Abstract
Running can improve health status from a biopsychosocial perspective. How- ever, isolation strategies, like the COVID-19 pandemic-induced lockdown, produce deleterious effects on both health status and sport performance. The aim of our study was to investigate recreational run- ners’ sporting habits, subjective vitality (SV), and well-being after the COVID-19 pandemic-induced lockdown.Methods: After data filtration, 5542 recreational runners (74.5% men and 25.5% women, >18 years) were selected for further analyses. The participants answered preliminary questions regarding sporting habits and completed the validated Spanish version of the Subjective Vitality as a Dynamic Reflection of Well-Being questionnaire for assessing their SV after lockdown.Results: Subjective vitality scores did not differ between men and women, nor between age groups (p= 0.41 andp= 0.11, respectively). Subjective vitality was greater with weekly training frequency up to 5 days/week, where this enhancement plateaued, while average training session duration was posi- tively related to SV, stabilizing at 91–120 min/session (p< 0.001 for both).Conclusions: There is a dose–response relationship between both weekly training frequency and training session duration,
and women, nor between age groups (p= 0.41 andp= 0.11, respectively). Subjective vitality was greater with weekly training frequency up to 5 days/week, where this enhancement plateaued, while average training session duration was posi- tively related to SV, stabilizing at 91–120 min/session (p< 0.001 for both).Conclusions: There is a dose–response relationship between both weekly training frequency and training session duration, and mental health benefits in recreational runners. Further longitudinal studies are needed in order to determine the optimal dose–response relationship for simultaneously enhancing mental health outcomes and running performance in recreational runners, especially regarding weekly training frequency, training session duration, and exercise intensity. Keywords:running; life satisfaction; mental health; dose-response; training volume 1. Introduction Recreational running is one of the most popular physical activities worldwide, as indicated by the increasing number of participants in running events (by around 58%) in the last decade [1]. Running can be considered a natural sporting activity inherent to the human species, and its effectiveness in maintaining or enhancing healthy fitness has been widely demonstrated [2]. Further, running also plays an important role in people’s health status at biological, psychological, and social levels, which is known as the biopsychosocial model [3,4]. This model considers health and disease as dynamic events based on the interactions of the three above-mentioned levels, which determines human functioning and, therefore, health status [5,6]. From this perspective, physical activity is considered as a complex, dynamic, and multidimensional process in which biological, psychological, and social aspects are interrelated and affected by individual differences [7,8]. The coronavirus disease (COVID-19) outbreak was declared a global pandemic by the World Health Organization (WHO) in March 2020, forcing governments to adopt preven- tion measures in order to slow the spread of COVID-19 (i.e., social distancing and limitation J. Funct. Morphol. Kinesiol.2024,9, 209.
J. Funct. Morphol. Kinesiol.2024,9, 209 2 of 13 of social interactions, wearing of surgical masks, school and sport facilities closures, can- cellation of sport events and, in most countries, an obligatory home quarantine) [9,10]. These measures differed between countries regarding the severity of the pandemic in each specific region. For instance, Spain and Italy, two of the most affected countries by the pandemic, adopted more restrictive strategies compared to France or Switzerland, which could affect the population’s health status, as well as their physical activity levels and sedentary behavior, in a different manner [11]. Concerning this, the unprecedented chal- lenging situation generated by COVID-19 produced a negative impact on both social and psychological outcomes, which some have suggested have received less attention compared to the biological responses to the pandemic [12]. Most scientific evidence indicates that pandemic-related circumstances such as isola- tion strategies (e.g., home confinement) have negatively affected people’s health and well- being, especially when physical activity levels and sports practice are considered[13–19] . In this regard, recent reviews focusing on physical activity levels in the general population during the COVID-19 pandemic showed that moderate-to-vigorous physical activity de- creased during lockdown, although overall physical activity levels were not substantially modified [20]. Furthermore, it was also suggested that simultaneously increasing sedentary time and decreasing physical activity led to psychological distress during the pandemic [21]. In this sense, other studies have also reported lower vitality scores associated with lower levels of physical activity, which negatively affects an individual’s well-being [22–24]. On the other hand, higher physical activity levels were associated with higher well-being and quality of life, and lower depressive symptoms, stress, and anxiety, regardless of age [25]. Concerning sports populations, adverse effects on athletes’ mental and emotional health during lockdown were reported [26]. More specifically, a recent study of recreational runners [11] also demonstrated that highly trained runners performed more and longer training sessions per week than lower-level runners during the lockdown. Moreover, in the first outdoor running session after confinement, highly trained runners performed a longer running session at a higher pace and covered a greater distance than lower-level
were reported [26]. More specifically, a recent study of recreational runners [11] also demonstrated that highly trained runners performed more and longer training sessions per week than lower-level runners during the lockdown. Moreover, in the first outdoor running session after confinement, highly trained runners performed a longer running session at a higher pace and covered a greater distance than lower-level runners, while enjoyment and motivation tended to be greater as runners’ level increased [11]. Well-being is considered an indicator of mental health [27]. Traditionally, well-being has been addressed from two well-differentiated perspectives [28]. On the one hand, the he- donistic perspective conceptualizes well-being as pleasure and affective experience, based on the presence of positive emotions and the absence of negative emotions [29]. On the other hand, eudaimonic well-being is oriented to meaning, excellence, and self-fulfillment, and represents the level at which an individual functions fully [30]. Thus, eudaimonic well- being is operationalized as a set of aspects that favor an individual’s well-being (pursuing intrinsic goals, behaving autonomously, and being mindful), satisfying basic psychological needs (i.e., autonomy, competence, and relatedness), and subjective vitality (SV) [31]. In this sense, SV refers to a positive feeling of aliveness and energy, where psychological and physiological factors converge [32], and has been considered a relevant outcome for evaluating psychological well-being [33]. In this regard, a recent review showed that a desirable health status is related to specific health behaviors (i.e., regular endurance and resistance training, sleep quality, and healthy nutrition) and to four psychological determi- nants (i.e., positive emotions, healthy mindsets, purposeful living, and social connectivity) that jointly interact to promote SV [34]. Thus, SV is positively related to general indicators of well-being, health, and moderate-intensity physical activity [35]. This is why individuals who perceive higher levels of energy are more likely to better manage stress, confront obstacles, pursue goals, or engage with the environment in other adaptive ways [36]. Recent scientific evidence suggests that participation in sports, at both recreational and elite levels, is related to better mental health. Specifically, sports participation promotes psychological well-being (e.g., higher self-esteem and life satisfaction), reduces psycholog- ical
higher levels of energy are more likely to better manage stress, confront obstacles, pursue goals, or engage with the environment in other adaptive ways [36]. Recent scientific evidence suggests that participation in sports, at both recreational and elite levels, is related to better mental health. Specifically, sports participation promotes psychological well-being (e.g., higher self-esteem and life satisfaction), reduces psycholog- ical ill-being (e.g., reduced levels of depression, anxiety, and stress) and enhances social outcomes (e.g., pro-social behavior, self-control, interpersonal communication, etc.) [37]. Therefore, the practice of physical activity during the pandemic has proven to be an effec-
J. Funct. Morphol. Kinesiol.2024,9, 209 3 of 13 tive strategy for recovering people’s subsequent well-being [38]. However, some studies have suggested that the benefits of physical activity are influenced by the frequency and intensity of activity. The WHO recommends performing at least 150 to 300 min per week of moderate-to-vigorous physical activity to obtain health benefits, although lower levels can also positively affect health status [39]. The intensity and frequency of sports practice are associated with SV and psychological well-being, revealing that these individual traits are related to health and well-being in specific environments (e.g., physical exercise prac- tice) [40]. Some studies have shown that running at high intensity and long-term training is associated with various positive mental health outcomes [41]. More specifically, previous studies have shown that recreational runners’ motivations to run are mainly related to eudaimonic well-being (e.g., maintain or improve health status, reach personal goals, and have fun) [42,43]. In this regard, motivation of recreational runners seems to be high in task-related goals (i.e., personal goal and mastery achievement) and low in ego-related goal orientation (i.e., competition and external achievement/social recognition) [42], while enjoyment and motivation for performance tend to be higher as the runner’s level in- creases [11]. Taken together, scientific evidence suggests that physical activity levels during the COVID-19 pandemic might play a key role in different-level athletes’ health status and sport-specific performance in the short- and long-term. In this sense, it was suggested that physical activity performed during lockdown (e.g., home-based training, quarantine training camps) attenuated the deleterious effects of the COVID-19 pandemic [26]. How- ever, the volume (duration), frequency (days per week), and intensity of physical activity needed to achieve health benefits is still unknown, especially when mental health outcomes are considered. This is of great importance since analyzing training characteristics after an unprecedented situation such as a lockdown and relating them to SV outcomes might shed some light on the effects of regular physical activity on mental health and the optimal dose–response relationship. Therefore, the aim of our study was to evaluate the sporting habits of recreational runners (frequency and duration of
is of great importance since analyzing training characteristics after an unprecedented situation such as a lockdown and relating them to SV outcomes might shed some light on the effects of regular physical activity on mental health and the optimal dose–response relationship. Therefore, the aim of our study was to evaluate the sporting habits of recreational runners (frequency and duration of training sessions) and SV after the COVID-19 pandemic- induced confinement in Spain. To the best of our knowledge, no studies have addressed these issues in recreational runners after home confinement. Based on previous studies that have established a strong relationship between exercise and mental health [34,37,44–46], we hypothesized that runners who accumulate a greater weekly training volume (i.e., greater weekly training frequency and longer training session duration) would obtain higher scores in SV outcomes until an optimal dose–response relationship was reached. We consider our study will help coaches and athletes to better manipulate the training variables in order to optimize both health status and athletic performance among different-level runners. 2. Materials and Methods 2.1. Study Design and Data Collection Our investigation was a cross-sectional study that included a self-reported question- naire to assess participants’ sporting habits and SV after the COVID-19 pandemic-induced lockdown in Spain. The study was approved by the local university Ethics Committee (ETK-52/21-22) and personal informed consent was obtained when the questionnaire was completed and submitted by the participants. The participants were contacted through the database of RUNNEA’s web page (www.runnea.com, accessed on 15 September 2024), a company focused on training recreational runners and analyzing running and lifestyle shoes. In order to reach as many people as possible, the company’s main social networks (Facebook, Instagram, X, and LinkedIn) were used to promote participation in the study. Data were collected between 18 and 29 November 2020. After preliminary questions re- garding sporting habits, the validated Spanish version [33] of the Subjective Vitality as a Dynamic Reflection of Well-Being (SVS) questionnaire [32] was sent via Google Forms. The reliability showed anαof Cronbach index of 0.86. This questionnaire is composed of six items containing a six-point Likert response scale (i.e.,
were collected between 18 and 29 November 2020. After preliminary questions re- garding sporting habits, the validated Spanish version [33] of the Subjective Vitality as a Dynamic Reflection of Well-Being (SVS) questionnaire [32] was sent via Google Forms. The reliability showed anαof Cronbach index of 0.86. This questionnaire is composed of six items containing a six-point Likert response scale (i.e., 1 = not at all, 4 = somewhat, and 7 = very true) and was designed to measure SV, reflecting the degree to which a person is
J. Funct. Morphol. Kinesiol.2024,9, 209 4 of 13 fully functioning and psychologically well [33]. The total score ranges from 6 to 42 points, with a higher score indicating a better condition [47]. The theoretical model was tested using the weighted least squares method with LISREL 8.80. The root-mean-squared error of approximation (RMSEA), comparative fit index (CFI), non-normative fit index (NNFI), and standardized residual mean root (SRMR) were used to evaluate the goodness of fit. CFI and NNFI values greater than 0.90, and RMSEA and SRMR values lower than 0.08, reflect an acceptable fit. We also used a one-factor model. Given that the items belonged to the same factor, measurement errors were allowed to correlate. The four-factor solution showed good fit, X 2 (714, n = 5542) = 1628, RMSEA = 0.076 (90% CI [0.072, 0.079],p= 0.36, CFI = 0.99, NNFI = 0.99. 2.2. Participants All participants volunteered to take part in this study and were previously informed of the aim of the investigation, data recording techniques, and data analyses. Personal data privacy was guaranteed in accordance with the European General Data Protection Regulation (EU GDPR; EU 2016/679). The inclusion criteria were to be over 18 years old and to run at least 1–2 days per week. The exclusion criteria were incomplete or incongruent, as well as duplicate responses. After the 12-day period for data collection, data from 5549 recreational runners were registered. Data filtration was performed by two researchers, and once the inclusion and exclusion criteria were applied, 5542 recreational runners (4126 men and 1416 women) were included for further data analyses. Table shows the sample sports habits according to sex and age group. Table 1.Sample sporting habits regarding sex and age group.Sporting Habits Age Group (Years) Sex n, % Total <30 min (n, %) 30–60 min (n, %) 61–90 min (n, %) 91–120 min (n, %) >120 min (n, %) Weekly Training Frequency (Days) (Mean±SD) <19 Female 9 0.16% 0 0.0% 2 0.0% 6 0.1% 1 0.0% 0 0.0% 4.33 ±1.50 Male 31 0.56% 1 0.0% 14 0.3% 10 0.2% 1 0.0% 5 0.1% 4.23 ±1.69 19–25 Female142 2.56%
Total <30 min (n, %) 30–60 min (n, %) 61–90 min (n, %) 91–120 min (n, %) >120 min (n, %) Weekly Training Frequency (Days) (Mean±SD) <19 Female 9 0.16% 0 0.0% 2 0.0% 6 0.1% 1 0.0% 0 0.0% 4.33 ±1.50 Male 31 0.56% 1 0.0% 14 0.3% 10 0.2% 1 0.0% 5 0.1% 4.23 ±1.69 19–25 Female142 2.56% 5 0.1% 84 1.5% 42 0.8% 10 0.2% 1 0.0% 3.55 ±1.32 Male 264 4.76% 11 0.2% 147 2.7% 86 1.6% 18 0.3% 2 0.0% 3.77 ±1.32 26–30 Female206 3.72% 5 0.1% 137 2.5% 57 1.0% 7 0.1% 0 0.0% 3.28 ±1.24 Male 313 5.65% 7 0.1% 172 3.1% 119 2.1% 14 0.3% 1 0.0% 3.55 ±1.24 31–35 Female214 3.86% 10 0.2% 127 2.3% 65 1.2% 12 0.2% 0 0.0% 3.36 ±1.31 Male 502 9.06% 6 0.1% 281 5.1% 194 3.5% 19 0.3% 2 0.0% 3.49 ±1.16 36–40 Female225 4.06% 4 0.1% 134 2.4% 75 1.4% 9 0.2% 3 0.1% 3.32 ±1.26 Male 686 12.38% 8 0.1% 370 6.7% 278 5.0% 26 0.5% 4 0.1% 3.45 ±1.12 41–45 Female289 5.21% 9 0.2% 141 2.5% 124 2.2% 13 0.2% 2 0.0% 3.37 ±1.18 Male 937 16.91% 8 0.1% 438 7.9% 435 7.8% 52 0.9% 4 0.1% 3.45 ±1.14 46–50 Female186 3.36% 3 0.1% 79 1.4% 92 1.7% 8 0.1% 4 0.1% 3.30 ±1.14 Male 757 13.66% 8 0.1% 343 6.2% 352 6.4% 52 0.9% 2 0.0% 3.59 ±1.16 51–55 Female96 1.73% 1 0.0% 40 0.7% 49 0.9% 5 0.1% 1 0.0% 3.38 ±1.01 Male 395 7.13% 9 0.2% 158 2.9% 197 3.6% 24 0.4% 7 0.1% 3.59 ±1.17 56–60 Female36 0.65% 1 0.0% 16 0.3% 15 0.3% 4 0.1% 0 0.0% 3.31 ±1.37 Male 162 2.92% 3 0.1% 60 1.1% 84 1.5% 11 0.2% 4 0.1% 3.72 ±1.35 61–65 Female 9 0.16% 1 0.0% 6 0.1% 2 0.0% 0 0.0% 0 0.0% 2.56 ±0.73 Male 50 0.90% 0 0.0% 13 0.2% 30 0.5% 7 0.1% 0 0.0% 3.92 ±1.14 66–70 Female 4 0.07% 0 0.0% 1 0.0% 2 0.0% 0 0.0% 1 0.0% 5.00 ±1.41 Male 25 0.45% 0 0.0% 6
1.5% 11 0.2% 4 0.1% 3.72 ±1.35 61–65 Female 9 0.16% 1 0.0% 6 0.1% 2 0.0% 0 0.0% 0 0.0% 2.56 ±0.73 Male 50 0.90% 0 0.0% 13 0.2% 30 0.5% 7 0.1% 0 0.0% 3.92 ±1.14 66–70 Female 4 0.07% 0 0.0% 1 0.0% 2 0.0% 0 0.0% 1 0.0% 5.00 ±1.41 Male 25 0.45% 0 0.0% 6 0.1% 14 0.3% 2 0.0% 3 0.1% 4.08 ±1.35 >70 Female 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% - Male 4 0.07% 0 0.0% 2 0.0% 2 0.0% 0 0.0% 0 0.0% 3.75 ±0.96 Data are presented in counts (n) and percentages of total (%), as well as mean and standard deviation (mean±SD ), when applicable. 2.3. Procedures Before the SVS questionnaire, the participants answered questions related to their sporting habits, which were included in the SVS questionnaire. Participants answered ques- tions about (a) age group and sex, (b) training session duration (minutes), and (c) weekly training frequency (days). In this regard, following the classification framework proposed
J. Funct. Morphol. Kinesiol.2024,9, 209 5 of 13 by McKay et al. [48], the great majority of our runners could be categorized as tier 1 (i.e., recreationally active) or tier 2 (i.e., trained/developmental). 2.4. Statistical Analysis The data homogeneity of variance test was performed using Levene’s test, while Kolmogorov–Smirnov, Cramer–von Mises, and Anderson–Darling tests were used to analyze if the variables were normally distributed. A confirmatory factor analysis was performed with LISREL 8.80 [49] to test the adequacy of the theoretical structure of the questionnaire for the present sample. A non-parametric Mann–Whitney U test was per- formed to analyze possible significant differences between men and women in the final SVS questionnaire score. A multiple regression model was set with SVS as the dependent variable and age group, training session duration, training frequency, interaction of age group×training frequency, age group×training duration, and training duration×training frequency as possible predictors. The final regression model was selected using a stepwise forward and backward method. For internal validation, k-fold cross-validation (10 folds and 5 repeti- tions) was performed. Internal validation was performed to reduce possible overfitting of the model [50]. Additionally, the R packages dplyr [51] and caret [52] were used to identify possible outliers and improve the fitting of the regression model. Outliers were identified and removed from the multiple regression model when the absolute value of the studen- tized residual (SRE) was≥3. Model performance was assessed using the root-mean-square error (RMSE) and Pearson’s R 2 . ANOVA tests were set to establish potential interaction training frequency and mean duration of training sessions on the SVS. When the ANOVA test showed significant differ- ences between factors, partial eta squared (η 2) was used as a measure of effect size (ES), using the reference values of small (η 2= 0.01), medium (η 2= 0.06), and large (η 2= 0.14). A subsequent post hoc Tukey’s test was performed to compare the potential differences between factors. For significant differences, Cohen’sdwas used as a measure of ES, using the reference values of small (d= 0.2), medium (d= 0.5), and large (d= 0.8) for interpreting them, as suggested by
Description
This study evaluates the relationship between training frequency, session duration, and vitality in recreational runners.