Health Management Research Articles

FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

SURVEY ON FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and Mask R-CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

Exploring the Experiences of Health Care Providers and Managers in the COVID-19 Pandemic: A Summative Content Analysiss

Background: The rapid morbidity and high mortality rates associated with COVID-19 have placed significant strain on healthcare systems. Mazandaran, as the third province, has been affected by this infectious pandemic. Objectives: The present study aimed to explore the experiences of healthcare providers and managers during the COVID-19 pandemic. Methods: The present qualitative research study was conducted using the content analysis approach between April 2022 and August 2022 to provide deep insights into the experiences of healthcare providers and managers during the COVID-19 pandemic. The participants included 15 hospital managers, comprising 2 clinical nurse supervisors, 3 matrons, and 10 chief executive officers. The participants were purposefully selected from hospitals affiliated with Mazandaran University of Medical Sciences, Iran. Data were collected through semi-structured individual interviews and the assessment of documents from the vice-chancellors of treatment, food and drug, development, and human resources. Results: The category of "challenges and strategies" emerged during the process of reducing and abstracting the codes. The challenges category evolved from three subcategories, including care and cure challenges, challenges related to providing physical resources and personal protective equipment (PPE), and challenges faced by healthcare providers. Similarly, the strategies category emerged from three subcategories, including strategies to improve patient care and treatment outcomes, strategies for procuring PPE and physical resources, and strategies for human resource management. Conclusions: The results of the present study revealed several challenges and strategies experienced by healthcare managers at Mazandaran University of Medical Sciences and hospital managers. Additionally, based on the existing infrastructure of Mazandaran University and the managerial skills of the managers, successful strategies were employed to tackle these challenges. These experiences can be utilized to inform revisions to health-related policies. Documenting these experiences can aid in better management of future infectious pandemics.

Versatile Biomass‐Based Injectable Photothermal Hydrogel for Integrated Regenerative Wound Healing and Skin Bioelectronics

AbstractThe continuously growing utilization of wound healing materials and skin bioelectronics urges the development of flexible hydrogels for personal therapy and health management. Versatile conductive hydrogels prepared from natural biomass are ideal candidates as one of the promising solutions for chronic wound management. Here, the study proposes a kind of robust (strain: 1560.8%), adhesive, self‐healing, injectable, antibacterial (sterilization rate: 99%), near‐infrared (NIR) photothermal responsive, biocompatible, and conductive hydrogel (CPPFe@TA) composed of carboxymethyl cellulose and tannic acid/iron ion complex (TA@Fe3+), featuring rapid self‐assembly and tunable crosslinking time. TA@Fe3+ facilitated the self‐catalysis of the polymerization reaction, and the crosslinking time could be controlled by adjusting Fe3+ concentration. Under NIR irradiation, the hydrogel exhibited remarkable photothermal antibacterial performance. In the full‐thickness skin defect repair experiment on mice, the prepared hydrogel dressing significantly enhanced wound healing. After 14 days, the wound healing rate (95.49%) of CPPFe@TA3 hydrogel + NIR treatment greatly exceeded that of commercial dressings. Meanwhile, the hydrogels has good electrical conductivity and thermo‐responsiveness, making them promising for skin bioelectronics in physiological signal monitoring and rehabilitation exercise management. This work therefore offers a promising strategy for developing versatile biomass‐based hydrogels, which is expected to be applicable to integrated regenerative wound healing and skin bioelectronics.

Exploring the Gut-Brain Axis: A Comprehensive Review of Interactions Between the Gut Microbiota and the Central Nervous System

The gut-brain axis represents a complex network of interactions among the gut microbiota, the enteric nervous system (ENS), the autonomic nervous system (ANS), and the central nervous system (CNS), exerting profound influences on both digestive and mental health. Dysfunction within this axis has been implicated in a spectrum of disorders ranging from irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) to depression, anxiety, and neurodegenerative conditions. While early observations of the relationship between emotions and gastrointestinal function date back to the early 20th century, recent decades have witnessed significant strides in understanding this intricate interplay, fueled by advancements in molecular biology, microbiology, and neuroscience. Key milestones include the discovery of the enteric nervous system, elucidation of gut hormone functions, and recognition of the pivotal role played by gut microbiota in shaping brain function and behavior. The gut microbiota, comprising trillions of microorganisms residing within the gastrointestinal tract, actively contribute to gut-brain communication by producing an array of metabolites, neurotransmitters, and immune modulators. This communication pathway underscores the critical importance of comprehending the intricate interplay between gut microbiota and the central nervous system for devising innovative therapeutic interventions targeting a broad spectrum of psychiatric and neurological disorders. Furthermore, lifestyle factors such as diet, stress, and antibiotic usage wield significant influence over the composition and functionality of gut microbiota, thereby emphasizing the pivotal role of lifestyle interventions in fostering and preserving gut-brain health. This abstract encapsulates the current understanding of the gut-brain axis, highlighting its multifaceted implications for health and disease management, and underscores the pressing need for continued research to unlock its therapeutic potential.

[Analysis of the results for genetic disease screening among 1 000 newborns from Huzhou].

To analyze the types and distribution of pathogenic variants for neonatal genetic diseases in Huzhou, Zhejiang Province. One thousand neonates (48 ~ 42 h after birth) born to Huzhou region were selected as the study subjects. Dry blood spot samples were collected from the newborns, and targeted capture high-throughput sequencing was carried out for pathogenic genes underlying 542 inherited diseases. Candidate variants were verified by Sanger sequencing. Among the 1 000 newborns, the male to female ratio was 1.02 : 1.00. No pathogenic variants were detected in 253 cases, whilst 747 cases were found to carry at least one pathogenic variant, which yielded a carrier rate of 74.7%. The most frequently involved pathogenic gene was FLG, followed by GJB2, UGT1A1, USH2A and DUOX2. The variants were classified as homozygous, compound heterozygous, and hemizygous variants. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), 213 neonates were verified to have carried pathogenic and/or likely pathogenic variants, with a positive rate of 21.3%. The most commonly involved genes had included UGT1A1, FLG, GJB2, MEFV and G6PD. Newborn screening based on high-throughput sequencing technology can expand the scope of screening and improve the positive predictive value. Genetic counseling based on the results can improve the patients' medical care and reduce neonatal mortality and childhood morbidity, while provide assistance to family members' health management and reproductive decisions.

Efficient Machine Learning Techniques to Classifying Cardiovascular Disease and Improve Prediction Analysis

Cardiovascular Disease (CVD) account for a large portion of the global health burden and are one of the main causes of decease worldwide. In the classification and forecasting of CVDs, Machine Learning (ML) techniques have demonstrated encouraging outcomes. In this research report, a comparative analysis of classification and prediction models for CVD is presented, including both linear and ensemble ML approaches. The paper compares ensemble models like Catboost, Histogram Gradient Boosting Machine (HGBM), and Extra Trees against linear models like Gaussian Nave Bayes, SVM, and KNN. The objective is to identify the most effective CVD prediction model by assessing its performance through accuracy, precision, sensitivity and F1 score as key evaluation metrics. Moreover, results show that ensemble models outperform linear models using advanced techniques such as boosting and histogram-based algorithms. The results underscore the critical role ensemble models play in accurately diagnosing and predicting cardiovascular disease and provide important new information to researchers and healthcare providers. Using these models has the potential to significantly improve patient outcomes and health management by enabling early detection and intervention

Probabilistic neural network based visual data mining for the healthcare sector.

The need for personalised care in the long-term management of patient health is paramount due to the variability in individual features and responses to specific medication. With the availability of large quantities of electronic patient records, big data analysis presents a valuable opportunity to gain insights into disease presentation and patient impact. This study aims to utilise data science in the medical field to extract unknown information from databases, validate previously obtained data, and enhance personalised patient care. An analytics suite is developed for monitoring patient health and treating cholesterol, thyroid, and diabetes disorders. This suite employs exploratory, predictive, and visual analytics to categorise patient data into multiple tiers and forecast related complication risk and treatment response. The study found that the analytics suite could successfully identify correlations between various biological indicators of patients and disorders. The suite also showcased potential in predicting health risks and responses to treatments. The analytics employed in this study suggest advanced methods of data analysis, which could serve as potential decision-making tools for healthcare providers. These methods might lead to improved treatment outcomes, contributing significantly to personalised patient care.

A comparative analysis of sleep quality and time management skills between shift and non-shift police officers.

The sleep quality of police officers working in shifts is negatively affected due to long working hours and sleeping less during the day. In our study, we aimed to examine the differences in sleep quality and time management skills in police officers working with shift and non-shift system. 106 police officers, 46 of whom worked in the shift system, 60 of whom worked in the non-shift system, and whose mean age was 27.6 ± 0.9 [min 20, max 40] were included in the study. Sleep quality was assessed with the Pittsburgh Sleep Quality Index[PSQI] and time management skills with the Time Management Questionnaire[TMQ]. While the TMQ total score was 80.91 ± 12.61 for police officers working in shifts, it was 72.41 ± 12.62 for police officers working non-shift. The PSQI total score was 5 in both groups. There was a difference between the TMQ time planning, TMQ time attitudes sub-dimensions, and TMQ total scores of police officers working in the shift system and those working in the non-shift system[p < 0.05]. While poor sleep quality was observed between both groups, there was no difference in sleep quality[p > 0.05]. There was no difference in sleep quality between police officers working in shifts and non-shifts. However, the time management skills of police officers working in shifts were better than those of police officers working in non-shifts. We believe that training programs to improve sleep health and time management skills in police officers are needed for both the sleep quality and well-being of police officers and public safety.

Psychosocial and supportive care concerns of young women living with advanced breast cancer: baseline findings from a prospective virtual support intervention study.

Adolescent and young adults (AYAs) with metastatic breast cancer (MBC) experience high physical and psychosocial burdens compounded by a disrupted life trajectory. We sought to determine the psychosocial and supportive care concerns of this population to better understand and address unmet needs. AYAs diagnosed with MBC (18-39years) participating in a prospective interventional study (Young, Empowered, and Strong) at Dana-Farber Cancer Institute completed an electronic survey following enrollment. Measures evaluated sociodemographics, health behaviors, quality of life, and symptoms, among others. We used two-sided Fisher's exact tests to determine associations between concerns (e.g., cancer progression, side effects, lifestyle, finances, fertility) and demographic variables. Among 77 participants enrolled from 9/2020-12/2022, average age at MBC diagnosis and survey was 35.9 (range: 22-39) and 38.3years (range: 27-46), respectively. Most were non-Hispanic white (83.8%) and 40.3% reported their diagnosis caused some financial problems. Many were concerned about fertility (27.0%), long-term treatment side effects (67.6%), exercise (61.6%), and diet (54.1%). Select concerns varied significantly by age, race/ethnicity, and education. Younger women at survey reported greater concern about familial cancer risk (p = 0.028). Women from minority racial/ethnic groups more frequently reported issues talking about their cancer to family/friends (p = 0.040) while those with more education were more frequently concerned with long-term effects of cancer on their health (p = 0.021). Young women living with MBC frequently report psychosocial, health, and cancer management concerns. Tailoring supportive care and communications to address prevalent concerns including disease progression and treatment side effects may optimize wellbeing.

A laser-Engraved Wearable Electrochemical Sensing Patch for Heat Stress Precise Individual Management of Horse.

In point-of-care diagnostics, the continuous monitoring of sweat constituents provides a window into individual's physiological state. For species like horses, with abundant sweat glands, sweat composition can serve as an early health indicator. Considering the salience of such metrics in the domain of high-value animal breeding, a sophisticated wearable sensor patch tailored is introduced for the dynamic assessment of equine sweat, offering insights into pH, potassium ion (K+), and temperature profiles during episodes of heat stress and under normal physiological conditions. The device integrates a laser-engraved graphene (LEG)sensing electrode array, a non-invasive iontophoretic module for stimulated sweat secretion, an adaptable signal processing unit, and an embedded wireless communication framework. Profiting from an admirable Truth Table capable of logical evaluation, the integrated system enabled the early and timely assessment for heat stress, with high accuracy, stability, and reproducibility. The sensor patch has been calibrated to align with the unique dermal and physiological contours of equine anatomy, thereby augmenting its applicability in practical settings. This real-time analysis tool for equine perspiration stands to revolutionize personalized health management approaches for high-value animals, marking a significant stride in the integration of smart technologies within the agricultural sector.

Barriers to recording calf health data on dairy farms in Ontario, Canada

Accurate data on illness and treatment rates in dairy calves are necessary for health management, yet calf health records are often incomplete. Due to the effects of illness in calves on future production, common use of antimicrobials in calf dis­ease, and concerns regarding judicious use of antimicrobials, we need to understand why calf health data are often poorly re­corded on dairy farms. Our objective was to investigate barriers for farmers for recording calf illnesses and treatments on dairy farms in Ontario, Canada.

Gender-specific associations between sleep quality, sleep duration and cognitive functioning among older Indians: findings from WHO-SAGE study

BackgroundSleep is an essential component of human health and well-being, playing a crucial role in several cognitive processes, including attention, memory, and executive function. In this study, we aimed to examine the association between sleep quality, sleep duration and cognitive functioning among older men and women in India.MethodsData come from the World Health Organization’s Study on global AGEing and adult health (WHO-SAGE), India wave-2, which was conducted in 2015 in six selected states of India, representing different country regions. The sample included 6,396 older adults aged 50 years and above. We used multivariable linear regression models to examine the associations between sleep quality, sleep duration and cognitive function, separately among older men and women.ResultsOlder men and women with poor sleep and short duration sleep had lower mean scores of cognition than their peers with good sleep and age-appropriate sleep duration. Poor sleep (aCoef: -5.09, CI: -8.66, -1.51) and short duration sleep (aCoef: -5.43, CI: -7.77, -3.10) were negatively associated with cognitive functioning among older men and the associations remained significant among older men with poor sleep (aCoef: -2.39, CI: -3.78, -1.00) and short duration sleep (aCoef: -4.39, CI: -6.46, -2.31) after adjusting for a large number of socio-demographic, health and behavioral factors. Similarly, poor sleep (aCoef: -3.15, CI: -5.79, -0.52) and short duration sleep (aCoef: -2.72, CI: -4.64, -0.81) were associated with cognitive functioning among older women, however, the associations were insignificant when the potential confounders were adjusted.ConclusionsThis study provides evidence for the significant association between sleep health and cognitive functioning in older Indian adults, especially older men, with poor sleep quality and insufficient sleep duration being detrimental to their cognitive health. Healthcare providers should routinely screen for sleep quality and age-appropriate sleep duration in their older adult patients and consider sex/gender-tailored sleep interventions as part of cognitive health management strategies.

Implementasi Platform Edukasi Pada Klinik Medikasih Pondok Kelapa Jakarta Timur

Medikasih Clinic, which is located in Pondok Kelapa Village, Duren Sawit District, East Jakarta City, Special Capital Region of Jakarta, currently relies on a paid platform to manage clinic operations. However, there is no specific health education platform available for patients/customers, and the importance of improving the quality of health services at this clinic is highly recognized. To overcome these challenges, the service team plans to launch a web-based health education platform. This platform aims to support increased patient participation and health literacy by presenting health information from various medical sources. Based on the analysis of popular medical record diagnoses at Medikasih Clinic, we are determined to provide users with easy and reliable access to understanding health conditions, preventive measures, and healthy lifestyle practices. In conclusion, the development of this platform is a progressive step to create a positive impact. A focus on increasing health literacy and patient participation will create a society that is more informed and involved in personal health management. Our recommendation is to engage health experts and specialists to contribute educational content, ensuring the accuracy and reliability of the information presented

Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS assessment tasks separately, without considering the correlation between these two tasks. Secondly, traditional deep learning can only extract single-scale features, which limits the ability to extract complex degradation features from high-dimensional condition monitoring data. Therefore, this work proposes a multi-scale and multi-task convolutional neural network for joint learning of aero-engine RUL prediction and HS assessment. Firstly, multi-sensor data with multiple cycles are converted into image samples to integrate more condition monitoring information that is beneficial to prognosis. Then, the multi-scale feature fusion block is designed as the shared network for multi-task, utilizing convolutional layers with filters of different sizes to enhance the ability to extract complex degradation features from high-dimensional condition monitoring data. And a multi-layer concatenation block is constructed to integrate multi-scale features at different levels to fully utilize the important information at different levels. On this basis, a multi-task joint learning block is constructed and a joint loss function is developed for joint learning of RUL prediction and HS assessment. Finally, experiments on two engine degradation datasets, CMAPSS and N-CMAPSS, demonstrate that the proposed network has excellent RUL prediction and HS assessment performance, and outperforms other state-of-the-art methods.

Digital applications for diet monitoring, planning, and precision nutrition for citizens and professionals: a state of the art.

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease. Tracking and monitoring one's diet has been shown to help improve health and weight management. However, this task can be complex and time-consuming, often leading to frustration and a lack of adherence to dietary recommendations. Digital applications for diet monitoring, diet generation, and precision nutrition offer the promise of better health outcomes. Data on current nutrition-based digital tools was collected from pertinent literature and software providers. These digital tools have been designed for particular user groups: citizens, nutritionists, and physicians and researchers employing genetics and epigenetics tools. The applications were evaluated in terms of their key functionalities, strengths, and limitations. The analysis primarily concentrated on artificial intelligence algorithms and devices intended to streamline the collection and organization of nutrition data. Furthermore, an exploration was conducted of potential future advancements in this field. Digital applications designed for the use of citizens allow diet self-monitoring, and they can be an effective tool for weight and diabetes management, while digital precision nutrition solutions for professionals can provide scalability, personalized recommendations for patients, and a means of providing ongoing diet support. The limitations in using these digital applications include data accuracy, accessibility, and affordability, and further research and development are required. The integration of artificial intelligence, machine learning, and blockchain technology holds promise for improving the performance, security, and privacy of digital precision nutrition interventions. Multidisciplinarity is crucial for evidence-based and accessible solutions. Digital applications for diet monitoring and precision nutrition have the potential to revolutionize nutrition and health. These tools can make it easier for individuals to control their diets, help nutritionists provide better care, and enable physicians to offer personalized treatment.

A knowledge distillation based cross-modal learning framework for the lithium-ion battery state of health estimation

AbstractThe accurate prediction of a lithium-ion battery’s State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network’s output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application.

The welfare of ill and injured feedlot cattle: a review of the literature and implications for managing feedlot hospital and chronic pens

By definition, ill and injured animals are on the negative valence of animal welfare. For beef cattle kept in feedlot settings, advances in cattle health management have resulted in a greater understanding and prevention of illness and injury. However, the management of cattle once they become ill and injured is an understudied area, and there are gaps in knowledge that could inform evidence-based decision-making and strengthen welfare for this population. The aim of this review is to provide a comprehensive overview of the acquired knowledge regarding ill and injured feedlot cattle welfare, focusing on existing knowledge gaps and implications for hospital and chronic pen management and welfare assurance. Ill and injured feedlot cattle consist of acutely impaired animals with short-term health conditions that resolve with treatment and chronically impaired animals with long-term health conditions that may be difficult to treat. A literature search identified 110 articles that mentioned welfare and ill and injured feedlot cattle, but the population of interest in most of these articles was healthy cattle, not ill and injured cattle. Articles about managing ill and injured cattle in specialized hospital (n = 12) or chronic (n = 2) pens were even more sparse. Results from this literature search will be used to outline the understanding of acutely and chronically ill and injured feedlot cattle, including common dispositions and welfare considerations, behavior during convalescence, and strategies for identifying and managing ill and injured cattle. Finally, by working through specific ailments common in commercial feedlot environments, we illustrate how the Five Domains Model can be used to explore feelings and experiences and subsequent welfare state of individual ill or injured feedlot cattle. Using this approach and our knowledge of current industry practices, we identify relevant animal-based outcomes and critical research questions to strengthen knowledge in this area. A better understanding of this overlooked topic will inform future research and the development of evidence-based guidelines to help producers care for this vulnerable population.

Empowering Sustainable Healthcare: The Role of Health Literacy

The imperative of sustainability in healthcare is becoming more pressing due to global health crises, climate change, and the rising burden of chronic diseases. Sustainability in healthcare involves social, economic, and environmental dimensions, each important for equitable and robust healthcare delivery. Health literacy may play a central role in embracing all three dimensions, bridging the gap between complex health information and individuals’ capability to understand and use it effectively: In fact, as a factor influencing the link between adverse social and economic conditions and subsequent health issues, health literacy could represent a practical target for mitigating health disparities within various demographic groups, thereby enhancing social sustainability. Furthermore, when people possess a solid understanding of their health conditions and the necessary steps for health management, they can contribute to a reduction in generated healthcare costs. Finally, health literacy and environmental health literacy equips individuals and communities with the knowledge and skills to understand how environmental factors affect health and empowers them to take proactive measures to protect the environment, potentially reducing the environmental impact of healthcare services. Therefore, integrating health literacy into education curricula and healthcare professional training is crucial for promoting sustainability. Despite some known barriers to the advancement and improvement of health literacy, such as limited awareness of its importance, the collaboration between academia, healthcare institutions, and community organizations is a path to follow to overcome these challenges.

Sustainable Projects Based on the Intersection of Clean Energy with the Health Sector: A Bibliometric Review

The objective of this work was to develop a bibliometric analysis of academic production related to sustainable projects based on clean, green and renewable alternative energies and their interaction with the health sector and health management on a global scale. To do this, a documentary research process based on bibliometric tools was proposed. The research is limited to the period between 2003 and 2023, executing the following search equation: (TITLE-ABS-KEY ("clean energy" ) OR TITLE-ABS-KEY ( "alternative energy" ) OR TITLE-ABS -KEY ("solar energy" ) OR TITLE-ABS-KEY ( "green energy" ) OR TITLE-ABS-KEY ( "renewable energy" ) AND TITLE-ABS-KEY ( "health sector" ) OR TITLE-ABS-KEY ( "healthcare sector" ) OR TITLE-ABS-KEY ( "public health service" ) OR TITLE-ABS-KEY ( "health management" ) ). The years with the highest production were 2020 (21), 2021 (23), 2022 (30) and 2023 (34), while the countries that have contributed the most to the topic are China (111), the USA (97) and the United Kingdom (71), which together contributed 57.76% of all publications. Of 145 sources found that published on the topic, the 18 main sources concentrate 48% of the publications. The three most cited articles were EMADIAN SM, 2017, WASTE MANAGE, WATTS N, 2018, LANCET and FULLER R, 2022, LANCET PLANET HEALTH, with a total of 1567 citations. Among the most relevant institutions in relation to the topic of study, UNIVERSITY COLLEGE LONDON stands out above all with 19 publications, followed by TSINGHUA UNIVERSITY (9) and WUHAN UNIVERSITY (8); between the three institutions they account for 20.57% of the publications found. This analysis provided critical insights for academics and professionals and underscored the relevance and dynamism of this research area in addressing clean energies and sustainable projects related with health management and health sector worldwide.