Background and significance

Brain-body interaction challenges of common chronic diseases: Major chronic diseases such as metabolic diseases, cardiovascular diseases, tumors, and chronic respiratory diseases have become major factors endangering human health. According to statistics, in 2018, chronic diseases accounted for 88.5% of deaths in China, of which cardiovascular diseases, cancer, chronic respiratory diseases and diabetes accounted for 47.1%, 24.1%, 8.8% and 2.5% of the total deaths, respectively. With the aging population and lifestyle changes, the burden of these chronic diseases continues to increase, and the morbidity and mortality rates are on the rise. Traditional medical research mostly focuses on the local organs and physiological mechanisms of diseases, and does not pay enough attention to the overall interaction between the central nervous system (brain) and peripheral organs (bodies). As a result, we lack a global understanding of the occurrence and development of chronic diseases, and many **brain-body interaction mechanisms have not been deeply revealed. In fact, a large number of studies have shown that psychological, neurological, endocrine, immune and other multi-system factors jointly affect the course of chronic diseases. For example, the brain communicates bidirectionally with organs throughout the body through neural pathways, endocrine hormones, and immune mediators: the gut forms a typical "**brain-gut axis" with the center, which involves neurological, endocrine, and immune signal reciprocation; Similarly, the "brain-lung axis" has been proposed to explain the multiplex communication between the brain and the lungs, and has been found to be closely related to a variety of lung and central disease states. There is also a significant two-way correlation between the cardiovascular system and the central nervous system – "brain health and heart health are closely related and mutually influential". These evidences suggest that chronic diseases are not simply localized, but rather systemic, physiological-psychological, and intertwined network disorders.

Emerging paradigm requirements for brain-body interaction: In the process of developing chronic diseases, the regulatory disorders of peripheral organs and the adverse effects of peripheral pathological changes on brain function often form a vicious circle. For example, in metabolic diseases such as obesity and type 2 diabetes, the regulatory mechanism of energy balance in brain regions such as the hypothalamus is impaired, resulting in appetite and metabolic disorders; At the same time, peripheral abnormalities such as hyperglycemia and inflammation in turn impair central cognitive function, leading to memory loss or mood disorders. The study highlights that the role of the nervous system in maintaining energy homeostasis goes far beyond the traditional understanding of endocrine regulation, including circadian rhythms, higher brain regions, and psychosocial factors that affect metabolic homeostasis. Another example is chronic psychological stress through the overactivation of the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system, resulting in long-term elevations of cortisol and catecholamines, inhibiting the body's immune surveillance function, thereby promoting tumorigenesis and development. Correspondingly, inflammatory factors released by the tumor microenvironment can affect the brain through the blood-brain barrier or vagus nerve, causing "disease behaviors" such as decreased appetite, fatigue, and depression. Studies have shown that chronic inflammation-induced "sickness behavior" has a protective effect in the acute phase, but if it persists for a long time, it will change to a state of maladaptation, aggravate symptoms such as depression and cognitive impairment, and further worsen the chronic disease process. Similarly, in chronic respiratory diseases such as COPD, chronic hypoxia and inflammation in the lungs can trigger central neuroinflammation and cognitive decline; Anxiety can aggravate asthma or chest tightness, creating a vicious cycle of mind and body. It can be seen that the imbalance of central-peripheral two-way regulation is an important reason for the prolongation and repeated aggravation of many chronic diseases. However, there is a lack of systematic theoretical models to characterize and explain these cognitive coupling patterns of brain-body interaction imbalance, and it is difficult to propose effective comprehensive intervention strategies.

New ideas provided by the DIKWP model: In response to the above challenges, this project uses the DIKWP model originally proposed by Professor Yucong Duan as the core theoretical support to explore a new paradigm of brain-body interaction. The DIKWP model was originally used as a cognitive framework for artificial intelligence, which was extended from the classic "Data-Information-Knowledge-Wisdom" system. The model introduces "Purpose" at the top level to form a cognitive chain of five levels: Data, Information, Knowledge, Wisdom (Wisdom), and Purpose (Purpose). This expansion emphasizes the central role of decision-making purpose in the process of intelligent cognition, and combines the subjective purpose of the subject with objective information processing, which is regarded as a major theoretical innovation in the field of artificial intelligence. The five-layer structure is abstracted from the bottom to the top: from the perception and processing of raw data, it is refined into information, integrated into knowledge, and then rises to Wisdom decision-making, and finally the top-level purpose guides and constrains the direction of action. Unlike the traditional linear hierarchical model, DIKWP uses a mesh structure to support two-way feedback and iterative updates between levels. In other words, the high-level decision-making goals of the Purpose layer can in turn affect the data collection and information interpretation process of the lower layers, and the knowledge and wisdom of each layer can also be continuously revised in the loop, so that the system has the ability to self-update and adapt. This system provides a transparent and decomposable framework for understanding complex cognitive processes, makes the internal processing process explicit and modular, and facilitates layer-by-layer analysis and diagnosis, thereby improving the interpretability and controllability of the overall system.

We foresee that the introduction of the DIKWP model into the study of brain-body interaction can unify the modeling of physiological signals and cognitive processes: the state and interaction between the brain and the periphery can be described at five levels: data, information, knowledge, wisdom, and purpose. For example, peripheral signs can be regarded as "data", which can be interpreted and interpreted by the central nervous system as "information", which is further integrated by the brain into "knowledge" of diseases and "wisdom" of coping strategies, and finally the patient forms the "purpose" of recovery or behavior adjustment. In turn, the patient's subjective purpose and emotions influence their behavioral decisions (Wisdom layer) and physiological responses (acting on the periphery through the endocrine neural pathway). The bidirectional network interaction structure of DIKWP fits into this cyclical brain-body information flow, which can be used as a theoretical tool to describe cognitive-physiological coupling.

Emerging Combination of Artificial Consciousness Theory and Digital Intervention: The DIKWP model is essentially an Artificial Consciousness framework. Its outstanding feature is that the autonomous purpose is incorporated into the computational model, so that the agent has the ability to regulate goal-driven behavior similar to consciousness. This provides the possibility for the introduction of "artificial consciousness" intervention in the field of chronic diseases: by building an artificial intelligence system with the ability to simulate consciousness, it can perceive the physical and mental state of patients and guide and regulate them. In recent years, the application of AI in healthcare has been increasing, but most of them are still limited to data analysis and decision-making. This project will be further expanded to develop an "artificial awareness-driven digital intervention system" with the help of the DIKWP artificial awareness model, which attempts to allow AI to not only analyze data, but also simulate the human cognitive purpose process and interact with patients. It provides a new technical approach for chronic disease management from the three levels of psychology, physiology and behavior. For example, the system can act like a "digital coach" or an "artificial consciousness companion" to monitor the patient's physiological and emotional indicators in real time (data/information layer), understand the patient's behavior patterns and health knowledge (knowledge layer), evaluate their decision-making ability and problems in their lifestyle habits (the Wisdom layer), and guide the patient to change bad behaviors by stimulating the patient's willingness and motivation to recover (the Purpose layer). This approach is expected to break through the traditional one-way digital health approach and truly realize personalized and proactive intervention for patients with chronic diseases. Previous studies have shown that AI-integrated digital health interventions can significantly improve the management of chronic diseases, such as improving blood sugar control and weight loss in diabetic patients, and the higher the patient engagement, the more significant the effect. This suggests that the introduction of smarter, more "aware" interactive systems may further improve patient compliance and disease management outcomes.

Project significance: This project closely follows the guidelines of the National Key R&D Program "Research on the Regulation Mechanism of Brain-Body Interaction in Common Major Chronic Diseases and Novel Intervention Strategies", and has important theoretical innovation value and practical significance.

(1) Theoretically, this study will establish a new paradigm of cognitive model of brain-body interaction. Through the construction of the DIKWP*DIKWP bidirectional interaction model, we can systematically characterize the information coupling and regulation law of the central and peripheral areas in the process of chronic diseases, and enrich the scientific understanding of the mechanism of mind-body unity in chronic diseases. This will expand the traditional biomedical model, introduce cognitive science and artificial intelligence perspectives, and promote the development of psychophysiological integrated medicine.

(2) In terms of application, this study explores artificial consciousness-driven intervention strategies, and is expected to propose new digital therapeutics and intelligent rehabilitation methods. By intervening in the behavior and physiology of patients by simulating their state of consciousness, we hope to enhance patients' self-management ability and treatment adherence, thereby improving the prognosis of chronic diseases. In particular, in view of the current pain points of patients' lack of motivation and poor long-term compliance in the management of chronic diseases, the intervention of strengthening the "purpose layer" will provide a new solution.

(3) This project is an interdisciplinary study, integrating artificial intelligence, neuroscience, endocrine immunity, biomedical engineering and other fields, in response to the requirements of the "Healthy China 2030" plan for innovative chronic disease prevention and control technologies. The implementation of the project will help enhance China's independent innovation capabilities in the field of smart medicine and digital health, and produce high-level scientific research results and transformable technical products. In short, this project is based on the national economy and people's livelihood problem of major chronic diseases, and proposes a new idea of brain-body interaction and artificial consciousness integration, which is expected to make breakthroughs in disease mechanism research and intervention technology, with significant scientific value and social benefits.

Overall objectives and technical roadmaps

Overall Objective: This project aims to elucidate the regulatory mechanisms of brain-body interactions in common major chronic diseases to construct a cognitive representation framework with the DIKWP*DIKWP interaction model as the core, and reveal the coupling mode of cognitive levels such as information, knowledge, wisdom and purpose in the disease process. On this basis, the theory of artificial consciousness was introduced, and a three-dimensional regulatory pathway model of "cognition-metabolism-immunity" was established to analyze the mechanism of abnormal higher-order neurological functions (such as self-perception, purpose and motivation) on the chronicity of chronic disease behavior. Finally, a set of artificial consciousness-driven digital intervention system was developed, including cognitive status monitoring, purpose activation feedback, digital behavior reconstruction and other modules, and its effectiveness in individualized regulation and chronic disease rehabilitation was verified. Specifically, the following objectives will be achieved through this project:

• **Objective 1: To establish a two-way cognitive model of brain-body interaction (DIKWP). **The central nervous system and peripheral organs were abstracted into the five-layer structural units of DIKWP, and the data flow, information exchange, knowledge coupling, Wisdom decision-making and purpose feedback mechanisms between the two were explained. Through this model, the interaction processes at different levels, such as neuro-endocrine-immunity, are described in a unified manner, and a new paradigm model of brain-body interaction is proposed.

• **Objective 2: To elucidate the mechanism of central-peripheral regulatory disorders in chronic diseases. **For four typical chronic diseases, namely metabolic diseases, cardiovascular diseases, tumors, and chronic respiratory diseases, this paper studies the pattern of disorders in the regulation of peripheral physiology of the brain in disease states, as well as the reverse regulation of peripheral pathological changes on central cognitive and emotional functions. To reveal the common and differential mechanisms of brain-body interaction imbalance in different diseases, and to provide a basis for intervention.

• **Objective 3: To uncover the relationship between Purpose layer disorder and worsening of chronic disease behavior. **Based on the theory of artificial consciousness, a three-dimensional interaction of "cognition-metabolism-immunity" regulatory pathway model is proposed to simulate the higher-order functional abnormalities such as weakened self-perception, impaired purpose, and degenerative motivation in patients with chronic diseases. To elucidate the mechanisms by which the disorder at the purpose level of patients leads to persistent poor lifestyle behaviors (e.g., poor adherence, lazy exercise, etc.), thereby contributing to the chronicity of the disease and difficulty in recovery.

• **Goal 4: Develop artificial awareness-driven digital intervention systems. **Design and implement an intelligent intervention platform integrating artificial consciousness, including: monitoring and evaluation module of central cognitive state and peripheral physiological indicators, patient purpose activation and feedback guidance module, and digital behavior reconstruction module. Through clinical trials or user studies, the effect of the system on improving patient initiative and improving the management effect of chronic diseases is verified, and a new path of individualized regulation and rehabilitation is explored.

In order to achieve the above goals, the project proposes the following technical routes:

1. Theoretical modeling stage (brain-body interaction model construction): First, the theoretical analysis and model construction of brain-body interaction are carried out. Based on a large number of literature and experimental data, the two-way action pathways of central nervous system and peripheral organs in chronic diseases (such as neuro-immune feedback circuit, endocrine compensation mechanism, etc.) were sorted out. Based on the DIKWP model, a dual-agent interaction architecture is introduced: a DIKWP*DIKWP interaction model composed of two five-layer DIKWP units is constructed, which represents the two subsystems of "brain" and "body" respectively. Clarify the specific meaning of each layer (data, information, knowledge, wisdom, purpose) in the brain and body and how it interacts across systems. A preliminary conceptual model and graphical framework of brain-body interaction were formed.

2. Mechanism Research Stage (Applied Research on Chronic Diseases): Focusing on four types of chronic diseases, the dysfunctional mechanism of brain-body interaction is studied in depth. The method of combining clinical research and animal experiments was used to collect central nervous system indicators (EEG, functional MRI, cognitive function test) and peripheral physiological indicators (hormone levels, immune factors, metabolic parameters, etc.) in patients with metabolic diseases (such as type 2 diabetes, obesity), cardiovascular diseases (hypertension, coronary heart disease), tumors (such as cancer with depression), COPD/asthma, etc., and analyze the signal correlation changes between the central and peripheral areas. These changes are characterized and explained by the DIKWP interaction model, and the characteristic patterns of coupling dysregulation of the information layer, knowledge layer, wisdom layer and purpose layer in each disease are refined. To summarize the common mechanisms of brain-body regulatory disorders, such as: how the imbalance of the HPA axis caused by chronic stress produces similar immunosuppressive effects in different diseases; The central "disease behavior" caused by peripheral inflammation is prevalent in a variety of chronic diseases. At the same time, it is necessary to identify points of difference, such as metabolic disease more prominent hypothalamic-vagus nerve pathway disorder, cardiovascular disease more involved autonomic cardiac regulation disorder, etc. Produce mechanistic models and scientific data support for brain-body interaction disorders.

3. Artificial consciousness fusion stage (Purpose layer disorder and intervention target): Incorporate the theory of artificial consciousness into the study of the mechanism of chronic diseases. Taking the top-level "Purpose" of the DIKWP model as the starting point, the role of patients' subjective state of consciousness in the process of chronic diseases was discussed. Constructing a cognitive-metabolic-immune three-dimensional regulatory model: A cognitive-psychological dimension is added to the brain-body interaction model to simulate the higher brain functions (including self-awareness, emotion, and willpower) of patients. Through literature research and clinical psychological evaluation data, the common psychological changes (such as chronic disease depression, burnout, and depression) of patients with chronic diseases were analyzed. Key simulations: decreased self-perception (sensitivity to one's own symptoms and physical state), impaired purpose function (willingness and goal to perform healthy behaviors), motivational system(reward-driven positivity) degradation. The DIKWP framework is used to characterize how these changes affect cognitive processes—for example, the weakened function of the Purpose layer will make it difficult for the Wisdom decision-makers to make health-friendly choices, and patients will know that they need to exercise but lack motivation to act, which will eventually lead to the solidification of unhealthy lifestyles and the chronicity of disease behavior patterns. Combining psychological questionnaires and behavioral data, the correlation between Purpose disorder and poor disease management was validated, revealing the causal circuits of both (e.g., depression leading to decreased adherence, poor adherence and aggravating the disease, thereby further deepening depression). On this basis, the hypothesis of the intervention target is proposed: that is, to break the vicious circle of chronic diseases by enhancing the function of the patient's Purpose layer (enhancing their initiative and sense of purpose). This stage will provide the theoretical basis and design direction of the digital intervention program.

4. System development stage (artificial consciousness-driven intervention system development): Based on the above research results, the intervention system design and implementation will be entered. Firstly, the overall architecture of digital intervention is determined: the interaction system between the agent (artificial consciousness agent) and the patient is constructed with the DIKWP artificial consciousness model as the core. The system consists of the following sub-modules:
   After the development of the system, it will be tested and optimized on a small scale. Patients with target chronic diseases were invited to participate in a pilot study to observe the effects of the system on their physiological indicators, psychological scales, and behavior patterns. Improve the human-computer interaction design and algorithm performance of the system based on user feedback to ensure safe and effective intervention.

• Cognitive layer status monitoring module: Develop multimodal data collection and analysis tools to monitor patients' physiological parameters (such as blood glucose, blood pressure, exercise level) and subjective feedback (such as pain scores, mood diaries) in real time, and map them to the data layer and information layer representation of the DIKWP model. IoT sensors and wearable devices are used to obtain peripheral data, combined with mobile phone applications to obtain patient self-reports, so as to achieve continuous perception of patients' physical and mental states. has shown that mobile applications with continuous monitoring and personalized feedback can significantly improve metabolic markers, and this module will draw on similar techniques.

• Purpose Activation and Feedback Module: Based on the research results of the above-mentioned Purpose layer mechanism, an artificial intelligence algorithm is designed to evaluate the patient's current Purpose intensity and direction (such as the willingness to follow the doctor's prescription and the degree of motivation to exercise). Conversational AI (similar to ChatGPT and other large models, embedded in the DIKWP architecture to make it have a purpose representation) is used to interact with patients and provide customized psychological support and motivational incentives. For example, when a patient is detected to be depressed, the system guides the patient to reset his or her health goals through communication, providing positive feedback and reinforcing his intrinsic motivation. This module emphasizes human-computer empathetic interaction, so that patients feel understood and supported, so as to gradually rebuild positive purpose.

• Digital Behavior Reconstruction Module: Translates the goal into a specific behavioral intervention plan, implements it digitally and tracks the effect. For example, for diabetic patients, the system formulates a daily diet and exercise plan (Wisdom layer decision-making) according to its Purpose-level goals (such as blood sugar control and weight loss), and supervises the implementation through the App (behavioral data is fed back to the information layer and re-enters the cycle). For patients with depressive tendencies, the module provides digital content such as mindfulness training and virtual reality relaxation therapy to help them improve their emotional and volitional states. All intervention behaviors will be recorded as data and entered into the cognitive monitoring module for closed-loop evaluation. Through continuous iteration (the two-way feedback mechanism of the DIKWP model), the intervention strategy is dynamically adjusted to adapt to the changes in the patient's state, so as to achieve individualized and precise intervention.

 Validation evaluation phase (effect evaluation and objective assessment): At the end of the project, the effectiveness of the intervention system is evaluated through rigorous comparative trials. Randomized controlled trials (RCTs) were conducted with typical diseases, such as a 6-month digital intervention trial in patients with type 2 diabetes, in which the control group received routine management and the experimental group used the artificial awareness intervention system developed in this project. Clinical indicators (such as glycosylated hemoglobin, blood pressure), patient compliance scores, quality of life scales, and cognitive function tests were measured before and after the trial. It is expected that the experimental group will be superior to the control group in terms of metabolic control and psychological state, thus verifying the clinical value of the system. At the same time, combined with the subjective evaluation of doctors and patients, the human-computer interaction experience and applicability of the system were comprehensively investigated. Collect multi-dimensional data to provide a quantitative basis for the achievement of project goals. Finally, the results of the project were summarized and refined, and a new theoretical model of the brain-body interaction mechanism of chronic diseases and a new technology and method of artificial consciousness intervention were formed.

The above technical route covers the complete chain of basic theory construction→ mechanism research→ model integration→ system development→ effect verification, and each stage is organically connected and gradually deepened. According to the progress of the research, the project will continue to iterate between the model and the system: the new findings of the mechanism research will feed back to improve the DIKWP model, and the model improvement will guide the optimization of the intervention algorithm to ensure that the theory and application complement each other. This route fully reflects the characteristics of interdisciplinary research, which will realize the deep integration of medicine and artificial intelligence technology and ensure the smooth achievement of the project goals.

(The figure below shows the schematic of the research content and technical route of this project, covering the construction of brain-body interaction model, the analysis of chronic disease mechanism, and the development of artificial consciousness intervention system.) (Purpose omitted)

What to study

Focusing on the above overall objectives, the research content of this project is divided into four interrelated aspects, covering all levels of basic theory, mechanism research and technological innovation:

1. A new paradigm of brain-body interaction and the construction of the DIKWP*DIKWP model

Overview: This part focuses on establishing a new theoretical paradigm of brain-body interaction in chronic diseases, and characterizes the bidirectional regulatory mechanism between the central and peripheral areas in the form of a DIKWP*DIKWP model. Main Studies:

(1) how to map the interaction process between the central nervous system and peripheral organs into the five-layer architecture of the DIKWP model;

(2) the cognitive coupling patterns of information, knowledge, wisdom, and purpose between brain and body and their changes in disease states.

Specific research contents:

• 1.1 Design of DIKWP dual-subject interaction model: The "brain" and "body" are regarded as two information processing subjects, which have five layers of data, information, knowledge, wisdom and purpose. The main pathways of brain-body communication were summarized through literature analysis, such as neural network signals corresponding to fast data exchange, hormones and cytokines corresponding to slow information transmission. How the medical knowledge stored in the brain (disease cognition, treatment experience) and the pathological knowledge at the body level (immune memory, metabolic adaptation) interact with each other. On this basis, the topological structure of the brain-body DIKWP interaction model is designed: the connection relationship and interaction rules between the two subjects of each layer are defined. For example, the data layer: peripheral sensory signals are transmitted to the brain, and cranial nerve impulses are exported to the organs; Information layer: the center interprets somatic signals (e.g., pain, hunger sensation), and the periphery receives central instructions and translates them into physiological responses (e.g., rapid heartbeat during stress); Knowledge: The brain's perception of the current state (e.g., "elevated blood pressure may mean pain") interacts with the body's intrinsic homeostatic settings (e.g., the mechanism by which the renin-angiotensin system maintains blood pressure); Wisdom layer: the center synthesizes multiple information to make regulatory decisions (such as initiating sympathetic responses), and peripheral multi-organ coordination reaches a new homeostasis; Purpose layer: The brain produces subjective purposes (e.g., awareness of pain and seeking pain relief) that influence lower-level decision-making, and local "purpose" of the body can be seen as an expression of intrinsic needs (e.g., signals to increase ventilation when tissues are deprived of oxygen) and ascend to affect brain perception. Through the two-way connection between these "five pairs of levels", a networked brain-body interaction model map is formed. Then, the mathematical or logical description framework of the model is formulated, such as using ontology + semantic network to define the concepts and relationships of each layer, or establishing a coupled equation system based on system science. Output: Brain-body DIKWP interaction model shows the purpose and formal description.

• 1.2 Analysis of brain-body cognitive coupling mode: The established model was used to analyze the changes in the coupling relationship between the brain-body cognitive layers in the health homeostasis and disease states. For example, at the Purpose level, the brain's goal (maintaining health) guides the body's physiological adjustment through autonomic and endocrine pathways, and the body's instinctive needs (hunger, thirst, fatigue signals) are accurately sensed and met by the brain at the right time, forming a closed-loop self-regulation as a whole. At the level of information and knowledge, the brain can correctly interpret body signals (not misjudge normal signals as abnormal), and the internal environment of the body is stable to provide the information basis needed for brain decision-making, and the decision-making at the Wisdom level can effectively maintain homeostasis. In disease states, this coupling is disrupted, and the following patterns may occur: (a) information mismatch: the brain misinterprets signals from the body or has abnormal thresholds, such as the brain of a patient with chronic pain who continues to receive over-amplified pain information, or the hypothalamus of a diabetic patient has a reduced sensitivity to blood glucose signals, resulting in a broken information chain; (b) Knowledge conflict: the original cognition of the brain does not match the real state of the body, such as the patient's lack of knowledge of the disease (knowledge deficit) leading to neglect of symptoms, or the body produces new pathological feedback (such as drug resistance) beyond the existing knowledge system of the brain, resulting in cognitive uncertainty; (c) Wisdom decision bias: the central decision is no longer optimal, for example, obese patients make overeating decisions driven by strong appetite (Purpose layer shift), and the Wisdom layer regulation fails; (d) Purpose disorder: the patient's subjective will is disconnected from the body's needs, such as depressed patients who know the need to exercise but are depressed, and the body's long-term lack of exercise leads to a decline in function, which further exacerbates the negative emotional feedback of the brain. Through the analysis of different types of dissonance modes, the general characteristic parameters of cognitive coupling imbalance (such as signal gain, threshold offset, feedback delay, etc.) are extracted. Then, based on these parameters, quantifiable evaluation indicators were established in the model to measure the health of brain-body coupling. For example, the definition of "brain-body synergy index" comprehensively reflects the consistency of information flow and the integrity of the feedback loop at each layer, and the decrease in synergy indicates abnormal interaction. Finally, the model and indicators were used to summarize and explain the phenomena of subsequent disease studies (see research content 2), and the applicability and generalization of the model were verified.

• 1.3 Empirical Validation and Model Refinement: In order to ensure that the DIKWP brain-body model conforms to the actual biological laws, this section will combine small-scale experiments or existing data to validate and modify the model. On the one hand, examples of brain-body interaction that are easy to control on animals are selected, such as the rodent stress model: by applying chronic stress to mice, the changes of the HPA axis and immune response are observed, and the obtained time series data is simulated and output against the model to test the prediction ability of the model on the interaction between data layer, information layer and knowledge layer. If the model can reproduce the actual observation (e.g., dynamic changes in hormones and cytokines caused by stress, as well as behavioral manifestations), then the model parameters are reasonable; If there is a large deviation, adjust the relevant connection weights or function forms in the model. On the other hand, clinical datasets were utilized for retrospective analysis. For example, for patients with heart failure, the association between autonomic function indicators (heart rate variability, etc.) and cognitive function scores can be extracted, and the relationship between autonomic nervous system (body information layer) and cognitive state (brain knowledge/wisdom layer) can be explained by the model, and whether the model can predict the change trend of cognitive status in heart failure patients. Through multi-case verification, the model structure is continuously improved to make it both theoretical and biologically reasonable. Final output: Published a set of theoretical models and index systems of brain-body interaction; The model may be provided in the form of an appendix in pseudocode or an open-source simulation program to provide a tool platform for further research in the academic community.

Expected innovation: This research will be the first to propose a DIKWP cognitive model of brain-body interaction to achieve a unified description of bio-psychological processes. Internationally, although there are conceptual models of brain-gut axis and brain-immune interaction, most of them focus on specific physiological pathways and lack an abstract cognitive framework. The introduction of our model into the cognitive level of artificial intelligence is expected to become a new theoretical tool in the field of psychosomatic medicine. The bidirectional interaction of this model also provides a paradigm for explaining complex system diseases, which can be generalized to the study of other systems (such as neuro-endocrine system).

2. Research on the mechanism of central-peripheral regulatory disorders of typical chronic diseases

Overview: This section focuses on four common major chronic diseases, namely metabolic diseases, cardiovascular diseases, tumors, and chronic respiratory diseases, and deeply studies the dysfunctional mechanisms of brain-body interaction in their respective pathological states. Through the combination of basic and clinical research, the disorder pattern of peripheral regulation of the center and the reverse regulation of the peripheral pathological environment on the central cognitive-emotional function are clarified**. To summarize** the commonality and specificity of brain-body interaction disorders in different diseases, and lay a foundation for the formulation of intervention strategies.

Specific research contents:

• 2.1 Brain-body regulatory imbalance in metabolic diseases: Obesity and type 2 diabetes mellitus are selected as representatives. To study the disturbance of the feedback mechanism between the weight regulation center of the brain (hypothalamus, etc.) and the peripheral metabolic organs (islets, adipose tissue, intestine) in the context of metabolic syndrome. noted that higher brain regions and circadian rhythms play an important role in regulating metabolism. We will observe the activation of the hypothalamus and reward center in obese/diabetic patients under the state of eating, insulin release, etc., through functional MRI and metabolic provocation test, and compare their neural responses with healthy controls. The secretion rhythm of peripheral hormones (e.g., leptin, insulin, ghrelin) and target tissue sensitivity were also measured. Hypothesis: Obese/diabetic patients have decreased central sensitivity (eg, leptin resistance leading to hypothalamic sluggishness to fat signaling, information layer disorder) and abnormal peripheral feedback (eg, insulin resistance leading to prolonged deviation of blood glucose signals from the normal range, brain misinformation). Using the brain-body DIKWP model, it can be described that the input of the brain information layer is distorted, and the cognition of the knowledge layer about energy balance is disrupted, so that the decision-making (appetite control) of the wisdom layer fails. Further, to investigate how these metabolic disorders affect cognitive function and mood: cognitive tests (such as executive function, memory) and mood scale assessments were performed to analyze whether patients with poor glycemic control had worse cognitive performance, and the correlation between chronic inflammatory markers (IL-6, TNF-α, etc.) and depression scores. Studies suggest that chronic inflammation and immune activation are risk factors for cognitive impairment and depression. We expect this to be verified in metabolic diseases such as diabetes: low-grade chronic inflammation affects hippocampal memory function through the blood-brain barrier or vagus nerve, resulting in mild cognitive impairment and depressed mood, i.e., the reverse regulation of the center by the periphery. Combining these results, we can draw a model of the brain-body-immune imbalance in metabolic diseases, reveal the mechanism of the vicious cycle between obesity, inflammation and cognition, and propose corresponding intervention targets (such as reducing inflammation or improving leptin sensitivity to restore brain-body information coupling).

• 2.2 Brain-body interaction disorders in cardiovascular diseases: hypertension and coronary heart disease are the main research objects. The cardiovascular system is regulated by the sympathetic/parasympathetic and endocrine systems (adrenal hormones, etc.), while heart disease can also affect the blood supply to the brain and the neuroendocrine state. We will first study the relationship between psychological stress and cardiovascular events: stress tests (public speaking or mental arithmetic) will be used to induce stress responses in subjects, changes in blood pressure, heart rate, and cortisol levels will be recorded, and limbic and autonomic activity will be measured by MRI or near-infrared. The goal is to elucidate the transient brain-heart interaction patterns in acute stress and the cumulative effects of chronic stress in patients with hypertension (e.g., persistent increase in sympathetic tension). Theoretically, chronic mental stress can lead to sympathetic overactivation and vagus nerve inhibition, resulting in vasoconstriction and persistently high heart rate, while repeated stimulation of the HPA axis leads to an increase in cortisol, promoting atherosclerosis and endothelial damage. We will measure levels of stress-related hormones and inflammatory factors in patients with hypertension and coronary heart disease, and assess their cognitive function (e.g., executive function, attention) and emotional state to verify that chronic stress passes through the nerves- The endocrine-immune pathway causes a chain of cardiovascular deterioration and cognitive and emotional alterations. If the data support it, we will be able to describe the "brain-cardioaxis" vicious cycle with a brain-body model: under stress stimulation, the amygdala/thalamus of the brain triggers sympathetic output (data/information layer overload), leading to an overload of the cardiovascular system (disorder of the body's Wisdom layer), in turn, hypertension and heart failure cause insufficient oxygen supply to the brain and an increase in inflammatory factors (the body feeds abnormal information back to the brain knowledge layer), which ultimately leads to depressed mood and cognitive decline (damage to the brain's Purpose/Wisdom layer). In addition, to study the direct effects of heart disease on the brain, such as cognitive decline common in patients with atrial fibrillation and coronary heart disease, we will use brain imaging to observe alterations in cerebral perfusion and functional connectivity, and speculate on how chronic decreased cardiac output, repeated microembolism, etc., affect brain networks, thereby adding these pathways to the model. Finally, the possibility of positive regulation, such as cardiac rehabilitation training (exercise, meditation, etc.) on the improvement of autonomic balance and brain plasticity, and its mechanism was incorporated into the model to provide ideas for intervention.

• 2.3 Mind-body interaction mechanism in tumor diseases: Malignant tumors are a special type of chronic diseases, and their progression is not only affected by the biological behavior of tumor cells, but also by neuroimmune factors. A large number of psychoneuroimmunological studies have shown that chronic psychological stress is closely related to the occurrence and development of certain tumors. In this section, we will examine the brain-body interaction characteristics of cancer patients, focusing on the neuro-immune-tumor tripartite relationship. On the one hand, to study how the imbalance in the regulation of immunity in the center affects tumors: recruit a group of treated cancer patients, assess their stress levels (e.g., anxiety and depression scales), measure sympathetic indicators (heart rate variability, norepinephrine) and HPA axis hormones, and peripheral immune function (peripheral blood NK cell activity, T cell subset ratio, inflammatory factor levels). Patients with high psychological stress were analyzed for hypersympathetic and immunosuppressive states, such as decreased NK cell activity and suppressed Th1 immune response. Combined with follow-up data, to see if this is associated with higher rates of tumor progression or metastasis. Expected results: Chronic stress can mediate immune evasion through sympathetic-adrenal glands, accelerating tumor growth. On the other hand, to study the impact of tumors and their treatment on the center: many cancer patients experience "chemobrain" (decline in cognitive function) and mood disorders. We will conduct neuropsychological testing and brain imaging examinations before, during, and after treatment for a group of breast cancer/lung cancer patients, combined with the detection of peripheral inflammatory factors and metabolites, to explore the ways in which chemotherapy drugs and tumor metabolites affect the brain through the periphery. For example, cytokines such as tumor necrosis factor may induce central inflammation, leading to impaired memory and attention; Anemia or malnutrition caused by certain chemotherapy drugs can affect the brain's energy supply. Through longitudinal data, we established a dynamic association model between the physical state **and brain function of cancer patients. Based on the above research, the "**brain-tumor-immunity" sub-model was improved: when patients are under psychological stress, the brain releases norepinephrine through sympathetic nerves to promote angiogenesis and immunosuppression in the tumor microenvironment (the body information layer changes the tumor knowledge layer); Factors released by the tumor and side effects of treatment in turn impair cognition and mood (peripheral lesions ascend to the central Wisdom/Purpose layer). This cycle will be used to explain the mechanisms by which some patients experience accelerated tumor progression after a stressful event, or remain chronically depressed after tumor remission.

• 2.4 Brain-body interactions in chronic respiratory diseases: represented by chronic obstructive pulmonary disease (COPD) and bronchial asthma. The respiratory system is closely connected to the central nervous system through blood and gas changes and nerve reflexes, and lung disease is often accompanied by central symptoms such as dyspnea, anxiety, and cognitive decline. This section will investigate the interaction between dyspnea, emotion, and cognition in patients with chronic lung disease. On the one hand, the changes in blood gas, cerebral oxygen, and brain electrical activity of COPD patients after resting and exercise, as well as the corresponding subjective dyspnea scores and anxiety scores, were measured. It is hypothesized that hypoxic hypercapnia triggers a broad response from the brain center, including bulbar respiratory center excitation and limbic system activation, causing the patient to feel the fear of choking (a centrally generated information-layer experience), which in turn induces anxiety attacks. This peripheral pathology-induced central panic can be described by the model as the abnormality of the body's data layer (hypoxia) is mistakenly amplified by the brain as a strong threat message, which is beyond the rational regulation of the Wisdom layer. On the other hand, the effects of anxiety on asthma are studied: asthma is a typical psychosomatic disorder, and mood changes often induce or worsen asthma symptoms. We will induce mild anxiety (e.g., watching stress films) in asthma patients, monitor airway response and autonomic markers, and see if anxiety causes bronchoconstriction through increased vagal tone. Expected results: Increased vagus nerve activity in the anxious state caused bronchial smooth muscle contraction and increased airway resistance, resulting in subjective chest tightness and the formation of an emotional-respiratory feedback loop。 In addition, the long-term effects of systemic chronic inflammation and repeated hypoxia on cognition in COPD patients are also worth paying attention to: we will conduct neuropsychological assessment of COPD patients, focusing on executive function and memory, measuring whether the levels of inflammatory markers (CRP, etc.) and brain-derived neurotrophic factor (BDNF) are abnormal, and exploring whether chronic respiratory failure promotes central chronic inflammation and neurodegenerative changes. Incorporate the findings into a brain-body model: pulmonary insufficiency prevents the body's Wisdom layer from providing adequate oxygen, and peripheral metabolic waste retention continues to stimulate the vagus nerve, which may lead to partial functional decline of the central body in the long term. This model could explain the symptoms of inattention and memory loss, which are common in patients with chronic lung disease. To summarize this section, a two-way imbalance model of the "brain-lung axis" is formed: chronic hypoxia and inflammation cause chronic fatigue and anxiety in the brain, which in turn further exacerbates dyspnea, and the cycle continues. Intervention hypotheses: a two-pronged approach, such as neuromodulation training of respiratory muscles and anti-anxiety therapy, may break this cycle.

Expected results: Through this study, we will obtain empirical data and mechanistic models of brain-body interaction disorders in four major chronic diseases. These models not only provide a detailed explanation of specific disease pathways, but also map to the DIKWP abstract model at a higher level. It is expected that a number of research papers will be published, such as: "Research on the mechanism of brain-gut-pancreatic axis information mismatch in metabolic syndrome", "The impact of brain-heart interaction on cognition in patients with chronic heart failure", "Study on stress-immunity-recurrence pathway in breast cancer patients", "Brain-lung axis function reconstruction in COPD patients", etc. More importantly, we will refine the common laws across diseases, such as the "inflammation-cognitive symptoms" connection that is prevalent in chronic diseases, and the mechanism of "autonomic imbalance-organ damage", so as to provide a targeted entry point for subsequent artificial consciousness intervention. These research contents will be at the forefront of international exploration, because there are few projects that can conduct a unified comparative analysis of the brain-body interaction mechanism of so many chronic diseases. The results will enrich the systematic understanding of chronic diseases and provide a scientific basis for the development of comprehensive intervention strategies.

3. Research on cognitive-metabolic-immune three-dimensional regulation and Purpose layer disorder based on artificial consciousness

Overview: Based on the above-mentioned brain-body models and disease mechanism studies, this section introduces the theory of artificial consciousness, focusing on the problem of higher-order neurological dysfunction (Purpose layer disorder) in chronic diseases. By establishing a three-dimensional regulatory model of "cognition-metabolism-immunity", this paper simulates the changes of patients' self-awareness, purpose and motivation in the state of chronic disease, and explores how the disorder of the purpose layer leads to the chronicity of disease behavior. The aim of this study is to reveal the deep coupling mechanism between psychological awareness factors and the biological processes of chronic diseases, and to provide a theoretical basis for Purpose layer intervention.

Specific research contents:

• 3.1 Cognitive-Metabolism-Immunity Three-dimensional Integration Model Construction: Based on the existing brain-body DIKWP model, a dimension is further expanded to include artificial consciousness elements, that is, an explicit representation of cognitive/conscious states is added to the model. This can be seen as embedding a meta-layer of "state of consciousness" on top of the five layers of the brain DIKWP (especially the Wisdom and Purpose layers), or refining the brain DIKWP model, where the Purpose layer includes subjective consciousness and motivation submodules. At the same time, the body-side model was refined to include key links in the immune system and metabolic system. Therefore, the new 3D model includes: cognitive dimension (cognitive/conscious processes of the brain, including perception, emotion, purpose, etc.), metabolic dimension (systemic metabolic internal environment, such as endocrine, energy state), and immune dimension(immune system status, e.g., inflammation levels, cellular immune function). The three form a three-dimensional network through multiple couplings. For example, cognitive-immune coupling: cerebral cortex and limbic activity can affect immune cells through the neuroendocrine pathway (eg, stress leads to decreased lymphocyte function), and cytokines released by immune cells can affect brain signaling through the blood-brain barrier (eg, IL-1 causes fever and drowsiness); Cognitive-metabolic coupling: the brain monitors and regulates the body's energy status (hunger, satiety, etc.), and metabolic hormones such as insulin and thyroid hormones also regulate brain function (affecting memory and attention); Metabolite-immune coupling: Metabolites can regulate immune cell activity (e.g., fatty acids promote inflammation), and the metabolic state of immune cells determines their function (e.g., immunodeficiency due to malnutrition). By collating the literature, we will identify the main interaction loops between the three dimensions and integrate them into the model structure. Next, a formal description and simulation scheme of the model are tried: for example, based on system dynamics, a system of differential equations is established to describe the changes of cognitive-metabolic-immune indicators over time and the interaction between them. Or use agent-based modeling to build three agents of brain, immunity, and metabolism, and reflect interaction through message transmission. The model will be tested with scenarios such as chronic stress or infection, and the correctness will be verified by known experimental rules (e.g., whether the pathways of long-term high-fat diet→ obesity→ chronic inflammation → cognitive decline can be simulated by the model). This three-dimensional integrated model is an important supplement to the DIKWP framework, which enables our theoretical framework to explain the synergistic regulation of psychology, metabolism, and immunity at the same time, and to have a more comprehensive systematic grasp of complex chronic diseases.

• 3.2 Simulation of Patient Self-perception and Purpose Dysfunction: The artificial consciousness theory, especially the Purpose layer of the DIKWP model, is used to simulate the changes of higher-order cognitive function in patients with chronic diseases. We will quantitatively model and simulate the diminished self-perception and impaired motivation that are prevalent in chronic diseases**. Firstly, the subjective state of patients with common chronic diseases (such as diabetes, heart failure, COPD, and cancer remission) was analyzed through patient surveys and psychological assessment scales**, including: illness perception, treatment intention, goal planning ability, motivation level, etc. Expected findings: Many patients with chronic diseases have a tendency of "learned helplessness", that is, due to long-term illness setbacks, which leads to a decrease in self-efficacy, and believes that no matter how hard they try, their condition will not improve, and then their willingness to actively manage is reduced. This manifests as intent strength decay at the Purpose layer. At the same time, chronic diseases are often accompanied by depression or cognitive decline, which makes the patient's perception of body signals dull or negative (such as pain, fatigue and other signals are habitually tolerated and lack of active response). In view of these characteristics, we will try to use the DIKWP model to characterize the target instructions generated by the Purpose layer in the DIKWP of the brain , and the driving force of the lower level Wisdom decision is insufficient; The information layer may desensitize to negative physical signals or over-ignore them (filtering at the cognitive level leads to the failure of risk information to rise to the knowledge level decision-making). We parameterize these cognitive impairments in the model, for example, by setting a "Purpose threshold" that represents motivation to act, and when this value falls below a certain threshold, the Wisdom layer will not act on a behavior (exercise, follow-up, etc.) even if it knows that it is good for health. Then, the long-term trend of the disease under different intensities was simulated on an integrated cognitive-metabolic-immune model: the changes in physiological parameters, such as blood glucose, blood pressure, and lung function, were compared between the "high purpose" (active cooperation with management) and the "low purpose" (patient passive procrastination) scenarios. If the model can reproduce the trends observed in reality (positive patients have a better prognosis, negative patients are prone to worsening), then further analyze the internal mechanism: low purpose leads to poor treatment adherence (e.g., not taking medication on time, not exercising), persists or worsens metabolic/immune abnormalities, and in turn, worsening chronic disease symptoms reinforces the patient's sense of helplessness, forming a positive feedback loop of Purpose layer disorder-disease exacerbation. We will pay special attention to the association between Purpose and immunity, hypothetizing that low Purpose leads to chronic stress and depression, and then inhibits immune function through neuroimmune pathways, such as the case of decreased immunity and accelerated tumor progression after cancer patients lose their will to survive. This phenomenon can be explained and predicted by models. These simulations are not only theoretical, but also provide a basis for interventions: we will propose a key hypothesis that enhancing the patient's Purpose layer can reverse the adverse trend of chronic disease, which will be tested in the next intervention study.

• 3.3 Validation of association between Purpose layer disorder and chronic disease behavior: To correspond model predictions to the real world, we will validate the association of Purpose disorder indicators with disease outcomes in a cohort of patients with chronic diseases. Design a prospective study: recruit a certain number of patients with chronic diseases (can be multi-disease), and evaluate their psychological and purpose-related indicators, such as Disease Perception Questionnaire (IPQ), Intrinsic Motivation Scale for Treatment, Depression and Anxiety Scores, etc., at baseline; Objective disease indicators and recent disease control levels were recorded at the same time. Follow-up for 1-2 years was followed up to monitor the clinical outcomes (occurrence of complications, number of hospitalizations, degree of disease progression, etc.). Patients were grouped by baseline purpose/motivation level and outcomes were compared between the different groups. Patients with low Purpose have a significantly worse prognosis if results show a significantly worse outcome (e.g., in diabetes, patients who are unwilling to engage in lifestyle interventions have more complications; In coronary heart disease, patients who lack the willingness to recover are at higher risk of second myocardial infarction), which confirms that the purpose layer disorder is indeed an important contributing factor to the chronicity of chronic disease behavior。 Furthermore, the multivariate regression model was used to eliminate other effects, and the independent contribution of the Purpose factor to the outcome was quantified. For example, calculate how much the risk of death increases for every certain increase in depression score; For every certain amount of reduction in intrinsic motivation for treatment, how much does the blood glucose attainment rate decrease. These statistical results will feed back to refine our theoretical model and make it more predictive. During the validation process, we will also take care to distinguish correlation from causation: there may be reverse causality (the severity of the disease causes the purpose to be low). Therefore, the structural equation model and other methods can be combined to construct a model of the mutual causal relationship between Purpose and disease, and the longitudinal data can be used to verify the causal direction. Causal inference is supported if model fitting shows that the Purpose layer variables have a significant impact on subsequent disease indicators. Finally, the findings are summarized as the theoretical framework of "Purpose-Chronic Disease Behavior": that is, in the process of chronic disease, subjective factors such as patients' purpose/motivation play a key moderating role, and their disorders can accelerate the deterioration of the disease, and adjusting these higher-order factors is expected to change the trajectory of the disease. This framework breaks through the traditional view of separating the biological and the psychological, and reflects the application potential of artificial consciousness theory in medicine.

Expected Results: This study will form a new understanding of the psychophysiological coupling of chronic diseases**, including the publication of cognitive-metabolic-immune** three-dimensional models (theoretical papers), and the data reporting of the impact of patients' motivation on chronic disease outcomes (clinical papers). At the same time, the new concept of purpose-level intervention is refined: patients are regarded as "cognitive systems" rather than just "organisms", and chronic diseases are managed by improving their subjective initiative. These results will directly guide the design of the next intervention system, provide a humanized and holistic perspective for chronic disease management, and also have enlightening significance for public health policies (such as chronic disease health education).

4. Research on the design and application of digital intervention system driven by artificial consciousness

Overview: On the basis of fully grasping the brain-body interaction mechanism and the law of the purpose layer, this part will enter the application transformation stage to design and develop an "artificial consciousness-driven digital intervention system". The system embeds the DIKWP artificial awareness model into the chronic disease management process, including three main functional modules: cognitive layer monitoring, purpose activation feedback, and digital behavior reconstruction. We will prototype and iteratively optimize the system, and conduct preliminary application studies to evaluate its effect on individualized regulation and rehabilitation, and explore new ways to translate research results into clinical practice.

Specific research contents:

• 4.1 The overall architecture and key technologies of the intervention system: According to the above design, the framework concept of the system has been proposed in the technical route. Here, the system architecture diagram and key module interfaces are formulated in detail, and the required technical solutions are clarified. In general, the system adopts a client-cloud combination model: the patient side is a mobile phone application or wearable device interface, and the cloud is an artificial awareness AI engine and data analysis platform. Key technical points include:

  • Multimodal data collection and fusion: Wearable sensor devices are integrated to obtain physiological data (heart rate, blood pressure, blood glucose, activity level, sleep, etc.), and mobile applications to obtain patients' subjective input (symptom punch-in, mood diary, questionnaire), environmental data (positioning, climate), etc. Data preprocessing and feature extraction algorithms are developed to convert heterogeneous data into data layer inputs (numerical normalization, event labeling, etc.) of the DIKWP model, and semantic annotations are performed at the information layer (such as detecting hyperglycemia events and insomnia events). Ensure reliable data collection and privacy security.

  • Artificial Consciousness Engine (DIKWP Cognitive Computing Module): This is the core of the system, which realizes cognitive reasoning and decision-making on the patient's state based on the DIKWP model. We will use the existing cognitive computing framework and reinforcement learning algorithms to customize the development according to the specific needs of this project. For example, a five-layer neural network or hierarchical Bayesian network is constructed to simulate the DIKWP process: the bottom sensing node receives data features, the middle layer performs pattern recognition (such as finding symptom patterns), the higher layer combines the knowledge base to judge risks, and the top layer outputs intervention suggestions (Purpose-driven). Introduce a knowledge base (including medical guidelines and expert rules) and a cognitive feedback mechanism to make AI decision-making conform to medical laws and be explainable. The two-way feedback mechanism will be implemented here: the top-level purpose of the AI will dynamically adjust the type and frequency of the data features of concern (for example, if the AI judges that the patient's psychological risk has increased in the near future, it will strengthen the frequency of emotion-related data monitoring), and at the same time, the data-driven correction of the AI's internal state (such as continuous blood glucose abnormalities will allow the AI to improve the assessment of the risk of complications). This design embodies the idea of DIKWP mesh interaction.

  • Personalized User Models and Purpose Computing: Maintain a digital twin model of each patient in the AI engine, recording their unique physiological-psychological characteristics and behavior patterns. Analyze historical data through machine learning and continuously update the parameters of the patient model to make interventions more personalized. For example, for patients with poor adherence, the model will mark their Purpose layer as less dynamic, and the intervention will be formulated with a focus on motivation and supervision; For patients with high self-discipline, more information level knowledge support is given. Purpose calculation refers to inferring the patient's intrinsic purpose state based on the patient's current context, such as using algorithms based on affective computing and motivational reasoning to detect the patient's emotions and intentions from the patient's language and behavior (for example, if the patient's tone is low and the motivation is low through the analysis of dialogue text, the Purpose level is judged to be low).

  • Human-computer interaction and feedback interface: Designed a user-friendly interface that supports multi-modal interaction (text, voice, expression, etc.). The system will interact with patients in the form of "artificial awareness assistants", such as providing personalized greetings and health advice every morning, reminding them to take medication and asking about subjective feelings at noon, and summarizing the day's status in the evening to give encouragement or advice. If an abnormality is detected (such as a persistent low mood or a sudden increase in blood pressure), the assistant will take the initiative to intervene or notify the medical staff. Interaction information is transformed into updates to patient models and subsequent decision-making in the background. The interface design will focus on incentive mechanisms, such as achievement badges, milestones, social sharing, etc., to improve patient stickiness and engagement.

• 4.2 Implementation and testing of core functional modules: According to the above architecture, the three core functional modules of the system are implemented one by one, and unit tests and integration tests are carried out in the experimental environment.
  After completing the development and testing of the above modules, enter the system integration and deploy all the modules together to run. By simulating the whole process trial operation of different user scenarios (various disease combinations and various abnormal working conditions), the stability and response speed of the system are checked, and the bugs are repaired in time. The result is a beta intervention system that can be piloted.

  • Cognitive state monitoring module development: Use the existing IoT development board and sensor components to build a prototype monitoring device; Develop mobile phone applications to access third-party wearable device data. Write a data collection daemon to realize real-time data upload. Develop basic data analysis scripts, such as heart rate variability calculations, sleep stage analysis, sentiment analysis (natural language processing can be used to determine the emotional polarity of diary text). The stability of the data link was tested in a laboratory setting with healthy subjects and some patients, and the data fusion algorithm was adjusted to reduce noise false positives. Ensure that the module can correctly process all types of input situations (normal/abnormal, physiological/behavioral) and output a uniform format of feature streams.

  • Purpose: Activation and feedback module development: Select an appropriate dialog system framework (e.g., GPT-based large model API, with custom constraints) to build an interactive agent. Write conversation scripts and reinforcement learning rewards to enable AI assistants to communicate with users in a guided manner. For example, when a patient expresses frustration, the assistant will comfort and propose a small goal for them to accomplish, activating their purpose. Implement "virtual coaching" features – such as real-time voice encouragement from an assistant during a workout and positive feedback at the end of the workout. For the Purpose calculation part, a classification model can be trained to map the sequence of recent user behaviors to the Purpose layer, such as "High Purpose", "Medium Purpose", and "Low Purpose" to determine the intensity of the interaction strategy (low Purpose means strong intervention, and High Purpose means weak reminder). When testing the module, you can ask the test person to pretend to be a different scenario and talk to the assistant to check that the response is expected and empathetic. Iteratively refine your conversation strategy and avoid blunt or offensive tones.

  • Digital Behavior Reconstruction Module Development: This part deals with the digital implementation of specific interventions. Different sub-functions are designed according to different diseases, such as: diet management plug-in for diabetic patients (recording diet and giving health scores and recommended recipes); Breathing and relaxation training for patients with hypertension (provide instructional audio, practice daily and monitor the effect); Videos of breathing exercises and rehabilitation exercises for COPD patients; Mindfulness meditation sessions for cancer survivors, etc. Integrate these capabilities into the app to allow AI to be pushed to patients as needed. Establish a correlation between behaviour and outcomes: e.g., assess how well the patient is performing the intervention (whether the number of steps is up to standard, whether the diet is in line with recommendations) in combination with monitoring data, and provide immediate feedback. Through reinforcement learning, the system gradually learns to choose the most appropriate intervention for the patient (e.g., if a patient is found to be more willing to accept a gamification challenge, a game-based intervention is used). During the test, let small patients experience these features for 1-2 weeks, and collect feedback questionnaires to see which features are popular and which are difficult to use, so as to improve the details of the interaction and the content fit.

• 4.3 Pilot application and effect evaluation of the intervention system: Select cooperative medical institutions or communities to carry out pilot application research of the system. Specific plan: Recruit about 30-50 patients with chronic diseases (which can be concentrated in one disease or include multiple diseases) and use the intervention system provided by the project for 3-6 months. The study used a single-group before-and-after control design or compared with historical controls to evaluate the effects of systematic interventions on all aspects of patients. The main observation indicators included:
  evaluating the effectiveness and safety of the intervention system through comprehensive analysis of the above data. If the effect is significant, for example, the improvement in metabolic markers in the AI intervention group is significantly higher than that of conventional management, then this will demonstrate the value of artificial awareness-driven intervention. AI personalized feedback has been shown to improve metabolic control, and this study is expected to further improve the effect. If something isn't working well, dig deeper into the cause and may need to improve the feature or algorithm in a subsequent release. Based on the results of the pilot, a version upgrade plan of the intervention system was formed to prepare for the next large-scale promotion.

  • Improvement of physiological indicators: such as changes in mean blood glucose and glycosylated hemoglobin levels in diabetic patients, blood pressure attainment rate in hypertensive patients, 6-minute walking distance and lung function in COPD patients, etc. Compare the differences before and after the intervention or between the control group and expect to see some improvement.

  • Psychological and cognitive indicators: Standard scales were used to evaluate whether patients had reduced levels of depression and anxiety, whether quality of life (SF-36, etc.) was improved, and whether cognitive test scores were improved. Particular attention is paid to changes in initiative and adherence, as measured by questionnaires or system logs (e.g., the percentage of homework completed per day). It is expected that the self-efficacy of health management will be improved after the intervention.

  • Medical outcomes: The number of acute exacerbations during the intervention period, the number of unplanned medical visits, and whether they were reduced compared with the same length before the intervention. Long-term follow-up to observe whether it affects the hospitalization rate.

  • User experience: Interviews or questionnaires are used to understand patients' acceptance and satisfaction with the system, including how much they like the image of an AIA, whether the interactive content is helpful, and which features are most useful. Gather suggestions for optimization.

  • Adverse events: monitor for adverse effects due to systemic interventions, such as overdependence and increased anxiety (generally low, but concerning).

• 4.4 System optimization and promotion preparation: According to the feedback from the pilot, the system will be optimized and improved in the final stage of the project. For example, improving the user-friendliness of the user interface, increasing the patient community communication function (allowing patients using the system to share their experiences with each other to increase motivation), enriching AI dialogue materials to avoid duplication, and strengthening data security and privacy protection measures. Technically, if necessary, higher performance AI models or knowledge bases can be introduced to augment the content of medical guidance. Then, the user manual and training materials of the system are developed to prepare for the subsequent roll-out. At the same time, condense the technical achievements of the project, apply for software copyright or invention patents, and ensure the protection of intellectual property rights. Finally, the application specifications and efficacy evidence of the digital intervention system were summarized and formed, and the system was promoted to the medical management department and the industry. For example, writing a report recommending to health authorities the introduction of the system as an adjunct to the management of chronic diseases, or exploring with insurance institutions the inclusion of digital therapeutics in health management services. Carry out academic exchanges, present the system at digital health-related conferences, and expand its influence. Actively look for industry partners, explore productization and commercial promotion paths, and lay the foundation for the subsequent transformation of the project.

Expected Results: This research will directly produce an intelligent intervention system with prototype application value. In the short term, the system can be piloted and promoted as a digital health product in chronic disease management; In the long run, the artificial consciousness model and human-computer interaction concept behind it can be extended to more medical fields (such as mental health and self-care). Through the implementation of the project, we expect to publish 1-2 papers or reports on the design and test results of the system, apply for relevant patents and software copyrights, and realize the closed loop from theoretical innovation to technological innovation. More importantly, this system verifies the feasibility of the new intervention strategy of "artificial awareness + digital health", creates a new path of comprehensive physical and mental intervention for chronic diseases, and provides a practical tool for improving the management effect of chronic diseases.

Feasibility analysis

The project has ambitious goals and complex contents, but it has a solid preliminary foundation and good implementation conditions, and has high feasibility:

1. Multidisciplinary research team: Prof. Yucong Duan, the project applicant, has been engaged in the basic theoretical research of cognitive computing and artificial intelligence for a long time, and is the proposer and leading expert of the DIKWP model. Since winning the Wu Wenjun Artificial Intelligence Award in 2020, his team has continuously deepened the theory of DIKWP and established a special artificial consciousness laboratory. As of 2022, Prof. Yucong Duan has applied for more than 240 domestic and international patents related to DIKWP, covering artificial intelligence, blockchain, semantic web and other directions. These achievements reflect the team's strong innovation and research and development capabilities. At the same time, the project has established a cross-field cooperation network with clinical medical experts and biologists, including clinical experts in endocrinology and metabolism, cardiovascular, oncology, and respiratory, as well as psychology and bioinformatics researchers. Such a multidisciplinary team ensures that the project has professional support in both medical mechanism and technical realization, and the cross-collaboration advantage is obvious.

2. Preliminary work and basic data: In terms of brain-body interaction and chronic disease research, our team members have carried out some preliminary explorations. For example, the medical experts of the team have participated in the project of "Psychological Stress and Cardiovascular Events" of the National Key Research and Development Program, and have accumulated data and analysis models on the impact of stress on hypertension. In the direction of tumors, the cooperative units have longitudinal cohorts of cancer patients, which can provide follow-up data of psychological and immune indicators; In the field of metabolic diseases, we have a pilot study on lifestyle interventions for diabetic patients, which has preliminarily validated the role of digital management in improving adherence. These preliminary works have provided valuable data and experience that will help to fast-track the relevant research in this project. In addition, the DIKWP Artificial Awareness Lab of the applicant team has developed an evaluation platform and some software frameworks for the DIKWP model, and has practical experience in the implementation of DIKWP in artificial intelligence systems. These technical reserves will be directly used for the system development of this project to reduce the difficulty of implementation.

3. Both technical feasibility and innovation: The project is innovative in theory, but the feasibility is fully considered in the selection of technical routes. For example, data collection will rely primarily on proven wearables and mobile application technologies, without the need to reinvent hardware; The artificial intelligence part will be transformed into our theoretical framework with the help of the current advanced large model API and reinforcement learning algorithms, which has a basis for implementation. When it comes to digital interventions, there are already a number of successful digital health products, such as mobile apps for diabetes management, that provide a template for us to innovate by adding an element of human awareness that doesn't completely deviate from existing technologies. Therefore, the risk is controllable: even if the artificial awareness module does not work as expected, we can still take a step back and use traditional algorithms to ensure that the basic functions are realized. At the same time, each module has parallel sub-goals, the research plan arranges iterative verification, and the results of each stage will feed back to the follow-up, and the incremental development reduces the overall risk. In response to possible challenges (such as difficulty in cross-system data fusion and high complexity of human-computer interaction), we have relevant experts in our team to tackle them, and reserve mobile resources to deal with them.

4. Abundant research support conditions: The relying unit has perfect scientific research conditions, including: the brain imaging center can support fMRI experiments, the cooperative departments of the hospital can recruit patients and collect clinical data, the high-performance computing server cluster can meet the needs of AI model training, and the laboratory animal center can carry out animal experiment verification. In addition, the project has received support and commitment from the universities and affiliated hospitals to facilitate ethical approval, data sharing, and equipment use. In terms of funding, this project belongs to the direction of the national key R&D plan, and the application funds are sufficient, which will be used for equipment purchase (such as wearable devices, test terminals), clinical trial subsidies and R&D manpower support, which can meet the research needs. In terms of management, the team will establish a regular communication mechanism, set up the heads of each module and node milestones, and the scientific research management department of the relying unit will also assist in monitoring the progress and financial implementation of the project to ensure the smooth implementation of the project.

5. Social and Ethical Acceptability: This project involves human research and AI applications, but we place a high value on ethical norms. All clinical subjects will sign informed consent, data will be kept strictly confidential, and the digital intervention protocol will follow medical norms and will not directly replace medical orders. The AI system is intended only as an aid, and its recommendations are reviewed by a doctor. We have received expert guidance on ethical design to ensure that the research process is ethical. As the project aims to improve the management of chronic diseases and benefit patients, the social benefits are clear, and the adoption of digital means is in line with the development direction of Wisdom Healthcare, it is expected to be understood and supported by the patient community and medical institutions. We will also establish feedback channels, listen to patients' opinions on the system, and continuously optimize to provide truly humane services. These measures provide a good basis for sustainable development of the project.

In summary, the project has a high degree of feasibility in terms of team, foundation, technology, conditions and ethics. Although the research content is extensive, it is guaranteed by solid multidisciplinary collaboration and previous results. With the unique strengths of the applicant team in the field of artificial intelligence and medicine in DIKWP and the strong support of our collaborators, we are confident that we will complete all research tasks as planned and achieve the expected goals.

Phased achievements and assessment indicators

In order to ensure the smooth progress of the project and the output of the results, a phased plan and quantitative assessment indicators are formulated as follows:

Phase 1 (initial project, 1-12 months): Model construction and mechanism explorationMain
tasks: Complete the construction and preliminary verification of the brain-body interaction DIKWP bidirectional model; Carry out basic research on the mechanism of brain-body regulation disorders in chronic diseases, focusing on metabolic diseases and cardiovascular diseases.
Expected Result:

• Proposed the DIKWP*DIKWP theoretical model of brain-body interaction, and wrote 1 model framework paper (submitted to domestic core journals or international conferences);

• Preliminary data of patients/animals with metabolic diseases and cardiovascular diseases were obtained, and one periodic report of brain-body regulatory disorders was formed.

• Applied for 1 model-related invention patent (such as "brain-body cognitive interaction modeling method");

• Stage assessment indicators: the prototype of the brain-body model has been completed, the mechanism experiments of at least 2 types of diseases have been completed, ≥ published/submitted papers, and 1 patent application has been ≥.

Phase 2 (mid-project, 13-24 months): Comprehensive mechanism research and artificial consciousness integration
Main tasks: Expand the study of the mechanism of chronic diseases to tumors and chronic respiratory diseases, and complete the comparison of the brain-body interaction mechanism of the four types of diseases; A cognitive-metabolic-immune three-dimensional model was constructed, and artificial consciousness simulation was introduced to analyze the effect of Purpose layer disorder.
Expected Result:

• 2-3 research papers on the mechanism of brain-body interaction disorders in four types of chronic diseases (each paper focuses on different diseases and is submitted to SCI journals);

• Establish a cognitive-metabolic-immune integration model, realize key simulation verification, and form a white paper on model description;

• Published or submitted 1 academic paper on the relationship between Purpose layer disorder and chronic disease behavior (based on patient longitudinal data);

• Stage assessment indicators: complete the data collection and analysis of four types of diseases, and realize 3D model simulation; A total of 3 papers have been published/submitted ≥ (including 1 SCI paper≥); He has made 2 presentations at academic conferences at home and abroad, ≥ demonstrated the theoretical progress of the project.

Phase 3 (mid-project, 25-36 months): Intervention system development and preliminary trial
Main tasks: Design and develop a prototype version of the artificial consciousness-driven digital intervention system, and complete the core module functions; A trial study of a small sample of patients was conducted to verify the feasibility of the system and the trend of effect.
Expected Result:

• The prototype of the artificial consciousness digital intervention system (software v1.0) realizes the basic functions and runs stably in the experimental environment;

• 1 technical report on system design and implementation, including architecture diagram, module description and test results; 1 software copyright registration may be formed;

• The small-scale pilot application was completed, and 1 preliminary data analysis report was issued, showing the trend of the impact of the system on patient behavior and indicators.

• Published 1 paper related to system research and development in international conferences or domestic journals, focusing on the application and innovation of artificial consciousness in digital health;

• Stage assessment indicators: the system prototype has been built and passed the internal test; Completed ≥ 30 patients with pilot intervention; Published/registered 1 soft copyright or patent ≥; 1 paper or conference paper ≥.

Phase 4 (end of the project, 37-48 months): main tasks of system improvement and comprehensive evaluation
: optimize the function of the intervention system, improve human-computer interaction and algorithms; conduct large-scale controlled trials to assess the effectiveness of the system; Summarize and form the final outcome of the project.
Expected Result:

• The optimized version of the artificial consciousness intervention system (software v2.0) increases the optimization function and is stably applied in the target population;

• Complete at least 100 controlled trials (or multicenter trials) with statistical results on the effectiveness of systematic interventions;

• He has published 2 papers in high-level SCI journals: one report on the brain-body interaction mechanism of chronic diseases and the innovation of artificial consciousness model (theoretical contribution), and the other report on the clinical trial effect of digital intervention system (application contribution);

• Form a general report of the project, covering the research background, model, mechanism discovery, system design, experimental effect, transformation prospect, etc., with a word count of no less than 50,000 words, providing guidance for subsequent research and development;

• Stage assessment indicators: the control trial was completed, and the system was proved to have a significant improvement effect in at least one chronic disease (P<0.05); The project has published a total of ≥ 3 SCI papers, of which at least 1 has an impact factor of > 5; 3 doctoral/master's students ≥; 1 scientific research award or media report ≥ the project (reflecting academic impact).

Overall assessment objectives: At the end of the project period, all the established goals will be achieved, and the specific indicators are: an innovative cognitive model of brain-body interaction has been proposed and recognized by the academic community (through paper citation, peer review, etc.); Elucidate new insights into the brain-body regulation mechanism of chronic diseases (results written in guidelines or cited by others); The developed artificial awareness intervention system has been tested and is ready for further replication (with positive regulatory or market feedback). In addition, the project has produced no less than 5 high-quality papers (3-4 SCI papers), more than 5 intellectual property rights such as patents and soft works, and a number of talent training. Achieving these targets will indicate that the project has met or even exceeded its mandate, and has achieved fruitful results in both science and application.

Convert app paths and campaigns

This project has a clear application orientation, and its research results will be transformed and promoted through multiple ways to benefit more patients with chronic diseases and serve the national health strategy:

1. Theoretical model and scientific cognition: The brain-body interaction DIKWP model and the cognitive-metabolic-immune three-dimensional model proposed by the project will be disseminated in the academic community as new theoretical tools. We plan to publish a monograph or chapter in the later stage of the project to systematically introduce the theoretical framework for reference by researchers in related fields. A new paradigm of "brain-body interaction" should be introduced in medical education and continuing education to improve clinicians' attention to the interaction mechanism between body and mind. For example, the evidence found in this project can be added to textbooks or guidelines such as endocrinology and psychosomatic medicine, and it is recommended that patients with chronic diseases should be evaluated and managed at the psychological purpose level. Through academic reports, seminars and other forms, the concept of physical and mental integration of chronic disease management was promoted to medical workers and the concept change was promoted. This will help promote the prevention and treatment of chronic diseases in China from simple organic treatment to comprehensive physical and mental intervention, and improve the overall efficacy.

2. Industrialization and application of digital intervention system: The artificial consciousness-driven intervention system developed by the project has significant application prospects. We will accelerate the industrialization process of the project after it is completed. Specific plans include:

• Partnering with companies in the digital health space to refine system functionality to meet medical product specifications and conduct large-scale testing. Consider setting up a startup team or company to upgrade a research prototype into a commercial digital therapeutics product.

• Apply for approval of the digital therapeutics from the NMPA (if the relevant regulatory system is in place) or as a Class II medical device. After obtaining legal qualifications, it can be formally promoted and used in clinical practice.

• A number of large hospitals or chronic disease management centers were selected for demonstration application. With technical support from the project team, the system was deployed at these pilot facilities to assist in the management of patients with chronic diseases who were followed up. Collect real-world results and feedback to further improve and establish success stories.

• With the help of the government's hierarchical diagnosis and treatment and family doctor service network, the system will be gradually extended to the grassroots level of the community. It is used by community doctors and patients in the form of SaaS services to realize remote management support for tens of thousands of patients with chronic diseases. Especially at the grassroots level, where doctors are understaffed, digital intervention systems can act as "assistants" to improve service coverage.

• Explore cooperation models with commercial insurance and medical insurance institutions. If the system can effectively improve the health of patients, consider including the cost of its services in the insurance payment to motivate patients to participate. At the same time, it is also a kind of system business model, a guarantee of sustainable operation.

Through the above measures, we strive to make the system occupy a place in the domestic chronic disease management market and serve millions of patients within 3-5 years after the end of the project. In the long run, if the effect is outstanding, it can also be promoted to the international market, participate in the global digital health industry competition, and strive for a leading position in the field of digital health for China.

3. Policy and standard impact: The research of this project is in line with the country's major strategic needs for chronic disease prevention and control and Wisdom Healthcare, and its results are expected to have an impact on relevant policy formulation and industry standards. At the end of the project, we will submit a report on policy recommendations to the National Health Commission and the competent departments of science and technology, including: promoting the integrated physical and mental management model of chronic diseases, supporting the integration of digital therapeutics into the formal healthcare system, and strengthening the supervision and guidance of the application of artificial intelligence in the medical field. Relevant data show that the premature mortality rate of chronic diseases in China has decreased, but the burden of chronic diseases is still heavy, and new strategies are needed. Our recommendations will provide a reference for the Healthy China Initiative. On the other hand, our digital intervention system, if successfully applied, can contribute to the development of guidelines and standards for digital health interventions. We will participate in standard discussions organized by industry associations, share our safety and efficacy evaluation methods, and promote the establishment of evaluation norms for digital therapeutics. In terms of AI ethics, we will also summarize our experience for policymakers to improve the ethical guidelines for AI healthcare. In this way, we ensure that the results of the project are valued at a broader level.

4. Continuous R&D and talent training: The completion of the project is not the end, we will continue to deepen relevant research and maintain technological leadership. On the one hand, using the data and models generated by the project, apply for follow-up scientific research projects (such as the National Natural Science Foundation of China and the Ministry of Science and Technology) to explore more scientific problems (such as the application of artificial consciousness in other diseases, the combination of brain-computer interface, etc.). On the other hand, the interdisciplinary talents cultivated by the project will become a valuable asset. These scientific research backbones who understand both medicine and AI can play an important role in universities and enterprises in the future, driving more cross-innovation. On the basis of the project, we plan to establish a joint laboratory for intelligent intervention for common chronic diseases, as a long-term platform for continuous R&D and incubation results, and realize the rolling development of talents and technologies.

5. Social Publicity and Public Health Awareness Enhancement: Using the influence of the project, we will also carry out science popularization and publicity to raise the public's awareness of the physical and mental management of chronic diseases. For example, we will produce popular books or new media popular science articles to introduce how to assist in the recovery of chronic diseases by regulating emotions and strengthening will, and promote simple physical and mental adjustment methods. Organize patient lectures or public welfare activities to teach patients with chronic diseases to use digital tools to self-manage, and establish a positive attitude of "my health is my decision". Expand social influence through official media on the progress and effectiveness of the project (e.g., Science and Technology Daily, Health News, etc.). These efforts have helped to improve public health literacy and create a climate for the large-scale application of the project's results.

To sum up, this project has planned a clear transformation path from theory to application. Relying on the strategy of simultaneous development of scientific research and industry, and interaction between policy and market, we are confident that the project results will go out of the laboratory, to the clinic, and into the society. It is expected that within a few years after the end of the project, China will be among the top in the world in the research on the brain-body interaction mechanism of chronic diseases and the practice of artificial intelligence intervention, and related technologies and products will benefit the majority of patients and produce significant health and economic benefits. The successful transformation of this project will become a national example of "medical-engineering intersection" scientific research, reflecting the strong supporting role of scientific and technological innovation in people's health.