Motivation for physical activity in the Jordanian military: Possible determinants of physical activity in male and female recruits

ABSTRACT

Given the obligatory nature of physical fitness training in the military and in order to guide intervention development, our study assessed possible motivational determinants as suggested by self-determination theory in addition to other possible determinants. A cross-sectional study was conducted among 218 military recruits during their basic training in Jordan. Physical activity and lifestyle behaviors were measured using the Arab Teens Lifestyle Study (ATLS). Psychosocial variables were assessed using the Exercise Self-Efficacy Scale (ESE), Behavioral Regulation Exercise Scale (BREQ-2) and Exercise Benefits/Barriers Scale (EBBS). Bivariate correlation analysis revealed that identified self-regulation, introjected regulation and exercise self-efficacy scores were positively associated with higher Metabolic Equivalent of Task (METs) and minutes per week of physical activity among male recruits and the overall sample respectively. Among females, only external regulation was positively associated with the total METs/week. Being a male was significantly associated with higher minutes of physical activity among the overall sample. Multivariate regression analyses showed that identified regulation was significantly and positively associated with higher minutes of physical activity among the overall sample and male recruits in addition to higher METs per week among the male recruits. Also, the amotivation score was significantly and positively associated with higher minutes of physical activity among the overall sample and male recruits. A multivariate regression analysis for female recruits showed no significant associations. Intervention developers are advised to increase autonomous forms of motivation through structured enjoyable physical fitness programs in order to enhance intrinsic motivation in the long term.

KEYWORDS:

• Self-determination theory
• determinants
• physical activity
• Jordan
• military

What is the public significance of this article?—Basic military training is a good opportunity to stimulate recruits to develop healthy habits and enhance their fitness level. To develop effective interventions that promote physical activity, it is crucial to understand the reasons why recruits are physically active; for example, whether they are internally motivated to be physically active, or only externally motivated. This study suggests that external motives and the confidence recruits have in that they can exercise also in difficult circumstances, are important factors for the activity level of Jordanian recruits.

Introduction

Nutrition, sleeping and physical activity are considered the performance triad in the military that supports health, ideal body composition, physical fitness and a positive psychological status (Purvis et al., 2013). Military personnel face challenges in terms of maintaining sufficient physical activity, especially in certain military branches which include administrative tasks. However, all military personnel are required to stay active and physically fit. In fact, the current military environment is not conducive to exercise as technology reduces the amount of manual labor associated with many tasks (Tanofsky-Kraff et al., 2013). Results of the Department of Defense Health Related Behaviors Survey in the US including 15,747 active-duty military personnel indicated that around 42% of respondents did not meet the Healthy People 2010 guidelines for moderate or vigorous physical activity (Smith et al., 2013).

Physical activity is a complex behavior that is affected by multiple factors (Pan et al., 2009). A cross-sectional study that included 5000 Belgian military men found that higher physical activity level demonstrated by total METs (Metabolic Equivalent of Task) of vigorous activity was associated with being younger, having a lower BMI, not smoking, having a lower educational level, and a higher Mediterranean diet score (Mullie et al., 2013). Worldwide, a systematic review of reviews suggests that some personal (e.g., self-efficacy), environmental factors (e.g., accessibility to facilities and the presence of sidewalks) and other factors such as age and health status were related to participation in physical activity among the general public (Choi et al., 2017).

The basic military training is not only a good chance to prepare young military recruits both mentally and physically for the military environment and training but also a possibility to have them develop healthy habits and enhance their fitness level (Santtila et al., 2015). Also when they return to citizen life after basic military training, they may benefit from these healthy habits, and remain prepared to participate in national defense tasks if requested.

The Social Cognitive Theory (SCT) developed by Bandura has been widely applied for predicting physical activity behaviors. Self-efficacy is one of the most important constructs of SCT, and represents people’s confidence in their ability to exercise in different situations (Young et al., 2014).

The self-determination theory (SDT) is also receiving attention in predicting and explaining physical activity behaviors. It is one of the theories that uniquely explain how different types of motivation affect behaviors such as physical activity (Teixeira et al., 2012). The theory differentiates between three forms of motivation: amotivation, extrinsic and intrinsic motivation. Amotivation represents a lack of intention to exercise. Extrinsic motivation is defined as doing an activity for instrumental reasons such as exercising to get a career promotion or to impress others. External motivation refers to a variety of regulatory styles: external, introjected, identified and integrated regulation. External regulation is the least self-determined as it represents behaviors motivated by external pressures or rewards. Introjected regulation is somewhat more internalized, the behavior is carried out to avoid feeling guilty and anxious. Identified regulation refers to the behavior being driven by reasons personally important to the individual like when military personnel recognize the benefit of physical fitness in military readiness and believe that it aligns with their long-term goals. Integrated regulation has the highest level of self-determination in the external forms of motivation, as individuals engage in the behavior because it is an integral part of who they are and because the behavior aligns with their personal values. Finally, intrinsic motivation represents the most self-determined motivation and refers to participation in military training for the feelings of pleasure and personal satisfaction regardless of external rewards or pressure (Ryan & Deci, 2017).

Also in a quite regulated environment such as the military environment, we expect different regulatory and motivational factors to be associated with different physical activity levels and intensities. A systematic review focusing on motivational factors that affect physical activity among the general public reported substantial evidence for a positive relationship between the more autonomous and intrinsic forms of motivation and exercise (Teixeira et al., 2012). Moreover, multiple studies aimed to gain knowledge of why soldiers exercise and understand how to increase motivation for physical training among soldiers (Dyrstad et al., 2007; Jenssen & Dillern, 2021, 2022; Wilson et al., 2012). For instance, Dyrstad et al. urged fostering intrinsic motivation among soldiers to increase and promote physical activity throughout their career.

To the best of our knowledge, research that assessed the behavioral determinants of physical activity in the military in Jordan and/or the Middle East is scarce. There is a need to conduct such studies to assess military-related determinants to guide intervention design and military-specific recommendations based on the intervention mapping approach (Brug et al., 2005). We assume that the motivation for exercise may differ between military personnel in the Middle East, particularly Jordan, and those in the West, due to variances in cultural, environmental, and health perception factors. Moreover, further research is needed to explore gender differences in terms of the determinants of physical activity such as assessing females’ physical activity barriers in the military (Shirazipour et al., 2019). The final goal is to reduce health risks, maintain a healthy body weight and improve physical readiness and performance, not only during military service but also when they go back to civilian life in both males and females.

The present study aims to provide a better understanding of the determinants of physical activity behaviors and to pay attention to gender differences herein. Such determinants include motivational factors (exercise self-regulation scores) in addition to age, BMI, screen time, exercise self-efficacy, and the perceived barriers and benefits of physical activity.

Methods

Study design

The study used a cross-sectional design. A simple random sample was taken from the first cohort of two military medical colleges of the Jordanian Royal Medical Services in April 2022. Randomness was undertaken using a random number generator to choose the required number of students based on their IDs from a list that had been provided by the military.

Participants

A total of 218 military first-year recruits fully participated in the study. One military college was only for females while the other one was for both sexes. Therefore, the sample included more females (138) than males (80). Young adults can enter medical military colleges in Jordan when they are 18–20 years old, single, have high school degree, have the minimum military body weight/height standards, not be convicted of any crime and are medically fit. Recruits receive 2–4 years of training to prepare them for full-time medical staff (nurses and other allied medical staff) serving in the Jordanian military until retirement. Throughout basic military training, recruits typically remain within the camp premises on weekdays, with weekends designated for returning home. The weekly training schedule includes daily walking and low-intensity running for 30 minutes in addition to weekly stretching and military marching sessions. Moreover, recruits have the chance to participate in sports competitions such as table tennis. Males and females follow the same schedule. The military provides three well-balanced meals containing nutrient-rich foods such as fruits and vegetables, along with essential macronutrients such as carbohydrates, proteins, and healthy fats, ensuring a sustained supply of energy. The meals are designed by licensed dietitians in the military kitchen. However, recruits can buy snacks from nearby canteens.

Procedure

Recruits completed the questionnaires on paper in a large lecture hall. The questionnaires were in Arabic. The participants were clearly informed about the purpose of the study and provided written informed consent. They voluntarily and anonymously answered the survey. Around 95% of recruits agreed to participate in the study. The average completion time was approximately 30 minutes.

Variables and measurements

Participants completed the Arab Teens Lifestyle Study (ATLS) questionnaire which includes a physical activity assessment (Al-Hazzaa & Musaiger, 2011), the Exercise Self-Efficacy (ESE) scale (Resnick & Jenkins, 2000), the Exercise Benefits/Barriers (EBBS) scale (Sechrist et al., 1987), and the Behavioral Regulation Exercise (BREQ-2) scale (D’Abundo et al., 2014). Moreover, self-reported smoking status, weight and height were collected besides sociodemographic information such as age, sex and place of residency.

The ESE, BREQ-2 and EBBS were translated from English into Arabic according to a systematic approach of translation and adaptation which is recommended by the WHO (Younan et al., 2019). The translation procedure for the three questionnaires is described in detail in a previous publication (Malkawi et al., 2022).

The Arab Teens Lifestyle Study Questionnaire (ATLS)

The ATLS questionnaire is an instrument to assess physical activity levels and sedentary behaviors among Arabic adolescents in addition to dietary behaviors. However, we only used the physical activity part for this study. Evidence for convergent validity and reliability of the questionnaire for assessing the level of physical activity were reported by the questionnaire developer (Al-Hazzaa & Musaiger, 2011; Al-Hazzaa et al., 2011). The questionnaire collects information on the frequency, duration, and intensity of a variety of light, moderate, and vigorous-intensity physical activities during a typical week. The physical activity questionnaire covers domains such as transport, the household, fitness and sports activities. Moderate-intensity physical activity includes activities such as normal-pace walking, brisk walking and recreational swimming, while vigorous-intensity physical activity includes activities such as weight lifting, running and cycling.

The total minutes of (moderate or vigorous intensity) physical activity per week in addition to the total METs-min/week were computed to assess physical activity levels. The METs-min/week for each activity is calculated by multiplying the intensity of different activities with the time spent (in minutes/week) (Al-Hazzaa et al., 2011). Then, the total METs-min/week was summed for all vigorous and moderate-intensity activities. We used three categories for physical activity (low, medium and high). The inactive level is less than 600 METs-min/week; the minimally active level is 600–1499 METs-min/week and the highly active level is 1500 or more METs-min/week (Al-Hazzaa & Musaiger, 2011).

The total screen time was computed by summing up the time spent per day on different sedentary activities, including television (TV) viewing, video games, and computer and internet use (Al-Hazzaa & Musaiger, 2011).

Exercise Self-Efficacy Scale (ESE)

The ESE is a 9-item scale developed by Bandura (1997) that assesses participants’ beliefs in their ability to continue exercising in different situations such as exercising when they are tired, under stress or when they are away from home. The total score is calculated by summing the responses from 1 (“not at all sure”) to 4 (“very sure”). The scale has a range from 9–28. A higher score indicates higher self-efficacy to exercise. The reliability and validity of ESE was demonstrated by Kroll et al. (2007). Our study showed that the internal consistency of the ESE (Cronbach’s α) was 0.729.

The Behavioral Regulation Exercise Scale (BREQ-2)

The BREQ-2 is a 19-item Likert scale questionnaire that measures different types of motivation to exercise based on the self-determination theory. The questionnaire is a 5-point scale ranging from 0 (“not true for me”) to 4 (“very true for me”). The five types of motivation are represented by five subscales: amotivation, intrinsic, external, introjected and identified motivation (Mahony et al., 2018), and one can score from 0–4 on each subscale. D’Abundo et al found that the BREQ-2 had adequate construct validity and reliability (Cronbach’s α = 0.75) among an American college student population (D’Abundo et al., 2014).

The Exercise Benefits/Barriers Scale (EBBS)

The EBBS is a validated Likert-type scale that measures the participant’s perceived benefits and barriers to exercise (Sechrist et al., 1987). It consists of 43 items, 14 on the barriers scale and 29 on the benefits scale. Answers vary from one to four: strongly disagree (1), disagree (2), agree (3), strongly agree (4). An overall score can be computed from these items, for this overall score the barrier scale items are reverse-scored. The total score of the instrument can range from 43–172. The barriers and benefits questions can also be used separately. The highest possible benefit score is 116, while the highest possible score of barrier score is 56 (Sechrist et al., 1987). Our study showed that the Cronbach’s α for the overall EBBS scale was 0.833.

Statistical analysis

The IBM SPSS Statistics version 28 was used for all analyses. Descriptive statistics were used to characterize the sample, including demographic characteristics and physical activity behaviors, using means, medians, interquartile ranges, standard deviations, and proportions.

Bivariate correlation analysis was conducted to assess correlations between the physical activity variables (total METs-min/week, the total minutes of physical activity) and all possible determinants including BMI, total screen time, exercise self-efficacy score, exercise benefit and barriers score, in addition to the BREQ-2 subscales. A backward multivariate linear regression method was used as an extension of the univariate correlational analysis, by including all measured variables. Assumptions of statistical tests were tested, and alternative tests were chosen when assumptions were not met. For instance, nonparametric tests were used for variables that were not normally distributed. Regarding the regression analysis, multicollinearity was assessed by executing a Variance Inflation Factor (VIF) test. Also, model fit was assessed with the F test, R² and adjusted R², standard error of the model and an ANOVA test. Models with ANOVA tests with p-values less than .05 were excluded from the results section. Only variables that have p-values less than .05 will be considered statistically significant. Categorical variables such as the smoking variable of the participant were inserted in the regression model using dummy variables.

Results

Participants

Table 1 presents the basic descriptive characteristics of the sample. The median age of participants was 19 years, while the median BMI was 22 kg/m2 indicating healthy body weight for the majority of recruits (BMI ranges from 16–29). Around one-third of the participants were males and two-thirds were females. The majority of participants (72.8%) attended the Royal Medical Services College of Allied Professions, while less than one-third (27%) attended Prince Muna College of Nursing. Approximately 20% of participants were current smokers. Around 14% of them lived in Amman (the capital of Jordan), while the rest of the recruits were distributed across various governorates such as Irbid, Madaba and Alkarak.

Table 1. Characteristic of the sample of recruits (n = 218).

Variables	Mean (sd)	Median	Interquartile range	Range
Age (years)	19.2 (0.8)	19.00	19–20	18–21
BMI (kg/m2) Mean ± SD	22.2 (2.7)	22.03	20.3–23.9	16.0–28.7
	Percentages
Military college
Prince Muna College of Nursing	27.2%
Royal Medical Services College of Allied Professions	72.8%
Gender
Males	36.7%
Females	63.3%
Current smoking
Yes	19.2%
No	80.8%
Original residency
Amman	13.8%
Outside Amman	86.4%

Basic characteristics of lifestyle behaviors

Table 2 presents the results on physical activity levels, screen time, exercise benefit score, exercise barriers score and exercise self-efficacy score in addition to the 5 subscales (amotivation, external regulation, identified, introjected and intrinsic scores). A high percentage of recruits showed a high physical activity level (80%). A small percentage of recruits were inactive (2%), while around 17% of recruits were minimally active. Males showed significantly more minutes of weekly physical activity than females. The median of amotivation and external regulation score was 0.5. In addition, the median of the introjected score was 1.3, while the median of introjected, identified and intrinsic scores was around 2.5 for all recruits (ranges of the 5 subscales are from 0–4). Male recruits showed significantly higher scores of identified, introjected and intrinsic motivation in comparison to females.

Table 2. Physical activity and psycho-social variables.

Physical activity and lifestyle behaviors		Males	Females	Cohen’s d	Overall
Total minutes of physical activity per week	Median* (Interquartile range)	212.5 (137.5–280)	157.5 (105–210)		170.0 (120–249)
	Mean (Sd)	241.0 (161.8)^☨	175.3 (98)	0.52	199.6 (129)
Total METs**-min/week for total physical activity (METs-min/week)	Median* (Interquartile range)	2905.3 (1853.6–5360.3)	3103.0 (1908–4011)		3023.0 (1908–4257)
	Mean (Sd)	4010.8 (3582.6)	3394.7 (2296.4)	0.22	3622.9 (2849)
Screen time (hours/day)	Median* (Interquartile range)	5.0 (2.81–3)	5.25 (3.25–3.5)		5.0 (3.18–3.28)
	Mean (Sd)	5.5 (4)	5.7 (3.14)	−0.06	5.6 (3.5)
Activity levels based on total METs (%):
Inactive (<600 METS-min/week)		3.8%	1.5%		2.3%
Minimally active (600–1499 METs-min/week)		15%	18.4%		17.1%
Highly active (≥1500 METs-min/week)		81.3%	80.1%		80.6%
Psycho-social variables
Exercise benefit score***	Median* (Interquartile range)	89.5 (81–101)	88.0 (79–96)		88.0 (80–97)
	Mean (Sd)	90.2 (12.6)	87.0 (13.6)	0.23	88.2 (13.3)
Exercise barrier score	Median* (Interquartile range)	29.0 (24–34)	27.0 (22–31)		28.0 (23–33)
	Mean (Sd)	30.7 (7.55)^☨	27.2 (6.15)	0.44	28.3 (6.8)
Exercise self-efficacy score****	Median* (Interquartile range)	20.0 (16–23)	17.0 (13–21)		18.0 (15–21)
	Mean (Sd)	20.0 (5)^☨	17.3 (4.5)	0.56	18.3 (4.8)
Amotivation score	Median* (Interquartile range)	0.5 (0–1)	0.5 (0–1)		0.5 (0–1)
	Mean (Sd)	0.7 (0.69)	0.7 (0.83)	−0.03	0.6 (0.8)
External regulation score	Median* (Interquartile range)	0.5 (0–1)	0.3 (0–1)		0.5 (0–1)
	Mean (Sd)	0.7 (0.71)	0.6 (0.81)	0.05	0.6 (0.8)
Introjected regulation score	Median* (Interquartile range)	1.8 (0.8–2.7)	1.0 (0.66–1.66)		1.3 (0.6–3.5)
	Mean (Sd)	1.7 (1.02)^☨	1.1 (0.86)	0.61	1.4 (0.9)
Identified regulation score	Median* (Interquartile range)	3.0 (2.5–3.5)	2.5 (1.8–3.3)		2.5 (2.0–3.3)
	Mean (Sd)	2.9 (0.8)^☨	2.4 (0.9)	0.52	2.6 (0.9)
Intrinsic regulation score	Median* (Interquartile range)	3.0 (2.25–3.5)	2.5 (2–3.5)		2.7 (2.0–3.5)
	Mean (Sd)	2.9 (0.9)^☨	2.6 (0.9)	0.34	2.7 (0.9)

*The median was calculated in addition to the mean because the variables were not normally distributed which is the case for most of variables **MET = metabolic equivalent. ***The benefit score ranges from 29–116, while the barrier score ranges from 14–56. ****The exercise self-efficacy score ranges from 9–28 (a higher score indicates higher exercise self-efficacy). ^☨means a significant difference between males and females using a Mann-Whitney U test.

The median exercise self-efficacy score was 18 (ranges from 9–28), while the median of exercise benefit and barrier scores was 88 (ranges from 29–116) and 28 (ranges from 14–56) respectively. Male recruits showed significantly higher exercise self-efficacy scores and higher perceived barrier scores than female recruits.

The median of total minutes of physical activity performed by recruits were 170 minutes per week with more minutes performed by male recruits (213 minutes for male recruit’s vs 158 minutes for female recruits). The median of total METs per week for the all over sample was around 3000 with no significant difference between males and female recruits. In fact, the majority of recruits demonstrated high physical activity levels (80%) with almost the same male and female percentages. Finally, the recruits demonstrated high screen time with a median of around 5 hours per day with no significant difference between males and females.

Correlates of physical activity behaviors

Table 3–5 depict the bivariate correlations between physical activity (total minutes of physical activity per week and METs minutes/week) and possible sociodemographic and psychosocial correlates for both males and females in addition to the overall sample.

Table 3. Bivariate correlations of different physical activity levels with socio-demographic, psychosocial and other potential variables for males.

Variables	1	2	3	4	5	6	7	8	9	10	11
1. Total minutes/week	–	0.88*	0.16	0.09	0.26*	0.15	0.17	−0.09	0.00	0.22	−0.07
2. Total METs-min/week	0.88*	–	−0.00	−.030	0.38*	0.17	0.23*	−0.21	0.14	0.25*	−0.07
3. Amotivation score	0.16	0.00	–	0.69*	−0.15	−0.05	0.18	0.46*	−0.23	0.04	0.02
4. External regulation score	0.09	−0.03	0.69*	–	0.03	−0.04	0.25*	0.40*	−0.07	0.09	0.08
5. Identified regulation score	0.26*	0.39*	−0.15	0.03	–	0.57*	0.50*	−0.15	0.50*	0.33*	−0.01
6. Intrinsic regulation score	0.15	0.17	−0.05	−0.04	0.57*	–	0.26*	−0.14	0.50*	0.14	0.01
7. Introjected regulation score	0.17	0.23*	0.18	0.25*	0.50*	0.26*	–	−0.08	0.33*	0.27*	0.14
8. Perceived barriers score	−0.09	−0.21	0.46*	0.40*	−0.15	−0.14	−0.08	–	−0.18	0.16	−0.02
9. Perceived benefit score	0.00	0.14	−0.23	−0.07	0.50*	0.50*	0.33*	−0.18	–	0.18	0.07
10. Exercise self-efficacy score	0.22	0.25*	0.04	0.02	0.33*	0.14	0.27*	0.16	0.18	–	−0.20
11. BMI	−0.07	−0.07	0.02	0.08	−0.01	0.01	0.14	−0.02	0.07	−0.20	–
12. Total screen time	−0.05	0.01	0.04	0.22	0.12	0.05	0.04	0.08	0.09	−0.19	−0.01

Table 4. Bivariate correlations of different physical activity levels with socio-demographic, psychosocial and other potential variables for females.

Variables	1	2	3	4	5	6	7	8	9	10	11
1. Total minutes/week	–	0.77*	0.08	0.05	0.00	−0.05	0.14	0.10	0.03	0.07	0.13
2. Total METs-min/week	0.77*	–	0.08	0.15	−0.14	−0.15	−0.05	0.08	−0.15	−0.14	0.11
3. Amotivation score	0.08	0.08	–	0.30*	−0.50*	−0.50*	−0.33*	0.42*	−0.61*	−0.16	−0.04
4. External regulation score	0.05	0.15	0.30*	–	−0.14	−0.25*	−0.08	0.23*	−0.34*	−0.32*	0.07
5. Identified regulation score	0.00	−0.14	−0.50*	−0.14	–	0.62*	0.63*	−0.29*	0.64*	0.32*	0.07
6. Intrinsic regulation score	−0.05	−0.15	−0.50*	−0.25*	0.62*	–	0.49*	−0.37*	0.61*	0.30*	0.01
7. Introjected regulation score	0.14	−0.05	−0.33*	−0.08	0.63*	0.49*	–	−0.17	0.47*	0.39*	0.08
8. Perceived barriers score	0.10	0.08	0.42*	0.23*	−0.29*	−0.37*	−0.17	–	−0.41*	−0.16	−0.02
9. Perceived benefit score	0.03	−0.15	−0.61*	−0.34*	0.64*	0.61*	0.47*	−0.41*	–	0.40*	0.04
10. Exercise self-efficacy score	0.07	−0.14	−0.16	−0.32*	0.32*	0.30*	0.39*	−0.15	0.40*	–	0.10
11. BMI	0.13	0.11	−0.04	0.09	0.07	0.01	0.08	−0.02	0.04	0.10	–
12. Total screen time	0.13	0.12	0.20*	−0.08	−0.22*	0.02	0.01	0.25*	−0.09	0.06	−0.14

Table 5. Bivariate correlations of different physical activity levels with socio-demographic, psychosocial and other potential variables for the overall sample.

Variable	1	2	3	4	5	6	7	8	9	10	11	12	13
1. Total minutes/week	–	0.83*	0.10	0.06	0.18*	0.09	0.22*	0.05	0.05	0.21*	0.03	0.02	0.28*
2. Total METs-min/week	0.83*	–	0.04	0.07	0.13	0.02	0.12	−0.06	−0.00	0.09	0.02	0.05	0.11
3. Amotivation score	0.10	0.04	–	0.41*	−0.38*	−0.35*	−0.14*	0.41*	−0.49*	−0.09	−0.02	0.13	−0.02
4. External regulation score	0.06	0.07	0.41*	–	−0.09	−0.18*	0.04	0.28*	−0.26*	−0.20*	0.07	0.04	−0.02
5. Identified regulation score	0.18*	0.13	−.038*	−0.09	–	0.61*	0.60*	−0.17*	0.60*	0.40*	0.05	−0.08	0.24*
6. Intrinsic regulation score	0.09	0.02	−0.35*	−0.18*	0.61*	–	0.42*	−0.23*	0.58*	0.27*	0.01	0.03	0.17*
7. Introjected regulation score	0.22*	0.12	−0.14*	0.04	0.60*	0.42*	–	−0.07	0.43*	0.38*	0.12	0.02	0.27*
8. Perceived barriers score	0.05	−0.06	0.41*	0.28*	−0.17*	−0.23*	−0.07	–	−0.28*	0.05	−0.01	0.16*	0.21*
9. Perceived benefit score	0.05	−0.00	−0.49*	−0.26*	0.60*	0.60*	0.43*	−0.28*	–	0.33*	0.05	−0.02	0.13
10. Exercise self-efficacy score	0.21*	0.09	−0.09	−0.20*	0.37*	0.27*	0.38*	0.05	0.33*	–	−0.01	−0.06	0.27*
11. BMI	0.03	0.02	−0.02	0.07	0.05	0.01	0.11	−0.01	0.05	−0.01	–	−0.09	0.03
12. Total screen time	0.02	0.05	0.13	0.04	−0.08	0.03	0.02	0.16*	−0.02	−0.06	−0.09	–	−0.03
13. Being a male	0.28*	0.11	−0.02	−0.02	0.24*	0.17*	0.27*	0.21*	0.13	0.27*	0.03	−0.03	–

Psychosocial correlates of physical activity behaviors

Table 5 shows that being a male and exercise self-efficacy, identified and introjected scores were positively correlated with the total minutes of physical activity per week among the whole sample. However, the perceived benefits and barriers, screen time and BMI were not significantly associated with either total METs or total minutes per week.

Among males, the identified regulation score was positively correlated with the total METs-min/week and the total minutes of physical activity (see Table 3). The introjected regulation score was only positively correlated with the total METs-min/week for males. The exercise self-efficacy score was positively correlated with the total METs-min/week in the male sample only. So, male recruits with a higher confidence to exercise in different situations had more energy expenditure as expressed by METs-min/week.

Regarding females, only the external regulation score was positively correlated with the total METs-min/week (see Table 4). This means that the female recruits whose physical activity level was more externally regulated had a higher level of physical activity (as measured by METs-min/week).

The perceived benefits and barriers, screen time and BMI showed no significant association with the total METs-min/week and total minutes of physical activity for both males and females.

The correlation coefficients were less than 0.3, which means that the effect sizes are weak to moderate, except for the relationship between males identified self-regulation score and the total METs-min/week (r = 0.39).

Regression analyses

Table 6 presents the results of the multiple linear regressions. The analysis for the overall sample showed that being a male, the amotivation score and the identified regulation score were significantly and positively associated with higher minutes of physical activity. These variables explain 10.4% of the variance in the total minutes of physical activity per week. Also, the multiple linear regression analysis among males only showed that the amotivation and the identified regulation scores were significantly and positively associated with a higher number of minutes of physical activity. These variables explain 12.2% of the variance in the total minutes of physical activity per week. No regression model was selected for the total METs among the whole sample because the p-value of ANOVA test was less than .05.

Table 6. Correlates of physical activity behaviors demonstrated by total minutes per week and total energy expenditure in a multivariate regression models.

				95% Confidence Interval for β
Physical activity variable	Correlates	Standardized β	P-value	Lower Bound	Upper Bound
Total minutes of physical activity per week for the whole sample	Amotivation score Identified regulation score Being a male	0.18 0.20 0.23	.02 .01 .00	5.27 6.79 . 25.67	52.06 49.97 99.44
Adjusted R^2  = 0.104
Total minutes of physical activity per week for male recruits	Amotivation score Identified regulation score Perceived barriers score Being a smoker	0.28 0.30 -0.20 0.19	.03 .01 .08 .09	8.73 16.84 −9.93 −11.56	123.38 103.75 0.61 135.52
Adjusted R^2  = 0.122
Total METs-min/week for physical activity (METs-min/week) for male recruits	Identified regulation score Perceived barriers score Being a smoker	0.40 −0.20 0.19	.00 .07 .08	792.12 −198.45 −174.67	2662.43 6658.00 3001.60
Adjusted R^2  = 0.21

multivariate regression analysis for female recruits showed no significant associations.

Regarding the total METs-min/week, multiple linear regression analysis showed that the identified regulation score was only significantly and positively associated with higher total METs-min/week for physical activity among males. The identified regulation score explains 21% of the variance in total METs-min/week for physical activity. Multiple linear regression analyses for the total METs and minutes per week for females show no significant effects.

Discussion

Our study aimed to gain insight into the possible determinants of physical activity among first-year military recruits in two medical colleges in Jordan, and gender differences therein. Physical activity level was assessed in this study by calculating the total weekly energy expenditure and total minutes of physical activity. We were especially interested in assessing the motivation the recruits had to be physically active, given the special situation of the military where a certain level of physical exercise is mandatory.

In general, our study found low levels of amotivation and external motivation in addition to a relatively high level of identified and intrinsic motivation. Furthermore, the majority of recruits (80%) were highly active with a higher number of minutes performed by males than females. A study that included 5,000 Belgian military men found that around 80% of military personnel are moderately to highly active (Mullie et al., 2013). In addition, in our study the mean of total METs per week performed was around 3600, which is higher than the recommended level for the general population.

Our study indicated that being a male was related to more physical activity in terms of total weekly minutes for the overall sample. This is consistent with (nonmilitary) large-scale data that was collected from 111 countries around the globe that showed that females made less daily steps than males (Althoff et al., 2017). Due to the results showing that the METs/per week were not significantly different between males and females, we may assume that females perceived performing higher-intensity physical activities. A study which was conducted during basic military training in South Africa showed that males had higher scores in all fitness measurements such as 2.4 km Run test and push-ups (Wood et al., 2017).

While in our study external regulation score was only associated with higher energy expenditure among female recruits in bivariate correlational analyses, identified self-regulation was associated with more energy expenditure among male recruits in multivariate analysis. Also, males demonstrated higher intrinsic regulation levels than females. This means that both male and female recruits were externally motivated to exercise, with more autonomous motivation among males. Some authors suggested that males are more regulated by factors related to strength and competition, while females are more regulated by social and media pressure to have slim and toned physiques (Teixeira et al., 2012). Teixeira et al conducted a systematic review that included 66 empirical studies worldwide (nonmilitary) and found a positive association between autonomous forms of motivation and exercise. For instance, the review found that the identified self-regulation score predicted short-term adherence, while the intrinsic motivation predicted long-term adherence to exercise after multivariate analysis among all samples. With respect to gender differences, the review stated that introjected regulation may be more positively associated with physical activity in females than in males (Teixeira et al., 2012). A possible explanation of our regression results that showed identified regulation and external regulation to be related to energy expenditure (METs/week), is that intense exercise is driven by a sense of obligation rather than a personally significant motive. Moreover, external motivation is more likely to thrive in a structured environment such as the military (Dyrstad et al., 2007).

In addition, the regression results of our study showed that amotivation was associated with more minutes of physical activity among the overall sample and male recruits. This is notable since the previously mentioned systematic review stated mixed results regarding the association of exercise and amotivation (Teixeira et al., 2012). However, the studies included in the review were usually carried out among nonmilitary populations. Our results are probably different due to the obligatory nature of the environment which encourages especially more exercise in less motivated people in order to reach the fitness level required by the military. This line of reasoning is supported by our finding that amotivation only predicts higher minutes of physical activity, and not METs. Recruits who are not motivated to be active themselves and are challenged by their superiors are more likely to spend more time being active, than spending more energy (METs) as this probably requires a higher level of motivation.

Higher exercise self-efficacy was associated with higher weekly energy expenditure and total minutes of physical activity in the overall sample only at the bivariate level. Also, the perceived benefits and barriers scores were not significantly associated with energy expenditure and duration of physical activity. A systematic review found that exercise self-efficacy is a consistent correlate of physical activity at the personal level among the general public (Bauman et al., 2012). A cross-sectional study that was conducted in Jordan and included 300 civilian adults from major cities in Jordan found that people with greater perceived self-efficacy and benefits, and fewer barriers, were more likely to be physically active (Ammouri et al., 2007). The differences between our results on exercise self-efficacy and perceived benefits and barriers and civilian studies can be due to the structured military environment which limits the extra physical activity recruits can do beyond the military training. Also, recruits who have less confidence to exercise still need to exercise to reach the minimum fitness level in the military.

Screen time was not significantly associated with physical activity in our study. A systematic review that included 26 studies from different settings and included adults aged 18–60 years found a small inverse association between general screen time and physical activity (Mansoubi et al., 2014). In our study, screen time may have no relationship with physical activity due to the nature of obligatory and structured military schedules where recruits have to exercise regardless of what they do in their rest time.

Limitations

A first limitation that is worth mentioning is that our study is limited to two military medical colleges in Jordan. In general, recruits who attend medical colleges in Jordan receive less intensive military and physical training than general military recruits. Therefore, one should be cautious with generalizing the results. Future studies might include other military branches such as the Air Force and the Army.

Another limitation is that self-reported measures were used for assessing physical activity behaviors. Self-reported tools such as questionnaires are subject to reporting and recall bias. For instance, one review stated that the ATLS questionnaire may lead to an overestimation of physical activity and METs performed (Chaabane et al., 2020). Using objective measures to assess physical activity behaviors such as accelerometers and pedometers would be recommended. However, these methods need well-trained staff (Trost & O’Neil, 2014).

A last limitation of our study that is worth mentioning is its cross-sectional nature, giving no insight into the causality of the found relations. Future studies could use stronger designs such as longitudinal and interventional studies to shed light on the causal relationship (Bauman et al., 2012).

Finally, the discrepancies observed between univariate and multivariate results, in addition to low variability in physical activity and other variables such as intrinsic regulation, challenge us to draw a definitive conclusion regarding the factors associated with physical activity.

Recommendations for research

The basic military training period can be a foundation of sustainable physical activity. Therefore, it is recommended to consider what happens after basic military training. It has been shown that physical fitness reduced significantly among military recruits after graduation or when moving to less demanding military tasks (Jenssen & Dillern, 2021). Therefore, it is suggested to assess the long-term performance of physical activity especially after finishing the obligatory training, and investigate the role of motivation quality therein.

Further research is needed to explore gender differences regarding the determinants of physical activity such as assessing females’ physical activity barriers in the military (Shirazipour et al., 2019).

Our study focused on possible motivational determinants of physical activity among military recruits. To effectively promote physical activity among military recruits, future research should gain a better understanding of the influence of factors such as outcome expectation, attitude and control over exercise (Choi et al., 2017). Moreover, it is important to better investigate the environmental determinants (e.g., the accessibility to exercise facilities in the military) as understanding these determinants may lead to more informal physical activity (Bauman et al., 2012).

Recommendation for practice

Physical fitness is extremely important for military personnel to equip them to respond to daily occupational demands effectively and to maintain a healthy weight and prevent chronic diseases. A physical activity level of 900 METs/week is recommended for military personnel, which is slightly higher than the civilian recommendations (Blacker et al., 2011). Although this recommendation is not difficult to be achieved during the basic military training period, it can be challenging after finishing it. Interventions need to emphasize maintaining the minimal recommendations of physical activity after finishing the basic military training.

Due to gender differences in physical activity levels, more focus should be paid to optimizing female physical performance in relation to their task requirements in military environments (Kyröläinen et al., 2018). However, it is not clear whether females need specific training programs as females demonstrated similar fitness improvement to males during the basic military training (Varley-Campbell et al., 2018).

Although the evidence from our study is only suggestive due to its cross-sectional design, we recommend that interventions promote exercise self-efficacy and more autonomous physical activity motivation without undermining external motivation which is likely to thrive in structured environments. For instance, promoting the identified regulation in recruits can be a good starting point, so that motivation can be slowly changed to be more internally regulated (Ryan & Deci, 2017). Physical training should be implemented within a context that emphasizes autonomy, competence, and relatedness. In general, it is recommended to enhance intrinsic motivation in the military because it is associated with less negative outcomes such as stress. This can be achieved by creating an environment that allows more autonomy in choosing different types of physical training in addition to nurturing recruits’ enjoyment, interest and mastery of physical activity (Dyrstad et al., 2007; Jenssen & Dillern, 2021). When applying this approach, amotivation is more likely to diminish as a determinant of physical activity. Moreover, intrinsic motivation is associated with long-lasting activity patterns and increased interest in physical activity (Teixeira et al., 2012).

Participating in physical activity programs does not necessarily lead to improvement in exercise self-efficacy (McAuley et al., 2011). Strategies to improve exercise self-efficacy within the military can include mastery experience, social persuasion, and social modeling (Rajati et al., 2014). McAuley et al. (2011) recommended targeting those with low self-efficacy and integrating boosting strategies for all participants at the end of the program.

Conclusion

To the best of our knowledge, this is one of the first studies that has assessed the possible determinants of physical activity in the military setting in Jordan. The study focused on possible psychosocial and motivational determinants such as exercise self-efficacy, exercise self-regulation scores and exercise benefits and barriers. Our results showed that identified regulation score was related to higher minutes and METS per week of physical activity among the whole sample and male recruits, while the amotivation score was related to higher minutes of physical activity. Future interventions may focus on promoting more autonomous forms of motivation and exercise self-efficacy through structured enjoyable physical fitness programs for sustained physical and fitness levels in the military. Moreover, identified regulation can be a starting point for promoting more autonomous forms of motivation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.