CN111798963A - Individual health management method based on energy metabolism and weight change - Google Patents
Individual health management method based on energy metabolism and weight change Download PDFInfo
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Abstract
The invention discloses an individual health management method based on energy metabolism and weight change, and belongs to the field of intelligent health management. The management method comprises the following steps: obtaining weight and body composition change difference values according to individual early and late stage weight and composition indexes, monitoring total intake energy, total consumption and total excretion energy through equipment such as an energy metabolism cabin to obtain an energy loss difference value, obtaining an individual energy-weight conversion rate according to the ratio of the total intake energy, the total consumption and the total excretion energy, then performing accurate weight loss prediction, and establishing an individual real-time weight loss target based on the energy loss difference value; and performing individual life style and exercise scheme intervention guidance based on metabolic parameters obtained by energy metabolism monitoring, and periodically detecting individual health indexes including body weight until the health indexes reach the standard. The method can realize accurate prediction of individual weight loss targets and maximize the weight loss effect of lifestyle intervention guidance, can increase the compliance of obese individuals and the confidence of adherence to healthy lifestyle, and has important guiding value for treating and preventing chronic diseases such as obesity and the like.
Description
Technical Field
The invention belongs to the field of intelligent health management, and particularly relates to an individual health management method based on energy metabolism and weight change.
Background
With the increase of high calorie food intake and the decrease of physical labor intensity, the incidence of obesity and various accompanying metabolic diseases of people is rapidly increased. By 2030, 38.5% of adults are expected to be overweight and 20% of people are obese worldwide. Obesity can exacerbate or cause many health problems, such as: causing type 2 diabetes, causing the body to be in a chronic systemic inflammatory state, promoting the development of cardiovascular diseases, increasing the morbidity of certain cancers (esophageal adenocarcinoma, colon cancer, breast cancer, endometrial cancer, kidney cancer and the like) and liver diseases, inducing respiratory complications (obstructive sleep apnea) and increasing the mortality. At present, obesity and related diseases (type II diabetes, ischemic heart disease, chronic kidney disease, cancer and the like) are the leading factors of human death worldwide, the increasing incidence rate brings huge economic burden and public health hazard to human society, and the search for a reasonable and effective obesity treatment mode is urgent.
The energy required by human body every day is from food intake, and the energy outflow includes the energy discharged from stool and urine and the total daily energy metabolism consumption (TDEE) of human body is composed of three parts of Basal Metabolic Rate (BMR), food heat effect (DIT) and energy consumption of physical Activity (AEE). Obesity occurs essentially because the body's energy intake exceeds consumption, resulting in excess energy being deposited in the body in the form of adipose tissue, factors that may contribute to this imbalance including genetics, high calorie diets, lack of physical activity, and the like. Although the heredity and family tendency of obesity are obvious, the gradual energy accumulation caused by diet, physical activity or exercise and change of life style is the main reason of obesity, the long-term life habits determine the overweight and obesity tendency of people, and how to control weight loss through effective life style intervention is very important.
Currently, there are various predictive models for weight loss, from the theory of food calories or exercise consumption, originally proposed by Wishnofsky, where the total energy required to subtract one pound of body weight is equivalent to 3500 calories, to various derived weight loss predictive formulas based on the first law of thermodynamics. Weight prediction is made by estimating the difference between daily energy intake and expenditure, different formulas have different side points, and the short-term or long-term weight loss tendency of a certain experimental population is still roughly estimated in the aspects of the intake of three major nutrients, the storage and conversion of energy to adipose tissue (FM) and non-adipose tissue (FFM), the expenditure of various exercises and the like. Due to the existence of individual differences, the application to individual body surfaces has great difference, the popularization is difficult to implement, and the lifestyle guidance cannot be given in a refined manner. Therefore, the method monitors the energy metabolism balance condition of the human body, predicts the weight loss trend and provides individual life style intervention measures, so that the compliance of obese patients is enhanced, the hunger sensation is overcome, the self-confidence is improved, and the method is very important for the treatment of obesity. However, the methods for predicting body weight in the prior art have the following disadvantages:
(1) because genetic and lifestyle differences result in individuals that vary greatly, there is a considerable difference in the conversion from the initial energy intake, to the body's energy expenditure (including food thermoeffect (DIT), Basal Metabolic Rate (BMR) and energy expenditure on physical Activity (AEE)), to the remaining energy and energy storage substances (glycogen, proteins or fat), which is a rather large and complex process. Because of the many factors involved, the existing formula cannot give accurate individual weight loss prediction. It is fatal for a person who insists on long-term weight loss to give up because his confidence of being struck cannot see the effect.
(2) In the process of losing weight, the human body components are dynamically changed, the energy metabolism is changed along with the change, the energy intake and the exercise consumption are correspondingly adjusted, but the existing formula cannot provide a new prediction guidance scheme according to the change in time.
(3) In the aspect of life style intervention guidance, on one hand, because a human body has an absorption rhythm, after three nutrient elements of food are taken, the energy discharged through excrement and the like has individual difference, the energy absorbed into the body needs to experience the food heat effect, and how to select the optimal diet style to reduce the energy absorption to the minimum; on the other hand, because the human body has a physiological rhythm, whether the energy consumed by exercise at different time is the same or not, how to select the optimal exercise mode and duration to maximize the weight-losing effect, how to increase the amount of exercise to offset the increase of the nutrient intake and other problems, and the like, the existing formulas are not solved.
Disclosure of Invention
Aiming at the technical problem that accurate weight loss prediction and life style intervention are difficult to provide due to large individual difference of various weight loss prediction formulas in the prior art, the invention provides an individualized health management method based on energy metabolism and weight change, which comprises the following steps:
s1: accurate weight reduction prediction is carried out through energy metabolism monitoring, and an individualized real-time weight reduction target is obtained, and the method comprises the following steps:
s101: detecting the early-stage body weight and component indexes of an individual as starting points;
s102: monitoring the energy metabolism index of the individual under the energy negative balance state that the total consumption is larger than the total intake based on the early-stage weight and component indexes, designing different energy intake and energy consumption modes so as to obtain the optimal life mode of the individual including the optimal exercise and intake scheme, and obtaining an energy loss difference value according to the total intake energy, the total consumption and the total excretion energy;
s103: detecting later-stage weight and component indexes of the individual as end points, and the method is the same as the step S101;
s104: obtaining a weight change difference value according to the early and later weight and component indexes of the individual to obtain an individual energy-weight conversion rate RConversionIs the ratio of said energy loss difference to said weight change difference and is related to the individualized food energy absorption rate RAbsorbtionIndividualized basal metabolic rate correction factor RCorrectionIndividualized activity coefficient RActiveAnd total energy consumption after weight loss on day X is ETEE_XCombined, individual day X predicted body weight W was predicted from both total energy intake and total energy expenditure and excretionX；
S2: predicted body weight W according to said individualized day XXPerforming energy intake regimen and exercise regimen intervention guidance on an individual comprising:
s201: predicted body weight W according to said individualized day XXObtaining the energy intake mode and the exercise scheme of the individual, and sequentially carrying out individualized intervention guidance;
s202: under the intervention guidance of the energy intake mode and the exercise scheme, the states of minimizing intake energy and maximizing consumed energy are achieved, and the optimal weight loss mode is ensured to accurately reach the target according to a plan;
s3: regularly detecting health indexes of individuals including weight and component indexes:
if the goal is reached, keeping the life style under the dynamic balance in the step S2; if not, repeating the steps S1 and S2 until reaching the standard, and keeping the life style reaching the standard.
According to some embodiments of the invention, in step S101, the body weight and body composition parameters of the individual are determined by dual energy X-ray measurement (DEXA), body fat apparatus and weight scale.
According to some embodiments of the invention, in step S102, the method for obtaining the best lifestyle includes: according to the individual food absorption rhythm, the energy intake is controlled by adopting the modes of taking foods with different proportions of three nutrients of sugar, fat and protein, adjusting the intake sequence of the three nutrients and limiting fasting, and the defecation energy and the food heat effect (DIT) are detected to obtain the life style with the minimum food energy absorption rate and the maximum food heat effect.
According to some embodiments of the present invention, in step S102, the method for acquiring the optimal motion scheme includes: according to the human body biological rhythm of the individual, the relationship between the exercise time period, the exercise intensity, the exercise time, the exercise mode and the exercise energy consumption of the individual is tested, and the exercise scheme with the most exercise energy consumption is obtained.
According to some embodiments of the invention, in step S102, energy metabolism monitoring is performed using one or a combination of two or more selected from a metabolic capsule, a metabolic mask, or a dual-marker tracer.
According to some embodiments of the invention, in step S104, the population basal metabolic rate predictor BMRPredictionThe method comprises the following steps of (1) obtaining a multiple linear regression based on the existing energy metabolism monitoring data of Chinese people by a factor of a x height + b x age + c x fat-free weight + d x body fat rate + e x resting heart rate + constant f, and gradually correcting the energy metabolism monitoring data to be close to the real condition along with the increase of the number of the determined people;
individualized energy-body weight conversion RConversion＝(ETEE-EIntake-EExcretion)/(WBefore-WAfter)，
Individualized food energy absorption rate RAbsorbtion＝1-(EExcretion/EIntake)；
Individualized basal metabolic rate correction factor RCorrection＝BMRMeasurement/BMRPrediction；
Individualized activity coefficient RActive＝ETEE/BMRMeasurement；
Predicting total energy consumption E on day X after weight lossTEE_X＝BMRPrediction×RCorrection×RActive；
Body weight W on day XX＝WBefore-[(ETEE+ETEE_X)×X/2-EIntake×RAbsorbtion×X]/RConversion；
Wherein:
ETEEis the total energy metabolism consumption;
EIntaketotal energy intake for food;
EExcretiontotal fecal energy by collecting all fecal material produced by ingestion of food by the individual during energy monitoring and measuring its total caloric content by the oxygen nitrogen method after lyophilization;
WBeforeinitial body weight at the beginning of energy metabolism monitoring;
WAfterbody weight at the end of energy metabolism monitoring;
BMRMeasurementbasal metabolic consumption total;
BMRPredictioncalibrating a population basal metabolic rate predicted value;
x is the number of days.
The invention also provides an individualized health management system based on energy metabolism and weight change, comprising: the accurate weight loss prediction module consists of an early-stage weight index monitoring unit, an energy metabolism monitoring unit and a later-stage weight index monitoring unit; wherein:
the energy metabolism monitoring unit is selected from one or the combination of more than two of a metabolism cabin, a metabolism mask and a double-standard water tracer;
the early-stage weight index monitoring unit is selected from one or the combination of more than two of a weighing machine, a body fat instrument and a dual-energy X-ray measuring instrument;
the later-stage weight index monitoring unit is selected from one or the combination of more than two of a weighing machine, a body fat instrument and a dual-energy X-ray measuring instrument;
further comprising: and the life style intervention guidance module consisting of a plurality of weight index monitoring units adjusts the weight and the component indexes in time through the optimal diet mode and the optimal exercise scheme until the weight and the component indexes reach the standard and keep the healthy exercise mode.
Compared with the prior art, the invention has the beneficial effects that:
(1) based on the conversion rate of observing the energy metabolism and the weight change of the individual, the individual difference factors are considered in a unified way, and accurate weight loss prediction can be realized.
(2) The life style and the motion scheme are adopted for intervention guidance and the weight reduction target is adjusted in time, so that the weight reduction effect of the life style intervention guidance can be maximized; energy intake absorption is reduced to the minimum through an individualized optimal diet mode, an individualized exercise scheme maximizes exercise weight loss benefits, and data integration is changed into an individualized behavior specification criterion until the healthy life style is reached.
(3) The individual health management method has high accuracy and strong scientificity, can greatly increase the compliance of fat individuals and the confidence of maintaining healthy life style, and has important guiding value for treating and preventing chronic diseases such as obesity and the like.
Drawings
FIG. 1 is a flow chart of the method for personalized health management based on energy metabolism and weight change according to the present invention.
FIG. 2 is a graph showing the comparison of body weight predicted by several individuals according to the present invention after 30 days with the actual value.
FIG. 3 shows the change of total energy intake and total energy expenditure in exercise for a certain individual X.
Fig. 4 is a schematic illustration of the individualized health management in example 2.
Detailed Description
The invention is further illustrated by the following specific examples.
As shown in fig. 1, the method for personalized health management based on energy metabolism and weight change comprises:
s1: accurate weight reduction prediction is carried out through energy metabolism monitoring, and an individualized real-time weight reduction target is obtained, and the method comprises the following steps:
s101: detecting the early-stage body weight and component indexes of an individual as starting points;
s102: monitoring the energy metabolism index of the individual under the energy negative balance state that the total consumption is larger than the total intake based on the early-stage weight and component indexes, designing different energy intake and energy consumption modes so as to obtain the optimal life mode of the individual including the optimal exercise and intake scheme, and obtaining an energy loss difference value according to the total intake energy, the total consumption and the total excretion energy;
s103: detecting later-stage weight and component indexes of the individual as end points, and the method is the same as the step S101;
s104: obtaining a weight change difference value according to the early and later weight and component indexes of the individual to obtain an individual energy-weight conversion rate RConversionIs the ratio of said energy loss difference to said weight change difference and is related to the individualized food energy absorption rate RAbsorbtionIndividualized basal metabolic rate correction factor RCorrectionIndividualized activity coefficient RActiveAnd total energy consumption after weight loss on day X is ETEE_XCombined, individual day X predicted body weight W was predicted from both total energy intake and total energy expenditure and excretionX；
S2: predicted body weight W according to said individualized day XXPerforming energy intake regimen and exercise regimen intervention guidance on an individual comprising:
s201: predicted body weight W according to said individualized day XXObtaining the energy intake mode and the exercise scheme of the individual, and sequentially carrying out individualized intervention guidance;
s202: under the intervention guidance of the energy intake mode and the exercise scheme, the states of minimizing intake energy and maximizing consumed energy are achieved, and the optimal weight loss mode is ensured to accurately reach the target according to a plan;
s3: regularly detecting health indexes of individuals including weight and component indexes:
if the goal is reached, keeping the life style under the dynamic balance in the step S2; if not, repeating the steps S1 and S2 until reaching the standard, and keeping the life style reaching the standard.
In step S101, the body weight and body composition parameters of an individual can be determined by dual energy X-ray measurement (DEXA), a body fat apparatus and a weight scale.
The optimal lifestyle in step S102 can be obtained by the following method: according to the individual food absorption rhythm, the energy intake is controlled by adopting the modes of taking foods with different proportions of three nutrients of sugar, fat and protein, adjusting the intake sequence of the three nutrients and limiting fasting, and the defecation energy and the food heat effect (DIT) are detected to obtain the life style with the minimum food energy absorption rate and the maximum food heat effect.
The optimal motion scheme in step S102 may be obtained by: according to the human body biological rhythm of the individual, the relationship between the exercise time period, the exercise intensity, the exercise time, the exercise mode and the exercise energy consumption of the individual is tested, and the exercise scheme with the most exercise energy consumption is obtained.
In step S102, energy metabolism monitoring may be performed using a tracer selected from a metabolic capsule, a metabolic mask, or dual tracer.
In step S104, the predicted body weight W on day XXCan be obtained by the following method:
(1) chinese population basic metabolic rate predicted value BMR is obtained by constructing multiple linear regression based on existing population energy metabolism monitoring dataPredictionThe weight of the patient is defined as a multiplied by height + b multiplied by age + c multiplied by fat-free weight + d multiplied by body fat rate + e multiplied by resting heart rate + constant f, and the patient is corrected to be close to the real condition gradually along with the increase of the number of the measured population;
(2) individualized energy-body weight conversion RConversion＝(ETEE-EIntake-EExcretion)/(WBefore-WAfter) Wherein E isTEEIs the total energy metabolism consumption; eIntakeTotal energy intake for food; eExcretionTotal energy of excreta and is obtained by collecting all excreta generated by the ingestion of food by an individual during energy monitoring, freeze-drying and then measuring the total energy by an oxygen nitrogen method; wBeforeInitial body weight at the beginning of energy metabolism monitoring; wAfterBody weight at the end of energy metabolism monitoring;
(3) based on total energy E of excretaExcretionObtaining individualized energy absorption rate R of foodAbsorbtion＝1-(EExcretion/EIntake)；
(4) Total basal metabolic consumption BMR as determined from energy metabolism monitoringMeasurementCalibrating the basal metabolic rate of the population in step (1)Predicting BMRPredictionObtaining individual basal metabolic rate correction coefficient RCorrection＝BMRMeasurement/BMRPredictionAnd an individualized activity coefficient RActive＝ETEE/BMRMeasurement；
(5) Predicting total energy consumption E on day X after weight lossTEE_X＝BMRPrediction×RCorrection×RActiveObtaining the predicted body weight W of day X from the energy balanceX＝WBefore-[(ETEE+ETEE_X)×X/2-EIntake×RAbsorbtion×X]/RConversion。
Example 1
Predicted body weight W on day XXThe weight of 25 patients to be reduced is predicted, based on energy metabolism monitoring and initial weight, the daily intake and exercise amount of the patients are kept consistent during 30 days of weight reduction, and the predicted weight after 30 days is compared with the actual value, and the result is shown in fig. 2 and table 1, wherein the average error is only 1.04% ± 0.8%.
TABLE 1
As the human body has movement rhythm and has difference in energy consumption in the morning, the total energy intake and the total energy consumption of movement of a certain individual X are recorded as shown in figure 3, and the maximum energy consumption of the individual X in the morning is realized. Meanwhile, under different exercise intensities of 20% VO2Max (maximum oxygen consumption), 40% VO2Max and 80% VO2Max, the energy consumption of an individual X has significant difference, and if the exercise time of the individual is fixed and the exercise time of 120 minutes is available today, the optimal exercise time and the optimal exercise intensity can be freely selected to match with the corresponding caloric intake, so that the predicted weight-reducing goal can be achieved. If the daily intake of the individual X is fixed, but the exercise time is not fixed, when the intake is 2000 kilocalories, the exercise time and exercise intensity most suitable for the individual X can be selected to achieve the predicted weight loss target, for example, the shortest time of carrying out 52 minutes 80% VO2max high intensity exercise in the morning or 202 minutes 20% VO2max low intensity exercise in the evening can be selected.
The diet heat effect DIT is influenced by the food type and the intake sequence, the food absorption has rhythmicity and is closely related to the eating time, and the diet mode with the highest individual DIT and the lowest food absorption rate is found out during the energy observation period.
Example 2
Fig. 4 shows the individual weight loss prediction and lifestyle intervention guidance for an individual by the individual health management method based on energy metabolism and weight change, which comprises the following steps:
(1) the weight and body composition of a patient are accurately measured as a starting point through a dual-energy X-ray measuring instrument, a body fat meter, a body weight meter and the like, and then the energy metabolism index of the patient is monitored in an energy negative balance state that the total consumption is larger than the total intake through a human body metabolism cabin, a human body metabolism mask or a dual standard water mode, so that the individual weight energy conversion rate is calculated conveniently.
(2) During the monitoring period of the energy metabolism index, obtaining an individual optimal life style and providing guidance for subsequent weight loss; the method comprises testing the optimal dietary intake mode, such as the patient ingesting foods with three nutrients in different proportions, adjusting the intake sequence of the three nutrients, assisting in time-limited fasting and the like, and obtaining the life mode with the minimum food energy absorption rate and the maximum food heat effect of an individual by detecting the excrement and urine discharge energy and the food heat effect so that the body absorbs less energy.
(3) During energy metabolism monitoring, testing to obtain an optimal exercise scheme; because the human body has biological rhythm, the patient does three times of motions in the morning, the noon and the evening in different time periods, under the condition of the same motion amount, the energy consumption is more, and the body benefit is larger. Since exercise in the morning consumes the most energy when exercising at the same intensity, exercise in the morning is recommended, and in addition, tests of different gradients are performed on exercise intensity and duration to find out when the individual does exercise of which intensity for which time the greatest energy is consumed, so that the body consumes more energy. And after the metabolic monitoring is finished, calculating an energy deficiency difference value according to the total energy intake and the total energy consumption.
(4) Accurately measuring the weight and body composition of the patient as end points, calculating the variation difference of the weight and body composition, and calculating the individual conversion rate according to the energy difference and the weight difference, namely the individual conversion rate is delta weight variation and delta energy variation.
(5) And predicting a weight loss target according to the individual conversion rate, such as X, reducing the intake of X kcal every day, and calculating the weight loss target according to the individual conversion rate of X, namely, how much weight can be reduced in a certain time period.
(6) And (3) combining the optimal diet way and the optimal exercise way, formulating an individualized optimal weight loss way according to the individualized conversion rate, and providing lifestyle intervention guidance, wherein the energy consumed by the individual X during morning exercise is the most as shown in figure 3. Meanwhile, under different exercise intensities of 20% VO2Max (maximum oxygen consumption), 40% VO2Max and 80% VO2Max, the energy consumption of the individual A has significant difference, and if the exercise time of the individual is fixed and the exercise time of 120 minutes is available today, the optimal exercise time and optimal exercise intensity can be freely selected to match with the corresponding caloric intake, so that the predicted weight-reducing goal can be achieved. If the daily intake of the individual X is fixed, but the exercise time is not fixed, when the intake is 2000 kilocalories, the exercise time and exercise intensity most suitable for the individual X can be selected to achieve the predicted weight loss target, for example, the shortest time of carrying out 52 minutes 80% VO2max high intensity exercise in the morning or 202 minutes 20% VO2max low intensity exercise in the evening can be selected. In addition, because the body composition is in dynamic change in the weight reduction process, key indexes such as energy consumption and conversion rate are changed correspondingly, the energy metabolism indexes of individuals need to be measured regularly, and the weight reduction scheme and the weight reduction target need to be adjusted in time. If the goal is reached, the healthy lifestyle of the optimal energy intake mode and the optimal exercise scheme is continuously maintained; and (3) if the standard is not met, repeating the steps (1) to (6) until the individual recovers the physical health level and keeps a healthy life style.
The health management method can realize accurate weight loss target prediction and maximize the weight loss effect of lifestyle intervention guidance. Because the weight loss prediction has high precision and strong scientificity, the compliance of fat individuals and the confidence of maintaining a healthy life style can be greatly improved, and the weight loss prediction method has important guiding value for treating and preventing chronic diseases such as obesity and the like.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. It will be readily apparent to those skilled in the art that various modifications to these embodiments and the generic principles defined herein may be applied to other embodiments without the use of the inventive faculty. Therefore, the present invention is not limited to the above-described embodiments. Those skilled in the art should appreciate that many modifications and variations are possible in light of the above teaching without departing from the scope of the invention.
Claims (7)
1. An individualized health management method based on energy metabolism and weight change, comprising:
s1: accurate weight reduction prediction is carried out through energy metabolism monitoring, and an individualized real-time weight reduction target is obtained, and the method comprises the following steps:
s101: detecting the early-stage body weight and component indexes of an individual as starting points;
s102: monitoring the energy metabolism index of the individual under the energy negative balance state that the total consumption is larger than the total intake based on the early-stage weight and component indexes, designing different energy intake and energy consumption modes so as to obtain the optimal life mode of the individual including the optimal exercise and intake scheme, and obtaining an energy loss difference value according to the total intake energy, the total consumption and the total excretion energy;
s103: detecting later-stage weight and component indexes of the individual as end points, and the method is the same as the step S101;
s104: obtaining a weight change difference value according to the early and later weight and component indexes of the individual to obtain an individual energy-weight conversion rate RConversionTo said energyThe ratio of the loss of quantity difference to the weight change difference and the individualized energy absorption rate R of the foodAbsorbtionIndividualized basal metabolic rate correction factor RCorrectionIndividualized activity coefficient RActiveAnd total energy consumption after weight loss on day X is ETEE_XCombined, individual day X predicted body weight W was predicted from both total energy intake and total energy expenditure and excretionX；
S2: predicted body weight W according to said individualized day XXPerforming energy intake regimen and exercise regimen intervention guidance on an individual comprising:
s201: predicted body weight W according to said individualized day XXObtaining the energy intake mode and the exercise scheme of the individual, and sequentially carrying out individualized intervention guidance;
s202: under the intervention guidance of the energy intake mode and the exercise scheme, the states of minimizing intake energy and maximizing consumed energy are achieved, and the optimal weight loss mode is ensured to accurately reach the target according to a plan;
s3: regularly detecting health indexes of individuals including weight and component indexes:
if the goal is reached, keeping the life style under the dynamic balance in the step S2; if not, repeating the steps S1 and S2 until reaching the standard, and keeping the life style reaching the standard.
2. The individualized health management method according to claim 1,
in step S101, the body weight and body composition parameters of an individual are measured by dual energy X-ray measurement (DEXA), a body fat meter, and a weighing machine.
3. The individualized health management method according to claim 1,
in step S102, the method for acquiring the optimal lifestyle includes: according to the individual food absorption rhythm, the energy intake is controlled by adopting the modes of taking foods with different proportions of three nutrients of sugar, fat and protein, adjusting the intake sequence of the three nutrients and limiting fasting, and the defecation energy and the food heat effect (DIT) are detected to obtain the life style with the minimum food energy absorption rate and the maximum food heat effect.
4. The individualized health management method according to claim 1,
in step S102, the method for acquiring the optimal motion scheme includes: according to the human body biological rhythm of the individual, the relationship between the exercise time period, the exercise intensity, the exercise time, the exercise mode and the exercise energy consumption of the individual is tested, and the exercise scheme with the most exercise energy consumption is obtained.
5. The individualized health management method according to claim 1,
in step S102, energy metabolism monitoring is performed using one or a combination of two or more selected from a metabolic capsule, a metabolic mask, or a dual-label water tracer.
6. The individualized health management method according to claim 1,
in step S104, the BMR is predicted value of the population basal metabolic ratePredictionThe method comprises the steps of obtaining a x height + b x age + c x fat-free weight + d x body fat rate + e x resting heart rate + constant f by means of multivariate linear regression based on the existing Chinese population energy metabolism monitoring data, and gradually correcting the energy metabolism condition to be close to the real Chinese population energy metabolism condition along with the increase of the number of the determined populations;
individualized energy-body weight conversion RConversion＝(ETEE-EIntake-EExcretion)/(WBefore-WAfter)，
Individualized food energy absorption rate RAbsorbtion＝1-(EExcretion/EIntake)；
Individualized basal metabolic rate correction factor RCorrection＝BMRMeasurement/BMRPrediction；
Individualized activity coefficient RActive＝ETEE/BMRMeasurement；
Predicting total energy consumption E on day X after weight lossTEE_X＝BMRPrediction×RCorrection×RActive；
Body weight W on day XX＝WBefore-[(ETEE+ETEE_X)×X/2-EIntake×RAbsorbtion×X]/RConversion；
Wherein:
ETEEis the total energy metabolism consumption;
EIntaketotal energy intake for food;
EExcretiontotal fecal energy by collecting all fecal material produced by ingestion of food by the individual during energy monitoring and measuring its total caloric content by the oxygen nitrogen method after lyophilization;
WBeforeinitial body weight at the beginning of energy metabolism monitoring;
WAfterbody weight at the end of energy metabolism monitoring;
BMRMeasurementbasal metabolic consumption total;
BMRPredictioncalibrating a population basal metabolic rate predicted value;
x is the number of days.
7. Individualized health management system based on energy metabolism and weight change, characterized by comprising: the accurate weight loss prediction module consists of an early-stage weight index monitoring unit, an energy metabolism monitoring unit and a later-stage weight index monitoring unit; wherein:
the energy metabolism monitoring unit is selected from one or the combination of more than two of a metabolism cabin, a metabolism mask and a double-standard water tracer;
the early-stage weight index monitoring unit is selected from one or the combination of more than two of a weighing machine, a body fat instrument and a dual-energy X-ray measuring instrument;
the later-stage weight index monitoring unit is selected from one or the combination of more than two of a weighing machine, a body fat instrument and a dual-energy X-ray measuring instrument;
further comprising: and the life style intervention guidance module consisting of a plurality of weight index monitoring units adjusts the weight and the component indexes in time through the optimal diet mode and the optimal exercise scheme until the weight and the component indexes reach the standard and keep the healthy exercise mode.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112349387A (en) * | 2020-11-30 | 2021-02-09 | 歌尔科技有限公司 | Data management method and device, intelligent wearable device and storage medium |
| CN112599222A (en) * | 2020-12-28 | 2021-04-02 | 重庆市汇人健康管理有限责任公司 | Diet and exercise guidance method for people with abnormal body weight |
| CN113936771A (en) * | 2021-12-17 | 2022-01-14 | 北京因数健康科技有限公司 | Iterative generation method and device of health index target |
| CN117373618A (en) * | 2023-10-23 | 2024-01-09 | 广州景腾医疗科技股份有限公司 | Local human body component data processing method for analyzer |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1723838A (en) * | 2005-07-21 | 2006-01-25 | 高春平 | Method and device for individualized and three-D type reducing weight |
| US20090131814A1 (en) * | 2007-11-19 | 2009-05-21 | Thompson Elizabeth S | Method and process for weight management |
| CN108010567A (en) * | 2018-01-02 | 2018-05-08 | 北京康萍科技有限公司 | A kind of pregnancy period individuation trophic analysis method and system |
| CN109285606A (en) * | 2018-11-28 | 2019-01-29 | 极力健生物科技（广州）有限公司 | A kind of health control method and system based on big data intelligent algorithm |
-
2020
- 2020-07-07 CN CN202010644441.0A patent/CN111798963A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1723838A (en) * | 2005-07-21 | 2006-01-25 | 高春平 | Method and device for individualized and three-D type reducing weight |
| US20090131814A1 (en) * | 2007-11-19 | 2009-05-21 | Thompson Elizabeth S | Method and process for weight management |
| CN108010567A (en) * | 2018-01-02 | 2018-05-08 | 北京康萍科技有限公司 | A kind of pregnancy period individuation trophic analysis method and system |
| CN109285606A (en) * | 2018-11-28 | 2019-01-29 | 极力健生物科技（广州）有限公司 | A kind of health control method and system based on big data intelligent algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 温少霞;严弘礼;: "膳食和运动疗法改善代谢综合征效果研究", 临床护理杂志, no. 03, pages 31 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112349387A (en) * | 2020-11-30 | 2021-02-09 | 歌尔科技有限公司 | Data management method and device, intelligent wearable device and storage medium |
| CN112599222A (en) * | 2020-12-28 | 2021-04-02 | 重庆市汇人健康管理有限责任公司 | Diet and exercise guidance method for people with abnormal body weight |
| CN113936771A (en) * | 2021-12-17 | 2022-01-14 | 北京因数健康科技有限公司 | Iterative generation method and device of health index target |
| CN117373618A (en) * | 2023-10-23 | 2024-01-09 | 广州景腾医疗科技股份有限公司 | Local human body component data processing method for analyzer |
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