Method for evaluating circadian rhythm disorder based on wearable device

Abstract

The invention provides a circadian rhythm disorder assessment method based on a wearable device, and relates to the technical field of circadian rhythm assessment, and the method comprises the steps: continuously collecting the illumination intensity data and body movement data of an individual in a natural life state through the wearable device; preprocessing the collected illumination intensity data and body movement data; discrete Fourier transform is carried out on the preprocessed illumination intensity data and body motion data, a fundamental frequency component is extracted and expressed as a phasor, and a difference phasor between a body motion phasor and a photogrammetry is obtained through calculation; and evaluating the circadian rhythm disorder degree of the individual according to the calculated difference phasor between the body movement phasor and the photogrammetry. According to the invention, convenient and non-invasive circadian rhythm monitoring is realized through the wearable device, and an effective tool is provided for early screening and intervention of circadian rhythm disorder.

Description

Technical Field

[0001] The present invention relates to the technical field of circadian rhythm assessment, and in particular to a circadian rhythm disorder assessment method based on a wearable device. Background Art

[0002] Circadian rhythm refers to the oscillation of physiological, biochemical and behavioral processes in an organism with a cycle of approximately 24 hours. These rhythms are regulated by the internal biological clock and synchronized with external environmental factors (especially the light cycle). Disturbances in circadian rhythms are closely related to the occurrence and development of a variety of diseases, including sleep disorders, metabolic diseases (such as diabetes and obesity), cardiovascular diseases, mental illnesses (such as depression and bipolar disorder), periodontal diseases and even cancer. Therefore, accurately assessing the circadian rhythm status of an individual is of great significance for the prevention, diagnosis and treatment of diseases.

[0003] Traditional circadian rhythm assessment methods mainly rely on measuring physiological indicators such as core body temperature or melatonin in a strictly controlled environment (such as a sleep laboratory). These methods are complex and costly, require professional equipment and personnel, and are difficult to monitor continuously in daily life, limiting their widespread application in clinical and scientific research. In recent years, with the development of wearable device technology, wrist actigraphy has been widely used in circadian rhythm research due to its convenience, non-invasiveness and long-term monitoring capabilities. The circadian rhythm disorder detection method based on actigraphy monitors human body movement through a built-in accelerometer and infers the individual's activity-rest rhythm based on the body movement data. However, body movement data alone cannot reflect the impact of light, an important circadian rhythm synchronization factor, on the human body.

[0004] Existing circadian rhythm disorder assessment methods have many limitations in their implementation, and it is difficult to meet the needs of convenient, accurate and comprehensive assessment. The polysomnography-based method is expensive and complex to operate. It needs to be operated by technicians in a professional sleep laboratory, and it is not possible to screen a large population. Moreover, it can only reflect the rhythm during sleep, and it is difficult to assess the circadian rhythm changes of individuals in the awake state. Another common method is based on a wrist activity recorder, which analyzes body movement data to infer circadian rhythms. However, this method only relies on a single body movement information and is easily interfered by factors such as individual activity habits. It also completely ignores light, a key circadian rhythm synchronization factor, and the accuracy of the assessment results is limited. Summary of the invention

[0005] In view of the defects in the prior art, the purpose of the present invention is to provide a circadian rhythm disorder assessment method based on wearable devices, which realizes convenient and non-invasive circadian rhythm monitoring through wearable devices, and combines spectrally weighted light data and standardized data processing procedures to improve the accuracy and reliability of the assessment results, providing an effective tool for early screening and intervention of circadian rhythm disorders.

[0006] To solve the above problems, the technical solution of the present invention is: A method for evaluating circadian rhythm disorder based on a wearable device comprises the following steps: Wearable devices are used to continuously collect light intensity data and body movement data of individuals in their natural living conditions; Preprocessing the collected light intensity data and body motion data; Perform discrete Fourier transform on the preprocessed illumination intensity data and body motion data, extract the fundamental frequency component and express it as a phasor, and calculate the difference phasor between the body motion phasor and the illumination phasor; The degree of circadian rhythm disorder of an individual is assessed based on the difference phasor between the calculated body movement phasor and the light exposure phasor.

[0007] Preferably, the step of continuously collecting light intensity data and body motion data of an individual in a natural living state through a wearable device specifically includes: using a wearable device integrated with a light sensor and a three-axis acceleration sensor to continuously collect light intensity data and three-axis acceleration data of an individual in a natural living state, the data collection time is not less than 24 hours, and the sampling frequencies of the light sensor and the acceleration sensor are not less than 1 Hz, so as to capture more subtle activities and light changes.

[0008] Preferably, the light intensity data preprocessing includes: Spectral weighting: Spectral weighting is performed on the original light intensity data to more accurately reflect the biological effects of light. Spectral weighting is performed using a spectral sensitivity function to calculate the circadian rhythm stimulation value. Temporal smoothing: The circadian rhythm stimulus values ​​were smoothed using a moving average filtering method to remove high-frequency noise; Binarization: The smoothed circadian rhythm stimulus value is binarized according to the preset threshold of the circadian rhythm stimulus value to obtain a light-dark mode sequence.

[0009] Preferably, the body motion data preprocessing includes: Calculate body motion amplitude: Calculate body motion amplitude based on triaxial acceleration data; Temporal smoothing: The moving average filtering method is used to smooth the body motion amplitude to remove high-frequency noise; Normalization: The smoothed body motion amplitude data is normalized to eliminate the influence of differences in activity levels among individuals.

[0010] Preferably, the steps of performing discrete Fourier transform on the preprocessed light intensity data and body motion data, extracting the fundamental frequency component and expressing it as a phasor, and calculating the difference phasor between the body motion phasor and the light photography phasor specifically include: performing discrete Fourier transform on the preprocessed light intensity data and body motion data with a period of 24 hours, respectively, extracting the fundamental frequency component of the 24-hour period and expressing it as a phasor, and calculating the difference phasor of the body motion phasor minus the light photography phasor.

[0011] Preferably, the calculation formula of the discrete Fourier transform DFT is: X(k) = Σ[n=0 to N-1] x(n) * e^(-j * 2π * k * n / N) Where: x(n) is the input signal, which can be light intensity data or body movement data; N is the signal length, corresponding to the number of sampling points in 24 hours; k is the frequency index; X(k) is the DFT coefficient; j is the imaginary unit.

[0012] Preferably, the phasor representation is as follows: the fundamental frequency components of illumination and body motion are represented as phasors L_phasor and A_phasor respectively: L_phasor = |L_phasor| * e^(j * θ_L) = X_L(1) A_phasor = |A_phasor| * e^(j * θ_A) = X_A(1) Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of illumination and body motion, respectively, corresponding to the modulus of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of illumination and body motion, respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

[0013] Preferably, the calculation of the difference phasor is: the difference phasor Δ_phasor obtained by subtracting the light phasor L_phasor from the body motion phasor is calculated as: Δ_phasor = A_phasor - L_phasor.

[0014] Preferably, the step of evaluating the degree of circadian rhythm disorder of an individual based on the calculated difference phasor between the body motion phasor and the light phasor specifically includes: based on the calculated difference phasor, evaluating the degree of circadian rhythm disorder of the individual by calculating its modulus and argument, the modulus of the difference phasor reflects the combined influence of the amplitude difference and phase difference between the light rhythm and the activity rhythm, and the calculation formula of the light-activity rhythm alignment index is: PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2) Where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor; the smaller the PM value, the higher the alignment between the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment between the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

[0015] Preferably, the step of evaluating the degree of circadian rhythm disorder of an individual based on the calculated difference phasor between the body movement phasor and the light illumination phasor specifically further comprises: calculating the argument of the difference phasor as a light-activity rhythm phase difference index, and the calculation formula is: PA = atan2(Im(Δ_phasor), Re(Δ_phasor)) Among them: atan2(y, x) is a four-quadrant inverse tangent function, which returns the inverse tangent value of y / x, and the result range is -π to π; the PA value reflects the phase difference between the light rhythm and the body movement rhythm. The phase characteristics of the individual's circadian rhythm are evaluated based on the PA value to determine the degree of circadian rhythm disorder of the individual.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention utilizes wearable devices to collect light intensity data and body movement data, thereby realizing portable, non-invasive and continuous monitoring of circadian rhythms in natural living conditions.

[0017] 2. The present invention adopts spectrally weighted light intensity data and standardized data processing flow to improve the accuracy and reliability of the evaluation results.

[0018] 3. The evaluation method of the present invention based on light-activity rhythm alignment can objectively and quantitatively evaluate the degree of circadian rhythm disorder of an individual. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is a flowchart of a circadian rhythm disorder assessment method based on a wearable device of the present invention; Figure 2 Schematic diagram of the cosine fitting cycle of minute-level light-dark data; Figure 3 Schematic diagram of the cosine fitting cycle of minute-level activity-rest data; Figure 4 This is a polar coordinate diagram of the difference between the body motion phase and the photoelectric phase measured using the phase analysis method. DETAILED DESCRIPTION

[0020] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0021] Specifically, the present invention provides a method, such as Figure 1 As shown, the method comprises the following steps: S1: Continuously collect light intensity data and body movement data of individuals in natural living conditions through wearable devices; Specifically, wearable devices with integrated light sensors and three-axis acceleration sensors, such as ActiGraph GT3X+ and smart bracelets, are used to continuously collect light intensity data and three-axis acceleration data of individuals in natural living conditions. The data collection time should be no less than 24 hours, and it is recommended to collect data for 7 days or longer to obtain more stable evaluation results. The sampling frequency of both the light sensor and the acceleration sensor should be no less than 1 Hz, and it is recommended to set it to 5 Hz or higher to capture more detailed activities and light changes.

[0022] S2: preprocessing the collected light intensity data and body motion data; Specifically, the light intensity data preprocessing includes: 1. Spectral weighting: Since different wavelengths of light have different effects on the human circadian rhythm, the original light intensity data needs to be spectrally weighted to more accurately reflect the biological effect of light. The present invention uses the spectral sensitivity function S(λ) recommended by the International Commission on Illumination (CIE) for spectral weighting and calculates the circadian stimulus value (CS).

[0023] The calculation formula for CS is: CS = C_A * [1 - (1 / (1 + (L / L_0)^p))], where: C_A is the spectrally weighted light intensity, and the calculation formula is: C_A = ∫ P(λ) * S(λ) dλ, where P(λ) is the light intensity at wavelength λ; S(λ) is the spectral sensitivity function defined by CIE, which describes the relative impact of different wavelengths of light on circadian rhythms; L is the original light intensity; L_0 and p are empirical parameters used to adjust the shape of the CS curve; L_0 usually ranges from 10 to 1000 lux, and p ranges from 0.5 to 1.5.

[0024] 2. Time smoothing: Use the moving average filter method to smooth the CS value to remove high-frequency noise. The mathematical expression of the moving average filter is: CS_s(t) = (1 / n) * Σ[i=t-n+1 to t] CS(i), where: t is the time index, corresponding to a specific time point or sampling point in the discrete time series, indicating the moment of calculating the current smoothed value CS_s(t); CS_s(t) is the smoothed CS value; CS(i) is the original CS value; n is the moving average window size, and it is recommended to set it to the number of sampling points corresponding to 5 minutes.

[0025] 3. Binarization: The smoothed CS value CS_s(t) is binarized according to the preset threshold CS_th to obtain the light-dark mode sequence LDP(t). The binarization formula is: LDP(t) = 1, if CS_s(t) ≥ CS_th LDP(t) = 0, if CS_s(t) <CS_th Here, CS_th is a preset threshold value, which may be set to 0.3, for example.

[0026] The body motion data preprocessing includes: 1. Calculate the body motion amplitude: Calculate the body motion amplitude A(t) according to the three-axis acceleration data a_x(t), a_y(t), and a_z(t) at time t. Common methods include vector amplitude and digital integration. In this embodiment, the vector amplitude method (VM) is used, and its calculation formula is: A(t) = sqrt(a_x(t)^2 + a_y(t)^2 + a_z(t)^2) Where t is the time index, corresponding to the sampling point in the discrete time series, representing the moment of calculating the current body motion amplitude A(t). A(t) is the body motion amplitude value at time t, indicating the intensity of the movement; a_x(t) is the acceleration value along the x-axis at time t; a_y(t) is the acceleration value along the y-axis at time t; a_z(t) is the acceleration value along the z-axis at time t.

[0027] 2. Time smoothing: Use the moving average filter method to smooth the body motion amplitude A(t) to remove high-frequency noise. The mathematical expression of the moving average filter is: A_s(t) = (1 / m) * Σ[i=t-m+1 to t] A(i), where: A_s(t) is the smoothed body motion amplitude; A(i) is the original body motion amplitude; m is the moving average window size.

[0028] 3. Normalization: The smoothed body motion amplitude data A_s(t) is normalized to eliminate the influence of differences in activity levels between individuals. In this embodiment, the maximum normalization method is adopted, and the body motion amplitude in each 24-hour cycle is divided by the maximum value of the cycle. The normalization formula is: A_n(t) = A_s(t) / max(A_s(t))_T, where: A_n(t) is the normalized body motion amplitude; max(A_s(t))_T is the maximum value of A_s(t) in the time period T (for example, 24 hours).

[0029] S3: Perform discrete Fourier transform on the preprocessed illumination intensity data and body motion data, extract the fundamental frequency component and express it as a phasor, and calculate the difference phasor between the body motion phasor and the illumination phasor; Specifically, Figure 2 and Figure 3 As shown in the figure, the pre-processed light intensity data (LDP(t)) and body movement data (A_n(t)) are processed with a discrete Fourier transform (DFT) with a period of 24 hours to extract the fundamental frequency component corresponding to the 24-hour period. The fundamental frequency component is used to represent the main change trend of the signal within the 24-hour period, and can be further represented in the form of phasor to quantify its amplitude and phase characteristics. The calculation formula of DFT is: X(k) = Σ[n=0 to N-1] x(n) * e^(-j * 2π * k * n / N) Where: X(k) is the complex coefficient of the (k)th frequency component after discrete Fourier transform, which contains the amplitude and phase information of the frequency component; x(n) represents the input discrete time series signal, which can be light intensity data (LDP(t)) or body movement data (A_n(t)), which has been sampled at a uniform time interval (such as 5 minutes); is the input signal, which can be LDP(t) or A_n(t); e^(-j * 2π * k * n / N) is the complex exponential kernel function, which is used to project the time domain signal into the frequency domain; N is the signal length, corresponding to the total number of sampling points in a 24-hour period; n is the discrete time index of the time series, which is used to mark the position of the current sampling point in the 24-hour period, and the value range is n = 0, 1, 2, ..., N-1; k is the frequency index, which represents the discrete frequency point in the frequency domain, which is used to mark the position of the extracted frequency component in the frequency domain, and the value range is k = 0, 1, 2, ..., N-1; j is the imaginary unit.

[0030] Phasor representation: The fundamental frequency components of illumination and body motion are represented as phasors L_phasor and A_phasor respectively: L_phasor = |L_phasor| * e^(j * θ_L) = X_L(1) A_phasor = |A_phasor| * e^(j * θ_A) = X_A(1) Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of illumination and body motion, respectively, corresponding to the modulus of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of illumination and body motion, respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

[0031] Calculate the difference phasor: Calculate the difference phasor Δ_phasor between the body motion phasor A_phasor and the light phasor L_phasor as follows: Δ_phasor = A_phasor - L_phasor.

[0032] S4. Assess the degree of circadian rhythm disturbance of an individual based on the calculated difference phasor between the body movement phasor and the light exposure phasor.

[0033] Specifically, Figure 4As shown, based on the difference phasor calculated in step S3, the degree of circadian rhythm disorder of the individual is evaluated by calculating its modulus and argument. The modulus of the difference phasor Δ_phasor reflects the combined influence of the amplitude difference and phase difference between the light rhythm and the activity rhythm, and can be used as an indicator for evaluating the degree of circadian rhythm disorder.

[0034] The calculation formula of the light-activity rhythm alignment index (Phasor Magnitude, PM) is: PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2) Where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor. The smaller the PM value, the higher the alignment between the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment between the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

[0035] Principle description: When the light rhythm and activity rhythm are completely aligned, the phase difference between the two is 0, and the amplitude is similar, then the modulus of Δ_phasor is close to 0. When the phase difference between the light rhythm and the activity rhythm is large or the amplitude difference is large, the modulus of Δ_phasor increases.

[0036] Calculate the phase difference index (Phasor Angle, PA) of light-activity rhythm: In addition to the PM index, the argument of the difference phase Δ_phasor can also be calculated as the phase difference index (Phasor Angle, PA) of light-activity rhythm. The calculation formula of PA is: PA = atan2(Im(Δ_phasor), Re(Δ_phasor)) Among them: atan2(y, x) is a four-quadrant inverse tangent function, which returns the inverse tangent value of y / x, and the result range is -π to π. The PA value reflects the phase difference between the light rhythm and the body movement rhythm. The phase characteristics of the individual's circadian rhythm can be evaluated based on the PA value, for example, to determine whether the individual is a "morning bird" or a "night owl".

[0037] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A circadian rhythm disorder assessment method based on wearable devices, characterized in that: The method comprises the following steps: Wearable devices are used to continuously collect light intensity data and body movement data of individuals in their natural living conditions; Preprocessing the collected light intensity data and body motion data; Perform discrete Fourier transform on the preprocessed illumination intensity data and body motion data, extract the fundamental frequency component and express it as a phasor, and calculate the difference phasor between the body motion phasor and the illumination phasor; The degree of circadian rhythm disorder of an individual is assessed based on the difference phasor between the calculated body movement phasor and the light exposure phasor.

2. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The step of continuously collecting light intensity data and body motion data of an individual in a natural living state through a wearable device specifically includes: using a wearable device integrated with a light sensor and a three-axis acceleration sensor to continuously collect light intensity data and three-axis acceleration data of an individual in a natural living state, wherein the data collection time is not less than 24 hours, and the sampling frequencies of the light sensor and the acceleration sensor are not less than 1 Hz, so as to capture more detailed activities and light changes.

3. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The light intensity data preprocessing comprises: Spectral weighting: Spectral weighting is performed on the original light intensity data to more accurately reflect the biological effects of light. Spectral weighting is performed using a spectral sensitivity function to calculate the circadian rhythm stimulation value. Temporal smoothing: The circadian rhythm stimulus values ​​were smoothed using a moving average filtering method to remove high-frequency noise; Binarization: The smoothed circadian rhythm stimulus value is binarized according to the preset threshold of the circadian rhythm stimulus value to obtain a light-dark mode sequence.

4. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The body motion data preprocessing includes: Calculate body motion amplitude: Calculate body motion amplitude based on triaxial acceleration data; Temporal smoothing: The moving average filtering method is used to smooth the body motion amplitude to remove high-frequency noise; Normalization: The smoothed body motion amplitude data is normalized to eliminate the influence of differences in activity levels among individuals.

5. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The steps of performing discrete Fourier transform on the preprocessed light intensity data and body motion data, extracting the fundamental frequency component and expressing it as a phasor, and calculating the difference phasor between the body motion phasor and the light photography phasor specifically include: performing discrete Fourier transform on the preprocessed light intensity data and body motion data with a period of 24 hours, respectively, extracting the fundamental frequency component of the 24-hour period, expressing it as a phasor, and calculating the difference phasor of the body motion phasor minus the light photography phasor.

6. The circadian rhythm disorder assessment method based on a wearable device according to claim 5, characterized in that: The calculation formula of the discrete Fourier transform DFT is: X(k) = Σ[n=0 to N-1] x(n) * e^(-j * 2π * k * n / N) Where: x(n) is the input signal, which is light intensity data or body motion data; N is the signal length, corresponding to the number of sampling points in 24 hours; k is the frequency index; X(k) is the DFT coefficient; j is the imaginary unit.

7. The circadian rhythm disorder assessment method based on a wearable device according to claim 6, characterized in that: The phasor representation is: The fundamental frequency components of illumination and body motion are represented as phasors L_phasor and A_phasor respectively: L_phasor = |L_phasor| * e^(j * θ_L) = X_L(1) A_phasor = |A_phasor| * e^(j * θ_A) = X_A(1) Where: |L_phasor| and |A_phasor| are the amplitudes of the fundamental frequency components of illumination and body motion, respectively, corresponding to the modulus of the DFT coefficients X_L(1) and X_A(1); θ_L and θ_A are the phases of the fundamental frequency components of illumination and body motion, respectively, corresponding to the arguments of the DFT coefficients X_L(1) and X_A(1).

8. The circadian rhythm disorder assessment method based on a wearable device according to claim 7, characterized in that: The difference phasor of the calculated body motion phasor minus the light illumination phasor is: the difference phasor Δ_phasor of the calculated body motion phasor A_phasor minus the light illumination phasor L_phasor is: Δ_phasor=A_phasor-L_phasor.

9. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The step of evaluating the degree of circadian rhythm disorder of an individual based on the calculated difference phasor between the body motion phasor and the light phasor specifically includes: based on the calculated difference phasor, evaluating the degree of circadian rhythm disorder of the individual by calculating its modulus and argument, the modulus of the difference phasor reflects the combined influence of the amplitude difference and phase difference between the light rhythm and the activity rhythm, and the calculation formula of the light-activity rhythm alignment index is: PM = |Δ_phasor| = sqrt(Re(Δ_phasor)^2 + Im(Δ_phasor)^2) Where: Re(Δ_phasor) represents the real part of Δ_phasor; Im(Δ_phasor) represents the imaginary part of Δ_phasor; the smaller the PM value, the higher the alignment between the light rhythm and the activity rhythm, and the more stable the circadian rhythm; the larger the PM value, the lower the alignment between the light rhythm and the activity rhythm, and the higher the degree of circadian rhythm disorder.

10. The circadian rhythm disorder assessment method based on a wearable device according to claim 1, characterized in that: The step of evaluating the degree of circadian rhythm disorder of an individual based on the calculated difference phasor between the body movement phasor and the light illumination phasor specifically includes: calculating the argument of the difference phasor as a light-activity rhythm phase difference index, and the calculation formula is: PA = atan2(Im(Δ_phasor), Re(Δ_phasor)) Among them: atan2(y, x) is a four-quadrant inverse tangent function, which returns the inverse tangent value of y / x, and the result range is -π to π; the PA value reflects the phase difference between the light rhythm and the body movement rhythm. The phase characteristics of the individual's circadian rhythm are evaluated based on the PA value to determine the degree of circadian rhythm disorder of the individual.

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