A pressure age test method based on saliva extraction

Abstract

This invention discloses a method for calculating stress age based on saliva extraction, belonging to the field of data processing technology. It includes collecting saliva samples and personal data from individuals, including individual characteristics, physiological data, and behavioral data; obtaining telomere detection data from the saliva samples; analyzing the telomere detection data, physiological data, and behavioral data to obtain telomere-related data; and generating a test report based on the telomere-related data. This method achieves the scientific quantification of emotional and psychological stress, and compared to traditional scales, the test results are more scientific and accurate.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method for calculating stress age based on saliva extraction. Background Technology

[0002] With the current fast pace of society and increasing life pressure, the incidence of mental illness continues to rise. The health risks caused by the long-term accumulation of psychological stress are becoming increasingly prominent. There is an urgent market demand for objective, scientific, and quantifiable psychological stress detection technology, but existing technologies have obvious limitations and cannot meet the actual needs.

[0003] Traditional psychological stress testing mainly relies on subjective scales and short-term physiological indicators: the former depends on individual self-reporting, is easily affected by emotions, cognitive biases and social expectations, and the results are highly subjective and have poor repeatability, making it impossible to achieve quantitative assessment; the latter can only reflect instantaneous stress response, cannot be linked to the cumulative damage of long-term stress to the body, and is difficult to distinguish between short-term tension and physiological imbalance caused by long-term stress, resulting in insufficient accuracy.

[0004] Telomeres, as protective structures at the ends of eukaryotic chromosomes, have had their length linked to cellular aging and stress damage, a fact verified by the scientific community. Long-term psychological stress can accelerate telomere shortening by activating oxidative stress and increasing chronic inflammation levels, making them a potential biomarker for long-term psychological stress. However, current telomere-related technologies only measure length from a biological and genetic perspective to assess aging or disease risk, failing to establish a link between telomeres and psychological stress. This makes it impossible to convert telomere length into an age index or quantitative indicator reflecting psychological stress, hindering a comprehensive and accurate assessment of an individual's emotional and psychological stress status. Overcoming these technological bottlenecks is urgently needed.

[0005] Therefore, the present invention provides a method for calculating stress age based on saliva extraction. Summary of the Invention

[0006] The purpose of this invention is to provide a method for calculating stress age based on saliva extraction, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a method for calculating stress age based on saliva extraction, which can be specifically implemented as follows: collecting saliva test samples and personal data from an individual, including individual characteristics, physiological data and behavioral data; obtaining telomere detection data from the saliva test samples; analyzing the telomere detection data, physiological data and behavioral data to obtain telomere-related data; and generating a test report based on the telomere-related data.

[0008] Based on the aforementioned technical methods, non-invasive collection of saliva samples along with individual characteristics, physiological and behavioral data reduces the operational threshold and discomfort for individuals, while avoiding the one-sidedness of single-data assessment. Furthermore, integrating telomere detection data with multi-dimensional personal data for collaborative analysis breaks through the limitations of traditional subjective assessment, making stress age calculation more accurate. Finally, a test report containing telomere-related data is generated, helping individuals intuitively understand their own stress status and providing a reliable basis for early detection of stress problems and scientific implementation of mental health interventions.

[0009] One possible approach to obtaining telomere detection data from saliva test samples is as follows: telomere detection data is obtained by performing telomere detection on saliva test samples based on real-time quantitative PCR technology. The telomere detection data includes: telomere length and fluorescence signal intensity data corresponding to the PCR reaction.

[0010] Based on the aforementioned technical methods, real-time quantitative PCR (qPCR) technology can accurately obtain the core data on telomere length while simultaneously recording the fluorescence signal intensity data during the PCR reaction. The fluorescence signal intensity can calibrate the calculated telomere length, eliminating errors such as sample concentration fluctuations and reaction system interference, ensuring the accuracy and validity of the test data. This dual-dimensional detection data provides a high-quality foundation for subsequent calculations of stress age and psychological stress index, avoiding bias from a single data point, significantly improving the scientific rigor and reliability of the results, and providing solid data support for accurately assessing psychological stress-related conditions.

[0011] Another possible implementation involves analyzing telomere detection data, physiological data, and behavioral data to obtain telomere-related data. Specifically, this can be achieved by: preprocessing the telomere detection data, physiological data, and behavioral data; fusing and analyzing the preprocessed telomere detection data, physiological data, and behavioral data to obtain multi-dimensional feature data; and obtaining telomere age and psychological stress index based on the pre-trained algorithm model and the multi-dimensional feature data.

[0012] Based on the aforementioned technical methods, data quality is ensured by preprocessing three types of data to remove noise and outliers; multi-dimensional information is integrated through fusion analysis to overcome the limitations of single-data evaluation; and telomere age and psychological stress index are calculated using a pre-trained model, leveraging extensive sample training experience to improve the accuracy of results. This approach achieves a scientific transformation from raw data to quantitative indicators, making the assessment more comprehensive and the results more reliable, providing strong support for accurately judging psychological stress levels and developing personalized intervention plans.

[0013] Another possible implementation involves fusing and analyzing the preprocessed telomere detection data, physiological data, and behavioral data to obtain multi-dimensional feature data. Specifically, this can be achieved by: extracting features from the preprocessed telomere detection data, physiological data, and behavioral data; aligning the extracted features along the time dimension; and using a multimodal data fusion algorithm to weight and integrate the features based on the influence weights of different features on the assessment of emotions and psychological stress, thereby obtaining multi-dimensional feature data.

[0014] Based on the aforementioned technical methods, the core features of the three types of data are extracted step by step to ensure that no key information is omitted. Time-series alignment is then performed to eliminate interference from data misalignment and ensure correlation. Finally, a multimodal algorithm is used to weight and integrate the data according to their influence, highlighting the dominant role of core features such as telomeres. This approach ensures that the multidimensional feature data not only covers comprehensive information but also reflects the priority of key features, providing high-quality input for subsequent model calculations and effectively improving the accuracy and reliability of telomere age and psychological stress index assessments.

[0015] Another possible implementation involves feature extraction from the preprocessed telomere detection data, physiological data, and behavioral data. Specifically, this can be achieved by extracting the absolute value of telomere length, the deviation of telomere length from the average value of healthy individuals in the same age group, and the stability parameter of fluorescence signal intensity from the telomere detection data; extracting the daily average heart rate, blood pressure fluctuation range, and frequency of abnormal physiological indicators from the physiological data; and extracting the daily average activity duration, sleep cycle integrity, percentage of deep sleep, and frequency of stress-related behaviors from the behavioral data.

[0016] Based on the aforementioned technical methods, core features related to stress and telomeres are extracted from three types of data. At the telomere level, the focus is on length baselines and deviations, and signal stability. At the physiological level, heart rate and blood pressure fluctuations and abnormal frequencies are captured. At the behavioral level, activity, sleep, and stress-related behavioral features are extracted, achieving precise capture of multi-dimensional information. This approach covers the entire molecular, physiological, and behavioral chain, while highlighting the correlation between key indicators and stress. It provides targeted feature inputs for subsequent fusion analysis, effectively ensuring the quality and relevance of multi-dimensional feature data, and laying a solid foundation for accurate assessment.

[0017] Another possible implementation is to obtain telomere age and psychological stress index based on pre-trained algorithm model and multi-dimensional feature data. Specifically, this can be achieved by inputting the telomere length feature from the multi-dimensional feature data into the pre-trained algorithm model, calling the pre-trained regression sub-model of telomere length and actual age to obtain the initial telomere age, and adjusting the initial telomere age based on the daily average heart rate, sleep cycle integrity, and deep sleep percentage to obtain the final telomere age.

[0018] Based on the aforementioned technical methods, the initial telomere age is first determined by combining telomere length characteristics with a pre-trained regression sub-model, ensuring a scientific model foundation for the calculation. Then, key physiological and behavioral characteristics such as daily average heart rate, sleep cycle integrity, and the percentage of deep sleep are introduced to adjust the initial values. This approach retains the dominant role of telomeres as a core biomarker while incorporating physiological and behavioral factors influencing stress. This avoids the one-sidedness of relying solely on telomere data, making telomere age more closely reflect an individual's actual biological state under stress, significantly improving the accuracy and relevance of the assessment, and laying a reliable foundation for subsequent calculations of the psychological stress index.

[0019] This invention provides a method for calculating stress age based on saliva extraction, which can be specifically implemented as follows: continuously monitoring telomere-related data for an individual, including telomere detection data, physiological data, behavioral data, and telomere age and psychological state index corresponding to each telomere detection; performing anomaly analysis based on all telomere-related data; and generating early warning information based on the anomaly analysis results.

[0020] Based on the aforementioned technical means, by continuously monitoring telomere detection data, physiological data, behavioral data, and multi-dimensional telomere-related data such as telomere age and psychological state index, the limitations of single static detection can be overcome, and the long-term impact of stress on an individual's physical and mental state can be dynamically tracked. Furthermore, by combining the full-dimensional data to conduct anomaly analysis, the risk of misjudgment based on a single indicator can be avoided, and the comprehensiveness and accuracy of anomaly identification can be improved. Timely generation of early warning information can enable individuals to detect signs of worsening stress as early as possible, creating a window of opportunity for early intervention and prevention of psychological crises, effectively enhancing the initiative and timeliness of mental health management.

[0021] This invention provides a method for calculating stress age based on saliva extraction, which can be specifically implemented as follows: performing trend analysis on all telomere-related data stored in the data storage module to obtain trend data on individual emotions and psychological stress status, and adapting and updating telomere age and psychological stress index based on trend data and individual characteristics.

[0022] Based on the aforementioned technical methods, trend analysis of the stored full set of telomere-related data can dynamically capture the long-term patterns of individual emotions and psychological stress, avoiding the limitations of single static data assessments. Furthermore, by incorporating individual characteristics (such as age and health status) to adapt and update telomere age and psychological stress indices, the indicators are made more closely aligned with individual differences and the actual evolution of stress. This significantly improves the dynamism and personalized accuracy of the assessment results, helping to accurately identify the triggers for stress changes and providing a scientific basis for developing long-term, tailored mental health management programs.

[0023] Another possible implementation is to generate a test report based on telomere-related data. Specifically, this can be achieved by generating diagnostic suggestions and intervention measures based on anomaly analysis results and individual characteristics, and then generating a test report based on the diagnostic suggestions, intervention measures, and early warning information.

[0024] Based on the aforementioned technical methods, diagnostic suggestions and intervention measures are generated by combining anomaly analysis results with individual characteristics. This avoids the limitations of generic recommendations and ensures that the solutions are tailored to the individual's specific situation. Furthermore, by integrating early warning information to generate a monitoring report, the report includes both problem solutions and risk warnings, making the content more comprehensive. This approach allows individuals to clearly understand their own stress anomalies, coping strategies, and potential risks, providing intuitive and practical guidance for precise intervention in psychological stress and efficient mental health management, thereby improving the targeting and effectiveness of management.

[0025] This invention provides a method for calculating stress age based on saliva extraction, which can be specifically implemented as follows: quality verification of saliva test samples, the quality verification indicators including saliva sample volume, telomere detection requirements, and the concentration and purity of DNA extracted from the sample, and pushing standardized operating instructions to individuals.

[0026] Based on the aforementioned technical methods, standardized operating instructions are disseminated from the source of collection to guide individuals in proper operation, reducing issues such as sample contamination and insufficient sample volume caused by improper operation. Sample quality is then verified using sample volume, DNA concentration, and purity as core indicators, and unqualified samples are discarded. This dual approach achieves end-to-end control over sample quality, preventing substandard samples from affecting subsequent test results and ensuring the accuracy of telomere detection data. Simultaneously, it reduces the frequency of resampling, lowering individual time costs and discomfort, providing a reliable data foundation for calculating stress age, and improving overall testing efficiency and experience. Attached Figure Description

[0027] Figure 1 This is a schematic flowchart of a pressure age calculation method based on saliva extraction provided in an embodiment of the present invention; Figure 2 This is a schematic flowchart of another pressure age calculation method based on saliva extraction provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of a test kit provided in an embodiment of the present invention; Figure 4 This is an example diagram of a test report provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 This invention provides a method for calculating stress age based on saliva extraction, the specific steps of which include: S101: Collect saliva samples and personal data from individuals, including individual characteristics, physiological data, and behavioral data.

[0030] Personal data includes: individual characteristics, physiological data, and behavioral data.

[0031] Individual characteristics refer to the basic attribute information of an individual that is innate or has been stable for a long time and may affect telomere status and psychological stress tolerance. They cover identity, health status and genetic related characteristics, and are the core reference for subsequent data adaptation analysis and result adjustment. Physiological data refers to the quantitative indicators of an individual’s physical function over a certain period of time. It focuses on dynamic physiological parameters related to stress response and metabolic state, and can directly reflect the impact of short-term or long-term psychological stress on the body. Behavioral data refers to recordable behavioral patterns such as an individual's daily activities and habits. These behaviors indirectly affect telomere length and psychological stress levels through factors such as work-rest patterns and energy consumption, and are a key dimension for assessing the cumulative effect of stress. In some embodiments, the collection of individual characteristics, physiological data, and behavioral data can be achieved through a combination of online questionnaires and synchronized smart devices: individual characteristics are collected through electronic questionnaires, physiological data is automatically collected through smart wearable devices adapted to the system, and behavioral data is supplemented by device records and brief daily logs of individuals, ensuring the convenience and completeness of data collection; at the same time, the collection period can be set according to the testing needs, such as individual characteristics being collected once, physiological data needing to be collected continuously for 7-14 days, and behavioral data collection period being consistent with physiological data collection period. Specifically, the collection of individual characteristics needs to cover three sub-items: basic information, health background, and genetic association. Basic information includes age, gender, height, weight, and occupation type, such as high-pressure mental work or light physical work. Health background includes history of chronic diseases, such as hypertension, depression, thyroid disease, history of major past illnesses, and long-term medication use, such as whether anti-anxiety drugs are taken. Genetic association information includes family history of telomere-related diseases and the incidence of mental illness in immediate family members. Optionally, physiological data collection should follow these guidelines: resting physiological indicators should be measured 30 minutes after fasting each morning; dynamic physiological indicators should be recorded in real time by the device; and special physiological indicators should be collected at fixed time points using special test strips. Behavioral data collection: Stress-related behaviors include daily overtime hours and frequency of sudden stressful events; daily rhythms include daily bedtime, wake-up time, and number of sleep interruptions; health habits include daily caffeine intake, frequency of smoking and drinking, and exercise behavior. For example, the personal data collection results of a 35-year-old female individual can be presented as follows: Individual characteristics: age 35, female, occupation: Internet product manager, height 162cm, weight 55kg, 3-year history of migraines, mother has a history of anxiety disorder; Physiological data: average resting heart rate of 72 beats / minute for 7 consecutive days, systolic blood pressure fluctuates between 110-135mmHg, the highest daytime heart rate of 105 beats / minute was reached on the 5th day due to working overtime; Behavioral data: average sleep time is 23:30 and wake-up time is 7:00, overtime is 4 times a week, each time for an average of 2 hours, drinking 4 cups of coffee per week, doing yoga 2 times a week, each time for 45 minutes, and experiencing 2 sudden stress events this week due to project launch. S102: Obtain telomere detection data from saliva test samples.

[0032] Telomere detection data refers to quantitative information extracted from saliva samples that reflects the biological state of telomeres. It is the core molecular-level data for subsequent calculation of telomere age and psychological stress index. It not only includes telomere length, which directly characterizes the protective function of telomeres, but also covers auxiliary verification data such as fluorescence signal intensity during PCR reaction. It can be used to calibrate telomere length calculation results, eliminate interference factors in the detection process, and ensure the authenticity and reliability of the data.

[0033] Telomere detection data includes: telomere length and corresponding fluorescence signal intensity data during the PCR reaction.

[0034] In some embodiments, telomere detection is performed on saliva test samples using real-time quantitative PCR technology to obtain telomere detection data.

[0035] Specifically, the telomere detection process includes: extracting DNA from saliva samples and purifying the DNA using magnetic beads to remove interfering substances such as proteins and impurities; designing specific primers and fluorescently labeled probes for telomere repeat sequences; configuring a quantitative real-time PCR reaction system containing purified DNA template, specific primers, fluorescent probes, Taq enzyme, dNTPs, and buffer; setting the amplification program in a quantitative real-time PCR instrument; monitoring the changes in fluorescence signal intensity for each cycle in real time to generate an amplification curve; and calculating the telomere length data corresponding to the saliva sample by analyzing the Ct value of the amplification curve and the standard curve. This telomere length data is the core telomere detection data. Real-time quantitative PCR (qPCR) is a technique that uses real-time fluorescence signals to monitor changes in the amount of amplified products during PCR amplification, enabling precise quantification of target sequences. Compared to traditional PCR methods that require subsequent electrophoresis, qPCR eliminates the need to open the container, avoiding cross-contamination. Furthermore, it directly correlates the concentration of the target sequence with the real-time fluorescence signal, significantly improving the efficiency and accuracy of telomere length detection. Simultaneously, it acquires dynamic data on fluorescence signal intensity, providing a basis for data quality verification. Optionally, to further ensure the accuracy of the test, an internal reference gene can be added to the PCR reaction system to correct the amplification deviation of the telomere sequence by the amplification efficiency of the internal reference gene; a blank control and a positive control can also be set up. If the blank control has no fluorescence signal and the amplification curve of the positive control meets the expectations, the test is considered valid; otherwise, the sample needs to be tested again.

[0036] S103: Analyze telomere detection data, physiological data, and behavioral data to obtain telomere-related data.

[0037] In some embodiments, telomere detection data, physiological data, and behavioral data are preprocessed, and the preprocessed telomere detection data, physiological data, and behavioral data are fused and analyzed to obtain multi-dimensional feature data. Based on the pre-trained algorithm model and the multi-dimensional feature data, telomere age and psychological stress index are obtained.

[0038] Preprocessing refers to targeted data optimization operations carried out on the characteristics of the three types of data to improve data quality and eliminate interference factors. The core purpose is to ensure the accuracy, consistency and completeness of the data used in subsequent analysis, and to avoid noise, outliers or missing values ​​in the original data from affecting the reliability of the analysis results. Specifically, it includes outlier removal, missing value imputation and data standardization operations.

[0039] For example, multi-dimensional feature data is obtained by fusing and analyzing preprocessed telomere detection data, physiological data, and behavioral data. This includes: extracting features from preprocessed telomere detection data, physiological data, and behavioral data; aligning the extracted features with the time dimension; and using a multimodal data fusion algorithm to weight and integrate the features according to the influence weights of different features on the assessment of emotion and psychological stress, thereby obtaining multi-dimensional feature data.

[0040] Among them, the time dimension alignment processing takes the time node of telomere detection as the benchmark and integrates the physiological data and behavioral data within the detection period according to the granularity of "daily average" and "weekly average" to ensure that the three types of data match in the time dimension.

[0041] The testing cycle refers to the period from the collection of saliva samples, i.e. the start time of telomere testing, as the core node, back 1-2 weeks and forward to the completion of sample testing. This cycle is designed to cover the individual's short-term physiological state and behavioral habits during telomere testing, ensuring that physiological and behavioral data can truly reflect the physical and mental state corresponding to telomere testing, and avoiding correlation bias caused by inconsistent data time spans.

[0042] For example, feature extraction is performed on the preprocessed telomere detection data, physiological data, and behavioral data, including: extracting the absolute value of telomere length, the deviation value of telomere length from the average value of healthy people of the same age group, and the stability parameter of fluorescence signal intensity from the telomere detection data; extracting the daily average heart rate, blood pressure fluctuation range, and frequency of occurrence of abnormal physiological indicators from the physiological data; and extracting the daily average activity duration, sleep cycle integrity, deep sleep ratio, and frequency of occurrence of stress-related behaviors from the behavioral data.

[0043] Feature extraction refers to the process of filtering, calculating, and refining key information directly related to telomere state and psychological stress from preprocessed raw data, eliminating redundant data, and transforming the raw data into quantitative features with clear biological or behavioral significance.

[0044] Specifically, the refined operations of feature extraction include: at the telomere detection data level, the stability parameter of fluorescence signal intensity is determined by calculating the deviation amplitude between the peak and mean values ​​of fluorescence signal in the PCR reaction and the maximum value of the difference in signal fluctuation between adjacent cycles; at the physiological data level, the blood pressure fluctuation range is taken as the average difference between the maximum and minimum values ​​of systolic blood pressure each day within the detection period, and the frequency of abnormal physiological indicators is statistically calculated according to medical standard thresholds such as heart rate > 100 beats / min and systolic blood pressure > 140 mmHg; at the behavioral data level, the completeness of the sleep cycle is judged as complete (recorded as 1) if there is no interruption after falling asleep and the sleep time is ≥ 7 hours, otherwise it is incomplete (recorded as 0), the proportion of deep sleep is calculated through sleep stage data recorded by smart devices, and stress-related behaviors (such as staying up late > 24:00, sitting for long periods of time > 8 hours / day) are calculated according to the number of times they occur each day and the weekly average frequency.

[0045] Specifically, telomere age and psychological stress index are obtained based on pre-trained algorithm models and multi-dimensional feature data, including: inputting telomere length features from multi-dimensional feature data into pre-trained algorithm models, calling pre-trained regression sub-models of telomere length and actual age to obtain initial telomere age, and adjusting the initial telomere age based on daily average heart rate, sleep cycle integrity, and deep sleep percentage to obtain telomere age.

[0046] Pre-trained algorithm models refer to models trained using machine learning algorithms based on a large number of labeled samples, covering people aged 18-65 with different levels of psychological stress and different health conditions, with a sample size of ≥5000 cases. These models enable rapid analysis and accurate calculation of new sample data.

[0047] Specifically, the input to the pre-trained algorithm model is multi-dimensional feature data after fusion processing; the output is telomere age and psychological stress index, and the reliability scores of the two indicators are also output.

[0048] Optionally, to improve the model's generalization ability, training samples will be stratified by region and occupation type to avoid regional or occupational bias caused by a single sample. At the same time, a 5-fold cross-validation method will be used during model training, dividing the samples into 5 groups, using 4 groups for training and 1 group for validation in turn, to ensure the stability of the model on different sample subsets.

[0049] For example, the psychological stress index is determined based on the daily average heart rate, blood pressure fluctuation range, frequency of abnormal physiological indicators, daily activity duration, sleep cycle integrity, percentage of deep sleep, and frequency of stress-related behaviors.

[0050] Specifically, determining the psychological stress index includes: The weights of each feature were determined based on the feature importance output by the pre-trained model. The telomere length deviation value had the highest weight, followed by the frequency of stress-related behaviors, the proportion of deep sleep, the range of blood pressure fluctuations, the frequency of abnormal physiological indicators, the daily average heart rate, the integrity of sleep cycles, and the daily average activity duration. Calculate the score for a single feature, such as: ; ; Among them, the lower the percentage, the higher the score, which represents greater pressure; To calculate the weighted total score, multiply each individual feature score by its corresponding weight and then sum the results to obtain the initial index. The fourth step involves fine-tuning the telomere age based on the difference between the telomere age and the actual age. For example, if the telomere age is more than 3 years older than the actual age, the initial index is increased by 10%, resulting in a psychological stress index, which is then divided into levels: 0-30 (mild stress), 31-60 (moderate stress), and 61-100 (severe stress).

[0051] Optionally, the psychological stress index calculation can incorporate a baseline calibration: an initial index is generated for each individual during the first test as a baseline, and the current index is compared with the baseline value during subsequent tests to calculate the magnitude of change. For example, if the index increases by 15 points compared to the previous test, the report will indicate the stress change trend corresponding to the index change, helping individuals to intuitively perceive the dynamic changes in their own stress.

[0052] S104: Generate a test report based on telomere-related data.

[0053] In some embodiments, diagnostic suggestions and intervention measures are generated based on anomaly analysis results and individual characteristics, and a test report is generated based on the diagnostic suggestions, intervention measures, and early warning information.

[0054] A test report is a professional document that integrates core telomere-related data, anomaly analysis conclusions, and personalized recommendations in a structured and visualized format. It must accurately present test results and ensure data reliability, while also transforming professional data into easily understandable information based on individual characteristics. Furthermore, it provides actionable intervention plans, offering comprehensive reference for individual self-health management and professional medical consultation. It serves as the core carrier connecting test data with health actions.

[0055] This invention provides a schematic flowchart of another method for calculating stress age based on saliva extraction, as shown in the embodiment of the invention. Figure 2 As shown, the specific steps are as follows: S201: Collect saliva samples and personal data from individuals.

[0056] For a description of this step, please refer to S101. It will not be elaborated further here.

[0057] S202: Obtain telomere detection data from saliva test samples.

[0058] For a description of this step, please refer to S102. It will not be elaborated further here.

[0059] S203: Analyze telomere detection data, physiological data, and behavioral data to obtain telomere-related data.

[0060] For a description of this step, please refer to S103. It will not be elaborated further here.

[0061] S204: Generate a test report based on telomere-related data.

[0062] For a description of this step, please refer to S104. It will not be elaborated further here.

[0063] S205: Perform quality verification on saliva test samples. Quality verification indicators include the amount of saliva sample required for telomere detection and the concentration and purity of DNA extracted from the sample.

[0064] Quality verification includes: baseline dose-response verification of samples, concentration verification after DNA extraction, purity verification after DNA extraction, and sample contamination screening. Specifically, the operation and judgment criteria for each step are as follows: Basic dose-response verification of samples: Use a special collection tube with a 0.1mL graduation to read the saliva volume - it must be ≥2mL, the minimum requirement for telomere detection, and the appearance must be clear with no obvious residue to be considered as qualified dose-response; if it is <2mL or contains a large number of impurities, the individual needs to be asked to collect the sample again. DNA concentration verification: Using a UV spectrophotometer, take 2μL of extracted DNA. A concentration ≥50ng / μL is acceptable (meets the template requirements for PCR reaction). If <50ng / μL, it is necessary to investigate whether it is due to insufficient oral cells or extraction loss during collection. If necessary, recollection is required. DNA purity verification: Detect OD260 / OD280 values; 1.8-2.0 is acceptable (low protein contamination); <1.8 requires secondary purification with phenol-chloroform; >2.0 requires adding RNase enzyme to degrade RNA. Sample contamination screening: PCR amplification of the 36B4 internal reference gene was performed. Electrophoresis showed only specific bands, indicating no exogenous contamination. The presence of impurities indicated contamination, requiring re-collection of samples after tracing the collection process.

[0065] S206: Push standardized operating instructions to individuals.

[0066] Optionally, the operating instructions include: 1. Before collection: Perform the procedure within 1 hour of waking up in the morning on an empty stomach, avoiding eating, drinking, or using mouthwash; check the collection kit for damage or leakage, rinse your mouth gently with water, and wait 10 minutes; 2. During collection: Do not touch the head with the throat swab, wipe each cheek 10-15 times clockwise, immerse the head in the preservation solution of the collection tube, break the cap tightly, and label with the name and collection time; 3. After collection: Refrigerate the sample at 2-8℃, and send it for testing using a shockproof box and ice pack. If the swab is contaminated or the tube leaks, immediately re-collect using a new tool. For any questions, please contact customer service.

[0067] S207: Perform trend analysis on all telomere-related data to obtain trend data on individual emotions and psychological stress.

[0068] Trend analysis refers to the process of dynamically tracking, statistically modeling, and pattern mining all telomere-related data of an individual over a certain period, with time as the core dimension. Its core purpose is to overcome the limitations of single static detection, identify the direction, magnitude, and rate of data changes, associate them with behavioral or environmental triggers, predict the evolution trend of emotions and psychological stress, and provide dynamic basis for subsequent indicator adjustments and intervention optimization, rather than simply focusing on the current data state. Specifically, the full data is first extracted from the data storage module, and invalid records caused by unqualified samples or equipment failures are removed. The data is then sorted by "year-month-day" to ensure uniform detection intervals. Next, linear regression is used to fit the time curves of telomere age and psychological stress index, and the trend slope is calculated. At the same time, correlation analysis is used to link physiological data and behavioral data to locate the triggers. Finally, the rate of change is quantified and output. Optionally, line charts can be used to display changes in core indicators, and heatmaps can be used to present the correlation between physiological and behavioral data, enhancing intuitiveness.

[0069] Optionally, a baseline comparison of trends among healthy individuals of the same age group can be introduced to highlight individual biases; quarterly reports can also be generated to extract key changes, such as "the stress index rose by 15 points in Q3, which is related to an increase of 2 hours of sitting."

[0070] S208: Telomere age and psychological stress index are adapted, adjusted, and updated based on trend data and individual characteristics.

[0071] Specifically, adaptation adjustment refers to the process of dynamically calibrating the initially calculated telomere age and psychological stress index by combining the long-term change patterns of individual telomere-related data with their own stable attributes.

[0072] Operationally, the adjustment dimensions are broken down first: Telomere age adjustment needs to refer to the telomere wear rate in the trend data, combined with the individual's chronic disease history, such as hypertension which accelerates telomere wear, with an adjustment coefficient of 1.1; genetic background, such as a family history of premature aging, with a coefficient of 1.08. The adjusted age is calculated as: initial telomere age + (trend rate × period) × characteristic coefficient. The psychological stress index adjustment is based on the "stress trigger correlation" in the trend data, such as the correlation between sedentary lifestyle and stress index r=0.85, combined with the individual's occupational stress level, such as the internet industry, with a coefficient of 1.05; stress tolerance, such as a history of anxiety, with a coefficient of 1.1. The adjusted index is calculated as: initial index + (trend increase × period) × characteristic coefficient. Optionally, a feature-coefficient correspondence table can be established (e.g., coefficient 1.0 for 25-35 years old, 1.05 for 36-45 years old) to simplify calculations; or an "emergency adjustment threshold" can be added for short-term sudden trends, such as a 20-point increase in the stress index in one month, to trigger rapid calibration; or the weight of the feature coefficient can be dynamically optimized by combining the individual's previous intervention effects, such as the trend slowing down after adjusting work and rest. For example, a 35-year-old internet professional with the following characteristics: high-pressure job, mild insomnia history, and trend data showing an average monthly increase of 0.3 years in telomere age and a monthly increase of 2.8 points in stress index: When adjusting for telomere age, a coefficient of 1.1 is set due to occupation and insomnia, and the adjusted age after 3 months = initial 36.2 years + (0.3 × 3) × 1.1 = 37.19 years; When adjusting for stress index, a coefficient of 1.05 is set due to occupation, and the adjusted index after 3 months = initial 55 points + (2.8 × 3) × 1.05 = 63.84 points. After the update, it is marked that priority should be given to intervention for occupation-related stress and sleep problems. S209: Continuously monitor telomere-related data for each individual.

[0073] Specifically, the monitoring data scope is clearly defined to include telomere detection data, physiological data, behavioral data, and derived indicators; a tiered monitoring cycle is set: saliva samples are collected every 3 months for telomere data testing in healthy individuals, while physiological / behavioral data is collected in real time via smart wearable devices; telomere data is tested every 2 months for individuals with mild abnormalities, and a daily summary report of physiological / behavioral data is generated; telomere data is tested monthly for individuals with severe abnormalities (deviation ≥ 2 years, stress index ≥ 61 points), and physiological data is monitored in real time with real-time alerts for abnormalities. Simultaneously, the data is encrypted and synchronized to a cloud storage module, automatically associated with a unique individual ID, forming a continuous personal data timeline to avoid data gaps or confusion.

[0074] S210: Based on all telomere-related data, perform anomaly analysis and generate early warning information based on the anomaly analysis results.

[0075] The early warning information includes: the name of the abnormal indicator and the degree of deviation, the risk level, the inference of the core cause, the immediate intervention suggestion, the time limit for review, and also indicates the information generation time and data reliability. Anomaly analysis refers to the process of identifying deviations, identifying contributing factors, and classifying risks in a comprehensive dataset of continuously monitored telomere-related data, using both individual and group benchmarks as dual references. This involves comparing an individual's own historical data with the indicator ranges of healthy individuals in the same age group to screen for indicators that deviate from the normal range. Simultaneously, through multi-dimensional data correlation analysis, the core behavioral or physiological factors leading to the abnormality are identified, avoiding misjudgments based on a single indicator and ensuring the comprehensiveness and relevance of the analysis results. Specifically, anomaly analysis and early warning generation are performed in three steps: Benchmark setting and deviation calculation: First, determine the individual benchmark value by taking the average of the individual's first three valid tests, such as a telomere age benchmark of 35.2 years and a stress index benchmark of 38 points, and group standard values, such as the average telomere length of 8.2 kb for healthy individuals aged 35-40 and a normal stress index range of 0-30 points. Then, calculate the deviation of the current indicator from the two benchmarks, such as "telomere age 36.7 years, 1.5 years higher than the individual benchmark and 0.8 years higher than the group standard; stress index 65 points, exceeding the individual benchmark by 27 points and exceeding the group standard by 35 points." Through correlation analysis, identify "insufficient deep sleep and frequent late nights" as the core contributing factors. Combine the deviation magnitude to determine the risk level: mild warning (single indicator exceeding the benchmark by 10%-20%, no multiple indicators showing abnormality), moderate warning (single indicator exceeding the benchmark by 20%-30%, or two indicators exceeding the benchmark by 10%), and severe warning (single indicator exceeding the benchmark by ≥30%, or three indicators exceeding the benchmark by ≥30%). (If any of the above indicators are abnormal), this case is classified as "Moderate Warning"; Warning information generation and push: Information is generated according to the structure of "Abnormality - Cause - Suggestion - Time Limit", such as "[Moderate Warning] Your telomere age has increased by 1.5 years compared to your personal baseline (currently 36.7 years old), and your psychological stress index has risen to 65 points (35 points above the group standard); Core cause: The percentage of deep sleep has decreased to 12% in the past month (baseline 22%), and you stay up late 4 times a week (baseline 1 time); Suggestion: Adjust your schedule immediately, go to bed before 23:00 every day, and improve sleep quality through pre-sleep meditation; Telomere data needs to be rechecked within 15 days to monitor changes in indicators." Optionally, a "dynamic benchmark update mechanism" can be introduced, adjusting the individual's benchmark every 6 months based on the latest 3 valid data to adapt to changes in indicators after long-term behavioral improvement (e.g., after 6 months of regular exercise, the stress index benchmark is lowered to 30 points); or multiple channels for early warning push can be added (e.g., APP pop-ups, SMS, WeChat official account notifications), and manual follow-up can be triggered for severe warnings (with professional health consultants calling to interpret and guide), further improving the timeliness of early warnings and the effectiveness of intervention.

[0076] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for calculating stress age based on saliva extraction, characterized in that, include: Collect saliva samples and personal data from individuals, including: individual characteristics, physiological data, and behavioral data; Acquire telomere detection data from the saliva test sample; Telomere-related data are obtained by analyzing the telomere detection data, physiological data, and behavioral data. A test report is generated based on the telomere-related data.

2. The method according to claim 1, characterized in that, The step of obtaining telomere detection data from the saliva test sample includes: Telomere detection data was obtained by using real-time quantitative PCR technology to detect telomeres in saliva samples. The telomere detection data included telomere length and fluorescence signal intensity data during the PCR reaction.

3. The method according to claim 1, characterized in that, The analysis of the telomere detection data, physiological data, and behavioral data to obtain telomere-related data includes: The telomere detection data, physiological data, and behavioral data are preprocessed. Multidimensional feature data are obtained by fusing and analyzing the preprocessed telomere detection data, physiological data, and behavioral data. The telomere age and psychological stress index are obtained based on the pre-trained algorithm model and the multi-dimensional feature data.

4. The method according to claim 3, characterized in that, The preprocessed telomere detection data, physiological data, and behavioral data are fused and analyzed to obtain multi-dimensional feature data, including: Feature extraction is performed on the preprocessed telomere detection data, physiological data, and behavioral data; The extracted features are then aligned along the time dimension. A multimodal data fusion algorithm is used to weight and integrate the features according to the influence weight of different features on the assessment of emotion and psychological stress, so as to obtain multi-dimensional feature data.

5. The method according to claim 4, characterized in that, Feature extraction is performed on the preprocessed telomere detection data, physiological data, and behavioral data, including: The absolute value of telomere length, the deviation of telomere length from the preset average value of healthy people of the same age, and the stability parameters of fluorescence signal intensity are extracted from the telomere detection data. Extract the daily average heart rate, blood pressure fluctuation range, and frequency of abnormal physiological indicators from the physiological data. Extract the average daily activity duration, sleep cycle integrity, percentage of deep sleep, and frequency of stress-related behaviors from the behavioral data.

6. The method according to claim 3 or 4, characterized in that, The telomere age and psychological stress index are obtained based on the pre-trained algorithm model and the multi-dimensional feature data, including: The telomere length feature from the multi-dimensional feature data is input into the pre-trained algorithm model, and the pre-trained regression sub-model of telomere length and actual age is called to obtain the initial telomere age. The initial telomere age is adjusted based on the daily average heart rate, sleep cycle integrity, and percentage of deep sleep to obtain the telomere age. The psychological stress index is determined based on the daily average heart rate, blood pressure fluctuation range, frequency of abnormal physiological indicators, daily activity duration, sleep cycle integrity, percentage of deep sleep, and frequency of stress-related behaviors.

7. The method according to claim 1, characterized in that, Also includes: The telomere-related data corresponding to the individual are continuously monitored. The telomere-related data includes: telomere detection data, physiological data, behavioral data, and telomere age and psychological state index corresponding to each telomere detection. Based on all the telomere-related data, anomaly analysis is performed, and early warning information is generated based on the anomaly analysis results.

8. The method according to claim 3 or 7, characterized in that, Also includes: Trend analysis is performed on all telomere-related data stored in the data storage module to obtain trend data on the individual's emotions and psychological stress. Based on the trend data and individual characteristics, the telomere age and psychological stress index are adapted, adjusted, and updated respectively.

9. The method according to claim 1 or 7, characterized in that, The generation of the detection report based on the telomere-related data includes: Based on the anomaly analysis results and the individual characteristics of the individuals, diagnostic suggestions and intervention measures are generated; A test report is generated based on the diagnostic recommendations, intervention measures, and early warning information.

10. The method according to claim 1, characterized in that, Also includes: The saliva test samples are subjected to quality verification, and the quality verification indicators include the saliva sample volume telomere detection requirements and the concentration and purity of DNA extracted from the sample. Standardized operating instructions are pushed to the individuals mentioned.

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