Diagnosed Health Conditions Research Articles

Design and Development of an IoT Based Wearable Vital Signs Measuring Smart Device for Diagnosing and Managing Various Health Conditions

The increasing demand for continuous health monitoring and early detection of medical conditions has led to the development of innovative wearable technologies. This paper presents the design and development of an IoT-based wearable smart device capable of measuring vital signs to aid in the diagnosis and management of various health conditions. The device integrates multiple sensors to monitor key physiological parameters, including heart rate, body temperature, and blood oxygen levels (SpO2). These sensors are connected to a microcontroller, which processes the data and transmits it to a cloud-based platform via wireless communication protocols. The system architecture leverages IoT technology to enable real-time data collection, storage, and analysis, making it possible for healthcare providers to remotely monitor patients and make informed decisions. The device is designed to be lightweight, energy-efficient, and comfortable for continuous wear, ensuring usability across different patient demographics. Additionally, the smart device includes features for alerting users and healthcare providers to abnormal readings, thereby facilitating timely interventions. This paper discusses the technical challenges encountered during the development process, such as sensor integration, power management, and ensuring data security and privacy. It also highlights the potential applications of the device in managing chronic diseases, post-operative care, and remote patient monitoring. The prototype’s performance is evaluated through a series of tests, demonstrating its reliability and accuracy in measuring vital signs. The study concludes that this IoT-based wearable device has significant potential to enhance healthcare delivery by providing a convenient, efficient, and scalable solution for continuous health monitoring.

A Wireless Wearable Sensor System Based on a Silver Nanowire‐Decorated Silicon Nanomembrane for Precise and Continuous Hazardous Gas Monitoring

AbstractWearable wireless gas sensors have attracted enormous interest due to precise, real‐time healthcare and environmental monitoring without temporal and spatial limitations. Among various toxic gases, detecting ammonia is crucial because of its highly hazardous nature and applicability as a noninvasive biomarker for diagnosing health conditions. In this study, silver nanowires‐decorated silicon nanomembrane (AgNW‐SiNM) chemiresistive gas sensor with high selectivity to ammonia is fully integrated with an ultrathin flexible Joule heater and wireless communication system to fabricate wearable wireless real‐time toxic gas monitoring system. The sensor exhibits improved performance to ammonia gas, attributed to heating‐induced changes in adsorption/desorption rates, along with electronic and chemical sensitization facilitated by AgNW decoration. The gas sensing system exhibits stable and high responses of ≈1.83, 1.47, and 1.19 on the ammonia exposure at concentrations of 10, 5, and 1 ppm even under mechanical deformations. The real‐time dynamic response of the sensor is wirelessly transmitted to portable electronics and displayed on the screen. Moreover, the system alerts the users in advance through the integrated haptic interface when exposed to dangerous gas environments for early evacuation. The system paves the way for timely warnings from hazardous gas and more accurate noninvasive medical diagnosis related to respiratory disorders.

Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied-An Exploratory Study.

Background/Objectives: This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals-cardiological patients, healthy participants, and athletes-based on cardiorespiratory features from short-term static measurements. It also examined the impact of cardiorespiratory coupling (CRC)-related features (from causal and information domains) on the modeling accuracy to identify a preferred cardiorespiratory feature set that could be further explored for specialized tasks, such as monitoring training progress or diagnosing health conditions. Methods: We utilized six self-prepared datasets that comprised various subsets of cardiorespiratory parameters and applied several ML algorithms to classify subjects into three distinct groups. This research also leveraged explainable artificial intelligence (XAI) techniques to interpret model decisions and investigate feature importance. Results: The highest accuracy, over 89%, was obtained using the dataset that included most important demographic, cardiac, respiratory, and interrelated (causal and information) domain features. The dataset that comprised the most influential features but without demographic data yielded the second best accuracy, equal to 85%. Incorporation of the causal and information domain features significantly improved the classification accuracy. The use of XAI tools further highlighted the importance of these features with respect to each individual group. Conclusions: The integration of ML algorithms with a broad spectrum of cardiorespiratory features provided satisfactory efficiency in classifying pediatric individuals into groups according to their actual health status. This study underscored the potential of ML and XAI in advancing the analysis of cardiorespiratory signals and emphasized the importance of CRC-related features. The established set of features that appeared optimal for the classification of pediatric patients should be further explored for their potential in assessing individual progress through training or rehabilitation.

Federated Learning for Privacy-Preserving Machine Learning: Decentralized Model Training with Enhanced Data Security

Abstract: Artificial Intelligence has found widespread use across various industries, from optimizing manufacturing workflows to diagnosing health conditions. However, the large volumes of data required to train AI models raise privacy concerns, especially when stored in centralized databases vulnerable to leaks. Federated Learning solves this problem by training models collaboratively by avoiding centralization of the sensitive data, preserving privacy while allowing decentralized models to be exported to edge devices. This paper explores Federated Learning, focusing on its technical aspects, algorithms, and decentralized architecture. By keeping raw data localized, Federated Learning enables global models while safeguarding individual privacy, fostering collaboration across sectors like healthcare, finance, and IoT. It also addresses challenges such as privacy vulnerabilities and model aggregation across devices, proposing solutions to strengthen Federated Learning's effectiveness. Ultimately, this study highlights Federated Learning's pivotal role in the future of AI, where privacy preservation and collaboration are key. By balancing model performance with data privacy, Federated Learning stands as a promising framework for responsible and inclusive AI development.

Nutrition beliefs, and practices of ultra-endurance runners in Ireland for gastrointestinal symptom management

Ultra-endurance running (UER) events are defined based upon duration, exceeding 4-6 hours, or distance, surpassing the 26.3-mile marathon(1). Oftentimes event courses comprise of extreme environmental conditions, altitudes, temperatures, terrains, or a combination. UER poses significant short- and long- term health implications. UER athletes’ risk chronic maladaptation of various systems, namely the cardiovascular, immune, and digestive(2). Research suggests that exercise-associated gastrointestinal symptoms (Ex-GIS) are among the leading cause of underperformance in distance running. Altered mesenteric blood flow, gastric emptying, and peristaltic activity from exercise are exacerbated by the conditions accompanying many ultraevents, impairing performance and recovery. Though Ex-GIS are generally temporary, there remains the potential for long-term medical conditions to arise, such as ischemic bowel disease and haemorrhagic gastritis. Ample nutritional planning and intake throughout training and racing is critical to enhance performance and recovery while minimizing Ex-GIS and its associated chronic conditions(3).The aim of this research was to investigate the nutrition beliefs, and practices of ultra-endurance runners in Ireland.An adapted electronic version of a validated questionnaire by Scrivin at al.(4) was used. A recruitment infographic of eligibility criteria (≥18 years old, healthy, no diagnosed health conditions except for irritable bowel syndrome, and training for/ competing in running events exceeding 4 hours) was distributed online and at events (Connemarathon, Kerry 50K ultra). Individuals that contacted the researchers and were deemed eligible for inclusion were directed to a Microsoft Forms page, where they gave consent and completed the questionnaire. Data were analysed using IBM SPSS Statistics version 28 (IBM Corporation New Orchard Road Armonk, NY 10504-1722, United States).Sixty-eight (n = 68) individuals completed the questionnaire with one excluded due to medical diagnosis. Remaining participants (n = 46 males, n = 21 females) ranged from 25-66 with one over 66 years old. Previous experience was the main factor influencing nutrition practices (N = 30, 45.5%). N = 3 (4.5%) sourced information from qualified professionals. N = 42 (59.4%) experienced Ex-GIS, usually evenly throughout training and competition. Many had not implemented dietary or nondietary strategies to manage Ex-GIS. Supplementing with nitrates (N = 9) and probiotics (N = 4) were the most common dietary practices to alleviate Ex-GIS, while other (N = 14) and portion control (N = 13) were the most reported non-dietary practice.This research cohort reflects the broader UER community, with 81% being >35 years old “masters” athletes, and male. This study suggests that Ex-GIS are commonplace throughout. Similarly, this research highlights the range of symptoms experienced. The absence in dietary and non-dietary practices employed for Ex-GIS management suggests a shortfall in the availability of nutrition information specific to this problem. Further research is required to understand the mechanisms behind UER Ex-GIS, its management, and best practices for communicating these to the target audience.

Is alcohol use disorder associated with higher rates of depression and anxiety among people with new onset type 2 diabetes? A cohort study using linked primary care data in England

IntroductionDepression and alcohol use disorder (AUD) in people living with Type 2 diabetes mellitus (T2DM) are associated with worse health outcomes. AUD is strongly associated with depression and anxiety, but it is not known how these conditions cluster in people with T2DM. We investigated rates of new episodes of depression and anxiety following T2DM diagnosis in people with and without prior AUD among an English primary care population.MethodsThe study population was people diagnosed with T2DM between 2004 and 2019. We used the Clinical Practice Research Datalink (CPRD) Aurum database and linked Hospital Episode Statistics Admitted Patient Care (HES APC) and Office for National Statistics (ONS) mortality data. We examined incidence of new episodes of anxiety or depression in people with T2DM with and without AUD. AUD was defined as any of i) clinical diagnosis; ii) alcohol withdrawal; or iii) chronic alcohol-related harm (physical or mental) using SNOMED-CT or ICD-10 codes. People were excluded if they had codes for depression/anxiety 12 months prior to T2DM diagnosis. Poisson regression models were fitted adjusting sequentially for a) age, gender, calendar time; b) region, Index of Multiple Deprivation, ethnicity, body mass index, smoking status, Charlson co-morbidity index; and c) history of a mental health condition.ResultsOur study population was 479,447 people of whom 10,983 (2.3%) had an AUD code prior to T2DM diagnosis.After adjusting for all measured confounders except history of a mental health condition, IRR for depression was 2.00 (95% CI 1.93, 2.06) for people with AUD compared to without AUD. This reduced to 1.45 (95% CI 1.41, 1.50) after further adjustment for history of a mental health condition.Findings for anxiety were substantially similar to those for depression (adjusted for all measured confounders except history of a mental health condition, IRR 2.08 95% CI 1.99, 2.18 fully adjusted IRR 1.48 95% CI 1.41, 1.55).ConclusionsPeople with AUD have over double the rates of depression and anxiety following T2DM diagnosis than those without AUD. This was only partially explained by pre-existing diagnoses of mental health conditions. A holistic approach incorporating mental health support is needed to improve health outcomes for people with AUD who develop T2DM.Trial registrationNot applicable.

Use of specialist health care among Norwegian-born children with one immigrant parent

Abstract Background An increasing number of children grow up with one immigrant and one native born parent. We know little about health in this group. Our aim was to compare the risk of receiving diagnoses of physical health conditions in specialist health care among Norwegian-born children with one immigrant parent compared to Norwegian background children. Methods Data from Medical Birth Registry of Norway were linked to data from the Norwegian Patient Registry (diagnoses in specialist health care), and Statistics Norway (parental country of origin, education, and household income). We estimated hazard ratios (HR) with Cox proportional hazard regressions, with Norwegian background children as reference category, adjusted for sex, birth year, parental education, and household income. Results Children with an immigrant parent had lower hazards than Norwegian background children of diagnoses of most types of infections, but higher hazards of some, including tuberculosis, and skin infections. They also had higher hazards than Norwegian background children of receiving diagnoses of obesity and other nutritional deficiencies, and skin diseases, but lower hazards for other conditions, including chronic lower respiratory disease, disease of the digestive system, and visual or hearing impairment. The hazards of diagnoses of any neurological condition, sleeping disorders and epilepsy were lower among children with an immigrant mother, but higher among those with an immigrant father. Differences between children with one immigrant parent and Norwegian background could not be explained by differences in parental education or household income. Conclusions Children of one immigrant parent do not overall use specialist health care for physical health issues more than other children. Key messages • Awareness of diagnoses given most frequently to children of one immigrant parent can help promote timely diagnoses and adequate use of services, which is important for prevention of later illness. • Barriers to health care use among children of immigrants should be further researched.

Debate: How much should nonspecialists be involved in mental health care for children and young people when resources are limited?

From the barber-surgeons of the Middle Ages to the digitally enabled clinical interventions of today, the role of nonspecialists in delivering accessible health care remains relevant and important. Their contribution in developing and supporting appropriate interventions and care pathways in the community can make a big difference for the overwhelming majority of children and young people with diagnosable mental health conditions who are not eligible for specialist treatment or do not seek help. Even if resources for specialist services were unlimited, there are independent benefits in involving nonspecialists in mental health care, albeit not without limitations and concerns. The natural shift over time in the boundaries of specialisms, the omnipresence and omnipotence of digital media in our lives and the vision of 'no wrong door' for service users, mean that it is not a question of whether, but of how, nonspecialists should be involved. We need to get past the point of considering them just as alternative providers of mental health interventions and services. Instead, they are best placed to protect children and young people's mental health through ubiquitous everyday actions, which counter the negative and maximise the positive elements in current practices, environments and interactions that influence families and communities.

The potential impact of AI innovations on US occupations.

An occupation is comprised of interconnected tasks, and it is these tasks, not occupations themselves, that are affected by Artificial Intelligence (AI). To evaluate how tasks may be impacted, previous approaches utilized manual annotations or coarse-grained matching. Leveraging recent advancements in machine learning, we replace coarse-grained matching with more precise deep learning approaches. Introducing the AI Impact measure, we employ Deep Learning Natural Language Processing to automatically identify AI patents that may impact various occupational tasks at scale. Our methodology relies on a comprehensive dataset of 17,879 task descriptions and quantifies AI's potential impact through analysis of 24,758 AI patents filed with the United States Patent and Trademark Office between 2015 and 2022. Our results reveal that some occupations will potentially be impacted, and that impact is intricately linked to specific skills. These include not only routine tasks (codified as a series of steps), as previously thought but also nonroutine ones (e.g. diagnosing health conditions, programming computers, and tracking flight routes). However, AI's impact on labor is limited by the fact that some of the occupations affected are augmented rather than replaced (e.g. neurologists, software engineers, air traffic controllers), and the sectors affected are experiencing labor shortages (e.g. IT, Healthcare, Transport).

Long COVID Is Not a Functional Neurologic Disorder.

Long COVID is a common sequela of SARS-CoV-2 infection. Data from numerous scientific studies indicate that long COVID involves a complex interaction between pathophysiological processes. Long COVID may involve the development of new diagnosable health conditions and exacerbation of pre-existing health conditions. However, despite this rapidly accumulating body of evidence regarding the pathobiology of long COVID, psychogenic and functional interpretations of the illness presentation continue to be endorsed by some healthcare professionals, creating confusion and inappropriate diagnostic and therapeutic pathways for people living with long COVID. The purpose of this perspective is to present a clinical and scientific rationale for why long COVID should not be considered as a functional neurologic disorder. It will begin by discussing the parallel historical development of pathobiological and psychosomatic/sociogenic diagnostic constructs arising from a common root in neurasthenia, which has resulted in the collective understandings of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and functional neurologic disorder (FND), respectively. We will also review the case definition criteria for FND and the distinguishing clinical and neuroimaging findings in FND vs. long COVID. We conclude that considering long COVID as FND is inappropriate based on differentiating pathophysiologic mechanisms and distinguishing clinical findings.

Cross-Recurrence Quantification Of Cardiovascular Signals In Newborns Is A Sensitive Marker Of Health Status

Background — Analysis of the state of complex biological systems requires the use of sensitive methods for diagnosing interactions using experimental time series. Objective — To evaluate the possibility of using the technique of cross-recurrence quantification (CRQ) in studying the health status of newborns using low-frequency components of RR-interval signals and photoplethysmograms, reflecting the dynamics of the circuits in autonomic regulation of blood circulation. Methods — The study included two groups of neonates: 10 full-term newborns and 10 preterm neonates. We carried out simultaneous recording of electrocardiographic and photoplethysmographic signals. CRQ analysis was employed as the primary tool. Results — We established that some indices of CRQ analysis, characterizing the degree of interaction of the studied circuits, act as sensitive markers. They make it possible to distinguish the dynamics of the studied contours between healthy newborns and preterm neonates. Conclusion — The results of our study confirmed that CRQ is a promising tool in creating methods for diagnosing health conditions, including in newborns.

Beyond physical pain: A large-scale cohort study on endometriosis trends and mental health correlates

BackgroundThis study investigates the increased prevalence of endometriosis in Israel and its association with psychiatric comorbidities, focusing on the timing of psychiatric diagnoses in relation to endometriosis diagnosis. MethodsEmploying a retrospective cohort analysis, we reviewed data from 1,291,963 patients in a large scale medical database, identifying 24,259 cases (1.88%) of endometriosis. The analysis included demographic details, ICD-10 diagnoses of endometriosis and mental health conditions, and medication use patterns. ResultsA marked rise in endometriosis diagnosis was observed, particularly among women born between 1973 and 1978. Those with endometriosis were more likely to have psychiatric disorders—such as mood disorders, anxiety, PTSD, and eating disorders—than the control group, with the majority of psychiatric diagnoses occurring prior to endometriosis detection, except for PTSD. The study also highlighted significant sociocultural and socioeconomic disparities in endometriosis diagnosis, suggesting barriers to healthcare access and the influence of cultural factors. Limitations include potential biases from the retrospective design and the specific context of Israel's healthcare system, which may limit generalizability. ConclusionsThe significant rise in endometriosis and its strong association with psychiatric comorbidities, predominantly preceding the diagnosis of endometriosis, underscores the necessity for integrated care approaches. The disparities in diagnosis rates call for culturally sensitive healthcare practices and early psychiatric interventions.

The embodiment of exclusionary displacement pressure: Intersections of housing insecurity and mental health in a Hispanic/Latinx immigrant neighborhood

Gentrification, growing income inequality, urban development, and the affordable housing crisis necessitate understanding the impact of the concern of displacement on health — prior to or even in the absence of a displacement event. In this paper, I use the term “exclusionary displacement pressure” to unify the literature on exclusionary displacement and displacement pressure, highlighting the disproportionate and inequitable impacts of displacement pressure among communities of color. Through following 35 residents over 2.5-years (2019–2022) in one predominantly low-income Hispanic/Latinx immigrant neighborhood in Denver, Colorado, I examine how exclusionary displacement pressure shapes their health and wellbeing over time. Through paying attention to how participants' lived experience is shaped by structural vulnerability (e.g. lack of documentation status, inadequate work, limited access to safety net systems), I identify how exclusionary displacement pressure is constantly internalized and responded to as a unique embodied health experience, wearing on individuals over time and reproducing population health inequities. The framework of embodied health experiences captures the wide range of health-related impacts, from diagnosable health conditions to idioms of distress, using participant's own language of suffering to express how they were feeling, battling, and enduring the pressure. Theorizing on structural vulnerability within specific subpopulations with intersecting identities, such as low-income immigrant Hispanic/Latinx communities, provides a bottom-up refinement to existing theories of embodied health. Understanding the place-health experiences of individuals in changing neighborhoods over time is also critically important to define time points at which context-specific supports and interventions are appropriate.

An Expanded Approach to the Ascertainment of Children and Youth With Special Health Care Needs.

To describe the prevalence, characteristics, and health-related outcomes of children with diagnosed health conditions and functional difficulties who do not meet criteria for having a special health care need based on the traditional scoring of the Children with Special Health Care Needs (CSHCN) Screener. Data come from the 2016 to 2021 National Survey of Children's Health (n = 225 443). Child characteristics and health-related outcomes were compared among 4 mutually exclusive groups defined by CSHCN Screener criteria and the presence of both conditions and difficulties. Among children who do not qualify as children and youth with special health care needs (CYSHCN) on the CSHCN Screener, 6.8% had ≥1 condition and ≥1 difficulty. These children were more likely than CYSHCN to be younger, female, Hispanic, uninsured, privately insured, living in a household with low educational attainment, have families with more children and a primary household language other than English. After adjustment, non-CYSHCN with ≥1 conditions and ≥1 difficulty were less likely than CYSHCN, but significantly more likely than other non-CYSHCN, to have ≥2 emergency department visits, have unmet health care needs, not meet flourishing criteria, live in families that experienced child health-related employment impacts and frustration accessing services. Including these children in the calculation of CYSHCN prevalence increases the national estimate from 19.1% to 24.6%. Approximately 4 million children have both a diagnosed health condition and functional difficulties but are not identified as CYSHCN. An expanded approach to identify CYSHCN may better align program and policy with population needs.

Exploring the co-occurrence of depression, anxiety and insomnia symptoms, diagnoses and treatments in primary care: observational study using UK primary care data.

Depression, anxiety and insomnia often co-occur. However, there is a lack of research regarding how they cluster and how this is related to medication used to treat them. To describe the frequencies and associations between depression, anxiety and insomnia, and treatment for these conditions in primary care. A retrospective cohort study using UK electronic primary care records. We included individuals aged between 18 and 99 years old with one or more records suggesting they had a diagnosis, symptom or drug treatment for anxiety, depression or insomnia between 2015 and 2017. We report the conditional probabilities of having different combinations of diagnoses, symptoms and treatments recorded. There were 1 325 960 records indicative of depression, anxiety or insomnia, for 739 834 individuals. Depression was the most common condition (n = 106 117 records), and SSRIs were the most commonly prescribed medication (n = 347 751 records). Overall, individuals with a record of anxiety were most likely to have co-occurring symptoms and diagnoses of other mental health conditions. For example, of the individuals with a record of generalised anxiety disorder (GAD), 24% also had a diagnosis of depression. In contrast, only 0.6% of those who had a diagnosis of depression had a diagnosis or symptom of GAD. Prescribing of more than one psychotropic medication within the same year was common. For example, of those who were prescribed an SNRI (serotonin-norepinephrine reuptake inhibitor), 40% were also prescribed an SSRI (selective serotonin reuptake inhibitor). The conditional probabilities of co-occurring anxiety, depression and insomnia symptoms, diagnoses and treatments are high.

Nadifit, data-driven diagnosis: a clinical study for evaluating the significance of traditional Chinese medicine organ patterns through pulse diagnosis (nadi pariksha) for accurate pathology predictions

Background: Pulse diagnosis or nadipariksha, is a traditional diagnostic technique utilized in ayurveda and traditional Chinese medicine, analyzing various pulse characteristics such as force, patterns, rate, and rhythm to discern underlying health conditions. This method integrates TCM's five-element energy levels and Ayurveda's Tridosha levels to accurately identify the root cause of diseases, enabling practitioners to tailor treatments accordingly. In today's fast-paced world, the need for efficient health monitoring is imperative. However, finding expert practitioners proficient in pulse diagnosis is becoming increasingly challenging, necessitating the development of systems capable of providing personalized health insights based on accurate root-cause analysis. Methods: In this study, we compared predicted symptoms derived from objective TCM organ pattern analysis using the Nadifit pulse diagnosis system with subjective clinical assessments of patients' symptoms. TCM organ patterns were determined based on the combination of five elements and their Yin/Yang energy levels. Pulse signals were collected from 105 individuals and compared with clinical evaluations of patients' symptoms. Results: The analysis revealed a high level of agreement between the clinical assessment of symptoms and the predicted organ patterns, with a Kappa coefficient of 0.82. This suggests that objective pulse analysis effectively identifies root causes, aligning with subjective assessments. Conclusions: The findings of this study underscore the validity and reliability of objective pulse analysis in diagnosing health conditions. By demonstrating significant agreement with subjective clinical assessments, this method provides a promising avenue for enhancing diagnostic accuracy and facilitating personalized treatment strategies based on individual health profiles.

Now is the time to advance mental health, addiction reform

In the complex patchwork of American health care, the rise in mental health illness affects millions of lives. With over 50 million Americans grappling with diagnosable mental health conditions, the urgency to address it cannot be overstated. Shockingly, this equates to more than one in five Americans navigating the often‐challenging terrain of mental illness. Among these, young adults find themselves particularly vulnerable, bearing a disproportionate burden of these challenges. However, the statistics become even more sobering when considering minority populations — a staggering 36% of multiracial Americans face mental health hurdles, underscoring the critical need for equitable access to care across all demographics.

The long shadow of accumulating adverse childhood experiences on mental health in the United Arab Emirates: implications for policy and practice.

This study investigates the cumulative effects of adverse childhood experiences (ACEs) on adult depression, anxiety, and stress in Abu Dhabi, controlling for demographic factors, lifestyle, and known health and mental health diagnoses. Utilizing a cross-sectional design and self-report measures, the research aims to fill a critical gap in understanding the specific impacts of ACEs in the UAE. Based on a multi-site, cross-sectional community sample of 697 residents of Abu Dhabi. The findings reveal significant variances in current screening values for depression, anxiety, and stress attributable to ACEs after controlling for demographic factors, lifestyle risk factors, and adult diagnoses of health and mental health conditions. The results underline the lifelong impact of ACEs and reinforce the importance of early identification and intervention. In particular, the implications for policy and practice in understanding and mitigating ACEs long-term effects on mental health are considered.

Audiologists’ Perception of Hearing and Balance Health-care Resources and Services in Saudi Arabia

Background:Hearing loss and balance disorders are the most commonly diagnosed health conditions. The prevalence of these conditions is on the rise as the population ages and diagnostic and treatment technologies become more advanced. The health-care system still struggles to meet many nations’ needs due to insufficient resources and services. The objective of the study was to evaluate audiologists’ perceptions of hearing and balance health-care resources and services in Saudi Arabia’s public health system.Materials and Methods:A questionnaire survey was conducted through E-mail, social media, mobile phone, and face-to-face with audiologists across the Kingdom of Saudi Arabia, with the final sample consisting of 66 audiologists.Results:The majority (92.4%) of audiologists in Saudi Arabia indicated that the services are inadequate because their hospitals lack adequate hearing health-care resources to provide efficient audiology services to patients. Video head impulse test and automated auditory brain stem response were perceived as unavailable in most hospitals. In addition, audiologists reported that vestibular rehabilitation services, cochlear implants, and the number of clinics were limited.Conclusion:Great efforts should be made to develop audiovestibular services, which include enhancing practitioners’ vestibular knowledge by implementing more vestibular workshops; more vestibular clinics are needed to overcome the shortage of vestibular services; creating public awareness campaigns about vestibular and audiology; and hiring additional audiologists where needed within Saudi Arabia.

Recurrent otitis media and behaviour problems in middle childhood: A longitudinal cohort study.

To investigate the long-term effects of early-life recurrent otitis media (OM) and subsequent behavioural problems in children at the age of 10 years. Data from the Raine Study, a longitudinal pregnancy cohort, were used to categorise children into those with three or more episodes of OM (rOM group) and those without a history of recurrent OM in the first 3 years of life (reference group). The parent report Strengths and Difficulties Questionnaire was used to assess child behaviour at the age of 10 years. Parental questionnaires were used to report past and present diagnoses of various mental health and developmental conditions, including attention, anxiety, depression, learning, and speech-language problems. Multiple linear and logistic models were used to analyse the data and were adjusted for a fixed set of key confounding variables. The linear regression analysis revealed significant, independent associations between a history of recurrent OM and higher Strengths and Difficulties Questionnaire scores, including total, internalising, externalising, emotional, attention/hyperactivity and peer problems subscales. Logistic regression analyses revealed an independent increased likelihood for children in the rOM group to have a diagnosis of attention, anxiety, learning and speech-language problems. Children at 10 years of age with an early history of recurrent OM are more likely to exhibit attentional and behavioural problems when compared to children without a history of recurrent OM. These findings highlight the association between early-life recurrent OM and later behavioural problems that may require professional allied health-care interventions.