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Understanding regular exercise behavior in frail older adults: a structural equation model based on social-cognitive variables

BMC Geriatrics volume 25, Article number: 73 (2025) Cite this article

Abstract

Background

Regularly engaging in exercise has been recognized as the only therapy found to consistently manage frailty. This study applies structural equation modeling (SEM) to integrate the theoretical perspective of Social cognitive theory (SCT) to better understand and promote regular exercise behavior among frail older adults, providing a foundation for enhancing exercise engagement in this vulnerable population.

Methods

A total of 306 frail older adults in Chengdu, China, were selected in this cross-sectional study. Participants completed the the FRAIL scale, the Self-Efficacy for Exercise (SEE) Scale, the behavioral regulation in exercise questionnaire (BREQ-2), the Outcome Expectations for Exercise (OEE), the Exercise Social Support (ESS) Scale. Structural equation modeling (SEM) was used to specify hypothesis between components of social-cognitive theory on regular exercise behavior among frail seniors.

Results

The percentage of regular exercise behavior was 29.1% among participants. There were statistically significant differences in the regular exercise behavior for demographic characters of occupation, disposable monthly income, frail phenotype of fatigue, resistance and weight loss (p < 0.05). SEM showed that the exercise self-efficacy (β = 0.52, p < 0.001) and exercise outcome expectation (β = 0.45, p < 0.011) are the strongest determinants of regular exercise behavior, while social support (β = 0.092, p > 0.05) and self-regulation (β = 0.05, p > 0.05) are non-determinant.

Conclusion

Our study underscores that adherence to regular and structured exercise regimens remains low among frail older adults, suggesting that this population may not be fully benefiting from scientifically guided exercise programs. Integrating self-efficacy and outcome expectations into further exercise promotion is critical. Patient-centered approach is essential for developing more effective and sustainable exercise interventions.

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Introduction

As lifespan increases, the concept of frailty has gained prominence. Frailty is a clinic syndrome manifested as a diminished biological reserves, due to poor regulation of physiological systems, leaving individuals vulnerable even to minor stressors [1]. It leads to poorer outcomes in terms of sarcopenia, disability, hospitalisation and premature death [2, 3]. Two critical features define frailty: if left untreated, it progresses to disability and, ultimately, death, but with timely intervention, its onset can be delayed [4].

Given this dynamic nature, it has been proposed that frailty can be shaped by patterns of living and in particular by the presence or absence of “healthful” levels of physical activity (PA) and exercise [5]. Exercise, a subcategory of PA, involves planned, structured, and repetitive bodily movements [6]. Regular exercise is defined as engaging in planned exercise for at least 30 min on three or more days a week, sustained for over three months [7, 8]. Mounting evidence has indicated regular exercise has a dose-response effect, critical for enhancing physiological health and cognitive function [9, 10]. According to the Centers for Disease Control and Prevention (CDC), older adults gain greater health benefits from more vigorous or prolonged exercise [11], improving aspects such as body composition, Vo2max, and plasma triglyceride levels [12].

Moreover, regularly engaging in exercise has been recognized as the only therapy found to consistently improve sarcopenia, physical function, cognitive performance and mood in frail older adults [13]. Studies of exercise training in frail elderly populations, particularly in nursing homes, report no significant cardiovascular incidents, such as sudden death or myocardial infarction, nor exacerbation of metabolic disorders [14, 15]. Promising data indicate that increased physical activity can reduce chronic low-grade inflammation, a key contributor to frailty [16]. Additionally, positive correlation between weekly energy expenditure from exercise and improvements in grip strength and walking speed has been proposed, suggesting that exercise levels significantly influence frailty development [17]. Therefore, the trajectory toward frailty is modifiable through regular exercise habits [18, 19].

However, there remains a scarcity of research investigating regular exercise behavior among frail older adults. Evidence shows that health behavior theories can effectively reveal and modify population-level behavior in real-world settings [20]. Social Cognitive Theory (SCT) is a widely accepted framework that identifies core psychosocial determinants, such as self-efficacy, outcome expectations, self-regulation, and social support, as essential for understanding health behaviors, including exercise and physical activity [21, 22]. SCT has been successfully applied across various age groups [23, 24]. However, factors influencing participation in health behaviors, including exercise, vary, and not all components of SCT have equal effects, particularly in older adults [22, 25, 26].

In conclusion, regular exercise is essential as a treatment for age-related frailty. A deeper understanding of exercise behavior in frail older adults is crucial. This study applies structural equation modeling (SEM) to integrate the theoretical perspective of SCT to better understand and promote regular exercise behavior among frail older adults, providing a foundation for enhancing exercise engagement in this target population.

Methods

Design and participants

Convenience sampling was employed to recruit older adults from February 2023 to May 2023 in Chengdu, China. Eligibility criteria included individuals aged 60 years or older, of either gender, who were community-dwelling and classified as frail according to the FRAIL scale. Exclusion criteria included self-reported significant depressive episodes, diagnosed dementia, severe hearing loss, noticeable language impairments, other communication disorders, and the inability or refusal to provide informed consent.

The sample size estimation method for multiple linear regression analysis was adopted, using the standard that requires a sample size of at least 10 times the number of items in the largest scale [27]. In this study, the Exercise Social Support Scale, with 24 items, represented the largest scale. Accounting for a 10–20% probability of invalid responses, the estimated minimum sample size was 288. However, 306 participants were included to account for potential dropouts and ensure sufficient statistical power for the study.

With the assistance of community health agents, eligible participants were identified and informed about the study’s objectives. Those who consented signed an informed consent form and participated in a face-to-face interview to provide the necessary information. Interviewers, who were trained in advance, ensured clarity in explaining the scale items to the older adults. Family caregivers assisted when participants had difficulty understanding the content. Ethical approval was obtained prior to the start of the study from the Ethics Review Committee of the First Affiliated Hospital of Chongqing Medical University (No.2023-019).

Instruments

Participants were asked to fill out their demographic information (i.e., age, gender, BMI, Nationality, education level, marital status, occupation, monthly income), frailty, exercise behavior (e.g., frequency of exercise, mode of exercise), and SCT variables.

The FRAIL scale

The FRAIL scale consists of 5 simple questions [28], which is entirely based on self-reported without any objective measurement. The 5 questions require a yes or no answer, with 1 point given to any affirmative response, i.e., the presence of fatigue, resistance, ambulation, comorbidity, and weight loss [29]. The score ranges from 0 to 5 points, and individuals can be classified as robust (0 point), prefrail (1 to 2 points), or frail (≥ 3 points). Good test-retest reliability (α = 0.708) in Chinese community-dwelling older adults has been reported [30].

Regular exercise behavior

Participants who continued regular exercise behavior for at least 3 months were definded as engaging in regular exercise [31]. The items of the questionnaire include: “do you have a habit of exercise” “ how many times do you exercise a day” “how many times do you exercise per week” “ what kinds of exercise do you usually do” “how long do you exercise at a time” “ how long have you been exercising”.

The self-efficacy for exercise (SEE) scale

SEE assessed how confident are the older adults right now that he/she could exercise three times per week for 20 min in the following circumstances: “The weather was bothering you”, “You were bored by the program or activity”, “You felt pain when exercising”, “You had to exercise alone” and so on. the participants choose an option from 0 (not confident) to 10 (very confident) that represent their perception [32]. The scale was scored by summing the numerical ratings for each response, with the higher scores representing greater exercise self-efficacy [32], with internal consistency in older adults being α = 0.89 and 0.90 [33].

The behavioral regulation in exercise questionnaire (BREQ-2)

BREQ-2 is a 19-item questionnaire that measures underlying motivational regulation relating to exercise participation [34]. A 5-point Likert scale ranging from 0 = “not true for me” to 4 = “very true for me” is used to rate each of its 19 items with the generation of each subscales score based on mean score across items, which include “I exercise because other people say I should”, “I feel guilty when I don’t exercise”, “I value the benefits of exercise”, “I exercise because it’s fun”, “I don’t see why I should have to exercise”and other items. Chinese version of BREQ-2 has been examined with good internal consistency and acceptable test-retest reliability [35].

The outcome expectations for exercise (OEE)

OEE is a 9-item scale focused on the outcome expectations and benefits associated with exercise in adults [36], which is effective in examining expected outcomes of involving exercise among older people [37]. To complete the OEE scale the participant is asked to listen to a statement about exercise (e.g., “Exercise makes me feel better physically”, “Exercise makes my mood better in general”, “Exercise melps me feel less tired”, “Exercise makes my muscles stronger”) and to strongly agree (1), agree (2), neither agree nor disagree (3), disagree (4), or strongly disagree (5) with the stated outcomes or benefits of exercising.The final score was calculated by summing the ratings of each response and dividing this number with the number of all responses. This scale possesses acceptable internal consistency (α = 0.85).

The exercise social support (ESS) scale

ESS was proposed by Yang and Huang (2015) [38], which includes four dimensions, namely emotional support (e.g., emotional encouragement and comfort), informational support (e.g., exercise place information, exercise related knowledge), instrumental support (e.g., exercise plan and goal setting, specific support provided at the operational level) and companion support (e.g., exercise companion from peers), and 24 items. Each item is rated on a 5-point Likert scale, with options range from strongly disagree (1) to strongly agree (5). The total score ranges from 24 to 120, with higher scores indicating higher level of social support. Good reliability and validity have been reported in Chinese adults (α = 0.94).

Kaiser-Meyer-Olkin (KMO) value indicates 0.926, and Bartlett’s test result was p < 0.001, suitable for factor analysis. Four factors were extracted, contributing to 62.8%of the sum of the squared loading (Table 1). Cronbach’s Coefficient Alpha (αvalue) is method used to measure the reliability of the questionnaire between each field and the mean of the whole fields of the questionnaire. Theαvalue for each construct were as follows: exercise self-efficacy (ESE) (0.93), exercise outcome expectation (EOE) (0.96), exercise self-regulation (ESR) (0.89), and exercise social support (ESS) (0.97) (Table 2). All the items are highly loaded on their constructs.

Table 1 Exploratory factor analysis
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Table 2 Survey statements and categorical confirmatory factor analysis
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Data analysis

All data analyses were conducted using SPSS version 26.0 (IBM Corp., Armonk, USA) and AMOS version 24.0 (IBM Corp.). Categorical variables were expressed as frequencies and percentages (%), and comparisons between groups were performed using the Chi-square test or Fisher’s exact test, as appropriate. Continuous variables were reported as means and standard deviations (SD). For normally distributed data, an independent samples t-test was applied; for non-normally distributed data, the Mann-Whitney U test was used. The relationships between variables were assessed using Pearson or Spearman correlation analysis, depending on data distribution [39].

Confirmatory factor analysis (CFA) was employed to identify the key factors contributing significantly to the SCT framework, as well as to test and validate the underlying model structure [40]. Structural equation modeling (SEM) was then utilized to quantify the hypothesized associations between latent variables within the SCT framework and regular exercise behavior. Multiple indicators were used to comprehensively evaluate the model’s goodness of fit.

Results

Descriptive statistics

Table 3 presents the participants’ demographic characteristics. A total of 306 frail older adults were recruited in this study, of whom 158 (51.6%) were males and 148 (48.4%) were females. Most of the participants were aged 60–69 years (62.4%), 52.6% of the participants had normal BMI, 33.3% had primary education, 82.4% were married, and 32% had 2001–3000 disposable income (yuan). This survey shows that the 75.5% of the participants were frail, while 24.5% were prefrail.

Table 3 Comparison of exercise behavior with different demographic characteristics (n = 306)
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As for the characteristics of exercise behavior, 47% participated exercise, while only 29.1% had regular exercise behavior. Specifically, 40.8% exercise once a day, 27.1% exercise twice a day, 69.3.% exercised less than 3 times per week. Walking (44.8%), traditional health exercise (28.4%), and jogging (14.7%) were the most popular exercise preference.

There were statistically significant differences in the regular exercise behavior for demographic characters of occupation, disposable monthly income, frail phenotype of of fatigue, resistance and weight loss (p < 0.05) (Table 3).

Confirmatory factor analysis (CFA)

In this section, CFA was performed to validate the consistency between the four factors and the theoretical model. The goodness of fit index (GFI), normed fit index (NFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA) are shown to described the goodness of fit if the model. The final result of the CFA indicated that the model fits samples well (x2/df ratio = 2.4; GFI = 0.93; NFI = 0.90; CFI = 0.91; RMSEA = 0.08). Composite reliability (C.R.) and average variance extraction (AVE) were used to evalute the convergent validity and discriminant validity of indicators. Generally, AVE value>0.5 and C.R. value>0.7 are recommended [41, 42]. The AVE values of the the four factors involved in this study (ESE, ESS, ESR, EOE) are all greater than 0.5, and C.R. value are all greater than 0.7, indicating that the data of this measurement scale has excellent convergent validity (Table 2). The results represented good reliability and validity of the scale.

Structural equation model (SEM)

SEM is a statistical method to analyze the relationship between variables. SEM can evaluate the theoretical model according to the consistency of the relationship between the theoretical model and the actual data to solve practical problems [43]. In this study, SEM was constructed based on the SCT to explore the relationship between latent variables. The maximum likelihood estimation method was adopted to estimate the parameters. Path coefficient was determined to describe the relationship between variables. Figure 1. depicts the direct effects of the SCT model. Direct paths from ESE and EOE to regular exercise behavior were statistically significant. As hypothesized by the SCT, exercise self-efficacy (β = 0.52, p < 0.001) and exercise outcome expectation (β = 0.45, p < 0.011) directly and positively influence exercise behavior (Table 4).

Fig. 1
figure 1

The structural equation model of the overall sample. Note: ***indicates p < 0.001, ** indicates p < 0.05

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Table 4 Results of estimation structural model
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The specific model fitting indicators have reached the corresponding requirements standards, indicating that the model fits well. The standardized parameter estimating the final structural model is shown in Fig. 2. The model with regular exercise behavior predicted by ESE, ESS, ESR, EOE showed a good fit to the data (x2/df = 2.758; GFI = 0.914; RMSA = 0.061; PNFI = 0.658, PCFI = 0.701, PGFI = 0.713).

Fig. 2
figure 2

Structural equation model of social cognitive theory (SCT)

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Discussion

Regular exercise is recognized as one of the primary strategies to counteract frailty-induced decline in older adults [44]. To inform the development of effective promotion strategies, we examined the current state of regular exercise behavior and explored the role of SCT constructs as predictive factors in frail older adults. Our study revealed that while 47.1% of frail older adults participated in exercise, only 29.1% engaged regularly. Previous studies have reported that 40–60% of older adults engage in regular exercise [45,46,47]. This discrepancy may suggest that frailty-induced declines in physical capacity may make it challenging for frail older adults to establish and maintain regular, structured exercise routines. Specifically, our study identified fatigue, resistance, and weight loss as critical frailty indicators that correlate with insufficient exercise in frail population. Fatigue, a persistent and marked sense of tiredness in daily life, is a well-established independent predictor of disability and mortality [48]. Physiological fatigue limits engagement in regular exercise due to a perceived lack of physical strength [49], while psychological fatigue may lead to negative emotions and difficulty focusing, thus reducing exercise motivation [49]. Resistance, the ability to resist force or perform tasks that require muscular effort, such as lifting objects or standing up unassisted, is another crucial factor. High-intensity exercise is closely linked to improved muscle strength and power in both frail and general older adults [50, 51]. However, poor muscle strength remains the most prevalent component of frailty [52]. For example, muscle power declines by approximately 3% annually, while muscle strength decreases by around 2% per year as people age [53]. Maintaining muscle strength is, therefore, essential for preserving the ability to perform activities of daily living [54]. Additionally, weight loss exacerbates nutritional deficiencies in frail older adults, contributing to sarcopenia and further declines in muscle mass and physical strength [55]. It may further exacerbate weakness and reduce the willingness to engage in regular exercise. These findings emphasize the importance of early and precise identification of frailty indicators, which is critical for timely prevention and intervention measures to avoid further health complications and exercise deficits.

As individuals age, the nature and type of exercise tends to alter [56]. Stephens and Craig [56] reported that less than 20% of older adults participated in swimming and cycling, while walking becomes more prevalent with advancing age [57]. This is the first study to investigate exercise preferences among frail older adults, revealing that walking, traditional health exercises, and jogging are preferred in this population. Research suggests that perceived control over exercise type and intensity, along with enjoyment, can significantly improve adherence and long-term engagement [58].It further underscores the importance of the patient-centered planning process that enables the development of more successful intervention [59].

More importantly, enhancing psychosocial factors is crucial in promoting regular physical activity. Our SEM results indicated that exercise self-efficacy and outcome expectations were strong predictors of regular engagement. Self-efficacy, the belief in one’s ability to overcome barriers to exercise, has consistently been shown to influence physical activity across various populations [60, 61]. It is a situation-specific form of self-confidence [62], commonly measured by assessing individuals’ confidence in overcoming barriers to exercise [63, 64]. Research highlighted the crucial role of self-efficacy in both the adoption and maintenance of exercise behavior [65]. In a 5-month exercise program for previously sedentary adults, self-efficacy was a significant predictor of adherence, especially in the initial three months. Even four months after the program ended, self-efficacy remained a key factor in sustained engagement, underscoring its importance in maintaining exercise routines when they become challenging.

For older adults, both self-efficacy and outcome expectations play pivotal roles in shaping exercise behavior [33]. Outcome expectations refer to beliefs about the benefits of specific behaviors, such as improved health or strength from exercise, and have been linked to higher exercise engagement [66]. Even with high self-efficacy, an older adult may not engage in regular exercise if they do not believe in its benefits, such as enhanced health or physical function [67]. Thus, health interventions that incorporate these two cognitive-behavioral strategies can significantly influence exercise behavior, aligning with our findings. However, researcher warns that while positive outcome expectations can motivate behavior, unrealistic expectations can hinder long-term adherence [68]. For instance, Wilcox et al. demonstrated that unmet high expectations at the outset of a program could undermine exercise maintenance [69]. Modeling exercise behaviors so that frail individuals can observe and visualize the outcomes may help address this issue.

Interestingly, our study did not support the hypothesized relationship between self-regulation and regular exercise. Previous research suggests that individuals who consistently engage in exercise possess higher self-regulatory capacities, such as planning, goal-setting, and impulse control [70]. However, our findings suggest a different narrative, possibly explained by age-related cognitive decline. The prefrontal cortex, responsible for self-regulatory behaviors, including impulse control and attention, often deteriorates with age [71]. This decline may affect older adults’ ability to regulate their exercise behavior effectively.

Furthermore, the non-significant relationship between social support and regular exercise in our study contrasts with earlier findings [72, 73]. Previous research has shown that simply knowing others who exercise or seeing people exercise in the neighborhood can positively influence meeting exercise recommendations [74]. However, for frail residence, the scope of social networks is limited and such external influences may not be as impactful. Research emphasizes that social interaction rates decline with age, and older adults become more selective in choosing social contacts [75]. As a result, the potential benefits of social networking may not be a significant motivating factor for promoting exercise among frail older adults. Moreover, our findings on exercise preferences suggest that frail older adults tend to favor convenient activities that can be performed independently, such as walking, which minimizes the need for social support to sustain the behavior.

Limitations

Our study has several limitations that warrant consideration. First, the reliance on self-reported questionnaires may have introduced biases. Objective measures, such as wearable activity trackers, is recommended. Second, cross-sectional design is not possible to establish causal relationships between the SCT constructs and regular exercise behavior. Longitudinal or experimental designs would be better. Third, while SCT model offers valuable insights into cognitive and behavioral predictors of exercise, it may not capture the full range of relevant factors influencing exercise behavior in frail older adults. Considering employing broader frameworks, such as social-ecological models, would provide a more comprehensive understanding of the complex factors that shape exercise behavior among frail older adults. Finally, our sample was limited to older adults in Chengdu, which may limit the generalization of the findings to other regions or populations.

Conclusion

In summary, our study underscores that adherence to regular and structured exercise regimens remains low among frail older adults, suggesting that this population may not be fully benefiting from scientifically guided exercise programs. From a theoretical perspective, exercise promotion strategies that integrate cognitive-behavioral approaches, particularly focusing on enhancing self-efficacy and shaping outcome expectations, are critical in improving exercise adherence. Furthermore, our findings highlight the importance of involving patients in the planning process, taking into account their individual preferences, enjoyment, and physical tolerance. This patient-centered approach is essential for developing more effective and sustainable interventions.

Data availability

The database used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

Special thanks to all the participants and researchers who provided their time and support to finish this paper.

Funding

Sichuan Provincial Department of Science and Technology Project, “Respecting the Elderly, Enhancing Oral Care-A Handbook for Elderly Dental Care,” Project Number: 2024JDKP0106.

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Authors and Affiliations

  1. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Department of Nursing, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China

    Shiqi Yu, Jie Lin, Siping Song, Shuqi Huang & Fan Liu

  2. Department of Nursing, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Mingzhao Xiao

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  1. Shiqi Yu
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Contributions

S.Q.Y, F.L and M.Z.X designed the study. J.L and S.P.S contributed to the acquisition, analysis and interpretation of the data. S.Q.Y. and S.Q.H analyzed and interpreted the data, wrote and edited the manuscript, administered the project, drafted the manuscript. F.L administered and supervised the project, acquired funding, and reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Fan Liu or Mingzhao Xiao.

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Ethics approval and consent for participate

The study was performed in accordance with the Declaration of Helsinki, and approved by the Ethics Review Committee of the First Affiliated Hospital of Chongqing Medical University (No.2023-019). Written informed consent clarifying the study purposes, significance, methods and risks was obtained from each participant before the survey.

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The authors declare no competing interests.

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Yu, S., Lin, J., Song, S. et al. Understanding regular exercise behavior in frail older adults: a structural equation model based on social-cognitive variables. BMC Geriatr 25, 73 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-025-05702-5

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BMC Geriatrics

ISSN: 1471-2318

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