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The idea of value co-creation involves the benefit actors gain from integrating resources through activities and interactions within a service network, with the environment enabling high-quality collaboration. This paradigm highlights customers’ ability to co-create value with service providers and other customers. This idea is gaining traction in health care. These days, patients are no longer passive recipients of health-care services; rather they have started taking proactive roles in their self-health management. This study aims to understand the phenomenon of value co-creation among patients within online health communities (OHCs).

A systematic literature review of papers published from 2003 to 2024 in Web of Science-indexed journals was conducted. The review highlights theories, contexts, characteristics and methodologies in this area, synthesizing insights from previous research and presenting a future research agenda for underexplored and unexplored contexts using emerging theoretical perspectives and analytical methodologies.

The review illuminates theoretical and empirical studies on value co-creation among patients in OHCs. Previous research shows that value co-creation among patients leads to cognitive, affective and physical benefits such as reduced anxiety and stress, increased assurance and self-confidence, improved quality of life, enhanced patient empowerment, acceptance of disease and treatment effectiveness and a sense of self-worth and well-being.

This review synthesizes insights from previous works and outlines a research agenda for future studies in underexplored and unexplored contexts using new theoretical perspectives and methodologies. Considering the role social media plays in an individual’s life, this work will help in deep diving into the role of such online communities in the health-care sector.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The study attempts to investigate the role of social media in spreading awareness regarding ayurvedic immunity boosters (AIB) and changes in diet. Further, the study examines the factors affecting the willingness to pay for ayurvedic immunity boosters (WPIB) during the pandemic and new normal situation with the moderating effect of the “fear of COVID-19 infection.”

The data were collected from millennials in two phases, i.e. the first phase (1 July–August 2021) with 300 respondents and a second phase with (June–August 2022) 257 respondents. An online questionnaire was shared with millennials using the snowball sampling technique. Descriptive statistics with SPSS and SmartPLS 4.0 software were applied to analyze the data.

The results found a variation in AIB content sharing on social media during 2021 and 2022. Results found that respondents reported significant changes in their lifestyle and diet, like consuming honey, khada, tulsi tea, etc. In 2021, health consciousness and trust significantly affected WPIB, whereas in 2022, only health consciousness was substantially affected. Fear of COVID-19 infection moderates the relationship between health consciousness, perceived fear and willingness to pay for ayurvedic products, whereas the effect on consumer preference and trust remains insignificant.

Results could help ayurvedic product manufacturing companies understand the consumers' mindset and the factors that stimulate consumers to buy these immunity boosters. Ayurvedic advertisers should design unambiguous messages that focus on health consciousness and have trustable components to encourage consumers to adopt a healthy lifestyle.

This is one of its kinds of studies that presents the contrasts of how the COVID-19 crisis has significantly changed individuals' dietary intake and affected lifestyle patterns.

This paper proposes a Web-based patient portal based on the electronic medical record. Such a portal can allow patients to manage their own health care, reduce health-care visits and significantly improve the quality of their health care.

A patient portal prototype and an accompanying online survey were distributed to assess the adoption readiness among a group of people in the United Arab Emirates (UAE).

The results from 470 survey participants demonstrated an enhanced awareness of this technology, and support the study hypotheses indicating that both intrinsic and extrinsic factors are important when considering the implementation of a patient portal in the UAE.

This study adds value to the few research studies undertaken in the Middle East discussing online health information technology and its adoption and usage among the population at large. The extended technology acceptance model, which contains two additional constructs, had not been previously validated in terms of a patient portal in the UAE, according to the author’s knowledge, adding more value. The UAE’s health-care system must use the benefits from the available IT infrastructure to provide a user-friendly online portal to encourage patients to manage their health care and health information.

Probiotic coadministration with soy beverages has been shown to have favorable results in human health. This study aims to conduct a systematic review of randomized clinical trials to examine the effect of probiotic soy beverages (PSB) on human health aspects such as lipid profile, oxidative stress and kidney function.

The study was conducted based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Relevant articles were extracted from PubMed, SCOPUS, Web of Science, Science Direct and Google Scholar up to April 2024. This paper searched all databases using the following keywords: (“Soy milk” OR “Soy beverage” OR “Fermented soy milk” OR “Soy Fermented Beverages”) AND (“Probiotic” OR “Probiotics”). The literature search did not consider language, age and publication date restrictions.

A total of 2,183 subjects were included in this review. PSB beneficially decreased low-density lipoprotein cholesterol and total cholesterol levels and increased high-density lipoprotein cholesterol. PSB beneficially affected superoxide dismutase, glutathione peroxidase, glutathione reductase and glutathione. PSB decreased serum creatinine and increased estimated glomerular filtration rate. Cystatin-C and progranulin decreased significantly in the PSB group. PSB had no significant effect on anthropometric indices, while decreased systolic and diastolic blood pressure.

This study provides a brief overview of the effect of PSB on lipid profile, oxidative stress, inflammation and kidney function for the first time.

The primary objective of this study is to investigate the ethical implications of deploying AI-enabled chatbots in the healthcare sector. In addition, the research underscores trust and reliability as critical factors in addressing the ethical challenges associated with these chatbots.

This study takes a qualitative approach, conducting 13 semi-structured interviews with a diverse range of participants, including patients, healthcare professionals, academic researchers, ethicists, and legal experts. This broad spectrum of perspectives ensures a comprehensive understanding of the ethical implications of AI-enabled chatbots in healthcare. The rich exploratory data gathered from these interviews is then analysed using thematic analysis.

The findings of this study are highly significant in the context of AI-enabled healthcare chatbots. They highlight four major themes: developing trust, ensuring reliability, ethical considerations, and potential ethical implications. The interconnectedness of these themes forms a coherent narrative, highlighting the pivotal role of trust and reliability in mitigating ethical issues.

This study contributes to the existing literature on AI-enabled healthcare chatbots. It not only reveals potential ethical concerns associated with these technologies, such as data security, patient privacy, bias, and accountability, but it also places a significant emphasis on trust and reliability as critical elements that can boost user confidence and engagement in using AI-enabled chatbots for healthcare advice.

The current work aims to present a review of academic literature that systematizes the body of knowledge related to marketing and consumer behavior in order to identify the most effective variables that encourage the consumer towards a proper and better lifestyle, accordingly the paradigm of management, marketing and technology efforts to promote a “better” society preventing obesity.

A literature review was carried out to examine the studies of marketing and consumer behavior published in international peer-reviewed journals over the last twenty-three years (2000–2023). Our review finally considered a total amount of 46 articles.

Findings elucidate three overarching themes and associated sub-hemes, encompassing: (1) Product design for obesity prevention, including aspects such as labeling, nomenclature, packaging and assortment; (2) Technology-supported preventive measures, involving mobile applications, self-monitoring, short message services and digital therapeutics; and (3) Marketing and communication strategies, incorporating social advertising, nudge, social influence and initiatives targeting childhood obesity prevention. Furthermore, a comprehensive research agenda is presented, delineating potential avenues for future investigations predicated on the utility of the results in fostering subsequent endeavors within the realms of: efficacy and effectiveness studies; personalization and tailoring; behavioral change techniques and gamification; user experience and acceptance; cost-effectiveness and implementation; as well as ethical and privacy concerns.

Main limitations are related to the characteristics of the analyzed literature, resulting in only English journal articles, book chapter and so on. Thus, other relevant contributions in different languages discussing interesting insights might have been neglected.

This study offers several insights to managers, marketers and policymakers involved in the issue of the obesity prevention. Since obesity represents a crucial challenge for public health at a global level, with its incidence reaching epidemic proportions in recent decades, the results may be extremely useful and powerful because suggesting – by employing a robust resulting corpus of knowledge on this domain – several practical features, actions and tactics to face such an important challenge. Moreover, this paper offers for scholar and researcher a systematized knowledge around the issues of obesity prevention, together with a detailed research agenda emerging by the critical analysis of the emerging insights, and to practitioners systematized useful insights to project and develop their future business strategies.

By providing several actions and tactics for obesity prevention (e.g. as for product labeling, naming, packaging, assortment; the exploitation of new technologies for mobile applications design, self-monitoring, short message service (SMS) alert systems, digital therapeutics; the role of social advertising, nudge, social influence) this work perfectly match the emerging societal orientation related to business, marketing and technology efforts to create a “better” society.

The study shed lights the need for a holistic approach to obesity prevention, involving interaction between individual main topics. Importantly this is the first study to analyze the issue of obesity prevention by considering a multidisciplinary corpus of literature, analyzed trough an individual-centric orientation.

The purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.

The approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).

The proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.

This work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.

The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.

Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.

The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.