Modeling of Chronic Disease Prevention and Nutritional Intervention for the Elderly Based on Big Data Analysis

The situation of chronic disease prevention and control is grim. On the one hand, because of the rapid advancement of industrialization and urbanization, the domestic production and living environment has changed dramatically. The national living environment has been polluted, and the lifestyle and behavior are surrounded by prevalent risk factors such as smoking, drinking, poor diet, and lack of physical exercise. A number of factors have led to a rapid increase in the prevalence of chronic diseases in Chinese nationals [1-3]. On the other hand, because of the aging of the population, chronic diseases, as a kind of accumulative disease, are positively correlated with age and the risk of chronic diseases. Along with the increase in the average life expectancy of nationals, the group of elderly people suffering from chronic diseases has been expanding [4-6].

Although there are currently more low-aged elderly people in China, and the problem of chronic diseases is relatively not very serious, according to the current aging situation, chronic diseases along with aging will inevitably bring heavy medical and economic burdens to society and families [7-9]. The community is the basic management unit of society, the main window of residents’ daily activities, and the most critical link in the promotion of policies such as home care, long-term care, and medical reform, which will provide important support for the implementation of chronic disease health management for the elderly [10-11]. In the context of population aging, rising prevalence of chronic diseases, and increasing healthcare burden, the implementation of urban community health management is imminent, and although theoretically, urban communities are the most effective and appropriate place to carry out chronic disease prevention, treatment, and health management, only grassroots community health service organizations with matching service capacity can make community health management work [12-13]. In the prevention and treatment of chronic diseases in the community, starting with disease prevention, the risk factors that lead to chronic diseases and threaten the health of the masses should be controlled, and the risk factors that lead to chronic diseases and threats to the health of the public should be controlled, so as to achieve the goal of “amplifying diseases, managing chronic diseases, and protecting health”, and implement the national chronic disease prevention and control strategy of strengthening the grassroots and moving the health work strategy forward and sinking the focus [14-16].

Malnutrition refers to the insufficient, excessive, or imbalanced intake of energy and/or nutrients and mainly includes non-disease-related malnutrition caused by starvation, for example, and disease-related malnutrition caused by cachexia or inflammation. Aging is clearly characterized by advancing age, especially in those older than 65 years [17-18]. Physiological changes are mainly reflected in the decline of metabolic capacity, respiratory function decline, cardio-cerebral function decline, and muscle attenuation, and these changes will affect the ability of the elderly to ingest, digest food, and absorb nutrients, which will lead to insufficient intake of proteins and micronutrients, and health problems such as emaciation, anemia, and lowered resistance, which will increase the risk of developing various diseases. Malnutrition usually leads to a decline in the functional status of the body of the elderly and even secondary to the occurrence, progression, and aggravation of a variety of chronic non-communicable diseases (hereinafter referred to as chronic diseases), such as hypertension, hyperlipidemia, diabetes mellitus, sarcopenia (hereinafter referred to as sarcopenia), chronic obstructive pulmonary disease (COPD), and neoplasms [19-21]. Chronic diseases in the elderly lead to excessive nutritional depletion of the organism, which will further increase the incidence of malnutrition. Therefore, the earlier the risk of malnutrition in the elderly is detected, i.e., nutritional screening is carried out to provide a comprehensive understanding of the nutritional status of the elderly and to provide a basis for adopting appropriate nutritional support, which is also of great significance for the management of chronic diseases [22-23].

This paper explores the prevention and treatment of chronic diseases in the elderly, explores the methods of health education, and establishes a reasonable nutritional intervention model based on the healthy lifestyles advocated in the Dietary Guidelines and the Dietary Pagoda, such as eating more fruits and vegetables, less salt and less oil, and strengthening physical exercise, etc., and provides one-year nutritional interventions for residents in the intervention community, and finally analyzes the effects of nutritional interventions through the SOC model. Finally, the effect of the nutritional intervention was analyzed by the SOC model to test the scientific validity of the nutritional intervention model.

2

Excavation methodology for the prevention and treatment of chronic diseases in the elderly

2.1

Chronic Disease Management Excavation Process for Older Adults

According to the general process of data mining, extracting useful information from the chronic disease system data of the elderly, obtaining the influencing factors related to the research object, establishing a mining model, and mining the chronic disease prevention and treatment intention of the elderly can be divided into the following steps with reference to CRISP-DM:

1) Selective extraction and new extraction from chronic disease data sources of the elderly form historical data and incremental data, respectively. Historical data refers to data that have already been generated, and incremental numbers are new data generated in future time.

2) Perform data exploratory analysis and preprocessing on the two data sets of the trip in (1), including exploratory analysis of data missing values and outliers, attribute statute, cleaning and transformation of data, correlation test, etc.

3) The processed data in (2) are divided into two categories: training sample set and test sample set. The training set is trained by a logistic regression algorithm, returning the regression parameters, generating the mining model, and then using the test set to test the model and optimize the model.

4) Use the established model to mine the elderly whether to carry out chronic disease prevention and treatment.

The mining process of chronic disease control for the elderly is shown in Figure 1.

2.2

logistic regression

Logistic regression is a binary regression with dependent variable y = {y|1 or 0, i.e. yes or no}. Assuming P(y = 1) = p under the action of independent variable x₁,x₂,...,x_p , then P(y = 0) = 1–p. The problem to be investigated is the probability of occurrence p in relation to the independent variable x₁,x₂,...,x_p when y = 1. The principles of the commonly used dichotomous logistic regression model are described below.

2.2.1

Logistic function

The dependent variable in a logistic regression model takes on only 1-0 (e.g., yes and no, occurrence and non-occurrence) values. Assuming that under the action of p independent independent variable x₁,x₂,...,x_p, note that the probability of y taking 1 is p = P(y = 1|X), the probability of taking 0 is 1–p, and the ratio of the probabilities of taking 1 and 0 is $\frac{p}{1 - p}$ , which is called the dominance ratio of the event, the logistic transformation $L o g i t (p) = \ln \frac{p}{1 - p}$ .

Let $L o g i t (p) = \ln \frac{p}{1 - p} = z$ , then $p = \frac{1}{1 + e^{- x}}$ , which is the logistic function [24]. When p varies between (0, 1), odds takes a positive value, then $\ln \frac{p}{1 - p}$ takes a value in the range (–∞,+∞).

2.2.2

Logistic regression model

The logistic regression model is: (1) $\ln \frac{p}{1 - p} = β_{0} + β_{1} x_{1} + \dots + β_{p} x_{p} + ε$

Since the range of values of $\ln \frac{p}{1 - p}$ is (–∞+∞), the independent variable x₁,x₂,...,x_p can take any value at this point.

Notation g(x) = β₀ + β₁x₁+…+β_px_p is obtained: (2) $p = P (y = 1 | X) = \frac{1}{1 + e^{- g (x)}}$ (3) $1 - p = P (y = 0 | X) = 1 - \frac{1}{1 + e^{- g (x)}} = \frac{1}{1 + e^{g (x)}}$

2.2.3

Logistic Regression Model Interpretation

(4)

\frac{p}{1 + p} = e^{β_{0} + β_{1} x_{1} + \dots + β_{p} x_{p} + ε}

In equation (4), β₀: the natural logarithm of the ratio of the probability of occurrence of y = 1 to y = 0 in the absence of an independent variable, i.e., x₁,x₂,...,x_p taking all zeros. β₁ : The logarithmic value of the y = 1 dominance ratio when a certain independent variable x_i changes, i.e., x_i = 1 compared to x_i = 0.

Compared with other data mining algorithms, logistic regression is characterized by a simple algorithm, mature application, stability, and reliability. In addition, the prevention and control of chronic diseases in the elderly belong to the simple 0-1 score judgment problem, which is particularly suitable for the logistic regression algorithm, so the model constructed in this paper mainly uses the logistic regression algorithm.

2.3

Feature evaluation based on Pearson’s correlation coefficient

Fusion of multi-domain and multi-category features for the prevention and treatment of chronic diseases in the elderly. There are many methods for feature fusion, but few analyze the relationship between features, and most of them use direct fusion, ignoring the connection between the original features. Therefore, it is necessary to analyze the features before fusion, and the features are first analyzed for correlation, on the basis of which KPCA is used for feature fusion [25], condensation of features, and obtaining features with complementary and streamlined information.

Pearson’s correlation coefficient was initially used in the field of statistics to decipher the correlation between two variables, and later developed in the field of natural sciences and was widely used to measure the correlation between two variables. Therefore, in order to analyze the correlation between features, the Pearson correlation coefficient is used. The Pearson correlation coefficient is calculated as follows: (5) $r = C o r r (X, T) = \frac{| K \sum_{k} X_{T} (t_{k}) t_{k} - \sum_{k} X_{T} (t_{k}) \sum_{k} t_{k} |}{\sqrt{[K \sum_{k} X_{T} {(t_{k})}^{2} - {(\sum_{k} X_{T} (t_{k}))}^{2}] [K \sum_{k} t_{k}^{2} - {(\sum_{k} t_{k})}^{2}]}}$

Where: Corr(X,T) table correlation between feature X and feature T. Because 20 different types of bearing vibration signal features are extracted, so each feature needs to calculate the correlation with the other 19 features, remember the first feature and the second feature correlation is r_1,2, and finally, a 20*20 feature evaluation matrix will be obtained, such as Eq: (6) $[\begin{matrix} r_{1, 1} & r_{1, 2} & \dots & r_{1, 20} \\ r_{2, 1} & r_{2, 2} & \dots & r_{2, 20} \\ ⋮ & ⋮ & ⋮ \\ r_{20, 1} & r_{2, 2} & \dots & r_{20, 20} \end{matrix}]$

Pearson correlation analysis was performed on the previously extracted characteristics of chronic disease prevention and treatment in the elderly to obtain the analyzed correlation feature matrix.

3

Nutritional interventions for older persons

3.1

Intervention methods

Through the method of health education, nutrition intervention activities with relevant contents and themes are carried out for farmers in the intervention community [26] and targeted nutritional education is provided for the farmer population in the five stages of the intervention community. The control group did not take any interventions and was a blank control.

3.2

Content of the intervention

From January to December 2019, a year-long nutritional intervention activity in various forms, mainly centered on eating more vegetables and fruits, eating less salt, and strengthening physical exercise as advocated in the Dietary Guidelines and the Dietary Pagoda, was successively carried out for farmers in the intervention communities:

1) A large-scale square publicity launching ceremony was held to publicize nutritional knowledge for the prevention and treatment of chronic non-communicable diseases.

2) Distributed low-sodium salt and salt spoons to households.

3) Regularly posted health posters with knowledge on eating more fruits and vegetables, eating less salt, and strengthening physical activity.

4) Distribute health promotion materials, especially for the 5 stages of the population, issue the “eat more fruits and vegetables, strengthen physical activity” theme of different levels and content of the promotional folders, targeted health knowledge guidance.

5) Lectures on the theme of “diet and health” were organized.

6) Famous chefs were hired to conduct live demonstrations of healthy dish production in rural areas.

3.3

Quality control

Questionnaires and physical tests were conducted by uniformly trained professionals as investigators and blood and urine samples were collected and tested by doctors from level IIIA hospitals.

Villagers are required to sign in their handwriting each time a publicity item is distributed so that telephone follow-up visits can be made. On a regular basis, a person is responsible for making telephone or household follow-up visits to the village doctors and community workers who visit households to promote education and distribute materials, inquiring about the receipt of promotional items by the farmers so as to ensure that nutritional interventions are carried out in practice.

The posters were posted, and the number of villagers who viewed the posters per unit of time was recorded to assess the effectiveness of the intervention.

3.4

Statistical analysis

Some of the interventions carried out were evaluated on-site using evaluation questionnaires to understand the effects achieved by the interventions.

SPSS11 statistical software was used to analyze the data, in which the t test was used for self-paired and group comparisons of measures, the x² test was used for comparison of the distributional composition of counts, the rank-sum test was used for trend analysis of unidirectionally ordered counts, and the analysis of variance (ANOVA) was used for comparison of differences in the measures of multiple groups. Nutritional interventions and effects were evaluated by applying SOC model analysis, and the flow of SOC model analysis is shown in Figure 2.

4

Analysis

4.1

Analysis of the correlation between the level of chronic disease prevention and literacy and health among the elderly

The correlation analysis of chronic disease prevention and control literacy level and health status among Chinese older adults is shown in Table 1. After the complex sampling module X² test, in the group of Chinese older adults aged 60-69 years who suffered from chronic diseases, the level of chronic disease prevention and treatment literacy varied among older adults with different numbers of types of chronic diseases, different years of suffering from chronic diseases, and different self-assessed health status, and the differences were all statistically significant (P<0.05). The level of chronic disease prevention and control literacy among older adults with 1 chronic disease was low at 10.67%. Elderly people with chronic diseases for more than 10 years had the highest level of chronic disease prevention and treatment literacy at 14.82%. The level of chronic disease prevention and treatment literacy is higher among those who assessed their health as good, relatively good, or average than among those who assessed their health as poor or bad.

Table 1.

Analysis of the prevention and health of chronic diseases in the elderly

Survey content		Sample rate/%	Weighted rate/%	95%CI	X² value	P value
Slow condition	Undiseased	10.39	10.40	9.77-10.89	3.648	0.058
Slow condition	Sickness	11.26	11.28	10.67-11.69	3.648	0.058
Slow number	One species	10.65	10.67	9.97-11.46	11.356	0.004
	Two kinds	13.62	13.65	11.75-15.98
	Greater than equal to 3	12.93	12.89	9.95-16.85
Period of slow disease	<One year	10.65	10.67	9.22-12.26	24.168	<0.001
	1-5 years	10.11	10.15	9.14-11.29
	5-10 years	10.74	10.75	9.48-12.29
	>Ten years	14.79	14.82	13.17-16.89
Self-assessment health	Good	10.09	10.11	9.17-11.19	78.816	<0.001
	Better	13.64	13.68	12.77-14.89
	General	10.21	10.22	9.77-10.89
	Difference	8.56	8.59	7.64-10.19
	Very bad	4.54	4.49	3.09-6.56

4.2

Percentage of correct answers to questions on the chronic disease prevention and control literacy assessment for older adults

The correct response rates for the questions of the Chronic Disease Management Literacy Assessment for Older Adults are shown in Table 2. Among the questions in the 2019 Chinese Residents’ Literacy Assessment for Chronic Disease Prevention and Control, the three questions with high response rates among 60-69 year-olds were “Depression may also occur in children and adolescents” and “Eating fruits is not a substitute for eating vegetables”, “Diseases susceptible to overweight and obese people”, with response rates of 75.14%, 66.99%, and 52.41% respectively. The three questions with low correct response rates were “the benefits of eating soybean products such as tofu and soy milk”, “understanding the dangers of smoking”, and “understanding the early danger signals of cancer”, with correct answer rates of 29.46%, 39.95%, and 39.96%, respectively. Only 33.33% of the questions had a correct answer rate of 50% or more.Q1-Eating fruits is not a substitute for eating vegetables, Q2-Depression can occur in children and adolescents, Q3-The concept of self-testing of blood pressure, Q4-Understanding of the dangers of smoking, Q5-Understanding of the early danger signals of cancer, Q6-Methods available to control body weight, Q7-Benefits of eating tofu, soymilk, and other soybean products, Q8- Health benefits of exercise, Q9-Diseases that overweight and obese people are prone to.

Table 2.

The accuracy of the evaluation of chronic diseases of chronic diseases

Test dimension	Test topic	Sample rate/%	Weighted rate/%	95%CI
Reasonable diet	Q1	66.98	66.99	66.35-67.68
Mental health	Q2	75.12	75.14	74.26-75.49
Slow disease management	Q3	42.56	42.58	41.22-42.36
Tobacco control	Q4	38.95	39.95	37.59-38.79
Tumor control	Q5	39.98	39.96	39.25-40.68
Weight management	Q6	48.75	48.76	47.89-48.95
Nutrition and health	Q7	29.44	29.46	28.56-29.87
Sports and health	Q8	41.25	41.23	39.79-41.22
Overweight and health	Q9	52.36	52.41	51.23-52.65

An analysis of the correctness rate of the questions on the health literacy test for the elderly shows that most of the correct answers to the various knowledge points were below 50%, indicating a general lack of knowledge about disease prevention and control among the elderly. “Implement health promotion actions for the elderly”, “carry out educational activities on fitness for the elderly, health care for the elderly, prevention and rehabilitation of geriatric diseases”, and “promote the elderly to know the core information of health” as the action goal. In the process of implementing health actions, the popularization of health knowledge and self-care skills for the elderly is still basic work, which is important and necessary.

4.3

Analysis of factors influencing the level of chronic disease prevention and treatment literacy

The multifactorial logistic regression analysis of chronic disease prevention and treatment literacy level of the elderly is shown in Table 3. The multifactorial logistic regression analysis was carried out under the complex sampling module with the availability of chronic disease prevention and treatment literacy as the dependent variable and urban and rural areas, regions, gender, age, literacy level, whether they suffer from chronic diseases, and self-assessment of health status as the independent variables, and the results showed that the 60-69-year-olds’ chronic disease The results showed that the level of chronic disease prevention and treatment literacy among the elderly aged 60-69 years was higher in urban than in rural areas, with an OR of 1.583. Compared with the western region, the level of health literacy among the elderly in the eastern part of the country was higher, with an OR of 1.523, and lower in the central part of the country, with an OR of 0.867. The higher the level of literacy, the higher the level of chronic disease prevention and treatment literacy, and the more the population had chronic disease prevention and treatment literacy, with reference to the illiterate/minor literacy, the more the population had chronic disease prevention and treatment literacy, with reference to the illiteracy/few literacies. Using illiteracy/little literacy as a reference, the ORs of chronic disease prevention and treatment literacy in the population were 1.845, 2.767, 3.875, and 5.058, respectively.

Compared with those with poor health status, those with good, relatively good, and average health status had higher levels of chronic disease prevention and treatment literacy, with ORs of 1.536, 1.975, and 1.638, respectively.

It was found that chronic disease health literacy was lower among older adults in rural areas, central and western regions, with lower literacy levels and poor health status, which is consistent with the distribution characteristics of the overall level of health literacy in different regions and populations. The proportion of older people who are illiterate is much higher in rural areas than in urban areas and much higher in central and western regions than in eastern regions, which is the main reason for the urban-rural and regional differences. In addition, in rural central and western regions, the health awareness of the elderly themselves is relatively weaker, and the limited educational and medical resources available also affect the improvement of the literacy level of the elderly in chronic disease prevention and treatment. It is suggested that the focus of health education and health promotion for the elderly should be further tilted towards rural residents, residents of central and western regions, and people with a lower level of literacy and that the grassroots level should be the main focus, giving full play to the role of grassroots medical and healthcare institutions, and strengthening the prevention of chronic diseases and health promotion for the elderly. On the other hand, research on appropriate technologies for promoting health literacy among the elderly should be strengthened in the light of the characteristics of the elderly and the resources available to them, with a view to summarizing appropriate intervention strategies and measures for the elderly and carrying out targeted interventions to enhance their health literacy in chronic diseases.

Table 3.

Logistic regression analysis of chronic disease prevention in the elderly

Variable		B	S.E.	T	P	OR	95%CI
Constant		-4.959	0.587	-8.412	<0.001	0.006	0.002-0.024
Urban and rural	City	0.462	0.053	8.445	<0.001	1.583	1.425-1.768
Urban and rural	Countryside					1.000
Region	East	0.422	0.054	7.286	<0.001	1.523	1.356-1.705
	Middle	-0.146	0.072	-2.097		0.867	0.756-0.993
	West					1.000
Gender	Man	-0.043	0.048	-0.805		0.965	0.875-1.056
Gender	Female					1.000
Age		0.018	0.008	1.978		1.016	1.000-1.034
Cultural degree	Illiterate					1.000
	Primary school	0.613	0.084	7.156	<0.001	1.845	1.556-2.184
	Junior high school	1.016	0.085	11.815	<0.001	2.767	2.336-3.281
	High school /high office	1.356	0.098	14.336	<0.001	3.875	3.218-4.659
	College /undergraduate /above	1.623	0.116	13.758	<0.001	5.058	4.016-6.369
Slow condition	Undiseased					1.000
Slow condition	Sickness	0.075	0.054	1.405	0.165	1.081	0.975-1.189
Self-assessment health	Good	0.426	0.215	1.994	0.048	1.536	1.008-2.336
	Better	0.684	0.210	3.256	0.001	1.975	1.316-2.968
	General	0.495	0.208	2.395	0.015	1.638	1.095-2.458
	Difference	0.423	0.222	1.942	0.056	1.515	0.996-2.357
	Very bad					1.000

5

Analysis of the results of nutrition interventions

One hundred and twenty cases of elderly maintenance hemodialysis patients admitted to a city hospital from January 2020 to June 2023 were selected and divided into 60 cases, each of intervention group and control group according to the random number table method, and nutritional intervention was provided to them.

After the intervention, the comparison of patients’ compliance behavior, comparison of nutritional status, comparison of coping styles, comparison of self-care ability and hope level, and comparison of comfort and quality of life scores were shown in Tables 4 to 8, respectively.

After the intervention, the scores of the confrontation dimension were higher, and the scores of the avoidance and submission dimensions were lower in both groups compared to the pre-intervention period (P<0.05). In the intervention group, the confrontation dimension score was higher than that of the control group. The avoidance and submission scores were lower than those of the control group (P<0.05), and their confrontation dimension scores were about twice as high as those of the control group. Their avoidance and submission scores were about 0.61 and 0.65, respectively, of those of the control group.

After the intervention, the ESCA and Herth scores of patients in both groups were higher than before the intervention, and the ESCA and Herth scores of the intervention group were significantly higher than those of the control group (P<0.05). After the intervention, the KKDQ and GCQ scores of patients in both groups were elevated compared to pre-intervention, and the KKDQ and GCQ scores of the intervention group were higher than those of the control group (P<0.05).

The results of this study showed that the improvement of compliance behavior, coping style, and hope level in the intervention group was better than that of the control group after the intervention (P<0.05), and the compliance behavior of the experimental group was 21.67% higher than that of the control group. It is suggested that the application of dietary nutrition intervention based on the trans-theoretical model can effectively improve patients’ hope level of medical compliance behavior and change patients’ coping styles. The reason for this analysis was that the intervention group helped to fundamentally motivate patients to change their coping styles through corresponding behavioral change strategies at each stage of the pre-intentional stage, intentional stage, preparatory stage, action stage, and maintenance stage, thus enhancing their hope level. Carrying out offline dietary health lectures and popularizing relevant nutritional knowledge helps to guide patients to establish a healthy concept and take the initiative to make changes, which in turn enhances their compliance behavior.

After the intervention, the improvement of nutritional status, self-care ability, comfort, and quality of life in the intervention group was greater than that in the control group (P<0.05), suggesting that the application of dietary nutritional intervention based on the transtheoretical model can significantly improve the nutritional status and quality of life of the patients, enhance their self-care ability, and improve their comfort. The reason for this is that the intervention group designed targeted recipes and adjusted them according to the actual situation (season, age, etc.), coupled with the fact that the patients perfected them according to their actual situation, clocked in, and recorded them after formulating the plan, and invited their families to supervise the implementation of the plan, and at the same time gave them encouragement and incentives, which was conducive to promoting the enhancement of the patient’s ability to take care of themselves, and fundamentally improving the patients’ nutritional status, enhancing their comfort and quality of life. The program will help promote patients’ self-care ability, fundamentally improve their nutritional status, and enhance their comfort and quality of life.

Table 4.

The two groups were compared to the practice of medical behavior

Group	N	Follow the doctor	As a doctor	Poor care	The compliance rate is at (%)
Intervention group	60	45	13	2	96.67
Control group	60	25	20	15	75

Note: Comparison of compliance rates between the two groups, chi-square test, X² = 7.115, p<0.05

Table 5.

Patient intervention before and after nutrition

Group	N	Time	ALB(g/L)	BMI(kg/m²)	PA(mg/L)	Hb(g/L)
Intervention group	60	preintervention	33.58±2.56	18.27±1.21	259.78±49.15	84.17±6.14
Intervention group	60	After intervention	40.04±2.15① ②	24.78±1.25① ②	316.75±18.79① ②	115.44±10.38① ②
Control group	60	preintervention	34.18±2.45	17.94±1.22	260.16±48.87	83.87±6.38
Control group	60	After intervention	35.89±3.1	20.05±0.68	292.24±15.98	104.55±11.27①

Note: Compared with the same group before the intervention,

①

P<0.05. Compared with the control group after the intervention,

②

P<0.05

Table 6.

Two groups of patients were compared before and after the intervention

Group	N	Time	Face	Avoidance	Yield
Intervention group	60	preintervention	13.12±1.55	22.21±1.05	14.92±1.76
Intervention group	60	After intervention	27.15±1.74① ②	13.55±1.28① ②	9.75±0.76① ②
Control group	60	preintervention	12.91±1.62	21.85±1.12	15.13±1.67
Control group	60	After intervention	22.66±1.24①	17.25±1.82①①	12.02±1.04①①

Note: Compared with the same group before the intervention,

①

P<0.05. Compared with the control group after the intervention,

②

P<0.05

Table 7.

The ability of self-protection before and after intervention, the hope level

Group	N	ESCA		Herth
Group	N	Preintervention	After intervention	Preintervention	After intervention
Intervention group	60	89.94±5.13	137.16±11.02① ②	18.86±1.72	31.08±2.82① ②
Control group	60	90.12±4.85	129.85±12.46①	19.18±1.85	25.35±3.37①

Note: Compared with the same group before the intervention,

①

P<0.05. Compared with the control group after the intervention,

②

P<0.05

Table 8.

The GCQ and KKKDQ score was compared

Group	N	ESCA		Herth
Group	N	Preintervention	After intervention	Preintervention	After intervention
Intervention group	60	49.15±4.66	87.15±6.05① ②	68.16±5.02	115.08±10.12① ②
Control group	60	47.12±4.82	79.87±7.46①	67.78±4.85	108.35±13.37①

Note: Compared with the same group before the intervention,

①

P<0.05. Compared with the control group after the intervention,

②

②P<0.05

6

Conclusion

This paper mines the literacy level of chronic disease prevention and control among the elderly and explores the effect of nutrition intervention modeling on it to provide relevant strategies for promoting chronic disease prevention and control among the elderly.

1) Elderly people with a high number of years of suffering from chronic diseases also have a high level of chronic disease prevention and control literacy. The chronic disease prevention and control literacy level of those with good self-assessed health status was higher than that of those with poor self-assessed status. Two-thirds of the questions in the health literacy test for the elderly had a correct answer rate of less than 50%, indicating that the knowledge of chronic disease prevention and control among the elderly is insufficient.

2) The level of chronic disease prevention and treatment literacy among the elderly aged 60-69 is higher in urban than in rural areas. In the eastern region, the health literacy level of the elderly is higher, with an OR of 1.523, and the ORs of chronic disease prevention and treatment literacy among people with lower to higher literacy levels are 1.845, 2.767, 3.875, and 5.058, respectively; those with good health conditions have a higher level of chronic disease prevention and treatment literacy. Therefore, on the whole, chronic disease health literacy is lower among the elderly in rural areas central and western regions, with lower literacy levels and poor health conditions.

3) After the nutritional intervention, the intervention group’s medical compliance behavior, coping style, and hope level improved better than that of the control group (P<0.05), in addition to which, the nutritional status, self-care ability, comfort, and quality of life of the intervention group improved better than that of the control group (P<0.05). It shows that a reasonable nutritional intervention model can effectively improve the patients’ hope level of medical compliance behavior, change the patients’ coping style, and significantly improve the nutritional status, quality of life, self-care ability, and comfort of elderly patients.

4) Teaching and dissemination of chronic disease prevention and treatment strategies for the elderly can be focused on rural residents, residents of central and western regions, and people with a low level of literacy, with an emphasis on the grass-roots level, to strengthen publicity for chronic disease prevention and treatment and health promotion strategies for the elderly. Since the elderly have limited ability to identify health information, professional organizations can carry out chronic disease health education tailored to the characteristics of the elderly and improve the effectiveness of health education. In addition, the purpose of popularizing knowledge about chronic diseases among older persons can be achieved by giving them reading books with information on the prevention of such diseases.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

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Modeling of Chronic Disease Prevention and Nutritional Intervention for the Elderly Based on Big Data Analysis

Min Tang

Publicado en línea: 19 mar 2025

Recibido: 06 nov 2024

Aceptado: 14 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0541

Palabras claveNutritional intervention, Chronic diseases in the elderly, Logistic regression, Soc model

© 2025 Min Tang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
Nutritional intervention, Chronic diseases in the elderly, Logistic regression, Soc model