WO2020235028A1 - Health information management system, mobile terminal device, health information management method, and health information management program for mobile terminal device - Google Patents

Abstract

Ingested protein (P), fat (F), and carbohydrate (C) and the amount of exercise have not been able to be displayed.　A wearable terminal 40 collects exercise data in Step S200, and, when determining that a request to transmit exercise data is made in Step S208, transmits the exercise data in Step S210. A smartphone 20 acquires the exercise data in Step S100, acquires information on ingested substances in Step S116, corrects the information on ingested substances in Step S128, and uploads the acquired information in Step S130. A cloud server C stands by for the uploading from the smartphone 20 in Step S300, identifies an image by using AI technology in Step S310 on the basis of the uploaded data, calculates ingested PFC on the basis of the image and corrects the ingested PFC, and causes the smartphone 20 to download PFC data in Step S320. The smartphone 20 displays the downloaded PFC data in Step S144, and displays exercise data in Step S146.

Description

Health information management system, mobile terminal device, health information management method, health information management program for mobile terminal device

The present invention is the world's first "My Health Anatomy (MHA) My Anatomy" application that makes it possible for users to understand what is the strongest KPI. For example, using AI, KPIs (Key Performance Indicators) related to health are captured and accumulated by wearables (including devices, chips, etc.) and smartphones, and KPIs are displayed on smartphones and the like. It is not a medical device, but is intended to support self-determination.

The obese population has already exceeded 2.1 billion, accounting for more than one in three human beings, and because it is expanding rapidly, it is called the Global Obesity War. War destroys everything. The same is true for obesity. It is widely known that obesity is associated with many diseases such as hypertension, diabetes, aspiration syndrome, kidney disease, myocardial infarction, cerebral infarction, cancer, dementia, psychiatric disorder, orthopedic disorder, dialysis, and blindness, and is accompanied by enormous sacrifice. Has been done. Obesity progresses chronically. Earth history states that there are no drugs with dramatic effects and that the efforts of obese people are often unrewarded. And it is accelerating not only individual financial collapse but also social collapse. The above diseases are the main efforts of WHO and the Japan Society for the Study of Obesity.

As a conclusion of the 2007 JAMA A to Z study, a low-carbohydrate, high-protein, high-fat diet was evaluated as a viable alternative recommendation for weight loss for the above-mentioned causative obesity. That is, it was important that it was high in protein and high in fat, rather than simply lowering carbohydrates. In N Engl J Med. 2008, a carbohydrate-restricted diet with unlimited calories improved glycemic control in diabetic patients. That is, it was a result showing the collapse of the calorie myth. The results of this publication in the world's No. 1 medical journal are currently being published at advanced medical centers in each country, and extremely excellent results have been reported in books and the like.

From the above points of view, it is assumed that it will be impossible to overturn those results even in the future by the world's best medical journal. The results of these two studies revealed how consuming sufficient protein and fat while managing carbohydrates is beneficial for weight loss and control of blood glucose levels. However, in modern society, slogans such as simply carbs off have become popular. Therefore, as practiced by leading medical institutions around the world, and as these treatises have shown, it is imperative to get enough protein and fat while managing carbohydrates.

Cite and explain major treatises about how carbohydrates are associated not only with obesity and diabetes, but also with numerous diseases.
As for the relationship between carbohydrates and illness, it has been clarified that the low-glycemic-load diet affects obesity and chronic diseases (Note: Chronic diseases are briefly summarized by the US CDC at the bottom, so here. Omitted). The sugar diet and the risk of water cancer have also been published in the US NIH magazine, showing a causal relationship. In addition, the risk of sugar and breast cancer, dietary glycemic load and colorectal cancer was revealed. Cancer Letters showed colon tumor correction and immune response in mice fed different sources of protein, fat, and carbohydrate. The association of advanced glycation end products (AGEs) with aging and the etiology of late-stage diabetic complications have been elucidated by Diabetes Research.

Since AGEs can be captured and accumulated by a smartphone camera, it is highly meaningful to accumulate and display them by MHA, and users can counter not only healthy skin but also arteriosclerosis (that is, aging). Conventionally, it has already been pointed out that AGEs are closely related to dementia, but the current situation is that extremely limited medical institutions measure them from the skin at a high cost, and they are inexpensive and inexpensive. There was no way to observe for a long time, but with the development of MHA and smartphone cameras, it has become possible to capture and display AGEs as one KPI. MHA is not necessary for simple capture, but since AGEs are a lifelong problem, self-management is desirable rather than spending high medical expenses. Similarly, further evidence of a link between periodontal disease and coronary artery disease has been reported in the Journal of Periodontology. In a meta-analysis of many studies, lipids have already been ruled out as having a causal relationship with coronary artery disease, and it is currently noted that AGEs are the worst, and AGEs are due to the Maillard reaction between sugar and protein. Produced in the body. Scary, a meta-analysis of the effects of sugar on children's behavior and cognition has been reported in JAMA. Currently, obesity in children is the greatest concern in the world. Not only does it cause behavioral and cognitive abnormalities, as shown in this paper, but it also has the depth of the problem that develops into obesity or diabetes in children. That is, the age of metabolic syndrome is getting younger. So far, we have described carbohydrates and various diseases.

Reference papers 1) Bell, SJ, Sears, B., “Low-glycemic-load diets: impact on obesity and chronic diseases.” Critical Reviews in Food Science & Nutrition, 43 (4), 2003, pages 357-77.
Reference paper 2) Michaud, DS, Liu, S., Giovannucci, E., et al., “Dietary Sugar, Glycemic Load, and Pancreatic Cancer Risk in a Prospective Study.” Journal of the National Cancer Institute, 94 (17) , 2002, pages 1293-1300.
References 3) Romieu, I., Lazcano-Ponce, E., Sanchez-Zamorano, LM, et al., “Carbohydrates and the Risk of Breast Cancer Among Mexican Women.” Cancer Epidemiology and Biomarkers Preview, 13 (8), 2004, pages 1283-1289.
References 4) Franceschi, S., Dal Maso, L., Augustin, L., et al., “Dietary Glycemic Load and Colorectal Cancer Risk.” Annals of Oncology, 12 (2), 2001, pages 173-178.
References 5) Nutter, RL, Gridley, DS, Kettering, JD, et al., “Modification of a transplantable colon tumor and immune responses in mice fed different sources of protein, fat and carbohydrate.” Cancer Letters, 18 (1) , 1983, pages 49-62.
Reference paper 6) Sensi, M., Pricci, F., Andreani, D., et al., “Advanced Nonenzymatic Glycation Endproducts (AGE): Their Relevance to Aging and the Pathogenesis of Late Diabetic Complications.” Diabetes Research, 16 ( 1), 1991, pages 1-9.
Reference Article 7) Gererts, SO, Legrand, V., Charpentier, J., et al. “Further evidence of the association between periodontal conditions and coronary artery disease.” Journal of Periodontology, 75 (9), 2004, pages 1274- 80.
References 8) Wolraich, ML, Wilson, DB, White, JW, “The effect of sugar on behavior or cognition in children. A meta-analysis.” JAMA, 274 (20), 1995, pages 1617-21.

Deep relationship between protein (P), lipid (F), carbohydrate (C), neurons, insulin resistance, blood glucose level, etc. as KPI (existence of connection)
Regarding proteins, in conventional nutrition, proteins are essential for cell formation, which is the main human tissue such as organs and bones. An important paper has been published by Nature, so I will describe it here. It has been shown that a variety of neurodegenerative diseases lead to common causes and pathological mechanisms-misfolding, aggregation and accumulation of proteins in the brain, which can lead to neuronal apoptosis. Studies in various fields strongly support this hypothesis and show in the paper 1) that general treatments for these catastrophic diseases may be possible. Next, in paper 2), insulin resistance in skeletal muscle plays a major role in the development of type 2 diabetes and may be causally related to an increase in intramuscular fatty acid metabolites. FATP1 deletion protected KO mice from fat-induced insulin resistance and intramuscular accumulation of fatty acyl-CoA without altering systemic hyperlipidemia. These findings demonstrated an important role of intramuscular fatty acid metabolites in inducing insulin resistance. That is, there is a relationship between intramuscular fat and insulin resistance. The Diabetes Society guides moderate exercise to obese and diabetic patients, but even from the relationship between the papers 1) and 2), it is highly important that sufficient protein and lipid intake are taken. For example, it is widely known that blood glucose level and insulin resistance (obesity hormone) have a twin relationship. Therefore, as instructed by the academic society, walking after a meal lowers the blood glucose level and improves insulin resistance. However, unfortunately, blood data do not reveal the quality and quantity of exercise and the appropriate time. In other words, how much exercise should I do? Also, what time should I do it? The Cleveland Clinic, the world's most advanced medical college, encourages exercise within 90 minutes of eating, which is the current limit of medicine. In order to know the in-depth answer, heart rate, blood pressure, exercise, sleep quality, food (including alcohol) obtained from blood tests, wearables, etc. are taken as KPIs, and MHA creates an AI database, collects and displays blood. Knowing the interrelationship between test data and these KPIs is exactly the mission of "my health anatomy = MHA". Unfortunately, although it is linked with devices such as wearables and smartphones, there is no connection regarding exercise, AGEs, blood data, drug history, heart rate, blood pressure and sleep quality, diet, and alcohol. Was not previously available. Furthermore, no similar interrelationships could be found for body weight, waist size, or BMI. Here is the essential background and significance of the development of MHA. Finally, since sleep is closely related to blood pressure and obesity, interlocking (devices such as wearables, smartphones, MHA) is a new invention necessary to form a bright and flat world in the future. We will dig deeper and describe the important relationships between KPIs, blood pressure, and sleep described above. Researchers at Mayo Clinic have recently discovered how poor sleep and quality affect a person's blood pressure. After monitoring eight participants for 16 days, they found that subjects recorded significantly higher blood pressure at night when they experienced longer, shorter sleeps. Blood pressure and sleep are closely related in this result and in other studies. Therefore, the significance and novelty of capturing and displaying as KPIs are high. Since obese people are highly associated with hypertension, the following KPIs such as diet and wearable devices and MHA of smartphones have theoretical medical rationality.

Paper 1) Nature Reviews Neuroscience volume 4, pages 49-60 (2003) Recent evidence indicates that diverse neurodegenerative diseases might have a common cause and pathological mechanism-the misfolding, aggregation and apoptosis of protein. from different disciplines strongly support this hypothesis and indicate that a common therapy for these devastating disorders might be possible. The aim of this article is to review the literature on the molecular mechanism of protein misfold therapeutic intervention in neurodegenerative diseases. Many questions still need to be answered and future research in this field will result in exciting new discoveries that might impact other areas of biology.

Paper 2) J Investigation. Clin Invest. 2004; 113 (5): 756-763. C 2004 The American Society for Clinical Inactivation of fatty acid transport protein 1 prevents fat-induced insulin resistance in skeletal muscle, Department of Internal Medicine, Yale University School of Medicine.
Insulin resistance in skeletal muscle plays a major role in the development of type 2 diabetes and may be causally associated with increases in intramuscular fatty acid metabolites. Fatty acid transport protein 1 (FATP1) is an acyl-CoA synthetase highly expressed in skeletal muscle and modulates fatty acid uptake and metabolism by converting fatty acids into fatty acyl-CoA. To investigate the role of FATP1 in glucose homeostasis and in the pathogenesis of insulin resistance, we examined the effect of acute lipid infusion or chronic high-fat feeding on insulin action in FATP1 KO mice. Whole-body adiposity, adipose tissue expression of adiponectin, intramuscular fatty acid metabolites, and insulin sensitivity were not altered in FATP1 KO mice fed a regular chow diet. In contrast, FATP1 deletion protected the KO mice from fat-induced insulin. resistance and intramuscular accumulation of fatty acyl-CoA without alteration in whole-body adiposity. These findings demonstrate an important role of intramuscular fatty acid metabolites in causing insulin resistance and suggest that FATP1 may be a novel therapeutic target for the treatment of insulin resistance and type 2 diabetes.

Paper 3) Glucose levels hit their peak within 90 minutes of a meal, according to a 2017 study. By Cleveland clinic
Paper 4) Mayo Clinic researchers recently set out to find how reduced sleep quantity and quality could affect a person's blood pressure. After monitoring their eight participants for 16 days, they found that when their subjects experienced prolonged periods of shorter sleep, they also registered substantially higher blood pressure numbers at night. While the size of the study was small, they presented their findings at the American College of Cardiology's 64th Annual Scientific Session in San Diego, California, on March 15.

Describes the 1988-2012 National Health and Nutrition Examination Survey by the US CDC due to obesity,
Among US adults aged 18 years and older, the prevalence of metabolic syndrome increased from 25.3% in 1988-1994 to 34.2% and increased by more than 35% between 1988 and 1994. By 2012, more than one-third of all adults in the United States have met the definition and criteria for metabolic syndrome agreed by several international organizations. (Note: Combining obesity with other diseases is defined as metabolic syndrome). The treatises for the following are shown at the bottom.

・ All causes of death (mortality rate) ⇒ Explained below.
・ Hypertension ⇒ Since hypertension in obese people is a global epidemic, explanations using papers are omitted.
High LDL cholesterol, low HDL cholesterol, or high levels of triglyceride (dyslipidemia) ⇒ December 2004 Diabetes Treatment 27 (12): 3009-3016 Many presentations at the American Diabetes Association (ADA) meeting in June , Aspects of lipid therapy in diabetics. Brown et al. (Summary 929) reports the correlation between fasting lipids and HbA1c (= abnormal blood glucose levels) in a diabetic patient with a population of 11,938 in the northwestern part of Kaiser Permanente. Comparing one or two people with dyslipidemia at risk of high cardiovascular disease (CVD), HbA 1c was 7.3, 7.5, and 7.9%, triglycerides 150, 210, and 381 mg / dl, and HDL 54, 40 and 37 mg 1 / dl suggest an association between poor glycemic control and dyslipidemia in type 2 diabetes, respectively. Pladevall etc. (Summary 948) A managed care database of 9,642 people, with 6,751 people followed between 1997 and 2001, was used to describe trends in the management of dyslipidemia in diabetic patients. Lipid tests were performed at 37, 44, 51, 55, and 6% in 1997, 1998, 1999, 2000, and 2001, respectively. During this period, 19, 23, 28, 33, and 41% of patients were prescribed lipid-lowering drugs, and the LDL cholesterol target <100 mg / dl was achieved by 22%, 27%, 32%, and 34%. Much is still being done in the treatment of lipids in diabetics in the United States, with 37% and 37% suggesting significant improvements, respectively. That is, there is a close relationship between changes in blood glucose levels (changes in HbA1c) and abnormal lipids. Describe the relationship between exercise and lipids.

・ About dyslipidemia About combined intervention of diet and exercise therapy for the treatment of dyslipidemia An effective preliminary strategy for lowering cholesterol levels. Currently, dyslipidemia is most commonly treated with drug therapy. However, as safety concerns about the use of medicines have arisen, the need for alternative non-pharmacological therapies is becoming increasingly apparent. The National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) includes a combination of diet and exercise therapy instead of medication for patients with moderate range of coronary heart disease (CHD). Lifestyle therapy is recommended. In this review, we examined the cholesterol-lowering effects of the following two NCEP-recommended combination therapies. 1) A low-saturated fat diet combined with exercise, and 2) Dietary supplements, namely fish oil, oats, or plant sterol supplementation and treatment of exercise dyslipidemia. Combination therapy is particularly advantageous because diet and exercise elicit complementary effects on lipid profiles. More specifically, with a few exceptions, diets have low total (TC) and LDL cholesterol (LDL-C) levels, whereas exercise interventions have HDL cholesterol (HDL-C). While increasing, it decreases triglyceride (TG) levels. For specific interventions, a low-saturated fat diet combined with exercise reduced TC, LDL-C, and TG levels by 7-18, 7-15, and 4-18%, respectively, and HDL-C levels of 5-. 14 improved. % Or, when combined with dietary supplements and exercise, TC, LDL-C, and TG levels decreased by 8-26, 8-30, and 12-39%, respectively, and HDL-C levels increased by 2-8%. did. These findings indicate that combination therapy is an effective and preliminary means of improving cholesterol levels in patients diagnosed with dyslipidemia, and is an alternative to drug therapy if cholesterol levels are slightly above the normal range. Suggest that it should be done.

Relationship between diabetes and exercise Effective exercise therapy to reduce insulin resistance in women with type 2 diabetes Diabetes Care 2003 The purpose is a training program that combines resistance and aerobic training in postmenopausal women with type 2 diabetes. However, it was to evaluate whether insulin sensitivity was improved compared to aerobic training alone. A second objective was to correlate improved insulin sensitivity with changes in abdominal adipose tissue (AT) and thigh muscle density. Study Design and Methods-A total of 28 obese postmenopausal women with type 2 diabetes were randomly assigned to one of three 16-week treatments: control, aerobic training (Ae only), or aerobic. Plus resistance training (Ae + RT). Measurements of outcomes before and after treatment included glucose treatment with a hyperinsulin normoglycemic clamp and computed tomography scans of abdominal AT and central femoral skeletal muscle. Results-Glucose infusion rate was significantly increased in the Ae + RT group (P <0.05). Both exercise groups reduced subcutaneous and visceral AT in the abdomen and increased muscle density. The Ae + RT training group showed a significantly greater increase in muscle density than the Ae-only group. Improved glucose treatment was independently associated with changes in subcutaneous AT, visceral AT, and muscle density. After controlling abdominal AT, muscle density remained associated with glucose processing. In conclusion, the addition of resistance training to aerobic training enhanced glucose treatment in postmenopausal women with type 2 diabetes. Improved insulin sensitivity is associated with loss of AT under the abdomen and viscera and increased muscle density. Therefore, it has been clarified that there is a deep relationship between insulin resistance and the above-mentioned enhancement of glucose treatment. Adding resistance training to such great aerobic training is not a low hurdle for older diabetics. So do you wait for death? This is another origin of MHA development, because it is possible to extract the difference between the data accumulated in the MHA AI database and the treatise when a new treatise with a high level of evidence is published. , You can change the exercise (although there are various types) little by little under the guidance of your doctor. MHA with exercise KPIs and other KPIs that can be referred not only to users but also to related parties including medical professionals will support the future of the elderly.

-Since I explained about coronary heart disease, I will omit it here.
Relationship with cerebral infarction J Stroke Cerebrovasc Dis. 2017 Risk of metabolic syndrome (MetS) and ischemic stroke Out of 5398 subjects, 2021 suffered from MetS with an incidence of 37.4%. The incidence of stroke with and without MetS was 2.6% and 1.1%, respectively, showing greater significance in the former group (P = 0.026). Compared to controls, stroke participants showed higher prevalence than some components of MetS, including hyperglycemia and hypertension. In a Cox proportional hazard analysis, the overall hazard ratio to long-term risk of ischemic stroke was 1.37 (95% confidence interval: 1.15-1.63, P <0.001) in MetS patients. Considering the different components of MetS, hyperglycemia (hazard ratio = 1.83, P = 0.01) and hypertension (1.74, P = 0.019) are effective in developing long-term ischemic stroke. I was able to predict it. CONCLUSIONS: MetS and its main components can be powerful predictors of long-term ischemic stroke. Therefore, it is necessary to focus on the identification and proper management of MetS components to prevent the occurrence of stroke.

・ Relationship between gallbladder disease, metabolic disorder, BMI, TG, waist circumference, blood pressure, triglyceride, etc. World J Gastroenterol. 2012 Metabolic syndrome and gallstone disease Metabolic syndrome (MetS) and cholelithiasis Results: Of 7570 subjects, cholelithiasis The prevalence was 12.1% (13.1% for men and 10.2% for women). Body mass index, waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose and serum triglyceride (TG) were higher than controls, but serum high-density lipids were lower than controls in the case group. There were significant differences in waist circumference, blood pressure, FPG (fasting blood glucose) and TG between the case and the control. In an age-adjusted logistic regression model, metabolic syndrome was associated with cholelithiasis. The age-adjusted odds ratio for MetS to GSD (glycogen storage disease) in men was 1.29 [95% confidence interval (CI), 1.09-1.52; P = 0.0030], and 1.68 (95% CI) in women. , 1.26 to 2.25; P = 0.0004). The overall age-adjusted odds ratio for MetS to GSD was 1.42 (95% CI, 1.23-1.64; P <0.0001). Men with more metabolic disorders had a higher prevalence of cholelithiasis, and the tendency was statistically significant (P <0.0001). The presence of the five components of MetS increased the risk of cholelithiasis by a factor of 3.4 (P <0.0001). The prevalence of GSD in women with the five MetS components was five-fold higher than in women without the MetS component. The higher the MetS component, the higher the prevalence of GSD (P <0.0001). The presence of the five components of MetS increased the risk of cholelithiasis by a factor of 4.0. Conclusion: GSD appears to be strongly associated with MetS, and the more components of MetS, the higher the prevalence of GSD. This paper also studies waist circumference, blood pressure, FPG (Fasting Blood Glucose) and TG to see how these are KPIs for GSD.

-Since it is widely known in the medical community that osteoarthritis (cartilage and bone collapse in joints) is more likely to occur due to obesity, the explanation in the treatise is omitted. It is also well known that there are cases in which artificial joint surgery can be avoided if the patient loses weight. Generally, in orthopedics, guidance is given to avoid surgery due to weight loss. This instruction includes biking, walking, and swimming with less strain on the joints. Of course, diet and exercise are important for weight loss, so MHA, which accumulates and displays such KPIs, plays a major role.
Sleep apnea and breathing problems Since it is often reported in newspapers and television, the explanation of the treatise is omitted. It has long been known that improving obesity improves sleep apnea syndrome. However, at this time, the correlation between this disease and devices such as wearables is unknown. The fact that MHA can be correlated with other KPIs using this device is expected to trigger early treatment and prevent bus and train accidents by appropriate medical care.

-Some cancers (endometrium, breast, colon, kidney, gallbladder and liver) are omitted as mentioned above. It is widely known that it is common in obese and diabetic patients. Therefore, a system that allows you to see the relationship between HBA1c and exercise for the rest of your life is desired, and that is the mission of MHA.
・ There are many inconveniences in low quality life if you get fat and have metabolic syndrome. Since it is widely reported, I will omit the explanation. Polypharmacy problems of clothes and medicines.
・ Body pain and difficulty in physical function. It has long been clinically known that obesity causes pain in the body, even in patients who do not require the above-mentioned orthopedic joint surgery. Therefore, the meaning of diet (slimming) is great. However, calorie restriction (in the old days of the Diabetes Society) diet was the mainstream in the world just because it was good, but now patients are affected by the carb counting method devised by Dr. Atkins and Dr. Bernstein in the United States. It turns out that you can lose weight without strong intensity and painful aerobic exercise. These groundbreaking proofs are evidenced in the 2007 A to Z test (the world's best test results).

Metabolic syndrome and all causes and mortality from cardiovascular disease: A prospective (JPHC) study based on the Public Health Center. Between 1990 and 2005, a Japanese health center-based prospective (JPHC) study found metabolic risk in 12,412 and 21,639 men aged 40-69 years with no history of cardiovascular disease (CVD) or cancer. Baseline measurements of the factors were performed. To clarify the role of obesity adopted by the definition of MetS in Japan, we investigated the clustering of risk factors in data grouped according to overweight status. During the 12.3 year follow-up, there were 2,040 deaths, of which 947 died of cancer and 304 died of CVD. MetS significantly increased the hazard ratios for all-cause mortality in women and CVD mortality in men. Non-overweight with two or more risk factors had similar effects on all-cause mortality and CVD mortality. Clustering of metabolic factors caused a linear increase in risk relative to mortality. CONCLUSIONS: MetS moderately increased all-cause mortality and CVD mortality.

As mentioned above, the risk of death of metabolic syndrome is high. To prevent and eliminate obesity first, as mentioned above, not only dietary KPIs, but also other KPIs such as exercise, sleep, heart rate, blood pressure, sleep, blood data, and drug history. There is design rationality and novelty of the application.

Show the relationship and effects of obesity metabolic syndrome and exercise. BMC Sports Sci Med Rehabil. 2018 2018 Abdominal Obesity and Metabolic Syndrome: Exercise as a medicine. Metabolic syndrome is defined as at least three clusters of five clinical risk factors: abdominal (visceral) obesity, hypertension, elevated serum triglycerides, low serum high density lipoprotein (HDL) and insulin resistance. It is estimated that more than 20% of the world's adult population is affected. Abdominal (visceral) obesity is considered to be a major risk factor for metabolic syndrome, and it is estimated that by 2030, 50% of adults will be classified as obese. According to health and economic evidence, regular and consistent exercise reduces abdominal obesity and results in favorable changes in body composition. Therefore, it has been suggested that exercise is a drug in itself and should be prescribed as such. This review provides a summary of current evidence for the pathophysiology of dysfunctional adipose tissue (lipidosis). It explains the relationship between steatosis and metabolic syndrome, and how exercise mediates these processes, and assesses current evidence for the clinical effectiveness of exercise in the management of abdominal obesity. The review also discusses the types and amounts of exercise needed to optimally improve health in relation to the available evidence and considers the difficulty of achieving adherence to an exercise program. CONCLUSIONS: The optimal dose and type of exercise are unknown at this time, but there is reasonable evidence to support the use of an exercise program that reverses metabolic syndrome. The main challenge for healthcare professionals is how to motivate individuals to participate in and comply with exercise programs that are used prophylactically and as a treatment for metabolic syndrome.

In the past, it was not possible to create a database of captured and integrated displays using devices such as smartphones and wearables.
According to the present invention, if AI is learned by signals including barcodes, voices, photographs, and manual corrections, the main KPIs of obese people can be displayed and the users themselves can use them for their own life management. ..

It is the schematic which shows the whole system to which the MHA application which concerns on one Example of this invention is applied. It is a figure which shows the image of the screen which a user can actually see. It is a block diagram of the smartphone corresponding to the information communication terminal B. It is a block diagram of the wearable terminal corresponding to the device A. It is a flowchart of a program carried out by a smartphone. It is a flowchart of a program executed by a wearable terminal. It is a flowchart of the program executed by the cloud server C.

(First Example)
FIG. 1 is a schematic diagram showing the entire system to which the MHA application according to an embodiment of the present invention is applied. Further, FIG. 2 is a diagram showing an image of a screen that the user can actually see. This is just an example, and can be changed by the user and customization at the time of manufacture and sale.

This system is composed of a device A worn by the user, an information communication terminal B such as a smartphone carried by the user, and a cloud server C.
The device A mainly collects exercise data and transmits it to the information communication terminal B. The information / communication terminal B mainly photographs foods to be ingested, uploads images together with exercise data to cloud server C, downloads PFC data from the cloud server C, and displays various information. The cloud server C uses the uploaded image, exercise data, etc., analyzes what the user has ingested by the AI engine, generates PFC data based on the analysis result, and downloads it to the information communication terminal B.

Hereinafter, the related technical contents will be explained with reference to the reference numerals in the drawings. 1 Explanation of exercise data and novelty in diet such as PFC and integration with external substance factors PFC (factor) is Protein (protein), It is derived from the acronyms Fat and Carbohydrate. The importance of accumulating this factor balance, consciously using it for eating habits, preventing illness, and maintaining health remains unwavering until today. However, it is not easy to capture the PFC factor of each meal and manage it. It is the origin and purpose of the present invention to enable people to practice these things instantly, easily, reasonably and continuously.

The user can obtain the PFC of the meal or beverage simply by automatically reading (sometimes taking a picture) the identification code such as the voice, photo, and bar code of the meal and uploading it. As a result, it becomes easy to recognize which factor of the PFC factor should be lowered or increased. The user can adjust the main factors of the diet by himself.

PFC is used as basic data to improve the eating habits of users, but exercise and sleep collected by devices such as smart watches shown in 1a, which are collectively called devices including healthcare devices. By adding blood pressure and heart rate data, it becomes an integrated integrated data, which enables integrated analysis of not only PFC but also exercise to support people's choices and decisions.

The data collected by healthcare devices is mainly exercise data such as steps, distance, and calories burned, but in recent years, devices that can collect health support data such as heart rate and sleep records have appeared. Sphygmomanometers, blood glucose meters, weight scales / body composition analyzers, activity meters, etc. equipped with communication functions have also been commercialized, and data collected from these devices can also be integrated and utilized with PFC.

It should be noted that the exercise data of 1 is generally accumulated in the built-in storage of the smartphone paired with the healthcare device via Bluetooth or the like. This data can be freely obtained from external systems and applications at any time via the library provided by the OS.

Recent smartphones are equipped with a motion coprocessor that processes sensor information such as gyro sensors, accelerometers, and compasses, which is less accurate than dedicated healthcare devices, but the smartphone itself can also be used as a pedometer. It is possible.

2 Meal photography 2a is a smartphone application (hereinafter referred to as MHA application) to which the present invention is applied.
The first step of the MHA app is to take pictures of food, beverages, and sometimes medicines that enter the body and read codes, but before that, I will briefly explain the work required.

The user downloads the MHA application to the smartphone and proceeds with the account registration procedure. If this procedure is completed successfully, a user-specific record will be created in the 7a database on the cloud. After the above, you can take pictures of food and drinks. Specifically, if you point the smartphone's camera at 2b and tap the shooting button in the MHA app, an image like 2c will be saved in the smartphone's internal storage. If you already have a photo taken, you can specify it. All you have to do is repeat this after each meal.

After that, the photos / codes, PFC, exercise data / health supplement data, etc. uploaded by the user will be integrated and further accumulated.
By the way, the 6a and 7a servers are built on the cloud platform.

3 Meals, beverages, drug images, cosmetics, skin, blood vessels Upload Meal photos taken with the MHA app are uploaded to storage on the 7a cloud server via the Internet, unless the user deletes the account thereafter. Stay in the cloud. If the upload is successful, the user may delete the photo from their smartphone.

The upload of 3 is managed by linking it to the database as a user-specific history, and it is also used for learning AI. Uploads can be treated as anonymous data, and it is possible to propose PFC, which is a dietary factor, with numerical values and photographs. When registering the account described in 2, specify the purpose of use and ask for consent for secondary use by the MHA app.

Basically, you upload photos of each meal, but there are more cases where you don't have to upload them (reading previously used photos, sounds, codes, etc.). For example, if there is a user who says "breakfast is always the same", give instructions (touch, voice) to MHA. The ones uploaded by the user in the past are stored and can be reused, saving time and effort.
Details will be described in 9 below, but the flow described in 3 to 8 is to calculate PFC from photographs that enter the body such as food and beverages.

4 Image input The process of recognizing 3 included in the photograph uses the external image recognition AI service described in 4a. Image recognition technology has been created by major IT companies based on many years of research, and in recent years it has been released in a form that can be used by general users. In other words, this technology already has sufficient practicality, and it is not the kind of technology that will be newly developed and competed in the future.
Needless to say, it is AI learning that improves the recognition accuracy of uploads. Since the development of blueprints and theories has been completed, improvement in recognition accuracy cannot be denied if learning is performed according to individual characteristics.

Currently, there are many AI options that can be adopted for 4a, both commercial and non-commercial, but here we will explain the flow when using the image recognition AI service provided in the market as an example.
A trained model is prepared for such an image recognition AI service, and for the present invention, "a recognition model for something that enters or touches the body" is selected, or another model is used. If you enter something that touches the body uploaded in 3 to this AI, all the objects contained in the image will be recognized. All means that one is recognized as one, and three is recognized as three.

5 Return of results All image recognition AI services basically return the recognized object and its accuracy. Like 5a, it is "toast 0.9354, bacon: 0.892, fried egg: 0.813". The closer the accuracy is to 1, the higher the match rate. In the case of the image recognition AI service described above, the result of 4 is formatted as JSON format and returned. The process for analyzing this content and passing it to the PFC engine is performed by the PFC server in 7a.

The accuracy of the present invention is increased by continuous use. In other words, it is the learning effect of AI. I want to clearly define the following behavior.
In addition, since it is difficult to calculate the amount of food and drink with AI at the beginning, if 4 to 7 are completed normally, there is a degree of freedom to have the user correct the amount before 8 or at the same timing as 8. Yes (numerical correction by human, voice correction) is possible. AI will relearn this correction as well.

6 The collation PFC engine works in conjunction with the database, as shown in 6a. Let's take breakfast of toast, fried egg, and bacon as an example. The PFC of each food is P: 4.9g, F: 2.6g, C: 30.3g per toast, P: 6.7g, F: 7.6g, C: 0.2g, bacon per fried egg. The primary assumption is P: 2.58g, F: 7.82g, C: 0.06g per sheet. Next, the PFC engine plays a role of collation by registering those PFC grams in the database and matching them with the correction of 5.

Assuming a case that was not captured in the PFC database, human input (hand, voice, use of reference photo) was set as a backup. This is the same method as 5.

7 Quantification When you find a record of things that fit in each body, the next step is to add them up and convert them from grams to percentages.
As an example, if AI recognizes the meal of 5a as cheese toast, fried egg, and bacon, and the user has one cheese toast, one fried egg, and two bacon, the total PFC is P: 20.16g, F: 30.34g, C: 31.12. Expressed as a ratio, P: about 25%, F: about 37%, C: about 31%.

8 Returning the result Returning the result of 7 to the MHA app is the final flow of PFC indexing.
The results of PFC will be returned in JSON format, which is compatible with Internet technology, with a view to cooperation with future external system development (medical institutions, trainers, schools, companies, governments).

9 Infographics
An example of the analysis in 8a is shown.
In the 1997 American Dietary Goal, P: 20%, F: 20%, and C: 60% are the ideal balance. By this criterion, the diet of 5a has a slight excess of P, a large excess of F, and a shortage of C. In addition, when there is a PFC plan intended by the user, the user execution and its result can be evaluated, for example, PF> C. Since MHA including PFC has tracking ability, it can be compared with past accumulation results.

The above is an example analyzed only by PFC, but if 1 motion data is added here, it becomes extremely active. As an example of adding the number of steps to PFC, the option "steering to PF> C, 8,000 steps" is posted for users who were "P: 10%, F: 20%, C: 70%, exercise: very few" it can.
The graph allows you to intuitively determine what you can reduce or increase. The viewing itself becomes fun, which motivates us to continue. Above all, it is possible to change the medium- to long-term dietary habits and exercise improvement plan guidelines by using the accumulation results in units of days, weeks, months, 3 months, 6 months, and 12 months.

Next, the self-input correction in 8a is supplemented.
No matter how much AI learning progresses, it is almost impossible to produce PFC with 100% accuracy. This is because the quality of the photographs (blown out, blackout, blurring, excessive processing, etc.) is not good, and the seasonings and oils used in cooking and the amount of each are not very clear. If the hamburger steak is green, it can be difficult to tell if it's a cheese-based sauce or a bean-based sauce. This is difficult to distinguish even with the human eye.

Also, for those who control their eating habits on a daily basis, they should think that the PFC they understand is more reliable than the AI PFC.
In such a case, 8a is the entrance to correct, input, and select PFC by yourself. If you include a few normal patterns that capture and upload what goes into your body, there are four main patterns to get PFC.

The first is a pattern that accepts the result of 8 as it is. The second is a pattern that manually corrects the result of 8 after performing 2 to 3, the third is a pattern that inputs PFC from the beginning in the second stage, and the fourth is the one that the user himself uploaded in the past. , It is a pattern (reuse of PFC) to select the one that is close to the meal that you ingested from the ones uploaded by other users. PFC can be calculated automatically, and can be input manually, by voice, barcode, etc., and the automatically calculated result can be corrected. We can meet all your needs.

The data accumulated through the MHA app is accumulated in the cloud. This data can be provided externally by installing an API on the PFC server of 7a, and in the future, cooperation with medical, national, corporate and school systems, external application systems and services, anonymous big The range of applications, such as sales as data, is endless.
So far, we have explained the connections related to KPIs of MHA applications, and at the same time, explained the significance and novelty of integrated display.

In FIG. 2, a display example on the information communication terminal B is shown on the left side, and an image recognition AI service is displayed on the right side.
In the information and communication terminal B (S-phone Wave), Protein (P: protein) Fat (F: lipid) Carbohydrates (C: carbohydrate) ingested from the upper row is shown as "P35 F55 C5", and the alcohol ingested next. (Al) is shown as "Alcohol 5". These are mainly data obtained from the image recognition AI service.

Next, the degree of sleep quality is shown as "SleepQ good", and the degree of exercise quality is shown as "Exercise perfect". Although these are data obtained from device A, an external recognition service may be used to improve quality analysis.
Next, the degree of heart rate and blood pressure quality is shown as "HR / BP good", the height-to-weight ratio (BMI index) is shown as "Body-w good", and the waist size is shown as "Waist size fine". .. These include data that cannot be obtained by device A alone, and data can be obtained manually or by connecting to an external measuring instrument.
Next, the change status of the dosage is shown as "Drug reductions", and the blood is shown as "Blood great". In this way, the status of blood in general may be shown, or individual measured values such as blood glucose level may be displayed. These also include data that cannot be obtained by device A alone, and data can be obtained manually or by connecting to an external measuring instrument.

Of course, the indications for heart rate and blood pressure quality, height-to-weight ratio (BMI index), waist size, dosage changes, and blood glucose levels are just examples.
In addition, the display can be changed as appropriate, such as displaying graphically.

What is realized in this embodiment.
A) Protein (P: protein) Fat (F: lipid) Carbohydrates (C: carbohydrate) + alcohol (Al) For the first time in the world, PFC and Al can be captured and accumulated. Also,
1. 1. 2. Capture and collect electronic signals such as barcodes (supermarkets, drug stores, convenience stores, etc.) with smartphones. Capture, integration, and correction with a voice input system 3. AI captures and accumulates PFC + Al by photographs 4. Improve the accuracy of captured data by manual correction (input)

B) A) It is an invention that can find out what is a KPI for the body regarding things that touch and enter the body. Integrated display using AI is an invention that can be displayed on the screen of a device such as a wearable, a smartphone, a PC, a VR, or the like.
C) It is an invention that can be linked with devices such as wearables and can be compared and referred to with A) to see how alcohol is related to sleep quality. Sleep quality is closely related to alcohol, exercise, and A). In the present invention, the user is the first invention in the world to be able to self-determine what and how much to change. The American Sleep Education Society (sleep education.com) recommends the following to the public:

I. Habituation to determine wake-up / bedtime
II. Stay awake during the day
III. Avoid smoking caffeine, alcohol and tobacco that interfere with sleep
IV. Review habits such as bathing, light meals, and reading before falling asleep
V. Keeping your bedroom dark, quiet, and a little cool will help you sleep better.
VI. Don't bring worries or worries to your bed

Nutrients. 2019: Children's Emotional Diet, Health Behavior, and Obesity: A Cross-sectional Study in 12 Countries. Eating in response to negative emotions (emotional diet, EE) can cause an individual to become obese. Still, it is not well known how a child's EE is associated with body mass index (BMI) and health behavior (ie, diet, physical activity, sleep, and watching television). In this study, we examined these associations in a cross-sectional sample of 5426 (54% girls) children aged 9-11 from 12 countries and 5 continents. EE, food intake, and TV viewing were measured using a self-managed questionnaire, and physical activity and nighttime sleep were measured using an accelerometer. BMI was calculated using the measured weight and height. EE factor scores were calculated using confirmatory factor analysis and dietary patterns were identified using principal component analysis. The association of EE with health behavior and BMI z-score was analyzed using a multilevel model that included age, gender, and household income as covariates. EE is honestly and consistently (over 12 research sites) associated with unhealthy dietary patterns (β = 0.29, SE = 0.02, p <0.0001), suggesting that this association is not limited to Western countries. ing. The positive link between EE and physical activity and watching television was inconsistent between sites. Results tended to be similar for boys and girls. In this sample, EE is independent of BMI, but prospective studies are needed to determine whether higher EE in children predicts unwanted dietary patterns and the development of obesity over time.
As described above, MHA collects data from devices such as wearables and smartphones for a long period of time and returns it to the user, so that the user can work on improvement for good sleep for a long period of time.

Next, the relationship between alcohol and sleep will be described. Prevalence of major depression in France in the general and specific populations from 2000 to 2018: a systematic review of the literature. RESULTS: The prevalence of major depression in France over the last 12 months was about 8% in the 2000s and increased to 10% in the 2010s. Studies on national databases tended to underestimate this prevalence (less than 2%), leaving only the most severe depression hospitalized and treated with antidepressants. In certain populations, data have been published for HIV, epilepsy, some cancers and cardiovascular diseases, cannabis and tobacco smokers. Conclusion: In France, the prevalence of depression appears to have increased between 2000 and 2010. In particular, with regard to alcohol intake disorders, cancer, cardiovascular diseases, and immunoinflammatory diseases, further research needs to be published due to the specific relationship between the high prevalence and MDD (major depressive disorders). Another study in the French general population should also be conducted. This data should help develop precision medicine to improve care for MDD in France.
As shown above, MHA accumulates sleep, so it captures and accumulates KPIs and helps users to know themselves. Sleep and blood pressure are linked, blood pressure affects cardiac function, and sleep problems are linked to obesity, which is a metabolic syndrome caused by obesity.

D) By using MHA, drug epidemiological tests and medical efficiency can be clarified more deeply. For example, A) has a very strong causal relationship with drugs and diseases. However, "good outpatients" are widely known in medicine, and the challenge is MHA for "patients outside the hospital." In this part, by tracing the MHA (with exceptions) in the category of "outside the hospital" that was the patient's free will, medical cost reduction that could not be achieved by medical care is dramatic and medical efficiency. This is the world's first invention that has made it possible to improve. In particular, C (C of PFC) is closely associated with obesity, and has been reported worldwide to be associated with diabetes, hypertension, myocardial infarction, cerebral infarction, psychiatric disorders, allergies, and dementia.

E) The drug history is exclusively owned by patients, medical institutions, and pharmacies (the number of information provided by smartphones is increasing a little in the world), but as MHA, drugs and other KPIs are compared and referred to. By doing so, we will increase the number of “good patients outside the hospital” and improve drug efficiency. As in the global discovery, there is a way to reduce C but increase PF (in Japan, women's medical colleges are famous for reducing C for difficult hypertension, and many are implemented in the world). Has been done. As a result, drug reduction is possible for many diseases, but the impact on humankind of capturing and accumulating PFCs on smartphones and the like is great. The scientific significance of this change in C has been published in New Engl, Lancet, JAMA, the world's top journals, and has a history of about 10 years. However, it was difficult to become a “good patient outside the hospital”. This is because patients do not know advanced nutrition and kinematics. These are usually out of insurance (patients are themselves). Worst of all, nutrition has changed dramatically in the last decade, but there are many doctors and nutritionists who do not know it. (Example: No patient has been cured by the old diabetic diet, but the fact that carb counts are relieving has been reported worldwide, but it is cumbersome to manage and you have to carry it with you even if you record it in a notebook. In addition, since it is not possible to integrate using AI, there is no choice but to rely on the feeling. Effective use of MHA by voice, bar, photograph, etc. has a novelty that far surpasses conventional science and methods.

F) Furthermore, it is an invention that has made it possible to improve the quality of long-term epidemiology of drugs and diseases. For example, if you compare the effects of 1 to 3 cosmetics and pharmaceuticals on the skin, since MHA exists on smartphones with basic numerical values, it is possible to perform comparative verification and cross-comparison tests by basic numerical values (by group). Become. In other words, fake slimming products can easily peel off ghosts. The body changes over time. Therefore, what used to be good PFC + Al and exercise is not effective at this time. That is why an AI database that can be integrated and displayed like MHA is needed. As an example, eating less because you are fat has been denied in the latest nutritional sciences for more than a decade, and PFC and exercise are the basic teachings.

G) MHA is an invention that allows patients to always discuss and support better medical care with their doctors.
H) A smartphone camera captures skin data and AI accumulates. It is an invention that allows users to compare and refer to past data and the present at any time and find the true value of cosmetics and esthetics. In reality, even in cases such as psoriasis in dermatology, case photo recording with a smartphone camera is useful, and it is an invention that can be integrated and examined with other KPIs possessed by MHA. Users can think deeply like never before.

I) What's happening to others? You can compare and refer to it. Consumers are deceived and confused and chaotic. Among them, MHA was developed. MHA is a new invention that can capture and accumulate everything that touches and enters the body as KPIs and can be compared and referenced (= yourself and others).
J) The causes of obesity in pets are exercise and diet, but by having MHA capture the causes as KPIs and display them in an integrated manner, users can not only provide pets with a better living environment than before, but also for many years. It will be possible to manage the health of pets over the years. Occasionally there are metabolic changes, weight gain, and blood sugar levels begin to rise (if you suspect that), from that point on, helping you decide to change your MHA and discuss it with your doctor. It is an invention that allows you to enjoy the best medical care.

Problems in Background Society The changes in the human body with age are well known, and as a result, unscrupulous cosmetics, diet products, and methods that seem to be fraudulent are rampant. It also suffers financial and health damage. But if you want to protect yourself from them, you need some way. Without it, users were forced to waste money, and in addition, they didn't have the opportunity to know what was a strong KPI because they weren't agglomerated by the MHA method.

In addition, users themselves understand the actual situation of "good patients in hospitals" = "patients ignoring the guidance of doctors and pharmacists", which is a common problem in the medical world, and sometimes it is a very serious problem of insurance administration. It can help recognize the accumulated KPIs by MHA and self-determine to change.

However, on the other hand, unfortunately in the medical world, patients always write down their medication history and medical history when they go to another hospital (although in some countries it is possible to give it electronically) to the doctor in charge. It is required to show. However, the best medical care cannot be realized only by the drug history and medical history. This is because metabolic syndrome caused by obesity cannot be seen in a single department alone and requires multidisciplinary treatment. It is necessary for the user to self-manage MHA, discuss not only the current medical history and drug history, but also other KPIs with medical professionals, and carry out "two-legged race multidisciplinary medical care" as a medical cost control strategy. If you compare it to war, you need what you need for the front, factor analysis = effective strategy planning and result analysis, orbit correction, and increase the probability of maximizing cost effectiveness. Upload barcodes and photos "including medicines and blood tests" at convenience stores, supermarkets, pharmacies, etc., MHA operates, accumulates, and heeds back, and users pursue costs not only with medical professionals but also with physical coaches and beauty specialists. It is an invention that has become possible. Again, being able to see what's going on with other people would be a hope for life.

The changes in the human body with aging are well known, but there was no suitable countermeasure. As a result, vicious diet products and methods that seem to be fraudulent are rampant. However, if you are willing to evaluate them, you need an evaluation method. Without it, customers were forced to spend unnecessary money, and because they did not perform cumulative evaluation by the MHA method, they did not have the opportunity to know the truth. In other words, MHA will be clarified if changes in the body and things that come into contact with the body are captured as KPIs and integrated display is possible. The present invention was designed and developed as a basic structure based on the results of the world's top medical journals as evidence level 1 of 2007/8.

Before and now before the 18th century Small victory: PF> C 1977 Great strategy: C> PF
Note 1) Evidence level 1 (strongest evidence and support) has been seen to win with PF dominating. However, in August of last year, research results showed that C and mortality rate have a U-shaped relationship. The fact that the 1977 Great Operation has a low ceasefire effect on obesity, diabetes, myocardial infarction, cancer, and dementia, and is worsening at an accelerating rate has not disappeared. MHA makes it clear what you should choose as a KPI and focus on it.

In recent years, devices (wearables, chips, etc.) that have been developing have captured factors other than the following (1) and (3) of individual users.
(1) PFC + alcohol (2) Exercise, heart rate, blood pressure, sleep (3) By medical institution: Blood data and drug history (delivered on paper or electronically)
(4) Weight, BMI, waist size According to this MHA, (1), (2), (3) and (4) can be simply integrated and displayed. Then, you can instantly understand what is a strong influence factor (KPI: key performance indicator). MHA with integrated KPIs is also a good radar.

(Second Example)
This embodiment is an example of a configuration that further embodies the first embodiment.
FIG. 3 is a block diagram of a smartphone corresponding to the information communication terminal B, and FIG. 4 is a block diagram of a wearable terminal corresponding to the device A. Although the cloud server C is not shown, it will be described as a computer system equipped with the AI engine described above.

In the smartphone 20, CPU 21, ROM 22, RAM 23 and the like are connected to each other via a bus 24 as main control elements. A basic program or data for the smartphone 20 to function is recorded in the ROM 22, and the CPU 21 expands the data or program in the memory area of the RAM 23 to perform predetermined control. While the RAM 23 is mainly a volatile device, the storage 25 is a non-volatile area, and the storage 25 is a variety of application programs that add functions in addition to the basic program, image data, audio data, and the like. Remember.

The display 26 displays information based on control by the CPU 21. The display 26 has a touch function, and provides an input function such as a software keyboard by displaying an image and detecting touch information.
The smartphone 20 can perform voice call communication and data communication via the network interface 27. Data communication supports both WAN and LAN. With this data communication function, the smartphone 20 performs data communication with the cloud server C. By uploading and downloading the predetermined data, the smartphone 20 and the cloud server C share the functions and the MHA application of the present invention is executed.

Various devices are connected to the smartphone 20 via the interface 28. As devices, a camera 29, a microphone 31, a Bluetooth interface (BT) 32, a barcode reader 33, and the like are connected. The barcode reader 33 can also be realized by software processing using the camera 28 as an image pickup element.

Further, a power button (not shown), a volume up / down button, and the like are also connected via the interface 28.
As an example, the smartphone 20 communicates with the wearable terminal 40 via the Bluetooth interface 32.
In the wearable terminal 40, a control unit 41 including a CPU and the like and a display 42 with a touch function are connected via a bus 43. An acceleration sensor 44, a heart rate sensor 45, a GPS sensor 46, and the like are further connected to the bus 43, and the control unit 41 obtains the detection signals of these sensors and obtains predetermined motion data. Further, a Bluetooth interface 47 is connected to the bus 43, and the control unit 41 can communicate with the smartphone 20 via the Bluetooth interface 47.

The wearable terminal 40 is supposed to be worn by the user on a daily basis, and the control unit 41 accumulates motion data based on the detection signal of the sensor and transmits the motion data to the smartphone 20 at a predetermined timing.

5 to 7 are flowcharts of programs executed by the smartphone 20, the wearable terminal 40, and the cloud server C, respectively.
Although the processing of each step does not match the execution instruction of the CPU 21 or the control unit 41, it is functionally expressed.

On the smartphone 20, the following processing is performed periodically or at the timing when the user gives an explicit instruction. First, the CPU 21 acquires motion data in step S100. Here, in this embodiment, as shown in FIG. 5, each step includes the step shown in the step. For example, the process of step S100 is a process of a higher concept including the process of steps S102 to S114, and in the process of step S100, the processes of steps S102 and S110 are executed, and the process of step S102. In the process, the processes of steps S104 to S108 are sequentially performed, and in the process of step S110, the processes of steps S112 and S114 are sequentially performed.

In step S100, the CPU 21 requests the wearable terminal to transmit data in step S102 and requests the non-wearable terminal to transmit data in step S110 in order to acquire the exercise data. Since it communicates with the other party that sends data, processing is performed for each device on the other party.

In step S102, the wearable terminal that makes the data transmission request performs the following processing. In the wearable terminal 40, the control unit 41 collects exercise data in step S200, and if there is a request for transmission of exercise data in step S208, the collected exercise data is transmitted in step S210. Normally, there is no request for transmission of exercise data, and exercise data is collected as an original function of the wearable terminal 40 in step S200.

The wearable terminal 40 of this embodiment includes an acceleration sensor 44, a heart rate sensor 45, and a GPS sensor 46, and counts a pedometer using the detection result of the acceleration sensor 44, and the acceleration sensor 44 and the GPS sensor 45 The movement amount is measured using the detection result, and the heart rate is measured using the detection signal of the heart rate sensor 45. In many cases, data based on the physical characteristics of the user is set in advance as a setting. For example, data such as height, weight, gender, age, and stride length are available.

In step S202, the control unit 41 collects exercise data as a pedometer, in step S204, collects exercise data as a movement amount, and in step S206, collects heart rate information associated with exercise as exercise data. Do. In the wearable terminal 40, when motion is detected, the detection result of each sensor may be used.

On the other hand, the partner device with which communication is performed in step S110 is a non-wearable terminal. The device that collects exercise data does not refer to direct exercise, whereas the wearable terminal 40 collects exercise data indicating the amount of activity, but the data of body weight and blood pressure have important meaning. And these are more accurate data to use with non-wearable instruments than to collect with wearables. Therefore, in this embodiment,
In step S110, the non-wearable data transmission request is made. Specifically, in step S112, the body weight data is acquired from the non-wearable weight scale, and in step S114, the blood pressure data is acquired from the non-wearable sphygmomanometer.

Although not shown, the number of devices that can communicate with the smartphone 20 is increasing due to Bluetooth for weight scales and blood pressure monitors, and by pairing in advance, when communication with the smartphone 20 becomes possible, it will be detected and described above. Make a data transmission request and acquire weight data and blood pressure data. Many weight scales have a function as a body composition meter, and by transmitting the measured body composition meter data together, exercise is performed when comparing with PFC data and exercise data including energy consumption. You will also be able to judge the efficiency of. For example, it becomes easy to determine whether exercise contributes to a decrease in body fat percentage.

As described above, the smartphone 20 makes a data transmission request via communication in Bluetooth at a predetermined timing, and when the data transmission request is made to the wearable terminal 40 in step S102, the wearable terminal 40 steps. The transmission request is received in S208, and exercise data including pedometer data, movement amount data, and heart rate data is transmitted in step S210.

The smartphone 20 acquires the exercise data and saves it in the storage 25. After that, the CPU 21 acquires ingestion information in step S116. The ingestion is all meals, snacks, etc. that the user ingests. In order to record what was ingested, in this embodiment, image data of a meal, bar code data purchased, image data of a receipt of a meal, image data of a beverage, and a beverage Use the image data of the receipt you ordered.

The user acquires these data as much as possible. Specifically, when the user has a meal, the smartphone 20 is operated so that the CPU 21 takes a picture of the meal and saves it as image data in step S118. When the foodstuff is purchased, the barcode is read and the barcode data is saved in step S120. Similarly, in step S122, the meal receipt is read and the order contents and purchase contents are saved, in step S124, the beverage is photographed and saved as image data, and in step S126, the receipt related to the beverage is read and the order contents are saved. And save your purchase. By repeating this on a daily basis, the smartphone 20 performs a process of acquiring ingestion information in step S116.

As mentioned above, in order to accurately accumulate the amount of food and beverages ingested, it is also effective for users to actively correct information. The image data is for two people, but in some cases it is shared by three people, and conversely it is all in one person. In such a case, the image data and the receipt data only tell us that we had a meal for two people. In step S128, the CPU 21 makes it possible to correct the information of the ingested food, and accepts the correction of the ingested amount by the user. As a result, accurate PFC data can be obtained.

After collecting the ingested data, the CPU 21 of the smartphone 20 uploads the acquired information in step S130. The other party to upload the information is the cloud server C, and the CPU 21 starts communication with the cloud server C using a predetermined protocol. Then, the CPU 21 uploads the image data in step S132, the command in step S134, the audio data in step S136, and the correction data in step S138.

The command means data that represents the content such as "same as the previous day's meal" or "usually eaten meal 1" instead of taking a picture of the meal and sending the image data. A command may be used in which a meal whose PFC data can be specified is registered in advance, and instructions such as meal 1 and meal 2 are given.

Further, the voice data is such that the user verbally explains the contents of the meal and the contents of the beverage, and the voice data is uploaded to the cloud server C.
The cloud server C waits for upload from the smartphone 20 in step S300, and when uploaded, acquires the uploaded data in steps S302 to S308. Specifically, in step S302, image data is acquired, in step S304, a command is acquired, in step S306, audio data is acquired, and in step S308, correction data is acquired.

After acquiring the data, the cloud server C identifies the image in step S310. In this embodiment, first, in step S312, the content of the meal is identified by using the AI engine for material determination based on the image data. The results are mainly the materials used and their amounts. Subsequently, in step S314, a PFC weighing AI engine is used based on the identified material. Primarily, once the material and amount are determined, the amount of protein, fat and carbohydrate contained in it can be identified. By adding other information included in the content of the meal, such as the name of the dish, the amount of protein, fat, and carbohydrate can be identified more accurately.

The main purpose of using this cloud server C is to measure the amount of protein, fat, and carbohydrate ingested by the user as accurately as possible. Therefore, as one means, the ingested meal is photographed, and the material and amount used are determined from the image. It is assumed that the amount of protein, fat, and carbohydrate contained in a predetermined unit weight for each material is digitized. Such data conversion is generally already completed. Therefore, if you know the ingredients and amount, you can calculate the amount of protein, fat, and carbohydrate ingested by the user.

As for the image data, not only the image of the meal but also the image of the receipt, the barcode, etc. can be used. In particular, in the process of accumulating data, if it is possible to identify a certain dish in a restaurant, the contents of that dish should be stored in a database in advance, or it should be associated with an image taken by another user. Then, you will be able to specify the contents of the meal.

In the cloud server C, the command, voice data, and correction data are identified in step S316. If there is any processing required for the image data based on the audio data and correction data, execute it. For example, the material and amount may be specified while specifying the meal based on the command and voice data. As a command, a command "same as what I ate yesterday" or a command "standard breakfast 1" in which a standard breakfast is registered can be assumed. Designate a meal that is so-called a meal that has already been registered. For voice data, the content of the meal is expressed by voice, and the amount of protein, fat, and carbohydrate ingested by the user is calculated based on the reproduced meal.

Further, the PFC data is corrected in step S318 based on the correction data. For example, if two people eat a meal, each value in the PFC data is halved.
When the correction process is completed in this way, the PFC data is downloaded in step S320. Downloading is a process of transmitting the generated PFC data to the smartphone 20.

On the other hand, in the smartphone 20, the CPU 21 downloads the PFC data in step S140 and stores the PFC data in the storage 25. Exercise data is also stored in the storage 25.
The smartphone 20 displays information in step S142. As shown in FIG. 2, the display is performed. Specifically, the PFC data is displayed in step S144, and the exercise data is displayed in step S146.

Various types of exercise data display have already been proposed. In this embodiment, the above-mentioned PFC data is displayed as the user's health management, and the user's exercise data corresponding to the above-mentioned PFC data can mainly determine the energy consumption and the content of the muscle training. Those are suitable.

It is also preferable to be able to display not only the total amount of energy consumed but also the time zone in which it is consumed. There is also an opinion that the degree of influence on obesity differs between the case of eating a meal and going to bed immediately and the case of sleeping after a certain period of time.

It is also preferable that the energy consumption reflects the content of the exercise performed at the fitness gym in addition to the measured number of steps and the amount of movement. Since various exercise devices are equipped with a function that can display the energy consumption, the energy consumption is communicated by Bluetooth and transferred to the smartphone 20, or the exercise device converts the energy consumption into a QR code and displays it on the smartphone. 20 may take a picture and read the energy consumption.

Similarly, as the content of muscle training, it is preferable to be able to notify the smartphone 20 from the exercise equipment of the fitness gym. However, the user may use the display 26 of the smartphone 20 to input information such as the type, load, number of times, and interval of muscle training as well as information on the time zone during exercise. The fact that the amount of protein intake and the time zone correspond to the content and time of muscle training is one of the elements of health management judgment based on PRC data.

As described above, this embodiment includes the following contents.
A health information management system that includes a mobile terminal device and a server device that can communicate with each other via a network.
When the server device receives the information indicating the content and amount of the meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and returns the data representing the amount of this protein, fat and carbohydrate. It is possible and
The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user.
Information indicating the content of the meal ingested by the user is transmitted to the server device,
The server device returns and receives data representing the amount of protein, fat and carbohydrate ingested by the user.
A health information management system that displays the exercise data for a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.
The mobile terminal device is provided with a camera, images the meal ingested by the user to obtain image data, and transmits the image data to the server device as information representing the contents of the meal ingested by the user.
The server device is a health information management system that receives image data of images of meals ingested by the user as information representing the contents and amount of meals.
The mobile terminal device is equipped with a microphone, and the user transmits a voice explaining the content and amount of the ingested meal as voice data to the server device.
The server device identifies the audio data, and if the audio data represents the content and amount of the ingested meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and this A health information management system that returns data representing the amount of protein, fat and carbohydrates.

The mobile terminal device captures a two-dimensional image showing the content and amount of the meal ingested by the user, and transmits the two-dimensional image data to the server device.
If the two-dimensional image data represents the content and amount of the ingested meal, the server device identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and the protein, fat and carbohydrate. A health information management system that returns data representing the amount of.
The mobile terminal device can communicate with a wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and provides health information by communicating with the mobile terminal device. Management system.

The server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal. A health information management system that has the function of improving the accuracy of meals.
It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate. A mobile terminal device capable of communicating with a server device capable of returning the representative data via the above network.
The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user.
Information indicating the content of the meal ingested by the user is transmitted to the server device,
Receives data representing the amount of protein, fat and carbohydrate ingested by the user, returned by the server device,
A portable terminal device that displays the exercise data for a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.

The mobile terminal device can communicate with a wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and communicates with the mobile terminal device to provide the mobile terminal. apparatus.
The server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal. A mobile terminal device having a function of improving the accuracy of the operation.

It is a health information management method implemented in a health information management system including a mobile terminal device and a server device that can communicate with each other via a network.
The mobile terminal device collects exercise data representing the content and amount of exercise of a predetermined user, and collects exercise data.
The mobile terminal device transmits information representing the contents of the meal ingested by the user to the server device, and the mobile terminal device transmits the information to the server device.
The server device receives information indicating the content and amount of the meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and outputs data representing the amount of this protein, fat and carbohydrate. Reply,
The mobile terminal device receives data indicating the amount of protein, fat, and carbohydrate ingested by the user, which is returned by the server device.
The mobile terminal device is a health information management method that displays the exercise data in a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.
It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate. It is a health information management program of a mobile terminal device that can communicate with a server device that can return the data to be represented via the above network.
The health information management program is applied to the mobile terminal device.
A function to collect exercise data showing the content and amount of exercise of a given user,
A function to send information indicating the contents of the meal ingested by the user to the server device, and
The function to receive the data representing the amount of protein, fat and carbohydrate ingested by the user, which is returned by the server device, and
A health information management program for a mobile terminal device that realizes a function of displaying the exercise data in a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.

Needless to say, the present invention is not limited to the above examples. Needless to say, those skilled in the art
-Applying the mutually replaceable members and configurations disclosed in the above-mentioned Examples by appropriately changing the combination thereof-Although not disclosed in the above-mentioned Examples, it is a known technique and the above-mentioned Examples. The members and configurations that can be mutually replaced with the members and configurations disclosed in the above are appropriately replaced, and the combinations thereof are changed and applied.-Although not disclosed in the above examples, known techniques and the like It is an embodiment of the present invention to appropriately replace the members and configurations that can be assumed as substitutes for the members and configurations disclosed by those skilled in the art based on the above, and to change and apply the combinations thereof. It is disclosed as.

A ... device, B ... information communication terminal, C ... cloud server, 20 ... smartphone, 21 ... CPU, 22 ... ROM, 23 ... RAM, 24 ... bus, 25 ... storage, 26 ... display, 27 ... network interface, 28 ... Interface, 29 ... Camera, 31 ... Microphone, 32 ... Bluetooth interface (BT), 33 ... Barcode reader, 40 ... Wearable terminal, 41 ... Control unit, 42 ... Display, 43 ... Bus, 44 ... Acceleration sensor, 45 ... Heartbeat Sensor, 46 ... GPS sensor.

Claims (11)

1. A health information management system that includes a mobile terminal device and a server device that can communicate with each other via a network.
   When the server device receives the information indicating the content and amount of the meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and returns the data representing the amount of this protein, fat and carbohydrate. It is possible and
   The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user.
   Information indicating the content of the meal ingested by the user is transmitted to the server device,
   The server device returns and receives data representing the amount of protein, fat and carbohydrate ingested by the user.
   A health information management system characterized by displaying the exercise data for a predetermined period and data representing the amount of protein, fat and carbohydrate ingested by the user.
2. The mobile terminal device is provided with a camera, images the meal ingested by the user to obtain image data, and transmits the image data to the server device as information representing the contents of the meal ingested by the user.
   The health information management system according to claim 1, wherein the server device receives image data of an image of a meal ingested by the user as information representing the content and amount of the meal.
3. The mobile terminal device is equipped with a microphone, and the user transmits a voice explaining the content and amount of the ingested meal as voice data to the server device.
   The server device identifies the audio data, and if the audio data represents the content and amount of the ingested meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and this The health information management system according to claim 1, wherein data representing the amounts of protein, fat and carbohydrate is returned.
4. The mobile terminal device captures a two-dimensional image showing the content and amount of the meal ingested by the user, and transmits the two-dimensional image data to the server device.
   If the two-dimensional image data represents the content and amount of the ingested meal, the server device identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and the protein, fat and carbohydrate. The health information management system according to claim 1, further comprising returning data representing the amount of.
   
5. The mobile terminal device can communicate with the wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and communicates with the mobile terminal device to provide the data. The health information management system according to claim 1, which is characterized.
6. The server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal. The health information management system according to claim 1, further comprising a function of improving the accuracy of the operation.
7. It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate. A mobile terminal device capable of communicating with a server device capable of returning the representative data via the above network.
   The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user.
   Information indicating the content of the meal ingested by the user is transmitted to the server device,
   The server device returns and receives data representing the amount of protein, fat and carbohydrate ingested by the user.
   A mobile terminal device characterized by displaying the exercise data in a predetermined period and data representing the amounts of protein, fat and carbohydrate ingested by the user.
8. The mobile terminal device can communicate with the wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and provides the data by communicating with the mobile terminal device. The mobile terminal device according to claim 7.
9. The server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal. The mobile terminal device according to claim 7, further comprising a function of improving the accuracy of the operation.
10. It is a health information management method implemented in a health information management system including a mobile terminal device and a server device that can communicate with each other via a network.
    The mobile terminal device collects exercise data representing the content and amount of exercise of a predetermined user, and collects exercise data.
    The mobile terminal device transmits information representing the contents of the meal ingested by the user to the server device, and the mobile terminal device transmits the information to the server device.
    The server device receives information indicating the content and amount of the meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and outputs data representing the amount of this protein, fat and carbohydrate. Reply,
    The mobile terminal device receives data indicating the amount of protein, fat, and carbohydrate ingested by the user, which is returned by the server device.
    The mobile terminal device is a health information management method characterized by displaying the exercise data in a predetermined period and data representing the amounts of protein, fat and carbohydrate ingested by the user.
11. It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate. It is a health information management program of a mobile terminal device that can communicate with a server device that can return the data to be represented via the above network.
    The health information management program is applied to the mobile terminal device.
    A function to collect exercise data showing the content and amount of exercise of a given user,
    A function to send information indicating the contents of the meal ingested by the user to the server device, and
    The function to receive the data representing the amount of protein, fat and carbohydrate ingested by the user, which is returned by the server device, and
    A health information management program for a mobile terminal device, which realizes a function of displaying the exercise data in a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.

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