Effect of Artificial Intelligence-based Health Education Accurately Linking System (AI-HEALS) for Type 2 diabetes self-management: protocol for a mixed-methods study

Explain the behavior change and maintenance in diabetic patients using MTM-HAPA framework

Objectives

The aim of the study was to to uncover the factors influencing the initiation and maintenance of health behaviors indiabetes mellitus (DM) patients, utilizing baseline data from a randomized controlled trial to construct a structural equation model based on the Multi-Theory Model (MTM) and Health Action Process Approach (HAPA) scales.

Methods

The study recruited participants with type 2 diabetes, aged between 18 and 75 years, from 45 distinct locations in Beijing, China.Patients [N = 406, n = 232 (57.1%) females, n = 232 (42.9%) males; Mean (SD) age = 56.7(10.9)] completed self-reported questionnaire about constructs from integrated theories concerning health behavior. To test the associations between the variables, structural equation modeling with latent variables was employed. Based on the path coefficients of Structural Equation Modeling(SEM), we verified all the hypotheses.

Results

Disadvantages, Advantages, Self-efficacy for Initiating Behavior, and Changes in Physical Environment are all prove to have an effect on intention, with the effect of Disadvantages being negative. Intention positively influenced Action Planning and Coping Planning, both of which in turn significantly predicted Initiation of Behavior Change. Practice for change, Emotional Transformation, Changes in Social Environment, and Self-efficacy for Sustaining Behavior were all affected by Outcome Expectancies and Risk Perception positively. Meanwhile, Practice for change, Emotional Transformation, Changes in Social Environment and Self-efficacy for Sustaining Behavior- would have a significant predictive effect on Maintenance of Behavioral Change.

Conclusion

The empirical evidence from this study robustly validates the majority of its theoretical constructs, affirming that MTM-HAPA possess significant explanatory capability in delineating the factors that underpin both the Maintenance of health-related behaviors and the Initiation of Behavior Changes in individuals suffering from DM.

Exploring the Relationship Between eHealth Literacy and Diabetes Knowledge, Self-Efficacy, and Self-Care Behaviors in Chinese Diabetic Patients: A Cross-Sectional Study

BACKGROUND

The importance of online educational resources enhancing self-management among patients is underscored by the growing prevalence of diabetes. It is crucial to comprehend how patients with diabetes in China seek diabetes-related information and use mobile applications (apps) designed for diabetes management. Although the Knowledge-Attitude-Practice theory plays an integral role in diabetes management, in-depth studies on eHealth literacy (eHL) and Knowledge-Attitude-Practice in populations with diabetes remain scarce.

PURPOSE

This study was developed to provide insights into the online information-seeking behaviors of and diabetes apps used by individuals living with diabetes by exploring the relationships among eHL, diabetes knowledge, self-efficacy, and self-care behaviors.

METHODS

A cross-sectional survey was conducted from November 2022 to June 2023 on 380 inpatients with diabetes at the Yantai Yuhuangding Hospital in Shandong Province. The participants voluntarily completed surveys covering sociodemographic characteristics, diabetes status, diabetes app usage, online diabetes information searching, eHL, diabetes knowledge, self-efficacy, and self-care behaviors. Structural equation modeling analyses were employed to assess model fitness and the interrelationships between latent constructs and observable variables.

RESULTS

Of the 380 participants, 57.1% (217/380) reported actively seeking diabetes information online, whereas only 3.7% (14/380) had used diabetes apps. eHL was shown to have a direct effect on diabetes knowledge (β = 0.377, p < .001) but no direct impact on self-care behaviors (β = 0.017, p = .860). However, an indirect effect on self-care behaviors was observed via diabetes knowledge and self-efficacy.

CONCLUSIONS/IMPLICATIONS FOR PRACTICE

The results of this study indicate that, despite widespread online information-seeking behavior, diabetes app utilization remains limited in China. Also, the findings indicate enhancing patients' eHL contributes to more comprehensive diabetes knowledge. Furthermore, eHL was shown to influence self-care behaviors via diabetes knowledge and self-efficacy. A self-managed intervention strategy should be developed to improve eHL that utilizes internet resources to improve patients' knowledge and self-efficacy and promote better self-care behaviors.

Assessing Visitor Expectations of AI Nursing Robots in Hospital Settings: Cross-Sectional Study Using the Kano Model

Background

Globally, the rates at which the aging population and the prevalence of chronic diseases are increasing are substantial. With declining birth rates and a growing percentage of older individuals, the demand for nursing staff is steadily rising. However, the shortage of nursing personnel has been a long-standing issue. In recent years, numerous researchers have advocated for the implementation of nursing robots as a substitute for traditional human labor.

Objective

This study analyzes hospital visitors' attitudes and priorities regarding the functional areas of artificial intelligence (AI) nursing robots based on the Kano model. Building on this analysis, recommendations are provided for the functional optimization of AI nursing robots, aiming to facilitate their adoption in the nursing field.

Methods

Using a random sampling method, 457 hospital visitors were surveyed between December 2023 and March 2024 to compare the differences in demand for AI nursing robot functionalities among the visitors.

Results

A comparative analysis of the Kano attribute quadrant diagrams showed that visitors seeking hospitalization prioritized functional aspects that enhance medical activities. In contrast, visitors attending outpatient examinations focused more on functional points that assist in medical treatment. Additionally, visitors whose purpose was companionship and care emphasized functional aspects that offer psychological and life support to patients.

Conclusions

AI nursing robots serve various functional areas and cater to diverse audience groups. In the future, it is essential to thoroughly consider users' functional needs and implement targeted functional developments to maximize the effectiveness of AI nursing robots.

Investigation and analysis of the status of cancer health popularization in China, 2023

BACKGROUND

Cancer presents a significant public health challenge in China, necessitating broad collaboration across society. The Chinese government has articulated a goal to increase the overall five-year survival rate for cancer by 15% by 2030. Achieving this objective requires not only advances in medical technology, but also an improvement in the dissemination of knowledge pertaining to cancer prevention and treatment.

AIM

To provide a comprehensive understanding of the status of cancer prevention and level of popularization in China in 2023.

METHODS

From January 2023 to May 2023, online questionnaires were distributed to 3000 participants, including medical personnel, patients with cancer, their families, and the general public. There were 2711 valid responses, covering the entire nation.

RESULTS

A total of 1020 medical personnel and 1691 patients with cancer, their family members, and the general public participated in the survey. Among medical personnel, 93.2% had popularized cancer health. Commonly addressed topics included cancer prevention (85.9%) and cancer screening (77.8%). Primary challenges included time constraints (73.9%), insufficient personnel and material support (66.7%), and uncertainty as to where to begin (49.3%). Among patients with cancer, their family members, and the general public, 93.4% reported reading or watching cancer science popularization materials and 56.9% expressed a desire for deeper understanding. The most sought-after topics in cancer science popularization included cancer screening (80.2%) and cancer prevention (75.8%). The greatest challenge encountered in accessing cancer health popularization was an abundance of misinformation (67.5%).

CONCLUSION

Most clinical doctors, patients, family, and the general public wish to participate in cancer education. However, improvement in the quality of content in cancer prevention and treatment education is required.

Assist me or replace me? Uncovering the influence of AI awareness on employees' counterproductive work behaviors

Objective

Drawing on the conservation of resources theory (COR), the research aims to reveal the influence of artificial intelligence (AI) awareness on employees' mental health and behaviors, particularly examining whether and how employees' AI awareness impacts their counterproductive work behaviors (CWB) in human-intelligence collaborations.

Methods

Data was collected from 327 Chinese employees who collaborated with AI in sales, manufacturing, logistics, and other industries. The measurement instruments included scales for AI awareness, psychological contract (PC), emotional exhaustion (EE), and counterproductive work behavior (CWB). We used Hayes's PROCESS macro to analyze the data.

Findings

AI awareness had a significant positive impact on CWB (β = 0.448, p < 0.01). PC and EE play a role as partial mediators in the relationship between AI awareness and CWB. The mediating pathways consist of three sequences: "AI awareness → PC → CWB," "AI awareness → EE → CWB" and "AI awareness → PC → EE → CWB," with the respective contributions to the overall effect amounting to 8.04, 18.53, and 4.91%.

Discussion

Our research contributes to the study of AI in the management field by elucidating the relationship between AI awareness and CWB, as well as the mediating mechanisms of this relationship, which enriches the literature on CWB and expands the understanding of the associations between AI and CWB.

A mobile serious game about diabetes self-management: Design and evaluation

Type 2 Diabetes Mellitus (T2DM) is a chronic condition that requires ongoing self-management and education. In recent years, there has been a growing interest in utilizing mobile serious games as a tool for patient education and engagement. This article presents the development of DiaPo, a mobile serious game designed to improve self-management education for patients with T2DM. DiaPo integrates gamification techniques to increase patient engagement and motivation while providing essential information about disease management. The development of DiaPo followed a structured design process, utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) educational system. This systematic approach allowed for the integration of best practices in educational game design and diabetes care. The development team consisted of experts in medical informatics, game design, and diabetes care, ensuring a multidisciplinary approach to the game's creation. The game's narrative focuses on a T2DM patient who earns positive points for making healthy lifestyle choices and negative points for poor ones. This gamified approach aims to reinforce positive behaviors and provide immediate feedback on negative ones. Interactive animations confirm or deny options selected by the player, further enhancing the learning experience. DiaPo offers a flexible and adaptable platform suitable for diverse audiences, promoting inclusiveness and accessibility in T2DM education. DiaPo represents a novel approach to self-management education for patients with T2DM, utilizing gamification techniques and a multidisciplinary design process to create an engaging and informative mobile serious game. By promoting inclusiveness and accessibility, DiaPo has the potential to empower patients with T2DM to take an active role in their disease management. As the field of mobile serious games continues to evolve, DiaPo stands as a promising tool for improving T2DM education and patient outcomes.

Artificial intelligence for diabetes care: current and future prospects

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.

Diabetes: a review of its pathophysiology, and advanced methods of mitigation

Diabetes mellitus (DM) is a long-lasting metabolic non-communicable disease often characterized by an increase in the level of glucose in the blood or hyperglycemia. Approximately, 415 million people between the ages of 20 and 79 years had DM in 2015 and this figure will rise by 200 million by 2040. In a study conducted by CARRS, it's been found that in Delhi the prevalence of diabetes is around 27% and for prediabetic cases, it is more than 46%. The disease DM can be both short-term and long-term and is often associated with one or more diseases like cardiovascular disease, liver disorder, or kidney malfunction. Early identification of diabetes may help avoid catastrophic repercussions because untreated DM can result in serious complications. Diabetes' primary symptoms are persistently high blood glucose levels, frequent urination, increased thirst, and increased hunger. Therefore, DM is classified into four major categories, namely, Type 1, Type 2, Gestational diabetes, and secondary diabetes. There are various oral and injectable formulations available in the market like insulin, biguanides, sulphonylureas, etc. for the treatment of DM. Recent attention can be given to the various nano approaches undertaken for the treatment, diagnosis, and management of diabetes mellitus. Various nanoparticles like Gold Nanoparticles, carbon nanomaterials, and metallic nanoparticles are some of the approaches mentioned in this review. Besides nanotechnology, artificial intelligence (AI) has also found its application in diabetes care. AI can be used for screening the disease, helping in decision-making, predictive population-level risk stratification, and patient self-management tools. Early detection and diagnosis of diabetes also help the patient avoid expensive treatments later in their life with the help of IoT (internet of medical things) and machine learning models. These tools will help healthcare physicians to predict the disease early. Therefore, the Nano drug delivery system along with AI tools holds a very bright future in diabetes care.

Digital Health in Diabetes and Cardiovascular Disease

BACKGROUND

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD).

PURPOSE OF THE REVIEW

In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease.

CONCLUSION

We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.