JP2024061019A - Toothbrushing assistance device, toothbrushing assistance system, and toothbrushing assistance program - Google Patents

Abstract

To provide a toothbrushing assistance device, a toothbrushing assistance system, and a toothbrushing assistance program for controlling a user's blood sugar through toothbrushing.SOLUTION: A toothbrushing assistance device 1 comprises: a blood sugar variation estimation unit 111 that estimates values related to variations in blood sugar of a user 9 based on data 61 related to a toothbrushing behavior derived from an oral care article 4 and data 62 related to blood sugar; a behavior candidate determination unit 112 that determines candidates for the toothbrushing behavior for controlling the variations in blood sugar; and a behavior candidate presentation unit 113 that presents the candidates for the toothbrushing behavior to the user 9.SELECTED DRAWING: Figure 2

Description

The present invention relates to a toothbrushing assistance device, a toothbrushing assistance system, and a toothbrushing assistance program that are used when a user brushes their teeth.

Maintaining oral function is important for living a physically and mentally healthy life. In order to extend an individual's healthy lifespan, dentists also recommend that people keep their teeth sufficiently clean by performing appropriate daily oral care, including brushing their teeth. Examples of conventional technologies that support daily tooth brushing using a toothbrush include the technologies described in Patent Document 1 and Patent Document 2 below.

JP 2009-240760 A JP 2011-524756 A

In recent years, reports have pointed out a link between periodontal disease and diabetes, and proper daily oral care is required from the perspective of preventing diabetes. Daily tooth brushing is the basis of oral care, and tooth brushing is expected to help healthy people prevent diabetes, and diabetic patients control and manage their daily blood sugar levels.

The present invention aims to provide a toothbrushing assistance device, a toothbrushing assistance system, and a toothbrushing assistance program for controlling a user's blood sugar through toothbrushing.

The present invention, which aims to solve the above problems, includes the following aspects, for example:

(Item 1)
A blood glucose fluctuation estimation unit that estimates a value related to the blood glucose fluctuation of a user based on data related to tooth brushing behavior and data related to blood glucose derived from oral care products;
A behavior candidate determination unit that determines a candidate toothbrushing behavior for controlling the blood sugar fluctuation;
a behavior candidate presentation unit that presents the tooth brushing behavior candidates to the user;
A tooth brushing aid comprising:
(Item 2)
The blood glucose fluctuation estimation unit estimates a value related to the user's blood glucose fluctuation based on a prediction model that takes data related to the tooth brushing behavior and data related to the blood glucose as input and outputs an estimated value related to the blood glucose fluctuation.
(Item 3)
3. The tooth brushing assistance device of claim 2, wherein the prediction model is a regression model, in which the value related to the blood glucose fluctuation is a dependent variable and items including each item of the data related to the tooth brushing behavior are explanatory variables.
(Item 4)
The tooth brushing behavior data includes one or more items selected from the group consisting of the number of times teeth are brushed within a specified period of time, the number of times teeth are brushed within a specified time period, the rate of tooth brushing within a specified period of time, the rate of tooth brushing within a specified time period, the time spent brushing teeth per session, the time spent brushing teeth within a specified period of time, the total time spent brushing teeth within a specified period of time, the time of brushing teeth, the time period of brushing teeth, a score regarding tooth brushing behavior per session or within a specified period of time, the frequency of use of oral care tools, and the type of oral care tool used, and combinations thereof.
(Item 5)
A tooth brushing assistance device described in any one of items 1 to 4, wherein the blood glucose data includes one or more items selected from the group consisting of glucose-derived values, HbA1c-derived values, insulin-derived values, and insulin resistance index (HOMA-R).
(Item 6)
An oral care product module that transmits a wireless signal related to tooth brushing behavior, and a tooth brushing assistant device according to any one of items 1 to 5 that receives the wireless signal,
The oral care product module includes:
A sensor for detecting a physical quantity related to a user's tooth brushing behavior;
a wireless transmission unit that transmits a wireless signal indicative of the physical quantity detected by the sensor;
Equipped with
The tooth brushing assist device is
The tooth brushing assistance system further comprises a wireless receiving unit that receives the wireless signal indicating the physical quantity.
(Item 7)
Item 7. The tooth brushing assistance system according to item 6, further comprising a tooth brushing assistance server communicatively connected to the tooth brushing assistance device and having a learning unit that learns a predictive model that receives data regarding the tooth brushing behavior and data regarding the blood glucose as input and outputs an estimated value regarding the blood glucose fluctuation.
(Item 8)
Item 8. The tooth brushing assistance system according to item 6 or 7, further comprising a blood glucose monitoring device that monitors data regarding the user's blood glucose and wirelessly transmits the data to the tooth brushing assistance device.
(Item 9)
A tooth brushing assistance program for causing a computer to function as each part of the tooth brushing assistance device according to any one of items 1 to 5.

According to the present invention, a toothbrushing assistance device, a toothbrushing assistance system, and a toothbrushing assistance program for controlling a user's blood sugar through toothbrushing can be provided.

1 is a conceptual diagram showing a schematic configuration of a tooth brushing assistance system according to an embodiment of the present invention. 1 is a block diagram for explaining functions of a tooth brushing assistance system according to an embodiment of the present invention. FIG. 1 illustrates an embodiment of a blood glucose monitoring device worn by a user. 1 is a flowchart for explaining the procedure of data processing performed by a tooth brushing assistance device in a tooth brushing assistance system according to one embodiment of the present invention. This is a graph in which CGM data showing changes in blood glucose levels over time are plotted for each total amount of time spent brushing teeth each day. This is a graph in which CGM data showing changes in blood glucose levels over time are plotted for each total amount of time spent brushing teeth each day. This is a graph plotting CGM data extracted from the population where teeth were brushed from early evening to night. This is a graph plotting CGM data extracted from the population where teeth were brushed from early evening to night.

Embodiments of the present invention will now be described in detail with reference to the accompanying drawings. In the following description and drawings, the same reference numerals indicate the same or similar components, and therefore, redundant descriptions of the same or similar components will be omitted.

[System configuration]
1 is a conceptual diagram showing a schematic configuration of a tooth brushing assistance system according to an embodiment of the present invention. An overview of the tooth brushing assistance system 100 will be described with reference to FIG.

The toothbrushing assistance system 100 according to one embodiment includes a toothbrushing assistance device 1, a toothbrushing assistance server 2, a toothbrush attachment 3, and a blood glucose monitoring device 5.

The toothbrushing assistance device 1 receives a wireless signal from the toothbrush attachment 3 indicating physical quantities related to the toothbrushing behavior using the toothbrush 4. The toothbrushing assistance device 1 records the physical quantities indicated in the received wireless signal as toothbrushing behavior data 61 together with other related data such as the time of brushing and the duration of brushing. Similarly, the toothbrushing assistance device 1 receives data related to the user 9's blood glucose together with other related data such as the measurement time from the blood glucose monitoring device 5 and records it as blood glucose data 62. The blood glucose monitoring device 5 continuously monitors the data related to the user 9's blood glucose.

The toothbrushing assistance device 1 estimates a value related to the blood glucose fluctuation of the user 9 based on the toothbrushing behavior data 61 and the blood glucose data 62. The value related to the blood glucose fluctuation of the user 9 obtained by the estimation represents a tendency related to the temporal fluctuation of the blood glucose level of the user 9. Exemplarily, the value related to the blood glucose fluctuation is a TIR (Time in Range) index of the blood glucose data 62. The blood glucose fluctuation includes a value obtained from one or more pieces of blood glucose data 62. At the time of estimation, one set of the toothbrushing behavior data 61 and the blood glucose data 62 is sufficient. In this embodiment, a prediction model 63 is used to estimate the value related to the blood glucose fluctuation. Exemplarily, the prediction model 63 is a regression model. The toothbrushing assistance device 1 acquires the prediction model 63 recorded in advance in the toothbrushing assistance server 2, and estimates the value related to the blood glucose fluctuation using the acquired prediction model 63. The toothbrushing assistance device 1 further determines candidates for toothbrushing behavior for controlling blood glucose fluctuation. Exemplarily, the candidates for toothbrushing behavior are the number of times of toothbrushing, the toothbrushing time, and the toothbrushing time period. The tooth brushing assistance device 1 then presents the determined tooth brushing behavior candidates to the user 9.

The toothbrushing assistance server 2 is communicatively connected to the toothbrushing assistance device 1 via the network 8, and acquires toothbrushing behavior data 61 and blood glucose data 62 of the user 9 from the toothbrushing assistance device 1. A plurality of toothbrushing assistance devices 1 are communicatively connected to the toothbrushing assistance server 2, and the toothbrushing assistance server 2 acquires a plurality of sets of toothbrushing behavior data 61 and blood glucose data 62 by a plurality of different users 9 from the plurality of toothbrushing assistance devices 1. The toothbrushing assistance server 2 records the plurality of sets of data 61, 62 acquired from the plurality of toothbrushing assistance devices 1 as teacher data 64 for learning a prediction model 63. The toothbrushing assistance server 2 includes a learning unit 211. The learning unit 211 uses the teacher data 64 to learn a prediction model 63 that receives the toothbrushing behavior data 61 and blood glucose data 62 as input and outputs an estimated value related to blood glucose fluctuation. In this embodiment, the toothbrushing assistance device 1 acquires the trained prediction model 63 from the toothbrushing assistance server 2, and estimates a value related to the blood glucose fluctuation of the user 9 based on the acquired trained prediction model 63. In this case, one set of the toothbrushing behavior data 61 and blood glucose data 62 of the user 9 is sufficient. The toothbrushing assistance device 1 can estimate a value related to the blood glucose fluctuation of the user 9 using at least one piece of measurement data related to the user 9, determine candidate toothbrushing behaviors for controlling blood glucose fluctuations, and present the determined candidate toothbrushing behaviors to the user 9.

According to such a toothbrushing assistance system 100, the toothbrushing assistance device 1 estimates a value related to blood glucose fluctuation of the user 9 based on the toothbrushing behavior data 61 and the blood glucose data 62. The toothbrushing assistance device 1 determines candidates for toothbrushing behaviors that control blood glucose fluctuations based on the estimated value related to blood glucose fluctuations (tendency of temporal fluctuations in blood glucose levels) and presents the determined candidates for toothbrushing behaviors to the user 9. As a result, specific candidates for toothbrushing behaviors that control blood glucose fluctuations are presented to the user 9 of the toothbrushing assistance device 1 via, for example, the touch panel display 15, and the user 9 is able to control and manage his or her own blood glucose through daily toothbrushing. For example, if the user 9 is a healthy person, prevention of diabetes is promoted through daily toothbrushing, and if the user 9 is a diabetic, daily blood glucose fluctuations can be controlled and managed through daily toothbrushing.

[Hardware configuration and functional blocks]
FIG. 2 is a block diagram for explaining the function of the tooth brushing assistance system according to one embodiment of the present invention.

<Tooth brushing aid>
The tooth brushing assistance device 1 includes a calculation unit 11, a recording unit 12, a communication unit 13, a wireless receiving unit 14, and a touch panel display 15. As an optional configuration, the tooth brushing assistance device 1 may further include a camera 16 and a speaker 17. Exemplarily, the tooth brushing assistance device 1 may be configured as a smartphone or a tablet computer.

The calculation unit 11 includes a CPU (not shown) for performing the data processing described below, and memory (not shown) used as a working area for the data processing.

The recording unit 12 is a non-volatile storage device that stores an operating system (OS), various control or calculation programs, and data generated by the programs, and is composed of, for example, a flash memory, an eMMC (embedded Multi Media Card), an SSD (Solid State Drive), etc.

The communication unit 13 transmits and receives data to and from external devices such as the tooth brushing assistance server 2 via a wired or wireless network 8.

The wireless receiving unit 14 receives wireless signals transmitted from the wireless transmitting unit 32 of the toothbrush attachment 3 and wireless signals transmitted from the blood glucose monitoring device 5.

The touch panel display 15 is configured to integrate the functions of a display device and an input device. Some items of the tooth brushing behavior data 61 and blood glucose data 62 of the user 9 can also be input to the tooth brushing assistance device 1 via the touch panel 15.

The calculation unit 11 includes a blood glucose fluctuation estimation unit 111, a behavior candidate determination unit 112, and a behavior candidate presentation unit 113. These functional blocks in the calculation unit 11 are realized by the CPU in the calculation unit 11 executing a program installed in the recording unit 12 or the memory in the calculation unit 11.

The blood glucose fluctuation estimation unit 111 estimates a value related to the user's blood glucose fluctuation based on the toothbrushing behavior data 61 and the blood glucose data 62. The behavior candidate determination unit 112 determines a candidate toothbrushing behavior that controls blood glucose fluctuation. The behavior candidate presentation unit 113 presents the candidate toothbrushing behavior to the user 9.

<Tooth brushing assistance server>
The tooth brushing assistance server 2 includes a calculation unit 21, a recording unit 22, and a communication unit 23. The calculation unit 21, the recording unit 22, and the communication unit 23 have the same hardware functions as the calculation unit 11, the recording unit 12, and the communication unit 13 of the tooth brushing assistance device 1. Illustratively, the tooth brushing assistance server 2 can be configured as a server computer.

The calculation unit 21 includes a learning unit 211. This functional block in the calculation unit 21 is realized by the CPU in the calculation unit 21 executing a program installed in the recording unit 22 or the memory in the calculation unit 21.

The learning unit 211 uses the teacher data 64 to learn a prediction model 63 that receives tooth brushing behavior data 61 and blood glucose data 62 as input and outputs an estimated value related to blood glucose fluctuation.

<Toothbrush attachment>
The toothbrush attachment 3 is attached to the toothbrush 4, and detects physical quantities related to the tooth brushing behavior of a user 9 using the toothbrush 4. The toothbrush attachment 3 includes a sensor 31 and a wireless transmission unit 32. Illustratively, the sensor 31 and the wireless transmission unit 32 can be implemented as a circuit block, and are housed in a toothbrush module 39 together with a power source (not shown). The power source may be a primary battery or a secondary battery.

The sensor 31 detects a physical quantity related to the toothbrushing action by the user 9. Illustratively, the physical quantity related to the toothbrushing action is the acceleration related to the toothbrushing action, and the sensor 31 can be, for example, a three-axis acceleration sensor that detects acceleration. Because the toothbrush attachment 3 is attached to the toothbrush 4, the sensor 31 can detect the physical quantity (acceleration) associated with the movement of the toothbrush 4 as the physical quantity (acceleration) related to the toothbrushing action by the user 9 using the toothbrush 4.

The sensor 31 can detect gravitational acceleration caused by the earth's gravity. Because the toothbrush attachment 3 is attached to the toothbrush 4, the posture of the toothbrush attachment 3 changes in response to changes in the posture of the toothbrush 4, and the direction of the gravitational acceleration detected by the sensor 31 changes. Therefore, the posture of the toothbrush 4 can be detected from the direction of the gravitational acceleration detected by the sensor 31. In the following, to simplify the explanation, detecting the posture of the toothbrush 4 based on the direction of the gravitational acceleration detected by the sensor 31 will simply be expressed as detecting the posture of the toothbrush 4 by the sensor 31.

The sensor 31 is not limited to an acceleration sensor. The sensor 31 may be a gyro sensor that detects the rotation angle and angular velocity of the toothbrush 4, a magnetic compass that detects geomagnetism, a pressure sensor that detects the pressure of the toothbrush 4 against the teeth of the user 9, a distance sensor that measures distance, a GPS that acquires position information, a microphone that acquires audio signals, or an intraoral camera (image sensor) that captures images of the oral cavity, or may include a combination of these sensors. The intraoral camera captures an image of the position of the head of the toothbrush 4 in the oral cavity, and by comparing the captured intraoral image with various physical quantities detected by the sensor 31, it is possible to verify which part of the tooth the toothbrush 4 is in contact with in the oral cavity and correct the position of the part in contact.

The wireless transmission unit 32 transmits a wireless signal indicating the physical quantity detected by the sensor 31. For example, various wireless methods such as Bluetooth (registered trademark) and Wi-Fi (registered trademark) can be used for wireless communication between the wireless transmission unit 32 and the wireless receiving unit 14 of the tooth brushing assistance device 1.

<Blood Glucose Monitoring Device>
The blood glucose monitoring device 5 monitors data related to the blood glucose of the user 9, and wirelessly transmits the data acquired by the monitoring to the tooth brushing assistance device 1. Illustratively, the blood glucose data is a glucose value at a certain time. The blood glucose monitoring device 5 according to this embodiment is a device that performs CGM (continuous glucose monitoring) or isCGM (intermittently scanned continuous glucose monitoring).

Figure 3 is a diagram showing a blood glucose monitoring device according to one embodiment worn by a user. As illustrated in Figure 3, the blood glucose monitoring device 5 is attached to, for example, the upper arm of a user 9. The illustrated blood glucose monitoring device 5 is provided with needle electrodes (not shown) for subcutaneous glucose measurement, an IC chip (not shown) for recording measurement data and for non-contact near field communication (NFC), and a power source (not shown).

The blood glucose monitoring device 5 measures blood glucose data at predetermined time intervals (e.g., every 15 minutes). Blood glucose data is measured continuously while the user 9 is wearing the blood glucose monitoring device 5, and the blood glucose monitoring device 5 monitors the time-series changes in the user 9's blood glucose data on a daily basis. The data obtained by monitoring is recorded in an IC chip built into the blood glucose monitoring device 5 together with data such as the measurement time.

[Data processing procedure]
FIG. 4 is a flowchart for explaining the procedure of data processing performed by the tooth-brushing assistance device in the tooth-brushing assistance system according to one embodiment of the present invention.

The processing of steps S1 to S5 shown in FIG. 4 is executed by each functional block of the calculation unit 11, that is, by the calculation unit 11. In the following explanation, it is assumed that the prediction model 63 used to estimate values related to blood glucose fluctuation of the user 9 when executing the processing of steps S1 to S5 has been learned in advance by the learning unit 211 of the tooth brushing assistance server 2 using the teacher data 64.

First, as preparation before using the toothbrushing assistance system 100, the toothbrush attachment 3 is attached to the toothbrush 4, and the toothbrush attachment 3 is turned on. The blood glucose monitoring device 5 is also attached to the user 9, for example, by attaching it to their upper arm. Next, the toothbrushing assistance program (app) installed in the toothbrushing assistance device 1 is started, and a state is set up in which data communication is possible between the toothbrushing assistance device 1 and the toothbrush attachment 3 and blood glucose monitoring device 5.

In step S1 (toothbrushing behavior data acquisition step), data on toothbrushing behavior (toothbrushing behavior data 61) is acquired. The toothbrushing behavior data 61 may include one or more items selected from the group consisting of the number of times teeth are brushed within a specified period, the number of times teeth are brushed within a specified time period, the rate of toothbrushing within a specified period, the rate of toothbrushing within a specified time period, the time spent brushing teeth per session, the time spent brushing teeth within a specified period, the total time spent brushing teeth within a specified period, the time of brushing teeth, the time period of brushing teeth, a score related to toothbrushing behavior per session or within a specified period, the frequency of use of oral care tools, and the type of oral care tool used, as well as combinations thereof. Illustratively, the specified period may be one day.

The toothbrushing assistance device 1 receives a wireless signal indicating a physical quantity related to the toothbrushing behavior from the toothbrush attachment 3. When the user 9 starts brushing their teeth using the toothbrush 4, the sensor 31 housed in the toothbrush module 39 detects a physical quantity related to the toothbrushing behavior of the user 9, and the wireless transmission unit 32 transmits a wireless signal indicating the detected physical quantity to the toothbrushing assistance device 1. The toothbrushing assistance device 1 receives the wireless signal via the wireless reception unit 14. In this embodiment, the toothbrushing assistance device 1 receives the wireless signal from the toothbrush attachment 3 for a predetermined time after the user 9 starts brushing their teeth. The toothbrushing assistance device 1 can appropriately calculate information related to the brushing position and information related to the brushing score using the physical quantity indicated in the received wireless signal.

The toothbrushing assistance device 1 records the physical quantities indicated in the received wireless signal as toothbrushing behavior data 61 together with other related data such as the time and duration of toothbrushing, and the calculated brushing position information and score information. Table 1 shows an example of the toothbrushing behavior data 61 of the user 9 that the toothbrushing assistance device 1 can acquire and calculate through the toothbrush attachment 3. The toothbrushing assistance device 1 can use these calculated values shown in Table 1 as they are, or can use values such as the amount of change, rate of change, and speed of change in the calculated values along the time axis.

In step S2 (blood glucose data acquisition step), data on blood glucose (blood glucose data 62) is acquired. The blood glucose data 62 may include one or more items selected from the group consisting of glucose-derived values, HbA1c-derived values, insulin-derived values, and insulin resistance index (HOMA-R). The term "derived value" will be described later.

The tooth brushing assistance device 1 receives the data on the blood glucose of the user 9 from the blood glucose monitoring device 5 together with other related data such as the time of measurement, and records it as blood glucose data 62. Illustratively, the data on blood glucose is a glucose value at a certain time, measured, for example, at 15-minute intervals.

In step S3 (blood glucose fluctuation estimation step), a value related to blood glucose fluctuation of the user 9 is estimated based on the tooth brushing behavior data 61 and the blood glucose data 62. In this embodiment, the value related to blood glucose fluctuation is a TIR (Time in Range) index of the blood glucose data 62, and a regression model is used as the prediction model 63 used to estimate the value related to blood glucose fluctuation (the TIR index of the blood glucose data 62).

The TIR (Time in Range) index for blood glucose fluctuation can be estimated based on continuously monitored blood glucose data 62. The TIR index indicates the tendency of blood glucose fluctuation over time, and indicates the degree to which the blood glucose data deviates from the optimal range. In other words, the TIR index reflects blood glucose control. When the blood glucose data (blood glucose data 62) is, for example, a glucose value, the TIR index indicates the proportion of data measuring the time-series change in glucose value by continuous blood glucose monitoring that meets the target range of interstitial fluid glucose value. Illustratively, the target range of glucose value is set to, for example, 70 to 180 mg/dL. The glucose value is not limited to the value in interstitial fluid as exemplified, but may be, for example, a value in serum, or may be a value equivalent to a glucose value obtained by non-invasive or slight invasion. In order to prevent the onset and progression of complications in diabetes, in addition to managing the absolute value of the glucose value, it is effective to control and manage blood glucose for each patient so as to maximize the TIR of glucose. If the proportion of the target range is a proportion of time, it can be the proportion of the time the target range is met to the total measurement time. If the proportion is a proportion of the number of measurements, it can be the proportion of the number of measurements that meet the target range to the total number of measurements.

The prediction model 63 predicts an estimated value of blood glucose fluctuation based on the tooth brushing behavior data 61 and the blood glucose data 62. For example, a regression equation or a correspondence table can be used for the prediction model 63, and machine learning models expressed in a form other than a regression equation, such as a neural network, a decision tree, or a state transition model, can also be used.

When a regression model is used as the prediction model 63, the prediction model 63 uses values related to blood glucose fluctuation as the objective variable, i.e., the TIR index of the blood glucose data 62 (e.g., the TIR index of glucose) as the objective variable, and items including each item of the tooth brushing behavior data 61 (e.g., each data item shown in Table 1) as the explanatory variable. The regression equation by which the prediction model 63 calculates the TIR index of the blood glucose data 62 (the TIR index of glucose) is, for example,

と表現することができる。

It can be expressed as follows.

In the formula, Y is the TIR index (objective variable) of the blood glucose data 62, X ₁ to X ₄ are factors (explanatory variables), and a ₁ to a ₄ are partial regression coefficients. The factors are, for example, scores of each item of the tooth brushing behavior data 61 (for example, each data item shown in Table 1), or scores of items that combine these items. The number of factors X (four in the formula shown as an example) is not particularly limited, but can be several tens, for example.

For example, if the number of factors n is 4, the following values can be set for Y and _X1 to _X4 in the formula. _X1 to _X3 are items obtained by combining the items of the tooth brushing behavior data 61, and _X4 is an item obtained from the blood glucose data 62.
Y: Expected change in TIR calculated from continuous glucose monitoring data _X1 : Average total tooth brushing time per day _X2 : Whether or not evening tooth brushing time is after 9 p.m. (True: -1, False: 0)
_X3 : Whether or not you have a midnight snack (True:2, False:1)
_X4 : Fasting blood glucose level at the time of the test

In this embodiment, the prediction model 63 is trained in advance by the learning unit 211 and teacher data 64 provided in the toothbrushing assistance server 2. The toothbrushing assistance server 1 obtains the trained prediction model 63 from the toothbrushing assistance server 2 via the network 8 and uses it.

In step S4 (candidate behavior determination step), candidate toothbrushing behaviors for controlling blood glucose fluctuations are determined. Exemplarily, the candidate toothbrushing behaviors are the number of times teeth are brushed, the duration of toothbrushing, and the time period for toothbrushing.

A procedure for determining a candidate tooth brushing behavior is illustrated. First, the TIR index of the blood glucose data 62 obtained from the current tooth brushing behavior data 61 and blood glucose data 62 of the user 9 is calculated (hereinafter referred to as value _E1 ) and recorded. Next, a new tooth brushing behavior plan is generated, and the TIR index of the blood glucose data 62 obtained by the new tooth brushing behavior plan is calculated (hereinafter referred to as value _E2 ) and recorded. The generation of a new tooth brushing behavior plan corresponds to, for example, generating a new combination of factors x _i in the regression equation described above. Next, a new tooth brushing behavior plan is generated, and the TIR index of the blood glucose data 62 obtained by the new tooth brushing behavior plan is calculated (hereinafter referred to as value _E3 ) and recorded. Thereafter, the generation of a new tooth brushing behavior plan and the calculation and recording of the TIR index of the corresponding blood glucose data 62 (hereinafter referred to as value Ex) are repeated a predetermined number of times. As a result, values E _{1 to Ex} of the TIR index of the blood glucose data 62 with different tooth brushing behavior plans are obtained. Of these calculated x values _E1 to _Ex , the combination of toothbrushing behavior suggestions corresponding to, for example, the highest value is determined to be the most effective toothbrushing behavior candidate among the toothbrushing behavior candidates for controlling (preferably suppressing) blood sugar fluctuations.

In step S5 (candidate behavior presentation step), the determined candidate tooth brushing behaviors are presented to the user 9. The tooth brushing assistance device 1 displays, for example, on the touch panel display 15, a message to encourage the user 9 to take specific tooth brushing behaviors to control blood sugar fluctuations, such as "limit evening snacking" and "we recommend brushing your teeth for at least three minutes in the evening to nighttime hours," as the most effective candidate tooth brushing behaviors determined in step S4.

According to the toothbrushing assistance system 100 according to one embodiment, the toothbrushing assistance device 1 estimates a value related to blood glucose fluctuation of the user 9 based on the toothbrushing behavior data 61 and the blood glucose data 62. The toothbrushing assistance device 1 determines candidates for toothbrushing behaviors that control blood glucose fluctuations based on the estimated value related to blood glucose fluctuations (tendency of temporal fluctuations in blood glucose levels) and presents the determined candidates for toothbrushing behaviors to the user 9. As a result, specific candidates for toothbrushing behaviors that control blood glucose fluctuations are presented to the user 9 of the toothbrushing assistance device 1 via, for example, the touch panel display 15, and the user 9 is able to control and manage his or her own blood glucose through daily toothbrushing. For example, if the user 9 is a healthy person, prevention of diabetes is promoted through daily toothbrushing, and if the user 9 is a diabetic patient, daily blood glucose can be controlled and managed through daily toothbrushing.

Diabetic patients and those on the borderline of diabetes (hereinafter referred to as patients, etc.) are at risk of falling out of the optimal blood glucose range, but the tooth brushing assistance system 100 according to one embodiment makes it possible to control the risk through the lifestyle habits of patients, etc., without relying on invasive, time-based blood glucose monitoring or medication. In particular, there are patients, etc. who are unable to fully improve their lifestyle habits due to the high hurdles of previous measures such as diet and exercise. The tooth brushing assistance system 100 according to one embodiment makes it possible to alleviate the risk of falling out of the optimal blood glucose range through tooth brushing actions that normalize the oral cavity of patients, etc., by encouraging the patients, etc. to perform effective oral cleaning based on blood glucose-related data from one or more sessions.

[Other embodiments]
Although the present invention has been described above using specific embodiments, the present invention is not limited to the above-mentioned embodiments. Unless otherwise specified, the configurations of the tooth brushing assistance system according to the other embodiments described below are the same as those of the tooth brushing assistance system according to the above-mentioned embodiments, and therefore, overlapping descriptions will be omitted.

In the above embodiment, the prediction model 63 is used to estimate the value related to blood glucose fluctuation, but the value related to blood glucose fluctuation of the user 9 can also be estimated without using the prediction model 63. For example, the correspondence table shown in Table 2 can be created in advance, and an estimate of the value related to blood glucose fluctuation of the user 9 (TIR index of glucose level) can be obtained by inputting the tooth brushing time included in the tooth brushing behavior data 61 of the user 9 and the fasting blood glucose level included in the blood glucose data 62 of the user 9 by referring to the correspondence table.

In the above embodiment, the value of the user's blood glucose fluctuation is estimated based on the tooth brushing behavior data 61 and the blood glucose data 62, but the value of the user's blood glucose fluctuation can be further estimated based on data on the daily habits of the user 9. The data on the daily habits is data on eating habits (food content, calorie intake, nutritional components, meal time, whether or not snacking, whether or not eating while doing other things), exercise habits, activity level (number of steps, etc.), sleep habits (time of waking up/going to bed, quality of sleep, etc.), alcohol intake habits, smoking habits, etc. Furthermore, the value of the user's blood glucose fluctuation can be further estimated based on data on the oral cavity condition of the user 9. The data on the oral cavity condition is data on dentition information (current number of teeth, number of functional teeth, bite, number and type of prostheses, presence or absence of crowding, presence or absence of implants, etc.) and oral cavity information (periodontal disease/caries morbidity, hypersensitivity, gingival recession, amount of plaque, etc.). In addition to the above examples, the value related to the blood glucose fluctuation of the user can be further estimated based on the age, sex, lipid metabolism-related (HDL-C, total cholesterol, triglyceride, etc.), kidney function-related (uric acid, etc.), liver function-related (γ-GTP, AST, ALT, etc.), inflammation-related (CRP (C-reactive protein), IL-6, IL1-β, TNF-α, etc.), weight, BMI, insulin treatment information, medication information (hypoglycemic drugs, etc.), diabetes classification (type 1, type 2, etc.), family history, genetic information, presence or absence of scaling/root planing (removal of plaque and tartar from areas that are difficult to clean by self-care, such as between the teeth and gums), presence or absence of periodontal disease treatment, stress state, blood pressure, etc. of the user 9. The above-mentioned data related to the daily habits of the user 9, data related to the oral cavity state of the user 9, the age, sex, etc. of the user 9 can be input to the tooth brushing assistance device 1 via, for example, the touch panel display 15, the camera 16, any sensor provided separately, etc. Examples of types of sensors that may be provided separately include a photosensor (for measuring oral bacteria and substances), a pH sensor for measuring saliva quality, such as caries risk, a sugar content sensor, and an ultrasonic measuring device.

In the above embodiment, the TIR index of the glucose value is estimated as a value related to the blood glucose fluctuation of the user 9, but the data related to blood glucose is not limited to the glucose value, and the index showing the tendency of fluctuation is not limited to the TIR index. In addition to the glucose value shown as an example, the data related to blood glucose can be, for example, the HbA1c value, the insulin value, and the insulin resistance index (HOMA-R). The values used for the estimation may be these measured values themselves, or the amount of change, rate of change, or speed of change of these measured values may be used for the estimation, or the values may be calculated based on these measured values. For example, a value calculated based on a glucose value may be a detection value obtained by non-invasive measurement using, for example, a glucose sensor mounted on a smart watch, and the glucose value and such a detection value can be referred to as a glucose-derived value or a glucose-related value in this specification. Similarly, the HbA1c value and the insulin value can be referred to as an HbA1c-derived value and an insulin-derived value in this specification, including values calculated based on these values. Instead of the TIR index, the index showing the tendency of fluctuation can be, for example, the difference from a predetermined comparison reference value (for example, the difference between the measured value and 70 mg/dL), the maximum value, the minimum value, the magnitude of the peak gradient, or the like. In addition, for example, general statistics such as the coefficient of variation and standard deviation, statistics specific to blood glucose data, and integral values in a range exceeding a specific standard (for example, 70 mg to 180 mg/dL) can be used as indexes showing the tendency of fluctuation instead of the TIR. The above-mentioned example statistics specific to blood glucose data can include, for example, the following three:
・TAR (Time above Range): Percentage of patients with hyperglycemia (e.g., 180 mg/dL or more)*
TBR (Time below Range): The percentage of hypoglycemia (e.g., less than 70 mg/dL)*
・ Mean amplitude of glycemic excursions (MAGE): An index that picks out blood glucose fluctuations that exceed the standard deviation (SD) and calculates the average

The type of blood glucose data used to calculate the TIR index, which indicates the trend of fluctuation, may be different from the type of blood glucose data input from the blood glucose monitoring device 5. For example, the blood glucose data used to calculate the TIR index may be a glucose value, and the blood glucose data input from the blood glucose monitoring device 5 may be an insulin value.

In the above embodiment, the teacher data 64 is based on the tooth brushing behavior data 61 and blood glucose data 62 acquired from a plurality of different users 9, but the teacher data 64 may be created based on the user 9's own past tooth brushing behavior data 61 and blood glucose data 62. In this case, the prediction model 63 is an autoregressive model, and the tooth brushing assistance device 1 is a standalone type having a learning unit 211 realized in the tooth brushing assistance server 2. Alternatively, the teacher data 64 may be created based on data 61, 62 that combines the tooth brushing behavior data 61 and blood glucose data 62 acquired from a plurality of different users 9 and the user 9's own past tooth brushing behavior data 61 and blood glucose data 62. In this case, for example, the tooth brushing assistance server 2 may update the teacher data 64 using the user 9's own past tooth brushing behavior data 61 and blood glucose data 62, and additionally learn the prediction model 63 based on the updated teacher data 64.

In each of the above-mentioned embodiments, the toothbrush 4 and the toothbrush attachment 3 are configured separately, but they can be configured as one unit. In this case, an electric toothbrush can be used as the toothbrush 4. The toothbrush 4 may be a replacement brush for the electric toothbrush. The attachment 3 is not limited to a toothbrush, and may be any device having a sensor that can detect the toothbrushing behavior of the user 9. As such a device having a sensor, various IoT devices such as a wristwatch-type wearable device worn on the wrist of the user 9 holding the toothbrush 4 and a smart mirror that reflects the image of the user 9 holding the toothbrush 4 can be used. The object attached to the attachment 3 is not limited to the toothbrush 4, and may be, for example, an oral care product such as an interdental brush or dental floss. In other words, the toothbrush attachment 3 and the toothbrush module 39 shown in the above-mentioned embodiments can be called an oral care product attachment and an oral care product module, respectively.

In the above embodiment, the blood glucose monitoring device 5 is a device that performs CGM or isCGM and measures the glucose level of the user 9 as the blood glucose data 62, but the blood glucose data 62 measured by the blood glucose monitoring device 5 is not limited to glucose values. For example, if a wristwatch-type wearable device worn on the wrist of the user 9 is used as the blood glucose monitoring device 5, the wearable device can measure a value equivalent to the glucose level of the user 9 (i.e., a glucose-derived value or a glucose-related value) as the blood glucose data 62 from a position near the veins of the user 9's wrist, for example.

In each of the above-described embodiments, the communication unit 13 and the wireless receiving unit 14 are configured separately in the tooth brushing assistance device 1, but they can be configured as an integrated unit.

In each of the above-mentioned embodiments, when the toothbrush attachment 3 is used, it is assumed that the wireless communication between the toothbrush attachment 3 and the toothbrushing assistance device 1 is online, but the wireless communication between the toothbrush attachment 3 and the toothbrushing assistance device 1 does not need to be always online. The toothbrush attachment 3 can be equipped with a non-volatile memory (not shown) and a micro control unit (not shown). When the wireless communication between the toothbrush attachment 3 and the toothbrushing assistance device 1 is offline, the toothbrush attachment 3 can store the physical quantities related to the toothbrushing behavior of the user 9 detected by the sensor 31 in the non-volatile memory together with, for example, time stamps (start and end times of the toothbrushing behavior). When the wireless communication becomes online, for example, when the toothbrushing assistance program (app) in the toothbrushing assistance device 1 is started or during background operation, the wireless transmission unit 32 of the toothbrush attachment 3 can transmit the physical quantities and time stamps related to the toothbrushing behavior of the user 9 stored in the non-volatile memory as offline data to the wireless reception unit 14 of the toothbrushing assistance device 1.

After executing step S5 (candidate behavior presenting step) illustrated in the above embodiment, an evaluation step illustrated below can be executed at any timing. The evaluation step is a step for evaluating or verifying whether the candidate toothbrushing behaviors presented to the user 9 in step S5 were effective for the user 9. The items to be evaluated can be, for example, the following two items.
Evaluation item 1: Whether or not the user 9 actually performed the proposed tooth brushing behavior. Evaluation item 2: Whether or not the proposed tooth brushing behavior was actually effective in controlling blood glucose fluctuations when the user 9 actually performed the proposed tooth brushing behavior in evaluation item 1.

In the evaluation step, the following two review steps can be carried out based on the evaluation results of the example evaluation items 1 and 2.
-Review step 1: Review of step S4 (action candidate determination step) -Review step 2: Review of prediction model 63 The review step 2 preferably includes a step of re-learning the prediction model 63 and updating the prediction model 63. Updating the prediction model 63 may include, for example, updating the values of the partial regression coefficients. The processing of the evaluation step including the illustrated review step may be performed by the tooth brushing assistance device 1 or the tooth brushing assistance server 2.

In the above embodiment, the value relating to the blood glucose fluctuation of the user 9 is estimated based on the tooth brushing behavior data 61 and the blood glucose data 62, but the value relating to the blood glucose fluctuation of the user 9 may be estimated based on data relating to the oral cavity condition and the blood glucose data 62 instead of the tooth brushing behavior data 61. The data relating to the oral cavity condition of the user 9 can be input to the tooth brushing assistance device 1 via, for example, the touch panel display 15, the camera 16, or any other sensor provided separately.

The following examples of the present invention will make the features of the present invention clearer.

In Example 1, the data trends were analyzed for the learning data used to train the predictive model. The analysis results are shown in the graphs in Figures 5 to 8.

Continuous glucose monitoring (CGM) data was collected for up to 12 weeks from more than 100 type 2 diabetes patients attending an internal medicine clinic using a FreeStyle Libre Pro sensor from Abbott Japan Co., Ltd. (Abbott Diabetes Care Inc., USA). Data on tooth brushing behavior and dietary habits were recorded along with the CGM data. Data on tooth brushing behavior was recorded using a toothbrush attachment, G.U.M PLAY (registered trademark) from Sunstar Oral Care Company. The recorded tooth brushing behavior items and dietary habits items were as follows. Note that the time periods were expressed as 5:00 PM to 9:00 PM (5:00 PM to 9:00 PM) in the evening and 5:00 PM to 3:00 AM (5:00 PM to 3:00 AM) at night.

Daily tooth brushing time Most frequent category value (value 1: less than 1 minute, value 2: 1 to 2 minutes, value 3: 2 to 3 minutes, value 4: 3 minutes or more)
- Nighttime tooth brushing time: Most frequent category value (value 1: less than 1 minute, value 2: 1 to 2 minutes, value 3: 2 to 3 minutes, value 4: 3 minutes or more)
- Tooth brushing in the evening from 5:00 pm to 9:00 pm (the percentage of days brushing was calculated by dividing the number of days brushing by the total number of recorded days, and individuals with an implementation rate of 50% or more were considered to have brushed their teeth in the evening)
- Tooth brushing after 9 p.m. (The percentage of days brushing was calculated by dividing the number of days brushing was done by the total number of recorded days, and those who brushed their teeth at night were considered to have brushed their teeth 50% or more.)
・Number of times brushing teeth per day (average, mode)
-Percentage of tooth brushing time (after waking up, after breakfast, after lunch, after dinner, and before bed)
- Percentage of food intake during the data recording period (breakfast, afternoon snack, lunch, afternoon snack, dinner, after-dinner snack, evening drink)
・Estimated total time spent brushing teeth per day Calculated using the following formula: Number of times brushing teeth per day (average value) x Most frequent category value of daily tooth brushing time ^*
( ^* If the daily tooth brushing time is less than 1 minute, a value of 0.5 is assigned; if it is between 1 and 2 minutes, a value of 1.5 is assigned; if it is between 2 and 3 minutes, a value of 2.5 is assigned; if it is 3 minutes or more, a value of 3.5 is assigned.)

Figures 5 to 8 are graphs of CGM data showing the time variation of blood glucose levels. Figures 5 and 6 are graphs plotting the CGM data by the total time spent brushing teeth in a day. Figures 7 and 8 are graphs plotting CGM data extracted from the population where teeth were brushed from early evening to night (5pm to 9pm).

<Considerations on total daily tooth brushing time and blood sugar fluctuations>
With reference to Figs. 5 and 6, the total time spent brushing teeth per day and blood sugar fluctuations were considered.

With reference to the graph in FIG. 5, the tendency of the average blood glucose level shown on the vertical axis of the graph was confirmed. It was confirmed that the fluctuation range of the average blood glucose level was the same in the graph plotted with the circle symbol (○) and the black triangle symbol (▲), and the value of the graph plotted with the cross symbol (+) was the largest. It was confirmed that the absolute value of the average blood glucose level was generally low in the graph plotted with the circle symbol (○), followed by the value of the graph plotted with the black triangle symbol (▲), and the value of the graph plotted with the cross symbol (+) generally tended to be high. From these, it was confirmed that in order to control and suppress the fluctuation of the average blood glucose level, the total tooth brushing time per day should be 6 minutes or more, as shown in the graph plotted with the circle symbol (○). In addition, in terms of the total tooth brushing time per day, the group (○) whose total tooth brushing time was 6 minutes or more tended to have stable blood glucose fluctuation in the morning. The average values of the TIR of each group were as follows.
Cross symbol (+) group: 76.31 ± 19.02 mg/dL
Black triangle symbol (▲) group: 80.51 ± 11.95 mg/dL
Circle symbol (○) group: 79.68 ± 16.42 mg/dL

As with Figure 5, the graph in Figure 6 was used to confirm the trend in average blood glucose levels. It was confirmed that the fluctuation range of average blood glucose levels was smaller in the graph plotted with black circle symbols (●) than in the graph plotted with inverted triangle symbols (▽). Similarly, it was confirmed that the absolute values of average blood glucose levels were generally smaller in the graph plotted with black circle symbols (●) than in the graph plotted with inverted triangle symbols (▽). From these findings, it was confirmed that in order to control and suppress fluctuations in average blood glucose levels, the total tooth brushing time per day should be 6 minutes or more, as shown in the graph plotted with black circle symbols (●).

Table 3 shows various descriptive statistics for each group shown in the graph in FIG.

<Considerations on tooth brushing time and blood sugar fluctuations from the evening to night>
With reference to Figs. 7 and 8, the relationship between tooth brushing time from the evening to night and blood sugar fluctuations was considered.

With reference to the graph in Figure 7, the tendency of the average blood glucose level shown on the vertical axis of the graph was confirmed. It was confirmed that the fluctuation of the average blood glucose level does not change significantly depending on the tooth brushing time. On the other hand, it was confirmed that the absolute value of the average blood glucose level tends to be lower overall the longer the tooth brushing time from the evening to the night. The average TIR of each group was as follows.
Cross symbol (+) group: 71.80 ± 23.48 mg/dL
Black triangle symbol (▲) group: 77.47 ± 18.88 mg/dL
Triangle symbol (△) group: 73.93 ± 23.59 mg/dL
Circle symbol (○): 81.25±11.10 mg/dL
Group marked with a black circle (●): 81.15 ± 11.62 mg/dL

The tendency of the average blood glucose level was confirmed by referring to the graph in FIG. 8 in the same manner as in FIG. 7. It was confirmed that the fluctuation of the average blood glucose level does not change significantly depending on the tooth brushing time. On the other hand, it was confirmed that the absolute value of the average blood glucose level is generally smaller in the graph plotted with black circle symbols (●) than in the graph plotted with cross symbols (+). From these facts, it was confirmed that in order to control and suppress the fluctuation of the average blood glucose level from the evening to the night time, the tooth brushing time should be at least 2 minutes or more per session. The average TIR of each group was as follows:
Cross symbol (+) group: 74.31 ± 22.09 mg/dL
Group marked with a black circle (●): 81.19 ± 11.30 mg/dL

Tables 4 and 5 show various descriptive statistics for each group shown in the graphs of FIGS.

1 Tooth brushing assistance device 2 Tooth brushing assistance server 3 Toothbrush attachment 4 Toothbrush 5 Blood glucose monitoring device 5
Reference Signs List 8: Network 9: User 11: Calculation unit 12: Recording unit 13: Communication unit 14: Wireless receiving unit 15: Touch panel display 16: Camera 17: Speaker 21: Calculation unit 22: Recording unit 23: Communication unit 31: Sensor 32: Wireless transmitting unit 39: Toothbrush module 100: Auxiliary system

Claims (9)

A blood glucose fluctuation estimation unit that estimates a value related to the blood glucose fluctuation of a user based on data related to tooth brushing behavior and data related to blood glucose derived from oral care products;
A behavior candidate determination unit that determines a candidate toothbrushing behavior for controlling the blood sugar fluctuation;
a behavior candidate presentation unit that presents the tooth brushing behavior candidates to the user;
A tooth brushing aid comprising: The tooth brushing assistance device according to claim 1, wherein the blood glucose fluctuation estimation unit estimates the value related to the blood glucose fluctuation of the user based on a prediction model that receives data related to the tooth brushing behavior and data related to the blood glucose and outputs an estimated value related to the blood glucose fluctuation. The tooth brushing assistance device according to claim 2, wherein the prediction model is a regression model, in which the value related to the blood glucose fluctuation is a response variable, and items including each item of the data related to the tooth brushing behavior are explanatory variables. The tooth brushing assist device according to claim 1, wherein the data on tooth brushing behavior includes one or more items selected from the group consisting of the number of times teeth are brushed within a specified period, the number of times teeth are brushed within a specified time period, the rate of tooth brushing within a specified period, the rate of tooth brushing within a specified time period, the time spent brushing teeth per session, the time spent brushing teeth within a specified period, the total time spent brushing teeth within a specified period, the time of brushing teeth, the time period of brushing teeth, a score regarding tooth brushing behavior per session or within a specified period, the frequency of use of oral care tools, and the type of oral care tool used, and combinations thereof. The tooth brushing aid according to claim 1, wherein the blood glucose data includes one or more items selected from the group consisting of glucose-derived values, HbA1c-derived values, insulin-derived values, and insulin resistance index (HOMA-R). An oral care product module that transmits a wireless signal related to tooth brushing behavior, and a tooth brushing assistant device according to any one of claims 1 to 5 that receives the wireless signal,
The oral care product module includes:
A sensor for detecting a physical quantity related to a user's tooth brushing behavior;
a wireless transmission unit that transmits a wireless signal indicative of the physical quantity detected by the sensor;
Equipped with
The tooth brushing assist device is
The tooth brushing assistance system further comprises a wireless receiving unit that receives the wireless signal indicating the physical quantity. The toothbrushing assistance system according to claim 6, further comprising a toothbrushing assistance server communicatively connected to the toothbrushing assistance device and equipped with a learning unit that learns a prediction model that receives data on the toothbrushing behavior and data on blood glucose as inputs and outputs an estimated value related to blood glucose fluctuation. The tooth brushing assistance system of claim 6 further comprises a blood glucose monitoring device that monitors data related to the user's blood glucose and wirelessly transmits the data to the tooth brushing assistance device. A toothbrushing assistance program for causing a computer to function as each part of the toothbrushing assistance device according to any one of claims 1 to 5.

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