Self-efficacy to reduce sedentary behavior status and its influencing factors among middle-aged or older adults with chronic diseases: a cross-sectional study

Abstract

Background

Sedentary behavior (SB) is an important public health risk factor and is common among people with chronic diseases. Self-efficacy to reduce SB represents a distinct construct. Nonetheless, studies investigating interventions for SB are lacking, which may be addressed in future research. Hence, understanding the factors influencing self-efficacy to reduce SB among middle-aged and elderly people with chronic diseases holds significance.

Methods

This study included 583 middle-aged and elderly people from March 2024-May 2024 in 6 communities in Changzhou. The Chinese version of the self-efficacy to reduce sedentary behavior (SRSB) questionnaire was used to measure individuals’ confidence in reducing SB. Data were analyzed using SPSS 27.0 using descriptive statistics, cluster analysis, chi-square test, and ordered multinomial logistic regression to explore the influencing factors of SRSB in people with chronic diseases.

Results

A survey completion rate of 97.2% was achieved. A total of 583 people from 6 communities participated in this study. The median (interquartile range, IQR) score of the Chinese version of the SRSB was 3.78 (3.00–4.44), with a range of 1.0 to 5.0. Factors associated with the self-efficacy to reduce SB were body mass index (BMI), education level, number of chronic diseases, smoking status, and physical exercise.

Conclusions

The findings of this study confirmed that the self-efficacy to reduce SB of people with chronic diseases in China was at a moderate level. In health management, emphasis should be placed on overweight or obese individuals, poorly educated, having more chronic diseases, smoking, and not exercising. Health education should focus on preventive interventions against SB, promoting the adoption of beneficial lifestyles for middle-aged and elderly people with chronic diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-25094-w.

Introduction

With the aging population, the prevalence of chronic diseases and comorbidity patterns pose significant challenges to global health, and the burden of chronic diseases shows a dramatically increasing trend worldwide. According to the World Health Organization, 74% of global deaths are attributed to chronic diseases [1]. Furthermore, chronic diseases are the primary cause of early death among adults worldwide, with older adults being more prone to developing most chronic diseases compared to younger adults [2]. According to the World Health Organization’s global report [1], 80% of chronic disease deaths occur in low- and middle-income countries. Evidence revealed that chronic diseases were highly prevalent among older adults in China and varied geographically [3]. According to the latest 2020 data released by the China Health and Retirement Longitudinal Study (CHARLS), common chronic diseases among middle-aged and elderly people in China include hypertension, diabetes, arthritis or rheumatism, stomach or digestive diseases, dyslipidemia, heart disease, and other conditions. The prevalence of chronic diseases in this population has reached 80.9%, with a comorbidity rate of 58.1% [4]. Zhang et al. [5] conducted a survey of 10,197 middle-aged and older adults and found that the prevalence of multimorbidity in this population was 55.12%, and 65.60% among people aged ≥ 65 years. Chronic diseases seriously threaten the health of Chinese residents and constitute a major public health problem affecting the social and economic development of China.

Sedentary behavior (SB) is an independent risk factor for chronic diseases and has emerged as the focus of research, clinical, and policy interest [6]. The Physical Activity and Sedentary Behavior Guidelines issued by the WHO in 2020 clearly state that people with chronic diseases should limit SB [7]. SB can be defined as any behavior in which the energy expenditure is less than 1.5 metabolic equivalents (METs) while sitting or lying down in an awake activity [8]. SB is common in patients with chronic diseases and affects the care and prognosis of the disease. Several studies have reported that patients with type 2 diabetes spend at least 50% of their total lifetime in sedentary behavior [9]. Moreover, COPD patients with an average age of 65 years are sedentary for at least 10 h per day [10], and the average daily SB time of stroke patients is (479.65 ± 112.65) min [11]. A large number of studies have confirmed that SB is associated with adverse health outcomes such as cardiovascular disease, all-cause mortality, type 2 diabetes, and cancer [12–14]; in addition, sitting for an extended period can also increase the risk of chronic disease and death, seriously affecting the quality of life of the patients with chronic diseases [15]. Previous research has reported that even physical activity at the recommended level does not offset the harm of sedentary behavior [16]. Additionally, SB tends to be unintentional and often occurs in conjunction with other behaviors, such as reading books, watching TV, and socializing [17]. Similar to smoking, SB has a delayed impact, and people may be exposed to potential health risks later in their lives. Therefore, reducing the SB of patients with chronic diseases is crucial.

Self-efficacy has been shown to be a reliable predictor of diverse health behaviors [18], and is usually conceptualized as situation- or domain-specific. Self-efficacy is a psychological concept defined as an individual’s belief in his or her capacity to execute behaviors necessary to produce specific performance attainments [19]. A substantial body of evidence has repeatedly demonstrated a strong link between self-efficacy and physical activity (PA) levels [20–22]. PA and self-efficacy are positively correlated [23], and exercise self-efficacy can predict subsequent PA [24]. A systematic review indicated that SB is significantly negatively associated with PA, particularly because SB often displaces time that could otherwise be used for light intensity activity, thereby reducing individuals’ overall PA levels [25]. However, the research on the relationship between self-efficacy to reduce SB and SB is still in the preliminary exploration stage. SB and PA, as two different behavioral patterns, should be explained by different predictors. Whipple stated that self-efficacy to reduce SB represents a distinct construct [26] and could be part of future research into interventions for SB. Therefore, exploring SB-specific self-efficacy is indispensable for reducing SB.

Based on Social Cognitive Theory, self-efficacy is regarded as one of the most central cognitive determinants of behavior change, and the influence of sociostructural factors on behavior is primarily exerted through their impact on self-efficacy beliefs [27]. Moreover, illness and treatment can also affect patients’ levels of exercise self-efficacy [28]. Therefore, we hypothesize that self-efficacy to reduce SB may also be affected by sociodemographic characteristics and chronic disease status. At present, studies exploring the related factors impacting the self-efficacy to reduce SB are scarce. Given the critical role of self-efficacy to reduce SB in predicting SB, identifying its influencing factors is of great significance for developing effective interventions to reduce sedentary time among patients with chronic diseases. Consequently, the present study aimed to assess the status of self-efficacy to reduce SB in people with chronic diseases in China and to investigate its influencing factors, in order to inform the design of future tailored intervention strategies for patients with chronic diseases.

Methods

Study participants and design

This study adopted a cross-sectional design. The project was carried out in 6 communities in Changzhou, PR China, from March to May 2024. The inclusion criteria were the following: (1) age ≥ 45 years old; (2) have clear consciousness, good language communication ability; (3) willing to participate in this study. The exclusion criteria were as follows: (1) mental illness or cognitive dysfunction; (2) suffering from other diseases resulting in limited PA or the need for wheelchair assistance out; (3) inability to communicate due to serious diseases, such as heart failure and respiratory failure. Based on the literature review, 12 independent variables were initially developed. The sample size was increased by 20% to account for a potential rejection rate and an invalid questionnaire rate of 20%, resulting in the required sample size of 150 ~ 225. In the sampling process, to ensure that the sample size meets the minimum standards, a larger number of middle-aged and elderly people with chronic diseases were included in the survey.

Measures

Demographic characteristics

Demographic characteristics included age, gender, BMI, place of residence, education level, living conditions, marital status, monthly income, working status, number of chronic diseases, smoking status, and lack of physical activity. In this study, individuals engaging in moderate or higher intensity PA at least 3 times per week for no less than 30 min per session or those performing moderate to heavy manual labor are considered physically active [29]. Specifically, moderate physical labor includes activities such as regular lifting, weeding, painting, and picking fruits and vegetables. Heavy labor includes more intense tasks like lifting heavy objects, shoveling, forging, or digging.

Self-efficacy to reduce sedentary behavior (SRSB)

The SRSB questionnaire was used to assess the confidence of chronic disease patients to reduce SB. Originally developed by Whipple et al. [26] in 2023, the SRSB demonstrated strong psychometric properties in adult samples aged 18 to 75 years, with a Cronbach α of 0.920. Confirmatory factor analysis supported a unidimensional model. The scale comprises nine items scored on a 5-point Likert scale ranging from ‘strongly disagree’ (1 point) to ‘strongly agree’ (5 points). A total score ranging between 9 and 45 points was obtained, with higher scores demonstrating higher confidence in reducing SB. In this study, we utilized the Chinese version of the SRSB, which was validated by our research team in previous studies [30]. This scale was translated using the Brislin back-translation method and further refined through expert consultation (see supplementary materials). The Chinese version of the scale showed a Cronbach α of 0.921 and a retest reliability of 0.951. In this study, the Cronbach α for the Chinese version of the SRSB was 0.920, which supported the reliability of this scale in middle-aged and elderly patients with chronic diseases.

Data collection

Data collection was completed by three experienced researchers, and each participant gave their informed consent. The objective, significance, and methods of this study were explained to the participants at the outset. Before the completion of the questionnaires, all participants were fully informed about the purpose of this study, the right to refuse participation in the study, and the method of anonymous data collection. Participants were required to fill out questionnaires, but if the participant was unable to read or write, the researcher would administer the survey in person while assisting the patient to comprehend the questions. During the filling process, the researchers avoided any inductive explanation. Clarification was provided by the investigator if participants reported difficulties in understanding any statements. After the participants answered, the researchers assisted the participants in filling in the facts and checked them immediately to guarantee the accuracy and completeness of the data.

Statistical analysis

SPSS software (version 27.0) was used for statistical analysis. The data normality was evaluated using the Kolmogorov–Smirnov test (P > 0.05 for all variables) and Q-Q normality plots. The data of all variables did not conform to the normal distribution and were analyzed using non-parametric methods. Continuous variables are presented as median (IQR), and categorical variables are shown as numbers (%). The scores of the SRSB were classified by cluster analysis. First, the between-group linkage method in hierarchical clustering was used to generate a dendrogram and determine the possible number of clusters. Then, the optimal number of clusters was determined using the iterative partitioning method of K-means clustering. Each cluster was required to contain more than 5% of the total sample size. The chi-square test was used to compare differences in SRSB among groups. In addition, ordered logistic regression was conducted to analyze the factors influencing SRSB in middle-aged and elderly people. For all analyses, a p-value < 0.05 was considered statistically significant (two-tailed).

Results

Descriptive characteristics of participants

A total of 600 questionnaires were distributed in this study, and 583 valid questionnaires were recovered, with an effective recovery rate of 97.2%. Most of the people with chronic diseases were female (53.7%) and over 60 years old (54.4%). Table 1 provides facts about middle-aged and elderly people with chronic diseases.

Table 1.

Univariate analysis of the influencing factors of the SRSB in people with chronic diseases

Variable	N (%)	Low-level group(n = 64)	Medium-level group(n = 240)	High-level group(n = 279)	χ²	P
Age (years)					3.621	0.164
45ཞ59	266 (45.6%)	23	107	136
≥ 60	317 (54.4%)	41	133	143
Gender					4.685	0.096
Male	270 (46.3%)	37	113	120
Female	313 (53.7%)	27	127	159
BMI (kg/m2)					14.503	0.006**
< 18.5	14 (2.4%)	5	8	1
18.5–23.9	289 (49.6%)	29	114	146
> 24.0	280 (48.0%)	30	118	132
Place of residence					8.361	0.015*
City	471 (80.8%)	50	182	239
Rural	112 (19.2%)	14	58	40
Education level					13.617	0.034*
Primary school or below	120 (20.6%)	19	51	50
Junior high school	250 (42.9%)	25	110	115
High school	122 (20.9%)	11	54	57
College or above	91 (15.6%)	9	25	57
Work status					14.658	< 0.001**
Full time	207 (35.5%)	9	89	109
Unemployed	376 (64.5%)	55	151	170
Marital status					2.630	0.268
Married (Spouse in tow)	516 (88.5%)	53	212	251
Single(divorced/widowed/unmarried)	67 (11.5%)	11	28	28
Monthly income (yuan)					10.914	0.028*
< 3k	239 (41.0%)	32	108	99
3-6k	197 (33.8%)	13	76	108
> 6k	147 (25.2%)	19	56	72
Residence status					12.73	0.044*
Living alone	53 (9.09%)	8	22	23
Living with children	86 (14.75%)	7	27	52
Living with a spouse	334 (57.29%)	41	140	153
Living with children and spouse	110 (18.87%)	8	51	51
Number of chronic diseases					20.090	< 0.001**
Without chronic diseases	236 (40.5%)	14	89	133
1 ~ 2 chronic diseases	292 (50.1%)	38	128	126
≥ 3 chronic diseases	55 (9.4%)	12	23	20
Whether or smoke					11.930	0.003**
Yes	143 (24.5%)	25	64	54
No	440 (75.5%)	39	176	225
Whether lack of physical activity					56.254	< 0.001**
Yes	225 (38.6%)	48	104	73
No	358 (61.4%)	16	136	206

due to rounding to one decimal place, the total percentage may not add up exactly to 100%; therefore, the percentages for some categories are displayed to two decimal places

* P < 0.05

** P < 0.01

Cluster analysis result

According to the cluster analysis dendrogram, 583 samples can be divided into 2, 3, and 4 categories, all meeting the requirement of the number of samples in each cluster exceeding 5% of the total number of samples. Considering the interpretability and practical significance of clustering and the suggestions of experts in related fields, the optimal k value was determined to be 3. Then, the K-means clustering method was utilized to set the maximum number of clusters as 10, and the SRSB scores were set as the clustering variable. According to the characteristics of each category, group 1 was named the ‘low-level group’, with 64 cases (11.0%); group 2 was the ‘medium-level group’, with 240 cases (41.2%); group 3 was the ‘high-level group’, with 279 cases (47.9%).

Univariate analysis of influencing factors of the SRSB in patients with chronic diseases

In the comparison of the SRSB among middle-aged and elderly patients with chronic diseases, factors such as BMI, residence place, education level, work status, monthly income, residence status, number of chronic diseases, smoking status, and lack of physical exercise showed statistically significant differences (P < 0.05). In contrast, age, gender, and marital status showed no statistically significant difference (Table 1).

The results of ordered multinomial logistic regression analysis of the influencing factors of the SRSB in patients with chronic diseases

The clustering results of the SRSB in people with chronic diseases were set as the dependent variable (low-level group = 1, medium-level group = 2, high-level group = 3). Subsequently, the independent variables with statistically significant differences in Table 1 were used for ordered multi-classification logistic regression analysis, as shown in Table 2. The likelihood-ratio test revealed that χ² = 101.349, P < 0.001; the goodness of fit test showed that χ² = 789.446, P = 0.959; the test of parallel lines showed that χ² = 24.738, P = 0.075. These results showed that the ordered multi-classification logistic regression analysis was feasible and the model was meaningful. The results of the ordinal logistic regression analysis indicated that BMI, education level, number of chronic diseases, smoking, and lack of physical exercise influenced the score of the SRSB in patients with chronic diseases (P < 0.05). Compared with overweight or obese patients with a college degree or above, non-smoking, and physical exercise, the scores of the SRSB were lower in those who were underweight, with primary school and below and junior high school, smoking, and lack of physical exercise (P < 0.05); compared with those with more than three kinds of chronic diseases, those without any diseases demonstrate a higher score (P < 0.05), as shown in Table 3.

Table 2.

Variable assignment

Variable	Assignment
BMI	< 18.5 = 1, 18.5–23.9 = 2, > 24.0 = 3
Place of residence	Rural (0, 0), City (0, 1)
Education level	Primary school and below = 1, Junior high school = 2, High school = 3, College or above = 4
Work status	Unemployed (0, 0), Full time (0, 1)
Monthly income	< 3k = 1, 3-6k = 2, >6k = 3
Residence status	Living alone (0, 0, 0, 0), Living with children (0, 1, 0, 0), Living with a spouse (0, 0, 1, 0), Living with children and spouse (0, 0, 0, 1)
Number of chronic diseases	Without chronic diseases = 1, 1 ~ 2 chronic diseases = 2, ≥ 3 chronic diseases = 3
Whether or smoke	Yes = 0, No = 1
Whether lack of physical activity	Yes = 0, No = 1

Table 3.

Multivariate ordinal logistic regression analysis of the influencing factors of the SRSB among people with chronic diseases

Variable	Eigenvalue	Reference group	β	SE	Wald χ2	P	OR (95%CI)
BMI (kg/m2)	< 18.5	> 24.0	−1.472	0.555	7.026	0.008*	0.229(0.077, 0.681)
	18.5–23.9		0.058	0.172	0.114	0.736	1.060(0.757, 1.486)
Place of residence	City	Rural	0.163	0.217	0.563	0.453	1.177(0.770, 1.799)
Education level	Primary school and below	College or above	−0.679	0.327	4.318	0.038*	0.507(0.267, 0.962)
	Junior high school		−0.581	0.285	4.155	0.042*	0.559(0.320, 0.978)
	High school		−0.34	0.296	1.32	0.251	0.712(0.399, 1.271)
Work status	Full time	Unemployed	0.326	0.192	2.891	0.089	1.385(0.951, 2.020)
Monthly income	< 3k	> 6k	−0.067	0.244	0.075	0.785	0.935(0.580, 1.508)
	3-6k		0.321	0.235	1.859	0.173	1.379(0.869, 2.186)
Residence status	Living alone	Living with children and spouse	−0.014	0.344	0.002	0.968	0.986(0.502, 1.937)
	Living with children		0.464	0.307	2.291	0.13	1.590(0.872, 2.904)
	Living with a spouse		−0.068	0.226	0.091	0.763	0.934(0.600, 1.455)
Number of chronic diseases	Without chronic diseases	≥ 3 chronic diseases	0.624	0.306	4.157	0.041*	1.866(1.024, 3.401)
	1 ~ 2 chronic diseases		0.237	0.289	0.671	0.413	1.267(0.719, 2.232)
Whether or smoke	Yes	No	−0.421	0.197	4.588	0.032*	0.656(0.446, 0.965)
Whether lack of physical activity	Yes	No	−1.099	0.176	38.833	< 0.001**	0.333(0.236, 0.471)

BMI(kg/m²) classification standard, <18.5 underweight, 18.5ཞ23.9 normal weight, 24.0ཞ27.9 overweight, ≥ 28.0 obesity, smoke: continuous or cumulative smoking for 6 months or more; physical activity: at least 30 min of physical exercise, more than 3 times per week; regular lifting, weeding, painting, picking fruits and vegetables, etc., are considered not lack of physical activity

* P < 0.05

** P < 0.01

Discussion

The trend of chronic diseases and comorbidities in middle-aged individuals and the elderly has emerged as a public health problem in China and throughout the world. Furthermore, SB is closely associated with the incidence and mortality rates of various chronic diseases [31]. Given the detrimental impact of sedentary behavior on the health of individuals with chronic diseases, it is particularly important to explore effective intervention strategies. Self-efficacy has long been regarded as one of the key variables influencing behavior change [32]. As an emerging construct, SRSB shows potential as a critical factor for promoting behavior change. Although previous studies have suggested that SRSB could be incorporated into future SB interventions [26], empirical research is scarce. Therefore, the importance of timely assessment of the SRSB among chronic patients cannot be over-emphasized. To date, this was the first study to investigate the level of SRSB among people with chronic disease in China. A key finding from this study showed that the predictors of chronic patients’ self-efficacy to reduce SB included BMI, education level, number of chronic diseases, smoking status, and physical exercise. These findings provide a reference for developing individualized strategies to help people with chronic diseases reduce SB.

In our study, across the entire sample (n = 583), the M (IQR) was 3.78 (3.00–4.44), with a range of 1.0 to 5.0. Compared with the median of the total score of the scale, the results of our study were in the middle level. These results are consistent with a previous study [26]. Currently, the development of the SRSB has not been standardized, and the item-level scores in our sample were largely consistent. Hence, the results were grouped artificially through cluster analysis. In the low-level group, item 4 scored the lowest, and item 5 scored the highest, while in the middle-level group and the high-level group, item 1 scored the highest and item 9 scored the lowest. The results of the low-level group may be attributed to a larger proportion of patients with more chronic diseases in the low-level group, leading to more time spent resting. Moreover, the environment at home is more comfortable, and reducing SB at home requires more self-discipline. However, such individuals may have more opportunities to engage in various activities while going out, so changes in the environment may promote activities to reduce SB [33], such as parks and walking trails. The results of the medium-level and high-level groups are related to stronger health awareness and behavioral control capabilities, as these individuals have more opportunities and conditions to choose to stand in their work or life. However, environmental restrictions and transport (such as cars and airplanes) also limit the possibility of standing.

This study revealed that underweight people reported lower levels of SRSB, which is consistent with previous research [26]. Overweight or obese patients may be aware of their high health risks, such as cardiovascular disease and diabetes, and have a stronger incentive to take measures to reduce SB to improve their health. In contrast, underweight people may think that their health risks are low and lack the motivation to reduce SB. Additionally, overweight or obese people may be subjected to more pressure and social support from society, family, and friends, prompting them to address their SB more actively. Conversely, underweight people may not be exposed to the same degree of social support and pressure to change their behavior, especially when they have no obvious health problems. Research has also shown that [34] weight does not affect self-efficacy, and overweight or obese people are less likely to seek weight loss help. In other words, this group of people may not pay much attention to their weight changes because of their high general self-efficacy. Therefore, despite being overweight or obese for extended periods, their scores on the SRSB remain high.

This study also reveals that education level was associated with levels of the SRSB, with higher education level being associated with a higher SRSB score. The education level can be used as an indicator to measure the individuals’ socio-economic status [35]. Individuals with higher socio-economic status have higher autonomy in choosing different lifestyles, often have higher health cognitive ability, and have a strong subjective willingness to choose a good lifestyle [36]. Compared with people with a primary school education and below, people with a college or university education and above generally attain more health knowledge and awareness. Therefore, these people may be more aware of the negative impact of SB on health and are more aware of the importance of reducing SB. Furthermore, research has shown that education level is the influencing factor of health behavior in hypertensive elderly patients [37]; patients with higher education levels exhibit better health behaviors in terms of reducing SB. In addition, people with higher education levels may live in a healthier environment, which encourages an active lifestyle. For example, some workplaces provide standing desks to reduce SB [38–40], which may lead to higher self-efficacy. Generally, patients with a higher education level have a stronger ability to absorb and master knowledge. Such individuals can efficiently use information resources to obtain accurate and scientific medical knowledge, developing a deeper understanding of their health status. This cognitive advantage enables them to face the challenges associated with their disease, allowing a more positive view of health behaviors such as reducing SB.

Findings from this study also indicate that the SRSB score of people with ≥ 3 chronic diseases was lower than that of people without chronic diseases. Previous studies have reported a higher incidence of SB in patients with chronic diseases [10, 41–43], which often coexist and interact with each other. Research has shown that the incidence of various chronic diseases increases as age increases, including cardiovascular disease, diabetes, osteoarthritis, and cancer [44–47]. As the number of diseases, sedentary time also tends to increase [48]. In addition, the coexistence of multiple diseases may lead to anxiety and depression. Research has shown that [49] the number of diseases is an influencing factor for depression; as the number of chronic diseases increases, the possibility of depression also increases. Individuals suffering from ≥ 5 chronic diseases have a 4 times higher risk of depression compared to people who are not sick. Depression patients may lack motivation for PA, thereby affecting self-efficacy and increasing SB. Therefore, the SRSB of people with a large number of diseases is lower than that of people without chronic diseases. A systematic review of qualitative research revealed [50] some misunderstandings in the elderly, such as ‘those with complications should be sedentary’ and ‘physical weakness caused by age growth cannot be avoided’, which hinders the adoption of SB-reducing activities in the elderly. This may partially be attributed to muscle atrophy in the elderly. Such changes limit the activities of elderly patients with chronic diseases, resulting in an increase in SB. Hence, for the population with more diseases, a lower SRSB score is associated with a higher difficulty to reduce SB, raising the difficulty in forming and maintaining good behavior habits.

The present study revealed that the SRSB score of non-smokers was higher than that of smokers. Non-smokers may have stronger health awareness and self-management ability. Research has shown that [51] non-smokers have a healthier overall lifestyle. As the score of healthy lifestyle increases, the proportion of smokers gradually decreases, with non-smokers in the high score group of healthy lifestyle. According to Yuan et al. [52], a history of smoking is negatively correlated with the level of self-efficacy in elderly patients with chronic diseases, which may be related to poor awareness of health management and a lack of self-care beliefs. Although elderly patients are aware of the dangers of smoking, their confidence in self-health management is reduced due to withdrawal symptoms and psychological cravings. Moreover, non-smokers may have developed a healthy lifestyle, which includes less sedentary time and more PA, which prompts them to adopt healthy behaviors in other aspects, such as reducing SB. Research has shown that [53] smokers often have poor health behaviors. A 7-year prospective observational research indicated that [54] smoking is associated with a lack of exercise, and smokers often lack exercise, which may be more likely to lead to sedentary behavior. Furthermore, the SRSB score of people who lack physical exercise was lower than that of people who do not lack physical exercise. People who exercise regularly are speculated to have stronger health awareness, are more concerned about their health, and are more likely to participate in various activities to reduce SB. People who lack physical exercise are often accustomed to a sedentary lifestyle, which makes it difficult for such people to confidently believe that they can reduce SB.

Limitations

Nevertheless, several limitations of the present study should be acknowledged. Firstly, this study adopted a cross-sectional design, which limits causal inference. Secondly, due to practical constraints in the study environment, objective tools were not employed to assess PA and SB, and comprehensive, accurate information on participants’ chronic disease status could not be fully obtained. Moreover, environmental factors such as digital media use, residential settings, and transportation that may influence SRSB were not assessed and should be considered in future research. Therefore, further research is needed to identify additional influencing factors. Thirdly, this study included only patients with chronic diseases in a specific city, which may not be fully representative of the broader or geographically diverse population of patients with chronic diseases. Thus, multi-center, multi-regional studies should be conducted to generalize the findings in future studies. Finally, due to the nature of self-reported measures, participants may have inaccurately reported their abilities.

Conclusion

In conclusion, this study demonstrated the self-efficacy to reduce SB of people with chronic diseases in China was at a moderate level and related to many factors. In daily life, the adverse effects of SB accumulate over time, and even when people are aware of the harms, it can be difficult to make changes due to a lack of effective methods, poor time management, or environmental constraints. The importance of modifiable factors in health promotion should be emphasized. In health management, emphasis should be placed on people who are overweight or obese, poorly educated, have more chronic diseases, smoke, and do not exercise, thereby strengthening health education and prevention interventions on sedentary behavior. At present, few studies have investigated self-efficacy to reduce SB. Therefore, the next study will use a wider sample and more comprehensive data collection methods to deeply explore the factors affecting self-efficacy to reduce SB and provide a reference for future interventions.

Abbreviations

IQR

Interquartile Range

CHARLS

China Health and Retirement Longitudinal Study

SB

Sedentary Behavior

PA

Physical Activity

WHO

World Health Organization

METs

Metabolic Equivalents

SRSB

Self-efficacy to Reduce Sedentary Behavior

Authors’ contributions

ZW led the project overall and are guarantor. ZW and YC conceptualized the study and designed the work. YC, JC, SZ, PT and GH acquired and interpreted the data. YC, JC were responsible for the statistical analysis and wrote the manuscript. ZW reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (No.72204029).

Data availability

The datasets used and analyzed in the current study are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Ethical approval was obtained from the Medical Ethics Committee of Changzhou University (No.20220303008). The purpose of the study was explained to all participants before the survey was conducted and informed consent was obtained, and all procedures were performed in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.