Next Article in Journal
Sex-Based Effects on Muscle Oxygenation During Repeated Maximal Intermittent Handgrip Exercise
Next Article in Special Issue
Towards Optimal Health Through Boredom Aversion Based on Experiencing Psychological Flow in a Self-Directed Exercise Regime—A Scoping Review of Recent Research
Previous Article in Journal
A Systematic Review and Meta-Analysis on Aerobic Fitness Dynamics in Post-COVID-19 Athletes: Implications in the Return-to-Play Performance
Previous Article in Special Issue
Intergenerational Judo: Synthesising Evidence- and Eminence-Based Knowledge on Judo across Ages
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sports
Volume 13
Issue 2
10.3390/sports13020041
sports-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

An Analysis of Young Women University Students’s Physical Activity Levels

by
Gema Díaz-Quesada
1,
Juan Francisco Jiménez-Jiménez
1,
Rosario Padial-Ruz
2,* and
Gema Torres-Luque
1
1
Faculty of Humanities and Education Sciences, University of Jaen, 23071 Jaen, Spain
2
Faculty of Education Sciences, University of Granada, 18071 Granada, Spain
*
Author to whom correspondence should be addressed.
Sports 2025, 13(2), 41; https://doi.org/10.3390/sports13020041
Submission received: 22 December 2024 / Revised: 23 January 2025 / Accepted: 26 January 2025 / Published: 5 February 2025
(This article belongs to the Special Issue Maintaining a Healthy Lifestyle: Sustainability of Promoting Physical Activity)
Browse Figure
Versions Notes

Abstract

:
The physical activity (PA) level in women, it seems, tends to decrease in adulthood. The aims of the study were: (i) to evaluate the degree of compliance with PA recommendations in young women university students and (ii) to measure steps and the level of PA in different periods during the week. Eighty-eight young adult girls (21.38 ± 2.71 years) were recruited for this study. Participants wore an “Actigraph GT3X” accelerometer for seven days, collecting minutes of moderate-to-vigorous physical activity (MVPA) and steps volume. The results show an 80% of compliance of the 10,000 steps per day and a 220% of compliance of the 300 min/week of MVPA. The analysis shows a trend towards higher steps and PA minutes at the Weekdays (steps/day, BF10 = 168.563, δ = 0.418; meeting recommendations 10,000 steps/day, BF10 = 168.563, δ = 0.419; MVPA minutes/day, BF10 = 10.648, δ = 0.323; meeting recommendations 300 min/week, BF10 = 10.648, δ = 0.324) and during the Out-of-University-Time (steps/day, BF10 = 1.387 × 1010, δ = −0.883; meeting recommendations 10,000 steps/day, BF10 = 1.387 × 1010, δ = −0.886; MVPA minutes/day, BF10 = 1.110 × 1015, δ = −1.138; meeting recommendations 300 min/week, BF10 = 1.1 × 1015, δ = −1.144). This study can provide strategies and motivational PA guidelines at university to enhance well-being in young female university students.

1. Introduction

Physical activity (PA) practice has numerous benefits across all age groups, as it prevents obesity, improves muscular fitness, enhances circulation, and reduces sedentary lifestyles [1,2,3,4,5]. It also mitigates risks associated with aging, such as high blood pressure, coronary diseases, diabetes, cancer, or heart conditions, while promoting brain health and overall well-being [6,7,8].
These recognized benefits have led global organizations, such as the World Health Organization (WHO) [9], to establish PA recommendations aimed at informing the population about the minimum requirements for maintaining a healthy state. For adults aged 18–64, it is recommended to engage in 150–300 min of moderate-to-vigorous physical activity (MVPA) per week, 75–150 min of vigorous physical activity (VPA), or a combination of both intensities, ensuring the same overall volume. Additionally, the WHO advises incorporating activities that strengthen major muscle groups at least two days a week, with moderate or higher intensity [9]. An alternative approach to measuring PA involves daily step counts, where adults are classified as “active” with a minimum of 10,000 steps per day and “very active” with 12,500 steps per day [10].
However, a concerning trend is the global decline in PA levels [11,12]. Currently, 31% of adults fail to meet the recommended PA guidelines, and this percentage is projected to increase to 35% by 2030 if the trend persists [13].
Women are disproportionately affected by this trend, as they are consistently less active than men. Data indicate that, on average, women are five percentage points less active than men, a gap that has persisted since 2000 [13]. Contributing factors include psychosocial barriers, such as weight stigma [14], higher rates of back pain associated with less individualized PA programs [15], and cultural elements linked to race or socioeconomic status [16]. Studies in adult women reveal that many prioritize other responsibilities over PA. For instance, in Latin America and Japan, women report engaging more frequently in light physical activity (LPA) than in MVPA or VPA [17,18]. In a similar study in Lithuania, 31% of young women (aged 18–24) reported not exercising, 10% participated in professional sports, 36% exercised independently, and 23% exercised at a gym or health center [19]. Additionally, research shows that only 28–35% of women meet PA recommendations based on step counts [20,21]. Controversies also exist regarding step-count compliance: some studies report adequate adherence [22].
These disparities are even more pronounced among younger women. Adolescents and young adults are particularly vulnerable, as PA levels decline rapidly during the transition from adolescence to adulthood. Adolescent girls are significantly less active than boys, with 85% of girls and 78% of boys worldwide not meeting daily PA recommendations [23]. A longitudinal study conducted in Spain revealed that among young women aged 18–30, 61% engaged in PA sporadically, while only 35% practiced PA several times a week [24]. Within university settings, this issue becomes more critical. The transition to higher education is often accompanied by significant lifestyle changes, such as increased academic stress, greater autonomy in time management, and social pressures, which collectively contribute to reduced PA levels [25]. Female university students face additional gender-specific barriers, including sociocultural expectations, time constraints, and limited access to PA programs tailored to their needs [25]. Research shows that only around 30% of women meet PA recommendations [26].
Understanding variations in PA levels across weekdays and weekends can provide critical insights into how structured schedules and periods of leisure impact young women’s routines. By identifying specific periods of inactivity, targeted interventions can be designed to address these gaps. In the scientific literature, different studies can be found that break down PA both at different times of the day and separating weekdays and weekends, these studies can be found in different educational stages, in early childhood [27,28], elementary school [29] and high school [30]. At the university stage, the literature focuses more on the days of the week and the weekend, rather than breaking down each day into different times [31].
Promoting morning activity on weekends or encouraging structured routines during weekdays could help young women achieve consistent PA levels. Social factors, such as peer support within academic institutions, and environmental considerations, like access to safe and well-equipped fitness spaces, further play a pivotal role in fostering engagement in PA [32,33].
These findings highlight the urgent need for interventions aimed at promoting PA among young university women. Understanding their adherence to PA recommendations and analyzing their step counts at different times of the week are essential steps in designing effective strategies to address this gap.
Therefore, the objectives of this study were: (a) To analyze the adherence to PA recommendations among young university women, and (b) To assess PA levels and step counts at different times of the week.

2. Materials and Methods

2.1. Subjects

A total of 88 young university women were selected (age = 21.38 ± 2.71; BMI = 23.95 ± 4.60). None of the participants had any physical limitations that would prevent their participation in the study. The participants were first-year students enrolled in the Early Childhood Education degree program at the local university. The objectives of the study were explained to the participants, who subsequently signed a consent form to confirm their participation. The sampling method used was voluntary sampling. The students were informed about the study that was to be conducted, and it was they who decided whether to participate or not. The study was approved by the Ethics Committee of the local institution (JUN.22/1.PRY).

2.2. Procedure and Measures

Participants were randomly selected, from those who voluntarily signed up to participate in the study (Figure 1). Only data from individuals who wore the activity tracker every day were included in the analysis (five weekdays and two weekend days) [34]. Participants used the ActiGraph GT3X accelerometer (ActiGraph, Pensacola, FL, USA) for seven consecutive days during a regular academic week [35]. The device collects and stores information on steps and PA across three orthogonal axes: vertical (Y), horizontal right-left (X), and horizontal front-back (Z). It also includes a “vector magnitude” metric, which calculates the square root of the sum of each axis squared, making the accelerometer valid for measuring PA [36]. The GT3X device was placed on the right hip, midway over the iliac crest, using an adjustable band [37].
In addition to verbal instructions during placement, participants were instructed to remove the device during aquatic activities (swimming or showering) and while sleeping. All data were subsequently analyzed using ActiLife 6.0 Software (Scientific Advisory Board, Pensacola, FL, USA) [38]. The data were expressed as minutes per day (min/day) and percentage (%) of MVPA, based on cut-off points established by Pate & O’Neill [39]. Daily step counts were also recorded. Data were analyzed across different time periods: weekdays (Monday to Friday), weekend (Saturday and Sunday), as well as morning (8:00–14:00) and afternoon (14:01–23:00) during weekdays, corresponding to university hours.
Weight, height and waist circumference measurements were taken in a designated classroom. Weight was measured twice with participants barefoot and lightly clothed, using a SECA electric scale (SECA LTD., Hamburg, HH, Germany), and recorded in kilograms. Height was measured twice with participants barefoot, standing with heels, buttocks and back against the wall, using a tape measure attached to the wall with tape, and recorded in centimeters. Waist circumference was measured twice with a Holtain fiberglass, millimetered, inextensible tape measure and recorded in centimeters. Body mass index (BMI) was calculated using the formula weight in kilograms divided by height in meters squared (kg/m2) [40].

2.3. Statistical Analysis

First, a descriptive analysis of the data was performed, presenting results as mean and standard deviation. Second, the Kolmogorov-Smirnov normality test confirmed a normal distribution. Third, Student’s paired t-test was used to evaluate differences between specific time periods (“weekdays” versus “weekend”; “morning” versus “afternoon”). Finally, Bayesian inferences were employed to perform paired and independent t-tests. This methodology quantifies the relative degree of evidence supporting two rival hypotheses: the null hypothesis (H0) versus the alternative hypothesis (H1), using Bayes Factors (BF01–BF10) [41,42]. Bayesian statistics have recently been proposed as a more robust alternative to traditional frequentist methods (based on confidence intervals and p-values) for hypothesis testing. Among its advantages, Bayesian analysis allows for the following: BF10 quantifies the evidence provided by the data in favor of H0 or H1, BF10 can quantify evidence supporting H0, and BF10 is not “violently biased” against H0 [43,44]. BF10 was interpreted using evidence categories suggested by Lee et al. [45]: <1/100 = extreme evidence for H0, 1/100 to <1/30 = very strong evidence for H0, 1/30 to <1/10 = strong evidence for H0, 1/10 to <1/3 = moderate evidence for H0, 1/3 to <1 = anecdotal evidence for H0, 1 to 3 = anecdotal evidence for H1, >3 to 10 = moderate evidence for H1, >10 to 30 = strong evidence for H1, >30 to 100 = very strong evidence for H1, and >100 = extreme evidence for H1. BF01 was calculated as the reciprocal of BF10 (e.g., >3 to 10 = moderate evidence for H0). Effect sizes were interpreted using criteria developed by Hopkins et al. [46]: 0.2 = trivial; 0.2–0.6 = small; 0.6–1.2 = moderate; 1.2–2.0 = large; 2.0–4.0 = very large; >4.0 = extremely large. The significance level for frequentist analyses was set at p < 0.05. Statistical analyses were performed using IBM SPSS Statistics 25.0 for Windows (IBM Software Group, Chicago, IL, USA) and Jamovi version 2.6.13, based on R graphical interface software.

3. Results

Table 1 shows the descriptive characteristics of the participants, including their adherence to PA recommendations across different periods of the week.
Table 2 shows the results of the Student’s paired samples t-test and Bayesian analysis, highlighting the differences in PA levels and the percentage of PA recommendations achieved between “weekdays” and the “weekend”.
Table 3 shows an analysis of PA levels and the percentage of PA recommendations achieved between “morning” and “afternoon” periods, using both frequentist (t-test) and Bayesian inference methodologies.
Table 1 shows that during the week (Monday to Sunday), approximately 80% of participants met the recommendation of 10,000 steps/day, while adherence to the 300 min/week recommendation reached around 200%. When analyzing weekdays, about 84% of participants met the 10,000 steps/day target, with adherence to the 300 min/week recommendation remaining at approximately 200%. During the weekend, adherence to the 10,000 steps/day recommendation dropped to around 68%, while the 300 min/week recommendation stayed consistent at 200%. In the morning, around 30% of participants achieved the 10,000 steps/day recommendation, while approximately 75% met the 300 min/week target. On the weekend, adherence to the 10,000 steps/day recommendation was about 50%, with the 300 min/week recommendation rising to approximately 130%.
Table 2 shows data analysis through Bayesian statistic showed extreme evidence (BF10 > 100) in favor of H1 in the following variables: Steps/day and AR 10,000 steps/day (%) and a strong evidence (BF10 from 10 to 30) in favor of H1 in the following variables: MVPA (min/day) and AR 300 min/week (%) compared between “weekdays“ and “weekend” (weekend < weekdays). This data indicates that it was 168.563 times more probable to find higher mean values in favor of “weekdays” than “weekend”. The numerical algorithm used to calculate the results indicated a great stability (error% 0.00). The robustness of Bayes factors was stable (e.g., max BF10: 195.975 at r = 0.3876, user prior: BF10 = 168.563; wide prior: BF10 = 137.275; ultra-wide prior: BF10 = 105.377. Moreover, the posterior distribution showed a large effect size in all variables (steps/day = δ > 1.2, 95% credible interval (95%CI) = 0.202 to 0.630; AR 10,000 steps/day (%) = δ > 1.2, 95%CI = 0.201 to 0.636; MVPA (min/day) = δ > 1.2, 95%CI = 0.106 to 0.534; AR 300 min/week (%) = δ > 1.2, 95%CI = 0.115 to 0.533). Statistically significant differences in steps/day, recommendations of 10,000 steps/day, MVPA min/day and recommendations of 300 min of MVPA per week (p < 0.005) were shown in the frequentist analysis.
Table 3 shows data analysis through Bayesian statistic showed extreme evidence (BF10 > 100) in favor of H1 in the following variables: Steps/day, AR 10,000 steps/day (%), MVPA (min/day) and AR 300 min/week (%) compared between “morning“ and “afternoon” (morning < afternoon). This data indicates that it was 1110 × 1015 times more probable to find higher mean values in favor of “afternoon” than “morning”. The numerical algorithm used to calculate the results indicated a great stability (error% 0.00). The robustness of Bayes factors was stable (e.g., max BF10: 1.24 × 1015 at r = 1.143, user prior: BF10 = 1.11 × 1015; wide prior: BF10 = 1.226 × 1015; ultra-wide prior: BF10 = 1.211 × 1015. Moreover, the posterior distribution showed a large effect size in all variables (steps/day = δ > 1.2, 95% credible interval (95%CI) = −1.131 to −0.636; AR 10,000 steps/day (%) = δ > 1.2, 95%CI = −1.122 to −0.636; MVPA (min/day) = δ > 1.2, 95%CI = −1.427 to −0.877; AR 300 min/week (%) = δ > 1.2, 95%CI = −1.412 to −0.889). Statistically significant differences in steps/day, recommendations of 10,000 steps/day, MVPA min/day and recommendations of 300 min of MVPA per week (p < 0.001) were shown in the frequentist analysis.

4. Discussion

This study evaluated adherence to PA and step recommendations among young university women and analyzed differences across various time periods throughout the week. It contributes to addressing a significant gap in the existing literature, as detailed analyses of PA levels in the female population, particularly among young adult women in a university context, remain scarce.
First, it was observed that participants achieved 80% compliance with the recommendation of 10,000 daily steps throughout the week. This finding is noteworthy due to the significant controversy in the literature. On the one hand, some studies have reported compliance rates around 30% [20,21], while others report 100% compliance [22,25]. Such a wide discrepancy could be attributed to the age of participants, as studies suggest that compliance tends to decrease with increasing age [20,25]. Participants in the current study were young adults, only recently transitioning out of adolescence. It is noteworthy that, despite the high compliance rates, these values still fall short of the 100% adherence observed in adolescent populations. This underscores the importance of understanding how PA behaviors evolve during this transitional phase into adulthood, especially as individuals adjust to new academic routines.
However, when analyzing PA levels, the findings were striking. Participants exceeded the recommended 150 or 300 min of MVPA by over 400% and 200%, respectively. Such high compliance rates are unprecedented in the literature, as previous studies have reported adherence to 150 min of MVPA ranging from 65% to 98% among Japanese and Thai women [47,48,49]. In contrast, adherence as low as 6% has been documented in Bangladeshi women [50], while other studies indicate stability around 70% in a seven-year longitudinal study in Thailand [47]. Although fewer studies have explored adherence to the 300-min MVPA recommendation, existing data suggest compliance rates around 60% [48].
Such findings suggest the need to examine the influence of specific cultural and regional factors. In Spain, PA, particularly walking and light-to-moderate activities, is culturally integrated into daily life. For instance, active commuting, such as walking or cycling to university, is a common practice among students, as public policies promote sustainable mobility in urban areas [51]. Additionally, traditional social norms and habits, such as outdoor leisure activities (e.g., walking in parks or engaging in community sports), may contribute to these high PA levels.
Beyond cultural factors, the academic context plays a significant role in shaping PA behaviors. Spanish young adults often experience a strong emphasis on education, with an intensive academic workload that structures much of their daily routine [52]. This structured environment may inadvertently create opportunities for PA, such as walking between classes or engaging in extracurricular activities organized by universities. Conversely, lower PA levels during weekends may reflect a shift in priorities, with weekends often reserved for rest, family commitments, or academic catch-up rather than PA. These findings align with the literature, where adherence to 10,000 step count during weekdays reached 84% of the recommendation, slightly below similar populations, which report approximately 91%. Conversely, on weekends, compliance dropped to 68%, aligning more closely with other studies that place it between 65% and 77% [53,54]. Hence, the importance of conducting a segmented analysis across different times of the week or day to identify periods with lower levels of PA.
Once again, the PA levels observed are striking. Both on weekdays (Monday to Friday) and weekends, adherence to the 150- or 300-min recommendations for MVPA exceeded 100%, ranging between 400% and 200%. The existing literature falls short of these findings, reporting adherence rates among young university women of 60–90% during weekdays and 30–50% on weekends [53,54]. Despite these notable differences, both the present study and prior research confirm that PA compliance is consistently lower on weekends than on weekdays. Several factors may explain these results. During the week, commuting to university may contribute to increased PA levels, as active transportation is a well-established and effective method for enhancing daily PA [52]. Additionally, young Spanish women (aged 12–25 years) demonstrate a heightened sense of responsibility toward their education and future, often associated with a significant academic workload [52]. The weekend, therefore, may serve as a designated period to meet these responsibilities, potentially reducing opportunities for engaging in PA.
This reflection is highly insightful because delving into a segmented analysis to explore differences between the morning (university hours) and the afternoon (extracurricular hours) reveals notable variations, even if the overall trend remains consistent (Table 3). During weekdays, the compliance with 150 min of MVPA in the morning still exceeds 100%, although not as significantly as in previous observations. However, adherence to the 300-min MVPA recommendation drops to 75%. Interestingly, other studies focusing on adolescent populations report compliance rates ranging from 30% to 55% in both morning and afternoon periods [55,56]. Various factors may explain these disparities. Academic schedules continue to be contexts with a predominantly sedentary nature, as observed among both adolescents and university students. This underscores the importance of implementing PA promotion strategies, such as active breaks in classrooms, to reduce sedentary behavior during academic hours [57]. What stands out, however, is that outside university hours, the young women participating in this study meet or exceed PA recommendations. This raises several points for consideration. At the university age, as young adults, time management tends to be more autonomous; some individuals live away from their family home, and awareness of the importance of PA is often heightened. Despite this, participants demonstrated remarkably high adherence to PA recommendations outside university hours, suggesting that young adults may actively compensate for sedentary periods during the day. Additionally, societal stereotypes related to appearance and fitness, where physical exercise is emphasized, may also play a role [58,59]. These pressures, combined with increasing autonomy in time management and exposure to health-promoting messages in academic and social settings, may explain the observed PA patterns.
One limitation of this study is the need to analyze the contextual variables influencing the daily routines of this population. Moreover, a more qualitative approach to understanding their habits, motivations, and barriers regarding PA is necessary. Such an approach would provide valuable insights into their routines and inform the development of strategies to ensure consistent adherence to PA throughout the week.
The findings from this study confirms the relevance of promoting physical activity among young university women and the especially low activity of this group during academic hours; hence, targeted interventions at universities become relevant. Strategies may also involve active breaks, which are inclusions of brief and structured physical activities into school classes to lessen sedentary behaviors; improvement in the infrastructure on campus, such as accessible recreational places, together with programs that endorse the use thereof; and an intervention on weekends like the organization of some physical and recreating time activities that can enhance PA levels not on weekdays. Coupling these initiatives with unique cultural and academic contexts among the university students, this will institute long-term healthy PA behavior both during and post-college.

5. Conclusions

Young female university students included in this study demonstrated a high level of adherence to daily MVPA recommendations. Despite these positive outcomes, a lower level of PA was observed during university hours compared to extracurricular hours and weekends. Implementing effective strategies is proposed, such as active breaks during classes, short physical activities within the classroom, and physical-recreational activities for the weekend.

Author Contributions

Conceptualization, G.D.-Q. and G.T.-L. methodology, G.D.-Q., J.F.J.-J., R.P.-R. and G.T.-L. formal analysis, G.D.-Q. and J.F.J.-J.; investigation, J.F.J.-J. and R.P.-R., writing—original draft preparation, G.D.-Q. and J.F.J.-J.; writing—review and editing, R.P.-R. and G.T.-L.; supervision, G.T.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. However, this study is part of the teaching innovation project: “ACTIVATE: Use of Smart Devices for the Promotion of Physical Activity” (PIMED31_202224) at the University of Jaén.

Institutional Review Board Statement

Informed consent was obtained from all subjects involved in the study. Ethics Committee of the local institution [University of Jaén, Spain (JUN.17/6)].

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Andradas-Aragonés, E.; Merino-Merino, B.; Campos-Esteban, P.; Gil-Luciano, A.; Zuza-Santacilia, I.; Terol-Claramonte, M.; Santaolaya-Cesteros, M.; Justo-Gil, S.; Ley-Vega de Seoane, V.; Aragonés-Clemente, M.T.; et al. Actividad Física para la Salud y Reducción del Sedentarismo. Ministerio de Sanidad, Servicios Sociales e Igualdad. 2015. Available online: https://recs.es/wp-content/uploads/2017/05/Recomendaciones_ActivFisica_para_la_Salud-1.pdf (accessed on 25 January 2025).
  2. Lavie, C.J.; Laddu, D.; Arena, R.; Ortega, F.B.; Alpert, M.A.; Kushner, R.F. Healthy Weight and Obesity Prevention: JACC Health Promotion Series. J. Am. Coll. Cardiol. 2018, 72, 1506–1531. [Google Scholar] [CrossRef] [PubMed]
  3. Lavie, C.J.; Ozemek, C.; Carbone, S.; Katzmarzyk, P.T.; Blair, S.N. Sedentary Behavior, Exercise, and Cardiovascular Health. Circ. Res. 2019, 124, 799–815. [Google Scholar] [CrossRef] [PubMed]
  4. Nystoriak, M.A.; Bhatnagar, A. Cardiovascular Effects and Benefits of Exercise. Front. Cardiovasc. Med. 2018, 5, 135. [Google Scholar] [CrossRef] [PubMed]
  5. Pandita, A.; Sharma, D.; Pandita, D.; Pawar, S.; Tariq, M.; Kaul, A. Childhood Obesity: Prevention Is Better Than Cure. Diabetes Metab. Syndr. Obes. Targets Ther. 2016, 9, 83–89. [Google Scholar] [CrossRef] [PubMed]
  6. Sánchez-Delgado, J.C.; Escobar-Pinzón, S.G.; Vega-Camacho, J.D.; Porras-Solano, A.J.; Angarita-Fonseca, A. Actitudes Hacia el Ejercicio Físico y Práctica de Actividad Física en Profesionales de la Salud: Estudio de Corte Transversal, 2015. Arch. Med. 2016, 16, 2339–3874. [Google Scholar] [CrossRef]
  7. Stamatakis, E.; Gale, J.; Bauman, A.; Ekelund, U.; Hamer, M.; Ding, D. Sitting Time, Physical Activity, and Risk of Mortality in Adults. J. Am. Coll. Cardiol. 2019, 73, 2062–2072. [Google Scholar] [CrossRef]
  8. The Lancet. A Sporting Chance: Physical Activity as Part of Everyday Life. Lancet 2021, 398, 365. [Google Scholar] [CrossRef]
  9. World Health Organization (WHO). WHO Guidelines on Physical Activity and Sedentary Behaviour; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
  10. Tudor-Locke, C.; Craig, C.L.; Brown, W.J.; Clemes, S.A.; De Cocker, K.; Giles-Corti, B.; Hatano, Y.; Inoue, S.; Matsudo, S.M.; Mutrie, N.; et al. How Many Steps/Day Are Enough? For Adults. Int. J. Behav. Nutr. Phys. Act. 2011, 8, 79. [Google Scholar] [CrossRef]
  11. Andersen, L.B.; Mota, J.; Di Pietro, L. Update on the global pandemic of physical inactivity. Lancet 2016, 388, 1255–1256. [Google Scholar] [CrossRef]
  12. Tremblay, M.S.; Aubert, S.; Barnes, J.D.; Saunders, T.J.; Carson, V.; Latimer-Cheung, A.E.; Chastin, S.F.M.; Altenburg, T.M.; Chinapaw, M.J.M.; SBRN Terminology Consensus Project Participants. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 75. [Google Scholar] [CrossRef]
  13. Strain, T.; Flaxman, S.; Guthold, R.; Semenova, E.; Cowan, M.; Riley, L.M.; Bull, F.C.; Stevens, G.A.; Country Data Author Group. National, regional, and global trends in insufficient physical activity among adults from 2000 to 2022: A pooled analysis of 507 population-based surveys with 5.7 million participants. Lancet Glob. Health 2024, 12, e1232–e1243. [Google Scholar] [CrossRef] [PubMed]
  14. Xu, P.; Chen, J.S.; Chang, Y.L.; Wang, X.; Jiang, X.; Griffiths, M.D.; Pakpour, A.H.; Lin, C.Y. Gender Differences in the Associations Between Physical Activity, Smartphone Use, and Weight Stigma. Front. Public Health 2022, 10, 862829. [Google Scholar] [CrossRef] [PubMed]
  15. Marijančić, V.; Peharec, S.; Starčević-Klasan, G.; Grubić Kezele, T. Gender Differences in the Relationship between Physical Activity, Postural Characteristics and Non-Specific Low Back Pain in Young Adults. J. Funct. Morphol. Kinesiol. 2024, 9, 189. [Google Scholar] [CrossRef] [PubMed]
  16. Armstrong, S.; Wong, C.A.; Perrin, E.; Page, S.; Sibley, L.; Skinner, A. Association of Physical Activity With Income, Race/Ethnicity, and Sex Among Adolescents and Young Adults in the United States: Findings From the National Health and Nutrition Examination Survey, 2007–2016. JAMA Pediatr. 2018, 172, 732–740. [Google Scholar] [CrossRef] [PubMed]
  17. de Moraes Ferrari, G.L.; Kovalskys, I.; Fisberg, M.; Gómez, G.; Rigotti, A.; Sanabria, L.Y.C. Socio-demographic patterning of objectively measured physical activity and sedentary behaviours in eight Latin American countries: Findings from the ELANS study. Eur. J. Sport Sci. 2019, 20, 670–681. [Google Scholar] [CrossRef] [PubMed]
  18. Amagasa, S.; Inoue, S.; Ukawa, S.; Sasaki, S.; Nakamura, K.; Yoshimura, A.; Tanaka, A.; Kimura, T.; Nakagawa, T.; Imae, A.; et al. Are Japanese Women Less Physically Active Than Men? Findings From the DOSANCO Health Study. J. Epidemiol. 2021, 31, 530–536. [Google Scholar] [CrossRef] [PubMed]
  19. Skurvydas, A.; Lisinskiene, A.; Lochbaum, M.; Majauskiene, D.; Valanciene, D.; Dadeliene, R.; Fatkulina, N.; Sarkauskiene, A. Physical Activity, Stress, Depression, Emotional Intelligence, Logical Thinking, and Overall Health in a Large Lithuanian Adult Sample: Age and Gender Differences. Int. J. Environ. Res. Public Health 2021, 18, 12809. [Google Scholar] [CrossRef] [PubMed]
  20. Hall, K.S.; McAuley, E. Individual, Social Environmental and Physical Environmental Barriers to Achieving 10,000 Steps per Day among Older Women. Health Educ. Res. 2010, 25, 478–488. [Google Scholar] [CrossRef]
  21. Paluch, A.E.; Gabriel, K.P.; Fulton, J.E.; Lewis, C.E.; Schreiner, P.J.; Sternfeld, B.; Sidney, S.; Siddique, J.; Whitaker, K.M.; Carnethon, M.R. Steps per Day and All-Cause Mortality in Middle-Aged Adults in the Coronary Artery Risk Development in Young Adults Study. JAMA Netw. Open 2021, 4, e2124516. [Google Scholar] [CrossRef]
  22. Ferrari, G.; Marques, A.; Barreira, T.V.; Kovalskys, I.; Gómez, G.; Rigotti, A.; Cortés, L.Y.; García, M.C.Y.; Pareja, R.G.; Herrera-Cuenca, M.; et al. Accelerometer-Measured Daily Step Counts and Adiposity Indicators among Latin American Adults: A Multi-Country Study. Int. J. Environ. Res. Public Health 2021, 18, 4641. [Google Scholar] [CrossRef]
  23. Guthold, R.; Stevens, G.A.; Riley, L.M.; Bull, F.C. Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1.6 million participants. Lancet Child Adolesc. Health 2019, 4, 23–35. [Google Scholar] [CrossRef] [PubMed]
  24. Laredo-Aguilera, J.A.; Cobo-Cuenca, A.I.; Santacruz-Salas, E.; Martins, M.M.; Rodríguez-Borrego, M.A.; López-Soto, P.J.; Carmona-Torres, J.M. Levels of Physical Activity, Obesity and Related Factors in Young Adults Aged 18–30 during 2009–2017. Int. J. Environ. Res. Public Health 2019, 16, 4033. [Google Scholar] [CrossRef] [PubMed]
  25. Bailey, B.W.; Borup, P.; Tucker, L.; LeCheminant, J.; Allen, M.; Hebbert, W. Steps Measured by Pedometry and the Relationship to Adiposity in College Women. J. Phys. Act. Health 2014, 11, 1225–1232. [Google Scholar] [CrossRef] [PubMed]
  26. Vella, C.A.; Ontiveros, D.; Zubia, R.Y.; Dalleck, L. Physical Activity Recommendations and Cardiovascular Disease Risk Factors in Young Hispanic Women. J. Sports Sci. 2011, 29, 37–45. [Google Scholar] [CrossRef]
  27. Díaz-Quesada, G.; Bahamonde-Pérez, C.; Giménez-Egido, J.M.; Torres-Luque, G. Use of Wearable Devices to Study Physical Activity in Early Childhood Education. Sustainability 2021, 13, 13998. [Google Scholar] [CrossRef]
  28. Díaz-Quesada, G.; Gálvez-Calabria, M.L.Á.; Connor, J.D.; Torres-Luque, G. When Are Children Most Physically Active? An Analysis of Preschool Age Children’s Physical Activity Levels. Children 2022, 9, 1015. [Google Scholar] [CrossRef]
  29. Lee, J.E.; Stodden, D.F.; Gao, Z. Young Children’s Energy Expenditure and Moderate-to-vigorous Physical Activity on Weekdays and Weekends. J. Phys. Act. Health 2016, 13, 1013–1016. [Google Scholar] [CrossRef]
  30. Beltrán Carrillo, V.J.; Sierra, A.C.; Jiménez Loaisa, A.; González-Cutre, D.; Martínez Galindo, C.; Cervelló, E. Diferencias Según género En El Tiempo Empleado Por Adolescentes En Actividad Sedentaria Y Actividad física En Diferentes Segmentos Horarios Del día (Gender Differences in Time Spent by Adolescents in Sedentary and Physical Activity in Different Day Segmen). Retos 2017, 31, 3–7. [Google Scholar] [CrossRef]
  31. Peñarrubia-Lozano, C.; Romero-Roso, L.; Olóriz-Nivela, M.; Lizalde-Gil, M. El desafío Como Estrategia Para La promoción De Actividad física En Universitarios (Challenge As a Strategy to Promote Physical Activity in University Students). Retos 2021, 39, 58–64. [Google Scholar] [CrossRef]
  32. Humpel, N.; Owen, N.; Leslie, E. Environmental factors associated with adults’ participation in physical activity: A review. Am. J. Prev. Med. 2002, 22, 188–199. [Google Scholar] [CrossRef]
  33. Wendel-Vos, W.; Droomers, M.; Kremers, S.; Brug, J.; van Lenthe, F.V. Potential environmental determinants of physical activity in adults: A systematic review. Obes. Rev. 2007, 8, 425–440. [Google Scholar] [CrossRef] [PubMed]
  34. Calahorro-Cañada, F.; Torres-Luque, G.; López-Fernández, I.; Carnero, E.A. Sedentariness and Physical Activity during School Recess Are Associated with VO2Peak. Int. J. Environ. Res. Public Health 2020, 17, 4733. [Google Scholar] [CrossRef] [PubMed] [PubMed Central]
  35. Trost, S.G.; Mciver, K.L.; Pate, R.R. Conducting accelerometer-based activity assessments in field-based research. Med. Sci. Sports Exerc. 2005, 37, 531–543. [Google Scholar] [CrossRef] [PubMed]
  36. Santos-Lozano, A.; Santín-Medeiros, F.; Cardon, G.; Torres-Luque, G.; Bailón, R.; Bergmeir, C.; Ruiz, J.R.; Lucia, A.; Garatachea, N. Actigraph GT3X: Validation and determination of physical activity intensity cut points. Int. J. Sports Med. 2013, 34, 975–982. [Google Scholar] [CrossRef] [PubMed]
  37. Sasaki, J.E.; John, D.; Freedson, P.S. Validation and comparison of ActiGraph activity monitors. J. Sci. Med. Sport 2011, 14, 411–416. [Google Scholar] [CrossRef]
  38. ActiGraph Software Department. ActiLife 6 User’s Manual; Actigraph: Pensacola, FL, USA, 2012; Available online: https://s3.amazonaws.com/actigraphcorp.com/wp-content/uploads/2018/02/22094137/SFT12DOC13-ActiLife-6-Users-Manual-Rev-A-110315.pdf (accessed on 25 January 2025).
  39. Pate, R.R.; O’Neill, J.R. After-School Interventions to Increase Physical Activity among Youth. Br. J. Sports Med. 2009, 43, 14–18. [Google Scholar] [CrossRef]
  40. Ruiz, J.R.; Castro-Piñero, J.; España-Romero, V.; Artero, E.G.; Ortega, F.B.; Cuenca, M.M.; Jiménez-Pavón, D.; Chillón, P.; Girela-Rejón, M.J.; Mora, J.; et al. Field-based fitness assessment in young people: The ALPHA health—Related fitness test battery for children and adolescents. Br. J. Sports Med. 2011, 45, 518–524. [Google Scholar] [CrossRef]
  41. Doncaster, G.; Page, R.; White, P.; Svenson, R.; Twist, C. Analysis of physical demands during youth soccer match-play: Considerations of sampling method and epoch length. Res. Q. Exerc. Sport 2019, 91, 326–334. [Google Scholar] [CrossRef]
  42. Linke, D.; Link, D.; Weber, H.; Lames, M. Decline in match running performance in football is affected by an increase in game interruptions. J. Sports Sci. Med. 2018, 17, 662–667. [Google Scholar]
  43. Ly, A.; Verhagen, J.; Wagenmakers, E.J. Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. J. Mathl. Psych. 2016, 72, 19–32. [Google Scholar] [CrossRef]
  44. Wagenmakers, E.J.; Marsman, M.; Jamil, T.; Ly, A.; Verhagen, J.; Love, J.; Selker, R.; Gronau, Q.F.; Smira, M.; Epskamp, S.; et al. Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications. Psychon. Bull. Rev. 2018, 25, 35–57. [Google Scholar] [CrossRef] [PubMed]
  45. Lee, M.D.; Wagenmakers, E.J. Bayesian Data Analysis for Cognitive Science: A Practical Course, 1st ed.; Cambridge University Press: New York, NY, USA, 2013. [Google Scholar]
  46. Hopkins, W.G.; Marshall, S.W.; Batterham, A.M.; Hanin, J. Progressive Statistics for Studies in Sports Medicine and Exercise Science. Med. Sci. Sports Exerc. 2009, 41, 3–12. [Google Scholar] [CrossRef] [PubMed]
  47. Katewongsa, P.; Yousomboon, C.; Haemathulin, N.; Rasri, N.; Widyastari, D.A. Prevalence of Sufficient MVPA among Thai Adults: Pooled Panel Data Analysis from Thailand’s Surveillance on Physical Activity 2012–2019. BMC Public Health 2021, 21, 665. [Google Scholar] [CrossRef]
  48. Hui, S.S.; Chin, E.C.; Chan, J.K.W.; Chan, B.P.; Wan, J.H.; Wong, S.W. Association of ‘Weekend Warrior’ and Other Leisure Time Physical Activity Patterns with Obesity and Adiposity: A Cross-Sectional Study. Diabetes Obes. Metab. 2025, 27, 482–489. [Google Scholar] [CrossRef] [PubMed]
  49. Machida, M.; Takamiya, T.; Fukushima, N.; Odagiri, Y.; Kikuchi, H.; Amagasa, S.; Kitabayashi, M.; Kitayuguchi, J.; Inoue, S. Bout Length-Specific Physical Activity and Adherence to Physical Activity Recommendations among Japanese Adults. Int. J. Environ. Res. Public Health 2019, 16, 1991. [Google Scholar] [CrossRef]
  50. Uddin, R.; Khan, A.; Burton, N.W. Prevalence and Sociodemographic Patterns of Physical Activity among Bangladeshi Young Adults. J. Health Popul. Nutr. 2017, 36, 31. [Google Scholar] [CrossRef]
  51. Molina-García, J.; Sallis, J.F.; Castillo, I. Active commuting and sociodemographic factors among university students in Spain. J. Phys. Act. Health 2014, 11, 359–363. [Google Scholar] [CrossRef]
  52. Luque-Casado, A.; Villar-Álvarez, F. Mujer Joven y Actividad Física; Fundación MAPFRE: Madrid, Spain, 2019. [Google Scholar]
  53. Arias-Palencia, N.M.; Solera-Martínez, M.; Gracia-Marco, L.; Silva, P.; Martínez-Vizcaíno, V.; Cañete-García-Prieto, J.; Sánchez-López, M. Levels and Patterns of Objectively Assessed Physical Activity and Compliance with Different Public Health Guidelines in University Students. PLoS ONE 2015, 10, e0141977. [Google Scholar] [CrossRef]
  54. Clemente, F.M.; Nikolaidis, P.T.; Martins, F.M.; Mendes, R.S. Physical Activity Patterns in University Students: Do They Follow the Public Health Guidelines? PLoS ONE 2016, 11, e0152516. [Google Scholar] [CrossRef]
  55. Hubbard, K.; Economos, C.D.; Bakun, P.; Boulos, R.; Chui, K.; Mueller, M.P.; Smith, K.; Sacheck, J. Disparities in Moderate-to-Vigorous Physical Activity among Girls and Overweight and Obese Schoolchildren during School- and Out-of-School Time. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 39. [Google Scholar] [CrossRef]
  56. Silva, D.A.S.; Chaput, J.P.; Katzmarzyk, P.T.; Fogelholm, M.; Hu, G.; Maher, C.; Olds, T.; Onywera, V.; Sarmiento, O.L.; Standage, M.; et al. Physical Education Classes, Physical Activity, and Sedentary Behavior in Children. Med. Sci. Sports Exerc. 2018, 50, 995–1004. [Google Scholar] [CrossRef] [PubMed]
  57. Donnelly, J.E.; Hillman, C.H.; Castelli, D.; Etnier, J.L.; Lee, S.; Tomporowski, P.; Lambourne, K.; SzaboReed, A.N. Physical activity, fitness, cognitive function, and academic achievement in children: A systematic review. Med. Sci. Sport. Exer. 2016, 48, 1197–1222. [Google Scholar] [CrossRef] [PubMed]
  58. Ahn, J.; Kim, I. The Effect of Autonomy and Self-Control on Changes in Healthy Lifestyles of Inactive College Students through Regular Exercise. Int. J. Environ. Res. Public Health 2022, 19, 10727. [Google Scholar] [CrossRef] [PubMed]
  59. Herbert, C. Enhancing Mental Health, Well-Being and Active Lifestyles of University Students by Means of Physical Activity and Exercise Research Programs. Front. Public Health 2022, 10, 849093. [Google Scholar] [CrossRef]
Sports 13 00041 g001
Figure 1. Experimental designer.
Figure 1. Experimental designer.
Sports 13 00041 g001
Table 1. Participants’ characteristics and meeting recommendations at different periods over a week.
Table 1. Participants’ characteristics and meeting recommendations at different periods over a week.
VariableGirls
(n = 88)
Age (years)21.38 ± 2.71
Weight (kg)64.14 ± 13.57
Height (m)1.64 ± 0.07
BMI (kg/m2)23.95 ± 4.60
Waist Circumference (cm)65.82 ± 10.34
Week steps/day8016.74 ± 2629.14
AR Week 10,000 steps/day (%)80.17 ± 26.29
Week MVPA (min/day)91.13 ± 26.57
AR Week 300 min/week (%)211.92 ± 61.79
Weekday steps/day8479.38 ± 2757.10
AR Weekdays 10,000 steps/day (%)84.79 ± 27.57
Weekdays MVPA (min/day)94.71 ± 27.49
AR Weekdays 300 min/week (%)220.26 ± 63.93
Weekend steps/day6860.14 ± 3910.54
AR Weekend 10,000 steps/day (%)68.60 ± 39.11
Weekend MVPA (min/day)82.17 ± 39.90
AR Weekend 300 min/week (%)191.09 ± 92.90
Morning steps/day3096.43 ± 1740.64
AR Morning 10,000 steps/day (%)30.96 ± 17.41
Morning MVPA (min/day)32.31 ± 16.10
AR Morning 300 min/week (%)75.15 ± 37.45
Afternoon steps/day5010.71 ± 1737.33
AR Afternoon 10,000 steps/day (%)50.11 ± 17.37
Afternoon MVPA (min/day)57.17 ± 18.59
AR Afternoon 300 min/week (%)132.96 ± 43.24
BMI: Body Mass Index Corporal; AR: Accomplishment Recommendations; MVPA: Moderate-to-vigorous Physical Activity; min: minutes.
Table 2. Differences in young women PA between weekdays and weekend.
Table 2. Differences in young women PA between weekdays and weekend.
VariableWeekdaysWeekendtpError %Bayes Factorδ
Steps/day8479.38 ± 2757.106860.14 ± 3910.544.04<0.0010.00BF10 = 168.5630.418
AR 10,000 steps/day (%)84.79 ± 27.5768.60 ± 39.114.04<0.0010.00BF10 = 168.5630.419
MVPA (min/day)94.71 ± 27.4982.17 ± 39.903.130.0020.00BF10 = 10.6480.323
AR 300 min/week (%)220.26 ± 63.93191.09 ± 92.903.130.0020.00BF10 = 10.6480.324
AR: Accomplishment Recommendations; MVPA: Moderate-to-vigorous Physical Activity; min: minutes.
Table 3. Differences in young women PA between morning and afternoon.
Table 3. Differences in young women PA between morning and afternoon.
VariableMorningAfternoontpError %Bayes Factorδ
Steps/day3096.43 ± 1740.645010.71 ± 1737.33−8.46<0.0010.00BF10 = 1.387 × 10100.418
AR 10,000 steps/day (%)30.96 ± 17.4150.11 ± 17.37−8.46<0.0010.00BF10 = 1.387 × 10100.419
MVPA (min/day)32.31 ± 16.1057.17 ± 18.59−10.94<0.0010.00BF10 = 1.110 × 10150.323
AR 300 min/week (%)75.15 ± 37.45132.96 ± 43.24−10.94<0.0010.00BF10 = 1.110 × 10150.324
AR: Accomplishment Recommendations; MVPA: Moderate-to-vigorous Physical Activity; min: minutes.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Díaz-Quesada, G.; Jiménez-Jiménez, J.F.; Padial-Ruz, R.; Torres-Luque, G. An Analysis of Young Women University Students’s Physical Activity Levels. Sports 2025, 13, 41. https://doi.org/10.3390/sports13020041

AMA Style

Díaz-Quesada G, Jiménez-Jiménez JF, Padial-Ruz R, Torres-Luque G. An Analysis of Young Women University Students’s Physical Activity Levels. Sports. 2025; 13(2):41. https://doi.org/10.3390/sports13020041

Chicago/Turabian Style

Díaz-Quesada, Gema, Juan Francisco Jiménez-Jiménez, Rosario Padial-Ruz, and Gema Torres-Luque. 2025. "An Analysis of Young Women University Students’s Physical Activity Levels" Sports 13, no. 2: 41. https://doi.org/10.3390/sports13020041

APA Style

Díaz-Quesada, G., Jiménez-Jiménez, J. F., Padial-Ruz, R., & Torres-Luque, G. (2025). An Analysis of Young Women University Students’s Physical Activity Levels. Sports, 13(2), 41. https://doi.org/10.3390/sports13020041

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop