Abstract
Aim
To explore the physical and mental health status of community residents and to identify the risk factors of chronic diseases.
Design
A cross‐sectional, descriptive correlational study was conducted.
Methods
A total of 579 participants were recruited from 15 communities in Tianjin. The demographic information sheet, 7‐item Generalized Anxiety Disorder scale (GAD‐7) and Patient Health Questionnaire (PHQ‐9) were used. Data collection was undertaken based on the health management system on mobile phones from April to May 2019.
Results
Eighty‐four participants of the total number of surveyed were with chronic disease. The incidence of depression and anxiety in participants was 44.2% and 41.3%. Logistic regression analysis showed that age (OR = 4.905, 95%CI: 2.619–9.187), religious belief (OR = 0.445, 95%CI: 1.510–11.181) and working condition (OR = 0.161, 95%CI: 0.299–0.664) entered the regression equation. Old age is a risk factor for chronic diseases. No religious belief and working condition are protective factors for chronic diseases.
Keywords: chronic diseases, community residents, health management systems, risk factors
1. INTRODUCTION
Globally, the ageing of populations increases exposure to risk factors for disease, and as such the prevalence of chronic diseases is likely to increase rapidly and compromising their health‐related quality of life (De Nardi et al., 2020; Zhao et al., 2021). Report on Nutrition and Chronic Diseases of Chinese Residents (2020) showed that there were about 500–600 million chronic patients in China in 2019, and the prevalence rate was about 35%–45%. The summary of China Cardiovascular Disease Report (2018) issued by the National Center for Cardiovascular Diseases shows that the mortality rates of cardiovascular diseases in rural and urban areas were 309.33/100,000 and 265.11/100,000 respectively; cardiovascular diseases accounted for 45.50% of the deaths in rural areas and 43.16% in urban areas, ranking first among all kinds of diseases (Hu et al., 2019). Report on Nutrition and Chronic Diseases of Chinese Residents (2015) issued that the mortality rate of chronic diseases among Chinese residents is 533/100,000, accounting for 86.6% of total deaths, which is much higher than that caused by infectious diseases and traffic accidents. In 2017, the Medium and Long‐term Plan for the Prevention and Control of Chronic Diseases in China (2017–2025), officially issued by the State Council, indicates that the prevention and control of chronic diseases should be strengthened, a long‐term working mechanism for health management should be established, and the healthy life expectancy of residents should be raised. Active monitoring of behavioural risk factors and scientific formulation of intervention measures are effective strategies for the prevention and control of chronic diseases (Yan et al., 2017). It can effectively accelerate the process of population ageing and urbanization in the context of the social status of high incidence of chronic diseases.
Existing studies have shown that the prevalence of chronic diseases is related to age, education level, occupation and other factors, but the results are inconsistent. Documents have reported that with the increase of age, the incidence of chronic diseases is increasing; the higher the education level of residents, the lower the incidence of chronic diseases; the higher the income of residents, the lower the incidence of chronic diseases (Hajjar & Kotchen, 2003; Parker et al., 2007). Some studies have shown that with the increase of education level, the risk of chronic diseases has increased; the higher the income of residents, the higher the incidence of chronic diseases (Kautzky‐Willer et al., 2012); related literature reports (Yu et al., 2013), the lowest incidence of chronic diseases for managers and technicians and the highest incidence of chronic diseases for retired and unemployed workers. Surveys showed that there was no statistically significant difference between the sexes, and gender factors had little effect on chronic diseases (Hajjar & Kotchen, 2003; Sun et al., 2020). The study shows that the incidence of chronic diseases in women is higher than that in men, which may be closely related to the physiological characteristics of women (Yu et al., 2013). Sun et al. (2020) think that married is the protective factor compared with unmarried. And the incidence of chronic diseases was highest in low education level, and the commercial insurance group was the lowest. The prevalence of chronic diseases with non‐smokers was higher than that of smokers.
The trend of health education on chronic diseases among community residents in China is mainly focused on the traditional questionnaire survey. With the development of modern information technology, the popularity of mobile Internet has affected all aspects of people's lives. Mobile Health supports medical and public health practices through mobile phones, patient monitoring devices, handheld computers and other wireless devices (Cole‐Lewis & Kershaw, 2010; Harris et al., 2010; Sweileh et al., 2017). Domestic survey shows that 85.71% of respondents want mobile medical App to be used in health monitoring, 62.75% want to be used to control hypertension and 58.54% want to be used in diabetes management (Che et al., 2016). The content of health management systems on mobile phones needs to be specialized and specific, and meets the personalized needs of patients (Chen et al., 2017). Relying on mobile phones, health information such as pulse, blood pressure and heart rate of the human can be obtained through communication with the health management system, and their health status can be obtained by combining the relevant information in their health files, analysing their influencing factors, promoting self‐monitoring and evaluation of patients and helping to carry out remote and continuous medical management based on families and communities. In this study, the prevalence of chronic diseases was taken as an evaluation index of the health status of community residents. With the help of the health management systems supported by mobile phones developed by the research team independently, the health status of residents in Tianjin areas was investigated and the factors affecting the health of community residents were analysed, so as to provide scientific basis for community to formulate effective health education measures.
2. METHODS
2.1. Study design
A convenience sample with a descriptive cross‐sectional survey design was used in this study.
2.2. Setting and samples
According to the health service management system of contracted doctors in the community, participants were recruited from 15 communities in Tianjin, China. The data collection was undertaken from April to May 2019. The inclusion criteria were as follows: (1) permanent resident population in Community; (2) age 18 or above; (3) voluntary participation in this study; (4) express clearly and communication easily; (5) normal spirit and intelligence. The exclusion criteria were as follows: (1) not living in the community recently and (2) residents in severe sickness or complications. After obtaining informed consent, the formal investigation was begun. A total of 596 residents volunteered for investigation and 579 questionnaires were considered suitable; among which, 17 (2.85%) were excluded for more than 5% missing data. Therefore, the effective recovery rate of questionnaire was 97.15%.
2.3. Ethical considerations
This study obtained the approval of Biomedical Ethic Committee of Peking University on March 25, 2019. The number is IRB00001052‐19018. All responses were voluntary and anonymity.
2.4. Measurements
2.4.1. Demographic information sheet
The demographic information sheet was designed by researchers. Previous literature was used to identify potential demographic variables that may affect chronic disease. Finally, 26 variables were considered, including age, gender, marital status, educational level, religious belief, working condition, eating habits and living habits. The occurrence of chronic conditions in East China was measured by self‐report. Chronic disease includes hypertension, diabetes mellitus, stroke, COPD, coronary heart disease, cerebrovascular disease, chronic gastritis, gastric ulcer and osteoporosis.
2.4.2. Seven‐item generalized anxiety disorder scale (GAD‐7)
GAD‐7 is a self‐rating anxiety disorder questionnaire consisting of seven short items. The seven items of the questionnaire are based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth edition (DSM‐IV). Therefore, the seven items of the questionnaire are consistent with DSM‐IV. GAD‐7 has been proven to have good reliability and validity in primary medical treatment and clinical application at home and abroad (Barthel et al., 2014; Simpson et al., 2014; Zeng et al., 2013). It is a reliable tool for identifying anxiety disorders in clinical practice. GAD‐7 reflects the mental and physical activity of patients in the past 2 weeks. Each item has four choices. Its content and score assignment are as follows: 0 point = absolutely no, 1 point = occasional days, 2 points = frequent, more than half of the days in the past 2 weeks, 3 points = almost days. The total score ranges from 0 to 21. (Total Anxiety Severity Score categorization: 1–4 minimal symptoms, 5–9 mild symptoms, 10–13 moderate symptoms, 14–18 moderately severe symptoms, 19–21 severe symptoms).
2.4.3. Patient health questionnaire (PHQ‐9)
PHQ‐9 is a concise self‐management questionnaire for depressive disorders. Overseas researches have proved to have good reliability and validity (Kroenke, 2014; Marc et al., 2014; Patten et al., 2015). In 2009, it was recommended as a depression screening tool for patients with tunnelling blood vessels by the preventive group of the American Society of Visceral Sciences (Carney & Freedland, 2017). The Chinese version of PHQ‐9, which is skilful in primary medicine and clinic in China, also shows a good value in identifying depression (Li et al., 2022; Liu et al., 2011; Xiong et al., 2015). The nine items of the questionnaire are based on nine of the DSM‐IV diagnostic criteria. The scores of each item are as follows: 0 point = never, 1 point = occasionally, 2 points = often, in the past 2 weeks or more than a week and 3 points = almost every day. The total score is the sum of the scores of each item. Cut‐offs of 5, 10, 15 and 20 represent mild, moderate, moderately severe and severe levels of depressive symptoms respectively.
2.5. Data analysis
Statistical analysis was performed by the Statistical Package for Social Sciences 23 (Chicago, Illinois). Descriptive statistics was used to summarize participants' characteristics and the prevalence of anxiety and depression. Univariate analysis was analysed by Chi‐square test, and logistic regression analysis was used to analysed multivariate factors; p < 0.05 showed statistical significance.
3. RESULTS
3.1. Participants’ characteristics
The average age of the participants is 29.88 ± 10.73. Most of the participants were women (n = 459, 79.3%), and most of the participants were 18–44 years old (n = 517, 89.3%). Most of the participants were unmarried (n = 311, 53.7%). In terms of educational level, most of the participants were undergraduates (n = 313, 54.1%). Most of the participants were incumbency (n = 336, 58.0%). The demographic characteristics are presented in Table 1.
TABLE 1.
Characteristics of participants (n = 579).
| General information | Grouping | Frequency | Percentage |
|---|---|---|---|
| Age | 18–44 | 517 | 89.3% |
| 45–59 | 50 | 8.6% | |
| ≥60 | 12 | 2.1% | |
| Gender | Male | 120 | 20.7% |
| Female | 459 | 79.3% | |
| Degree of education | Primary school and below | 9 | 1.6% |
| Junior middle school | 29 | 5.0% | |
| Special secondary school | 13 | 2.2% | |
| High school | 11 | 1.9% | |
| Junior college | 144 | 24.9% | |
| Undergraduate | 313 | 54.1% | |
| Postgraduate | 60 | 10.4% | |
| Marital status | Unmarried | 311 | 53.7% |
| Married | 261 | 45.1% | |
| Bereavement | 1 | 0.2% | |
| Divorce | 6 | 1.0% | |
| Living conditions | Live alone | 167 | 28.8% |
| Living with others | 412 | 71.2% | |
| Working condition | Unemployment | 31 | 5.4% |
| Incumbency | 336 | 58.0% | |
| Retirement | 13 | 2.2% | |
| Student | 199 | 34.4% | |
| Religious belief average monthly income of family | No | 555 | 95.9% |
| Yes | 24 | 4.1% | |
| ≤2000yuan | 129 | 22.3% | |
| 2001–5000yuan | 196 | 33.9% | |
| >5000yuan | 254 | 43.9% | |
| Medical expenses | Private treatment | 114 | 19.7% |
| Public medical treatment | 24 | 4.1% | |
| Medical insurance | 432 | 74.6% | |
| Commercial insurance | 9 | 1.6% | |
| Dining conditions | Eat every day | 489 | 84.0% |
| Do not eat occasionally (<1/week) | 90 | 84.5% | |
| Breakfast | Eat every day | 318 | 54.9% |
| Do not eat occasionally (<1/week) | 154 | 26.6% | |
| Sometimes do not eat (2‐3/week) | 60 | 10.4% | |
| Often do not eat (4‐6/week) | 40 | 6.9% | |
| Never eat | 7 | 1.2% | |
| Additional meals in the morning | Eat every day | 36 | 6.2% |
| Do not eat occasionally (<1/week) | 14 | 2.4% | |
| Sometimes do not eat (2‐3/week) | 35 | 6.0% | |
| Often do not eat (4–6 /week) | 188 | 32.5% | |
| Never eat | 306 | 52.8% | |
| Lunch | Eat every day | 505 | 87.2% |
| Do not eat occasionally (<1/week) | 54 | 9.3% | |
| Sometimes do not eat (2‐3/week) | 12 | 2.1% | |
| Often do not eat (4–6 /week) | 7 | 1.2% | |
| Never eat | 1 | 0.2% | |
| Additional meals in the afternoon | Eat every day | 29 | 5.0% |
| Do not eat occasionally (<1/week) | 19 | 3.3% | |
| Sometimes do not eat (2‐3/week) | 52 | 9.0% | |
| Often do not eat (4–6 /week) | 191 | 39.7% | |
| Never eat | 230 | 43.0% | |
| Dinner | Eat every day | 415 | 71.7% |
| Do not eat occasionally (<1/week) | 90 | 15.5% | |
| Sometimes do not eat (2‐3/week) | 46 | 7.9% | |
| Often do not eat (4–6 /week) | 22 | 3.8% | |
| Never eat | 6 | 1.0% | |
| Midnight snack | Eat every day | 11 | 1.9% |
| Do not eat occasionally (<1/week) | 19 | 3.3% | |
| Sometimes do not eat (2–3/week) | 60 | 10.4% | |
| Often do not eat (4–6 /week) | 277 | 47.8% | |
| Never eat | 212 | 36.6% | |
| Dietary status | Buckwheat in half | 433 | 74.8% |
| Carnivorous dietary | 67 | 11.6% | |
| Vegetarian diet | 79 | 13.6% | |
| Dietary preferences | Fatty | 18 | 3.1% |
| Salty | 56 | 9.7% | |
| Moderate | 427 | 73.7% | |
| Sweeter | 18 | 3.1% | |
| Vegetarian | 60 | 10.4% | |
| Special diet | Diabetic diet | 3 | 0.5% |
| low‐salt diet | 3 | 0.5% | |
| Low‐fat diet | 8 | 1.4% | |
| None | 560 | 96.7% | |
| Others | 5 | 0.9% | |
| Frequency of eating sweet | Seldom | 198 | 34.2% |
| Sometimes | 285 | 49.2% | |
| Often | 96 | 16.6% | |
| Frequency of eating fruit | Seldom | 70 | 12.1% |
| Sometimes | 243 | 42.0% | |
| Often | 266 | 45.9% | |
| Frequency of drinking sugary drinks anyone smoking around passive smoking | Seldom | 210 | 36.3% |
| Sometimes | 223 | 38.5% | |
| Often | 146 | 25.2% | |
| No | 191 | 33.0% | |
| Yes | 388 | 67.0% | |
| 0 day | 320 | 55.3% | |
| 1–2 days/week | 97 | 16.8% | |
| 3–5 days/week | 34 | 5.9% | |
| Almost every day | 45 | 7.8% | |
| Unclear | 83 | 14.3% | |
| Frequency of drinking | Never drink | 389 | 67.2% |
| Occasionally drinking (<1/month) | 115 | 19.9% | |
| Sometimes drinking (2–4 /month) | 49 | 8.5% | |
| Often drinking (>1/week) | 26 | 4.5% | |
| Frequency of smoking | Never | 495 | 85.5% |
| Every day | 51 | 8.8% | |
| Sometimes | 18 | 3.1% | |
| Once | 15 | 2.6% |
3.2. Descriptive statistics
Overall, the prevalence of chronic diseases and depression and anxiety among 579 participants is shown in Table 2. The top two chronic diseases are hypertension and chronic gastritis, with prevalence rates of 6.39% and 4.15% respectively. There are 84 patients with chronic diseases, 14.51% of the total number surveyed. In which 19 patients with comorbidity, including 15 patients with two chronic diseases and four patients with three or more chronic diseases. The percentage of depression in participants was 44.2%. The percentage of anxiety in participants was 41.3%.
TABLE 2.
depression and anxiety of participants (n = 579).
| Scales categorization | Frequency | Percentage |
|---|---|---|
| PHQ‐9 | ||
| No depressive symptoms | 329 | 56.8% |
| Mild depression | 184 | 31.8% |
| Moderate depression | 45 | 7.8% |
| Moderately severe depression | 14 | 2.4% |
| Severe depression | 7 | 1.2% |
| GAD‐7 | ||
| No anxiety | 340 | 58.7% |
| Mild anxiety | 198 | 34.2% |
| Moderate anxiety | 24 | 4.1% |
| Moderately severe anxiety | 9 | 1.6% |
| Severe anxiety | 8 | 1.4% |
| Chronic disease | ||
| Hypertension | 37 | 6.39% |
| Chronic gastritis | 24 | 4.15% |
| Others | 17 | 2.94% |
| Diabetes | 10 | 1.73% |
| Hyperlipidaemia | 9 | 1.55% |
| Cerebrovascular disease | 3 | 0.52% |
| Coronary heart disease | 2 | 0.35% |
The participants who had no condition, single condition, double conditions and three or more than three conditions of chronic disease, anxiety and depression are shown in Figure 1. Otherwise, there are 39 participants having both physical and mental comorbidities.
FIGURE 1.
The conditions of chronic disease, anxiety, depression of participants.
3.3. Univariate analysis of influencing factors of chronic disease
Chi‐square test was used for single‐factor analysis. The results showed that age, gender, marital status, education level, working condition, religious belief, medical expenses and dietary status had statistical significance on chronic disease. Meaning (p < 0.05), others had no significant impact on chronic disease (p > 0.05), as shown in Table 3.
TABLE 3.
Univariate analysis of chronic diseases (n = 579).
| General information | Grouping | Chronic disease | X 2 | p | |
|---|---|---|---|---|---|
| no | Yes | ||||
| Age | 18–44 | 470 | 47 | 118.113 | 0.000 |
| 45–59 | 18 | 32 | |||
| ≥60 | 7 | 5 | |||
| Gender | Male | 98 | 22 | 1.786 | 0.191 |
| Female | 397 | 62 | |||
| Degree of education | Primary school and below | 1 | 8 | 93.851 | 0.000 |
| Junior middle school | 16 | 13 | |||
| Special secondary school | 11 | 2 | |||
| High school | 4 | 7 | |||
| Junior college | 123 | 21 | |||
| Undergraduate | 288 | 25 | |||
| Postgraduate | 52 | 8 | |||
| Marital status | Unmarried | 289 | 22 | 33.038 | 0.000 |
| Married | 199 | 62 | |||
| Bereavement | 1 | 0 | |||
| Divorce | 6 | 0 | |||
| Living conditions | Live alone | 144 | 23 | 0.102 | 0.796 |
| Living with others | 351 | 61 | |||
| Working condition | Unemployment | 15 | 16 | 61.067 | 0.000 |
| Incumbency | 280 | 56 | |||
| Retirement | 8 | 5 | |||
| Student | 192 | 7 | |||
| Religious belief | No | 479 | 76 | 7.154 | 0.014 |
| Yes | 16 | 8 | |||
| Average monthly | ≤2000yuan | 113 | 16 | 0.784 | 0.676 |
| Income of family | 2001–5000yuan | 168 | 28 | ||
| >5000yuan | 214 | 40 | |||
| Medical expenses | Private treatment | 108 | 6 | 13.099 | 0.004 |
| Public medical treatment | 17 | 7 | |||
| Medical insurance | 362 | 70 | |||
| Commercial insurance | 8 | 1 | |||
| Depression | No depressive symptoms | 275 | 54 | 4.191 | 0.381 |
| Mild depression | 162 | 22 | |||
| Moderate depression | 41 | 4 | |||
| Moderate to severe depression | 12 | 2 | |||
| Severe depression | 5 | 2 | |||
| Anxiety | No anxiety | 288 | 52 | 5.446 | 0.244 |
| Mild anxiety | 174 | 24 | |||
| Moderate anxiety | 18 | 6 | |||
| Moderate to severe anxiety | 9 | 0 | |||
| Severe anxiety | 6 | 2 | |||
| Dining conditions | Eat every day | 421 | 68 | 0.919 | 0.331 |
| Do not eat occasionally (<1/week) | 74 | 16 | |||
| Breakfast | Eat every day | 275 | 43 | 6.468 | 0.167 |
| Do not eat occasionally (<1/week) | 133 | 21 | |||
| Sometimes do not eat (2–3/week) | 48 | 12 | |||
| Often do not eat (4–6/week) | 35 | 5 | |||
| Never eat | 4 | 3 | |||
| Additional meals in the morning | Eat every day | 30 | 6 | 1.047 | 0.903 |
| Do not eat occasionally (<1/week) | 13 | 1 | |||
| Sometimes do not eat (2–3/week) | 31 | 4 | |||
| Often do not eat (4–6 /week) | 160 | 28 | |||
| Never eat | 261 | 45 | |||
| Lunch | Eat every day | 433 | 72 | 1.265 | 0.867 |
| Do not eat occasionally (<1/week) | 46 | 8 | |||
| Sometimes do not eat (2–3/week) | 9 | 3 | |||
| Often do not eat (4–6 /week) | 6 | 1 | |||
| Never eat | 1 | 0 | |||
| Additional meals in the afternoon | Eat every day | 27 | 2 | 5.204 | 0.267 |
| Do not eat occasionally (<1/week) | 19 | 0 | |||
| Sometimes do not eat (2–3/week) | 45 | 7 | |||
| Often do not eat (4–6 /week) | 195 | 35 | |||
| Never eat | 209 | 40 | |||
| Dinner | Eat every day | 358 | 57 | 2.154 | 0.707 |
| Do not eat occasionally (<1/week) | 73 | 17 | |||
| Sometimes do not eat (2–3/week) | 39 | 7 | |||
| Often do not eat (4–6 /week) | 20 | 2 | |||
| Never eat | 5 | 1 | |||
| Midnight snack | Eat every day | 10 | 1 | 4.178 | 0.382 |
| Do not eat occasionally (<1/week) | 15 | 4 | |||
| Sometimes do not eat (2–3/week) | 56 | 4 | |||
| Often do not eat (4–6 /week) | 235 | 42 | |||
| Never eat | 179 | 33 | |||
| Dietary status | Buckwheat in half | 367 | 66 | 4.784 | 0.091 |
| Carnivorous dietary | 63 | 4 | |||
| Vegetarian diet | 65 | 14 | |||
| Dietary preferences | Fatty | 17 | 1 | 6.003 | 0.199 |
| Salty | 47 | 9 | |||
| Moderate | 359 | 68 | |||
| Sweeter | 18 | 0 | |||
| Vegetarian | 54 | 6 | |||
| Special diet | Diabetic diet | 3 | 0 | 2.220 | 0.695 |
| Low‐salt diet | 2 | 1 | |||
| Low‐fat diet | 6 | 2 | |||
| None | 480 | 80 | |||
| Others | 4 | 1 | |||
| Frequency of eating sweet | Seldom | 172 | 26 | 0.675 | 0.713 |
| Sometimes | 243 | 42 | |||
| Often | 80 | 16 | |||
| Frequency of eating fruit | Seldom | 59 | 11 | 0.311 | 0.856 |
| Sometimes | 210 | 33 | |||
| Often | 226 | 40 | |||
| Frequency of drinking sugary drinks anyone smoking around passive smoking | Seldom | 187 | 23 | 4.257 | 0.119 |
| Sometimes | 183 | 40 | |||
| Often | 125 | 21 | |||
| No | 169 | 22 | 2.054 | 0.168 | |
| Yes | 326 | 62 | |||
| 0 day | 273 | 47 | 4.803 | 0.308 | |
| 1–2 days/week | 83 | 14 | |||
| 3–5 days/week | 28 | 6 | |||
| Almost every day | 43 | 2 | |||
| Unclear | 68 | 15 | |||
| Frequency of drinking | Never drink | 331 | 58 | 0.806 | 0.848 |
| Occasionally drinking (<1/month) | 98 | 17 | |||
| Sometimes drinking (2–4 /month) | 44 | 5 | |||
| Often drinking (>1/week) | 22 | 4 | |||
| Frequency of smoking | Never | 427 | 68 | 3.214 | 0.360 |
| Every day | 43 | 8 | |||
| Sometimes | 13 | 5 | |||
| Once | 12 | 3 | |||
3.4. Regression analysis of the influencing factors of chronic diseases in community residents
Bivariate logistic regression analysis was conducted with the condition of chronic diseases as dependent variables (with chronic disease assignment 1, without chronic disease assignment 0). The specific variables and assignment descriptions are shown in Table 4. The introduction level was 0.05, and the elimination level was 0.10. The results of regression analysis are shown in Table 5.
TABLE 4.
Variable assignment.
| Independent variable | Assignment |
|---|---|
| Age | <18 = 1; 18–44 = 2; 45–59 = 3; ≥ 60 = 4 |
| Degree of education | Primary school and below =1; Junior middle school =2; special secondary school = 3; high school =4; junior college =5; undergraduate =6; postgraduate = 7 |
| Marital status | Setting dumb variable with unmarried as reference |
| working condition | Setting dumb variables with unemployment as a reference |
| Religious belief | no = 1; yes = 0 |
| Medical expenses | Setting dumb variables with private treatment as reference |
TABLE 5.
logistic regression analysis.
| Variable | B | SE | Wald | P | OR | 95%CI |
|---|---|---|---|---|---|---|
| constant | −4.253 | 1.402 | 9.208 | 0.002 | 0.014 | ‐ |
| Age | 1.590 | 0.320 | 24.678 | 0.000 | 4.905 | 2.619–9.187 |
| Religious belief | 1.413 | 0.511 | 7.652 | 0.006 | 0.445 | 1.510–11.181 |
| Working condition | −0.809 | 0.203 | 15.810 | 0.000 | 0.161 | 0.299–0.664 |
4. DISCUSSION
At present, there are many studies on the evaluation of residents' comprehensive health status, but the evaluation methods are different. This study relies on health management systems supported by mobile phones and takes the morbidity of chronic diseases as the objective health evaluation index to evaluate residents' health status. The morbidity rate of chronic diseases is an important indicator to measure the health status of residents, which attracts more and more attention and can well reflect the objective health status of residents (Liu & Bai, 2013). The results show that the prevalence of chronic diseases among community residents in Tianjin is 14.51%, which is lower than 20.5% of rural residents in the Fifth National Health Service Survey in 2013 (Jia et al., 2017). To analyse the reasons, on the one hand, it may be related to the low rate of residents' health examination. Rural residents do not meet the requirements of routine physical examination at least once a year, and fail to find out and treat in time. On the other hand, it may be due to the limitation of residents' own education level on their understanding of diseases and preventive health care, so the incidence is higher. Chronic diseases are caused by long‐term mental stress and physical fatigue, the lack of bad lifestyle and self‐care awareness and even the exposure of environmental pollutants, which lead to the disorder of physiological balance and gradually accumulated diseases (Williams et al., 2015). Chronic diseases not only threaten the quality of life of residents, but also bring heavy economic burden to the families of patients and the whole society (Li et al., 2022). Therefore, the state and local governments should vigorously support the development of grass‐roots medical and health undertakings, promote the improvement of the quality and efficiency of medical services, promote residents to master the basic knowledge and ability of disease prevention and strengthen self‐health awareness, so as to reduce the incidence of chronic diseases among residents. The results of logistic regression analysis show that age, religious belief and occupation are the main factors of residents' self‐rated health status.
In this study, the chronic disease with the highest prevalence is hypertension. With the improvement of living standards, residents' dietary structure and lifestyle have also changed, and the prevalence of hypertension has risen. It is important to carry out hypertension health education for residents as soon as possible, so as to improve their health habits. The second most common chronic disease is chronic gastritis, which may be the reason why irregular eating is a high‐risk factor for chronic diseases. Often do not eat breakfast and never eating breakfast are the main manifestations of irregular eating. The current fast‐paced life intensifies the occurrence of eating irregularities. It is particularly important to strengthen health education on regular eating.
This study shows that old age is a risk factor for chronic diseases. No religious belief and working condition are protective factors for chronic diseases. Age is not only the influencing factor in the prevalence of chronic diseases but also the main influencing factor of the 2‐week prevalence and self‐rated health of residents. With the control of other factors unchanged, with the increase of age, the incidence of chronic diseases is gradually increasing, which is consistent with the relevant research results at home and abroad (Foraker et al., 2011; Xu et al., 2016). On the one hand, the function of various organs of the body is gradually degraded, the physiological function is gradually declining, and the disease of various diseases of the body is caused by the ageing population compared with the younger population. Religious beliefs and occupations have an impact on chronic diseases of residents. Non‐religious beliefs and students are protective factors for chronic diseases, which is consistent with previous research results (Cong, 2013). The reasons may be that there is no religious belief residents actively participate in recreational activities, social interaction, emotional pursuit and so on. Because of their younger age and higher level of knowledge, students have lower incidence of chronic diseases.
The strength of the present study is that the diet and lifestyle of study participants were collected by the community residents' health monitoring platform independently developed by the research team, and these values were used to determine influencing factors in this study. Compared with the traditional data collection method, the intelligent health monitoring platform used in this study is highly efficient, timely and dynamic. The system has completed the collection of health behaviour, physiological and mental health status data in short time. At the same time, personalized electronic health files of residents and baseline data for dynamic data collection in the future can be established by using it. This system avoids the problems of cumbersome filling and data missing in traditional questionnaires. Through intelligent health management systems by mobile phone, it explores the health status monitoring and management mode of community people, laying the foundation for targeted disease prevention and early warning models. Therefore, in community health monitoring, we should actively promote information‐based health management and establish a systematic platform to effectively predict and warn residents' health status.
These findings can provide scientific basis for the community to formulate effective health education measures. However, our study has some limitations. First, in this survey, the participants were recruited by convenience sampling from 15 communities in Tianjin, but the sample was not equal, leading to inadequate representativeness of the sample, stratified random sampling should be used in future research. In addition, this study is a cross‐sectional study, cross‐sectional studies can reflect the prevalence and influencing factors of chronic diseases in the target population, but cannot determine the causal relationship between exposure and outcomes. Therefore, cohort studies should be conducted in future studies. Third, residents' diet, exercise and other living habits should be monitored dynamically. Fourth, in the future, the health management system can be used to conduct a larger scale investigation to analyse the risk factors of different chronic diseases. In this way, health care professionals can give timely guidance, so as to improve residents' health status and improve residents' health management level.
This study investigated the health status of community residents in Tianjin. We found that the prevalence rate of chronic diseases among community residents was 14.51%, which was generally better. The incidence of depression and anxiety was 44.2% and 41.3%, respectively, indicating that the mental health of the residents is poor. Old age is a risk factor for chronic diseases. No religious belief, retirement and students are protective factors for chronic diseases.
AUTHOR CONTRIBUTIONS
All authors have made substantial contributions to this study. Ru‐zhen Luo and Qi Lu were responsible for the acquisition of data, or analysis and interpretation of data and drafting the article. Yumei Sun assisted in analysing and interpreting data. Hongyu Sun, Yanhui Liu and Yue Zhao were responsible for conception and design and revising it critically for important intellectual content. Finally, all authors were approval of the version to be published.
FUNDING INFORMATION
This study was supported by Caofeidian College of Technology and the National Natural Science Foundation of China (Project Approval Number: 72174012).
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no conflict of interests.
ACKNOWLEDGEMENTS
The authors also gratefully acknowledge the 579 residents who volunteered to participate in the study, as well as the experts and members of the group for their help and advice.
Luo, R.‐z. , Lu, Q. , Sun, Y. , Sun, H. , Liu, Y.‐h. , & Zhao, Y. (2023). Investigation on risk factors of chronic diseases among community residents: A study based on health management systems supported by Mobile phones. Nursing Open, 10, 5117–5128. 10.1002/nop2.1747
DATA AVAILABILITY STATEMENT
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.