Abstract
To investigate the status quo and influencing factors of overall frailty and its 3 domains among rural community-dwelling older adults. This is a cross-sectional study. A convenience sample of 195 older adults from 6 villages in Bashang Area of Zhangjiakou City, Hebei Province, China, were recruited from August to September, 2022. The demographic characteristics, the Chinese version of Tilburg Frailty Indicator, Charlson Comorbidity Scale and Hospital Anxiety and Depression Scale were used to investigate frailty and its influencing factors. Univariate analysis and multiple linear regression analysis were employed. The prevalence of overall frailty among the older adults in Bashang Area was 85.13%. Multiple linear regression analysis showed that age, gender, marital status, regular exercise, comorbidity, and anxiety were the influencing factors of overall frailty. While anxiety was the only shared influencing factor for physical frailty, psychological frailty, and social frailty, age, gender, marital status, financial burden, the comorbidity, and regular exercise were factors which influenced 1 or 2 domains of frailty. The prevalence of overall frailty among the older adults in rural areas, Zhangjiakou City is high. It is influenced by many factors. Medical staff and policy makers should work hand in hand to improve frailty among rural community-dwelling older adults in China.
Keywords: frailty, older adult, rural area, status quo, influencing factor
What do we already know about this topic?
The older adults in rural areas differ from those living in urban areas in terms of their living arrangements, income levels, dietary preferences, exercise routines, and the like. Nevertheless, frailty among older adults in rural areas which may be attributed to those factors is less investigated than that in urban areas.
How does your research contribute to the field?
Our study is among one of the first to investigate not only the overall frailty but also the 3 domains and their influencing factors among rural community-dwelling older adults in China based on Gobbens’ integral conceptual model of frailty.
What are your research’s implications toward theory, practice, or policy?
By adopting Gobbens’ integral conceptual model to examine the multi-dimensional frailty among rural community-dwelling older adults in China, our research not only validates the model but also provides insight into how medical staff and policy makers can do to improve frailty for rural community-dwelling older adults.
Introduction
With the continuous acceleration of global aging, frailty has become a worldwide public health concern. 1 Frailty was first proposed as a complex and age-related clinical syndrome, characterized by decreased physiological capacity of multiple organ systems and an increase in susceptibility to stressors. 2 With the deepening of research, the concept has gradually expanded to domains: physical, psychological and social frailty.3,4 According to Gobbens et al, 5 frailty is defined as “a dynamic state affecting an individual who experiences losses in one or more domains of human functioning (physical, psychological, social), which is caused by the influence of a range of variables and which increases the risk of adverse outcomes.” In addition to a decrease in physical function, frailty can also include a reduction in the ability to cope with mental pressure, 6 and a state of being at persistent risk of losing, or having lost, resources to fulfill basic social needs, including a lack of social behaviors, social activities, and self-management. 7 Frailty increased the possibilities of negative health-related events, leading to a range of serious problems, among which falls, hospitalization, and death were the most common ones. 1
Older adults are at a high risk of frailty due to the weakening of physiological function and the co-existence of multiple diseases. 2 A systematic review and meta-analysis by Zhou et al 8 showed that the overall prevalence of frailty among older community dwellers in China was 10.1%, with people in rural areas having a higher prevalence of frailty. According to the Chinese Geriatrics Society, 9 frailty poses a heavy burden on the Chinese health system and society. Frailty is an ongoing process of development and evolution which can be reversed in the early stage. Therefore, formulating effective nursing strategies to prevent and control frailty in older adults is of great significance to improve their physical, mental health, social adaptability and quality of life and reduce the burden of public health. 10
Report of the Seventh Population Census issued by the National Bureau of Statistics of China 11 has shown that the proportion of older adults in rural areas is higher than that in urban areas, and the gap is widening. However, research regarding frailty to date tend to focus on older adults in cities 12 or nursing homes 13 rather than in rural areas, who differ from those living in urban areas in terms of their living arrangement, education level, income level, exercise routine, and the like. Therefore, it is necessary to strengthen research on the status quo and influencing factors of frailty among rural community-dwelling older adults, so as to identify and manage the influencing factors of frailty.
Although it is well recognized that frailty is a multidimensional syndrome,14,15 only the domain of physical frailty among rural community-dwelling older adults has been studied extensively in China.16 -18 It was found that the prevalence of frailty among these populations was between 36.76% and 67.3%. Age, education level, income level, employment status, income sources, suffering from a variety of chronic diseases, drinking, and smoking were the main influencing factors. However, the previous studies mainly investigated the physical frailty, with less focus on the analysis of psychological and social frailty. Relevant study has summarized an integral conceptual model of frailty. 5 In this model, demographic, socioeconomic and illness-related factors collectively affect physical, psychological, and social frailty. Up to now, little research focuses on influencing factors of frailty among rural community-dwelling older adults using this integral conceptual model of frailty. Therefore, the aim of our study is to investigate the status quo and influencing factors of overall frailty and its 3 domains among rural community-dwelling older adults based on the integral conceptual model to provide a theoretical basis to delay and alleviate frailty.
Method
Participants
A cross-sectional study was conducted with a convenience sample of older adults in 6 villages, Bashang Area of Zhangjiakou City, Hebei Province, China from August to September 2022. The inclusion criteria were: (1) Age ≥60 years old; (2) Permanent residents living in rural areas for more than 6 months per year; (3) Be able to communicate and comprehend; and (4) Be informed consent and willing to participate in this study. Those who had severe mental illness, acute diseases, and acute exacerbation of chronic diseases were excluded. To estimate the sample size, the rule of 10 to 20 participants per parameter was used. 19 There were 13 parameters in our study. Therefore, 130 to 260 participants were needed. A total of 200 questionnaires were distributed in our study, 195 valid questionnaires were completed, with a valid questionnaire return rate of 97.5%.
Measurements
General information form
It was a self-designed form to collect demographic and disease-related data. Demographic data included gender, age, marital status, living arrangement, education level, income level, financial burden, and medical service payment method. Disease-related data included the types of chronic diseases participants had such as degenerative osteoarthropathy, hypertension, diabetes, and coronary heart disease, the types of medications taken, and lifestyle information such as smoking, drinking, and exercise habits.
Assessment of the frailty
Tilburg Frailty Indicator (TFI) 20 was used to assess the frailty in this study. It was translated into Chinese and revised by Xi et al. 21 The scale showed a good construct validity and the Cronbach’s α was .686 in a group of Chinese older adults. 21 It is a 15-item multi-dimensional self-assessment tool that contains 3 domains: physical (8 items), psychological (4 items), and social (3 items). TFI has 2 response categories as yes and no except for the items of cognition, depressive symptoms, anxiety, and lack of social relations. The item of cognition was dichotomized into sometimes or no and yes, and the other 3 items were dichotomized into no and yes or sometimes. Total score for frailty has a range of 0 to 15, a score of no less than 5 is considered frailty. The score ranges for physical, psychological, and social frailty are 0 to 8, 0 to 4, and 0 to 3, respectively. High scores on the total scale and the subscales indicate a greater degree of frailty. In the current study, the Cronbach’s α coefficient for TFI was .738.
Assessment of comorbidity
The age-adjusted Charlson Comorbidity Index (age-CCI) 22 was used to assess the comorbidity. The age-CCI score is the sum of the weighted score of number and the seriousness of 17 comorbid diseases and the score of the participant’s age. The age of 50 to 59 years old is calculated as 1 point, after which each additional 10 years is worth 1 point. Age-CCI ≥ 2 is identified as having comorbidity, higher score indicates more comorbidities. 22 It demonstrated good validity and reliability among Chinese population. 23
Assessment of the mental status
The Hospital Anxiety and Depression Scale (HADS) 24 was used to evaluate the mental status, which was divided into 2 subscales of anxiety and depression, each containing 7 items. The scale has a total of 14 items and uses a 4-point scoring on a range of 0 to 3, yielding a total score ranging from 0 to 21 for each subscale. According to the HADS subscale, a score of 0 to 7 in each subscale is defined as no anxiety or no depression, a score of 8 to 10 as mild anxiety or mild depression, a score of 11 to 14 as moderate anxiety or moderate depression, and a score of 15 to 21 as severe anxiety or severe depression. 24 HADS has good reliability and validity in both hospital and community settings.25,26
Data Collection
The researchers contacted the director of the Bashang area for the permission to collect the data in that area. Two young villagers were selected as surveyors to help screen the eligible participants and collect data in participants’ houses. The surveyors were trained on the purpose, significance, and procedure of data collection. Because most participants had low literary or vision or hearing problems, after participants signed the informed consent sheet, the surveyors used face-to-face interview to help participants comprehend the questions and fill in the questionnaires according to their responses. To ensure the fidelity of responses, the surveyors informed the participants that the questionnaires were anonymous, and their responses would be kept confidential. The researchers went with the surveyors and were available to answer surveyors’ questions they had during the interview. The researchers double checked the questionnaires when they were returned to ensure data completeness. The questionnaire was regarded as invalid if 10% or more of the items were missing.
Data Analysis
IBM SPSS Statistics 26.0 was used for data analysis. Participants’ demographic characteristics and disease-related information were analyzed by using descriptive statistics. Categorical variables were described as frequency and percentage. All variables in this study did not conform to the normal distribution. Therefore, continuous variables were reported as median and quartile (P25, P75). Non-parametric test, such as Mann-Whitney U test and Kruskal-Walls H test were used, respectively, for comparisons between the 2 groups and multiple groups to explore the differences of frailty scores among the rural community dwelling older adults with different characteristics; Spearman correlation analysis was performed to evaluate the associations between variables. 27 Multiple linear regression was performed to explore the influencing factors of frailty and its domains. The total score and scores for each domain of the TFI scale were treated as dependent variables, variables with statistically significant differences in univariate analysis were treated as independent variables. P < .05 was considered statistically significant.
Ethical Consideration
This study was approved by the Ethics Committee of Beijing University of Chinese Medicine (2022BZYLL1210). The study was conducted according to the principles outlined in the Helsinki Declaration. Participants were informed that their participation in the study was voluntary, and the questionnaires were anonymous. Their refusal to participate in this study would not affect their rights or interests in the area. The written informed consent was obtained from participants.
Results
Demographic Characteristics
Among the 195 rural community-dwelling older adults, the average age was 71.52 ± 7.59 years old, ranging from 60 to 96 years old. The median and quartile of monthly income (RMB) were 1000 Yuan (700, 1000). Ninety-eight point forty six used the rural cooperative medical service for payment. Details are reported in Table 1.
Table 1.
Demographic Characteristics of the Rural Community-Dwelling Older Adults (N = 195).
| Characteristics | Number of people | (%) |
|---|---|---|
| Gender | ||
| Male | 76 | 38.97 |
| Female | 119 | 61.03 |
| Age (years old) | ||
| 60-69 | 86 | 44.10 |
| 70-79 | 76 | 38.97 |
| ≥80 | 33 | 16.93 |
| Education level | ||
| Primary school and below | 149 | 76.41 |
| Junior high school and above | 46 | 23.59 |
| Marital status | ||
| With a spouse | 168 | 86.15 |
| Without a spouse | 27 | 13.85 |
| Living arrangement | ||
| Living alone | 22 | 11.28 |
| Living together with family | 135 | 69.23 |
| Living in nursing homes | 38 | 19.49 |
| Monthly income (RMB) | ||
| <1000 | 89 | 45.64 |
| 1000-2499 | 95 | 48.72 |
| ≥2500 | 11 | 5.64 |
| Financial burden | ||
| No | 4 | 2.05 |
| Light | 140 | 71.80 |
| Medium | 43 | 22.05 |
| Heavy | 8 | 4.10 |
Disease-Related Information
The average number of chronic diseases participants had was 1.08 ± 0.97, with a maximum of 5. Degenerative osteoarthropathy (110, 56.41%) and hypertension (110, 56.41) were the most prevalent. The average number of medications taken was 0.94 ± 0.90, with a maximum of 4. A total of 69 (35.38%) had smoking habits, 68 of whom were males. Seventy-two (36.92%) had drinking habits, including 71 males and 1 female. Forty-seven exercised regularly, only accounting for 24.10%.
Comorbidity
The total score of the age-CCI ranged from 0 to 27, with a median score of 4 (P25 = 3, P75 = 5). A total of 147 (75.38%) had comorbidities.
Mental Status
The anxiety subscale scored from 3 to 17, with a median score of 7 (P25 = 7, P75 = 10). The depression subscale scored from 0 to 27, with a median score of 11(P25 = 9, P75 = 13). One hundred seventy-seven cases (90.77%) were identified as having depressive syndromes and 93 cases (47.69%) having anxiety.
The Status Quo of Frailty
The total score of TFI among 195 rural community-dwelling older adults ranged from 1 to 14, with a median and quartile score of 8(6, 10). One hundred sixty-six cases (85.13%) were identified as having frailty. Frailty scored differently across domains, with a median score of 4 for physical frailty, a median score of 2 for psychological frailty, and a median score of 2 for social frailty. The results for each item are shown in Table 2.
Table 2.
The Status Quo of Frailty Among Rural Community-Dwelling Older Adults (N = 195).
| Domain | Item | Score | n | % |
|---|---|---|---|---|
| Physical frailty | Good physical health | 0 (Yes) | 22 | 11.28 |
| 1 (No) | 173 | 88.72 | ||
| Unexplained weight loss | 0 (No) | 185 | 94.87 | |
| 1 (Yes) | 10 | 5.13 | ||
| Difficulty in walking | 0 (No) | 73 | 37.44 | |
| 1 (Yes) | 122 | 62.56 | ||
| Difficulty in maintaining balance | 0 (No) | 70 | 35.90 | |
| 1 (Yes) | 125 | 64.10 | ||
| Poor hearing | 0 (No) | 155 | 79.49 | |
| 1 (Yes) | 40 | 20.51 | ||
| Poor vision | 0 (No) | 158 | 81.03 | |
| 1 (Yes) | 3 | 18.97 | ||
| Lack of strength in hands | 0 (No) | 90 | 46.15 | |
| 1 (Yes) | 105 | 53.85 | ||
| Physical tiredness | 0 (No) | 38 | 19.49 | |
| 1 (Yes) | 157 | 80.51 | ||
| Psychological frailty | Cognition | 0 (No or sometimes) | 171 | 87.69 |
| 1 (Yes) | 24 | 12.31 | ||
| Depressive symptoms | 0 (No) | 95 | 48.72 | |
| 1 (Yes or sometimes) | 100 | 51.28 | ||
| Anxiety | 0 (No) | 76 | 38.97 | |
| 1 (Yes or sometimes) | 119 | 61.03 | ||
| Coping with problems well | 0 (Yes) | 16 | 8.21 | |
| 1 (No) | 179 | 91.79 | ||
| Social frailty | Living alone | 0 (No) | 170 | 87.18 |
| 1 (Yes) | 25 | 12.82 | ||
| Lack of social relations | 0 (No) | 38 | 19.49 | |
| 1 (Yes or sometimes) | 157 | 80.51 | ||
| Enough social support | 0 (Yes) | 43 | 22.05 | |
| 1 (No) | 152 | 77.95 |
Univariate Analysis of Associated Factors of Overall and 3 Domains of Frailty Among Rural Community-Dwelling Older Adults
Table 3 shows the results of univariate analysis of frailty and its domains. Statistically significant differences (all P < .05) were found in the scores of overall frailty and its domains in terms of gender, marital status, living arrangement, education level, financial burden, lifestyle (including exercise, smoking, and drinking habits), and comorbidities. According to the Spearman correlation analysis, age, anxiety score, and depression score were positively correlated with scores of overall frailty and its domains (r = .389-.617, P ＜ .001); and monthly income was negatively correlated with overall frailty.
Table 3.
Univariate Analysis of Influencing Factors of Overall and 3 Domains of Frailty Among Rural Community-Dwelling Older Adults (N = 195).
| Domains of frailty | Gender a | Age c | Education level a | Marital status a | Living arrangement b | Monthly income c | Financial burden b | Age-CCI a | Regular exercise a | Smoking a | Drinking a | Anxiety score c | Depression score c |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Physical frailty | −1.836 | 0.407*** | −3.118** | −3.311** | 6.565* | −0.258*** | 9.673* | 3.465** | −1.684 | −1.544 | −2.298* | 0.463*** | 0.450*** |
| Psychological frailty | −3.315** | 0.179*** | −1.830 | −1.015 | 4.950 | −0.218*** | 9.610* | 2.180* | −3.519*** | −3.246** | −3.117** | 0.577*** | 0.474*** |
| Social frailty | 0.780 | 0.238** | −0.562 | −3.828*** | 21.413*** | 0.063 | 5.908 | 0.632 | −2.086* | −0.253 | 0.558 | 0.319*** | 0.320*** |
| Overall frailty | −2.281** | 0.389*** | −2.850** | −3.406** | 12.214** | −0.242** | 13.047** | −3.276** | −3.023** | −2.313* | −2.624** | 0.617** | 0.566*** |
Mann-Whitney U test.
Kruskal-Walls H test.
Spearman correlation analysis.
P < .05.**P < .01. ***P < .001.
Multiple Linear Regression Analysis of Influencing Factors of Overall and 3 Domains of Frailty Among Rural Community-Dwelling Older Adults
The coding of independent variables is shown in Table 4. The results in Table 5 showed that age, gender, marital status, regular exercise, comorbidity and anxiety explain 49.4% of the variance for the overall frailty score. Anxiety was the only shared influencing factor of physical frailty, psychological frailty, and social frailty. Age, gender, marital status, financial burden, comorbidity, and regular exercise were factors which influenced 1 or 2 domains of frailty.
Table 4.
The Coding of Independent Variables.
| Independent variable | Coding |
|---|---|
| Age, monthly income, anxiety, depression | Enter as the original value |
| Gender | Female = 0; Male = 1 |
| Marital status | With no spouse = 0; With a spouse = 1 |
| Living arrangement | |
| Living alone | Alone = 0; Together = 0; Nursing home = 0 |
| Living together with family | Alone = 0; Together = 1; Nursing home = 0 |
| Nursing home | Alone = 0; Together = 0; Nursing home = 1 |
| Education level | Primary and below = 0; Junior high school and above = 1 |
| Financial burden | No = 0; Light = 1; Medium = 2; Heavy = 3 |
| Regular exercise | No = 0; Yes = 1 |
| Smoking | No = 0; Yes = 1 |
| Drinking | No = 0; Yes = 1 |
| Comorbidity | 0 ~ 1 = 0; ≥ 2 = 1 |
Table 5.
Multiple Linear Regression Analysis of Influencing Factors of Overall and 3 Domains of Frailty (N = 195).
| Dependent variable | Independent variable | Beta | t | P | Adjusted R2 |
|---|---|---|---|---|---|
| Overall frailty | Age | 0.225 | 4.109 | <.001 | .494 |
| Comorbidity | 0.195 | 3.668 | <.001 | ||
| Marital status | −1.616 | −0.199 | <.001 | ||
| Regular exercise | −0.183 | −3.352 | .001 | ||
| Gender | −0.135 | −2.606 | .010 | ||
| Anxiety | 0.439 | 7.850 | <.001 | ||
| Physical frailty | Age | 0.275 | 4.493 | <.001 | .364 |
| Comorbidity | 0.213 | 3.673 | <.001 | ||
| Marital status | −0.152 | −2.577 | .011 | ||
| Financial burden | 0.132 | 2.251 | .026 | ||
| Anxiety | 0.327 | 5.337 | <.001 | ||
| Psychological frailty | Gender | −0.206 | −3.515 | .001 | .350 |
| Regular exercise | −0.150 | −2.501 | .013 | ||
| Anxiety | 0.496 | 8.279 | <.001 | ||
| Social frailty | Marital status | −0.293 | −4.446 | <.001 | .164 |
| Anxiety | 0.274 | 4.163 | <.001 |
Discussion
Our study is among one of the first to investigate not only the overall frailty but also the 3 domains and their influencing factors among rural community-dwelling older adults in China based on Gobbens’ integral conceptual model of frailty. 18 By surveying 195 older adults in Bashang area, we reveal that the frailty among rural elderly is prevalent, and is determined by a variety of factors. The identification of the underlying factors may help develop interventions which are tailored to the special needs of older adults in the rural areas.
The prevalence of overall frailty among rural community-dwelling older adults in China is high. In our study, the overall frailty rate was 85.13%, which was much higher than 36.76% to 67.3% in other rural areas,16 -18 and 12.8% to 44.3% in urban areas.14,15 It may be due to the different tools used to measure frailty. We used the Chinese version of the TFI scale to investigate the prevalence of multidimensional frailty, while other researchers mainly investigated the prevalence of physical frailty by using Fried’s phenotype (FP). In addition, the average age of participants in our study was 71.52 ± 7.59 years old. The relatively young healthy older adults usually go out of the villages to earn a living, and those stay in the villages are usually older, less healthy and may be frailer.
In terms of physical frailty, nearly 90% of participants had poor physical health, which may be due to the substantial number of comorbidities and the significant decline of physical fitness with age. More than 80% of participants had frequent tiredness, which was related to the fact that the data collection time was within the busy farming season, and participants who engaged in manual labor were often in a state of fatigue. More than 60% of participants had difficulty in maintaining balance or walking, which are mainly associated with excessive physical labor and waist-leg diseases such as degenerative osteoarthropathy.
Regarding psychological frailty, more than 90% of the participants had a decline in coping ability. They generally felt that they had a low education level and had less contact with the outside world. Therefore, they lacked confidence and thought that they were not good at dealing with problems. More than 50% of the participants had anxiety or depressive syndromes. Anxiety may be due to their concerns about their health conditions, economic burden, and worries about the development of their children. The possible explanation of depressive syndromes may be related to the death of participants’ spouses, their declined health and income level, and insufficient social support.
As to social frailty, approximately 80% of the participants felt that they lack social relations or social support. The possible reasons were as follows: (1) the death of spouses, relatives, friends, and especially children, led to the loss of categories of social ties and a significant decline in social ties; (2) The increasing number of comorbidities and the decreased physiological capacity limited their participation in social activities and their social connections; (3) There is a lack of support from their children. Due to the lockdown of COVID-19 pandemic in China during the data collection period, only a few could return home to visit their families. In addition, due to the low information literacy, some older adults did not have the ability to use smartphones and could not video-chat with their children and grandchildren, further increased their sense of helplessness.
The most striking finding of this study is that anxiety affects frailty and all the 3 domains. Spearman correlation analysis showed that the anxiety scores were associated positively with the overall frailty and its domains. The multiple linear regression analysis further confirmed these trends. For every 1-point increase in anxiety score, frailty scores will increase by 0.274 to 0.496 points. This finding is in line with previous research. 28 This may be related to the dysfunction and the decrease of their psychological adjustment ability, which limits their activities and affects their physical conditions. Therefore, they are more subject to physical, psychological and social frailty.
The result of this study showed that both age and comorbidity could affect physical frailty and overall frailty. The older the age, the more serious the frailty, which is consistent with previous studies.16 -18,29 With the growth of age, physiological aging can lead to the weakening of physiological function, the decline of body reserve capacity, and the cumulative decline of multiple systems, leading to a variety of diseases. The older adults usually suffer from one or more chronic diseases. Relevant studies have shown that comorbidity will lead to the decline of physical function in older adults, 30 and then cause physical frailty.
Previous studies31 -33 have pointed out that the degree of overall frailty or physical frailty of women was more serious than those of men, few studies have focused on the gender difference in the psychological frailty of the elderly. In addition to the gender difference in the overall frailty, a new finding in our study is that gender can also affect the psychological frailty. The univariate analysis showed that female was more serious than male in terms of psychological frailty. The result was further confirmed by the multiple linear regression analysis. Studies34,35 have pointed out that female older adults have poorer mental health, greater psychological stress, and higher levels of depressive syndromes than males. The female older adults in rural area in China usually have fewer social activities, do most farm work and took care of children at home, receive less attention from their families, feel lonelier, and therefore, are prone to various psychological problems.
The results of this study showed that marital status could affect physical frailty, social frailty, and overall frailty. Frailty scores of participants without a spouse were higher than those with a spouse, which is consistent with those in Fan et al’s 36 and Panda et al’s 37 studies. It may be due to the fact that the older adults without spouses usually live alone, do all the house chores, and have insufficient social support. The results of this study also showed that economic burden could affect physical frailty. The older adults in the rural area usually have limited income sources and a heavy economic burden. When feeling unwell, they usually choose not to seek medical treatment, which further leads to the decline of physical condition. We also found that that regular exercise could affect psychological frailty and overall frailty. Those who exercise regularly had lower scores for psychological frailty and overall frailty. It is believed that exercise or physical activity can promote health. 17 It is common that the older adults are chatting together while exercising on the fitness equipment. Therefore, their physiological and psychological health may be improved at the same time.
Limitations
Although our study is novel in terms of the use of an integrated framework of frailty to examine physical, psychological and social frailty and their influencing factors, several limitations should be acknowledged. Firstly, the use of convenience sampling may limit the generalizability of the results of this study. Secondly, due to the cross-sectional design, the causal relationship between frailty and the relevant factors could not be determined. Lastly, due to the time limit, the data collection time of this study is between August and September, which lies in the busy farming season. Therefore, the prevalence of frailty may be overestimated. Future study could extend the data collection time to analyze the frailty across different seasons or times.
Implications to Theory, Practice and Policy
Our research is among one of the first to adopt Gobbens’ integral conceptual model to examine the multi-dimensional frailty and in turn validate the model among rural community-dwelling older adults in China. It is suggested that more attention from the medical staff should be paid to screen the frailty for the rural community-dwelling older adults, especially those who are anxious, in the older age, women, without a spouse, with heavy financial burden and with more comorbidities. Furthermore, the research findings indicate the necessity of providing economic support and mental health services for economically disadvantaged frail rural community-dwelling older adults. They are encouraged to participate in regular exercise to improve their frailty.
Conclusion
The overall frailty of the older adults in Bashang Area of Zhangjiakou is very serious, with a high prevalence rate of 85.13%. Frailty is due to multiple factors, such as anxiety, age, gender, marital status, financial burden, comorbidity, and regular exercise. Medical staff and policy makers should work hand in hand to improve frailty among rural community-dwelling older adults in China.
Acknowledgments
We would like to thank the director and the 2 surveyors of the Bashang area for their assistance to the data collection.
Footnotes
Author Contributions: Shaobo Guo: Data analysis, manuscript drafting, manuscript revision.
Hongxia Liu: Study design.
Bei Zhang: Data collection, data analysis, manuscript drafting.
Xiangru Li: Data analysis.
Keke Lin: Result execution, manuscript revision, supervision of the whole process.
Data Availability Statement: The data used in this study is available from the corresponding author upon request.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The authors received no financial support for the research, authorship, and/or publication of this article.
Ethical Approval and Informed Consent Statements: This study was approved by the Ethics Committee of Beijing University of Chinese Medicine (2022BZYLL1210). The study was conducted according to the principles outlined in the Helsinki Declaration. Participants were informed that their participation to the study was voluntary, and the questionnaires were anonymous. Their refusal to participate in this study would not affect their rights or interests in the area. The written informed consent was obtained from participants.
Consent to Participate: Written informed consent was obtained from participants.
Consent for Publication: Not applicable
ORCID iD: Keke Lin 
https://orcid.org/0000-0003-1755-0415
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