Investigating Factors Affecting Behavioral Intention among Gym-Goers to Visit Fitness Centers during the COVID-19 Pandemic: Integrating Physical Activity Maintenance Theory and Social Cognitive Theory

Abstract

The COVID-19 pandemic has led to the closure of many fitness centers and has significantly affected the behavioral intentions of gym-goers. This study aimed to determine factors affecting the behavioral intentions of gym-goers regarding fitness centers during the COVID-19 pandemic in the Philippines by utilizing the Physical Activity Maintenance Theory within the framework of Social Cognitive Theory. A total of 1048 gym-goers voluntarily answered an online, self-administered survey comprising 68 questions. Structural Equation Modeling indicated that physical activity maintenance through understanding COVID-19 and self-motivation had the highest significant effect on behavioral intentions. Interestingly, life stress was found to have a significant negative direct effect on physical activity maintenance. The current study is one of the first to have analyzed factors affecting the behavioral intentions of gym-goers during the COVID-19 pandemic. Finally, the application of Physical Activity Maintenance Theory and Social Cognitive Theory in this study provided accurate predictors of behavioral intention. As a result, this integrated model could serve as a theoretical foundation that could be applied and extended to assess behavioral intentions among gym-goers during the COVID-19 pandemic worldwide.

1. Introduction

The demand for fitness centers and their popularity have increased rapidly in recent years [1]. The industry has capitalized on this, and over 201,000 fitness centers are operating worldwide [2]. Accordingly, fitness centers have become a trend for millions of people, rapidly increasing industry size [3]. More people are willing to join the fitness industry because they prioritize health and body image. One country that has taken advantage of the increasing growth of fitness centers is the Philippines.

In the Philippines, going to fitness centers has become a popular pastime. A recent report by Ken Research [4] noted that between 2013 and 2018, the number of fitness centers in the Philippines increased at a compound annual growth rate (CAGR) of nearly 7%. The market revenue of fitness centers in the Philippines was around 8% during the revenue period, and total membership is expected to grow at a double-digit rate in the coming years [2]. Given these facts, it is not surprising that entrepreneurs have capitalized on the popularity of fitness centers.

Aside from basic fitness center operations, the addition of activities and services, such as yoga, swimming, and personal training, has played a significant part in the increase in the popularity of these businesses. Leeman and Ong [5] found that attitudes and subjective norms significantly affected the intention to join a gym. In the case of current gym members, attitude—rather than subjective norms—was found to influence such behavioral intentions, while the opposite was true for non-members [5].

However, this rapid increase in popularity and demand has left fitness centers struggling, especially during the COVID-19 pandemic. Due to community lockdowns resulting from the COVID-19 pandemic, fitness centers and gyms have been required to follow safety protocols and halt all non-essential activities by the policies approved by the Inter-Agency Task Force (IATF). Subsequently, most members have tended to ask for either a discount or a refund [6]. As such, the COVID-19 pandemic has significantly impacted entrepreneurs with businesses related to fitness centers and gyms.

The emergence of new COVID-19 variants has further impacted people’s physical activity, health, and fitness worldwide. A study by Petersen et al. [7] explored how this pandemic has affected gym-goers’ health perceptions and behavioral intentions. Individuals with a range of characteristics, physical activity levels, and perceptions related to COVID-19 were selected for evaluation in that study. The results discussed the different degrees of disruption in daily routines and behavior regarding workouts and other physical activities [7]. Such research promotes a healthy lifestyle by supporting physical activity in fitness centers, even during COVID-19.

As the number of fitness centers increases, more and more related studies are being conducted around the world. In Canada, Petersen et al. [7] proposed intervention strategies to support physical activity during the COVID-19 pandemic; the objective was to target the contextual behavior of individuals in sports facilities and fitness centers. In Australia, Choi [8] analyzed the causal relationship between fitness center satisfaction and re-registration intention among Australian Taekwondo leaders. The results of that study showed that the leadership of trainers positively affects the satisfaction level experienced by fitness center patrons. Jeon et al. [9] examined the structural relationship between choice attributes, perceived values, customer satisfaction, and loyalty to youth sports in Korea. It was seen that a high level of satisfaction with a youth swimming club or a Taekwondo group positively impacted the service sub-dimensions of the chosen attributes. In the Philippines, Ong et al. [2] discussed how catering to preferences would increase gym-goers’ satisfaction levels [10,11,12].

Different studies in the field of physical activity, as well as other sectors, have also confirmed the link between customer satisfaction and behavioral intentions [10,12,13]. A study in Spain by García-Pascual et al. [12] analyzed the impact of different management variables along with psychological and demographic variables on the satisfaction, perceived value, and future intentions of fitness center users. It was found that perceived value and satisfaction are vital in forming future intentions. A comparative study was conducted by Fontanoza et al. [14] on the effects of supporting physical activities in fitness centers in Indonesia and the Philippines. That study found that many factors must be considered to achieve customer satisfaction in centers, i.e., compelling customers to do physical activities. These studies showed how satisfaction influences physical activity maintenance and behavioral intentions. However, there were no studies regarding the behavioral intention of gym-goers during the COVID-19 pandemic in the Philippines.

The behavioral intentions of gym-goers could be evaluated by using Social Cognitive Theory (SCT) and the Physical Activity Maintenance Theory (PAMT). According to Jekauc et al. [15], in the context of the physical activity maintenance process, it can be considered a part of the development and specification of the SCT. Compared to non-maintenance theories and models, Nigg et al.’s [16] PAMT was specifically developed to explain Physical Activity (PA) maintenance. The PAMT focuses explicitly on physical activity maintenance and considers goal setting, self-motivation, and self-efficacy determinants. According to this theory, goal setting is task-oriented and linked to behavior through satisfaction, achievement, and commitment to goals. Self-motivation is a persistent inclination to carry out behavioral goals regardless of beliefs about reinforcement history, capacity, or control. According to Bandura [17], self-efficacy is the belief in one’s own capacities to complete a particular behavior. Although there is a reciprocal relationship between the three factors, each variable has a direct and unique effect on physical activity maintenance. Furthermore, Nigg et al. [16] suggested that a supportive environment positively impacts goal setting, self-motivation, self-efficacy, and physical activity maintenance, while life stress negates the impact.

Moreover, SCT is a well-known theory in the field of physical activity, which proposes a critical approach to understanding human behavior [18]. SCT proposes a reciprocal deterministic relationship between the individual, their environment, and behavior. All three components form a model for reciprocal causation to provide the basis for behavior and potential interventions to modify behavior [17]. A study by Jekauc et al. [15] compared three different theories used in explaining physical activity behavior, the theory of planned behavior (TPB), SCT, and PAMT. The study aimed to compare the predictive power of the three PA maintenance theories. It was found that SCT had the most predictive power compared to PAMT and TPB regarding PA maintenance. Results from different studies regarding health behavior state that satisfaction is an important determinant of maintenance [19,20,21]. Therefore, the need to assess human behavior should be explored to link behavioral intention and satisfaction.

This study aimed to determine the factors affecting the gym-goers’ behavioral intentions with fitness centers during the COVID-19 pandemic in the Philippines. Specifically, this study extended the PAMT and integrated it with the SCT. Factors such as Understanding of COVID-19 (covering the understanding of risk and protocols in general), Self-Motivation, Life Stress, Goal Setting, Self-efficacy, Impediments, Facilitator, Physical Activity Maintenance, Physical Activity Environment, and Behavioral Intention were considered in this study. The framework utilized could be beneficial for academicians and researchers in analyzing the relationship between the gym-goers’ behavioral intentions or other service-provider businesses. Finally, the results of this study would be beneficial to fitness centers to promote and improve their service quality, which affects the gym-goers’ behavioral intentions in different countries, even after the COVID-19 pandemic.

2. Literature Review and Conceptual Framework

Factors such as Understanding of COVID-19, Self- Motivation, Life Stress, Goal Setting, Self-efficacy, Impediments, Facilitator, Physical Activity Maintenance, Physical Activity Environment, and Behavioral Intention were evaluated using structural equation modeling. Specifically, factors under PAMT were Life Stress, Goal Setting, Self-Efficacy, Physical Activity Maintenance, and Physical Activity Environment. However, several studies evaluated behavioral intentions using PAMT considered extension or integration of other frameworks [15,16,17,18,19,20,21] to measure an individual’s intention holistically. Thus, this study considered the SCT, wherein factors under the Personal or Cognitive Aspects (Understanding and Self-Motivation), Environmental Aspects (Facilitators and Impediments), and behavioral aspects (Behavioral Intentions) were considered. Figure 1 represents the theoretical framework from the integrated theories utilized in this study. The following are related studies that justify the hypotheses for our theoretical research framework.

With the current COVID-19 pandemic, people experienced different life stresses. People’s life satisfaction and psychological well-being during the COVID-19 pandemic were found to be moderately high [22]. Accordingly, the study of providing resilience during the COVID-19 pandemic to sustain their psychological well-being and improve their life satisfaction is important. According to Kim and Kang [23], the reflection and brooding sequentially mediated the relationship between the COVID-19 pandemic and the perceived life stress and life satisfaction. The results were found to be necessary to develop ways to guide people to counteract negative effects while maintaining reflective self-focus and preserving healthy well-being. The circumstances surrounding this pandemic sufficiently affected life stress which is connected to the self-motivation of consumers and buyers regarding purchasing or obtaining services and self-care. As stated by Nelson-Coffey [24], programs that focus on health behaviors such as prosocial motivation, self-focused motivation, and COVID-19 stress or worries can be affected by other impediments; rather than just oneself. In the context of this study, both the risk and protocols were covered by the latent variable ‘Understanding of COVID-19′. Therefore, the following were hypothesized:

Hypothesis (H1).

Understanding COVID-19 has a significant effect on Life Stress.

Hypothesis (H2).

Understanding COVID-19 has a significant effect on Self-motivation.

The ongoing civil unrest from the pandemic exacerbated dire social-economic conditions, resulting in COVID-19 having an additional effect on the psychological well-being of people. Tresh [25] stated that perceived social support helps decrease the stress that is brought about by the COVID-19 pandemic. A stressful situation (i.e., pandemic and confinement) together with critical events (e.g., illness and death of a friend/relative due to COVID-19) increases anxiety levels and influences the perception of self-efficacy [26]. With this in mind, Vagni [27] said that exposed adults also have greater levels of emergency stress and anxiety, which is more willing to use problem-focused coping. These individuals involved in the treatment of COVID-19 are exposed to large amounts of stress and could experience secondary trauma. Moreover, individual efficacy in stopping negative emotions could be a protective strategy against stress and trauma [27]. Yildirim [28] stated that COVID-19 severity and its subsequent effects, self-efficacy, and preventive behaviors uniquely predicted mental health over other chronic diseases. These results may underscore the development of interventions aiming to improve individuals’ mental and physical health during the pandemic. Therefore, it was hypothesized that:

Hypothesis (H3).

Understanding COVID-19 has a significant effect on Self-efficacy.

Self-efficacy for exercise is an independent predictor of physical activity and obstructions gym-goers from attending fitness centers while overcoming barriers to exercise, trait anxiety, and fear of falling [29]. According to McAuley et al. [30], self-efficacy plays a critical role in gym-goers’ physical activity, with protective factors and impediments having a significant direct relationship with their functional performance. Legraux [31] stated that an increase in self-efficacy was also associated with an increase in self-determination, which significantly impacts gym-goers’ behavior in the face of impediments. Several studies also included the social environment or facilitator as a predictor of self-efficacy, with high attendance fitness centers significantly outperforming low attendance fitness centers in terms of self-efficacy exercises [32]. In line with this, Dionigi [33] correlates self-efficacy and social interaction as important mechanisms in enhancing the gym-goers’ well-being and various physical, mental, and social health. Furthermore, self-efficacy reinforced the impact of facilitators as an intermediate variable to the physical activity of the gym-goers [34]. Therefore, the following were hypothesized:

Hypothesis (H4).

Self-efficacy has a significant effect on Impediments.

Hypothesis (H5).

Self-efficacy has a significant effect on Facilitators.

According to Teixiera [35], self-motivation is an important factor in sustaining goals in the fitness centers, which is linked to positive health outcomes. Keith and Hopfner [36] explained that setting high and specific goals is one of the most well-established management tools for increasing performance and motivation. Consequently, self-motivation positively and significantly impacts gym-goers’ goal setting because they will receive non-judgmental reminders [37]. Therefore, it was hypothesized that:

Hypothesis (H6).

Self-motivation has a significant effect on Goal Setting.

Adherence to structured exercise programs significantly and directly relates to the barriers of gym-goers attending the fitness centers [38]. Moreover, goal setting is an effective way of helping gym-goers achieve their health and fitness benefits of structured exercise and increased lifestyle physical activity. Kaur et al. [39] stated that health concerns such as the COVID-19 pandemic significantly affect commitment, schedule, and goal-seeing in terms of fitness. Therefore, it was hypothesized that:

Hypothesis (H7).

Impediments have a significant effect on Goal Setting.

Facilitators are described as individuals who share significant characteristics with a demographic, allowing them to serve that demographic directly [40]. Peer-facilitated interventions are consistent with established theories relating to health-behavior changes [18,41]. These emphasize interpersonal relationships to increase self-efficacy and motivation for behavior change, often through vicarious experiences and verbal persuasion. Abrantes et al. [42] concluded that facilitators significantly affect goal setting, which can be essential for initiating and maintaining physical activity in the long term. Therefore, it was hypothesized that:

Hypothesis (H8).

The Facilitator has a significant effect on Goal Setting.

Physical activity involving inactive participants usually aims to achieve any increase in physical activity rather than solely focusing on increasing it to a target level [43]. Given the lack of consensus in defining physical activity maintenance, some studies also examined an alternative definition that is consistent with how intervention studies typically operationalize maintenance [44]. The PAMT considers the significant effect of life stress in redirecting personal resources away from focusing on physical activity and increasing adverse effects [16]. Therefore, it was hypothesized that:

Hypothesis (H9).

Life stress has a significant effect on Physical Activity Maintenance.

Goal setting is thought to function to reinforce continued efforts towards a goal. A goal is most successfully achieved when a person commits to that goal and has the confidence to reach that goal [45]. Scholz et al. [46] noted the difficulty for healthcare providers to effectively design and plan programs to increase adult physical activity levels without first identifying the various factors that influence the initiation and maintenance of regular physical activities. Amireault et al. [47] found that motivation and goals are among the strongest predictors of physical activity maintenance. Iwasaki et al. [48] also state that goal setting is a standard method in behavioral interventions—one of the classified interventions for achieving increased physical activity [43]. In addition, Shilts et al. [49] found that goal setting has proved effective in modifying adults’ physical activity behavior. Therefore, it was hypothesized that:

Hypothesis (H10).

Goal setting has a significant effect on Physical Activity Maintenance.

Summerbell et al. [50] stated that both environmental and individual factors are directly related to intention and maintenance. According to Humpel [51], there is a consistent link between physical environmental factors, PA maintenance, and behavior. Consequently, strategies that target both individual and environmental physical activity can have a powerful and long-lasting impact on maintenance [16]. In line with this, Bandura [17,18] stated that environmental characteristics emerge as the overriding factors when they exert strict limitations on behavior, such as physical exercise. Thus, environmental factors have a significant impact on physical exercise. Moreover, Scholz et al. [46] stated that there is a significant number of involved behavioral factors regarding regular physical activity. Thus, the following were hypothesized:

Hypothesis (H11).

Physical Activity Environment has a significant effect on Physical Activity Maintenance.

Hypothesis (H12).

Physical Activity Maintenance has a significant effect on Behavioral intention.

3. Methodology

3.1. Participants

The study utilized an online self-administered survey using Google Forms. The survey was distributed online through social media platforms such as Facebook, Viber, Instagram, and Twitter due to the COVID-19 pandemic. A convenience sampling approach was utilized for this study, making the survey available from 21 September to 22 December 2021 to people who are going to the gym. Before answering the questionnaire for this study, respondents were requested to sign a consent form. From the survey form, the participants were allowed to cancel and their responses would not be recorded if the form was not finished. Thus, only recorded participants were considered in this study. A total of 1048 participants voluntarily answered the survey, as seen in Table 1. However, we only analyzed the gym members’ respondents (1002 individuals).

The demographics are composed of 856 males and 192 females, wherein the majority came from the 15–24 age group (48.4%) and 28–34 years old (44.8%). In the Philippines, individuals with ages below 18 years old are considered minors. However, IHRSA [1] reported that gym-goers under 18 make up 16.10% of gym-goers, while those above it (18–35 years old) make up 60.60%. Thus, our study opted to segregate the demographic age group into 15–24 and 28–34 years old, similar to the study of Ong et al. [2]. Moreover, 48.3% are college graduates and are still studying (45.3%). About 42.4% have a monthly salary/allowance of less than 15,000, mainly from the NCR (45.6%). The majority of the respondents are either enrolled in a gym (95.6%), do home workouts (79.3%), or regularly go to the gym (80.9%).

Table 1. The demographic of the respondents (n = 1048).

Characteristics	Category	n	%
Gender	Male	856	81.7
	Female	192	18.3
Age	15–24 years old	507	48.4
	28–34 years old	469	44.8
	35–44 years old	63	6.00
	45–54 years old	8	8.00
	55–64 years old	1	0.10
	More than 64 years old	0	0
Education Status	Elementary Graduate	3	0.30
	Junior High School Graduate	72	6.90
	Senior High School Graduate	424	40.5
	Vocational Graduate	7	0.70
	College Graduate	506	48.3
	Master’s Degree Graduate	26	2.50
	PhD Graduate	10	1.00
Employment Status	Student	475	45.3
	Unemployed	9	0.90
	Self-Employed	103	9.80
	Employed	461	44.0
Monthly Income/ Allowance	Less than 15,000 PHP	444	42.4
	15,000–30,000 PHP	110	10.5
	30,001–45,000 PHP	138	13.2
	45,001–60,000 PHP	140	8.2
	More than 60,000 PHP	130	12.4
Location	Cordillera Administrative Region (CAR)	13	1.20
	National Capital Region (NCR)	477	15.5
	Region I—Ilocos Region	8	0.80
	Region II—Cagayan Valley	5	0.50
	Region III—Central Luzon	54	5.20
	Region IV-A—CALABARZON	360	34.4
	Region IV-B—MIMAROPA Region	101	9.60
	Region V—Bicol Region	13	1.20
	Region VI—Western Visayas	7	0.70
	Region VII—Central Visayas	3	0.30
	Region VIII—Eastern Visayas	3	0.30
	Region IX—Zamboanga Peninsula	1	0.10
	Region X—Northern Mindanao	2	0.20
	Region XI—Davao Region	0	0
	Region XIII—Caraga	1	0.10
	BARMM—Bangsamoro Autonomous Region in Muslim Mindanao	0	0
Are you enrolled in a gym/fitness center?	Yes	1002	95.6
	No	46	4.40
Are you doing a home workout?	Yes	831	79.3
	No	217	20.7
Do you regularly go to the gym/fitness center?	Yes	848	80.9
	No	200	19.1

Table 1. The demographic of the respondents (n = 1048).

Characteristics	Category	n	%
Gender	Male	856	81.7
	Female	192	18.3
Age	15–24 years old	507	48.4
	28–34 years old	469	44.8
	35–44 years old	63	6.00
	45–54 years old	8	8.00
	55–64 years old	1	0.10
	More than 64 years old	0	0
Education Status	Elementary Graduate	3	0.30
	Junior High School Graduate	72	6.90
	Senior High School Graduate	424	40.5
	Vocational Graduate	7	0.70
	College Graduate	506	48.3
	Master’s Degree Graduate	26	2.50
	PhD Graduate	10	1.00
Employment Status	Student	475	45.3
	Unemployed	9	0.90
	Self-Employed	103	9.80
	Employed	461	44.0
Monthly Income/ Allowance	Less than 15,000 PHP	444	42.4
	15,000–30,000 PHP	110	10.5
	30,001–45,000 PHP	138	13.2
	45,001–60,000 PHP	140	8.2
	More than 60,000 PHP	130	12.4
Location	Cordillera Administrative Region (CAR)	13	1.20
	National Capital Region (NCR)	477	15.5
	Region I—Ilocos Region	8	0.80
	Region II—Cagayan Valley	5	0.50
	Region III—Central Luzon	54	5.20
	Region IV-A—CALABARZON	360	34.4
	Region IV-B—MIMAROPA Region	101	9.60
	Region V—Bicol Region	13	1.20
	Region VI—Western Visayas	7	0.70
	Region VII—Central Visayas	3	0.30
	Region VIII—Eastern Visayas	3	0.30
	Region IX—Zamboanga Peninsula	1	0.10
	Region X—Northern Mindanao	2	0.20
	Region XI—Davao Region	0	0
	Region XIII—Caraga	1	0.10
	BARMM—Bangsamoro Autonomous Region in Muslim Mindanao	0	0
Are you enrolled in a gym/fitness center?	Yes	1002	95.6
	No	46	4.40
Are you doing a home workout?	Yes	831	79.3
	No	217	20.7
Do you regularly go to the gym/fitness center?	Yes	848	80.9
	No	200	19.1

3.2. Questionnaire

Presented in

Table 2. Questionnaires.

Construct	Items	Measures	Supporting References
Understanding of COVID-19	U1	I understand the transmission of the COVID-19 pandemic.	Nicola et al. [52]
	U2	I understand the symptoms that the COVID-19 pandemic brings.	Coccia [53]
	U3	I understand the protocols given if I have symptoms similar to COVID-19.	Prasetyo et al. [54]
	U4	I understand the given quarantine period of COVID-19.	Prasetyo et al. [54]
	U5	I know which hospital patients can go to get treatment for COVID-19.	Nicola et al. [52]
	U6	I know what to do when I get COVID-19.
Self-Motivation	SM1	Once I have made up my mind to regularly go to the gym, I will not be easily discouraged.	Jekauc et al. [15]
	SM2	When I get to the gym, I will not stop until I accomplish the goal I have set in my mind.
	SM3	I go to the gym because I want to stay in shape, especially during the COVID-19 pandemic.	Kopp et al. [55]; Vancampfort et al. [56]
	SM4	I have increased energy when I regularly attend a gym.	Jekauc et al. [15]; Vancampfort et al. [56]
	SM5	I believe it is essential to make an effort to attend the gym on a regular basis, especially during the COVID-19 pandemic.	Vancampfort et al. [56]
Life Stress	LS1	My performance in the gym is impacted by the death of a family member during the COVID-19 pandemic.	Erlangsen & Pitman [57]
	LS2	My performance in the gym is impacted by financial problems during the COVID-19 pandemic.
	LS3	My performance in the gym is impacted by emotional problems during the COVID-19 pandemic.	Gerber et al. [58]
	LS4	My performance in the gym is impacted by physical fatigue during the COVID-19 pandemic.	Gerber et al. [58]
	LS5	My performance in the gym is impacted by academic fatigue during the COVID-19 pandemic.
	LS6	My performance in the gym is impacted by social fatigue during the COVID-19 pandemic.
Goal Setting	GS1	By going to the gym regularly, I am committed to making it a priority during the COVID-19 pandemic.	Som et al. [59]
	GS2	By going to the gym regularly, I am committed to holding myself accountable.
	GS3	By going to the gym regularly, I am committed to following a schedule during the COVID-19 pandemic.	Pearson [60]
	GS4	By going to the gym regularly, I am committed to achieving my goals during the COVID-19 pandemic.	Som et al. [59]
	GS5	I want to go to the gym to be physically fit during the COVID-19 pandemic.	Seidel [61]
	GS6	I want to go to the gym to be mentally fit during the COVID-19 pandemic.	Gerber & Pühse [62]; Vankim & Nelson [63]
	GS7	I want to go to the gym to be more sociable during the COVID-19 pandemic.	Pels & Kleinert [64]
	GS8	I want to go to the gym to become more confident during the COVID-19 pandemic.	Mazereel et al. [65]; Spence et al. [66]
	GS9	I want to regularly attend the gym to be satisfied with the goal I set during the COVID-19 pandemic.
Self-Efficacy	SE1	I am sure that I will attend the gym even if I am physically drained during the COVID-19 pandemic.	Chamorro-Koc et al. [67]
	SE2	I am sure that I will attend the gym even if boredom attacks me during the COVID-19 pandemic.	Buman et al. [68]
	SE3	I am sure that I will attend the gym even if depression attacks me during the COVID-19 pandemic.	Alonzo et al. [32]
	SE4	I am sure that I will attend the gym even if I have responsibilities for my family during the COVID-19 pandemic.	Radhakrishnan et al. [69]
	SE5	I am sure that I will attend the gym even if I have responsibilities for my work during the COVID-19 pandemic.	Coulson & McKenna [70]
	SE6	I want to attend the gym because I promised myself to be consistent during the COVID-19 pandemic.
Impediments	I1	When I do not go to the gym, it is because of the distance of it from my location.	Mizzi [71]
	I2	When I do not go to the gym, it is because I am sick during the COVID-19 pandemic.
	I3	When I do not go to the gym, it is because I don’t have available time during the COVID-19 pandemic.	Lenhart [72]
	I4	When I do not go to the gym, it is because I don’t have the resources/equipment.	Lockett et al. [73]
	I5	When I do not go to the gym, it is because I am on vacation.	Lockett et al. [73]
Facilitator	F1	I am encouraged by my friends to pursue my physical fitness journey during the COVID-19 pandemic.	De Brujin et al. [74]
	F2	I am encouraged by my relatives to pursue my physical fitness journey during the COVID-19 pandemic.	Martínez et al. [75]
	F3	I am easily affected by my mood when going to the gym during the COVID-19 pandemic.	De Brujin et al. [74]
	F4	My decision to go to the gym is influenced by my educational and academic workload and schedule during the COVID-19 pandemic.	De Brujin et al. [74]
	F5	My decision to go to the gym is influenced by my other responsibilities like household chores, relationships, etc.	Martínez et al. [75]
Physical Activity Maintenance	PAM1	I am satisfied with my current body structure and my intention to go to the gym.	Choi [8]
	PAM2	I am determined to maintain this psyche towards my intention to go to the gym during the COVID-19 pandemic.	Thakur & Joshi [76]
	PAM3	Even with a hectic schedule and the pandemic, I make sure that going to the gym is never left out during the COVID-19 pandemic.	Thakur & Joshi [76]
	PAM4	Even with a pandemic and health crisis, I can improvise at-home gym in order to maintain my physical fitness.	Buman et al. [68]
	PAM5	I am more focused on maintaining my long-term physical fitness in the gym even during the COVID-19 pandemic.	Buman et al. [68]
Physical Activity Environment	PE1	The gym that I go to offers sufficient equipment, which encourages me to be more physically active even during the COVID-19 pandemic.	Jekauc et al. [15]
	PE2	Having a coach/instructor in the gym is a big factor in my workout during the COVID-19 pandemic.	Jekauc et al. [15]
	PE3	My companions and gym-mates affect my performance in the gym during the COVID-19 pandemic.	Nigg et al. [16]
	PE4	Having a fixed schedule for each body component is more efficient for my physical fitness during the COVID-19 pandemic.	Nigg et al. [16]
	PE5	The ventilation and airflow in the gym greatly affect my performance during the COVID-19 pandemic.	Kapalo et al. [77]
Behavioral Intention	B1	The disruption in my daily gym routine is largely affected by my doubts about COVID-19 infection.	Peterson [7]
	B2	I can easily adapt to changes in my gym routine despite the COVID-19 infection.	Peterson [7]
	B3	I intend to attend a fitness center on a regular basis for the next six months despite the COVID-19 pandemic.	Peterson [7]
	B4	It is mostly up to me whether or not I attend the gym during the COVID-19 pandemic.	Peterson [7]
	B5	I enjoy going to the gym despite the COVID-19 pandemic.	Peterson [7]

are the constructs which were adapted to form a questionnaire that can determine the factors affecting the behavioral intentions of gym-goers during the COVID-19 pandemic in the Philippines. A five-point Likert scale was utilized to measure all the latent constructs using the SEM following the study of Ong et al. [2].

3.3. Structural Equation Modeling

SEM is a multivariate tool that measures the causal relationship among different variables to measure different aspects, such as human behaviors [78]. It measures the relationships of numerous observed and latent variables through direct, indirect, and total effects [79]. Jekauc et al. [15] considered SEM comparing TPB, PAMT, and SCT to explain physical activity behaviors, showing SCT followed by PAMT as the best theories to be utilized. Thus, this study considered SEM to determine the factors affecting the gym-goer’s behavioral intentions with fitness centers during the COVID-19 pandemic by integrating the SCT and PAMT models.

4. Results

The initial SEM (Figure 2) was run using AMOS 25 following the study of Ong et al. [80]. Based on the results, several indicators were not within the 0.5 thresholds set by Hair [79]. Hair [79] suggested that removing these indicators would enhance the model fit for the acceptability of the SEM. From which, U5, U6, LS5, and GS1 were removed.

The final SEM is presented in Figure 3. It could be seen that all indicators are within the threshold of 0.5 [78,79,80]. The following are the descriptive statistics of the factor loading, seen in

Table 3. Indicators statistical analysis.

Variable	Item	Mean	StD	Factor Loading
				Initial	Final
Understanding of COVID-19	U1	4.9227	0.310	0.542	0.540
	U2	4.6832	0.536	0.858	0.825
	U3	4.8149	0.439	0.868	0.870
	U4	4.6002	0.566	0.836	0.823
	U5	4.6469	0.613	0.288	-
	U6	4.7023	0.499	0.410	-
Self-Motivation	SM1	4.3855	0.863	0.795	0.717
	SM2	4.3053	0.893	0.824	0.812
	SM3	4.2729	0.962	0.861	0.878
	SM4	4.0906	0.861	0.754	0.742
	SM5	3.7080	1.030	0.821	0.899
Life Stress	LS1	4.1059	1.084	0.720	0.738
	LS2	3.9361	0.901	0.860	0.854
	LS3	4.0515	0.894	0.884	0.880
	LS4	3.9313	0.926	0.872	0.866
	LS5	3.6784	0.997	0.465	-
	LS6	4.0210	0.888	0.842	0.856
Goal Setting	GS1	3.5181	0.957	0.493	-
	GS2	4.2052	0.969	0.795	0.843
	GS3	4.1116	1.017	0.801	0.843
	GS4	4.0830	1.044	0.847	0.878
	GS5	4.3302	0.951	0.856	0.892
	GS6	4.2462	1.056	0.870	0.899
	GS7	4.1899	1.044	0.772	0.816
	GS8	4.2405	0.974	0.799	0.844
	GS9	3.9828	1.102	0.740	0.787
Self-Efficacy	SE1	2.5954	0.975	0.721	0.865
	SE2	2.7996	1.116	0.770	0.834
	SE3	2.2767	0.984	0.631	0.818
	SE4	2.5821	1.042	0.875	0.870
	SE5	2.8731	1.108	0.857	0.879
	SE6	3.5105	1.168	0.802	0.894
Impediments	I1	3.6288	0.907	0.667	0.641
	I2	4.2204	1.173	0.802	0.882
	I3	3.8397	0.941	0.722	0.740
	I4	3.5859	0.942	0.805	0.819
	I5	3.6336	0.920	0.614	0.772
Facilitator	F1	4.2796	1.088	0.822	0.884
	F2	3.9695	1.036	0.755	0.726
	F3	4.1918	0.921	0.757	0.751
	F4	4.0210	1.108	0.804	0.786
	F5	4.0372	1.043	0.851	0.805
Physical Activity Maintenance	PM1	4.1784	0.950	0.706	0.730
	PM2	4.3053	0.996	0.774	0.794
	PM3	4.2586	1.002	0.685	0.702
	PM4	4.2910	0.916	0.642	0.678
	PM5	4.1718	1.052	0.784	0.808
Physical Activity Environment	PE1	4.3397	1.038	0.880	0.864
	PE2	4.2872	1.002	0.851	0.845
	PE3	4.2586	1.019	0.856	0.869
	PE4	4.2910	0.905	0.766	0.752
	PE5	4.2099	1.062	0.854	0.834
Behavioral Intentions	BI1	4.2939	1.012	0.715	0.743
	BI2	4.2395	0.930	0.707	0.737
	BI3	4.0811	0.933	0.847	0.878
	BI4	4.1708	0.921	0.561	0.591
	BI5	3.9046	1.207	0.680	0.707

.

Presented in Table 4 is the model fit parameters showing values within the cut-off set by Gefen et al. [81] and Steiger [82]. Based on the results, the IFI, TLI, CFI, GFI, and AGFI are greater than 0.80, indicating that the model is a good fit [78,79,80]. Moreover, the RMSEA value is 0.065, less than the cut-off, 0.07, which is considered acceptable [78]. Hair [79] explained how having these values within the cut-off presents an acceptable and good model fit.

Table 5 presents Cronbach’s alpha, average variance extracted (AVE), and composite reliability (CR). From Cronbach’s alpha and CR are acceptable with values greater than 0.70 [79]. In addition, Hair [79] indicated that greater than 0.50 values for AVE further justify internal validity and validity among the constructs of the study. Thus, this study has constructs that present internal validity and reliability.

Table 6 presents the model’s direct, indirect, and total effects. All hypotheses were significant, as seen from the results with a p-value less than 0.05. Moreover, running the common method bias (CMB) from Harman’s Single Factor Test showed a value of 41.26%, resulting in no CMB from the latent constructs of the study (CMB < 50%) [83].

Table 4. The Model Fit.

Goodness of Fit Measures of SEM	Parameter Estimates	Minimum Cut-Off	Suggested by
Incremental Fit Index (IFI)	0.883	>0.80	Gefen et al. [81]
Tucker–Lewis Index (TLI)	0.862	>0.80	Gefen et al. [81]
Comparative Fit Index (CFI)	0.883	>0.80	Gefen et al. [81]
Goodness of Fit Index (GFI)	0.869	>0.80	Gefen et al. [81]
Adjusted Goodness of Fit Index (AGFI)	0.824	>0.80	Gefen et al. [81]
Root Mean Square Error (RMSEA)	0.065	<0.07	Steiger [82]

Table 4. The Model Fit.

Goodness of Fit Measures of SEM	Parameter Estimates	Minimum Cut-Off	Suggested by
Incremental Fit Index (IFI)	0.883	>0.80	Gefen et al. [81]
Tucker–Lewis Index (TLI)	0.862	>0.80	Gefen et al. [81]
Comparative Fit Index (CFI)	0.883	>0.80	Gefen et al. [81]
Goodness of Fit Index (GFI)	0.869	>0.80	Gefen et al. [81]
Adjusted Goodness of Fit Index (AGFI)	0.824	>0.80	Gefen et al. [81]
Root Mean Square Error (RMSEA)	0.065	<0.07	Steiger [82]

Table 5. Composite Reliability and Validity.

Factor	Cronbach’s α	Composite Reliability (CR)	Average Variance Extracted (AVE)
Understanding of COVID-19	0.769	0.854	0.602
Self-Motivation	0.862	0.906	0.661
Life Stress	0.880	0.923	0.706
Goal Setting	0.955	0.954	0.724
Self-Efficacy	0.907	0.945	0.740
Impediments	0.847	0.881	0.601
Facilitator	0.897	0.893	0.628
Physical Activity Maintenance	0.917	0.861	0.554
Physical Activity Environment	0.924	0.919	0.695
Behavioral Intention	0.872	0.854	0.543

Table 5. Composite Reliability and Validity.

Factor	Cronbach’s α	Composite Reliability (CR)	Average Variance Extracted (AVE)
Understanding of COVID-19	0.769	0.854	0.602
Self-Motivation	0.862	0.906	0.661
Life Stress	0.880	0.923	0.706
Goal Setting	0.955	0.954	0.724
Self-Efficacy	0.907	0.945	0.740
Impediments	0.847	0.881	0.601
Facilitator	0.897	0.893	0.628
Physical Activity Maintenance	0.917	0.861	0.554
Physical Activity Environment	0.924	0.919	0.695
Behavioral Intention	0.872	0.854	0.543

Table 6. Direct, Indirect, and Total Effects.

No	Variable	Direct Effect	p-Value	Indirect Effect	p-Value	Total Effect	p-Value
1	PAE → PAM	0.579	0.002	-	-	0.579	0.002
2	U → SE	0.661	0.001	-	-	0.661	0.001
3	U → SM	0.861	0.001	-	-	0.861	0.001
4	U → LS	0.413	0.001	-	-	0.413	0.001
5	SE → F	0.548	0.002	-	-	0.548	0.002
6	SE → I	0.608	0.002	-	-	0.608	0.002
7	F → GS	0.272	0.001	-	-	0.272	0.001
8	I → GS	0.285	0.002	-	-	0.285	0.002
9	SM → GS	0.663	0.002	-	-	0.663	0.002
10	GS → PAM	0.794	0.002	-	-	0.794	0.002
11	LS → PAM	−0.113	0.003	-	-	−0.113	0.003
12	PAM → BI	0.973	0.004	-	-	0.973	0.004
13	PAE → BI	-	-	0.563	0.002	0.563	0.002
14	U → F	-	-	0.362	0.001	0.362	0.001
15	U → I	-	-	0.402	0.001	0.402	0.001
16	U → GS	-	-	0.783	0.001	0.783	0.001
17	U → PAM	-	-	0.575	0.001	0.575	0.001
18	U → BI	-	-	0.559	0.001	0.559	0.001
19	SE → GS	-	-	0.322	0.002	0.322	0.002
20	SE → PAM	-	-	0.255	0.001	0.255	0.001
21	SE → BI	-	-	0.248	0.002	0.248	0.002
22	F → PAM	-	-	0.216	0.001	0.216	0.001
23	F → BI	-	-	0.210	0.001	0.210	0.001
24	I → PAM	-	-	0.226	0.001	0.226	0.001
25	I → BI	-	-	0.220	0.001	0.220	0.001
26	SM → PAM	-	-	0.526	0.001	0.526	0.001
27	SM → BI	-	-	0.512	0.001	0.512	0.001
28	GS → BI	-	-	0.772	0.002	0.772	0.002
29	LS → BI	-	-	−0.110	0.003	−0.110	0.003

Table 6. Direct, Indirect, and Total Effects.

No	Variable	Direct Effect	p-Value	Indirect Effect	p-Value	Total Effect	p-Value
1	PAE → PAM	0.579	0.002	-	-	0.579	0.002
2	U → SE	0.661	0.001	-	-	0.661	0.001
3	U → SM	0.861	0.001	-	-	0.861	0.001
4	U → LS	0.413	0.001	-	-	0.413	0.001
5	SE → F	0.548	0.002	-	-	0.548	0.002
6	SE → I	0.608	0.002	-	-	0.608	0.002
7	F → GS	0.272	0.001	-	-	0.272	0.001
8	I → GS	0.285	0.002	-	-	0.285	0.002
9	SM → GS	0.663	0.002	-	-	0.663	0.002
10	GS → PAM	0.794	0.002	-	-	0.794	0.002
11	LS → PAM	−0.113	0.003	-	-	−0.113	0.003
12	PAM → BI	0.973	0.004	-	-	0.973	0.004
13	PAE → BI	-	-	0.563	0.002	0.563	0.002
14	U → F	-	-	0.362	0.001	0.362	0.001
15	U → I	-	-	0.402	0.001	0.402	0.001
16	U → GS	-	-	0.783	0.001	0.783	0.001
17	U → PAM	-	-	0.575	0.001	0.575	0.001
18	U → BI	-	-	0.559	0.001	0.559	0.001
19	SE → GS	-	-	0.322	0.002	0.322	0.002
20	SE → PAM	-	-	0.255	0.001	0.255	0.001
21	SE → BI	-	-	0.248	0.002	0.248	0.002
22	F → PAM	-	-	0.216	0.001	0.216	0.001
23	F → BI	-	-	0.210	0.001	0.210	0.001
24	I → PAM	-	-	0.226	0.001	0.226	0.001
25	I → BI	-	-	0.220	0.001	0.220	0.001
26	SM → PAM	-	-	0.526	0.001	0.526	0.001
27	SM → BI	-	-	0.512	0.001	0.512	0.001
28	GS → BI	-	-	0.772	0.002	0.772	0.002
29	LS → BI	-	-	−0.110	0.003	−0.110	0.003

5. Discussion

SEM was utilized to determine the factors affecting the gym-goers’ behavioral intentions during the COVID-19 pandemic through the integration of PAMT and SCT. Factors such as Understanding of COVID-19 (U), Self-Motivation (SM), Life Stress (LS), Goal Setting (GS), Self-efficacy (SE), Impediments (I), Facilitator (F), Physical Activity Maintenance (PAM), Physical Activity Environment (PAE), and Behavioral Intention (BI) were considered in this study.

The results indicated that PAM had the highest direct significant effect on BI (β = 0.973; p = 0.004). The indicators of PAM regarding the overall satisfaction, attainment, and commitment of the gym-goers impacted BI. The result implies that an individual’s behavioral intention and contextual constructs contributed to maintaining physical activity. This result coincides with the findings of other studies in which gym-goers’ satisfaction and the quality of the fitness center directly influence PAM [10,12,84]. Additionally, Hagger [85] found that once the behavior is initiated, key self-regulatory volitional mechanisms like action control and planning are thought to influence behavior maintenance. Having the aim to maintain physical activities would lead to intention. The current technology has led people to consider online workouts on different social media platforms. It was explained in the study of Cronshaw [86] and Kaur et al. [39] how web training and web workouts are widely utilized, especially in lockdowns and gym closures during the COVID-19 pandemic. The PAM of people who are committed to doing regular workouts despite the lockdown was seen to be evident; thus, the reopening and lifting of lockdown also presented people’s BI to continue with the actual gym workouts.

Second, it was also seen that U had a significant direct effect on SM (β = 0.861; p = 0.001). The transmission, symptoms, and protocols of the COVID-19 are key indicators of U. The adjustment brought about by the pandemic has a significant impact on gym-goers’ motivation. Through the understanding and knowledge of the COVID-19 pandemic, people are more aware of the consequences of being infected. As mentioned by Kim and Crimmins [87], considering a nationally representative sample of the U.S. population at younger and older ages, people adjust their behavior in reaction to the pandemic. Sequentially, Westcott et al. [88] adopted Rogers’ social cognitive model of protection motivation theory to investigate how threat and coping appraisal processes were connected to protective actions engaged in when confronted with danger. As a result, this disparity between young and old persons in the routes to participating in preventative behaviors shows that different intervention techniques for different age groups are required [89]. With this study having younger generations who participated (15–34 years old), it confirms that younger people are more active gym-goers and are more health-conscious.

With that, educating the young generation on the necessity and effectiveness of pandemic preparedness behaviors is easier. On the other hand, local governments and city public health agencies can effectively promote pandemic mitigating behaviors to older people through local media. The role of media exposure is to transmit information effectively to individuals. Kim and Crimmins [87] also presented a suitable theoretical foundation and practical examples. Through this, insights were presented that can help better comprehend people’s motives during the pandemic. Implementing and understanding behavioral changes is essential concerning the COVID-19 pandemic and how it differs for younger and older generations.

Third, GS had a significant direct effect on PAM (β = 0.794; p = 0.002). It was observed that setting one’s goal in the gym plays a significant role in maintaining physical activity in the long term. Indicators such as commitment, accountability, and specific goals of the gym-goers were seen in the study. Amireault et al. [47] explained that GS and motivation are two of the most potent indicators of continued physical activity. Several studies support that GS is essential for the initiation and PAM on behavioral change in general [89] and in physical activity in particular [90]. Iwasaki et al. [48] also stated that GS may lead to increased physical activity and defining specific goals can help bridge the gap between getting the desired result by changing one’s lifestyle and habits.

Fourth, U had a significant direct effect on SE (β = 0.661; p = 0.001), and LS (β = 0.413; p = 0.001). The knowledge of COVID-19 among gym-goers was found to be the indicator that influences the individual’s belief in carrying out the behaviors required to achieve specific performance goals. The indicators such as the death of a family member, financial problems, and different fatigue signify that U has an impact on LS. It implies that gym-goers’ concerns about COVID-19 are positively related to the different types of stress they are experiencing. A study by Vagni [27] stated that the primary healthcare group exhibits higher levels of emergency stress and arousal than the emergency worker group and is more inclined to utilize problem-focused coping.

In addition, individual effectiveness in halting unpleasant feelings and ideas might be a stress and secondary trauma protective approach. This correlates to a study by Zhang et al. [91] in which two forms of scientific self-efficacy as measured by scientific learning ability and scientific learning behavior, were found to be adversely linked with cognitive anxiety. Furthermore, cognitive anxiety was inversely connected to four categories of science involvement, as shown by cognitive engagement, emotional engagement, behavioral engagement, and social engagement. It was also seen that U had an indirect effect on PAM (β = 0.575; p = 0.001) and BI (β = 0.559; p = 0.001). Further support was seen from the study by Weinstein [92]. Weinstein [92] stated that because of the unexpected and abrupt increases in time spent alone due to the COVID-19 pandemic, protective or aggravating predictive characteristics would play a bigger role in predicting change.

Interestingly, it was seen from the results that U had an indirect effect on several factors such as GS (β = 0.783; p = 0.001), F (β = 0.362; p = 0.001), and I (β = 0.402; p = 0.001). A study by Malureanu et al. [93] confirmed the belief that although frequently surprising, both cognitive and non-cognitive elements have a clear predictive role in job performance. As a result, it is advised that cognitive and non-cognitive elements be evaluated on a regular basis in various lifelong learning situations to increase job motivation and, indirectly, work performance. Thus, to effectively promote lifelong learning in a virtual setting, corporate trainers should employ e-platforms and tailor their tactics to particular psychological circumstances. Meyer [94] correlated F to SE, similar to this study. The need to develop and evaluate intervention techniques that promote appropriate levels of SE in people to promote a healthy lifestyle would lead to positive intentions. As per BI, a study by Clements [95] said that there were differences in knowledge about COVID-19 based on age group. Contrary to recent US media, baby boomers were more knowledgeable about COVID-19 than all other age groups and were less likely to engage in purchasing behavior that could be considered hoarding. Therefore, SM and its antecedent, GS, are the mediators for U since they have the understanding to protect themselves from the COVID-19 virus.

Furthermore, SE had a significant direct effect on I (β = 0.608; p = 0.002) and F (β = 548; p = 0.002). It showed that the gym’s location, availability, and gym equipment were the indicators seen in the study. Indicators such as friends, relatives, mood, educational and academic workload, and responsibilities were seen as key constructs in the study. It shows how SE dramatically affects a gym-goer’s willingness and perception. According to Dogan [96], specific relationships and factors affect the link between the length of the gym and the choice of gym-goers. The result shows that the said determinant, such as the distance of the intention of the gym-goers plays a significant factor in the SE to I. This also correlates with why there is an indirect effect of SE on GS (β = 0.322; p = 0.002), PAM (β = 0.255; p = 0.001), and BI (β = 0.248; p = 0.002). Thus, the need for gyms to strategize on their location to enhance membership and gym-goers could be beneficial on their end. Gyms and fitness centers could capitalize on these findings as people are seen to be willing but are not motivated due to several constraints.

Consequently, the SEM also indicated that SM had a significant direct effect on GS (β = 0.663; p = 0.002). It was observed that SM plays a significant role in GS and provides standards for accomplishments. Key indicators that were seen in the study are the mindset, energy, and effort of the gym-goers in relation to their attendance at the gym. This result was similar to several studies that showed the significance of SM to GS as a determinant of intention to go to the gym. As Deci and Ryan [41] mentioned, humans are inherently active, which means that they are intrinsically motivated to do various activities in which rewards are inherent after the goals are achieved. Given that the primary motivators should be spontaneous and have internal experiences accompanying the behavior, it would result in positive intention among people. Furthermore, a study by Locke and Latham [97] stated that the mechanisms by which the goals perform moderated the effects of goals as mediators of incentives, leading to new directions in GS that play practical significance in the motivation of a gym-goer.

Moreover, the PAE also had a significant direct effect on the PAM of a gym-goer (β = 0.579; p = 0.002). Determination and satisfaction to maintain the current body structure, willingness to maintain physical activity at home, and focusing on long-term physical fitness were the indicators seen in the study. It is explained that gym-goer’s surroundings determine the individual’s willingness to maintain their activity. Lawton [98] discussed the impact of the PAE on anxiety levels, similar to outdoor activities, and the well-being of individuals’ physical activities. These are considered essential determinants of the latent.

The relationship between F (β = 0.272; p = 0.001) and I (β = 0.285; p = 0.002) to GS showed that it is essential for a gym-goer to have role models that provide support, encouragement, and commitment to a structured exercise program to adequately set and achieve his/her goals in the gym. The findings are similar to the study of Brinks and Franklin [38], which indicated that adherence to a structured exercise program is directly related to the impediments of the gym-goers attending fitness centers. Abrantes et al. [42] also stated that facilitators significantly impact GS, which is crucial for establishing and maintaining physical activity in the long term. This also supports why F has an indirect effect on PAM (β = 0.216; p = 0.001) and BI (β = 0.210; p = 0.001). Moreover, Impediments were also seen to have an indirect effect on PAM (β = 0.226; p = 0.001) and BI (β = 0.220; p = 0.001), which was also mentioned in Kaur et al. ‘s [39] study.

Interestingly, LS had a negative direct significant effect on PAM (β = −0.113; p = 0.003). It could be considered that the death of a family member, financial and emotional problems and various fatigue caused by the COVID-19 pandemic have effects on gym-goers’ performance. The results from the study of Stults-Kolehmainen and Sinha [99] indicated that individuals utilize exercise to cope with stress, which explains why life stress negatively affects physical activity maintenance. They also mentioned in their study that other factors, such as stages of change for exercise, might attenuate stress and physical activity interactions. In the face of stress, those who are habitually active exercise more, whereas those who are just starting out exercise less. As a result, stress affects exercise adoption, maintenance, and relapse differently.

Given the results, life stress harms one’s attempt to maintain physical activity through both individual psychosocial variables and contextual constructs [16]. As mentioned by Holmes and Rahe [100], life stress or any event or transition that causes dramatic changes to one’s life can result in concomitant changes in behavior and health, specifically PAM. Alternatively, being inundated with minor nuisances may also weaken one’s attempts for healthy behavior—perhaps to a similar degree as the experience of a small number of significant life events [101]. A study by Nigg et al. [16] supports these statements since the PAMT considers that LS has a negative direct significant effect on PAM in terms of redirecting personal resources away from focusing on physical activity and increasing adverse effects.

Lastly, PAE (β = 0.563; p = 0.002) and GS (β = 0.772; p = 0.002) had significant indirect effects on BI. Laddu [102] stated that the function of the built environment in promoting the promotion and engagement of physical activity, particularly in the context of active transportation and leisure time, are domains of physical activity. A special emphasis will be placed on discrepancies in access to activity-promoting settings and the unequal benefits of environmental interventions in disadvantaged communities.

This paper will review the opportunities for public health and policy to advocate for and prioritize the implementation of equitable and effective interventions aimed at expanding or improving activity and supportive infrastructures within neighborhoods and communities that are meaningful to the population, thus increasing physical activity. Similarly, Harris [103] explained that participants who were sedentary at the start reported 3.4 and 3.8 days of activity after one and two years, respectively. These findings provide intriguing early evidence that gamification can help reduce physical inactivity, and game-design techniques that can help with behavior change are highlighted. Early research found that maintenance self-efficacy, behavioral regulation techniques, emotional judgments, perceived control/opportunity, habit, and extraversion were all consistent predictors of post-intention physical activity. There are several intention-behavior discordance models in the literature but they are seldom applied. Therefore, PAM mediates the influence of PAE and GS on BI.

5.1. Theoretical Contribution

The integrated framework of this study indicated a strong correlation between the variables, resulting in the determination of gym-goers ’ behavioral intentions. The emphasis of these theories is on comprehending the cognitive psychology of the individual, either alone or in the context of the individual’s social environment, and from the standpoint of several key constructs. The integration of PAMT and SCT in this study revealed the holistic measurement of BI among physical-related activities. According to the findings, PAM, through the understanding of COVID-19 and self-motivation under the PAMT, had the highest direct relationship affecting BI. This implies that, when compared to the other factors, gym-goers’ PAM and motivation are the most significant factors influencing behavioral intention. The integration of PAMT and SCT can be used to identify the motivational factors that influence a given behavior wherein the stronger an individual’s intention is, the more likely they will perform. Consequently, this integrated model could serve as a theoretical foundation and be applied and extended to assess gym-goers’ behavioral intentions worldwide, even after the COVID-19 pandemic.

5.2. Practical Contribution

Some of the practical contributions of the cases in this study and the aforementioned results that were discussed provided some comprehensive insights. Incorporating the PAMT in this study highlighted the influence of numerous factors on gym-goer behavioral intentions. According to the findings, PAM had the most direct meaningful influence on BI. PAM variables relating to gym-goers’ overall pleasure, accomplishment, and commitment influenced BI. This suggests that an individual’s behavioral intention and environmental factors contributed to maintaining physical activity. Thus, people should have the proper environment to be influenced and have the intention to maintain their physical activity. Lastly, the direct and indirect significance, particularly the highest of which, addresses how public health and policy can advocate for and prioritize the implementation of equitable and effective interventions aimed at expanding or improving activity-supportive infrastructures within neighborhoods and communities.

6. Conclusions

There has been a significant increase in gym-goers in line with the growth of fitness centers. During the COVID-19 pandemic, the Inter-Agency Task Force imposed a lockdown in the Philippines, resulting in the closure of fitness centers. Consequently, the COVID-19 pandemic affected gym-goers’ health perceptions and behavioral intentions [7].

The study integrated Physical Activity Maintenance Theory (PAMT) and Social Cognitive Theory (SCT) to determine the factors affecting the behavioral intentions of gym-goers during the COVID-19 pandemic in the Philippines. Several latent variables were analyzed in the study, such as understanding of COVID-19, self-motivation, life stress, goal setting, self-efficacy, impediments, facilitator, physical activity maintenance, physical activity environment, and behavioral intention. A total of 1048 gym-goers voluntarily answered the online self-administered survey containing 68 questions. Structural Equation Modeling (SEM) indicated that among the latent variables, physical activity maintenance through the understanding of COVID-19 and self-motivation had the highest significant effect on behavioral intentions. Interestingly, life stress was found to have a negative direct significant effect on physical activity maintenance.

The current study is one of the first to analyze factors affecting the gym-goers’ behavioral intentions during the COVID-19 pandemic. Compared to different theories, the integration of the PAMT and SCT in this study provided a better predictor. However, it should be considered that since the data collection was made online due to the COVID-19 pandemic, the control for responses was based on being an active member in gym and fitness centers. Moreover, the convenience sampling method was employed; thus, future studies may consider specifications underscoring the risk of bias such as recall, and selection bias, among others, for future research extension. Moreover, other methods, such the use of a machine learning algorithm, may be applied in evaluating behavioral intentions [104,105] to measure other limitations of the current method as applied. On another note, it was seen that the integration of these theories holistically measured the behavioral intention of gym-goers during the COVID-19 pandemic in the Philippines. As a result, this integrated model could serve as a theoretical foundation and be applied and extended to assess gym-goers’ behavioral intentions during the COVID-19 pandemic worldwide [106,107,108,109,110,111,112,113,114].