Levels of Physical Activity and Their Relationship With Motivational Determinants,Self-Regulation,and Other Health-Related Parameters in University Students

Abstract

Motivational processes and emotional self-regulation are relevant factors for predicting the practice of physical exercise and for certain health-related parameters. The aim of the present work, developed along two different phases, was, on the one hand, to determine the prevalence of physical inactivity and, on the other hand, to carry out a comparative analysis between physically active and inactive university students in relation to their motivational determinants, emotional self-regulation, physical condition, health-related quality of life and other health parameters. The results obtained showed a prevalence of physical inactivity of 12.78%. Physically inactive participants showed significantly lower scores in intrinsic motivation, emotion regulation, and in some dimensions of health-related quality of life, as well as significantly high levels of body fat mass. Lastly, logistic regression analysis identified low scores in Emotional Clarity (p= .009) and Identified Regulation (p = .011), and high scores in Amotivation (p = .006) as reliable predictors of a physically inactive lifestyle. These results are useful for the design and implementation of programs aimed at promoting health and physical activity, with special attention on physically inactive youth populations.

Keywords

Sedentary lifestyle physical inactivity motivation emotional self-regulation quality of life healthy behavior

Introduction

Environmental and socioeconomic changes, as well as the technological development that characterizes contemporary society, promote unhealthy lifestyles, including physical inactivity and sedentary behavior. These behaviors, widely extended among the population, adversely affect health, and are one of the main risk factors for pathologies such as cardiovascular disease, diabetes, cancer, or chronic lung diseases, which are responsible for three out of five deaths worldwide and with the highest morbidity rates (Hallal et al., 2012; Trachte et al., 2016; Tremblay et al., 2017).

It is relatively common for the terms sedentariness and physical inactivity to be used interchangeably, causing confusion among researchers (Chacónet al., 2015; González et al., 2017). Faced with this situation, the Sedentary Behaviour Research Network (2012) proposed a definition to differentiate the two concepts. Thus, sedentary behavior is understood as “any type of activity carried out by the individual in a seated or inclined position with an energy expenditure ≤1.5 METs, while he is awake” (p. 540). The Metabolic Equivalent Index (MET) is the unit of measure that describes the energy expenditure of a physical activity, which allows calculating the oxygen consumption (IPAQ Core Group, 2005). One MET is equal to the approximate amount of oxygen consumed in one minute by a person in a state of rest, and corresponds to 3.5 ml Or₂/kg per minute. On another hand, physical inactivity is defined as “an insufficient level of physical activity with respect to current recommendations” (Sedentary Behaviour Research Network, 2012, p. 540). In this regard, according to the World Health Organization, the recommendations of physical activity for people from 18 to 64 years old indicate that they should “accumulate a minimum of 150 minutes of moderate aerobic physical activity, or 75 minutes of vigorous aerobic physical activity every week, or an equivalent combination of moderate and vigorous activities” (World Health Organization, 2010, p. 8).

As pointed out by Hallal et al. (2012), individuals are considered physically inactive when they do not meet any of the following three criteria: 1) perform 30 minutes of moderate or intense physical activity at least 5 days per week; 2) practice 20 minutes of intense physical activity at least 3 days per week; or 3) accumulate a weekly physical activity of at least 600 METs.

Insufficient levels of physical activity are related to an increase in morbidity and mortality worldwide (Trachte et al., 2016; Tremblay et al., 2017; WHO, 2010). Similarly, Pedersen (2009) first presented the term “diseasome of physical inactivity” to describe a series of chronic diseases – type 2 diabetes, cardiovascular diseases, colon cancer, breast cancer, dementia and depression – to which physical inactivity acts as a risk factor. Later, Booth et al. (2012) expanded the previous list to more than thirty-five diseases, confirming that physical inactivity negatively affects almost all cells, organs and systems of the human body.

Epidemiological studies indicate that, in more than half of the countries, the level of physical inactivity is specially higher in women than in men, and this condition correlates negatively with individuals' educational level and socioeconomic status (Ekelund et al., 2015; Guthold et al., 2018).

In the university population, the scientific literature on the subject shows the existence of a great variability in the prevalence of physical inactivit (23 to 50%) y, both in international studies and in those carried out by different researchers from the same country (Gaweł-Dąbrowska et al., 2016; Hasse et al., 2004; Práxedes et al., 2016). This variability is mainly due to the disparity of methods, instruments, and criteria used for its determination (Arvidsson, 2009). On another hand, researchers do not seem to agree on whether, during the transition stage of secondary to university education, young people's physical activity increases or decreases (Maselli et al., 2018).

Both physically active and inactive behaviors are determined by multiple factors, among which are cognitive variables such as self-regulation or motivation (Annesi, 2013). Emotional self-regulation, a dimension of emotional intelligence, is a cognitive strategy that allows one to adequately manage thoughts, emotions, and behaviors, as well as to maintain motivation to persevere in the attainment of objectives or pre-established goals (Baumeister et al., 2005; Hall & Fong, 2015; Muraven & Slessareva, 2003). It is thus revealed as an important factor to persist in the practice of physical activity (Hall & Fong, 2015), allowing people to regulate negative emotional states such as depression, anxiety, or anger, which can interfere with the practice of physical activity and even induce one to abandon it (Cano & Miguel, 2001).

In a similar vein, motivational processes play an important role in predicting physically active and inactive behavior by influencing their beginning, maintenance and abandonment (Friederichs et al., 2015). Motivation is therefore essential to regulate the practice of physical exercise. In the last decades, the self-determination theory (SDT; Ryan & Deci, 2017) has been one of the most extensively used theoretical developments to study the extent to which the practice of physical exercise is self-determined; that is, the individual performs it on his own initiative and as a freely chosen act (Franco et al., 2017).

Although university students are a key group, given their potential to promote healthy lifestyles in their family, work, or social environment (Bennassar, 2011), concern for health promotion (HP) in the University community is relatively recent because, until not long ago, university life was considered to be carried out in environments (classrooms or libraries) that did not have a relevant impact on students' health. This conception seems to have changed in recent decades, among other aspects, due to the emergence of studies that relate health status to academic performance (Martín, 2007), but also, thanks to the growing importance granted to the promotion of health and healthy environments (Bravo-Valenzuela et al., 2013).

Thus, the point, the objective of the present work, developed along two different phases, was, on the one hand, to determine the prevalence of physical inactivity and, on the other hand, to perform a comparative analysis between physically active and inactive university students in relation to its motivational determinants, emotional self-regulation, physical condition, health-related quality of life and other health parameters.

Material and method

Participants

By means of stratified randomized multistage sampling according to scientific area and gender, 665 first-year students of the University of Vigo (Spain) were selected. Regarding their distribution by scientific area, 59.30% of the students belonged to the legal-social area, 24.70% to the technological area, 8.30% to the scientific area, 4.70% to the health sciences, and the remaining 3% to the humanistic area. Taking into account the distribution by gender of the sample, 291 were men (43.76%) and 374 women (56.24%).

From the previous sample, as a function of their levels of physical activity, 170 students were selected (85 with a level lower than 600 METs/week and another 85 with a value greater than 600 METs/week). According to their distribution by scientific areas, 62.4% of the students belonged to the juridical-social field, 20% to the technological area, 8.8% to the scientific area, 5.3% to the health sciences, and the 3.5% remaining to the humanistic area. Regarding gender, 73 were men (42.99%) and 97 were women (57.01%).

Instruments

International Physical Activity Questionnaire, reduced version (IPAQ-SFF; IPAQ Core Group, 2005). This instrument provides comparable estimates of physical activity in population aged 18 to 69 years, measuring in METs (unit of energy consumption) the amount of physical activity that each participant has carried out in the past seven days prior to its administration. It has 7 items, the first 6 evaluate intensity (mild, moderate, and intense) and frequency of three specific types of activities carried out in four domains (free time, household jobs, occupational, and transportation domains), and the 7th item assesses the time spent sitting down during a working day. This questionnaire provides two types of results: 1) quantitative (in METs-minutes/week); and 2) categorical (classifying the level of physical activity as low, moderate, or high). It detects physical inactivity when the individual obtains levels below 600 METs/week.

Health-Related Quality of Life Questionnaire SF-12v2, validated version in Spanish by Vilagut et al. (2008). This instrument is commonly used to evaluate both physical and mental health-related quality of life in different groups. Its administration is fast and simple and it has excellent psychometric qualities. Its 12 items assess 8 dimensions: Physical Function (2 items), Social Function (1 item), Role Physical (2 items), Role Emotional (2 items), Mental Health (2 items), Vitality (1 item), Bodily Pain (1 item), and General Health (1 item). It is rated on a multiple-choice response format ranging between three and five alternatives, depending on the item. To obtain the raw scores of each dimension, the corresponding items are recoded and added. The raw scores are then transformed on a scaler ranging from 0 (the worst health status) to 100 (the best health status), producing a profile of the health status based on the score obtained in each one of the eight dimensions analyzed, grouped into two summary indices: physical health and mental health. The SF-12V2 has adequate levels of reliability with a Cronbach alpha of .85 (Vilagut et al., 2008).

Trait-Meta Mood Scale (TMMS-24), adapted to Spanish by Fernández-Berrocal et al. (2004 from the original Trait Meta-Mood Scale (TMMS-48; Salovey et al., 1995). This is a reduced version, but it maintains the conceptual structure and dimensions of the original scale. The TMMS-24 evaluates knowledge of one's own moods through three subscales: Emotional Attention (Items 1 to 8), Emotional Clarity (9 to 16), and Emotional Repair (17 to 24). The Cronbach alpha for each of the dimensions is very adequate, ranging between .86 and .90 (Fernández-Berrocal et al., 2004).

Behavioral Regulation Exercise Questionnaire (BREQ-3; Wilson et al., 2006), in the Spanish adaptation of González-Cutre et al., 2010). This version of the BREQ-3 is composed of 23 items that assess six dimensions: Intrinsic Regulation (4), Integrated Regulation (4), Identified Regulation (3), Introjected Regulation (4), External Regulation (4) and Amotivation (4). It is rated on a five-point Likert-type scale, ranging 0 (Not at all true) to 4 (Completely true). The reliability (Cronbach alpha) is adequate, with scores that exceed the value of .70 in all dimensions, except for Identified Regulation, which obtained a value of .66 (González-Cutre et al., 2010).

Ruffier Dickson Test (Barbany, 1990). This test is used to assess cardiorespiratory physical fitness through the record of the resting heart rate (RHR) and after a delay of 15 and 60 seconds after performing 30 deep knee bends in 45 seconds (HR15 and HR60, respectively). With these scores, the Ruffier-Dickson index can be calculated by means of the formula: [(HR15-70) + (HR60-RHR)]/10. This coefficient determines cardiac resistance to exertion and the capacity of cardiac recovery. The results can be grouped into four categories: excellent (<0 – 0), very good (1 – 5), good (5 – 10), sufficient (10 – 15), and poor (>15).

Anthropometric Evaluation: Anthropometry is a technique to evaluate the proportions, size, and composition of the body. In the present study, waist and hip measurements were carried out using a tape measure; height with a stadiometer with a plastic cursor (Seca Professional model), calibrated to measure up to two meters. Using a digital scale (Tanita BC418) with bioimpedance-reading electrodes, weight, body mass index (BMI), percentage of body fat mass, lean mass, and body water were determined. The aforementioned scale was calibrated before each measurement.

Sociodemographic data questionnaire. An ad hoc questionnaire was designed to collect basic socio-descriptive data (title, gender and age) and other aspects related to marital status and substance use (alcohol, tobacco, and other drugs).

Procedure

The study was carried out in two different phases (see Figure 1). Phase I (screening) took place in the first fortnight of September 2017 and consisted of administering the reduced version of the IPAQ (IPAQ-SF) and the demographic data questionnaire to a sample of 665 first-year university students. The objective was to identify the physically inactive students, adopting as criterion levels lower than 600 METs/week on the IPAQ-SF.

Figure 1.

Phases of the study.

Phase II (health status assessment) of the study took place between October and November 2017. Participants were 170 students who were selected from the participant sample in Phase I. For the empirical contrast, two groups were formed according to their physical activity levels (85 with a level lower than 600 METs/week and 85 with a higher value than this). All of them completed the battery made up of the instruments that evaluated health-related quality of life, emotional self-regulation, and regulation for the practice of physical exercise. Likewise, sociodemographic information was expanded their cardiorespiratory physical fitness was assessed and an in-depth anthropometric evaluation was carried out (see Figure 1).

All the participants were informed of the purpose of the study and of its anonymous and voluntary nature, and their consent was obtained prior to their participation. At all times, the intervention was guided by the Declaration of Helsinki of ethical principles for research with human subjects.

Data analysis

For data analysis, univariate and multivariate descriptive statistic techniques were used, using central tendency and dispersion measures (means and standard deviations), frequency and percentage analysis, analytical contrasts through univariate analysis of variance (ANOVA), chi-square analysis (χ²), and logistic regression analysis. Following the verification of the assumption of normality by means of the algebraic method of Kolmogorov Smirnov, we decided to complement the ANOVA contrasts with the non-parametric statistic Mann-Whitney-U (for independent samples) in order to detect significant differences (p < .05). These analyses were carried out with the statistical program IBM SSPS (Statistical Package for Social Sciences), version 22 for Windows.

Results

Demographic data of the sample

As can be seen in Table 1, the mean age of the students participating in Phase I (screening) was 19.57 years (SD = 3.62), and there were no statistically significant gender differences in this variable. The overall mean height was 169.72 cm (SD = 11.26), and there were statistically gender significant differences (F_1,664 = 397.52, p = .000), with, as usual, men being taller than women, with a mean height of 177.55 and 163.57 cm, respectively. Finally, the mean weight was 64.9 Kg (SD = 13,91 with, as expected, a significantly higher weight in the group of men (F_1,664 = 150.45, p = .000). On another hand, the variables gender and level of physical activity had a statistically significant relation (χ² = 16.20, p = .000).

Table 1.

Demographic data of students evaluated in Phase 1 according to gender.

Variables	Total n = 665 Mean (SD)	Men n = 291 Mean (SD)	Women n = 374 Mean (SD)	F	p
Age	19.57 (3.62)	19.45 (3.38)	19.66 (3.81)	.54	.461
Height	169.72 (11.26)	177.55 (8.07)	163.57 (9.46)	397.52	.000
Weight	64.94 (13.91)	71.75 (11.32)	59.44 (13.36)	150.45	.000

Levels of physical activity and cardiorespiratory physical fitness

In Phase I (screening), 580 participants (87.22%) obtained scores higher than 600 METs/week in the IPAQ-SF and were classified as physically active. The remaining 85 (12.78%) reported levels of activity below 600 METs/week and were classified as physically inactive.

In relation to the student participants in Phase II (see Table 2), no statistically significant relationship was detected between the variables physical activity profile (physically active versus inactive) and distribution by scientific area (χ² = 5.94, p = .204). In contrast, there was a statistically significant relation between the variables gender and scientific area (χ² = 153.7, p = .000).

Table 2.

Distribution of students by scientific area in Phase II depending on the level of physical activity (>600 METs versus <600 METs) and gender.

	IPAQ-SF>600 METsn = 85		IPAQ-SF<600 METsn = 85		χ²	p	Men n = 73		Women n = 97		χ²	p
Scientific area	n	%	n	%	χ²	p	n	%	n	%	χ²	p
Legal-social	49	57.8	57	67.1	5.94	.204	30	41.1	76	78.3	153.7	.000
Technological	22	26.1	12	14.1			27	37.0	7	7.2
Humanistic	3	3.0	3	3.5			4	5.5	2	2.1
Scientific	7	8.3	8	9.4			9	12.3	6	6.2
Health sciences	4	4.8	5	5.9			3	4.1	6	6.2

IPAQ-SF: International Physical Activity Questionnaire, reduced version; MET: Metabolic Equivalent Index.

As can be seen in Table 3, the mean total physical activity of the participants in Phase II was 2423.28 METs (SD = 1742.80). The group of physically active participants had significantly higher levels in intensive (U_1,169 = 126, p = .000), moderate (U_{1, 169} = 131, p = .000), and total (U_{1, 169} = .00, p = .000) physical activity.

Table 3.

Levels of physical activity (METs) of students participating in Phase II according to the activity profile (physically inactive versus active).

Variable	Total n = 170Mean (SD)	Physically inactive (>600 METs) n = 85 Mean (SD)	Physically active (<600 METs) n = 85 Mean (SD)	Mann–Whitney U	p
IPAQ-SF _Total Intense Physical Activity	1355.76 (1491.74)	40.00 (123.48)	1709.25 (1493.07)	126.00	.000
IPAQ-SF Total Moderate Physical Activity	681.64 (840.21)	54.44 (96.96)	850.15 (871.75)	131.00	.000
IPAQ-SF Total Hikes	385.87 (145.44)	360.07 (157.65)	392.80 (142.44)	533.50	.451
IPAQ-SF Total Physical Activity	2423.28 (1742.80)	454.51 (170.04)	2952.20 (1586.57)	.00	.000
IPAQ-SF Total Minutes Seated	263.86 (131.64)	247.91 (149.51)	267.00 (128.99)	350.00	.810

IPAQ-SF: International Physical Activity Questionnaire, reduced version; MET: Metabolic Equivalent Index.

Regarding cardiorespiratory physical fitness, no statistically significant relation was detected between this variable and the physical activity profile -active versus inactive- (χ² = 5.01, p = .286)

When dividing the sample according to gender (see Table 4), we found no statistically significant relation between physical fitness and the physical activity profile (active versus inactive). The pattern found was similar both for men and women.

Table 4.

Cardiorespiratory physical fitness as a function of the level of physical activity (physically inactive versus physically active) divided by gender.

		Inactive n = 85		Active n = 85		χ²	p
Cardiorespiratory physical fitness		n	%	n	%	χ²	p
Men	Excellent	0	0.0	2	6.3	3.52	.474
	Very good	0	0.0	14	25.0
	Good	57	67.1	20	53.3
	Sufficient	23	27.1	12	12.6
	Poor	5	5.8	2	0.0
Women	Excellent	0	0.0	5	5.9	7.20	.126
	Very good	21	24.7	25	29.4
	Good	29	34.1	35	41.2
	Sufficient	21	24.7	20	23.5
	Poor	14	16.5	0	0.0

Relationship between the physical activity profile and other parameters

Regarding health-related quality of life, the SF-12v2 showed adequate levels of internal consistency, with a Cronbach alpha of .72. As it can be seen in Table 5, active students showed higher and statistically significant levels in the domains Bodily Pain (U_{1, 169} = 446.0, p = .039), Vitality (U_{1, 169} = 368.5, p = .039), Role Emotional (U_{1, 169} = 346.0, p = .032), and in the Summary Index of Mental Health (U_{1, 169} = 332.5, p = .019).

Table 5.

Dimensions of health-related quality of life, emotional self-regulation and regulation for the practice of physical activity as a function of the physical activity profile (inactive versus active).

Variable	Inactive n = 85 Mean (SD) Mean ranges	Active n = 85 Mean (SD) Mean ranges	Mann–Whitney U	p
SF12v2_Physical Function	88.33 (15.99) 43.60	85.87 (20.32) 42.26	501.0	.826
SF12v2_Role Physical	65.83 (15.99) 39.40	68.75 (18.39) 43.77	471.0	.521
SF12v2_Bodily Pain	81.67 (24.03) 39.07	87.14 (18.91) 43.84	446.0	.039
SF12v2_General health	53.33 (18.58) 36.70	60.00 (18.25) 44.35	430.5	.236
SF12v2_Vitality	65.00 (15.81) 32.57	73.93 (16.17) 53.01	368.5	.039
SF12v2_ Social Function	80.00 (28.66) 40.17	85.71 (21.94) 43.61	482.5	.568
SF12v2_Role Emotional	72.50 (20.70) 31.07	84.28 (17.24) 45.56	346.0	.032
SF12v2_ Mental Health	70.83 (17.70) 36.03	77.14 (14.43) 44.49	420.5	.210
SF12v2_Summary Index Physical Health	72.08 (10.26) 32.93	76.90 (11.56) 44.58	374.0	.089
SF12v2_Summary Index Mental Health	72.08 (13.17) 29.50	80.27 (10.28) 45.89	322.5	.019
TMMS-24: Emotional Attention	24.47 (5.51) 45.23	23.97 (5.17) 42.52	491.5	.699
TMMS-24_ Emotional Clarity	24.93 (7.28) 33.54	27.58 (5.84) 41.39	364.5	.244
TMMS-24_Emotional Repair	24.93 (5.10) 30.33	28.16 (6.33) 44.00	335.0	.044
BREQ-3_ Intrinsic Regulation	11.20 (3.63) 30.17	13.14 (3.02) 44.61	332.5	.033
BREQ-3_ Integrated Regulation	9.50 (3.65) 29.32	11.65 (3.66) 3.44	305.5	.040
BREQ-3_Identified Regulation	7.79 (2.01) 23.04	9.79 (1.96) 45.30	217.5	.001
BREQ-3_ Introjected Regulation	6.47 (1.99) 35.03	7.40 (2.59) 42.95	405.5	.240
BREQ-3_ External Regulation	5.71 (1.80) 28.07	4.94 (1.53) 21.45	90.5	.216
BREQ-3_Amotivación	5.80 (2.11) 52.17	4.77 (1.37) 37.81	312.5	.015

TMMS-24: Trait-Meta Mood Scale; BREQ-3: Behavioral Regulation Exercise Questionnaire.

As for meta-knowledge of emotional states, the TMMS-24 scale also showed adequate levels of internal consistency, with a Cronbach Alpha ranged between .82 and .84. In the three subscales which make it up, statistically significant differences were only detected in the dimension Emotional Repair (U_{1, 169} = 335.0, p = .044), with physically active students showing higher levels of emotional self-regulation (Table 5). If we take a closer look at its analysis, as well as segmenting the sample based on the level of physical activity (active versus inactive) and gender, no statistically significant differences are detected between men and women, in the meta-knowledge of emotional states, both in the group of physically active and inactive subjects.

Regarding the regulation of behavior in physical exercise, in our study, the internal consistency of BREQ-3 was higher than .70, except for Introjected Regulation (α = .67). A it can be seen in Table 5, physically active students showed significantly higher levels in the dimensions Intrinsic Regulation (U_{1, 169} = 332.5, p = .033), Integrated Regulation (U_{1, 169} = 305.5, p = .040), and Identified Regulation (U = 217.5, p = .001). In contrast, physically inactive students scored significantly higher in the Amotivation factor (U_1,169 = 312.5, p = .015). Delving into the analysis and segmenting the sample based on the level of physical activity (active versus inactive) and gender, we find that in the group of physically inactive students statistically significant differences were detected between males and females in the dimensions External Regulation (U_{1, 69} = 12.5, p = .031) and in the Amotivation factor (U_{1, 69} =312.5, p = .015). In both cases the males score higher in these dimensions. With regard to the group of physically active students, intergender differences were detected in Integrated Regulation dimensions (U _{1, 69} = 423.5, p = .028), External Regulation (U_{1, 69} = 448.0, p = .047) and in the Amotivation factor (U _{1, 69} = 452.0, p = .033): Males also obtain higher scores in these dimensions.

On another hand, physically inactive students showed significantly higher levels of body fat mass (U_{1, 169} = 337, p = .035), with no differences between the two groups in the levels of BMI, lean mass, body water, basal metabolism, or waist perimeter (see Table 6).

Table 6.

BMI, body fat levels and other health parameters as as a function of the physical activity profile (inactive versus active).

Variable	Inactive n = 85 Mean (SD) Mean ranges	Active n = 85 Mean (SD) Mean ranges	Mann–Whitney U	p
BMI	23.66 (4.74) 48.57	22.48 (4.63) 40.55	411.5	.244
Lean mass	46.26 (10.15) 37.5	50.04 (12.49) 43.59	442.5	.381
Body fat mass	21.25 (12.79) 54.53	15.21 (12.86) 39.88	337.0	.035
Body water	34.85 (6.34) 40.40	36.79 (8.32) 42.44	486.0	.713
Basal metabolism	1498.80 (245.20) 40.00	1560.47 (322.33) 42.44	480.0	.723
Waist perimeter	75.60 (13.08) 43.60	75.26 (14.88) 42.26	501.0	.847

BMI: body mass index.

Lastly, in order to examine in depth, the degree of relationship between physically inactive behavior, emotional self-regulation, behavior regulation in the practice of physical activity, physical fitness, and some anthropometric and demographic factors, a logistic regression equation (forward conditional method), with a 95% confidence level was adjusted to determine the reliable predictors of the physically inactive style (see Table 7).

Table 7.

Final logistic regression model with reliable predictors and quantifiable risk coefficients related to physically inactive behavior.

Variables	B	SE	Wald	p	df	OR	95% CI
TMMS-24 Emotional Clarity	–.179	.076	6.755	.009	1	1.220	(1.050–1.417)
BREQ-3 Identified Regulation	–.499	.188	6.482	.011	1	1.615	(1.117–2.336)
BREQ-3 Amotivation	3.674	.253	7.429	.006	1	.502	(.306–.824)

Note. n = 170. TMMS-24: Trait-Meta Mood Scale; BREQ-3: Behavioral Regulation Exercise Questionnaire.

The Hosmer-Lemeshow test indicated a good level of fit of the model, and no statistically significant differences between the estimated and the observed values were detected (p = .145). According to the Nagelkerke statistic, 43.6% of the variance was explained by the model. The analyses identified the following aspects as reliable predictors of the physically inactive style: low scores in Emotional Clarity (p = .009) and Identified Regulation (p = .011) and high scores in Amotivation (p = .006).

Discussion

Considerable scientific evidence indicates that physical exercise on a regular basis and at appropriate levels improves quality of life and life expectancy, constituting a protective factor against multiple diseases (Ambroa de Frutos, 2016; Hallal et al., 2012; Kotseva et al., 2016; Trachte et al., 2016). On the contrary, physical inactivity is one of the main risk factors for mortality worldwide, as well as suffering from NCDs, such as cardiovascular disorders, cancer or diabetes (WHO, 2010).

Among the tools most used by researchers to determine physical activity levels is the International Physical Activity Questionnaire in its reduced version (IPAQ-SF; IPAQ Core Group, 2005). Taking this as a reference, an individual is physically inactive when he does not generate an energy outlay of at least 600 METs per week (Pate et al., 2008).

Thus, taking into account the scores obtained in the IPAQ-SF (IPAQ Core Group, 2005), nearly 13% of the students were classified as physically inactive for reporting an energy expenditure of less than 600 METs/week (Pate et al., 2008). These levels of physical inactivity are significantly lower than those usually reported in the general population (Ambroa de Frutos, 2016), which, in some ways, was expected, as the scientific literature indicates that the population with a higher level of education performs more physical activity and practices more sports (Ekelund et al., 2015), whereas the population with low educational level presents higher prevalence of physical inactivity and overweight (Devaux & Sassi, 2013). However, the prevalence levels were also significantly lower than those reported in research conducted with university students samples (Gaweł-Dąbrowska et al., 2016; Práxedes et al., 2016), which is between 23% and 50% (Hasse et al., 2004). This result probably has to do with the important effort made by the University of Vigo in the promotion of physical activity, but this aspect should be the subject of further research.

Despite these data, relatively low, it should not be forgotten that a considerable percentage of the students did not perform adequate levels of physical activity. In this sense, the detection of physical inactivity should be an essential task in view of its well-known negative effects on health and quality of life (Kohl et al., 2012).

Another aspect we have addressed in this study was to analyze the relationship between the physical activity profile (active versus inactive) and physical fitness. A causal relationship between physical activity and cardiorespiratory physical fitness is usually presumed. Thus, in the adult population, there seems to be enough evidence to support the hypothesis that regular practice of physical exercise improves physical fitness (Biniaminov et al., 2018). However, the data from our study indicate that there is no association between these variables. In spite of this, if we look closely at these data, we can observe that, both in male and female students, physically inactive subjects accumulate higher percentages of poor physical fitness and null or lower levels of excellent or very good physical fitness. The explanation for the lack of statistical association between the above-mentioned variables may be the age of the subjects of our sample (all of them were very young). In this sense, some studies indicate that this relationship is not as strong in children and young adults as in the adult population (García-Artero et al., 2007; Martínez-Vizcaíno & Sánchez-López, 2008). In addition, the effects of physical inactivity on health are not always observable in the short term, especially in the young and healthy population (Mutikainen et al., 2009). Another possible explanation can be derived from the instrument used to determine physical inactivity, as it evaluates the level of activity carried out in the past week, so we do not know the history or the time that the individual has maintained his or her lifestyle of physical activity or inactivity beyond the period covered by this questionnaire (IPAQ Core Group, 2005).

In relation to BMI, body fat, and other anthropometric variables, statistically significant differences were only detected as a function of the physical activity profile in relation to fat mass values, with physically inactive subjects showing significantly higher levels. In this sense, several studies find that physical inactivity is one of the factors that is highly related to the presence of higher levels of body fat mass (Deforche et al., 2015; Rodríguez et al., 2016).

On another hand, the practice of regular physical activity (as a behavior that is maintained over time) may be affected by cognitive factors such as emotional self-regulation (Laborde et al., 2016) or various motivational states (Castonguay et al., 2018). In this sense, incorporating into our lifestyle habits the regular practice of physical exercise requires, among other aspects, having the ability to regulate emotions, so that they do not interfere with the achievement of this goal (Millgram et al., 2019). Our results indicate that physically inactive subjects obtain significantly lower levels in Emotional Repair, which means that they have a smaller capacity to regulate and adequately manage their moods (Mayer et al., 2001). In line with our results, some studies indicate that physical inactivity is related to emotional deregulation (Tull et al., 2018), while the practice of moderate and intense physical exercise can help improve emotional regulation (Bernstein & McNally, 2017; Rodriguez-Ayllon et al., 2019). Yet, we must recognize that research on the effect of emotional self-regulation on our lifestyle habits is still incipient (Wilms et al., 2020).

In line with our data, other researchers have found that self-regulation is a relevant variable in order to persevere and achieve specific goals (Shapiro & Schwartz, 2000), such as those required by academic contexts (Nota et al., 2004) or the commitment to the practice of regular physical exercise (Castonguay et al., 2018; Halberstadt et al., 2017; Hall & Fong, 2015).

As far as motivational processes are concerned, physically active students obtained significantly higher scores in the motivational dimensions, with a higher level of self-determination (Intrinsic Regulation, Integrated Regulation, and Identified Regulation) and significantly higher levels of Amotivation, which seems to indicate that their behavior related to the practice of physical exercise is essentially regulated by external factors, as postulated by the sub-theory of organismic integration derived from the SDT (Deci & Ryan, 2012). Our results are consistent with various studies that indicate that self-determined motivation is related to a greater commitment to the practice of physical exercise, as well as to higher levels of physical activity (Martínez, et al., 2018; Symons-Downs et al., 2013).

On another hand, the scientific evidence has consistently documented the positive relationship between quality of life and the practice of physical activity (Bize et al., 2007). The results indicate that, in general, physically active students report higher levels of quality of life, particularly mental health, data that are consistent with previous studies (Cadarso et al., 2017). This is of extraordinary interest for the design of health promotion programs.

Lastly, in this study, we performed a logistic regression analysis to examine in more depth the relationship between physically inactive behavior and emotional self-regulation, the motivational aspects that affect behavior regulation for the practice of physical activity, physical fitness, and some anthropometric and demographic factors. The final model identified low scores in Emotional Clarity and Identified Regulation and high scores in Amotivation as reliable predictors of the physically inactive style. Which means that physically inactive students have greater difficulty knowing, understanding, and differentiating their emotions (Herazo-Beltrán et al., 2019). Similarly, predictably, their intrinsic motivational levels are lower therefore they are more likely to be demotivated by the practice of regular physical exercise, a behavior that involves a long-term commitment (Bernstein, & McNally, 2017).

These data indicate the importance of meta-knowledge of emotional states (Castonguay et al., 2018; Hall & Fong, 2015) and of the motivational processes (Castonguay et al., 2018) in the determination of physically active behavior and engagement in beneficial behaviors for health (Hall & Fong, 2015).

The present study presents some limitations. Firstly, we must refer to the sample size. Although the number of participants in Phase I (screening) was relatively large (N = 665), in Phase II (health status assessment), only 170 students were assessed. However, we must consider that the average evaluation time was between 70 and 90 minutes per individual, which made the process highly laborious. Another limitation has to do with the measuring instruments used; thus, the levels of physical activity were determined only by a subjective procedure, not including other measures derived from criterial or objective methods (Arvidsson, 2009). This was mainly due to the considerable size of the sample in Phase I, which required the use of an instrument to determine the intensity and levels of physical activity by means of an economic, fast, and simple procedure. In addition, despite their drawbacks, self-reports are one of the most widely used methods, as they are considered appropriate measures to evaluate the type of activity and to classify the levels of its practice (Corder et al., 2009). And within these, the IPAQ-SF is one of the most used worldwide (IPAQ Core Group, 2005) However, for future studies, we consider it advisable to extend the sample size and complement the evaluation of physical activity by combining recall surveys with more objective methods such as accelerometers, pulsometers or monitoring the heart rate; methods that have demonstrated their quality and accuracy in evaluating physical activity (Arvidsson, 2009).

The results of this study highlight the importance of motivational processes and emotional self-regulation on the determination of the practice of physical exercise. Data that are useful for the design and implementation of programs aimed at promoting health and physical activity, with special attention on physically inactive youth populations.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partially funded by the INOU Project (ref. INOU17-06) under the call for grant applications for research groups at Ourense University Campus. Funding Institutions: Vigo University/Ourense Provincial Council.

ORCID iD

José María Faílde-Garrido

Notes

References

Ambroa de Frutos

(2016). Impacto del sedentarismo sobre la práctica de actividad física y la salud. Análisis de la situación en españa [Impact of physical inactivity on the practice of physical activity and health analysis of the situation in Spain]. Revista Española de Educación Física y Deportes, 412, 33–44.

Annesi

(2013). Effects of treatment differences on psychosocial predictors of exercise and improved eating in obese, Middle-age adults. Journal of Physical Activity & Health, 10(7), 1024–1031. https://doi.org/10.1123/jpah.10.7.1024

Arvidsson

(2009). Physical activity and energy expenditure in clinical settings using multisensor activity monitors. University of Gothemburg.

Barbany

J. R.

(1990). Fundamentos de fisiología del ejercicio y del entrenamiento [Fundamentals physiology of exercise and of training]. Barcanova.

Baumeister

R. F.

DeWall

C. N.

Ciarocco

N. J.

Twenge

J. M.

(2005). Social exclusion impairs self-regulation. Journal of Personality and Social Psychology, 88(4), 589–604. https://doi.org/10.1037/0022-3514.88.4.589

Bennassar

(2011). Estilos de vida y salud en estudiantes universitarios [Lifestyle and health in college students] [Doctoral thesis]. Universidad de las Illes Balears. www.tdx.cat/bitstream/10803/84136/1/tmbv1de1.pdf

Bernstein

E. E.

McNally

R. J.

(2017). Acute aerobic exercise helps overcome emotion regulation déficits. Cognition & Emotion, 31(4), 834–843. https://doi.org/10.1080/02699931.2016.1168284

Biniaminov

Bandt

Roth

Haertel

Neumann

Bub

(2018). Irisin, physical activity and fitness status in healthy humans: No association under resting conditions in a cross-sectional study. PLoS One, 13(1), e0189254. https://doi.org/10.1371/journal.pone.0189254

Bize

Johnson

J. A.

Plotnikof

R. C.

(2007). Physical activity level and health-related quality of life in the general adult population: A systematic review. Preventive Medicine, 45(6), 401–415. https://doi.org/10.1016/j.ypmed.2007.07.017

10.

Booth

F. W.

Roberts

C. K.

Laye

M. J.

(2012). Lack of Exercise is a Major Cause of Chronic Diseases. Comprehensive Physiology , 2(2), 1143–1211. https://doi.org/10.1002/cphy.c110025 23798298

11.

Bravo-Valenzuela

Cabieses

Zuzulich

M. S.

Muñoz

Ojeda

(2013). Glosario Para universidades promotoras de la salud [A glossary for healthcare-promoting universities]. Revista de Salud Pública, 15(3), 465–477. https://www.scielosp.org/pdf/rsap/2013.v15n3/465-477/es

12.

Cadarso

Dopico

Iglesias-Soler

Suárez

Gude

(2017). Calidad de vida relacionada con la salud y su relación con la adherencia a la dieta mediterránea y la actividad física en universitarios de Galicia [Health-related quality of life and its relationship with adherence to the Mediterranean diet and physical activity in university students of Galicia]. Nutrición Clínica y Dietética Hospitalaria, 37(2), 42–49. https://doi.org/10.12873/372cadarso

13.

Cano

Miguel

J. J.

(2001). Emociones y salud [Emotions and health]. Ansiedad y Estrés, 7, 111–121.

14.

Castonguay

Miquelon

P. A.

Boudreau

(2018). Self-regulation resources and physical activity participation among adults with type 2 diabetes. Health Psychology Open, 5(1), 2055102917750331. https://doi.org/10.1177/2055102917750331

15.

Chacón

Espejo

Cabrera

Á.

Castro

López

J. F.

Zurita

(2015). “Exergames” Para la mejora de la salud en niños y niñas en edad escolar: Estudio a partir de hábitos sedentarios e índices de obesidad [«Exergames» to improve the health of school children: Study of sedentary lifestyle and obesity rates]. Revista Latinoamericana de Tecnología Educativa, 14(2), 39–50. https://doi.org/10.17398/1695288X. 14.2.39

16.

Corder

van Sluijs

E. M. F.

Wright

Whincup

Wareham

N. J.

Ekelund

(2009). Is it possible to assess free-living physical activity and energy expenditure in young people by self-report? The American Journal of Clinical Nutrition, 89(3), 862–870. https://doi.org/10.3945/ajcn.2008.26739

17.

Deci

E. L.

Ryan

R. M.

(2012). Self-determination theory. In A. W.Kruglanski, P. A. M. Van Lange, & E. T. Higgins (Eds.), Handbook of theories of social psychology (pp. 416–437). Sage.

18.

Deforche

Dyck

Deliens

Bourdeaudhuij

(2015). Changes in weight, physical activity, sedentary behaviour and dietary intake during the transition to higher education: A prospective study. International Journal of Behavioral Nutrition and Physical Activity, 12(1), 1–10. https://doi.org/10.1186/s12966-015-0173-9

19.

Devaux

Sassi

(2013). Social inequalities in obesity and overweight in 11 OECD countries. European Journal of Public Health, 23(3), 464–469. https://doi.org/10.1093/eurpub/ckr058

20.

Ekelund

Ward

H. A.

Norat

Luan

May

A. M.

Weiderpass

Sharp

S. S.

Overvad

Østergaard

J. N.

Tjønneland

Johnsen

N. F.

Mesrine

Fournier

Fagherazzi

Trichopoulou

Lagiou

Trichopoulos

Kaaks

, … Riboli

(2015). Physical activity and all-cause mortality across levels of overall and abdominal adiposity in European men and women: The European prospective investigation into cancer and nutrition study (EPIC). The American Journal of Clinical Nutrition, 101(3), 613–621. https://doi.org/10.3945/ajcn.114.100065

21.

Fernández-Berrocal

Extremera

Ramos

(2004). Validity and reliability of the Spanish modified version of the trait Meta-Mood scale. Psychological Reports, 94(3), 751–755. https://doi.org/10.2466/pr0.94.3.751-755

22.

Franco

Coterón

Martínez

H. A.

Brito

(2017). Perfles motivacionales en estudiantes de educación física de tres países y su relación con la actividad física [Motivational profiles in physical education students from three countries and their relationship with physical activity]. Suma Psicológica, 24(1), 1–8. https://doi.org/10.1016/j.sumpsi.2016.07.00

23.

Friederichs

S. A. H.

Bolman

Oenema

Lechner

(2015). Profiling physical activity motivation based on self-determination theory: A cluster analysis approach. BMC Psychology, 3(1), 1. https://doi.org/10.1186/s40359-015-0059-2

24.

García-Artero

Ortega

F. B.

Ruiz

J. R.

Mesa

J. L.

Delgado

González-Gross

García

Vicente

Gutiérrez

Castillo

M. J.

(2007). El perfil lipídico metabólico en los adolescentes está más influido por la condición física que por la actividad física (estudio AVENA) [The metabolic lipid profile in adolescents is more influenced by physical condition than by physical activity (AVENA study)]. Revista Española de Cardiología, 60(6), 581–588. https://doi.org/10.1157/13107114

25.

Gaweł-Dąbrowska

Einhorn

Połtyn-Zaradna

Zatońska

(2016). A physical activity assessment of Wroclaw medical university students with IPAQ – long questionnaire: A cross-sectional study. Family Medicine & Primary Care Review, 18(1), 12–18. https://doi.org/10.5114/fmpcr/41842

26.

González-Cutre

Sicilia

Fernández

(2010). Hacia una mayor comprensión de la motivación en el ejercicio físico: Medición de la regulación integrada en el contexto español [Towards a greater understanding of the motivation in physical exercise: Measurement of integrated regulation in the Spanish context]. Psicothema, 22, 841–847. http://www.psicothema.com/pdf/3810.pdf

27.

González

Fuentes

Márquez

J. L.

(2017). Physical inactivity, sedentary behavior and chronic diseases. Korean Journal of Family Medicine, 38(3), 111–115. https://doi.org/10.4082/kjfm.2017.38.3.111

28.

Guthold

Stevens

G. A.

Riley

L. M.

Bull

F. C.

(2018). Worldwide trends in insufficient physical activity from 2001 to 2016: A pooled analysis of 358 population-based surveys with 1·9 million participants. The Lancet. Global Health, 6(10), e1077–1086. https://doi.org/10.1016/S2214-109X(18)30357-7

29.

Halberstadt

de Vet

Nederkoorn

Jansen

van Weelden

O. H.

Eekhout

Heymans

M. W.

Seidell

J. C.

(2017). The association of self-regulation with weight loss maintenance after an intensive combined lifestyle intervention for children and adolescents with severe obesity. BMC Obesity, 4(13). https://doi.org/10.1186/s40608-016-0140-2

30.

Hall

P. A.

Fong

G. T.

(2015). Temporal self-regulation theory: A neurobiologically informed model for physical activity behavior. Frontiers in Human Neuroscience, 9, 117. https://doi.org/10.3389/fnhum.2015.00117

31.

Hallal

P. C.

Andersen

L. B.

Bull

F. C.

Guthold

Haskell

W. L.

Ekelund

, & Lancet Physical Activity Series Working Group. (2012). Global physical activity levels: Surveillance progress, pitfalls, and prospects. Lancet (London, England), 380(9838), 247–257. https://doi.org/10.1016/S0140-6736(12)60646-1

32.

Hasse

Steptoe

Phil

Sallis

J. F.

Wardle

(2004). Leisure-time physical activity in university students from 23 countries: Associations with health beliefs, risk awareness, and national economic development. Preventive Medicine, 39(1), 182–190. https://doi.org/10.1016/j.ypmed.2004.01.028

33.

Herazo-Beltrán

Campo-Ternera

García-Puello

Méndez

Suarez-Villa

Vásquez-De la Hoz

Núñez-Bravo , N. (2019). Relationship between physical activity and emotional intelligence and bullying among school children. Journal of Sport Psychology, 28(1), 97–104. https://archives.rpd-online.com/article/download/v28-n1-herazo-campo-garcia-etal/2000-12641-1-PB.pdf

34.

IPAQ Core Group. (2005). Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. http://www.ipaq.ki.se/scoring.pdf

35.

Kohl

H. W.

Craig

C. L.

Lambert

E. V.

Inoue

Alkandari

J. R.

Leetongin

Kahlmeier

(2012). The pandemic of physical inactivity: Global action for public health. The Lancet, 380(9838), 294–305. https://doi.org/10.1016/S0140-6736(12)60898-8

36.

Kotseva

Wood

De Bacquer

De Backer

Rydén

Jennings

Gyberg

Amouyel

Bruthans

Castro Conde

Cífková

Deckers

J. W.

De Sutter

Dilic

Dolzhenko

Erglis

Fras

Gaita

Gotcheva

, … EUROASPIRE Investigators. (2016). EUROASPIRE IV: A European society of cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. European Journal of Preventive Cardiology, 23(6), 636–648. https://doi.org/10.1177/2047487315569401

37.

Laborde

Dosseville

Allen

M. S.

(2016). Emotional intelligence in sport and exercise: A systematic review. Scandinavian Journal of Medicine & Science in Sports, 26(8), 862–874. https://doi.org/10.1111/sms.12510

38.

Mart í n

(2007). Estrés académico en estudiantes universitarios [Academic stress in university students]. Apuntes de Psicología, 25(1), 87–99. http://www.cop.es/delegaci/andocci/files/contenidos/ VOL 251_7.pdf

39.

Martínez

Mayorga

Viciana

(2018). Factores predictores de la actividad física en escolares españoles de acuerdo a su estado de peso [Predictive factors of physical activity in Spanish students based on their weight status]. Retos, 33, 74–80.

40.

Martínez-Vizcaíno

Sánchez-López

(2008). Relación entre actividad física y condición física en niños y adolescentes [Relationship between physical activity and physical fitness in children and adolescents]. Revista Española de Cardiología, 61(2), 108–111. https://doi.org/10.1157/13116196

41.

Maselli

Ward

P. B.

Gobbi

Carraro

(2018). Promoting physical activity among university students: A systematic review of controlled trials. American Journal of Health Promotion: AJHP, 32(7), 1602–1612. https://doi.org/10.1177/0890117117753798

42.

Mayer

J. D.

Salovey

Caruso

D. R.

Sitarenios

(2001). Emotional intelligence as a standard intelligence. Emotion (Washington, D.C.), 1(3), 232–242. https://doi.org/10.1037//1528-3542.1.3.232

43.

Millgram

Sheppes

Kalokerinos

E. K.

Kuppens

Tamir

(2019). Do the ends dictate the means in emotion regulation? Journal of Experimental Psychology. General, 148(1), 80–96. https://doi.org/10.1037/xge0000477

44.

Muraven

Slessareva

(2003). Mechanisms of self-control failure: Motivation and limited resources. Personality & Social Psychology Bulletin, 29(7), 894–906. https://doi.org/10.1177/0146167203029007008

45.

Mutikainen

Perhonen

Alén

Leskinen

Karjalainen

Rantanen

Kaprio

Kujala

U. M.

(2009). Effects of long-term physical activity on cardiac structure and function: A twin study. Journal of Sports Science and Medicine, 8(4), 533–542.

46.

Nota

Soresi

Zimmerman

B. J.

(2004). Self-regulation and academic achievement and resilience: A longitudinal study. International Journal of Educational Research, 41(3), 198–251. https://doi.org/10.1016/j.ijer.2005.07.001

47.

Pate

R. R.

O'Neill

J. R.

Lobelo

(2008). The evolving definition of “sedentary”. Exercise and Sport Sciences Reviews, 36(4), 173–178. https://doi.org/10.1097/jes.0b013e3181877d1a

48.

Pedersen B. K. (2009). The diseasome of physical inactivity–and the role of myokines in muscle--fat cross talk. The Journal of physiology, 587(Pt 23), 5559–5568. https://doi.org/10.1113/jphysiol.2009.179515

49.

Práxedes

Sevil

Moreno

del Villar

García-González

(2016). Niveles de actividad física en estudiantes universitarios: Diferencias en función del género, la edad y los estados de cambio [Levels of physical activity in university students: Differences in terms of gender, age and states of change]. Revista Iberoamericana de Psicología Del Ejercicio y el Deporte, 11(1), 123–132.

50.

Rodriguez-Ayllon, M., Acosta-Manzano, P., Coll-Risco, I., Romero-Gallardo, L., Borges-Cosic, M., Est evez-L opez, F., & Aparicio, V. A. (2019). Associations of physical activity, sedentary time and physical fitness with mental health during preg-nancy: The GESTAFIT project. Journal of Sport and Health Science (Science). Advance online publication. https://doi.org/10.1016/j.jshs.2019.04.003

51.

Rodríguez

Santibañez

Montupin

Chávez

Solis

(2016). Diferencias en la composición corporal y actividad física en estudiantes universitarios según año de ingreso [Differences in body composition and physical activity in university students according to year of entrance]. Universidad y Salud, 18(3), 474–481. https://doi.org/10.22267/rus.161803.52

52.

Ryan

R. M.

Deci

E. L.

(2017). Self-determination theory: Basic psychological needs inmotivation, development, and wellness. Guilford Press. https://doi.org/10.7202/1041847ar

53.

Salovey

Mayer

J. D.

Goldman

S. L.

Turvey

Palfai

T. P.

(1995). Emotional attention, clarity, and repair: Exploring emotional intelligence using the trait meta-mood scale. In J. W. Pennebaker (Ed.), Emotion, disclosure, and health (pp. 125–151). American Psychological Association. https://doi.org/10.1037/10182-006

54.

Sedentary Behaviour Research Network. (2012). Letter to the editor: Standardized use of the terms “sedentary” and “sedentary behaviours. Applied Physiology, Nutrition, and Metabolism, 37, 540–542. https://doi.org/10.1139/h2012-024

55.

Shapiro

S. L.

Schwartz

G. E.

(2000). The role of intention in self-regulation: Toward intentional systemic mindfulness. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 253–273). Academic Press. https://doi.org/10.1016/B978-0-12-109890-2.X5027-6

56.

Symons-Downs

Savage

J. S.

DiNallo

J. M.

(2013). Self-determined to exercise? Leisure-time exercise behavior, exercise motivation, and exercise dependence in youth. Journal of Physical Activity & Health, 10(2), 176–184. https://doi.org/10.1123/jpah.10.2.176

57.

Trachte

Geyer

Sperlich

(2016). Impact of physical activity on self-rated health in older people: Do the effects vary by socioeconomic status? Journal of Public Health (Oxford, England), 38(4), 754–759. https://doi.org/10.1093/pubmed/fdv198

58.

Tremblay

M. S.

Aubert

Barnes

J. D.

Saunders

T. J.

Carson

Latimer-Cheung

A. E.

Chastin

S. F. M.

Altenburg

T. M.

Chinapaw

M. J. M

, & SBRN Terminology Consensus Project Participants. (2017). Terminology consensus project process and outcome. The International Journal of Behavioral Nutrition and Physical Activity, 14(1), 75. https://doi.org/10.1186/s12966-017-0525-8

59.

Tull

M. T.

Lee

A. A.

Geers

A. L.

Gratz

K. L.

(2018). Exploring the role of sedentary behavior and physical activity in depression and anxiety symptom severity among patients with substance use disorders. Mental Health and Physical Activity, 14, 98–102. https://doi.org/10.1016/j.mhpa.2018.03.001

60.

Vilagut

Valderas

J. M.

Ferrer

Garín

López

Alonso

(2008). Interpretación de los cuestionarios de salud SF-36 y SF-12 en españa. Componentes físico y mental [Interpretation of SF-36 and SF-12 in Spain. Physical and mental components]. Medicina Clínica (Clínica), 130(19), 726–735. https://doi.org/10.1157/13121076

61.

Wilms

Lanwehr

Kastenmüller

(2020). Emotion regulation in everyday life: The role of goals and situational factors. Frontiers in Psychology, 11, 877. https://doi.org/10.3389/fpsyg.2020.00877

62.

Wilson

P. M.

Rodgers

W. M.

Loitz

C. C.

Scime

(2006). “It’s who I am… really!” The importance of integrated regulation in exercise contexts. Journal of Applied Biobehavioral Research, 11(2), 79–104. https://doi.org/10.1111/j.1751-9861.2006.tb00021.x

63.

World Health Organization. (2010). Global recommendations on physical activity for health.