosology. DID as described in the international classifications has little similarity with the clinical picture of “DID” met in current youth psychiatry. From this Perspective, we hypothesize that this current clinical presentation does not satisfy the categorical criteria of the international classifications. Based on the two terminological challenges related to the definition of DID (i.e., the notion of dissociative disorders and the different meanings of the term identity), we propose to differentiate two distinct entities from each other. The first is medical and listed in diagnostic criteria of international classifications; the second comes from popular culture and refers to the vast majority of clinical presentations received in daily clinical practice—presented under the term Dissociative Identity Conditions (DIC). Since the status of DIC is a hot topic in current clinical psychiatry, we aim to identify eight possible explanations that can be provided to support its occurrence: (1) impact of iatrogenicity; (2) factors of suggestibility and desire for social acceptability; (3) psychoanalytic explanations; (4) neuropsychological explanations; (5) socio-cognitive explanations; (6) emotional labeling; (7) narrative explanations; (8) and transient illnesses explanations. In conclusion, we sustain that DIC results from a narrative interpretation of medical discourse by popular culture, developing in patients presenting undeniable distress. Such a transient disease fits in an ecological niche, which echoes the values of society, persisting under the action of a need for narrative continuity of the self.

Sustainable consumption refers to the choice, purchase, use, and disposal of goods so as to enable all people to meet their present and future objective needs. However, panic caused by the gradual onset of the pandemic had a considerable impact on the behavior of consumption and purchase of medication, and the question remains whether sustainable consumption still matters during a crisis or not. The present study aims to analyze whether the pandemic has influenced the behavior of medication use among residents of Brasov County amid panic caused by the virus and to determine the primary sources of information on methods of the prevention and treatment of health conditions. The study focusses on the differences between the two target groups, young people, and adults, considering three socio-demographic factors, such as age, gender, and background. The present research uses two methods of data collection: an interview with 5 doctors and a questionnaire with 543 respondents. This research draws attention to the fact that the behaviors of purchasing and consuming medication during the COVID-19 pandemic has changed due to the spreading of the new virus and the state of emergency, as well as with medication use becoming less sustainable. The results showed that adults were more engaged in the panic buying of medication compared to young people during the pandemic; however, in normal times, young people use more medication. Our study shows that females tend to consume more medicines than males. Moreover, they are more susceptible for seeking media guidance and following advice from the digital realm, because they avoid going to the doctor, especially during the COVID-19 period. Depending on the environment of residence, it was found that people in rural areas generally tend to consume more medication than those in urban areas. However, during the pandemic, the drug use of this category of population has decreased because they do not tend to self-medicate and avoid visiting the doctor because of the fear of becoming contaminated. The results of the research could help to establish measures in order to prevent the panic buying of self-medication and pharmaceuticals, both during pandemics and in normal times, because such behaviors can have negative effects on both health and the environment. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is considered a major cause of death worldwide, and various studies have been conducted for its early diagnosis. Our work developed a scoring system by predicting and validating COPD and performed predictive model implementations. Participants who underwent a health screening between 2017 and 2020 were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES) database. COPD individuals were defined as aged 40 years or older with prebronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC < 0.7). The logistic regression model was performed, and the C-index was used for variable selection. Receiver operating characteristic (ROC) curves with area under the curve (AUC) values were generated for evaluation. Age, sex, waist circumference and diastolic blood pressure were used to predict COPD and to develop a COPD score based on a multivariable model. A simplified model for COPD was validated with an AUC value of 0.780 from the ROC curves. In addition, we evaluated the association of the derived score with cardiovascular disease (CVD). COPD scores showed significant performance in COPD prediction. The developed score also showed a good effect on the diagnostic ability for CVD risk. In the future, studies comparing the diagnostic accuracy of the derived scores with standard diagnostic tests are needed. [ABSTRACT FROM AUTHOR]

Currently, the need for high-quality dialogue systems that assist users to conduct self-diagnosis is rapidly increasing. Slot filling for automatic diagnosis, which converts medical queries into structured representations, plays an important role in diagnostic dialogue systems. However, the lack of high-quality datasets limits the performance of slot filling. While medical communities like AskAPatient usually have multiple rounds of diagnostic dialogue containing colloquial input and professional responses from doctors. Therefore, the data of diagnostic dialogue in medical communities can be utilized to solve the main challenges in slot filling. This paper proposes a two-step training framework to make full use of these unlabeled dialogue data in medical communities. To promote further researches, we provide a Chinese dataset with 2,652 annotated samples and a large amount of unlabeled samples. Experimental results on the dataset demonstrate the effectiveness of the proposed method with an increase of 6.32% in Micro F1 and 8.20% in Macro F1 on average over strong baselines. [ABSTRACT FROM AUTHOR]