Abstract
Background
Multiple co-morbidities of chronic diseases tend to increase patients' medical costs and psychological stress, and reduce their quality of life. However, few studies have examined the mental health of older adults from cognitive perspectives and chronic disease co-morbidities.
Objectives
The aim is to investigate the current status of intolerable uncertainty and the factors that influence it in elderly patients with chronic comorbidities.
Design
Cross-sectional study design.
Settings
Departments of Gastroenterology, Endocrinology, Cardiovascular Medicine, and Respiratory Medicine, a tertiary hospital in Henan Province, China.
Participants
Elderly patients with chronic co-morbidities hospitalized in the departments of gastroenterology, endocrinology, cardiovascular medicine and respiratory medicine.
Methods
A non-probability convenient sampling method was used to select 295 elderly chronic disease co-morbid patients who were hospitalized in a tertiary-level hospital in Henan Province from June 2023 to December 2023, and general information questionnaires, the short version of the Intolerable Uncertainty Scale (IUS-12), the Family Caringness Rating Scale (APGAR), and World Health Organization Five Physical and mental health indicators (WHO-5) were used to investigate and analyze the influencing factors of intolerable uncertainty in elderly chronic disease co-morbid patients.
Results
The total IUS-12 score in elderly patients with chronic disease co-morbidities was (35.58 ± 3.24), the total APGAR score was (7.27 ± 2.29), and the total WHO-5 score was (17.37 ± 2.69). The total IUS-12 score of elderly chronic disease co-morbidities was negatively correlated with the total APGAR score (r=-0.884, P<0.01) and with the total WHO-5 score (r=-0.920, P<0.01). The results of multiple linear regression showed that rural area, living alone, family care and physical and mental health were independent influences on the intolerable uncertainty of elderly chronic co-morbid patients (P < 0.05).
Conclusions
The intolerable uncertainty of elderly patients with chronic disease co-morbidities is at a medium level, and clinical work should focus on the phenomenon of intolerable uncertainty of elderly patients with chronic disease co-morbidities, strengthen the early identification and nursing interventions for high-risk groups in rural areas, living alone, with low family care, and with poor physical and mental health, and give full play to the strengths of humanistic care in nursing to improve the quality of life. Urge policymakers to allocate targeted funding to underserved regions in order to expand access to specialized geriatric nursing services, telehealth infrastructure, and community-based support networks for patients living alone.
Keywords: Elderly, Chronic disease, Multimorbidity, Intolerance of uncertainty, Influencing factors
What is already known about the topic?
China has entered a deeply aging society, and population aging has accelerated the process of chronic disease prevalence in the elderly population to a certain extent.
Multimorbidity is defined as the coexistence of ≥ 2 chronic diseases in an individual, and compared with a single disease, multimorbidity tends to increase the medical costs and psychological stress of patients, and reduce their quality of life.
Intolerance of Uncertainty, as a cognitive bias, tends to cause individuals to negatively perceive, interpret, and react to uncertain events or situations, which has a negative effect on individuals’ emotions, cognitions, and behaviors, and is a key impediment to achieving healthy aging.
What this paper adds?
The intolerance of uncertainty among older chronic multimorbidity is at an intermediate level.
Ruralness, living alone, family caregiving, and physical and mental health are independent influences on the intolerance of uncertainty among older chronic multimorbidity patients.
The total intolerance of uncertainty score of elderly chronic multimorbidity was negatively associated with family caring and physical and mental health.
Introduction
According to demographic data released by the official statistics authority, by the end of 2024, the percentage of China’s population aged 65 and above reached 15.6%, significantly higher than the world average, indicating that China has entered a deeply aging society and that the aging process will continue to accelerate in the future [1]. Population aging throughout the 21 st century, the aging pattern is the basic national conditions of the Chinese-style modernization period, the aging tide as a whole on the economic and social development of a huge impact, and the pressure has a magnifying effect [2].
Under the dual pressures of population aging and the continuing prevalence of metabolic risk factors, the incidence of chronic diseases is gradually increasing and has gradually become a serious threat to human public health problems [3, 4]. At the same time, the incidence and prevalence of chronic diseases in the elderly are gradually increasing, more than 78% of the elderly have chronic disease co-morbidities, and the co-morbidity rate is on the rise, which is a serious threat to the life and health of the elderly [5, 6]. Studies have shown that the chronic disease co-morbid state will deplete the patient’s ability to work, leading to a decline in the quality of life, the economic burden and mental health and a series of problems will ensue [7, 8].
China has a Huge burden of chronic diseases, which accounts for about 70% of the total burden of disease, and the death rate of chronic diseases is as high as 86.6% [9]. In the context of the national implementation of the strategy of actively coping with population aging, how to improve the quality of life of elderly patients with chronic diseases and enhance the sense of well-being and accessibility of the elderly has become an urgent issue in the process of healthy aging [10].
Multimorbidity is the coexistence of individuals with ≥ 2 chronic diseases. With the aggravation of population aging and changes in the disease spectrum, the prevalence of chronic disease co-morbidities among the elderly in China remains high, and has become a key population of concern for primary health management [11, 12]. Compared with a single chronic disease, the coexistence of multiple chronic diseases has more complex influencing factors, which not only increases the difficulty of assessing and managing patients’ health status and makes medical decision-making more difficult, but also greatly reduces the quality of patients’ life and health and aggravates their economic burden [2].
Intolerance of Uncertainty (IU), also known as Uncertainty Tolerance, as a cognitive bias, tends to make individuals perceive, interpret, and react negatively to uncertain events or situations, which has negative effects on individuals’ emotions, cognition, and behaviors, and is a key impediment to achieving healthy aging [13, 14].It has been shown that intolerance of uncertainty is significantly associated with negative affective reactions such as worry and anxiety [15]. Individuals with a high degree of uncertainty intolerance experience stronger negative emotions in response to an impending uncertain event [16].Compared with healthy elderly people, elderly patients with multimorbidity have to face more uncertainties and less tolerance for uncertainties due to the superimposition, coexistence and combination of multiple chronic diseases, the cumulative effect of co-morbidities, and the complexity of their conditions [17].
The family is a fundamental part of the social network of older persons, while family care is the main source of warmth for older persons and has a direct impact on their physical and mental health. Family caring is an important indicator of how well a family is functioning and evaluates an individual’s quality of life. According to the theory of differential order pattern, interpersonal relationships in Chinese society are driven by blood relations centered on the family [18]. Maintaining close contact among family members and regularly participating in cultural and recreational activities together can provide adequate care and warmth for the elderly and effectively promote the level of active ageing.
Under the Transactional Model of Stress and Coping (TMSC), the physical and mental health, family care, and intolerable uncertainty (IU) of elderly chronic disease co-morbid patients form a dynamic interaction [19]. Family care, as a key buffer resource, enhances patients’ psychological resilience through emotional support, life care and health guidance, relieves IU-induced anxiety and cognitive avoidance, and promotes the maintenance of physiological functioning and improvement of social adaptive capacity, while IU, as a cognitive-emotional variable, may increase the psychological burden of patients due to the intensification of disease uncertainty, and may be alleviated through intergenerational communication and information sharing in the family support system, influencing patients’ choice of coping strategies for chronic disease management.
Previous studies have focused on symptom management and medication management in older adults, ignoring the mental health of older adults with chronic disease co-morbidities, and few studies have been conducted from a cognitive perspective and chronic disease co-morbidities. Therefore, this study investigated the current status of IU in elderly patients with multimorbidity from a cognitive perspective and explored IU-related influencing factors, with the aim of providing references for the development of precise intervention programs for clinical nursing practice.
Methods
Study design, setting and participants
Elderly patients with multimorbidity who were hospitalized in the departments of gastroenterology, endocrinology, cardiovascular medicine, and respiratory medicine in a tertiary-level hospitals in Henan Province from June to December 2023 were selected as the study subjects by convenience sampling method. Inclusion criteria: ① age ≥ 60 years old; ② have been diagnosed with at least two chronic diseases by a secondary- or tertiary-level hospital in China; ③ stable condition, informed consent, voluntary participation in this study; ④ have a certain understanding and communication ability, can complete the questionnaire independently or with the assistance of the investigator. Exclusion criteria: ① being involved in other intervention studies; ② serious condition unable to cooperate. According to Kendall’s sample size estimation method [20], the sample size should be at least 5–10 times of the study variables, and considering 20% of invalid questionnaires, a total of 10 variables were included, and the required sample size was at least 120 cases, which was sufficient for this study. This study was carried out in compliance with the STROBE statement and the Declaration of Helsinki [21, 22]. This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Ethics Approval No. 2023-KY-1159). All participants provided signed informed consent.
Procedure
A specific survey team was established, and the team members were trained before the survey to unify the instructional language. The purpose and significance of this study were explained to patients in detail before the survey, and questionnaires were administered after obtaining informed consent from the study subjects. After the investigators collected and verified the questionnaires, if some items and omissions needed to be added, the study subjects were immediately asked to provide them. However, in cases where the missing information could not be obtained despite these efforts, the questionnaires were considered incomplete. If there were obvious logical inconsistencies or contradictory answers within the questionnaire that could not be resolved through further verification with the study subjects, the questionnaire was deemed invalid. Two people checked data entry to eliminate logical errors in the questionnaires to ensure validity. A total of 320 questionnaires were distributed in this study, and 295 valid questionnaires were recovered, for a valid recovery rate of 92.19%.
Measures
General information questionnaire
The questionnaire developed by the investigators drawing on references [23]. It included age, gender, type of health insurance, education level, marital status, per capita monthly household income, place of residence, and mode of residence.
Intolerance of uncertainty Scale-12 (IUS-12)
It was developed by Carleton et al. [24] and to assesses individuals’ perceptions, interpretations and cognitive biases of uncertainty situations or events. The scale consists of three dimensions, namely anticipatory behavior (6 entries), inhibitory behavior (3 entries), and anticipatory emotion (3 entries), with a total of 12 entries, and has a good reliability and Cronbach′s α coefficient of 0.79. The Likert 5-point scale was used, ranging from “extremely non-compliant” to “fully compliant”, with scores ranging from 1 to 5, and the higher the score, the higher the level of intolerable uncertainty. The total score on the Intolerance of Uncertainty Scale − 12 (IUS − 12) ranges from 12 to 60.
Family APGAR index (APGAR)
It was developed by Smilkstein et al. [25], with a total of 5 entries and a Cronbach′s α coefficient of 0.87. A Likert 3-point scale was used, with scores ranging from 0 to 2 for “almost never” to “often”, and total scores ranging from 0 to 10. The higher the total score, the better the family functioning. A total score of ≤ 3 was classified as severely impaired, 4–6 as moderately impaired, and ≥ 7 as well-functioning.
World health Organization-5 Well-being index (WHO-5)
It was developed by BECH et al. [26], with a total of 5 entries and a Cronbach′s α coefficient of 0.82. It was scored on a 6-point Likert scale, with scores ranging from 0 to 5, from “never” to “all the time”. Likert 6 scores were used, ranging from 0 to 5 for “never” to “all the time”, with a total score of 0 to 25, with higher scores indicating better physical and mental health.
Statistical analysis
Data analysis were performed by statistic package for social science (IBM SPSS, version 26.0 for Windows, Armonk, NY, USA). The Harman one-factor method was used to test for common method bias for all entries, and there were four factors with an eigenroot greater than 1, and the first factor explained 34.28% of the total variance, which was less than the critical value of 40%, indicating that there was no significant common method bias in this study. Measurement information that conformed to normal distribution was expressed as mean ± standard deviation, and counting information was expressed as number of cases (%). Pearson’s correlation analysis was used to explore the relationship between the variables, and multiple linear regression was used to analyze the factors influencing the intolerable uncertainty of elderly patients with chronic co-morbidities. All tests were two-sided, and the statistical significance level for all analyses was defined as P < 0.05.
Results
Participant characteristics
The patient selection flow is outlined in Fig. 1. A total of 295 elderly patients with chronic disease co-morbidities were included in this study, of whom 132 cases (44.7%) were male and 163 cases (55.3%) were female; age, 60–91 (69.23 ± 7.12) years old; type of medical insurance: self-funded, 16 cases (5.4%), New Rural Cooperative Medical Insurance (NRCMI), 82 cases (27.8%), Resident’s Medical Insurance (RMI), 134 cases (45.4%), Employee’s Medical Insurance (EMI), 63 cases (21.4%); education level: junior high school and below 211 cases (71.5%), senior high school/secondary school 58 cases (19.7%), college and above 26 cases (8.8%); marital status: married 288 cases (97.6%), other 7 cases (2.4%); per capita monthly household income: <2000 RMB 90 cases (30.5%), 2000 ~ 3000 RMB 107 cases (36.3%), > 3000 RMB 98 cases (33.2%); place of residence: 88 cases (29.8%) in rural areas, 207 cases (70.2%) in urban areas; mode of residence: 171 cases (58.0%) living with their children, 119 cases (40.3) living with their spouses, 5 cases (1.7%) living alone.
Fig. 1.
Flowchart of the participants
IUS-12, APGAR, and WHO-5 scores in elderly patients with chronic co-morbidities
See Table 1.
Table 1.
The IUS-12, APGAR, and WHO-5 scores of elderly patients with chronic co-morbidities (N = 295) (scores, ‾x ± s)
| Items | Minimum values | Maximum values | Overall score | Average score of entries |
|---|---|---|---|---|
| IUS-12 | 27 | 45 | 35.58 ± 3.24 | 2.96 ± 0.27 |
| Anticipatory behavior | 12 | 23 | 16.26 ± 2.10 | 2.71 ± 0.35 |
| Inhibitory behavior | 8 | 13 | 10.48 ± 0.89 | 3.49 ± 0.29 |
| Anticipatory emotion | 6 | 12 | 8.83 ± 0.98 | 2.94 ± 0.33 |
| APGAR | 3 | 10 | 7.27 ± 2.29 | 1.45 ± 0.46 |
| WHO-5 | 12 | 23 | 17.37 ± 2.69 | 3.48 ± 0.54 |
Univariate analysis of factors influencing IUS-12 in elderly patients with chronic co-morbidities
The IUS-12 score differences in elderly patients with chronic comorbidities across medical insurance types, education level, monthly household income per capita, residence place, and modes of residence were statistically significant (P < 0.05). See Table 2.
Table 2.
Univariate analysis of factors influencing IUS-12 in elderly patients with chronic co-morbidities (N = 295)
| Items | Classification | Number of cases (n) | IUS-12 | Test value | P |
|---|---|---|---|---|---|
| Gender | Male | 132 | 35.59 ± 3.44 | −0.070 | 0.945 |
| Female | 163 | 35.56 ± 3.09 | |||
| Age(years) | 60 ~ 64 | 86 | 36.13 ± 3.53 | 1.557 | 0.200 |
| 65 ~ 69 | 86 | 35.07 ± 3.12 | |||
| 70 ~ 74 | 57 | 35.61 ± 3.36 | |||
| ≥ 75 | 66 | 35.48 ± 3.24 | |||
| Medical insurance type | Self-payor | 16 | 36.63 ± 3.16 | 73.339 | 0.000 |
| Villagers’ medical insurance | 82 | 38.88 ± 3.08 | |||
| Residents’ medical insurance | 134 | 34.16 ± 1.97 | |||
| Staff’ s medical insurance | 63 | 34.02 ± 3.24 | |||
| Education level | Junior high school and belowl | 211 | 36.27 ± 3.34 | 20.346 | 0.000 |
| Senior high school/secondary school | 58 | 34.12 ± 2.16 | |||
| College and above | 26 | 33.15 ± 2.11 | |||
| Marital Status | Married | 288 | 35.53 ± 3.22 | −1.413 | 0.159 |
| Other | 7 | 37.29 ± 4.23 | |||
| Monthly household income per capita | <2000RMB | 90 | 38.78 ± 3.10 | 110.324 | 0.000 |
| 2000 ~ 3000RMB | 107 | 34.31 ± 2.06 | |||
| >3000RMB | 98 | 34.02 ± 2.18 | |||
| Residence place | Village | 88 | 38.95 ± 3.03 | 13.633 | 0.000 |
| City | 207 | 34.14 ± 2.05 | |||
| Mode of residence | Live with spouse | 119 | 35.17 ± 3.41 | 9.040 | 0.000 |
| Live with children | 171 | 35.70 ± 2.99 | |||
| Alone | 5 | 41.20 ± 2.28 |
Correlations between intolerable uncertainty and family caring, physical and mental health in older patients with chronic co-morbidities
The IUS-12 total score was negatively correlated with the APGAR and WHO-5 total scores in elderly patients with chronic co-morbidities (both P < 0.05), as shown in Table 3.
Table 3.
Correlation analysis between intolerable uncertainty and family caring, physical and mental health of elderly patients with chronic co-morbidities
| IUS-12 | Anticipatory behavior | Inhibitory behavior | Anticipatory emotion | WHO-5 | APGAR | |
|---|---|---|---|---|---|---|
| IUS-12 | 1 | — | — | — | — | — |
| Anticipatory behavior | 0.875** | 1 | — | — | — | — |
| Inhibitory behavior | 0.697** | 0.317** | 1 | — | — | — |
| Anticipatory emotion | 0.798** | 0.462** | 0.715** | 1 | — | — |
| WHO-5 | −0.920** | −0.774** | −0.701** | −0.744** | 1 | — |
| APGAR | −0.884** | −0.768** | −0.647** | −0.688** | 0.974** | 1 |
**P<0.01
Multifactorial analysis of factors influencing IUS-12 in elderly patients with chronic co-morbidities
Multiple linear regression analyses were performed using the total IUS-12 score of elderly patients with chronic co-morbidities as the dependent variable, and the statistically significant variables in the univariate analyses as well as the APGAR and WHO-5 scores as the independent variables, and the values assigned to the independent variables are shown in Table 4. The results are shown in Table 5.
Table 4.
Assignment of independent variables
| Items | Assignment Description |
|---|---|
| Medical insurance type | Dummy variables are set using self-payor as a reference. Villagers’ medical insurance = X1(1,0,0), Residents’ medical insurance = X2(0,1,0), Staff’ s medical insurance = X3(0,0,1) |
| Education level | Junior high school and belowl = 1, Senior high school/secondary school = 2, College and above = 3 |
| Monthly household income per capita | <2000RMB = 1, 2000 ~ 3000RMB = 2, >3000RMB = 3 |
| Residence place | Village = 1, City = 2 |
| Mode of residence | Dummy variables are set using living with spouse as a reference. living with children = X1(1,0), alone = X2༈0,1༉ |
| APGAR | Original Value Carried |
| WHO-5 | Original Value Carried |
Table 5.
Multiple linear regression analysis of factors influencing IUS-12 in elderly patients with chronic co-morbidities (N = 295)
| Variables | Unstandardized coefficient | Beta | t | P | |
|---|---|---|---|---|---|
| B | Standard error | ||||
| Constant | 56.945 | 1.002 | — | 56.844 | 0.000 |
| Residence place | −1.450 | 0.302 | −0.205 | −4.805 | 0.000 |
| Mode of residence | |||||
| Alone | 1.562 | 0.617 | 0.062 | 2.530 | 0.012 |
| APGAR | −1.034 | 0.041 | −0.257 | −5.178 | 0.000 |
| WHO-5 | −1.247 | 0.112 | −1.034 | −11.132 | 0.000 |
R2=0.884, Adjust R2=0.881, F=310.580, P=0.000
Discussion
The current state of IU for older patients with chronic co-morbidities
In this study, the score of intolerable uncertainty of elderly patients with chronic disease co-morbidities was 35.58 ± 3.24, which was at a medium level, higher than that of otolaryngology patients [27], individuals who received liver transplant [28], and university students [29], which was consistent with the results of the study conducted by Sara et al. [30].The reason for this is that due to physiological weakness and social role cognitive changes, the elderly are prone to become a high-risk group for chronic disease co-morbidities, are more concerned about chronic disease cognition and multiple medication management, and have a relatively high level of disease-related uncertainty and intolerable uncertainty, and often take a conservative approach to medical decision-making, with a subjective tendency to treat conservatively in order to circumvent uncertainty and risk. This finding fits with Dugas et al.‘s [31] model of empirical avoidance, which states that when faced with uncertainty in a life situation, individuals who are highly intolerant of uncertainty tend to respond in an avoidant manner and with negative attitudes in order to prevent themselves from falling into the same situation again. Dugas regards IU as a cognitive bias that predisposes individuals to negatively perceive and react to uncertain events or situations and has a certain negative impact on their decision-making ability. Individuals with a high inability to tolerate uncertainty are prone to indecision in the face of uncertain situations, have a high sense of risk aversion, and are more inclined to adopt conservative solutions and responses. The transdiagnostic model of intolerance of uncertainty posits that individuals who are intolerant to uncertainty utilise rumination and experiential avoidance when faced with uncertain situations in order to cope with their emotional arousal [32]. Therefore, it is suggested that clinical nursing practice should pay attention to the current situation of intolerable uncertainty in elderly patients with chronic disease co-morbidities, learn from the cognitive point of view to draw on the relevant theories and techniques of psychology, and adopt psychological nursing interventions, such as group health education and counseling and cognitive-behavioral therapy, in order to reduce the patients’ sense of uncertainty about their diseases and their inability to tolerate the level of uncertainty, and to improve the quality of their lives. The study’s findings suggest that this can help improve the physical and mental health of patients with chronic disease comorbidities, and reduce the burden of family caregiving. This study establishes a foundation for future intervention studies on intolerable uncertainty in older adults with chronic disease comorbidities, and also provides a reference point for the management of chronic disease comorbidities.
Factors influencing the intolerable uncertainty of older patients with chronic co-morbidities
Residence place and mode of residence
The results of this study showed that older chronic disease co-morbid patients in urban areas had lower intolerable uncertainty scores compared to those living in rural areas. The reason for this analysis is that most of the low-income groups in rural areas are not highly educated and are unwilling to bear the high cost of examinations and treatment risks relative to their income levels, and tend to prefer conservative treatments, with low levels of treatment compliance and examination cooperation, and are unable to tolerate high levels of uncertainty about their illnesses. While the financial cost of access can be a significant barrier, it is not the only factor to be considered. The potential for increased Medicare reimbursement or the implementation of free testing programs for older adults as part of a comprehensive solution is acknowledged, however, it is also emphasised that a multifaceted approach is necessary to address all potential factors.
Clinical nursing workers are recommended to focus on the physical and mental health of rural elderly people, encourage them to participate in regular health checkups, actively participate in early screening for chronic diseases, and carry out targeted health education and counseling activities for elderly people with different levels of literacy, focusing on improving their health awareness of chronic disease prevention and treatment, so as to achieve forward movement of the gateway to prevention in the first place.
In addition, the level of intolerable uncertainty is higher among elderly co-morbid patients living alone compared to those living with their spouses and children. This may be due to the fact that elderly patients with chronic disease co-morbidities who live alone lack the necessary support of a companion, and their psychological ability is often more fragile, and they need to face multiple stressors alone. The long-term disease status, economic pressure from chronic disease co-morbidities treatment, and mental stress all aggravate the patients’ sense of uncertainty to different degrees, and they remain anxious or fearful of the unknown uncertainty, and their intolerance of uncertainty level is higher. The dyadic coping theory suggests that partners communicate and assess when facing uncertainty about a stressful event, rely on each other and influence each other, and can facilitate joint coping [33]. The dyadic coping system interaction model promotes supportive dyadic coping, in which partners provide emotional support to each other in the face of a stressful event and adopt problem-solving-centered coping strategies, as an important form of positive interaction between partners [34]. It is suggested that clinical practice should pay attention to the residence situation and family structure of elderly chronic disease co-morbid patients, and give more help and support to elderly chronic disease patients living alone. Concurrently, it is imperative to fortify the interface with the community health care service system and to empower the community to provide comprehensive assistance to the elderly with chronic co-morbidities.
Degree of family care
The results of this study showed that family caring was the main factor influencing the intolerable uncertainty of elderly chronic disease co-morbid patients (B = −1.034, P < 0.01), which means that the higher the family caring, the lower the score of intolerable uncertainty of elderly chronic disease co-morbid patients. As an important part of the social support system for the elderly, family care can provide basic life care and emotional support for elderly patients, enhance their positive emotions, help them establish good health beliefs and health behaviors, and reduce their uncertainty tolerance. It is suggested that dyadic coping intervention strategies based on the patient-spouse dichotomous holistic perspective can be used to enhance the family care of elderly chronic disease co-morbid patients, and to help patients establish positive health beliefs and treatment confidence, in order to reduce the intolerable uncertainty of their illness and enhance their physical and mental health. The information motivation behavioral skills model (IMB) suggests that social support, health behavior beliefs, and an individual’s self-efficacy constitute the major cognitive and affective factors in their health-related behaviors [35]. Intergenerational support, as a special component of the social support system, refers to the financial, life and emotional help provided by parents for their children or by children for their parents. At present, intergenerational support has become the main way of care for the elderly, and plays an important role in improving the quality of life and subjective well-being of the elderly [36]. Strengthening the intergenerational support of children is an important way to increase the family companionship of elderly patients with chronic diseases, which helps to fulfill the role of family support and care, assisting them to actively manage their illnesses, improving the family care of elderly patients with chronic co-morbidities, and decreasing their sense of uncertainty about their illnesses. Therefore, clinical nursing practice should start from a variety of social support forms such as spousal support, intergenerational support and healthcare support in an all-round way, at multiple levels and from multiple perspectives to assist elderly chronic disease co-morbid patients in obtaining more family resource support, which in turn enhances the degree of their family care, in order to reduce their intolerable uncertainty.
Mental and physical health
The results of this study showed that the lower the WHO-5 scores, the higher the IUS-12 scores of elderly chronic co-morbidities. This is, the poorer the level of physical and mental health of the individual, the higher the level of intolerable uncertainty. The cognitive model of Generalized Anxiety Disorder (GAD) states that the inability to tolerate uncertainty is a central factor in an individual’s worry anxiety, directly affecting the way the individual perceives, emotions, and behaviors [37]. Previous studies have confirmed the correlation between intolerance of uncertainty and anxiety subtypes such as obsessive-compulsive disorder [38], social anxiety disorder [39], and panic disorder [40], suggesting that intolerance of uncertainty has some neurobiological association with anxiety [41]. Therefore, it is suggested that clinical nursing practice should pay close attention to the physical and mental health of elderly patients with chronic co-morbidities, actively understand and regularly assess the existing or potential psychological problems of the patients, patiently listen to the real thoughts of the patients’ hearts, and do a good job in the corresponding psychological care, emotional management and life guidance to improve their mental health, implement more comprehensive nursing humanistic care, and improve the quality of nursing services. Actively guiding patients to participate proactively in the whole process of self-management of chronic disease co-morbidities and disease diagnosis and treatment, enhancing their confidence in disease treatment, improving their physical and mental health, and reducing their sense of uncertainty about their diseases and their inability to tolerate uncertainty.
Limitations
One notable limitation of this study lies in the sampling method employed. Specifically, we utilized convenience sampling to recruit participants, which, while pragmatic given the constraints of time, resources, and accessibility within our specific healthcare setting, may introduce selection bias. This sampling approach inherently limits the generalizability of our findings to the broader population, as it may not fully capture the diversity and characteristics of the entire target group. To address this limitation in future research, we recommend the adoption of probability sampling methods, such as random sampling or stratified sampling, which can enhance the representativeness of the sample and improve the external validity of the study findings. Additionally, expanding the recruitment scope to include multiple healthcare facilities or regions could further mitigate the impact of sampling bias.
It is important to note that this study was conducted as a cross-sectional study in only one tertiary hospital in Henan Province, resulting in a small sample size and potential selection bias. The regional limitations involved recommend that large-sample cohort studies be targeted in different regions to gain a comprehensive understanding of the current status of chronic disease co-morbidity intolerable uncertainty among the elderly in China. Most of the questionnaires in this study were completed by the patients themselves, and therefore there is some risk of self-reporting bias. The influencing factors for the intolerable uncertainty of older patients with chronic co-morbidities that were collated and summarized in this study are limited, and more influencing factors should be further explored in future studies. In addition, this study focused only on hospitalized elderly patients with chronic co-morbidities, to some extent limiting the generalizability of the results. Furthermore, the study was limited by its cross-sectional design, precluding the determination of causal relationships between factors.
Conclusions
In conclusion, this study revealed that elderly patients with chronic co - morbidities experience moderate levels of intolerable uncertainty. Rural residence, living alone, family caregiving, and physical and mental health status emerged as independent influencing factors. Early identification, assessment, and management of intolerable uncertainty in this population are crucial.
Acknowledgements
The study expresses its sincerest gratitude to all those who participated in the questionnaire.
Author contributions
Tie hao (First Author): Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft; Zhang Jingru (Corresponding Author): Conceptualization, Resources, Supervision, Writing - Review & Editing.
Funding
No external funding.
Data availability
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
Declarations
Ethical approval and consent to participate
This study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (Ethics Approval No. 2023-KY-1159). All participants provided signed informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Hao Tie, Email: 1669393107@qq.com.
Jingru Zhang, Email: zhangjingru2016@126.com.
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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 that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.