Advice provision device

Abstract

To provide an advice provision device that provides advice related to exercises for effectively reducing body fat or preventing lifestyle diseases in accordance with users.MEANS FOR SOLVING THE PROBLEM: An advice provision device according to one embodiment of the present invention provides advice on body fat reduction to a user to whom the advice is to be supplied. The advice provision device comprises: an information acquisition unit for acquiring dietary habit information that pertains to the dietary habit of the user to whom the advice is to be supplied; and an advice generation unit for generating advice for presenting a recommended exercise intensity and / or a recommended exercise time slot for body fat reduction to the user to whom the advice is to be supplied, on the basis of an exercise intensity and / or an exercise time slot, related data indicating a relationship of body fat reduction, and the dietary habit.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an advice providing device that provides 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 food 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 the conditions is presented to the user. Here, since the causes of body fat accumulation and lifestyle-related diseases vary among users, even if advice is presented according to common conditions among users, it may not be effective advice for reducing body fat or preventing lifestyle-related diseases. In particular, since exercise greatly contributes to reducing body fat and preventing lifestyle-related diseases, if appropriate advice regarding exercise can be provided according to the user, high effects on reducing body fat and preventing lifestyle-related diseases can be expected.

[0005] The present invention relates to an advice providing device that effectively provides advice regarding exercise for reducing body fat and preventing lifestyle-related diseases according to the user.

Means for Solving the Problems

[0006] An advice providing device according to one embodiment of the present invention is an advice providing device that provides advice for reducing body fat to a user to be provided, and includes: an information acquisition unit that acquires eating habit information, which is information about the eating habits of the user to be provided; an advice generation unit that generates advice for presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction and the eating habit information.

[0007] An advice providing system according to one embodiment of the present invention is an advice providing system that provides advice for reducing body fat to a user to be provided, and includes: an information acquisition unit that acquires eating habit information, which is information about the eating habits of the user to be provided, from a first information terminal; an advice generation unit that generates advice for presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction and the eating habit information, and provides the advice to a second information terminal.

[0008] An advice providing program according to one embodiment of the present invention is an advice providing program executed by an information processing device that provides advice for reducing body fat to a user to be provided, and causes the information processing device to execute: a step of acquiring eating habit information, which is information about the eating habits of the user to be provided; a step of generating advice for presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction and the eating habit information.

[0009] An advice providing method according to an aspect of the present invention is an advice providing method for providing advice on body fat reduction to a target user, and acquires eating habit information, which is information on the eating habits of the target user, and generates advice for presenting at least one of a recommended exercise intensity and a recommended exercise time zone for body fat reduction to the target user based on relationship data indicating the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction and the eating habit information.

Advantages of the Invention

[0010] According to the present invention, it is possible to effectively provide advice on exercise for body fat reduction and lifestyle disease prevention according to the user.

Brief Description of the Drawings

[0011]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Best Mode for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present invention will be described with reference to the drawings. Also, the present invention is not limited only to the embodiments shown below, and various modifications can be made without departing from the gist of the present invention. Hereinafter, an embodiment of the present invention in which there is one information processing apparatus will be described, but when a plurality of information processing apparatuses cooperate and operate by distributed processing technology, that is, when a series of processes are shared by a plurality of information processing apparatuses, it is also within the scope of the present invention.

[0013] (First Embodiment) An advice providing system according to the first 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.

[0014] [Configuration of Advice Providing System] The configuration of the advice providing system according to the present embodiment will be described. As shown in FIG. 1, the advice providing system 100 according to the present embodiment includes a dietary habit classification generation device 10 and an advice providing device 20.

[0015] 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.

[0016] The advice-providing system 100 utilizes biometric indicators. Biometric 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), and DBP (Diastolic Blood Pressure).

[0017] The advice-providing system 100 utilizes specific biometric indicators according to the symptoms of the advice recipient. Hereinafter, the biometric indicators utilized by the advice-providing system 100 are referred to as "specific biometric indicators". Table 1 below shows examples of symptoms and specific biometric indicators. Note that the symptoms and specific biometric indicators are not limited to those shown here. For example, the specific biometric indicators for visceral fat accumulation may be abdominal circumference or body weight.

[0018]

Table 1

[0019] The eating habit classification generation device 10 is an information processing device that generates eating habit classifications. The eating habit classification generation device 10 analyzes users whose specific biometric indicators are equal to or higher than the reference values shown in Table 1 as analysis targets and generates eating habit classifications. Hereinafter, this user is referred to as the "analysis target user". The number of analysis target users is not particularly limited, but 100 or more is preferable.

[0020] As shown in FIG. 1, the eating habit classification generation device 10 includes 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 regarding the eating habits (including eating life, eating behavior, life behavior, exercise habits, and personality) of each analysis target user, and data on specific biometric indicators for a plurality of analysis target users.

[0021] The eating habit information is the answers to 35 questions about eating habits as shown below. Note that it is not limited to these. Q01 I tend to eat when I smell something delicious even when I'm not hungry. Q02 I end up eating when someone around me is eating something. Q03 I overeat when I'm in a depressed mood. Q04 I consciously control my food intake to avoid gaining weight. Q05 I feel hungry throughout the day. Q06 I feel guilty about overeating and have reduced the amount I eat. Q07 I try to buy low - calorie foods. Q08 I think I eat faster than others. Q09 I actively eat green and yellow vegetables. Q10 I like hamburgers, donuts, and potato chips. Q11 I actively choose foods rich in dietary fiber. Q12 I prefer fish to meat. Q13 I often have a late - night snack after dinner. Q14 I limit animal fat and consume plant - based fat and fish fat instead. Q15 I think I am more prone to gaining weight than others. Q16 I think I won't have energy if I don't eat. Q17 My meal times are irregular. Q18 I have a weakness for sweet things. Q19 I have a late - night snack. Q20 I can't help but reach for fruits and snacks when they are available. Q21 I feel it's a waste not to eat the food I'm given, so I end up eating it. Q22 I feel uneasy unless I buy groceries in larger quantities than I need. Q23 I often have a late dinner due to work. Q24 I use the elevator or escalator if available. 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 Almost never chews Q29 I think it's lack of exercise rather than overeating Q30 I'm a night owl who is weak in the morning Q31 Lazy person Q32 Has insight Q33 Cooperative Q34 Positive Q35 Prone to getting nervous

[0022] The user to be analyzed answers each question item by selecting one of "Doesn't apply = 1", "Doesn't really apply = 2", "Can't say either way = 3", "Somewhat applies = 4", "Applies = 5" to generate eating habit information. Note that the above question items are just examples and other question items may be used. Also, the number of question items is not limited to 35. The data acquisition unit 11 supplies the acquired eating habit information and the data of the biological indicators to the eating habit classification generation unit 12.

[0023] The eating habit classification generation unit 12 performs factor analysis on the data supplied from the data acquisition unit 11 and generates a plurality of eating habit classifications for each factor with respect to the biological indicators of the eating habit information. The eating habit classification is a classification of the eating habits of the user to be analyzed. For example, it includes classifications such as "Low health orientation or health orientation", "Snacking", "Nighttime eating", "Eating breakfast early", "Lack of exercise or exercise", "Eating late at night or having a late dinner", "Decrease in basal metabolism or metabolism", "Others (not applicable to any item)".

[0024] "Low health orientation or health orientation" is an eating habit classification in which the user is indifferent to diet and exercise and eats without being conscious of health. For example, it has the following characteristics. [Diet] · Not trying to buy low-calorie foods · Not actively eating green and yellow vegetables · Not actively choosing foods rich in dietary fiber · Not refraining from animal fat and instead taking vegetable fat or fish fat · Likes hamburgers, donuts, and potato chips · Prefers fish over meat · Does not consciously control food intake to avoid weight gain · Does not reduce food intake due to guilt about overeating · Meal times are irregular · Eats snacks at night [Exercise] · Uses elevators and escalators when available · Takes few steps · Does not exercise much · Thinks it is more lack of exercise than overeating [Personality] · Is lazy · Lacks insight · Is not proactive

[0025] "Snacking" is a classification of eating habits that cannot resist sweet foods and always ends up eating snacks. For example, it has the following characteristics. [Diet] · Likes hamburgers, donuts, and potato chips · Cannot resist sweet foods · Always reaches for fruits and snacks when available · Eats along with others when someone around is eating · Eats when smelling something delicious even when not hungry · Overeats when feeling depressed · Eats food received because it seems wasteful not to [Diet content] · High intake of lipids, vegetable lipids, saturated fatty acids, monounsaturated fatty acids, and sucrose [Exercise] · Low muscle mass [Personality] · Thinks one is more prone to gaining weight than others · Thinks one will lose energy without eating · Feels uneasy unless buying more food than necessary · Short in height · Low basal metabolic rate

[0026] "Night eating" is a classification of eating habits for night owls who eat a late-night snack after dinner. For example, it has the following characteristics. [Dietary habits] · Often eat a late-night snack after dinner · Eat a late-night snack · Dinner is often late due to work · Meal times are irregular · Eat along with others when someone around is eating something · Tend to eat when smelling something delicious even when not hungry · Do not consciously control the amount of food intake to avoid gaining weight · Overeat when feeling depressed · Feel hungry throughout the day · Have a weakness for sweet things · Can't help but reach for fruits and snacks when available · Eat food given because it seems wasteful not to [Dietary content] · High intake of energy, carbohydrates, and sucrose · Low intake of animal protein, vitamin D, niacin, vitamin B6, vitamin B12, and alcohol [Exercise] · Do little exercise [Personality] · Think that one is more prone to gaining weight than others · Feel uneasy unless buying more food than necessary · Are night owls who are weak in the morning · Are lazy · Tend to be nervous · Are young · Are emotionally unstable

[0027] "Fast eating" is a classification of eating habits for people with a good build who eat food without chewing it much. For example, it has the following characteristics. [Dietary habits] · Think that one eats faster than others · Hardly chew · Eat food given because it seems wasteful not to · Have not finished dinner more than 2 hours before going to bed [Personality] · Heavy weight · High muscle mass · High basal metabolic rate

[0028] "Lack of exercise or exercise" is a dietary habit classification where a person dislikes exercise and has a diet lacking in meat and vegetables. For example, it has the following characteristics. [Dietary habits] · Not actively eating green and yellow vegetables · Not actively choosing foods rich in dietary fiber · Not preferring fish over meat · Not refraining from animal fat and instead taking plant fat and fish fat · Having a sense of guilt about overeating and not reducing the amount eaten [Diet content] · Low intake of β - carotene [Exercise] · Not liking to walk or ride a bicycle · Few steps taken · Not exercising much · Thinking it is more lack of exercise than overeating [Personality] · Lazy · High body fat percentage

[0029] "Late-night meal or late dinner" is a dietary habit classification where a person eats dinner while drinking alcohol late at night. For example, it has the following characteristics. [Dietary habits] · Dinner is often late due to work · Have not finished dinner more than 2 hours before going to bed · Meal times are irregular · Having a weakness for sweet things [Diet content] · Low intake of lipid, K, Ca, Fe, Zn, Cu, vitamin B1, vitamin C, saturated fatty acid, water-soluble dietary fiber, insoluble dietary fiber, total dietary fiber, and sucrose · High alcohol intake

[0030] "Low or abnormal basal metabolism" refers to a dietary habit classification in which one gains weight even when eating the same as before. For example, it has the following characteristics. [Dietary content, amount of food, meal time, exercise] .[No problem] [Personality] .[Older age]

[0031] The dietary habit classification generation unit 12 performs factor analysis on the dietary habit information and biometric index data of a plurality of users to be analyzed. As the factor analysis method, although not limited, data obtained by statistically investigating data related to dietary habit information and biometric indexes are preferably shown. For example, multiple regression analysis using statistical data, logistic regression models, neural networks such as multilayer perceptrons, CNN (Convolutional Neural Network), and RNN (Recurrent Neural Network), support vector machines using arbitrary kernel functions such as Gaussian kernels, random forests modeled as regression trees, models using hidden Markov models, statistical models, probability models, factor analysis, and correspondence tables utilizing tables, etc. can also be adopted. Also, a model that combines various models to make a comprehensive determination can be adopted. Among them, in particular, it is preferable to use factor analysis using the principal factor method with varimax rotation.

[0032] In Tables 2 and 3 below, the "factor loadings" of each question item, the "eigenvalue", "contribution rate", and "cumulative contribution rate" of each factor calculated by the "principal factor method with varimax rotation" for VFA, which is a biometric index of visceral fat accumulation, are shown.

[0033]

Table 2

[0034]

Table 3

[0035] As shown in Tables 2 and 3, the eating habit classification generation unit 12 selects each question item for each factor according to the factor loading. For example, since the factor loadings (within the black squares) of Q04, Q07, Q06, Q09, and Q11 for "Factor 1" exceed the reference value, they are selected for "Factor 1". The reference value is, for example, 0.4. Similarly, Q02, Q01, and Q20 are selected for "Factor 2", Q19 and Q13 are selected for "Factor 3", and Q08 and Q28 are selected for "Factor 4", respectively. Also, Q27 and Q29 are selected for "Factor 5", and Q25 and Q23 are selected for "Factor 6", respectively. The eating habit classification generation unit 12 generates an eating habit classification such as "low health orientation or health orientation" for each factor according to the content of the question items selected for each factor.

[0036] Similarly, in the case of biological indicators other than VFA, the eating habit classification generation unit 12 selects each question item for each factor according to the factor loading. Table 4 below shows the eating habit classification for each factor regarding each symptom. As shown in Table 4, the eating habit classification for each factor regarding each symptom and the corresponding question items are different. The eating habit classification generation unit 12 supplies the generated eating habit classification and the "contribution rate" to the ranking determination unit 13.

[0037]

Table 4

[0038] The ranking determination unit 13 determines the priority order in descending order of the contribution rate among the eating habit classifications. In the examples of Tables 2 and 3, the contribution rate of "low health orientation or health orientation" (0.12) is the largest, and the contribution rate of "snacking" (0.11) is the second largest. Hereinafter, the contribution rates gradually decrease in the order of "nighttime eating", "eating quickly in the morning", "lack of exercise or exercise", and "eating late at night or having a late dinner". Therefore, the ranking determination unit 13 sets the highest priority for "low health orientation or health orientation", and then assigns priorities in the order of "snacking", "nighttime eating", "eating quickly in the morning", "lack of exercise or exercise", and "eating late at night or having a late dinner".

[0039] Table 4 shows the priority order of dietary habit classification according to the contribution rate for each symptom. As shown in Table 4, for "lipid abnormality", the ranking determination unit 13 sets the highest priority for "nighttime snacking", and then assigns priorities in the order of "overeating", "lack of exercise or exercise", and "low health orientation or health orientation". For "hypertension", the ranking determination unit 13 sets the highest priority for "nighttime snacking", and then assigns priorities in the order of "between-meal snacking", "lack of exercise or exercise", "low health orientation or health orientation", and "eating breakfast early". For "hyperglycemia", the ranking determination unit 13 sets the highest priority for "between-meal snacking", and then assigns priorities in the order of "nighttime snacking", "lack of exercise or exercise", "irregular meal times", and "meal portion restriction". The ranking determination unit 13 supplies the dietary habit classification and the priority order to the advice providing device 20. Note that the values obtained for the dietary habit classification name, contribution rate, and priority order differ depending on the data used. The results disclosed in this specification are examples. The priority order may be changed according to the improvement purpose desired by the user.

[0040] The advice providing device 20 is an information processing device that provides advice to the user for reducing body fat or preventing lifestyle-related diseases. Hereinafter, the user to whom the advice is provided is referred to as the "target user". As shown in FIG. 1, the advice providing device 20 includes a target user determination unit 21, a dietary habit information acquisition unit 22, a dietary habit classification unit 23, a record information acquisition unit 24, an evaluation unit 25, an advice generation unit 26, and a presentation information generation unit 27.

[0041] The target user determination unit 21 determines the target user. The target user determination unit 21 can determine a user whose specific biological index exceeds the specified value as the target user. For example, in the case of VFA, the specified value is 10 cm 2 or more, preferably 50 cm 2 or more, more preferably 80 cm 2 or more, still more preferably 100 cm 2 or more for men, and 10 cm 2 or more, preferably 30 cm 2 or more, more preferably 50 cm 2 or more, still more preferably 80 cm 2The above is the case. Also, for example, in the case of abdominal circumference, for men, it is 85 cm ≥, and for women, it is 90 cm ≥.

[0042] The target user determination unit 21 can determine a user whose specific biological index input by the user exceeds the specified value as a target user to be provided. Also, the target user determination unit 21 can estimate the specific biological index of the user from the information input by the user, and determine a user whose estimated value exceeds the above specified value as a user to be provided.

[0043] For example, the target user determination unit 21 estimates the amount of visceral fat accumulation such as VFA from the body information input by the user, such as gender, age, height, weight, abdominal circumference, and image data from which these information can be estimated. A user whose estimated value exceeds the specified value can be determined as a target user to be provided. Note that the target user determination unit 21 may determine the target user based on information other than the specific biological index, or may determine all users as target users to be provided.

[0044] The eating habit information acquisition unit 22 acquires the eating habit information of the target user to be provided. The eating habit information is the answer to the 35 question items regarding eating habits described above. The eating habit information acquisition unit 22 can generate a question item presentation screen as shown in FIG. 2, and can make it easier to answer by displaying one question item on one screen. Also, the eating habit information acquisition unit 22 may further acquire personal data such as the gender, age, place of residence, place of origin, occupation, and income of the target user to be provided, which affect the eating habits.

[0045] The eating habit classification unit 23 classifies the eating habits of the target user to be provided based on the eating habit information into any of the eating habit classifications generated by the eating habit classification generation unit 12. As shown in FIG. 1, the eating habit classification unit 23 includes an eating habit classification determination unit 28 and an eating habit classification determination unit 29.

[0046] The eating habit classification determination unit 28 determines whether the eating habits of each user to be provided correspond to each eating habit classification based on the eating habit information of each user to be provided. The eating habit classification determination unit 28 can determine whether the eating habits of the user to be provided correspond to each eating habit classification according to the response scores of the question items selected for each eating habit classification. The rotation scores are the scores of "does not apply = 1", "barely applies = 2", "neither applies = 3", "somewhat applies = 4", and "applies = 5" described above.

[0047] For example, regarding visceral fat accumulation, if the response scores of Q04, Q07, Q06, Q09, and Q11, which are the question items selected for "low health orientation or health orientation" (see Table 4), are all "does not apply = 1" or "barely applies = 2", the eating habits of the user to be provided are determined to correspond to "low health orientation or health orientation". Also, if the response scores of Q02, Q01, and Q20, which are the question items selected for "snacking", are all "applies = 5" or "somewhat applies = 4", the eating habits of the user to be provided are determined to correspond to "snacking". Similarly, the eating habit classification determination unit 28 determines whether the eating habits of the user to be provided correspond to these according to the response scores of the question items classified into each eating habit of "night snacking", "eating quickly in the morning", and "lack of exercise or exercise". Furthermore, if the response score of Q25, which is the question item selected for "late-night meal or late dinner", is "does not apply = 1" or "barely applies = 2", and the response score of Q23 is "applies = 5" or "somewhat applies = 4", the eating habits of the user to be provided are determined to correspond to "late-night meal or late dinner".

[0048] Here, if the response scores meet the conditions in a plurality of eating habit classifications, the eating habit classification determination unit 28 determines that the eating habits of the user to be provided correspond to the plurality of eating habit classifications. Also, if the response scores do not meet any of the conditions in any eating habit classification, the eating habit classification determination unit 28 determines that the eating habits of the user to be provided do not correspond to any eating habit classification.

[0049] Also, as another method, if the sum of the response scores of Q04, Q07, Q06, Q09, and Q11, which are question items selected as "low health orientation or health orientation" (see Table 4), is equal to or greater than the threshold value, the eating habits classification determination unit 28 determines that the eating habits of the target user to be provided correspond to "low health orientation or health orientation". Also, if the sum of the response scores of Q02, Q01, and Q20, which are question items selected as "snacking", is equal to or greater than the threshold value, the eating habits classification determination unit 28 determines that the eating habits of the target user to be provided correspond to "snacking". Similarly hereinafter, the eating habits classification determination unit 28 determines whether or not the eating habits of the target user to be provided correspond to these according to the response scores of the question items classified into each eating habit of "nighttime snacking", "eating quickly", "lack of exercise or exercise", and "late dinner or late evening meal".

[0050] In this case, if the response scores are equal to or greater than the threshold value in a plurality of eating habits classifications, the eating habits classification determination unit 28 determines that the eating habits of the target user to be provided correspond to the plurality of eating habits classifications. Also, if the response scores are less than the threshold value in any of the eating habits classifications, the eating habits classification determination unit 28 determines that the eating habits of the target user to be provided do not correspond to any of the eating habits classifications.

[0051] The eating habits classification decision unit 29 determines the eating habits classification of the target user to be provided based on the determination result by the eating habits classification determination unit 28. When the eating habits classification determination unit 28 determines that the eating habits of the target user to be provided correspond to one eating habits classification, the eating habits classification decision unit 29 determines the eating habits of the target user to be provided as the said eating habits classification.

[0052] In addition, when the eating habit classification determination unit 29 determines that the eating habits of the user to be provided correspond to multiple eating habit classifications according to the eating habit classification determination unit 28, the eating habit classification determination unit 29 classifies the eating habits of the user to be provided into the eating habit classification with the highest priority (see Table 4) determined by the priority determination unit 13. For example, regarding visceral fat accumulation, when it is determined by the eating habit classification determination unit 28 that the eating habits of a specific user to be provided correspond to "snacking" and "lack of exercise or exercise", the eating habit classification determination unit 29 determines that the eating habits of the user to be provided shall be the eating habit classification of "snacking" with a higher priority.

[0053] In addition, when the eating habit classification determination unit 29 determines that the eating habits of the user to be provided do not correspond to any eating habit classification according to the eating habit classification determination unit 28, the eating habits of the user to be provided can be classified into a classification different from the eating habit classifications generated by factor. For example, regarding visceral fat accumulation, when it is determined that the eating habits of the user to be provided do not correspond to any eating habit classification, the eating habit classification determination unit 29 classifies the eating habits of the user to be provided into "decreased basal metabolism or metabolism". This is because although there is no problem with the eating habits of the user to be provided, the amount of visceral fat accumulation exceeds the specified value, so it can be determined that it is due to decreased basal metabolism.

[0054] The eating habit classification unit 23 classifies the eating habits of the user to be provided into one of the above. The eating habit classification unit 23 can generate a presentation screen for eating habit classification as shown in FIG. 3 and visually notify the classification result to the user to be provided. The radar chart is the score for each eating habit classification. Note that the eating habit classification unit 23 may accept a selection of an eating habit classification from the user to be provided and set the eating habit classification selected by the user to be provided as the eating habit classification of the user. This is effective when the user to be provided grasps his / her own eating habit classification notified previously. The eating habit classification unit 23 supplies the eating habit classification of the user to be provided to the recording information acquisition unit 24 and the advice generation unit 26.

[0055] The recording information acquisition unit 24 acquires recording information which is information regarding the meal content and activity content recorded by the user to be provided. The recording information acquisition unit 24 generates a group of recording items including recording items corresponding to the eating habit classification for each user to be provided. The following Table 5 and Table 6 are examples of the group of recording items generated by the recording information acquisition unit.

[0056]

Table 5

[0057]

Table 6

[0058] Note that, as an option, the intake amounts of tea catechins, chlorogenic acid, ALA-DAG (α-linolenic acid diacylglycerol), etc., which are functional components contributing to the suppression of body fat accumulation and the prevention of lifestyle-related diseases, may be added to the group of recording items.

[0059] As shown in Table 5 and Table 6, the group of recording items generated by the recording information acquisition unit includes "items classified by eating habit". The "items classified by eating habit" are recording items classified by eating habit by the eating habit classification unit 23, and the recording items for "low health orientation" are exemplified in Table 5 and Table 6. The following Table 7 shows the recording items classified by other eating habits. As shown in these tables, the recording items classified by eating habit have different contents for each eating habit classification. Also, the recording items classified by eating habit may include calories consumed other than walking.

[0060]

Table 7

[0061] Furthermore, as shown in Table 5, the record item group may include "nutrient intake amount". "Nutrient intake amount" is a record item regarding the intake amount of one or more nutrients selected from protein, omega-3 fatty acids, and dietary fiber. Also, as shown in Table 6, the record item group may include record items such as "lifestyle points", "daily calorie intake", "number of steps", and "body measurement values". Items other than the "items classified by eating habits" in the record item group are common among each target user to be provided. Hereinafter, each of the "items classified by eating habits", "protein", "dietary fiber", "omega-3 fatty acids", and "lifestyle points" is regarded as a "major item".

[0062] The record information acquisition unit 24 presents the generated record item group to the target user to be provided. When the target user to be provided inputs the record content, the record information acquisition unit 24 acquires the record information. The input of the record content by the target user to be provided is performed daily or weekly.

[0063] The record information acquisition unit 24 may further acquire "exercise record" as the record information. The exercise record is a record of the exercise performed by the target user to be provided, and includes the start and end times of the exercise, the type of exercise, the number of steps, the energy consumed in activities, and the like. The record information acquisition unit 24 supplies the acquired record information to the evaluation unit 25.

[0064] The evaluation unit 25 evaluates the cause of body fat accumulation or the cause of lifestyle-related diseases for each target user to be provided based on the record information. Tables 8 and 9 below show the evaluation methods by the evaluation unit 25.

[0065]

Table 8

[0066]

Table 9

[0067] The evaluation unit 25 can aggregate the recording information for each target user for a certain evaluation period, for example, one week, and obtain the score of each recording item by calculating the average value of the number of positive responses for each recording item. For example, in the recording item of "ate a meal considering nutritional balance" under "low health orientation", the evaluation unit 25 takes the average value of the number of "〇" in one week as the score of this recording item. Similarly, for other recording items such as "didn't eat until full for any meal", the average value of the number of "〇" in one week is taken as the score of the corresponding recording item.

[0068] The evaluation unit 25 can extract the recording items with higher scores (hereinafter referred to as the above recording items) and the recording items with lower scores (hereinafter referred to as the lower recording items) among each recording item. The upper recording items are, for example, the recording items ranked 1st to 3rd in terms of scores, and the lower recording items are, for example, the recording items ranked last 1st to 3rd in terms of scores. The evaluation unit 25 can evaluate that the lower recording items are items that require improvement to eliminate the causes of body fat accumulation or lifestyle diseases.

[0069] Furthermore, the evaluation unit 25 can evaluate the intake amount of nutrients for each target user based on the recording information. The evaluation unit 25 can estimate the intake ratio and intake status of each nutrient from the scores of the recording items of "protein", "omega-3 fatty acid" and "dietary fiber". In addition, the evaluation unit 25 can also estimate the "protein / lipid ratio", "omega-3 fatty acid / lipid ratio", and "dietary fiber / carbohydrate or carbohydrate ratio" from the scores of the recording items of "protein", "omega-3 fatty acid" and "dietary fiber".

[0070] In addition, the evaluation unit 25 can also evaluate the "eating behavior" or "personal information" for each target user based on the recording information. "Eating behavior" is one or more of the quality, quantity, time, eating method and activity level of the target user's diet, and "personal information" is at least one or more of weight and abdominal circumference, etc. The priority order of the evaluation by the evaluation unit 25 is not limited, but as an example, it is preferable to set the priority of "quantity of food" as the highest, and then assign priorities in the order of "activity level", "time of meal", "quality of meal", "eating method", "weight".

[0071] The advice generation unit 26 generates advice for presenting at least one of "recommended exercise intensity" and "recommended exercise time zone" for reducing body fat to the user to be provided. The recommended exercise intensity is an exercise intensity that, when executed by the user to be provided, has a high efficiency in reducing body fat, and the recommended exercise time zone is a time zone that, when executed by the user to be provided, has a high efficiency in reducing body fat. Table 10 is a table showing factors that affect the change in visceral fat area by eating habit classification. As shown by the dashed frame in Table 10, exercise is an important factor in reducing visceral fat.

[0072]

Table 10

[0073] The advice generation unit 26 identifies at least one of the recommended exercise intensity and the recommended exercise time zone based on the "relationship data" and the eating habit information of the user to be provided acquired by the eating habit information acquisition unit 22. The relationship data is data showing the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction. The following Tables 11 and 12 are examples of relationship data. As shown in Table 11, the relationship data is the correlation coefficient between the exercise intensity in each exercise time zone and the change in visceral fat area for each eating habit classification. The change in visceral fat area is also denoted as ΔVFA (Visceral Fat Area).

[0074]

Table 11

[0075]

Table 12

[0076] The relational data shown in Table 11 is obtained by collecting eating habit information, changes in visceral fat area, information on the exercise time zone and exercise intensity of the exercises performed from a large number of users, classifying each user into one of the eating habit classifications, and then calculating the correlation coefficient between the exercise intensity in each exercise time zone and the change in visceral fat area for each eating habit classification. The relational data as shown in Table 11 may be generated by the advice generation unit 26, or the advice generation unit 26 may obtain and use the pre-prepared relational data.

[0077] As shown in Table 11, priorities are assigned in descending order of the correlation coefficient for each eating habit classification. The exercise time zones with high priorities vary depending on the eating habit classification. For example, in the case of "snacking", the time zone from 12:00 to 15:00 has the first priority, the time zone from 15:00 to 18:00 has the second priority, and the time zone from 9:00 to 12:00 has the third priority. On the other hand, in the case of "dinner", the time zone from 21:00 to 24:00 has the first priority, the time zone from 15:00 to 18:00 has the second priority, and the time zone from 12:00 to 15:00 has the third priority. In the example shown in Table 11, no correlation was found between the exercise intensity in each exercise time zone and the change in visceral fat area for "early eater" and "lack of exercise or exercise". Table 12 shows the "average value ± standard deviation of exercise intensity (Ex)" for the exercise time zones with the first to third priorities in Table 11.

[0078] Regarding exercise intensity, Table 13 is a table showing exercise intensity (Ex), exercise intensity (Mets), and specific examples of exercises. For example, an exercise intensity (Ex) of 5 corresponds to an exercise intensity (Mets) of 4.3, and specifically corresponds to an exercise intensity such as brisk walking or walking in water. Exercise intensity (Ex) and exercise intensity (Mets) can be converted by the following formula (1). The exercise intensity used in this embodiment may be either one of exercise intensity (Ex) and exercise intensity (Mets), or both. Exercise intensity (Mets)=0.043X 2 +0.379X+1.361 (1) X = exercise intensity (Ex)

[0079]

Table 13

[0080] As described above, the advice generation unit 26 identifies at least one of the recommended exercise intensity and the recommended exercise time zone based on the relationship data and the eating habit information of the target user obtained by the eating habit information acquisition unit 22. Specifically, the advice generation unit 26 refers to the eating habit classification of the target user supplied from the eating habit classification unit 23. This eating habit classification is determined by the eating habit classification unit 23 based on the eating habit information of the target user obtained by the eating habit information acquisition unit 22.

[0081] Furthermore, the advice generation unit 26 identifies the exercise time zone with the highest correlation coefficient in the eating habit classification of the target user among the relationship data. For example, when the eating habit classification of the target user is "snacking", the advice generation unit 26 identifies "12:00 - 15:00", which is the exercise time zone with the highest correlation coefficient, i.e., the highest priority, in the "snacking" in Table 11.

[0082] Subsequently, the advice generation unit 26 identifies the exercise intensity of the identified exercise time zone. As shown in Table 12, the exercise intensity (Ex) of the identified exercise time zone is "2.8 ± 0.7" with the mean value ± standard deviation. The advice generation unit 26 sets the identified exercise time zone as the "recommended exercise time zone", which is the exercise time zone recommended for body fat reduction, and sets the identified exercise intensity as the "recommended exercise intensity", which is the exercise intensity recommended for body fat reduction. Then, the advice generation unit 26 generates advice presenting at least one of the recommended exercise time zone and the recommended exercise intensity. The advice generation unit 26 can generate advice to the effect that, for example, "the recommended exercise time zone is 12:00 - 15:00, and the recommended exercise intensity (Ex) is 2.8 ± 0.7".

[0083] In addition, the advice generation unit 26 may further specify lower-priority exercise time zones and exercise intensities in the eating habit classification of the user to whom advice is provided. For example, the advice generation unit 26 can specify "15:00 to 18:00", which has the second-highest priority in "snacking", and its exercise intensity (Ex) of "2.8 ± 0.3". Also, the advice generation unit 26 can specify "9:00 to 12:00", which has the third-highest priority in "snacking", and its exercise intensity (Ex) of "2.6 ± 0.6". The advice generation unit 26 can use these exercise time zones as recommended exercise time zones and these exercise intensities as recommended exercise intensities to generate advice that presents at least one of the recommended exercise time zone and the recommended exercise intensity. The advice generation unit 26 supplies the generated advice to the presentation information generation unit 27.

[0084] The presentation information generation unit 27 generates presentation information including the advice supplied from the advice generation unit 26. FIG. 4 is an example of the presentation information presented by the presentation information generation unit 27, and is an example when the eating habit classification of the user to whom advice is provided is "snacking". As shown in the figure, the presentation information presented by the presentation information generation unit 27 includes advice S1 presenting the recommended exercise time zone and advice S2 presenting the recommended exercise intensity. Note that the recommended exercise intensity with the highest priority (exercise intensity (Ex) 2.1 to 3.5) is the "average value ± standard deviation (2.8 ± 0.7)" from 12:00 to 15:00 for "snacking" shown in Table 12, and the same applies to other priorities. As shown in the figure, the advice S2 may include the specific types of exercises corresponding to the recommended exercise intensity (see Table 13).

[0085] FIG. 5 is another example of the presentation information presented by the presentation information generation unit 27, and is an example when the eating habit classification of the user to be provided is "night snack". Also in this case, the presentation information includes advice S1 presenting the recommended exercise time zone and advice S2 presenting the recommended exercise intensity. In FIGS. 4 and 5, the recommended exercise time zone and the recommended exercise intensity are different. Similarly, in the case of other eating habit classifications such as "low health orientation or health orientation", "late meal or late dinner", "basal metabolism decrease or metabolism", presentation information including advice S1 presenting each recommended exercise time zone and advice S2 presenting the recommended exercise intensity is generated.

[0086] Note that the presentation information generation unit 27 may include advice S1 presenting the recommended exercise time zone and advice S2 presenting the recommended exercise intensity only for the exercise time zone with the highest priority. Also, the presentation information generation unit 27 may include only one of advice S1 presenting the recommended exercise time zone and advice presenting the recommended exercise intensity S2.

[0087] The advice providing system 100 has the above configuration. Each configuration of the eating habit classification generation device 10 and the advice providing device 20 is a functional configuration realized by the cooperation of hardware and software of an information processing device composed of a CPU (Central Processing Unit), a RAM (Random Access Memory), etc. The software is a program executable by the information processing device, and may be a program recorded on a recording medium readable by the information processing device. Also, the eating habit classification generation device 10 and the advice providing device 20 may be separate information processing devices, or may be a single information processing device.

[0088] [Other configurations of the advice generation unit] As described above, in "early eater" and "lack of exercise or exercise" of the eating habit classification, no correlation was found between the exercise intensity in each exercise time zone and the amount of change in visceral fat area (see Table 11). However, also for these, the advice generation unit 26 can generate advice presenting at least one of the recommended exercise intensity and the recommended exercise time zone using other relational data.

[0089] Tables 14 and 15 are examples of relational data when the eating habit classification is "lack of exercise or exercise". As shown in Table 14, the relational data is the correlation coefficient between the number of steps in each exercise time zone and the change amount of visceral fat area. As shown in Table 10, the number of steps is a factor highly related to the change amount of visceral fat area in the eating habit classification of "lack of exercise or exercise". This relational data also shows the relationship between the exercise time zone and body fat reduction, that is, it corresponds to "relational data showing the relationship between at least one of the exercise intensity and the exercise time zone and body fat reduction". Table 15 shows the "average value ± standard deviation of the number of steps" for the exercise time zones ranked 1st to 3rd in Table 14.

[0090]

Table 14

[0091]

Table 15

[0092] When the eating habit classification of the target user supplied from the eating habit classification unit 23 is "lack of exercise or exercise", the advice generation unit 26 refers to the relational data as shown in Table 14. The relational data as shown in Table 14 may be generated by the advice generation unit 26, or the advice generation unit 26 may obtain and use the pre-prepared relational data. The advice generation unit 26 identifies the exercise time zone with the highest correlation coefficient among the relational data shown in Table 14. For example, the advice generation unit 26 identifies the "21:00 to 24:00" which is the exercise time zone with the highest correlation coefficient, that is, the highest priority in Table 14.

[0093] The advice generation unit 26 sets the identified exercise time zone as the "recommended exercise time zone" which is the exercise time zone recommended for body fat reduction. Then the advice generation unit 26 generates advice presenting the recommended exercise time zone. The advice generation unit 26 can generate advice to the effect that, for example, "the recommended exercise time zone is 21:00 to 24:00".

[0094] Note that the advice generation unit 26 may further specify a lower-priority exercise time zone and exercise intensity. For example, the advice generation unit 26 can specify "18:00 to 21:00" with a priority of 2 and "15:00 to 18:00" with a priority of 3. The advice generation unit 26 can also use these exercise time zones as recommended exercise time zones and generate advice to present them. The advice generation unit 26 supplies the generated advice to the presentation information generation unit 27 for presentation. FIG. 6 is an example of the presentation information presented by the presentation information generation unit 27 in this case. As shown in the figure, the presentation information presented by the presentation information generation unit 27 includes advice S1 presenting the recommended exercise time zone.

[0095] Here, as the relationship data, the correlation coefficient between the number of steps in each exercise time zone and the change amount of visceral fat area has been described, but the number of steps may be other activity amounts. Examples of other activity amounts include walking time and walking distance.

[0096] Table 16 is an example of relationship data when the eating habit classification is "eating quickly". As shown in Table 16, the relationship data is the correlation coefficient between the exercise intensity and the change amount of visceral fat area. As shown in Table 10, in the eating habit classification of "eating quickly", the low-intensity activity time is a factor highly related to the change amount of visceral fat area. This relationship data also shows the relationship between exercise intensity and body fat reduction, that is, it corresponds to "relationship data showing the relationship between at least one of exercise intensity and exercise time zone and body fat reduction".

[0097]

Table 16

[0098] When the eating habit classification of the target user supplied from the eating habit classification unit 23 to the advice generation unit 26 is "eating quickly", the advice generation unit 26 refers to the relationship data as shown in Table 16. The relationship data as shown in Table 16 may be generated by the advice generation unit 26, or the advice generation unit 26 may obtain and use the pre-prepared relationship data.

[0099] The advice generation unit 26 identifies the exercise intensity with the highest correlation coefficient among the relationship data shown in Table 16. For example, the advice generation unit 26 identifies "Exercise intensity (Ex) 1 (equivalent to 1.8 Mets)", which has the highest correlation coefficient in Table 16, that is, the exercise intensity with the highest priority.

[0100] The advice generation unit 26 sets the identified exercise intensity as the "recommended exercise intensity", which is the exercise intensity recommended for reducing body fat. Then, the advice generation unit 26 generates advice presenting the recommended exercise intensity. For example, the advice generation unit 26 can generate advice to the effect that "the recommended exercise intensity is Exercise intensity (Ex) 1 (equivalent to 1.8 Mets)".

[0101] Note that the advice generation unit 26 may further identify the exercise time zones and exercise intensities with lower priorities. For example, the advice generation unit 26 can identify "Exercise intensity (Ex) 2 (equivalent to 2.3 Mets)" with the second highest priority and "Exercise intensity (Ex) 3 (equivalent to 2.9 Mets)" with the third highest priority. The advice generation unit 26 can also set these exercise intensities as the recommended exercise intensities and generate advice presenting them. The advice generation unit 26 supplies the generated advice to the presentation information generation unit 27 for presentation. FIG. 7 shows an example of the presentation information presented by the presentation information generation unit 27 in this case. As shown in the figure, the presentation information presented by the presentation information generation unit 27 includes advice S2 presenting the recommended exercise intensity.

[0102] [Advice generation according to the reduction target value of visceral fat area] The advice generation unit 26 can also generate advice presenting, as the recommended exercise intensity, the exercise intensity determined according to the reduction target value of the visceral fat area specified by the user to whom the advice is provided.

[0103] First, the advice generation unit 26 identifies the exercise time zone with the highest correlation coefficient in the eating habits classification of the target user among the relevant data. For example, when the eating habits classification of the target user is "snacking", the advice generation unit 26 identifies "12:00 - 15:00", which is the exercise time zone with the highest correlation coefficient in the "snacking" section of Table 11.

[0104] Subsequently, for this exercise time zone, the advice generation unit 26 applies the reduction target value of the visceral fat area to the correspondence relationship between the exercise intensity and the change amount of the visceral fat area prepared in advance to obtain the exercise intensity. Specifically, the advice generation unit 26 calculates the exercise intensity by substituting the reduction target value of the visceral fat area into the approximate formula calculated from the exercise intensity and the change amount of the visceral fat area. Table 17 is a table showing the approximate formula and the calculation results when the eating habits classification is "snacking", and Table 18 is a table showing the approximate formula and the calculation results when the eating habits classification is "night snacking". As shown in these tables, the approximate formula is set for each exercise time zone of each eating habits classification.

[0105]

Table 17

[0106]

Table 18

[0107] For each exercise time zone, the advice generation unit 26 can substitute the reduction target value of the visceral fat area into the approximate formula and generate advice to present the obtained exercise intensity as the recommended exercise intensity. FIG. 8 is an example of presentation information including the recommended exercise intensity determined by the advice generation unit 26 according to the reduction target value of the visceral fat area. As shown in the figure, the presentation information includes advice S3 that presents the recommended exercise intensity according to the reduction target value of the visceral fat area.

[0108] [Operation of the Advice Providing System] The operation of the advice providing system 100 will be described. As described above, the eating habit classification generation device 10 generates an eating habit classification based on the eating habit information of a plurality of analysis target users and data related to biological indicators. The generation of the eating habit classification may be executed once when the advice providing system 100 is constructed, but may also be executed again after the construction.

[0109] The advice providing device 20 provides a body fat reduction or lifestyle disease prevention program to the target user. As shown in FIG. 9, the advice providing device 20 operates respectively "before program execution" and "during program execution". Table 19 below shows the contents of "input", "judgment", and "output" before and during program execution.

[0110]

Table 19

[0111] Before program execution, as shown in FIG. 9, the target user determination unit 21 determines the target user (St11). As shown in Table 19, the target user determination unit 21 can receive the input of body information from the user and calculate specific biological indicators. Further, the target user determination unit 21 can determine whether each user corresponds to a target user for whom the program is to be executed based on the specific biological indicators, and output whether each user is a target user for whom the program is to be executed.

[0112] Subsequently, the eating habit information acquisition unit 22 acquires the eating habit information of the target user (St12), and the eating habit classification unit 23 determines the eating habit classification of the target user (St13). As shown in Table 8, the eating habit information acquisition unit 22 receives the input of eating habit information from the target user, and the eating habit classification unit 23 determines the eating habit classification of the target user. Further, the eating habit classification unit 23 can output the eating habit classification corresponding to each target user (see FIG. 3).

[0113] Next, the advice generation unit 26 generates advice for presenting at least one of the recommended exercise intensity and the recommended exercise time zone for reducing body fat based on the related data and the eating habit information (St14). Specifically, the advice generation unit 26 uses the eating habit classification of the user to be provided and the related data supplied from the eating habit classification unit 23 to identify at least one of the recommended exercise intensity and the recommended exercise time zone as described above, and generates advice for presenting it. The presentation information generation unit 27 generates presentation information including this advice (St15).

[0114] At this time, the advice generation unit 26 may generate advice for eliminating the cause of body fat accumulation or the cause of lifestyle diseases according to the eating habit classification, and output the advice generated for each user to be provided. In addition, the presentation information generation unit 27 may predict the physical information of the user to be provided after the program is executed from the physical information input by the user to be provided, and output the predicted physical information to the user to be provided.

[0115] During the execution of the program, the recording information acquisition unit 24 acquires the recording information (St16), and the evaluation unit 25 evaluates the recording information (St17). The recording information is a record of the exercise performed by the user to be provided, such as the start and end times of the exercise, the type of exercise, the number of steps, and the energy consumed in activities. The evaluation unit 25 calculates and evaluates the total exercise time, the average number of steps, the average energy consumed in activities, etc.

[0116] Subsequently, the advice generation unit 26 generates advice for reducing body fat according to the evaluation result by the evaluation unit 25, and the presentation information generation unit 27 generates presentation information including the advice (St18). The advice generation unit 26 can generate advice for praising the execution of the exercise or prompting improvement. In addition, the advice generation unit 26 can also generate advice for encouraging the exercise with the praised exercise intensity or the praised exercise time zone as the next focus item.

[0117] In addition, during program execution, the recording information acquisition unit 24 may acquire recording information regarding meals as shown in Table 5 (St14). The evaluation unit 25 can calculate the scores for each recording item and each major item, and extract the upper-level recording items and lower-level recording items (St14). Further, the evaluation unit 25 may evaluate the nutrient intake. The presentation information generation unit 27 can generate presentation information including these pieces of information and output it to each target user.

[0118] After the advice providing device 20 executes the operation "before program execution", it executes the operation "during program execution" for each evaluation period. The evaluation period is, for example, one week. The program execution period is, for example, one month, and the advice providing device 20 executes the operation "during program execution" once or multiple times within the program execution period.

[0119] In this way, the advice providing system 100 provides advice for eliminating the causes of visceral fat accumulation or lifestyle disease for each target user. The operation of the advice providing system 100 described above is an example, and the advice providing system 100 may provide advice for eliminating the causes of visceral fat accumulation or lifestyle disease according to the eating habit classification for at least one of before and during program execution to the target user. Note that the eating habit classification of the target user can be used for other purposes such as product recommendation, service provision / cooperation, future prediction (body type, lifestyle disease, etc.), and introduction of friends (community formation) in addition to advice provision.

[0120] [Effect by Advice Providing System] As described above, in the advice providing system 100, it is possible to present at least one of the exercise time zone and exercise intensity that has the highest correlation with visceral fat reduction according to the eating habit information acquired from the target user, and the target user can efficiently reduce visceral fat. In addition, the advice providing system 100 can also present advice for presenting the exercise intensity according to the reduction target value of the visceral fat area, and the target user can grasp the exercise intensity that enables efficient reduction of visceral fat according to the reduction target value.

[0121] In addition, an optimal insurance sales material may be proposed in combination with the advice-providing system 100. Furthermore, as part of the services provided by a health guidance institution or the like, the advice-providing system 100 may be provided in combination with health consultations, health examinations, health education, health coaching, and health programs for smoking cessation. Also, as part of the services provided by a medical examination institution or the like, the advice-providing system 100 may be provided in combination with cancer screenings, lifestyle disease screenings, prenatal check-ups, and screenings implemented by companies for the health management of their employees.

[0122] [Regarding the System Configuration of the Advice-Providing System] The advice-providing system 100 may be realized by a single information processing device, or may be realized by a plurality of information processing devices directly or connected to each other via an information communication line. For example, the eating habit information acquisition unit 22 can acquire the eating habit information input by the first information terminal possessed by the user to be provided, and the advice generation unit 26 can provide the generated advice to the second information terminal possessed by the personal trainer.

[0123] As described above, the embodiments of the present invention have been explained, but the present invention is not limited to the above-described embodiments, and it goes without saying that various changes can be made without departing from the gist of the present invention.

Explanation of Reference Numerals

[0124] 20... Advice-providing device 23... Eating habit classification unit 26... Advice generation unit

Claims

1. An advice providing device that provides advice for reducing body fat to a target user, comprising: an information acquisition unit that acquires eating habit information, which is information about the eating habits of the target user; an advice generation unit that generates advice for presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the target user based on relationship data indicating the relationship between at least one of exercise intensity and exercise time zone and body fat reduction and the eating habit information; An advice providing device comprising the above.

2. The advice providing device according to claim 1, further comprising an eating habit classification unit that classifies the eating habits of the target user into any one of a plurality of eating habit classifications according to the cause of body fat accumulation based on the eating habit information, wherein the relationship data is data indicating the relationship between at least one of exercise intensity and exercise time zone and body fat reduction for each eating habit classification. The advice providing device according to claim 1.

3. The relationship data is a correlation coefficient between the exercise intensity in each exercise time zone and the change amount of visceral fat area for each eating habit classification, and the advice generation unit generates advice for presenting, as the recommended exercise time zone, the exercise time zone in which the correlation coefficient is the highest in the eating habit classification of the target user among the relationship data. The advice providing device according to claim 2.

4. The advice generation unit generates advice for presenting, as the recommended exercise intensity, the exercise intensity in the exercise time zone in which the correlation coefficient is the highest in the eating habit classification of the target user among the relationship data. The advice providing device according to claim 3.

5. The relationship data is a correlation coefficient between the activity amount in each exercise time zone and the change amount of visceral fat area for each eating habit classification, and the advice generation unit generates advice for presenting, as the recommended exercise time zone, the exercise time zone in which the correlation coefficient is the highest in the eating habit classification of the target user among the relationship data. The advice providing device according to claim 2.

6. The relationship data is a correlation coefficient between exercise intensity and the change amount of visceral fat area for each eating habit classification, and the advice generation unit generates advice for presenting, as the recommended exercise intensity, the exercise intensity in which the correlation coefficient is the highest in the eating habit classification of the target user among the relationship data. The advice providing device according to claim 2.

7. The relationship data is a correlation coefficient between the exercise intensity in each exercise time zone and the change amount of visceral fat area. The advice generation unit generates advice that presents, as the recommended exercise time zone, the exercise time zone having the highest correlation coefficient among the relationship data. The advice providing apparatus according to claim 1.

8. The advice generation unit generates advice that presents, as the recommended exercise intensity, the exercise intensity of the exercise time zone having the highest correlation coefficient among the relationship data. The advice providing apparatus according to claim 7.

9. An advice providing system that provides advice for reducing body fat to a user to be provided, an information acquisition unit that acquires eating habit information, which is information regarding the eating habits of the user to be provided, from a first information terminal; an advice generation unit that generates advice presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of exercise intensity and exercise time zone and body fat reduction and the eating habit information, and provides the advice to a second information terminal; An advice providing system comprising the above.

10. An advice providing program executed by an information processing apparatus that provides advice for reducing body fat to a user to be provided, a step of acquiring eating habit information, which is information regarding the eating habits of the user to be provided; a step of generating advice presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of exercise intensity and exercise time zone and body fat reduction and the eating habit information; An advice providing program for causing the above to be executed.

11. An advice providing method for providing advice for reducing body fat to a user to be provided, acquiring eating habit information, which is information regarding the eating habits of the user to be provided; generating advice presenting at least one of a recommended exercise intensity and a recommended exercise time zone for reducing body fat to the user to be provided, based on relationship data indicating the relationship between at least one of exercise intensity and exercise time zone and body fat reduction and the eating habit information An advice providing method.

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