Cognitive function determination program, cognitive function determination method, and cognitive function determination device

Abstract

To provide a highly reliable cognitive function determination program in which a complicated device is not needed, a burden on a subject is reduced, and false recognition is reduced, and to provide a cognitive function determination method, and a cognitive function determination device.SOLUTION: A cognitive function determination program causes a computer to execute a determination step for determining a cognitive function using the lowest pulse during sleep. A cognitive function determination method includes a lowest pulse acquisition process for acquiring the lowest pulse by measuring a pulse during sleep, and a determination process for performing determination of a cognitive function using the acquired lowest pulse. A cognitive function determination device includes a computer in which the aforesaid program is stored.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a cognitive function assessment program, a cognitive function assessment method, and a cognitive function assessment device that can diagnose dementia without requiring complicated operations and without imposing a great deal of effort on the subject. [Background technology]

[0002] Diagnosis of dementia requires the subject to undergo numerous tests, which is a great burden on the subject and therefore poses a problem of subjects being reluctant to undergo a dementia diagnosis. As a result, various methods have been proposed for diagnosing dementia that utilize IT to reduce the burden on subjects. For example, Patent Document 1 proposes a cognitive function diagnostic device that automatically creates comments regarding cognitive function. Specifically, the proposed cognitive function diagnostic device has an age information acquisition means for acquiring age information indicating the age of the subject, a gender acquisition means for acquiring gender information indicating the gender of the subject, a blood test information acquisition means for acquiring blood test information indicating the result of the general blood test of the subject, and a comment creation means for creating a comment regarding cognitive function using the subject's age information acquired by the age information acquisition means, the subject's gender information acquired by the gender acquisition means, and the subject's blood test information acquired by the blood test information acquisition means. Patent Document 2 proposes a device for assessing a patient's absolute and / or relative risk of cognitive decline and / or dementia, the device comprising a probe configured to be positioned adjacent to the patient's common carotid artery, internal carotid artery, or external carotid artery, and at least two sensors associated with the probe, the sensors configured to measure one or more of carotid pulsation wave intensity, carotid pulsation wave force, carotid pulsation pressure waveform, pulse wave velocity, arterial compliance, arterial stiffness, arterial diameter, number of microemboli, heart rate variability, and changes to the eye or retina. Moreover, Non-Patent Document 1 reports that the higher the resting pulse rate, the higher the risk. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-92371 A [Patent Document 2] Special Publication No. 2022-504781 [Non-patent literature]

[0004] [Non-Patent Document 1] Deng et al. Alzheimer's Research & Therapy (2022)14:147 https; / / doi.org / 10.1186 / s13195-022-01088-3 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above proposal still requires complicated operations and a significant burden on subjects, and the operations are not simple. In addition, although the report in Non-Patent Document 1 certainly shows a correlation, there is still a problem that the probability of misidentification is high and reliability is low when determining cognitive decline. In short, there is currently a demand for the development of a highly reliable method for assessing cognitive function that reduces the need for complicated equipment and the burden on subjects, and that also reduces the incidence of erroneous determinations, as well as a device that uses this assessment method. Therefore, an object of the present invention is to provide a cognitive function assessment program, a cognitive function assessment method, and a cognitive function assessment device that do not require complicated equipment, reduce the burden on the subject, and further reduce the incidence of erroneous determinations, and are highly reliable. [Means for solving the problem]

[0006] As a result of intensive research aimed at solving the above problems, the inventors discovered that physical information obtained during sleep is highly reliable in diagnosing dementia, and thus completed the present invention. That is, the present invention provides the following inventions. 1. A cognitive function assessment program that causes a computer to execute an assessment step of assessing cognitive function using the minimum pulse rate during sleep. 2. A method for assessing cognitive function comprising a minimum pulse rate acquisition step of measuring the pulse rate during sleep to obtain the minimum pulse rate, and an assessment step of assessing cognitive function using the obtained minimum pulse rate. 3. A cognitive function assessment device comprising a computer having the program described in 1 stored therein. Effect of the Invention

[0007] According to the cognitive function assessment program and cognitive function assessment method of the present invention, there is no need for complicated equipment, the burden on the subject is reduced, and furthermore, there are fewer erroneous diagnosis, making it possible to reliably assess dementia. Since the cognitive function assessment device of the present invention has the cognitive function assessment program of the present invention, it reduces complicated equipment and the burden on the subject, and further reduces erroneous recognition and is highly reliable. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a schematic diagram showing an overview of the apparatus of the present invention. [Diagram 2] FIG. 2 is a schematic diagram showing an example of a flow sheet of the program of the present invention. [Diagram 3] FIG. 3 is a flow sheet showing a method for narrowing down the candidates from the experimental results in the examples. [Explanation of symbols]

[0009] 1: cognitive function assessment device, 10: computer, 20: sleep measurement device, 30: pulse measurement device DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] The present invention will now be described in further detail. [Cognitive function assessment device] First, an embodiment of a cognitive function assessment device according to the present invention will be described, and then an embodiment of a cognitive function assessment program according to the present invention will also be described. 1, the cognitive function assessment device 1 of this embodiment includes a computer 10 storing a program described below. It also includes a sleep measurement device for grasping the sleep state of the subject, and a pulse measurement device for measuring the pulse of the subject. First, the device will be described in detail below.

[0011] <Computer> 1, the computer 10 in this embodiment includes a random access memory (RAM) 11, at least one central processing unit (CPU) 13, a recording medium 15, one or more input means 14 such as a keyboard or a pointing device (e.g., a mouse or a touch pad), a network interface (not shown) for connecting the computer system to a data communication network such as the Internet, and a local interface (not shown) for receiving signals from a sleep measuring device 20 and a pulse measuring device 30, which will be described later. The computer 10 is also connected to an output means 12 such as a liquid crystal display (LCD) panel device to display various information to the subject. In the present invention, a server can also be used as the computer. Furthermore, a mobile terminal such as a so-called smartphone, tablet terminal, or smart watch can also be used as the computer. In this case, the user interface device 14 and the display device 12 are both provided as an integral part of the computer. When the sleep measurement device 20 and the pulse measurement device 30 are provided separately from the computer, the network interface may also serve as the local interface. In addition to using a typical 32-bit or 64-bit operating system, the operating system also includes a number of standard software modules, such as web server software and script language modules, that are generally used in computer systems. This computer, having the programs described below stored therein, functions as a storage unit that stores the data acquired by the sleep measurement device 20 and the pulse measurement device 30 in a recording medium or memory, and as a determination unit that processes the stored data to determine when the person is asleep and extracts the pulse rate during sleep, and determines decline in cognitive function from the pulse rate during sleep.

[0012] <Sleep measuring device> The sleep measuring device 20 used in this embodiment can be any device for measuring a normal sleep state without any particular restrictions, but for example, a device that combines a heart rate monitor, a pulse wave sensor, or a microphone (measuring the volume and rhythm of breathing sounds) with an acceleration sensor to measure the heart rate or breathing state and body movement, converts the data, and measures whether or not the person is sleeping, and further, what kind of sleeping state the person is in. Note that when a smart watch or a smartphone (in this case, a microphone captures breathing sounds and measures their volume and rhythm) is used as the computer, the sleep measuring device is integrated with the computer. <Pulse measuring device> The pulse measuring device 30 used in this embodiment can be any device for measuring a normal pulse without any particular restrictions, and can be, for example, a heart rate monitor or a pulse wave sensor. In addition, when a heart rate monitor or a pulse wave sensor is used as the sleep measuring device 20, the sleep measuring device 20 can also function as the pulse measuring device 30. Furthermore, when using a smartphone as a computer, a microphone and an acceleration sensor are used in combination as a sleep measurement device, so a separate pulse measurement device, such as a smart watch, must be used. Examples of devices that can be used as both a sleep measurement device and a pulse measurement device include devices with optical sensors such as the "WatchPad Unified (product name)" manufactured by Philips, various smart watches, and devices with pressure sensors such as "Nemuri Scan (registered trademark)" manufactured by Paramount Bed Co., Ltd., and "Sleep Scan (registered trademark)" manufactured by Tanita Corporation.

[0013] <Other materials> In the cognitive function assessment device of the present invention, various sensors and the like that are usually used in this type of device can be provided and various data can be set to be stored in a computer.

[0014] [Cognitive function assessment program] In the cognitive function assessment device of the present invention, in order to cause the computer 10 to function as the storage unit and the assessment unit, the cognitive function assessment program of the present invention is stored in a recording medium of the computer 10. Hereinafter, one embodiment of the cognitive function assessment program of the present invention will be described. The cognitive function assessment program of the present embodiment is stored in a computer and causes the computer to execute a judgment step S2 of judging cognitive function using the minimum pulse rate during sleep. In addition, the computer can be set to execute a sleep state judgment step S1 in addition to the judgment step. Each step will be described below with reference to the flow chart shown in FIG.

[0015] <Sleep state determination step S1> The sleep state determination step S1 obtains sleep-related data from the sleep determination device 20, specifically, pulse data, respiratory data, and body movement data, although this varies depending on the sensor used, and uses this data to determine whether the subject is asleep or not. The method of judgment can be any known method without any particular restrictions. Specifically, depending on the sleep state judgment device used, a data analysis program may be set and stored, and by utilizing these data analysis programs as they are or using the same judgment algorithm, it can be judged whether the subject is in a sleep state, and further, what state the sleep state is (REM sleep state, non-REM sleep state, etc.). This step can be substituted by acquiring data on whether or not the subject is asleep from an existing sleep state determination device by simply using the data stored in the sleep state determination device. In that sense, this step is not essential to the program of the present invention.

[0016] <Determination step S2> In the judgment step S2, if the patient is judged to be in an "asleep state" in the sleep state judgment step S1, pulse data for the patient in that sleep state is obtained, the minimum pulse rate during sleep is extracted from the pulse data, and cognitive function is judged based on the obtained minimum pulse rate. Here, the "lowest pulse rate" refers to the lowest pulse rate measured during a time period during a day when the subject is determined to be in a sleeping state. The standard measurement period is one night, but to improve the reliability of the determination, the average value of the measurement results for two to three nights, preferably one week, may be used. In addition, when using a device that can simultaneously measure the pulse arrival time (PAT), the time period during which the PAT signal could not be acquired is regarded as the time period during which there was an improper attachment of the measuring device, and the corresponding time period is excluded from the evaluation of the pulse rate in order to improve the accuracy of the determination, thereby obtaining more reliable pulse rate data. Then, a threshold value is set for the extracted minimum pulse rate, and the risk of cognitive decline is determined by comparing the threshold value with the actual measured value. In this case, the threshold value is calculated based on a relationship with the Locomo 2 step measurement result based on a predetermined number of samples measured in advance, as described in the embodiment below, and set in this step. Here, the case where two threshold values ​​of 45 bpm and 55 bpm are set is shown in FIG. 2 and will be described. However, the threshold value may vary as the number of samples increases in the future. If the extracted minimum pulse rate is less than 45 bpm, it is determined that the risk of cognitive decline (possibility of suspecting dementia or mild dementia) is low, if it is 45 bpm or more, there is a risk of cognitive decline, and if it is more than 55 bpm, the risk of cognitive decline is determined to be greater. Therefore, when making such a determination, 45 bpm and 55 bpm are the threshold values. The program is programmed to compare the measured minimum pulse rate with these thresholds and determine whether it is below, above, or above these thresholds, and to make a determination of low risk of cognitive decline, presence of risk of cognitive decline, or high risk of cognitive decline, and the computer is caused to execute the determination step. In addition, the score of the MMSE (Mini Mental State Examination), which is the most internationally standard simplified dementia test, can be estimated using the following prediction formula. The estimated MMSE score is compared with the normal MMSE score to determine the risk of cognitive decline. In this case, the program is set to substitute the measured minimum pulse rate into the following formula, apply the obtained MMSE prediction score to a correlation table between the MMSE score and the risk of cognitive decline stored in advance, and calculate the risk of cognitive decline for the MMSE prediction score. Note that the coefficient (0.286) was calculated using the current sample, and may fluctuate as the number of samples increases in the future. MMSE predicted score = 40.406 ‐ 0.286 × lowest pulse rate during sleep

[0017] <Other steps> (Preliminary step S0) In the cognitive function assessment program of this embodiment, it is preferable to perform a pre-step S0 in which a database is constructed in advance. As will be described in detail in the examples below, it is preferable to store data on multiple subjects using test data on cognitive function so that the above-mentioned threshold value can be calculated by regression analysis. In addition, the data can be updated appropriately by inputting the measurement results according to the present invention, and this can also be set as this pre-step S0 and executed by the computer. (Post-processing step S3) In the cognitive function assessment program of this embodiment, the assessment result in assessment step S2 can be displayed on an output means, or can be notified to the subject, a doctor, a care facility, etc. via a communication means such as email, and a post-processing step S3 can be configured to have a computer execute this series of operations. (Other steps) In addition, in the present invention, in addition to the steps described above, it is possible to configure a program so as to execute various steps as appropriate without departing from the spirit of the present invention.

[0018] <Usage example> The cognitive function assessment device 1 of the present invention can be used as follows. The subject is asked to sleep with the sleep state measuring device 20 and the pulse rate measuring device 30 attached to the bedding or body of the subject. Based on data from the sleep state measuring device 20, the computer executes the sleep state determining step S1 to determine whether or not the subject is asleep. Next, if it is determined that the subject is in a sleeping state, the cognitive function assessment method of the present invention carries out a minimum pulse rate acquisition step of measuring the pulse rate during sleep to obtain the minimum pulse rate, and a assessment step of assessing cognitive function using the obtained minimum pulse rate. That is, the computer executes the judgment step S2, extracts pulse data when it is judged that the subject is asleep, and extracts the minimum pulse rate from the extracted pulse rate data when the subject is asleep. The obtained minimum pulse rate is used to judge the decline in cognitive function. For this reason, the pulse rate data is accurately linked to time data (year, month, day, hour, minute, and second), and whether or not the subject is asleep is also accurately linked to this time data. In this way, it is necessary to realize data acquisition and storage in anticipation of utilization so that the pulse rate data during the time judged to be asleep can be accurately grasped.

[0019] The present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the present invention. For example, the accuracy of predicting the risk of cognitive decline may be improved by combining the minimum pulse rate during sleep with other known indicators related to cognitive decline (such as the stride length of two steps, grip strength, and questions related to cognitive function evaluation). In addition, in the method of the present invention, the risk can be determined from the measured value using a correspondence table or the like without using a computer. EXAMPLES

[0020] The present invention will be described in more detail below with reference to examples, but the present invention is not limited to these examples.

[0021] [Example] As shown in Figure 3, the correlation between the results of the MMSE test and the various tests was confirmed by narrowing down the results by the P value according to the usual method. The subjects and survey items for each test are listed below, and the results are also shown in Table 1.

[0022] [Table 1]

[0023] As a result, it was confirmed that there was a high correlation between the Locomo 2-Step Test and measuring the minimum pulse rate during sleep, with P values ​​of 0.022 (Locomo 2-Step Test) and 0.026 (minimum pulse rate during sleep), respectively. From these results, it can be seen that the cognitive function assessment method of the present invention has a high correlation with the MMSE test, has high accuracy in assessing cognitive function, and is a highly reliable assessment method. On the other hand, the P value of the minimum pulse rate at rest was 0.378. This shows that the "minimum pulse rate during sleep" of the present invention can be used to more reliably assess cognitive function than the minimum pulse rate at rest, which has been said to be effective in assessing cognitive function in the past. Participants: Elderly people without nursing care certification (average age 86.89 years, 32 people, none of whom had been diagnosed with dementia) Survey items: MMSE, basic items (medical history, age, blood pressure, etc.), physical measurements such as body composition (measured using a Tanita body composition analyzer), dementia simple test kit, 3-axis activity meter (Actigraph), physical ability test (grip strength, Locomotion 2-step test, etc.), blood test (white blood cell count, red blood cell count, blood sugar level, etc.), dental examination (number of remaining teeth, bite force, etc.), urinary incontinence, nutritional intake analysis, pulse (at rest, while asleep) Analysis method: Statistical analyses were performed, including correlation analysis, two-group comparison analysis, and linear multiple regression analysis. Based on the MMSE results, 57.1% of participants, both male and female, were suspected of having MCI or dementia (MMSE score ≦27 points).

[0024] Details of each survey item in this example are as follows. Basic items: All participants were asked to fill out a questionnaire to obtain information about their health, including their medical history. Blood pressure, etc. were measured according to standard methods. Body composition and other physical measurements: Participants' weight, body fat, muscle, and water percentages were measured using a body composition monitor (product name "Tanita MC-780A-N" manufactured by Tanita Corporation, Tokyo, Japan). Accurate weight values ​​were measured after removing as much clothing as possible and subtracting the estimated weight of remaining clothing from the actual value (assuming 1.0 kg in January and February, and 0.5 kg from March to December). Height was measured using a stadiometer (height monitor product name "HM 200P" manufactured by Charder Electronic Co., Taichung, Taiwan). Systolic blood pressure, diastolic blood pressure, and pulse were measured using a blood pressure monitor (product name "Terumo ES-W300ZZ" manufactured by Terumo Corporation, Tokyo, Japan). MMSE: (Mini-Mental State Examination) Participants' cognitive function was assessed using the Japanese version of the MMSE. Pulse: Measured using a portable monitoring device (trade name: WatchPAT Unified, manufactured by Philips) that records peripheral arterial pressure measurement signals, heart rate, oxygen saturation, and actigraphy. WatchPAT uses an automated proprietary algorithm to calculate respiratory events and clinical parameters such as the 4% oxygen saturation index. This is less burdensome for patients than a full polysomnography test and is recommended by the American Academy of Sleep Medicine guidelines for obstructive sleep apnea. The data obtained was automatically analyzed to estimate respiratory events such as AHI, respiratory disorder index, and sleep state. Three-axis activity monitor: Daily activity was measured using a three-axis activity monitor (product name "ActiGraph wGT3X-BT"; manufactured by ActiGraph Corp.) For analysis, the average values ​​of the number of steps per day, total sleep time, sleep efficiency (total sleep time / total sleep time), number of awakenings, awakening time (min), wakefulness after sleep onset (WASO), number of activities, activity index, fragmentation index, and sleep fragmentation index, which were output from the dedicated software, were used. Grip strength measurement: Grip strength was measured twice using a grip strength measuring device consisting of a digital force gauge (product number ZP-500N, IMADA, Toyohashi City) and a computer / display system. Locomotive syndrome 2-step test, etc.: This test consists of (1) a stand-up test to evaluate the muscle strength required to stand up from seats of different heights, (2) a 2-step test to evaluate the stride length of two steps, and (3) a questionnaire consisting of 25 questions about physical movement that correlates with EQ-5D (European Quality of Life Scale-5 Dimensions) (Hoshino et al.). The risk level of locomotive syndrome (locomotor score) was determined as described above. Blood tests: Blood samples were collected in appropriate tubes (for plasma, clot formation, and whole blood). The specimens were centrifuged and stored separately. The following laboratory values ​​were evaluated by SRL (Tokyo, Japan) or Japan Medical (Yamanashi, Japan): blood urea nitrogen (BUN), creatinine, total cholesterol, high-density lipoprotein cholesterol, HbA1c, white blood cell count, red blood cell count, hematocrit, and cystatin C. Dental examination: Biting force was measured using a bite force measuring system (trade name "Dentalplescale", GC, Tokyo, Japan). Urinary incontinence: To assess symptoms and impact of urinary incontinence, we used the Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI SF), a questionnaire used to assess and measure urinary incontinence symptoms and their impact on an individual's quality of life. Statistical analysis (analysis method): Statistical analysis was performed using JMP (registered trademark) Pro 15.1.0 (SAS Institute Inc., Cary, NC, USA). Spearman correlation analysis was performed to calculate the correlation coefficient (ρ) and p-value between MMSE and other measurement data, and variables with |ρ| > 0.400 were selected. Pooled t-tests were performed to compare cognitively healthy participants (MMSE score ≥ 28) and patients (MMSE score < 28) in the selected variables. Variables with statistically significant differences between the two groups were selected with a p-value < 0.05, and their relationship with MMSE was reevaluated using a simple linear regression model to calculate the estimated value (β) and p-value. In addition, stepwise multiple regression analysis was performed to remove variables with high variance inflation factors (VIF). Briefly, MMSE was first estimated using a multiple linear regression model using the initially selected MMSE-related variables and representative MMSE-related variables that were not selected in the initial screening, and the R2, adjusted R2, p-value, and each significant β, p-value, and VIF were calculated for the entire model. The variables with the highest VIFs over 3.0 were then removed. As a result, the same estimation was performed using the remaining variables. This process of removing variables with high VIFs was repeated stepwise until multicollinearity was eliminated (VIFs for all variables < 3.0).

[0025] The cognitive function assessment device used in this experiment is a device equipped with the cognitive function assessment program described above, and the sleep measurement device and pulse measurement device (combined) used was the "Watchpad Unified (product name)" manufactured by Philips, as described above. A commercially available personal computer was used as the computer. The cognitive function assessment program was configured as shown in Figure 2, and the above threshold value was adopted.

[0026] The cognitive function assessment method, cognitive function assessment device, and cognitive function assessment program of the present invention can accurately assess the decline in cognitive function, i.e., dementia, with simple operations and little burden on the subject, that is, by having the subject simply lie down. By deploying the devices on a smartphone or smartwatch, individuals can easily know the state of their cognitive function, and are therefore expected to be used in situations where it is necessary to easily grasp cognitive function, such as in medical settings and local health and welfare services.

Claims

1. A cognitive function assessment program that causes a computer to execute an assessment step of assessing cognitive function using the minimum pulse rate during sleep.

2. A cognitive function assessment method comprising: a minimum pulse rate acquisition step of measuring the pulse rate during sleep to obtain the minimum pulse rate; and an assessment step of assessing cognitive function using the obtained minimum pulse rate.

3. A cognitive function assessment device comprising a computer having the program according to claim 1 stored therein.

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