Determinants physical activity in middle-aged women: application trans theoretical model

Abstract

Background and aims

Physical activity is one of the most important indicators of health in the society and plays an important role in the lives of people, especially women. Nevertheless, one of the major challenges in modern society is the inactivity and lack of optimal physical activity of women, which has caused a rising prevalence in chronic diseases. Models and theories help to better understand these behaviors and better planning for behavior change in target groups. This study was conducted with the aim of investigating the predictors of regular physical activity among middle-aged women based on the trans-theoretical model.

Design

The present study was conducted cross-sectionally on 250 middle-aged women (age range 45–59) covered by comprehensive health databases.

Method

Inclusion conditions included willingness to participate, living in the area under study, not having certain diseases and disorders that would cause changes in lifestyle or physical activity. The random sampling method was simple. In this study, questionnaires of transtheoretical model constructs and short international questionnaire of physical activity were used. Data were analyzed using SPSS version 26 and Amos 24 software.

Results

In path analysis, change methods with path coefficient β = 0.20 are the strongest predictors of physical activity behavior in middle-aged women, and it clearly shows a significant positive relationship with the amount of physical activity (P < 0.05). Also, the stimulus control substructure with a factor loading of β = 0.17 and a confidence interval (CI) of 95% also has a high predictive power of the tendency to physical activity behavior. Chi-square ratio to degrees of freedom (χ²/DF) < 3 and RMSEA = 0.065 indicate a good fit of the model with the data (GFI = 0.91, CFI = 0.98).

Conclusion

The path analysis revealed that the proposed model by Prochaska fits well with the research data, indicating that change processes are strong predictors of physical behavior. These findings can serve as a foundation for developing targeted, evidence-based interventions to promote physical activity among middle-aged women.

Introduction

Regular physical activity is the first priority of a healthy lifestyle. Physical activity refers to any physical movement by voluntary muscles, which requires energy consumption. This definition includes any daily life activities, including job duties, household chores, and other daily duties to sports. The recommendation of the World Health Organization regarding the amount of physical activity required for people aged 18–65 is at least 150 min of moderate physical activity per week or 75 min of vigorous physical activity per week or a balanced combination of these moderate and vigorous physical activities. Also, muscle strengthening exercises that involve large body muscles should be done 2 or more days a week [1,2,3].

According to data from the World Health Organization, physical inactivity is responsible for approximately 2.3 million deaths annually and significantly contributes to 21–25% of breast and colon cancers, 27% of diabetes cases, and around 30% of ischemic heart diseases [3, 4]. The Eastern Mediterranean region exhibits the highest levels of inactivity globally, with over one-third of men and nearly half of women classified as physically inactive [5]. A report from the World Health Organization highlights that the prevalence of inactivity, particularly in terms of leisure-time physical activity, among Iranian individuals aged 18 to 64 years is 76.3% for women and 58.8% for men, resulting in an overall inactivity rate of 67.5% for this age group [6]. Some studies conducted in Iran have indicated even higher levels of inactivity [7, 8]. According to global and Iranian statistics, women are less active than men [8, 9]. Physical activity is a key determinant of energy intake, energy balance, and weight control. In addition, it increases life expectancy and improves the quality of life [10]. For the elderly, participation in physical activities can positively influence their quality of life and functional capabilities, helping them maintain independence in daily tasks. Conversely, elderly individuals living alone may experience challenges in their basic activities and functional abilities due to a lack of companionship, which can diminish their motivation to remain active [11]. Understanding the factors that contribute to a high level of health satisfaction among the elderly is essential. Key determinants include maintaining sufficient functional abilities, the absence of physical and mental health issues, and sustaining an adequate level of physical activity [12]. Many individuals in Iran and various other nations fail to engage in sufficient physical activity, thereby missing out on the significant advantages associated with both physical and mental health [10, 13]. Research indicates that the primary strategies to encourage middle-aged women to engage in consistent physical activity include enhancing intrinsic motivators, minimizing obstacles, and broadening access to opportunities. Several factors influencing the physical activity behaviors of middle-aged individuals in Iran encompass intrapersonal, interpersonal, cultural, environmental, and socio-economic dimensions. Additionally, the absence of structured and consistent approaches to facilitate the involvement of the elderly in physical activities and sports, along with the lack of an appropriate environment for their physical and athletic development, are identified as further challenges to promoting physical activity among the elderly population in Iran [14,15,16].

Numerous challenges and issues can impede behavior modification. The dynamics of behavior change play a crucial role in recognizing and addressing these factors while aligning them with prevailing cultural and social frameworks. Among the various theoretical models available, the transtheoretical model, developed by Prochaska, DiClemente, and colleagues, has gained prominence as a holistic and integrative approach to fostering behavioral changes that encourage participation in sports activities [17, 18]. This model posits that behavior change is a gradual process rather than a singular event, with individuals exhibiting varying degrees of motivation and readiness across five distinct stages: pre-intention (pre-contemplation), intention (contemplation), preparation, action, and maintenance [19, 20]. The pre-contemplation stage is characterized by an individual’s lack of consideration for behavior change, typically extending for at least six months. In the contemplation stage, the individual begins to contemplate the possibility of change within the next six months, although they may not yet feel prepared to act. The preparation stage involves a serious commitment to change, with intentions to initiate modifications in the near future, often within the following month. The action stage reflects a period during which the individual has actively implemented lifestyle changes over the past six months. Finally, the maintenance phase represents a prolonged period during which the individual strives to solidify and sustain these changes, requiring ongoing and deliberate effort beyond the six-month mark [20, 21].

Prochaska proposes 10 processes under the title of cognitive and behavioral processes to transition from the stages of change. Change processes include activities and strategies or processes that help a person to advance in the stages of change and include two main categories; Cognitive processes that deal with people’s thinking and feelings about behavior and are used in the initial stages of change, and behavioral processes that cause behavior change and are used in the final stages of change. The structure of balance and balance in decision-making is another structure that is formed based on the conflict pattern in decision-making and its focus is on the importance of positive (pros) and negative (cons) perceptions of a person regarding the results of his behavior or changing his behavior. In this structure, it is assumed that the person will not change his behavior unless he realizes that the benefits of changing the behavior outweigh the disadvantages. Another construct is the self-efficacy construct, which represents the confidence that people have about their ability to cope with the situation in returning to their previous habit [20, 22].

Comprehensive health centers in Iran play an important role in communicating with women and identifying risk factors in these people. Considering the lack of physical activity behavior in Iranian women and the need to determine the predictors of this behavior based on a successful theoretical framework such as the transtheoretical model. Therefore, this study was conducted with the aim of determining the relationship between the constructs of the meta-theoretical model and the physical activity behavior of middle-aged women referring to health centers and providing the most fit model.

Materials and methods

In this cross-sectional-analytical study, 250 middle-aged women (age range 45–59) covered by the comprehensive health centers of a city in Fars’s province in the south of Iran in 2020 were examined. The required sample size for this study was calculated according to the sample size calculation formula in correlational studies and based on a similar study [23]. The required sample size was calculated by considering the confidence level of 95% and the power of 80%, based on GPower software, and the drop rate of 15%, the number of 250 people. The inclusion’s conditions included willingness to cooperate, living in the area under study, not suffering from certain diseases and disorders that would cause changes in lifestyle or physical activity Such as severe ischemic heart disease, heart failure, cerebrovascular accidents and high blood pressure. After obtaining the necessary permits, 250 middle-aged women (from 5 comprehensive health centers in the city) were selected to enter the study, based on the inclusion criteria and using random numbers.

Data collection tools

• 1. Individual demographic information questionnaire include age, marital status, education level, disease status, medication use.

• 2. Transtheoretical model (TTM) constructs questionnaire in Physical Activity Stage of Change (PASOC) by Marcus et al. was used in this study. which has been used in the study of Farmanbar et al., Keshavarz Mohammadian et al., Shirazi et al., and Nigg et al. [24,25,26,27,28]. There are 18 questions including 12 cognitive questions (Subconstructs: Consciousness Raising, Dramatic Relief, Environmental Reevaluation, Self-Reevaluation, and Self-Liberation) and 6 behavioral questions (Subconstructs: Counterconditioning, Helping Relationships, Social Liberation, Stimulus Control) in this questionnaire to measure the construct of change processes. Also, physical activity self-efficacy with 10 questions and physical activity decision-making balance includes 7 questions about pros and 6 questions about cons. Answering the questions is based on a 5-point Likert scale, from 5 completely agree to 1 completely disagree. In Farmanbar et al.‘s research in Iran, the retest coefficient was 90% and Cronbach’s alpha was 86% [26].

• 3. International Physical Activity Questionnaire (IPAQ) [29]: It includes 7 questions about intense, moderate and walking and sitting physical activity in the last seven days. This Questionnaire was developed by an expert group in 1998 to facilitate basic physical activity on a global standard with the aim of assessing the importance of physical activity. The validity and reliability of the Persian version of the IPAQ short form has been evaluated and confirmed by Moghadam et al. [30]. Rate physical activity in this questionnaire categorized based on the severe, moderate, and low physical activity. The validity of the questionnaire in Iran was confirmed in the study of Vasheghani-Farahani et al. and its reliability was reported to be 0.83 [31].

Implementation

At the beginning of the research, the necessary information about how to conduct the research and confidentiality was given to the participants and their written consent was obtained for their willingness to enter the study. Questionnaires were distributed among middle-aged women referring to the comprehensive health center based on the conditions of inclusion in the study. The duration of completing the questionnaire for each person was 15–20 min.

After entering information using SPSS software version 25, the correlation matrix between the structure of the TTM model and the relationship of the structures with the amount of physical activity and its related factors was done. Then the relationships between the variables were analyzed in the form of path analysis model using AMOS version 24 tools.

Results

The average age of the participants was 52.74 ± 4.79 years. 83.2% of women were married. 52.4% have a low literacy level and 26.8% have a high literacy level. 54% of participants were patients, 51.2% of whom were taking medication.

Based on the distribution of physical activity change stages, 69.2% (n = 173) were in the pre-contemplation and contemplation stage, 13.2% (n = 33) were in the preparation stage, and 17.6% (n = 44) were in the action and maintenance stage.

The average score of the structure of the change process was 36.18 ± 7.56 in the structure of cognitive processes and 21.29 ± 4.73 in the structure of behavioral processes. The average score of the self-efficacy structure was 26.06 and the decision balance structure was 43.18 (Table 1).

Table 1 Mean and standard deviation of transtheoretical model constructs questionnaire in physical activity stage of change

To assess the correlation among the research variables, Pearson’s correlation method was employed. The correlation matrix for the TTM constructs is presented in Table 2. An analysis of the relationship between the stages of change and the level of physical activity reveals a positive and significant correlation between the stages of change and various factors, including the amount of physical activity, cognitive strategy, behavioral strategy, self-efficacy, and the pros and cons. Additionally, a significant relationship exists between the level of physical activity and both cognitive and behavioral strategies, as well as the pros. Furthermore, a notable statistical relationship is observed between the pros and cons.

Table 2 Correlation matrix of constructs model (TTM) and rate physical activity (n = 250)

The relationships between TTM constructs and physical activity levels is shown in Fig. 1. Subconstructs consciousness raising, dramatic relief, self-reevaluation, and self-liberation are cognitive and counterconditioning strategies, stimulus control, helping relationships, and social liberation are behavioral strategies. The pros construct in relation to physical activity levels. The stimulus control substructure with a standardized coefficient path coefficient of β = 0.17 has a strong predictive power of willing physical activity behavior. Also, pros and social liberation sub-structures have the lowest predictability with the path coefficient of the standard coefficient of β = 0.10, respectively.

Fig. 1

Theoretical model of TTM structures with physical activity levels of middle-aged women

* In this form, only the paths that are meaningful are shown

The relationship between TTM structures and the level of activity and stages of behavior change with the rest of the structures and the determination of the strongest predictive structures are shown in Fig. 2.

The constructs process of change including self-efficacy, pros, cons, and decision balance respectively have standard path coefficients in connection with the structure of the stages of change in the structure of change processes has a greater relative influence on the stages of change in physical activity than the rest of the independent variables. Stages of change in physical activity behavior with a path coefficient of β = 0.20 standard coefficient has the predictive power of physical activity behavior.

Fig. 2

The fitted model predicting physical activity behavior of middle-aged women based on TTM structures

The relationship between the stages of change, cognitive strategy, and behavioral strategy with physical activity levels is shown in Fig. 3. In fact, Stages of Change has more predictive power with the standard path coefficient β = 0.20. This model was chosen as a predictor of physical activity behavior after examining the fit indices and paying attention to the significance or non-significance of the t statistic.

Fig. 3

The fitted model predicting the physical behavior levels of middle-aged women based on TTM structures

In order to choose the most suitable model, two indicator groups were used:

1- Appropriate fit indices include goodness of fit index (GFI) ≤ 0.90% indicates good fit, chi-square/DF ratio < 3 or even > 4 or 5 is suitable and the closer to zero, the more suitable it is. Root mean square error of approximation (RMSEA) is the closer to zero, it indicates a better fit of the model, which generally < 0.05 indicates a very good fit (Chi-Square = 1.505, RMSEA = 0.065, GFI = 0.91, AGFI = 0.90).

2- Model comparison indexes including comparative fit index (CFI) and normal fit index (NFI), if they are close to one, it indicates a good fit and values ​​>0.90% are acceptable (CFI = 0.98, NFI = 0.96).

Discussion

This study aimed to explore the relationships between the constructs of the Transtheoretical Model (TTM) and the physical activity behaviors of middle-aged women. Our findings indicate a significant positive relationship between the stages of change, the amount of physical activity, cognitive and behavioral strategies, self-efficacy, and the pros and cons associated with engaging in physical activity.

The results of this investigation reveal that the Goodness of Fit Index (GFI) is 0.91, while the Adjusted Goodness of Fit Index (AGFI) stands at 0.90, both of which suggest a favorable model fit. Furthermore, the chi-square to degrees of freedom ratio, which is below 3 in this study, indicates an acceptable fit for the model. The Root Mean Square Error of Approximation (RMSEA) is recorded at 0.065, reflecting a good fit, albeit not an exceptional one, as values under 0.05 are typically regarded as indicative of a very good fit. Additionally, the Comparative Fit Index (CFI) is 0.98 and the Normed Fit Index (NFI) is 0.96, both of which signify an excellent model fit due to their proximity to one. Consequently, the model proposed by Prochaska is substantiated. The research conducted by Kang et al. (2022) [32] examined the influence of the Transtheoretical Model (TTM) on identifying psychological factors associated with physical activity among adults with physical disabilities. The findings underscore the impact of psychological variables on the behavior change process, emphasizing the critical role of psychological transformations in promoting physical activity. Similarly, Kim’s study investigated the connections between physical activity and TTM constructs among Korean adults, revealing that different stages of readiness to change and the processes of change significantly affect physical activity levels. These outcomes are consistent with our findings, which highlight that change processes are the most robust predictors of physical behaviors [33]. Furthermore, these insights indicate that interventions aimed at enhancing psychological readiness may significantly improve the effectiveness of behavior change strategies in encouraging physical activity. Future studies should investigate how these psychological factors interact with individual characteristics to further refine and enhance behavior change models.

Self-efficacy emerged as a critical predictor of physical activity engagement. When individuals believe in their capabilities, they are more likely to initiate and sustain physical activity. Therefore, enhancing self-efficacy should be a focal point in behavioral interventions aimed at increasing physical activity. This is supported by Taymoori and Lubans [34], who found that cognitive and behavioral strategies, including goal setting and self-efficacy enhancement, significantly impacted engagement in physical activity among Iranian adolescent girls.

Our research uncovered a notable correlation between the advantages and disadvantages of exercise, suggesting that individuals assess the benefits of physical activity in relation to perceived obstacles. Grasping this interplay is crucial for developing interventions that not only amplify perceived advantages but also tackle psychological impediments. This observation aligns with the findings of Horiuchi et al., who studied 403 Japanese students. Their research indicated that the varying influences of self-efficacy and perceived disadvantages on the long-term commitment to regular exercise not only correlated with students’ exercise persistence but also reinforced the relevance of the Transtheoretical Model (TTM) stage classification. Furthermore, they demonstrated that during the pre-action phases, the pros and/or cons were more significant than self-efficacy in differentiating between adjacent stages. These findings imply that enhancing the pros and mitigating the cons are essential for promoting the initiation of regular exercise, while reducing cons is critical for sustaining it [35]. Understanding this dynamic is essential for crafting interventions that effectively enhance perceived benefits and address psychological barriers.

It’s noteworthy that the model’s substructure regarding stimulus control demonstrated robust predictability for physical activity behaviors, whereas the pros and social liberation exhibited lower predictability. This suggests that while understanding the benefits of physical activity is essential, it may not be sufficient in driving behavioral change alone. Downs et al. [36] emphasized this complexity, discussing how behavior change can be effectively modified through tailored reinforcement techniques and a comprehension of individual motivation.

The positive correlation between stages of change and physical activity underscores the importance of interventions designed to help individuals advance through these stages. Such interventions could significantly enhance physical activity levels, thereby improving overall health. Cognitive strategies, such as goal setting and self-monitoring, should be integrated into these interventions, as they have been shown to effectively promote adherence to exercise regimens.

This research presents several limitations. Firstly, the sample size may restrict the ability to generalize findings to a broader population. Additionally, the cross-sectional nature of the study inhibits the establishment of clear cause-and-effect relationships. The use of questionnaires and self-report reports may lead to reporting errors, because people may not be able to accurately record their activities. Furthermore, the study did not explore the cultural and social factors that could influence women’s physical activity levels. The Iranian cultural context, characterized by its unique heritage and modern influences, offers a specific environment for engaging in physical activities. Traditional perspectives on gender roles, family structures, and community significance can significantly impact participation rates. Despite an expanding body of research on physical activity, there remains a considerable gap in the incorporation of these cultural and social factors within research methodologies.

Conclusion

The path analysis results indicated that the modified model proposed by Prochaska aligns well with the data collected in this study. This finding suggests that the processes of change exert the most significant influence on physical behavior. Specifically, these change processes emerge as the most robust predictors of physical activity. Consequently, the Transtheoretical Model (TTM) is frequently employed as a framework for comprehending changes in physical behavior. The current study’s findings support the conclusion that the stages of change related to physical activity possess predictive validity regarding physical activity behavior. The notable correlations identified in this research establish a basis for the development of targeted, evidence-based interventions designed to encourage physical activity across diverse populations. By utilizing the constructs of the TTM, practitioners can customize strategies to bolster self-efficacy, implement effective cognitive and behavioral strategies, and address the perceived advantages and disadvantages of engaging in physical activity. Such focused initiatives have the potential to enhance health outcomes, increase participation in physical activity, and improve the overall quality of life for middle-aged women at various stages of change.