Advice provision system, advice provision program, and advice provision method

Abstract

The present invention relates to an advice provision system, an advice provision program, and an advice provision method that provide advice to users to encourage the effective and efficient implementation of practical items. [Solution] An advice provision system according to one embodiment of the present invention is an advice provision system that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, comprising: an implementation status acquisition unit that acquires the implementation status of the target user for at least one of diet and exercise; and an advice generation unit that generates advice that proposes increasing or decreasing the number of practice items to be implemented by the target user based on the implementation status.

Description

Technical Field

[0001] The present invention relates to an advice providing system, an advice providing program, and an advice providing method for providing advice for reducing body fat or preventing lifestyle-related diseases.

Background Art

[0002] Techniques have been developed to evaluate a user's diet and activity content and generate advice for reducing body fat or preventing lifestyle-related diseases according to the evaluation results. For example, Patent Document 1 discloses a lifestyle improvement proposal device that generates a message for proposing lifestyle improvement based on lifestyle information such as a user's weight gain rate, number of steps, and dietary intake, and environmental information such as weather and temperature.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the invention described in Patent Document 1 above, when lifestyle information and environmental information satisfy predetermined conditions, a predetermined message corresponding to those conditions is presented to the user. Here, even if advice for reducing body fat or preventing lifestyle-related diseases is presented to the user, no effect can be obtained unless the user practices the advice.

[0005] The present invention relates to an advice providing system, an advice providing program, and an advice providing method for providing advice that prompts a user to practice effective and efficient practice items.

Means for Solving the Problems

[0006] An advice provision system according to one embodiment of the present invention is an advice provision system that provides advice to target users for reducing body fat or preventing lifestyle-related diseases, An implementation status acquisition unit that acquires the implementation status of the target user for at least one of the practical items related to diet and exercise, The system includes an advice generation unit that generates advice suggesting increases or decreases in the practical items to be implemented for the target users based on the aforementioned implementation status.

[0007] An advice provision program according to one embodiment of the present invention is an advice provision program that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, and the information processing device comprises: A step of obtaining the implementation status of the target user for at least one of the practice items related to diet and exercise, Based on the aforementioned implementation status, the system performs the step of generating advice that suggests increasing or decreasing the number of practical items to be implemented by the target users.

[0008] An advice provision method according to one embodiment of the present invention is an advice provision method that provides advice to a target user for reducing body fat or preventing lifestyle-related diseases, wherein the advice provision device is The implementation status of the aforementioned target users for at least one of the practice items related to diet and exercise is obtained. Based on the aforementioned implementation status, advice is generated suggesting increases or decreases in the practical items to be implemented for the target users. [Effects of the Invention]

[0009] According to the present invention, it is possible to provide users with advice that encourages them to implement effective and efficient practices. [Brief explanation of the drawing]

[0010] [Figure 1] This is a block diagram showing the configuration of an advice provision system according to an embodiment of the present invention. [Figure 2]This is an example of a screen for presenting question items presented to a user from an advice providing device included in the above advice providing system. [Figure 3] This is an example of a screen for presenting a dietary habit classification result presented to a user from the above advice providing device. [Figure 4] This is an example of advice presented to a user from the above advice providing device. [Figure 5] This is an example of advice presented to a user from the above advice providing device. [Figure 6] This is an example of advice presented to a user from the above advice providing device. [Figure 7] This is an example of advice presented to a user from the above advice providing device. [Figure 8] This is a flowchart showing the operation of the above advice providing device.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the configurations, numerical values, processing flows, functional elements, etc. described in the following embodiments are merely examples, and modifications and changes thereof are free, and are not intended to limit the technical scope of the present invention to the following description. In this specification, "system" shall include one or more information processing devices. For example, a single information processing device can also constitute a system, and when a plurality of information processing devices cooperate to perform functions such as a web server, these information processing devices can also constitute a system. Further, as described in the following embodiments, the "system" may include an information processing device functioning as a web server and one or more terminal devices.

[0012] The advice providing system according to an embodiment of the present invention will be described. Note that since the advice providing system according to the present embodiment also functions as a dietary habit classification system, the following advice providing system can also be read as a dietary habit classification system.

[0013] [Configuration of the Advice Providing System] The configuration of the advice providing system according to this embodiment will be described. As shown in FIG. 1, the advice providing system 100 according to this embodiment includes a dietary habit classification generation device 10 and an advice providing device 20.

[0014] The advice providing system 100 is a system that provides advice to a user for reducing body fat or preventing lifestyle-related diseases. "Reducing body fat" includes reducing visceral fat, abdominal circumference, body weight, body fat percentage, and eliminating metabolic syndrome. Also, "preventing lifestyle-related diseases" includes improving hypertension, hyperglycemia, lipid abnormalities, and eliminating obesity.

[0015] The advice providing system 100 generates advice using biological indicators. Biological indicators are indicators representing the state of the living body such as abdominal circumference, VFA (Visceral Fat Area), FBS (Fasting blood sugar), TG (Triglyceride), HDL-C (High Density Lipoprotein Cholesterol), SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), etc.

[0016] The advice providing system 100 uses specific biological indicators according to the symptoms of the advice recipient. Hereinafter, the biological indicators used by the advice providing system 100 are referred to as "specific biological indicators". Table 1 below shows examples of symptoms and specific biological indicators. Note that the symptoms and specific biological indicators are not limited to those shown here. For example, the specific biological indicators of visceral fat accumulation may be abdominal circumference or body weight.

[0017]

Table 1

[0018] The dietary habit classification generator 10 is an information processing device that generates dietary habit classifications. The dietary habit classification generator 10 analyzes users whose specific biological indicators are equal to or greater than the standard values ​​shown in Table 1 and generates dietary habit classifications. Hereinafter, these users will be referred to as "analyzed users." The number of analyzed users is not particularly limited, but 100 or more is preferable.

[0019] As shown in Figure 1, the eating habit classification generation device 10 comprises a data acquisition unit 11, an eating habit classification generation unit 12, and a ranking determination unit 13. The data acquisition unit 11 acquires "eating habit information," which is information about each target user's eating habits (including dietary habits, eating behavior, lifestyle behavior, exercise habits, and personality), and data of specific biometric indicators for multiple target users.

[0020] The dietary habit information consists of responses to 35 questions regarding dietary habits, as listed below. However, it is not limited to these items. Q01 Even if I'm not hungry, I can't help but eat if something smells delicious. Q02 If someone around me is eating something, I'll eat it too. Q03 When I'm feeling depressed, I tend to overeat. Q04 I consciously limit my food intake to avoid gaining weight. Q05 I feel like I'm hungry all day long. Q06 I feel guilty about overeating, so I'm reducing the amount I eat. Q07 I try to buy low-calorie foods. Q08 I think I eat faster than other people. Q09 Do you actively eat green and yellow vegetables? Q10 I like hamburgers, donuts, and potato chips. Q11 Do you actively choose foods that are high in dietary fiber? Q12 I prefer fish to meat. Q13 I often eat a late-night snack after dinner. Q14 I'm trying to reduce my intake of animal fats and increase my intake of plant-based fats and fish fats. Q15 I think I have a body type that gains weight more easily than other people. Q16 I think I won't have any energy if I don't eat. Q17 Meal times are all over the place. Q18 I have a sweet tooth. Q19 Eating a late-night snack Q20 I can't help but reach for fruit and sweets when they're out there. Q21 When I receive food as a gift, I eat it because I don't want to waste it. Q22 I can't help but buy more groceries than I actually need. Q23 I often have late dinners because of work. Q24 If there is an elevator or escalator, use it. Q25 I finish dinner more than two hours before going to bed. Q26 I don't like walking or cycling. Q27 I don't exercise much. Q28 I hardly chew at all Q29 I think it's more about not getting enough exercise than overeating. Q30 I'm a night owl and not a morning person. Q31 I'm lazy. Q32 Insightful Q33 Cooperative Q34 I am proactive. Q35 I get nervous easily

[0021] The user being analyzed selects one of the following options for each question: "Does not apply = 1", "Does not apply very well = 2", "Neither agree nor disagree = 3", "Somewhat agree = 4", or "Applies = 5", and answers accordingly to generate dietary habit information. Note that the above questions are just examples, and other questions may be used. Also, the number of questions is not limited to 35. The data acquisition unit 11 supplies the acquired dietary habit information and biometric indicator data to the dietary habit classification generation unit 12.

[0022] The dietary habit classification generation unit 12 performs factor analysis on the data supplied from the data acquisition unit 11 and generates multiple dietary habit classifications based on factors for biometric indicators of dietary habit information. The dietary habit classifications categorize the dietary habits of the target user and include classifications such as "low health consciousness," "snacking," "late-night eating," "eating quickly," "lack of exercise," "late-night meals," "low basal metabolism," and "other (does not fall into any of the above categories)."

[0023] "Low health consciousness" refers to a category of eating habits characterized by indifference to diet and exercise, and eating without considering health. For example, it has the following characteristics: [Diet] I don't try to buy low-calorie foods. • I don't actively eat green and yellow vegetables. • I don't actively choose foods that are high in dietary fiber. • I am trying to reduce my intake of animal fats and avoid plant-based fats and fish fats. I like hamburgers, donuts, and potato chips. I don't like fish more than meat. • I don't consciously restrict my food intake to avoid gaining weight. I feel guilty about overeating, so I haven't reduced the amount I eat. • Meal times are irregular. • Eat a late-night snack [motion] • Use the elevator or escalator if available. • Few steps I don't exercise much. I think it's more about not getting enough exercise than overeating. [Personality] • Lazy • Lack of insight Not proactive

[0024] "Snacking" is a category of eating habits characterized by a strong craving for sweets and a tendency to eat snacks without thinking, and it has the following characteristics, for example: [Diet] I like hamburgers, donuts, and potato chips. • I have a sweet tooth. I can't help but reach for fruit and sweets when they're out there. If someone around me is eating something, I'll eat it too. Even if I'm not hungry, I can't help but eat if something smells delicious. I tend to overeat when I'm feeling down. I eat food when I receive it because I don't want to waste it. [Meal contents] • High intake of lipids, plant-based lipids, saturated fatty acids, monounsaturated fatty acids, and sucrose. [motion] • Low muscle mass [Personality] I think I have a body type that gains weight more easily than other people. I feel like I won't have any energy if I don't eat. I can't rest easy unless I buy more groceries than I actually need. • Short • Low basal metabolic rate

[0025] "Late-night snacking" is a classification of eating habits where people are night owls and eat a late-night snack after dinner, and it has the following characteristics, for example. [Diet] • I often eat a late-night snack after dinner. • Eat a late-night snack • I often have late dinners because of work. • Meal times are all over the place. If someone around me is eating something, I'll eat it too. Even if I'm not hungry, I can't help but eat if something smells delicious. • I don't consciously restrict my food intake to avoid gaining weight. I tend to overeat when I'm feeling down. I feel like I'm hungry all day long. • I have a sweet tooth. I can't help but reach for fruit and sweets when they're out there. I eat food when I receive it because I don't want to waste it. [Meal contents] • High intake of energy, carbohydrates, and sucrose. • Low intake of animal protein, vitamin D, niacin, vitamin B6, vitamin B12, and alcohol. [motion] I don't exercise much. [Personality] I think I have a body type that gains weight more easily than other people. I can't rest easy unless I buy more groceries than I actually need. I'm a night owl and not a morning person. • Lazy • Easily gets nervous • Young ·Emotionally unstable

[0026] "Eating quickly" is a category of eating habits characterized by a large physique and a tendency to eat food without chewing it properly. For example, it has the following characteristics: [Diet] I think I eat faster than other people. • I hardly ever chew. I eat food when I receive it because I don't want to waste it. • I haven't finished dinner more than two hours before going to bed. [Personality] • Overweight • High muscle mass • High basal metabolic rate

[0027] "Lack of exercise" is a classification of eating habits characterized by a dislike of exercise and insufficient consumption of meat and vegetables, and has the following characteristics, for example: [Diet] • I don't actively eat green and yellow vegetables. • I don't actively choose foods that are high in dietary fiber. I don't like fish more than meat. • I am trying to reduce my intake of animal fats and avoid plant-based fats and fish fats. I feel guilty about overeating, so I haven't reduced the amount I eat. [Meal contents] • Low intake of beta-carotene [motion] I don't like walking or cycling. • Few steps I don't exercise much. I think it's more about not getting enough exercise than overeating. [Personality] • Lazy • High body fat percentage

[0028] "Late-night eating" is a classification of eating habits that involve having dinner late at night while drinking alcohol, and has the following characteristics, for example: [Diet] • I often have late dinners because of work. • I haven't finished dinner more than two hours before going to bed. • Meal times are all over the place. • I have a sweet tooth. [Meal contents] • Low intake of lipids, potassium, calcium, iron, zinc, copper, vitamin B1, vitamin C, saturated fatty acids, soluble dietary fiber, insoluble dietary fiber, total dietary fiber, and sucrose. • High alcohol consumption

[0029] "Decreased basal metabolism" refers to a type of eating habit where you gain weight even if you eat the same amount as before, and it has the following characteristics, for example. [Meal contents, portion sizes, meal times, exercise] No problem [Personality] • Older

[0030] The dietary habit classification generation unit 12 performs factor analysis on the dietary habit information and biometric indicator data of multiple target users. While the method of factor analysis is not limited, statistically analyzed data related to dietary habit information and biometric indicators are preferred. For example, various models can be employed, such as multiple regression analysis using statistical data, logistic regression models, multilayer perceptrons, neural networks such as CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), support vector machines using arbitrary kernel functions such as Gaussian kernels, random forests modeled as regression trees, models utilizing hidden Markov models, statistical models, probabilistic models, factor analysis, and correspondence tables. Furthermore, models that combine various models to perform a comprehensive judgment can also be employed. Among these, factor analysis using the principal component method with varimax rotation is particularly preferred.

[0031] Tables 2 and 3 below show the "factor loadings," "eigenvalues," "contribution rates," and "cumulative contribution rates" for each questionnaire item, calculated using the "principal component method with varimax rotation" for VFA, a bioindicator of visceral fat accumulation.

[0032] [Table 2]

[0033] [Table 3]

[0034] As shown in Tables 2 and 3, the dietary habit classification generation unit 12 sorts each question item into factors according to their factor loadings. For example, Q04, Q07, Q06, Q09, and Q11 are sorted into "Factor 1" because their factor loadings (indicated by black squares) for "Factor 1" exceed the standard value. The standard value is, for example, 0.4. Similarly, Q02, Q01, and Q20 are sorted into "Factor 2," Q19 and Q13 into "Factor 3," and Q08 and Q28 into "Factor 4." In addition, Q27 and Q29 are sorted into "Factor 5," and Q25 and Q23 into "Factor 6." The dietary habit classification generation unit 12 then generates dietary habit classifications such as "low health orientation" for each factor, according to the content of the question items sorted into each factor.

[0035] Similarly, for biomarkers other than VFA, the dietary habit classification generation unit 12 selects each question item for each factor according to its factor loading. Table 4 below shows the dietary habit classification for each factor for each symptom. As shown in Table 4, the dietary habit classification and the corresponding question items differ for each symptom. The dietary habit classification generation unit 12 supplies the generated dietary habit classification and "contribution rate" to the ranking determination unit 13.

[0036] [Table 4]

[0037] The ranking unit 13 determines the priority order among the dietary habit classifications in order of their contribution rate. In the examples in Tables 2 and 3, the contribution rate of "low health consciousness" (0.12) is the highest, followed by the contribution rate of "snacking" (0.11). The contribution rates then gradually decrease in the order of "eating late at night," "eating quickly," "lack of exercise," and "eating late at night." Therefore, the ranking unit 13 assigns the highest priority to "low health consciousness," followed by "snacking," "eating late at night," "eating quickly," "lack of exercise," and "eating late at night."

[0038] Table 4 shows the priority order of dietary habit classifications based on their contribution rates for each symptom. As shown in Table 4, the ranking unit 13 assigns the highest priority to "late-night eating" for "dyslipidemia," followed by "overeating," "lack of exercise," and "poor health consciousness." Similarly, for "hypertension," the highest priority is given to "late-night eating," followed by "snacking," "lack of exercise," "poor health consciousness," and "eating too quickly." For "hyperglycemia," the highest priority is given to "snacking," followed by "late-night eating," "lack of exercise," "irregular meal times," and "restricted food intake." The ranking unit 13 supplies the dietary habit classifications and priorities to the advice provision device 20. Note that the dietary habit classification names, contribution rates, and priorities will vary depending on the data used. The results disclosed in this specification are examples only. Priorities may be changed according to the user's desired improvement objectives.

[0039] The advice-providing device 20 is an information processing device that provides users with advice for reducing body fat or preventing lifestyle-related diseases. Hereinafter, users who are targeted to receive advice will be referred to as "target users." As shown in Figure 1, the advice-providing device 20 comprises a target user determination unit 21, a dietary habit information acquisition unit 22, a dietary habit classification unit 23, an ideal state acquisition unit 24, an advice generation unit 25, an implementation status acquisition unit 26, and a presentation information generation unit 27.

[0040] The target user determination unit 21 determines which users are eligible for the service. The target user determination unit 21 can determine which users are eligible for the service if the above-mentioned specific biometric indicator exceeds a predetermined value. For example, in the case of VFA, the predetermined value is 10 cm for males. 2 Preferably 50 cm 2 More preferably 80 cm 2 More preferably 100 cm 2 For women, 10cm 2 Preferably 30 cm 2 More preferably 50cm 2 More preferably 80 cm 2 That's all. Also, for example, in the case of waist circumference, it is 85cm or greater for men and 90cm or greater for women.

[0041] The target user determination unit 21 can determine that a user is a target user if their specific biometric indicators, as entered by the user, exceed a predetermined value. Furthermore, the target user determination unit 21 can estimate the user's specific biometric indicators from the information entered by the user, and determine that a user is a target user if this estimated value exceeds the predetermined value.

[0042] For example, the target user determination unit 21 estimates the amount of visceral fat accumulation, such as VFA, from the physical information entered by the user, such as gender, age, height, weight, waist circumference, and image data that can estimate this information, and can determine that users whose estimated value exceeds a predetermined value are target users. The target user determination unit 21 may also determine target users based on information other than specific biometric indicators, or it may determine all users as target users.

[0043] The dietary habit information acquisition unit 22 acquires dietary habit information of the target user. The dietary habit information consists of answers to the 35 questions about dietary habits mentioned above. The dietary habit information acquisition unit 22 can generate a screen displaying the questions as shown in Figure 2, making it easier to answer by displaying one question per screen. The dietary habit information acquisition unit 22 may also acquire further personal data that affects the dietary habits of the target user, such as gender, age, place of residence, place of origin, occupation, and income.

[0044] The eating habit classification unit 23 classifies the eating habits of the target user based on the eating habit information into one of the eating habit classifications generated by the eating habit classification generation unit 12. As shown in Figure 1, the eating habit classification unit 23 includes an eating habit classification determination unit 28 and an eating habit classification determination unit 29.

[0045] The eating habit classification determination unit 28 determines whether each user's eating habits fall under each eating habit classification based on the eating habit information of each user. The eating habit classification determination unit 28 can determine whether each user's eating habits fall under each eating habit classification based on the answer scores of the question items selected for each eating habit classification. The rotation scores are the scores for "does not apply = 1", "does not apply very well = 2", "neither agree nor disagree = 3", "somewhat applies = 4", and "applies = 5" as described above.

[0046] For example, regarding visceral fat accumulation, the eating habit classification unit 28 determines that the target user's eating habits fall under "low health orientation" if the answer scores for Q04, Q07, Q06, Q09, and Q11, which are selected as "low health orientation" (see Table 4), are all "does not apply = 1" or "somewhat does not apply = 2". Similarly, the eating habit classification unit 28 determines that the target user's eating habits fall under "snacking" if the answer scores for Q02, Q01, and Q20, which are selected as "snacking," are all "applies = 5" or "somewhat applies = 4". In the same manner, the eating habit classification unit 28 determines whether the target user's eating habits fall under "late-night eating," "eating too quickly," and "lack of exercise" based on the answer scores for the questions classified as these eating habits. Furthermore, the eating habit classification unit 28 determines that the eating habit of the target user falls under "eating late at night" if the answer score for question Q25, which is selected as "not applicable = 1" or "somewhat not applicable = 2", and the answer score for Q23 is "applicable = 5" or "somewhat applicable = 4".

[0047] Here, the eating habit classification determination unit 28 determines that the eating habits of the target user fall under multiple eating habit classifications if the response score meets the conditions in multiple eating habit classifications. Conversely, if the response score does not meet any of the conditions in any of the eating habit classifications, the eating habit determination unit 28 determines that the eating habits of the target user do not fall under any of the eating habit classifications.

[0048] Alternatively, the eating habit classification unit 28 determines that a user's eating habits fall under "low health orientation" if the sum of the answer scores for questions Q04, Q07, Q06, Q09, and Q11, which are classified as "low health orientation" (see Table 4), is above a threshold. Similarly, the eating habit classification unit 28 determines that a user's eating habits fall under "snacking" if the sum of the answer scores for questions Q02, Q01, and Q20, which are classified as "snacking," is above a threshold. In the same manner, the eating habit classification unit 28 determines whether a user's eating habits fall under each of the following categories based on the answer scores for the questions classified as "late-night eating," "eating too quickly," "lack of exercise," and "late-night meals."

[0049] In this case, the eating habit classification determination unit 28 determines that the eating habits of the target user fall under multiple eating habit classifications if the response score is above the threshold for multiple eating habit classifications. Conversely, if the response score is below the threshold for any eating habit classification, the eating habit classification determination unit 28 determines that the eating habits of the target user do not fall under any of the eating habit classifications.

[0050] The eating habit classification determination unit 29 determines the eating habit classification of the target user based on the determination result from the eating habit classification judgment unit 28. If the eating habit classification judgment unit 28 determines that the target user's eating habits belong to one eating habit classification, the eating habit classification determination unit 29 decides to classify the target user's eating habits as that eating habit classification.

[0051] Furthermore, if the Eating Habit Classification Determination Unit 28 determines that a target user's eating habits fall under multiple eating habit classifications, the Eating Habit Classification Determination Unit 29 will classify that target user's eating habits into the eating habit classification with the highest priority (see Table 4) determined by the Ranking Determination Unit 13. For example, if the Eating Habit Classification Determination Unit 28 determines that a particular target user's eating habits regarding visceral fat accumulation fall under both "snacking" and "lack of exercise," the Eating Habit Classification Determination Unit 29 will classify that target user's eating habits into the "snacking" category, which has a higher priority.

[0052] Furthermore, if the Eating Habit Classification Determination Unit 28 determines that the target user's eating habits do not fall under any of the specified eating habit classifications, the Eating Habit Classification Determination Unit 29 can classify the target user's eating habits in a different category than the one generated for each factor. For example, if the Eating Habit Classification Determination Unit 29 determines that the target user's eating habits do not fall under any of the specified eating habit classifications regarding visceral fat accumulation, it will classify the target user's eating habits as "decreased basal metabolism." This is because, even though there are no problems with the target user's eating habits, the amount of visceral fat accumulation exceeds a specified value, and it can be determined that this is due to a decrease in basal metabolism.

[0053] The eating habit classification unit 23 classifies the eating habits of the target user into one of the following categories as described above. The eating habit classification unit 23 can generate a display screen of the eating habit classification as shown in Figure 3, and visually notify the target user of the classification result. The radar chart shows the response score for each eating habit classification. The eating habit classification unit 23 may also accept a selection of an eating habit classification from the target user and use the classification selected by the target user as the user's eating habit classification. This is effective when the target user is aware of their previously notified eating habit classification. The eating habit classification unit 23 supplies the target user's eating habit classification to the advice generation unit 25.

[0054] The Ideal State Acquisition Unit 24 acquires the ideal state of at least one of the target user's body shape and body composition. The Ideal State Acquisition Unit 24 presents the target user with a selection of ideal state options as shown in Table 5 below, and the target user can select one to acquire the target user's ideal state. The Ideal State Acquisition Unit 24 can also acquire the ideal state based on the target user's free-form description. In addition to the body shape shown in Table 5, the ideal state can be any ideal state related to body shape or body composition, such as "I want to reduce body fat in my lower body," "I want to lose 10 kg of weight," or "I want to reduce my body fat percentage by 5%." The Ideal State Acquisition Unit 24 supplies the acquired ideal state of the target user to the Advice Generation Unit 25.

[0055] [Table 5]

[0056] The advice generation unit 25 selects practical items relating to at least one of diet and exercise, and generates advice to present the selected practical items to the target user. The advice generation unit 25 may select practical items according to the target user's dietary habit classification supplied by the dietary habit classification unit 23, or according to the target user's ideal state supplied by the ideal state acquisition unit 24. The advice generation unit 25 may also select practical items according to both the target user's dietary habit classification and ideal state. The following are examples of practical items selected by the advice generation unit 25.

[0057] • Eat breakfast within one hour of waking up. • Eat dinner by 8 p.m. • Limit your intake of sweets and alcohol to less than 200kcal per day. • Measure your weight once a week, and if it has increased, review your diet and activity level. • Eat a main dish containing fish, meat, soy products, eggs, etc. with every meal. • Eat lean meats (such as beef, pork leg or tenderloin, chicken breast or tenderloin). • Eat soy products such as tofu, natto, and soybeans. • Eat milk or yogurt (fat-free or low-fat products are recommended). • Avoid eating fried foods such as karaage, fried chicken, and tempura. • Do not eat processed meat products such as bacon, sausage, and salami.

[0058] The advice generation unit 25 can select practice items to present to the target user from common practice items, practice items prepared for each dietary habit classification, or practice items prepared for each ideal state. Furthermore, the advice generation unit 25 generates advice to the target user suggesting increases or decreases in practice items, depending on the user's progress in implementing the practice items. Details of this will be described later.

[0059] The implementation status acquisition unit 26 acquires the implementation status of target users for the practice items. The implementation status acquisition unit 26 can acquire the implementation status of target users at predetermined intervals, and this period will be referred to as the "practice period" below. The practice period is typically 7 days, but is not particularly limited. Table 6 below shows examples of implementation status acquired by the implementation status acquisition unit 26. In Table 6, "practice items" are practice items selected by the advice generation unit 25.

[0060] [Table 6]

[0061] As shown in Table 6, the target user selects the practical items they intend to implement from the list of practical items. Hereafter, these practical items will be referred to as "selected items." In Table 6, the practical items with a checkmark in the "Implemented" column are selected items. There is no particular limit to the number of selected items; one or all of the practical items may be selected. The target user records whether or not they were able to complete the selected items each day during the "implementation period."

[0062] In Table 6, the implementation period is 7 days, and each day, selected items that were achieved are marked with a "○" and selected items that were not achieved are marked with a "×". The implementation status acquisition unit 26 can acquire the implementation status as shown in Table 10 in this way. The implementation status acquisition unit 26 supplies the acquired implementation status to the advice generation unit 25.

[0063] The presentation information generation unit 27 generates presentation information including advice supplied from the advice generation unit 25. The presentation information generation unit 27 generates a presentation image including the generated presentation information, transmits the presentation information to the terminal of the target user, and can present it to the target user.

[0064] As described above, the advice generation unit 25 generates advice suggesting to the target user to increase or decrease the number of practice items, based on the implementation status of the practice items by the target user, as obtained by the implementation status acquisition unit 26.

[0065] Specifically, the advice generation unit 25 can generate advice suggesting an increase or decrease in the number of selected items based on at least one of the number of selected items and the "implementation rate". As mentioned above, the number of selected items is the number of practice items that the target user intends to implement, and in the example in Table 6, it is "3". The implementation rate is the ratio of the total number of selected items achieved by the target user each day to the product of the number of days in the practice period and the number of selected items. In the example in Table 6, the ratio of the total number of selected items achieved by the target user each day (5 items) to the product of the number of days in the practice period (7 days) and the number of selected items (3 items), which is "21", is approximately 24%, meaning the implementation rate is approximately 24%.

[0066] Table 7 below shows the criteria for determining whether to increase or decrease advice provided by the advice generation unit 25. If the number of selected items is less than half of the number of practice items presented to the target user, it is judged as "few," and if it is half or more of the number of practice items, it is judged as "few." In addition, other criteria may be used to determine whether the number is "few" or "few." If the practice rate is less than 50%, it is judged as "low," and if it is 50% or higher, it is judged as "high." In addition, other criteria may be used to determine whether the practice rate is "low" or "high."

[0067] [Table 7]

[0068] The advice generation unit 25 generates advice suggesting the reduction or maintenance of selected items when the number of selected items is small and the implementation rate is low. In the example in Table 6, the number of selected items is "3" and the implementation rate is "24%", which falls under the category of a small number of selected items and a low implementation rate. In this case, the advice generation unit 25 generates advice suggesting the reduction of selected items if the number of selected items is 2 or more, and generates advice suggesting the maintenance of selected items if the number of selected items is 1. Figure 4 shows an example of advice generated by the advice generation unit 25. As shown in the figure, this advice includes advice S1 suggesting the reduction of selected items.

[0069] The selection items that the advice generation unit 25 proposes to reduce are preferably those that have the smallest contribution to the target user or those that have the lowest implementation rate among the target user. Table 8 below is an example showing the factors that affect the change in visceral fat area by dietary habit classification, in order of their contribution rate, with factor 1 having the largest contribution rate and factor 5 having the smallest contribution rate. The advice generation unit 25 can propose to reduce the selection items from the practical items with the lowest implementation rate or the practical items related to the factors with the lowest contribution rate ranking shown in Table 8.

[0070] [Table 8]

[0071] When the number of selection items is small and the implementation rate is low in the implementation situation, it can be inferred that the target users have low motivation to work on the selection items. Therefore, by suggesting a reduction in the number of selection items, the advice generation unit 25 can enable the target users to concentrate on other selection items. Furthermore, by suggesting the deletion of the selection item that has the least contribution to the target user or the selection item with the lowest implementation rate among the target users, the advice generation unit 25 can propose more effective and efficient selection items.

[0072] The advice generation unit 25 generates advice suggesting the addition of selection items when the number of selection items is small and the implementation rate is high. In the example in Table 9 below, the number of selection items is "3" and the implementation rate is "90%", which falls under the category of a small number of selection items and a high implementation rate. In this case, the advice generation unit 25 generates advice suggesting the addition of selection items. Figure 5 shows an example of advice generated by the advice generation unit 25. As shown in the figure, this advice includes advice S2, which suggests the addition of selection items.

[0073] [Table 9]

[0074] The advice generation unit 25 suggests adding an optional item that is most beneficial to the target user. The advice generation unit 25 can also suggest adding an optional item to the practical items that are not currently selected, specifically the practical item related to the factor with the highest contribution rate, as shown in Table 8.

[0075] When the number of selection items is small and the implementation rate is high, it can be inferred that the target user is a selective and focused individual. Therefore, by suggesting the addition of more selection items, the advice generation unit 25 can encourage the target user to implement more selection items. Furthermore, by suggesting the addition of the implementation item that will have the greatest contribution to the target user, the advice generation unit 25 can propose more effective and efficient selection items.

[0076] The advice generation unit 25 generates advice suggesting a reduction in the number of selected items when the number of selected items is large and the implementation rate is low. In the example in Table 10 below, the number of selected items is "8" and the implementation rate is "13%", which falls under the category of having a large number of selected items and a low implementation rate. In this case, the advice generation unit 25 generates advice suggesting a reduction in the number of selected items. Figure 6 shows an example of advice generated by the advice generation unit 25. As shown in the figure, this advice includes advice S3, which suggests a reduction in the number of selected items.

[0077] [Table 10]

[0078] The selection items that the advice generation unit 25 proposes to reduce are preferably those that have the smallest contribution to the target users or those that have the lowest implementation rate among the target users. The advice generation unit 25 can propose to remove from the selection items the implementation item with the lowest implementation rate or the implementation item related to the factor with the lowest contribution rate ranking shown in Table 8.

[0079] If the number of selection items is large and the implementation rate is low, it can be inferred that the target users are highly motivated but not taking action, and therefore need encouragement to continue. For this reason, the advice generation unit 25 can suggest reducing the number of selection items to encourage the target users to implement other selection items. Furthermore, by suggesting the deletion of the selection item that has the least contribution to the target user or the selection item with the lowest implementation rate among the target users, the advice generation unit 25 can propose more effective and efficient selection items.

[0080] The advice generation unit 25 generates advice suggesting the addition or maintenance of selected items when there are many selected items and the implementation rate is high. In Table 11 below, the number of selected items is "8" and the implementation rate is "90%", which falls under the category of having many selected items and a high implementation rate. In this case, the advice generation unit 25 generates advice suggesting the addition of selected items if the selected items are not all of the implementation items presented to the target user, and generates advice suggesting the maintenance of selected items if the selected items are all of the implementation items presented to the target user. Figure 7 shows an example of advice generated by the advice generation unit 25. As shown in the figure, this advice includes advice S4, which suggests the addition of selected items.

[0081] [Table 11]

[0082] The advice generation unit 25 suggests adding optional items that are most beneficial to the target user. The advice generation unit 25 can also suggest adding optional items to the list of practical items that are not currently selected, specifically those related to factors with high contribution rates as shown in Table 8.

[0083] If the number of selected items is small and the implementation rate is high, it can be inferred that the target users are highly motivated and have a strong desire for self-improvement. Therefore, by suggesting the addition of more selected items, the advice generation unit 25 can encourage the target users to implement more of the selected items. Furthermore, by suggesting the addition of the implementation item that will have the greatest contribution to the target user, the advice generation unit 25 can propose more effective and efficient selection items.

[0084] The advice generation unit 25 generates advice in this manner, suggesting to the target user an increase or decrease in the number of selected items based on the user's progress in implementing the practice items. In the above description, the advice generation unit 25 suggests an increase or decrease in selected items based on the number of selected items and the implementation rate, but it is not limited to this; it may also suggest an increase or decrease in selected items based only on the number of selected items, or based only on the implementation rate. Furthermore, the advice generation unit 25 only needs to suggest an increase or decrease in selected items based on the progress of implementing the practice items, and may also suggest an increase or decrease in selected items based on criteria other than the number of selected items and the implementation rate.

[0085] The information generation unit 27 presents the advice supplied by the advice generation unit 25 to the target user, as described above. The information generation unit 27 may also present the advice supplied by the advice generation unit 25 to the target user's instructor. Examples of instructors for the target user include registered dietitians, public health nurses, pharmacists, and other healthcare professionals. The information generation unit 27 can tailor the advice presented to the instructor to the instructor's area of ​​expertise. Specifically, the information generation unit 27 can generate information including dietary advice and send it to a registered dietitian's terminal, or generate information including exercise advice and send it to a healthcare professional's terminal, for example.

[0086] The advice provision system 100 has the configuration described above. The configurations of the dietary habit classification generation device 10 and the advice provision device 20 are functional configurations realized through the cooperation of hardware and software of an information processing device consisting of a CPU (Central Processing Unit) and RAM (Random Access Memory), etc. The software is a program that can be executed by the information processing device, and may also be a program recorded on a recording medium that can be read by the information processing device. Furthermore, the dietary habit classification generation device 10 and the advice provision device 20 may be separate information processing devices, or they may be a single information processing device.

[0087] [How the advice provision system works] The operation of the advice provision system 100 will now be described. As described above, the dietary habit classification generator 10 generates a dietary habit classification based on dietary habit information and biometric indicator data of multiple target users. The generation of the dietary habit classification only needs to be performed once when the advice provision system 100 is built, but it may be performed again after the system is built.

[0088] The advice-providing device 20 provides target users with a body fat reduction or lifestyle-related disease prevention program. As shown in Figure 8, the advice-providing device 20 operates both "before program implementation" and "during program implementation." Table 12 below shows the contents of "input," "judgment," and "output" before and during program implementation.

[0089] [Table 12]

[0090] Before program implementation, as shown in Figure 8, the target user determination unit 21 determines the target users (St11). As shown in Table 12, the target user determination unit 21 can receive physical information input from the user and calculate specific biometric indicators. Furthermore, based on the specific biometric indicators, the target user determination unit 21 can determine whether or not each user is a target user for the program and output to each user whether or not they are a target user for the program.

[0091] Next, the dietary habit information acquisition unit 22 acquires the dietary habit information and ideal state of the target user (St12), and the dietary habit classification unit 23 determines the dietary habit classification of the target user (St13). As shown in Table 12, the dietary habit information acquisition unit 22 receives input of dietary habit information from the target user, and the dietary habit classification unit 23 determines the dietary habit classification of the target user. Furthermore, the dietary habit classification unit 23 can output the dietary habit classification corresponding to each target user (see Figure 3).

[0092] In this case, the advice generation unit 25 may generate advice that presents practical items selected according to at least one of the dietary habit classification and the ideal state, and present the generated advice to each target user. In addition, the presentation information generation unit 27 may predict the target user's physical information after program implementation from the physical information entered by the target user, and output the predicted physical information to the target user.

[0093] During program implementation, the implementation status acquisition unit 26 acquires the implementation status by the target users (see Table 7) (St14), and the advice generation unit 25 generates advice suggesting increases or decreases in the number of selected items and the implementation rate (St15). Subsequently, the presentation information generation unit 27 generates presentation information including the advice (St16). The presentation information generation unit 27 generates presentation information including the advice and outputs it to at least one of each target user and their instructor. It is preferable that the presentation information generation unit 27 generates the advice presented to the instructor as presentation information that corresponds to the instructor's area of ​​expertise.

[0094] The advice-providing device 20 performs the "pre-program execution" operation, and then performs the "program execution" operation at intervals of the execution period. The advice-providing device 20 can perform the "program execution" operation once or multiple times.

[0095] The advice provision system 100 provides advice to each target user in this manner. The operation of the advice provision system 100 described above is just one example; the advice provision system 100 only needs to provide advice to target users suggesting increases or decreases in the practical items they will implement. In addition to providing advice, the classification of target users' eating habits can be used for product recommendations, service provision and collaboration, future predictions (body type, lifestyle-related diseases, etc.), and introductions to peers (community building), etc.

[0096] [Effects of the advice provision system] As described above, the advice provision system 100 can generate advice from the advice generation unit 25 that suggests increasing or decreasing the number of practice items to be implemented based on the implementation status of the practice items by the target user. This makes it possible for the advice provision system 100 to generate advice that encourages the target user to implement practice items effectively and efficiently. Furthermore, the advice provision system 100 can select practice items to be added or reduced based on the contribution to the target user and the target user's implementation rate, enabling it to propose more effective and efficient practice items.

[0097] Furthermore, the system may be combined with the advice system 100 to propose the most suitable insurance products. In addition, the advice system 100 may be offered as part of services provided by health guidance institutions, etc., in combination with health consultations, health checkups, health education, health coaching, health programs for smoking and lack of exercise, etc. Furthermore, the advice system 100 may be offered as part of services provided by health checkup institutions, etc., in combination with health checkups, cancer screenings, lifestyle-related disease checkups, prenatal checkups, health checkups conducted by companies for employee health management, etc.

[0098] Although embodiments of the present invention have been described above, the present invention is not limited to the embodiments described above, and various modifications can be made without departing from the spirit of the invention. In addition, preferred examples of the advice provision program and advice provision method are the same as those described above for the advice provision system.

[0099] Furthermore, the present invention may be applied to a system composed of multiple devices or to a single device. Moreover, the present invention is also applicable when an information processing program that realizes the functions of the embodiment is supplied to a system or device and executed by a built-in processor. Therefore, the technical scope of the present invention includes programs installed on a computer to realize the functions of the present invention on a computer, the medium on which the program is stored, the WWW (World Wide Web) server that allows the program to be downloaded, and the processor that executes the program. In particular, at least a non-transitory computer-readable medium containing a program that causes a computer to execute the processing steps included in the above-described embodiment is included in the technical scope of the present invention. [Explanation of Symbols]

[0100] 20…Advice-providing device 23…Eating habits classification department 24...Ideal state acquisition unit 25... Advice generation unit 26…Implementation Status Acquisition Department 27…Presentation information generation unit 100... Advice provision system

Claims

1. An advice provision system that provides advice to target users for reducing body fat or preventing lifestyle-related diseases, An implementation status acquisition unit that acquires the implementation status of the target user for at least one of the practical items related to diet and exercise, Based on the aforementioned implementation status, an advice generation unit generates advice that proposes increasing or decreasing the number of practical items to be implemented for the aforementioned target users, An advice provision system equipped with the necessary features.

2. The advice generation unit generates advice that suggests increasing or decreasing the number of practice items to be implemented based on the number of practice items started by the target user during the practice period in which the target user implements the practice items. The advice provision system according to claim 1.

3. If the practice rate is defined as the ratio of the total number of practice items achieved by the user each day to the product of the number of days in the practice period during which the user practices the practice items and the number of practice items started by the user, then The advice generation unit generates advice that suggests increasing or decreasing the number of practice items to be implemented based on the implementation rate. The advice provision system according to claim 1.

4. If the practice rate is defined as the ratio of the total number of practice items achieved by the user each day to the product of the number of days in the practice period during which the user practices the practice items and the number of practice items started by the user, then The advice generation unit generates advice that suggests increasing or decreasing the number of practice items to be implemented based on the number of practice items started by the target user during the practice period and the practice rate. The advice provision system according to claim 1.

5. The advice generation unit generates advice suggesting the addition of practice items to be implemented if the number of practice items initiated by the target user is less than a predetermined number, and advice suggesting the reduction or maintenance of practice items to be implemented if the implementation rate is above a predetermined percentage, and advice suggesting the reduction or maintenance of practice items to be implemented if the implementation rate is below the predetermined percentage. The advice provision system according to claim 4.

6. The advice generation unit, when the number of practical items started by the target user exceeds a predetermined number, generates advice suggesting the addition or maintenance of practical items if the implementation rate is above a predetermined percentage, and generates advice suggesting the reduction of practical items if the implementation rate is below the predetermined percentage. The advice provision system according to claim 4.

7. The advice generation unit, when proposing the addition of practice items to be implemented, generates advice suggesting the addition of the practice item that will have the greatest contribution to the target user. When proposing the reduction of practice items to be implemented, it generates advice suggesting the reduction of the practice item that will have the least contribution to the target user or the practice item that has the lowest implementation rate among the target user. The advice provision system according to claim 5 or 6.

8. The advice generation unit generates advice that presents the selected practice items to the target user, and generates advice that suggests increasing or decreasing the number of practice items to be implemented by the target user based on the implementation status. The advice provision system according to claim 1.

9. The advice generation unit obtains an ideal state regarding at least one of body shape and body composition from the target user and selects action items according to the ideal state. The advice provision system according to claim 8.

10. The advice generation unit obtains dietary habit information, which is information about the target user's eating habits, and selects practical items according to the dietary habit classification of the target user. The advice provision system according to claim 8.

11. The system further comprises a presentation information generation unit that presents the advice generated by the advice generation unit to the target user and the target user's instructor, and that the advice presented to the instructor is tailored to the instructor's area of ​​expertise. The advice provision system according to claim 1.

12. An advice provision program that provides advice to target users for reducing body fat or preventing lifestyle-related diseases, wherein the information processing device includes: A step of obtaining the implementation status of the target user for at least one of the practice items related to diet and exercise, Based on the aforementioned implementation status, the step of generating advice that suggests increasing or decreasing the number of practical items to be implemented for the aforementioned target users, A program that provides advice to help you implement solutions.

13. An advice provision method that provides advice to target users for reducing body fat or preventing lifestyle-related diseases, wherein the advice provision device is The implementation status of the aforementioned target users for at least one of the practice items related to diet and exercise is obtained. Based on the aforementioned implementation status, the system generates advice suggesting increases or decreases in the practical items to be implemented by the target users. How to provide advice.

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