Determinants of Movement Quality in Obese and Overweight Healthcare Students: A Cross-Sectional Analysis

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Alkhathami, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8101901/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Feb, 2026 Read the published version in Discover Public Health → Version 1 posted 14 You are reading this latest preprint version Abstract Background Healthcare students demonstrate high prevalence of overweight and obesity, which may compromise movement quality essential for their professional demands. Poor movement patterns in healthcare professionals are associated with increased injury risk and reduced work performance. This study aimed to investigate factors influencing movement quality in overweight and obese healthcare students in order to identify potential candidates for intervention. Methods A cross- sectional study was carried out on 111 young male healthcare students with BMI ≥ 25 kg/m². Participants underwent comprehensive assessments including anthropometric measurements, Functional Movement Screen (FMS), Six-Minute Walk Distance (6MWD) test, and physical activity evaluation using the International Physical Activity Questionnaire (IPAQ). Results The mean FMS score was 14.2 ± 2.1. The final regression model explained 80% of variance in FMS scores (R² = 0.800, F (3,107) = 142.7, p < 0.001), representing a very large effect size (Cohen's f² = 4.0). 6MWD emerged as the strongest predictor, followed by physical activity level, and BMI. BMI significantly moderated the relationship between 6MWD and FMS (β = 0.187, p = 0.021), with stronger associations observed in participants with lower BMI values. Conclusion Functional exercise capacity, as measured by 6MWD, is the primary determinant of movement quality in overweight and obese healthcare students. The moderating effect of BMI suggests that targeted interventions should prioritize cardiovascular fitness improvement, particularly for students with higher BMI. These findings support the implementation of fitness and movement quality programs in healthcare education to better prepare students for their professional demands. Movement quality healthcare students obesity functional movement screen exercise capacity Figures Figure 1 1. Introduction Healthcare professionals face significant physical demands in their work environment, requiring optimal movement patterns to prevent injury and maintain professional effectiveness[ 1 ]. Tasks such as patient lifting, prolonged standing, repetitive movements, and emergency response procedures require high levels of functional movement quality and physical fitness[ 2 ]. Recent studies reporting rates of 35–45% globally, significantly higher than age-matched populations in other academic disciplines[ 3 – 5 ]. Movement quality, defined as the ability to perform fundamental movement patterns with proper biomechanical alignment and neuromuscular control, is crucial for optimal functioning in healthcare professionals[ 6 ]. Poor movement quality has been consistently associated with increased risk of musculoskeletal injuries, reduced work performance, and early career burnout in healthcare settings[ 7 , 8 ]. Over recent decades, Saudi Arabia has undergone swift socio- cultural change as a result of economic reform. Incremental “modernization” of society and continuing development of the economy has brought along with it diet changes and sedentary lifestyle for several people in the country[ 9 ]. Many studies reported from Saudi Arabia reveal that healthcare students tend to be prone to obesity and overweight. Several factors such as irregular meal timings, consumption of fast food, disordered eating habits, unhealthy life style and psychological stress could cause this tendency[ 10 ]. The Functional Movement Screen (FMS) has been seen to be a reliable and valid tool for assessing movement quality in various populations, demonstrating good inter-rater reliability (ICC = 0.87–0.98) and predictive validity for injury risk[ 6 , 11 , 12 ]. Previous studies have identified several factors to be potential influences on FMS performance, including body composition, cardiovascular fitness, physical activity levels, and anthropometric characteristics[ 13 ][ 14 ]. Despite the established relationships between obesity, physical activity, and movement patterns in general populations, there remains a significant knowledge gap regarding the specific factors that influence movement quality in healthcare students who are overweight or obese. Furthermore, no previous studies have examined the potential moderating role of BMI on the relationship between functional exercise capacity and movement quality in healthcare student populations. The Six-Minute Walk Distance (6MWD) test, a well-established measure of functional exercise capacity, has shown strong correlations with movement quality in clinical populations, but its predictive value in young, overweight healthcare students remains unclear[ 15 , 16 ]. Keeping this research gap in mind, the present study was designed with the purpose of investigating the anthropometric and fitness variables which best predict movement quality in overweight and obese healthcare students. The study also aimed to find if BMI moderated the relationship between functional exercise capacity and movement quality in this population and explore the relative contributions of different predictor variables to overall movement quality as measured by FMS scores. 2. Methods 2.1 Study Design and Participants This cross-sectional study was conducted at the Department of Health Rehabilitation, Shaqra University between January and June 2025. Healthcare students aged 18–25 years with BMI ≥ 25 kg/m² were recruited through convenience sampling from nursing, physiotherapy, and medical programs. The study received ethical approval from the Ethics Research Committee of the University (ERC_SU_F_202300004), and written informed consent was provided by all participants prior to participation. Male students aged 18–22 years, enrolled in nursing, physiotherapy or medical undergraduate programs who had BMI more than or equal to 25, and who were able to complete the assessment protocols were included in the study as participants. Those with acute musculoskeletal injury within the past 3 months, with chronic medical conditions affecting movement or exercise capacity (e.g., cardiovascular disease, respiratory disorders, neurological conditions), those who were on medications affecting physical performance and those who had previously participated in any formal movement screening programs were excluded. 2.2 Sample Size Calculation Sample size calculation was done with G- power version 3.1 software. With α = 0.05, power = 0.80, medium effect size (f² = 0.15), and 6 predictors, the minimum required sample was n = 98. To account for potential dropouts and ensure adequate power for moderation analysis, we aimed to recruit 130 participants. 2.3 Participant Flow A total of 131 healthcare students were assessed for eligibility. Of these, 16 were excluded based on inclusion/exclusion criteria (8 due to recent injuries, 6 due to chronic conditions, and 2 due to age restrictions), and 4 dropped out during data collection due to scheduling conflicts. The final analysis was conducted on 111 participants. 2.4 Outcome Measures 2.4.1 Anthropometric Measurements All anthropometric measurements were conducted by trained research assistants following standardized protocols. Height was measured to the nearest 0.1 cm using a stadiometer and weight was measured to the nearest 0.1 kg using a digital scale (Tanita BC-418, Tokyo, Japan) with participants in light clothing and bare feet. BMI was calculated as weight (kg) divided by height squared (m²). Waist circumference was measured at the narrowest point between the lower costal margin and iliac crest using a non-elastic tape measure, following WHO guidelines. Hip circumference was measured at the widest point over the greater trochanters. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were again calculated according to standard procedure[ 17 ]. 2.4.2 Functional Movement Screen (FMS) The FMS was administered by a certified FMS practitioner following standardized protocols established by Cook et al.[ 18 ][ 6 ]. Before starting evaluation using the FMS participants were provided basic information about the usage of the scale, which comprises of seven patterns of movement for which both stability and mobility are required. These movement patterns are hurdle step, deep overhead squat, shoulder mobility, trunk stability, active straight leg raising, rotatory stability and in- line lunges. Each movement was scored on a 0–3 scale where 3 indicated performance of movement correctly without compensation, 2 indicated performance of movement with some compensation, 1 indicated inability to perform movement even with compensations, and 0 meant experiencing pain during movement. The total FMS score ranges from 0–21, with higher scores indicating better movement quality. Three trials were performed for each movement, with the best score recorded. Inter-rater reliability for FMS has been reported to be good, with an ICC of 0.87–0.98 in similar populations[ 19 ]. 2.4.3 Six-Minute Walk Distance (6MWD) The 6MWD test was conducted according to American Thoracic Society guidelines on a 30-meter straight corridor marked every 3 meters[ 20 ]. Participants were instructed to "walk as far as possible in 6 minutes" while maintaining a pace they could sustain throughout the test. Standardized encouragement was provided every minute using phrases such as "You are doing well," "Keep up the good work," and "You have [time remaining] minutes left." Heart rate was continuously monitored using a chest strap monitor (Polar H10, Kempele, Finland), and oxygen saturation was measured using a pulse oximeter (Nonin 9590, Plymouth, MN) before the test, at 3 minutes, and immediately after completion. Participants were allowed to slow down, stop, and rest if necessary, but were encouraged to resume walking as soon as possible. The total distance covered in 6 minutes was recorded to the nearest meter. 2.4.4 Physical Activity Assessment Physical activity levels were assessed using the International Physical Activity Questionnaire Short Form (IPAQ-SF), which has demonstrated acceptable test-retest reliability (ρ = 0.8) and criterion validity (ρ = 0.3) for assessing physical activity in adults[ 21 ]. Participants reported frequency, duration, and intensity of walking, moderate-intensity activities, and vigorous-intensity activities performed in the previous 7 days. Total physical activity, including Walking, Moderate Activity and Vigorous Activity, was calculated as MET-minutes per week using the formula specified in the IPAQ- SF, and participants were classified according to IPAQ scoring protocol as Low (< 600 MET-min/week), Moderate (600–2999 MET-min/week) or High (≥ 3000 MET-min/week). 2.4.5 Physiological Measurements Resting heart rate and blood pressure were measured using an automated sphygmomanometer (Omron HEM-7120, Kyoto, Japan), after 5 minutes of seated rest. Oxygen saturation was measured using a pulse oximeter (Nonin 9590, Plymouth, MN). All measurements were taken in triplicate, and the average value used for analysis. 2.5 Data Collection Procedure All data collection sessions were conducted in the university's Human Performance Laboratory under standardized environmental conditions (temperature 20–22°C, humidity 40–60%). Participants attended a single 2-hour assessment session during which all measurements were completed. 2.6 Statistical Analysis Data analysis was performed using IBM SPSS version 28.0 (IBM Corp., Armonk, NY). Normality of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. Descriptive statistics were calculated as mean ± standard deviation for normally distributed variables and median (interquartile range) for non-normally distributed variables. Pearson correlation coefficients were calculated to examine bivariate relationships between predictor variables and FMS scores. Variables showing significant correlations (p < 0.05) with FMS scores were entered into a stepwise multiple linear regression model with FMS total score as the dependent variable. Moderation analysis was conducted using the PROCESS macro for SPSS (Model 1) to examine whether BMI moderates the relationship between significant predictors and FMS scores[ 22 ]. All continuous variables were mean-centered to reduce multicollinearity and improve interpretation of interaction terms. Simple slope analysis was performed to probe significant interactions at ± 1 SD from the mean moderator value. 3. Results 3.1 Participant Characteristics A total of 111 male healthcare students completed all assessments (Table 1 ) with a mean age of 20.54 ± 1.16 years (range: 18–23 years). The mean BMI was 28.7 ± 3.2 kg/m², with 75 participants (67.6%) classified as overweight (BMI 25.0-29.9) and 36 participants (32.4%) classified as obese (BMI ≥ 30.0). Table 1 Participant Characteristics Characteristic Mean ± SD Range Normal Values/References Age (years) 20.54 ± 1.16 18–23 18–25 years Height (cm) 165.2 ± 8.4 148–182 — Weight (kg) 78.5 ± 12.3 58–108 — BMI (kg/m²) 28.7 ± 3.2 25.0-36.8 18.5–24.9 (normal) Waist circumference (cm) 89.4 ± 8.7 72–106 < 94 cm (men), < 80 cm (women) Hip circumference (cm) 105.8 ± 7.9 88–124 — Waist-to-Hip Ratio 0.85 ± 0.06 0.72–0.98 < 0.90 (men), < 0.85 (women) Waist-to-Height Ratio 0.54 ± 0.05 0.45–0.65 < 0.50 (normal) Systolic BP (mmHg) 118.3 ± 11.2 95–142 90–120 mmHg Diastolic BP (mmHg) 76.8 ± 8.9 60–95 60–80 mmHg Resting HR (bpm) 78.4 ± 12.1 55–105 60–100 bpm SpO₂ (%) 98.2 ± 1.4 94–100 95–100% 6MWD (meters) 542.8 ± 78.9 380–720 400–700 m (age-matched) Physical Activity (MET-min/week) 2847 ± 1205 420–6180 ≥ 600 (active) FMS Total Score 14.2 ± 2.1 9–19 ≥ 14 (low injury risk) 3.2 Movement Quality Assessment The mean FMS total score was 14.2 ± 2.1 (range: 9–19). The individual test component scores of the FMS were, deep squat 2.1 ± 0.6, hurdle step 2.3 ± 0.5, in-line lunge 2.2 ± 0.6, shoulder mobility 2.4 ± 0.7, active straight leg raise 2.0 ± 0.7, trunk stability push-up 1.8 ± 0.8, and rotary stability 1.4 ± 0.9. Based on established FMS cutoff scores, 23 participants (20.7%) scored ≤ 13, indicating high injury risk, while 15 participants (13.5%) scored ≥ 17, indicating optimal movement quality. The majority (n = 73, 65.8%) scored between 14–16, representing moderate movement quality with room for improvement. 3.3 Functional Exercise Capacity and Physical Activity The mean 6MWD was 542.8 ± 78.9 meters (range: 380–720 meters), which is below age-predicted normal values (typically 600–700 meters for this age group). Peak heart rate during the test averaged 142.3 ± 18.7 bpm (70.1 ± 9.2% of age-predicted maximum), and oxygen saturation remained stable throughout testing (pre-test: 98.2 ± 1.4%, post-test: 97.8 ± 1.6%). Physical activity levels varied considerably, with a mean of 2847 ± 1205 MET-minutes per week. According to IPAQ classifications, 28 participants (25.2%) were classified as having low physical activity (< 600 MET-min/week), 57 participants (51.4%) as moderate (600–2999 MET-min/week), and 26 participants (23.4%) as high (≥ 3000 MET-min/week). 3.4 Correlation Analysis Table 2 Correlation Matrix FMS BMI WHtR WHR 6MWD PA Level HR SpO₂ FMS 1.000*** -0.445* -0.398* -0.321* 0.742*** 0.589** -0.287 0.312* BMI -0.445* 1.000*** 0.823*** 0.756*** -0.512** -0.398* 0.234 -0.198 WHtR -0.398* 0.823*** 1.000*** 0.698** -0.456* -0.345* 0.198 -0.167 WHR -0.321* 0.756*** 0.698** 1.000*** -0.389* -0.298 0.176 -0.145 6MWD 0.742*** -0.512** -0.456* -0.389* 1.000*** 0.634** -0.345* 0.398* PA Level 0.589** -0.398* -0.345* -0.298 0.634** 1.000*** -0.298 0.287 HR -0.287 0.234 0.198 0.176 -0.345* -0.298 1.000*** -0.234 SpO₂ 0.312* -0.198 -0.167 -0.145 0.398* 0.287 -0.234 1.000*** FMS total score showed significant correlations with multiple variables (Table 2 ). The strongest positive correlations were observed with 6MWD (r = 0.742, p < 0.001, 95% CI = [0.668, 0.802]) and physical activity level (r = 0.589, p < 0.001, 95% CI = [0.484, 0.678]). Moderate positive correlations were observed with SpO₂ (r = 0.312, p < 0.001, 95% CI = [0.176, 0.436]). Age showed no significant correlation with FMS scores (r = 0.089, p = 0.356), indicating that movement quality differences were not attributable to age variations within this narrow age range. 3.5 Multiple Linear Regression Analysis Table 3 Regression Analysis Variable B (Unstandardized) 95% CI for B β (Standardized) 95% CI for β p-value Effect Size Additional Info 6MWD (meters) 0.032 [0.009, 0.039] 0.494 [0.312, 0.676] 0.002 Large Primary predictor Physical Activity Level 0.0035 [0.0015, 0.0055] 0.298 [0.156, 0.440] 0.008 Medium Secondary predictor BMI (kg/m²) -0.298 [-0.567, -0.029] -0.203 [-0.345, -0.061] 0.026 Small-Medium Significant negative Model Statistics — — — — < 0.001 Very Large (f² = 4.0) R² = 0.800, Adj R² = 0.794 The effect size for the overall model was very large (Cohen's f² = 4.0), indicating exceptional practical significance. Of the individual predictors, the 6MWD (β = 0.494, p = 0.002) was the strongest predictor, with a 95% CI of [0.312, 0.676]. For every 100-meter increase in 6MWD, FMS scores increased by approximately 2.5 points, representing a large individual effect. Physical Activity Level (β = 0.298, p = 0.008) was the second strongest predictor, with a 95% CI of [0.156, 0.440]. Higher physical activity levels were associated with better movement quality, representing a medium individual effect. BMI (β = -0.203, p = 0.026) was shown to be a significant negative predictor, with a 95% CI of [-0.345, -0.061]. 3.6Moderation Analysis Table 4 Moderation Analysis Analysis Component β (Standardized) 95% Confidence Interval p-value Interpretation Main Effect: 6MWD 0.494 [0.312, 0.676] 0.002 Strong positive predictor Main Effect: BMI -0.203 [-0.345, -0.061] 0.026 Negative predictor Interaction: 6MWD × BMI 0.187 [0.028, 0.346] 0.021 Significant moderation Model R² — — < 0.001 80.0% variance explained ΔR² (Interaction) — — 0.021 2.3% additional variance Simple Slopes Analysis : — — — Effect decreases with BMI Low BMI (-1 SD = 25.5) 0.041 [0.025, 0.057] < 0.001 Strongest effect Mean BMI (28.7) 0.033 [0.021, 0.045] < 0.001 Moderate effect High BMI (+ 1 SD = 31.9) 0.017 [0.003, 0.031] 0.014 Weakest effect BMI significantly moderated the relationship between 6MWD and FMS scores (β = 0.187, 95% CI = [0.028, 0.346], p = 0.021, ΔR² = 0.023). The interaction term added 2.3% additional variance to the model, representing a small but meaningful improvement in prediction. Simple slope analysis revealed that the positive relationship between 6MWD and FMS varied significantly across BMI levels. For Low BMI (-1 SD = 25.5 kg/m²), the values were β = 0.041, 95% CI = [0.025, 0.057], p < 0.001, for Mean BMI (28.7 kg/m²), they were β = 0.033, 95% CI = [0.021, 0.045], p < 0.001, and for High BMI (+ 1 SD = 31.9 kg/m²), β = 0.017, 95% CI = [0.003, 0.031], p = 0.014 These results indicate that the positive association between functional exercise capacity and movement quality was strongest in participants with lower BMI values and diminished as BMI increased. The Johnson-Neyman technique identified the region of significance for the moderation effect between BMI values of 24.8 and 33.2 kg/m². 3.7 Post-hoc Power Analysis With the final sample size of 111 participants and the observed large effect size (f² = 4.0), the achieved power for the multiple regression analysis was > 0.99, well exceeding the planned 80% power. This indicates that the study was adequately powered to detect the observed relationships and that the results are statistically robust. 4. Discussion 4.1 Principal Findings This study identified functional exercise capacity, as measured by the 6MWD test, as the primary determinant of movement quality in overweight and obese healthcare students. The finding that 6MWD explained nearly 50% of the variance in FMS scores (β = 0.494) represents a large effect size and highlights the fundamental importance of cardiovascular fitness and functional capacity for optimal movement patterns. Additionally, BMI significantly moderated this relationship, with the association between functional exercise capacity and movement quality being strongest in participants with lower BMI values. 4.2 Physiological Mechanisms The strong predictive value of 6MWD for movement quality likely reflects several underlying physiological mechanisms. The 6MWD test simultaneously assesses cardiorespiratory fitness, muscular endurance, and neuromuscular coordination- all components essential for optimal movement patterns[ 23 ]. Individuals with better functional exercise capacity typically demonstrate superior motor control, proprioceptive awareness, and muscle activation patterns, which are fundamental to achieving high FMS scores[ 24 ]. Healthcare students with higher functional exercise capacity likely possess better cardiovascular health, enhanced oxygen delivery to working muscles, improved fatigue resistance, and more efficient movement patterns—all factors that contribute to maintaining proper movement mechanics during functional tasks[ 25 ]. From a biomechanical perspective, the 6MWD test requires sustained activation of multiple muscle groups while maintaining postural control and movement efficiency. These same neuromuscular demands are assessed by the FMS, albeit through different movement patterns. The strong correlation suggests that individuals who can efficiently coordinate multiple systems during prolonged walking are also capable of demonstrating quality movement patterns in the fundamental movements assessed by the FMS. 4.3 BMI Moderation Effect: Clinical Significance The significant moderation effect of BMI on the 6MWD-FMS relationship represents a novel and clinically important finding. Contrary to our initial hypothesis, the results demonstrate that the positive association between functional exercise capacity and movement quality becomes weaker as BMI increases, not stronger. This finding has several important implications for understanding movement quality in overweight and obese populations. Several mechanisms may explain this moderation effect. First, individuals with higher BMI face greater biomechanical constraints during movement, which may limit the extent to which improved cardiovascular fitness can translate into better movement quality[ 26 ]. The additional mass creates altered joint loading patterns, changed center of gravity, and increased metabolic demands that may persist regardless of fitness level[ 27 ]. Second, excess adipose tissue, particularly central adiposity, may create physical barriers to optimal movement patterns. Even individuals with good cardiovascular fitness may struggle to achieve ideal movement mechanics when excess body mass interferes with joint range of motion or creates compensatory movement patterns[ 28 ]. This suggests that for individuals with higher BMI, movement quality interventions may need to address body composition in addition to cardiovascular fitness. Third, the diminishing returns observed at higher BMI levels may reflect a threshold effect, where the benefits of improved fitness become progressively smaller as biomechanical constraints increase. This finding suggests that weight management strategies may be particularly important for optimizing movement quality in obese healthcare students, even when cardiovascular fitness is adequate. 4.4 Physical Activity as a Secondary Predictor Physical activity level emerged as the second strongest predictor of movement quality (β = 0.298), which aligns with previous research demonstrating the importance of regular physical activity for maintaining optimal movement patterns[ 29 ]. However, the fact that 6MWD remained a stronger predictor suggests that functional exercise capacity may be more important than overall physical activity volume for movement quality. This distinction is clinically relevant because it suggests that interventions should focus on improving functional fitness rather than simply increasing overall activity levels. Healthcare students may benefit more from structured exercise programs that specifically target cardiovascular fitness and functional movement patterns rather than general recommendations to "be more active.” 4.5 Comparison with Previous Literature Our findings align with previous research demonstrating relationships between cardiovascular fitness and movement quality in various populations. Studies in athletic populations have consistently shown positive correlations between aerobic fitness and FMS scores, with correlation coefficients ranging from 0.45 to 0.68[ 30 ][ 31 ]. Our observed correlation of 0.742 is at the higher end of this range, suggesting that the relationship may be particularly strong in overweight and obese populations. The mean FMS score of 14.2 ± 2.1 observed in our sample is comparable to values reported in other young adult populations (13.8–15.2) but lower than those typically seen in athletic groups (16.1–17.8)[ 32 ][ 33 ]. This suggests that movement quality may indeed be compromised in overweight and obese healthcare students, supporting the need for targeted interventions. The 6MWD results (542.8 ± 78.9 meters) are below age-predicted normal values for this population, which typically range from 600–700 meters for healthy young adults[ 34 ]. This finding indicates that functional exercise capacity is indeed reduced in overweight and obese healthcare students, which may contribute to their compromised movement quality. 4.6 Clinical and Educational Implications Healthcare education programs should consider implementing both functional movement assessments (FMS) and fitness evaluations (6MWD) as part of comprehensive student health monitoring. Early identification of students with poor movement quality or low functional capacity could enable timely intervention before clinical rotations begin, potentially preventing work-related injuries and improving clinical performance. The strong predictive value of 6MWD suggests that interventions should prioritize cardiovascular fitness improvement as a primary strategy for enhancing movement quality. Students with lower 6MWD scores should be prioritized for structured exercise programs that combine cardiovascular training with movement quality exercises. The moderation effect of BMI indicates that intervention strategies should be tailored based on individual body composition. Students with higher BMI may require more comprehensive programs that address both cardiovascular fitness and weight management to achieve optimal movement quality improvements. Physical fitness and movement quality training should be integrated into healthcare curricula to prepare students for the physical demands of their future professions. This integration could include mandatory fitness courses focusing on functional exercise capacity development, movement screening programs conducted at program entry and annually thereafter, workplace ergonomics training emphasizing proper body mechanics for clinical tasks, injury prevention education based on individual FMS and fitness assessment results. Early identification and intervention for movement quality issues may prevent work-related musculoskeletal disorders, reduce healthcare costs, and improve career longevity for healthcare professionals. Given the high rates of injury and burnout in healthcare professions, proactive approaches to optimizing physical preparedness during education may yield significant long-term benefits. 4.7 Study Limitations The study was conducted at a single institution on only male healthcare students, which may limit generalizability to female healthcare students or students from different educational settings. Longitudinal or intervention studies are needed to establish causality and determine the effectiveness of targeted interventions. Physical activity data were self-reported using the IPAQ, which may be subject to recall bias and social desirability effects. The FMS, while widely used and validated, may not capture all aspects of movement quality relevant to healthcare practice, and finally, some factors like previous injury history, sleep quality and duration, stress levels, years of healthcare training, specific healthcare discipline demands and nutritional status were not assessed. These parameters are potentially able to influence movement quality and fitness levels, physical performance and movement efficiency. 4.8 Future Research Directions Future studies should longitudinally track changes in exercise capacity, BMI, and movement quality to establish causality and optimal intervention timing. Randomized controlled trials examining exercise interventions on movement quality would guide healthcare education programs. Including diverse healthcare disciplines, institutions, and cultural contexts would improve generalizability. Objective assessment of physical activity using wearables and exploration of underlying physiological and biomechanical mechanisms are recommended to inform targeted interventions. 5. Conclusion This study demonstrates that functional exercise capacity, as measured by the 6MWD test, is the primary determinant of movement quality in overweight and obese healthcare students. The exceptional effect size achieved (Cohen's f² = 4.0) indicates very strong practical significance and suggests that interventions targeting functional fitness could yield substantial improvements in movement quality. The significant moderation effect of BMI reveals that the relationship between fitness and movement quality is complex and varies according to body composition. Specifically, the benefits of improved functional exercise capacity for movement quality are greatest in individuals with lower BMI and diminish as BMI increases. This finding has important implications for tailoring interventions based on individual characteristics. The strong relationships observed between fitness, body composition, and movement quality highlight the importance of addressing physical health as a core component of healthcare professional development. By identifying and addressing movement quality deficits early in training, healthcare education programs can potentially reduce future injury risk, improve job performance, and enhance career longevity for their graduates. Future research should focus on longitudinal studies and intervention trials to establish causal relationships and develop evidence-based programs for optimizing movement quality in healthcare students. The ultimate goal should be preparing healthcare professionals who not only possess the clinical knowledge and skills necessary for patient care but also maintain the physical capacity to deliver that care safely and effectively throughout their careers. Declarations Institutional Review Board Statement The research protocol was reviewed and approved by the Ethical Research Committee of Shaqra University (Approval number – ERC_SU_F_202300004). The study was reported in accordance with the Standards for Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. Data Availability Statement The raw data supporting the conclusions of this article will be available by the authors on request. Funding Declaration The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the project (SU-ANN-202303). Clinical trial number Not applicable. Author Contribution Conceptualization, A.S., A.H. and K.A.; Data Collection, A.S., B.A., A.Z and H.M.; Methodology, A.S., A.H., K.A. and B.A.; Writing- Original Draft Preparation, A.S., K.A. and A.H.; Writing- Review & Editing, A.S., H.M., K.A. and B.A.; Supervision, A.Z. and H.M.; Project Administration, A.S. and K.A.; Funding Acquisition, A.S., K.A., A.Z. and B.A. Acknowledgement The authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the project (SU-ANN-202303). References Kugler HL, Taylor NF, Brusco NK (2024) Patient handling training interventions and musculoskeletal injuries in healthcare workers: Systematic review and meta-analysis. Heliyon 10:e24937. https://doi.org/10.1016/J.HELIYON.2024.E24937 Zaitoon RA, Said NB, Snober RH, et al (2024) Low back pain prevalence and associated factors among nurses: cross sectional study from Palestine. 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Am J Sports Med 45:725–732. https://doi.org/10.1177/0363546516641937 Kiesel K, Plisky PJ, Voight ML (2007) Can Serious Injury in Professional Football be Predicted by a Preseason Functional Movement Screen? N Am J Sports Phys Ther 2:147 Chapman RF, Laymon AS, Arnold T (2014) Functional Movement Scores and Longitudinal Performance Outcomes in Elite Track and Field Athletes. Int J Sports Physiol Perform 9:203–211. https://doi.org/10.1123/IJSPP.2012-0329 Delfa-de-la-Morena JM, Paes PP, Júnior FC, et al (2025) Relationship of Physical Activity Levels and Body Composition with Psychomotor Performance and Strength in Men. Healthcare 13:1789. https://doi.org/10.3390/HEALTHCARE13151789 Enright PL, McBurnie MA, Bittner V, et al (2003) The 6-min walk test: A quick measure of functional status in elderly adults. Chest 123:387–398. https://doi.org/10.1378/chest.123.2.387 Ross R, Blair SN, Arena R, et al (2016) Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation 134:e653–e699. https://doi.org/10.1161/CIR.0000000000000461 Czernichow S, Kengne A-P, Stamatakis E, et al (2011) Body mass index, waist circumference, and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk? Evidence from an individual-participant meta-analysis of 82,864 participants from nine cohort studies Europe PMC Funders Group. Obes Rev 12:680–687. https://doi.org/10.1111/j.1467-789X.2011.00879.x Cook G, Burton L, Hoogenboom B (2006) Pre-Participation Screening: The Use of Fundamental Movements as an Assessment of Function – Part 1. N Am J Sports Phys Ther 1:62 Gulgin H, Hoogenboom B (2014) THE FUNCTIONAL MOVEMENT SCREENING (FMS) TM : AN INTER‐RATER RELIABILITY STUDY BETWEEN RATERS OF VARIED EXPERIENCE. Int J Sports Phys Ther 9:14 Crapo RO, Casaburi R, Coates AL, et al (2002) ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med 166:111–117. https://doi.org/10.1164/AJRCCM.166.1.AT1102 Craig CL, Marshall AL, Sjöström M, et al (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35:1381–1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB Hayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis A Regression-Based Approach (Methodology in the Social Sciences) (2nd ed.). New York, NY The Guilford Press. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=3016713. Accessed 20 Oct 2025 Hulens M, Vansant G, Lysens R, et al (2001) Study of differences in peripheral muscle strength of lean versus obese women: an allometric approach. Int J Obes Relat Metab Disord 25:676–681. https://doi.org/10.1038/SJ.IJO.0801560 Wearing SC, Hennig EM, Byrne NM, et al (2006) The biomechanics of restricted movement in adult obesity. Obes Rev 7:13–24. https://doi.org/10.1111/J.1467-789X.2006.00215.X Lafortuna CL, Maffiuletti NA, Agosti F, Sartorio A (2005) Gender variations of body composition, muscle strength and power output in morbid obesity. Int J Obes (Lond) 29:833–841. https://doi.org/10.1038/SJ.IJO.0802955 Corbeil P, Simoneau M, Rancourt D, et al (2001) Increased risk for falling associated with obesity: mathematical modeling of postural control. IEEE Trans Neural Syst Rehabil Eng 9:126–136. https://doi.org/10.1109/7333.928572 Maffiuletti NA, Agosti F, Proietti M, et al (2005) Postural instability of extremely obese individuals improves after a body weight reduction program entailing specific balance training. J Endocrinol Invest 28:2–7. https://doi.org/10.1007/BF03345521 Pataky Z, Armand S, Müller-Pinget S, et al (2014) Effects of obesity on functional capacity. Obesity (Silver Spring) 22:56–62. https://doi.org/10.1002/OBY.20514 Warburton DER, Nicol CW, Bredin SSD (2006) Health benefits of physical activity: the evidence. CMAJ 174:801–809. https://doi.org/10.1503/CMAJ.051351 Parchmann CJ, McBride JM (2011) Relationship between functional movement screen and athletic performance. J Strength Cond Res 25:3378–3384. https://doi.org/10.1519/JSC.0B013E318238E916 Lockie RG, Schultz AB, Callaghan SJ, et al (2015) A preliminary investigation into the relationship between functional movement screen scores and athletic physical performance in female team sport athletes. Biol Sport 32:41–51. https://doi.org/10.5604/20831862.1127281 Schneiders DAG, Davidsson Å, Hörman E, Sullivan PSJ (2011) FUNCTIONAL MOVEMENT SCREENTM NORMATIVE VALUES IN A YOUNG, ACTIVE POPULATION. Int J Sports Phys Ther 6:75 Abraham A, Sannasi R, Nair R (2015) NORMATIVE VALUES FOR THE FUNCTIONAL MOVEMENT SCREENTM IN ADOLESCENT SCHOOL AGED CHILDREN. Int J Sports Phys Ther 10:29 Casanova C, Celli BR, Barria P, et al (2011) The 6-min walk distance in healthy subjects: reference standards from seven countries. Eur Respir J 37:150–156. https://doi.org/10.1183/09031936.00194909 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8101901","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553676602,"identity":"cb0ddafe-f833-48f2-b178-bef695643b9b","order_by":0,"name":"Ajith Soman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFCCgw3MEAbzARDJ2ECElsZmCIMtgVgtDIxQLTwGxGnhbzzc/rjgj000/+yez595GGxkNxxgf/gBnxaJA0CHzWxLy51x5+w2aR6GNOMNB3iMJfBaA9LC23A4t+FG7jZmHobDiUAtDHi1yIO08Pw5nDv/Rs5joMP+A7WwP/6BT4sBWAvb4dwNN3IYgA47ANTCYIbXFkOgltm8QL9svJFmJjnHINl45mEeMwt8WuRuHH/wmeePTe68G8mPP7ypsJPtO97++AY+LcAgQ3EnEDPjVQ8E/A2EVIyCUTAKRsGIBwAkE1Vb1DlnqwAAAABJRU5ErkJggg==","orcid":"","institution":"Shaqra University","correspondingAuthor":true,"prefix":"","firstName":"Ajith","middleName":"","lastName":"Soman","suffix":""},{"id":553676603,"identity":"d065be02-b62f-4a8a-9ff7-b6687739c4a6","order_by":1,"name":"Abdullah Ibrahim Alhusayni","email":"","orcid":"","institution":"Shaqra University","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"Ibrahim","lastName":"Alhusayni","suffix":""},{"id":553676604,"identity":"54c2720e-fdee-4949-be72-3bc721ffa44b","order_by":2,"name":"Khalid M. 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Introduction","content":"\u003cp\u003eHealthcare professionals face significant physical demands in their work environment, requiring optimal movement patterns to prevent injury and maintain professional effectiveness[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Tasks such as patient lifting, prolonged standing, repetitive movements, and emergency response procedures require high levels of functional movement quality and physical fitness[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Recent studies reporting rates of 35\u0026ndash;45% globally, significantly higher than age-matched populations in other academic disciplines[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMovement quality, defined as the ability to perform fundamental movement patterns with proper biomechanical alignment and neuromuscular control, is crucial for optimal functioning in healthcare professionals[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Poor movement quality has been consistently associated with increased risk of musculoskeletal injuries, reduced work performance, and early career burnout in healthcare settings[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Over recent decades, Saudi Arabia has undergone swift socio- cultural change as a result of economic reform. Incremental \u0026ldquo;modernization\u0026rdquo; of society and continuing development of the economy has brought along with it diet changes and sedentary lifestyle for several people in the country[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Many studies reported from Saudi Arabia reveal that healthcare students tend to be prone to obesity and overweight. Several factors such as irregular meal timings, consumption of fast food, disordered eating habits, unhealthy life style and psychological stress could cause this tendency[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Functional Movement Screen (FMS) has been seen to be a reliable and valid tool for assessing movement quality in various populations, demonstrating good inter-rater reliability (ICC\u0026thinsp;=\u0026thinsp;0.87\u0026ndash;0.98) and predictive validity for injury risk[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Previous studies have identified several factors to be potential influences on FMS performance, including body composition, cardiovascular fitness, physical activity levels, and anthropometric characteristics[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the established relationships between obesity, physical activity, and movement patterns in general populations, there remains a significant knowledge gap regarding the specific factors that influence movement quality in healthcare students who are overweight or obese. Furthermore, no previous studies have examined the potential moderating role of BMI on the relationship between functional exercise capacity and movement quality in healthcare student populations. The Six-Minute Walk Distance (6MWD) test, a well-established measure of functional exercise capacity, has shown strong correlations with movement quality in clinical populations, but its predictive value in young, overweight healthcare students remains unclear[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Keeping this research gap in mind, the present study was designed with the purpose of investigating the anthropometric and fitness variables which best predict movement quality in overweight and obese healthcare students. The study also aimed to find if BMI moderated the relationship between functional exercise capacity and movement quality in this population and explore the relative contributions of different predictor variables to overall movement quality as measured by FMS scores.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design and Participants\u003c/h2\u003e\u003cp\u003eThis cross-sectional study was conducted at the Department of Health Rehabilitation, Shaqra University between January and June 2025. Healthcare students aged 18\u0026ndash;25 years with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2; were recruited through convenience sampling from nursing, physiotherapy, and medical programs. The study received ethical approval from the Ethics Research Committee of the University (ERC_SU_F_202300004), and written informed consent was provided by all participants prior to participation.\u003c/p\u003e\u003cp\u003eMale students aged 18\u0026ndash;22 years, enrolled in nursing, physiotherapy or medical undergraduate programs who had BMI more than or equal to 25, and who were able to complete the assessment protocols were included in the study as participants. Those with acute musculoskeletal injury within the past 3 months, with chronic medical conditions affecting movement or exercise capacity (e.g., cardiovascular disease, respiratory disorders, neurological conditions), those who were on medications affecting physical performance and those who had previously participated in any formal movement screening programs were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Sample Size Calculation\u003c/h2\u003e\u003cp\u003eSample size calculation was done with G- power version 3.1 software. With α\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;0.80, medium effect size (f\u0026sup2; = 0.15), and 6 predictors, the minimum required sample was n\u0026thinsp;=\u0026thinsp;98. To account for potential dropouts and ensure adequate power for moderation analysis, we aimed to recruit 130 participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Participant Flow\u003c/h2\u003e\u003cp\u003eA total of 131 healthcare students were assessed for eligibility. Of these, 16 were excluded based on inclusion/exclusion criteria (8 due to recent injuries, 6 due to chronic conditions, and 2 due to age restrictions), and 4 dropped out during data collection due to scheduling conflicts. The final analysis was conducted on 111 participants.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Outcome Measures\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1 Anthropometric Measurements\u003c/h2\u003e\u003cp\u003eAll anthropometric measurements were conducted by trained research assistants following standardized protocols. Height was measured to the nearest 0.1 cm using a stadiometer and weight was measured to the nearest 0.1 kg using a digital scale (Tanita BC-418, Tokyo, Japan) with participants in light clothing and bare feet. BMI was calculated as weight (kg) divided by height squared (m\u0026sup2;).\u003c/p\u003e\u003cp\u003e Waist circumference was measured at the narrowest point between the lower costal margin and iliac crest using a non-elastic tape measure, following WHO guidelines. Hip circumference was measured at the widest point over the greater trochanters. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) were again calculated according to standard procedure[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Functional Movement Screen (FMS)\u003c/h2\u003e\u003cp\u003eThe FMS was administered by a certified FMS practitioner following standardized protocols established by Cook et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e][\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Before starting evaluation using the FMS participants were provided basic information about the usage of the scale, which comprises of seven patterns of movement for which both stability and mobility are required. These movement patterns are hurdle step, deep overhead squat, shoulder mobility, trunk stability, active straight leg raising, rotatory stability and in- line lunges.\u003c/p\u003e\u003cp\u003eEach movement was scored on a 0\u0026ndash;3 scale where 3 indicated performance of movement correctly without compensation, 2 indicated performance of movement with some compensation, 1 indicated inability to perform movement even with compensations, and 0 meant experiencing pain during movement. The total FMS score ranges from 0\u0026ndash;21, with higher scores indicating better movement quality. Three trials were performed for each movement, with the best score recorded. Inter-rater reliability for FMS has been reported to be good, with an ICC of 0.87\u0026ndash;0.98 in similar populations[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3 Six-Minute Walk Distance (6MWD)\u003c/h2\u003e\u003cp\u003eThe 6MWD test was conducted according to American Thoracic Society guidelines on a 30-meter straight corridor marked every 3 meters[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Participants were instructed to \"walk as far as possible in 6 minutes\" while maintaining a pace they could sustain throughout the test. Standardized encouragement was provided every minute using phrases such as \"You are doing well,\" \"Keep up the good work,\" and \"You have [time remaining] minutes left.\"\u003c/p\u003e\u003cp\u003eHeart rate was continuously monitored using a chest strap monitor (Polar H10, Kempele, Finland), and oxygen saturation was measured using a pulse oximeter (Nonin 9590, Plymouth, MN) before the test, at 3 minutes, and immediately after completion. Participants were allowed to slow down, stop, and rest if necessary, but were encouraged to resume walking as soon as possible. The total distance covered in 6 minutes was recorded to the nearest meter.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.4.4 Physical Activity Assessment\u003c/h2\u003e\u003cp\u003ePhysical activity levels were assessed using the International Physical Activity Questionnaire Short Form (IPAQ-SF), which has demonstrated acceptable test-retest reliability (ρ\u0026thinsp;=\u0026thinsp;0.8) and criterion validity (ρ\u0026thinsp;=\u0026thinsp;0.3) for assessing physical activity in adults[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Participants reported frequency, duration, and intensity of walking, moderate-intensity activities, and vigorous-intensity activities performed in the previous 7 days.\u003c/p\u003e\u003cp\u003eTotal physical activity, including Walking, Moderate Activity and Vigorous Activity, was calculated as MET-minutes per week using the formula specified in the IPAQ- SF, and participants were classified according to IPAQ scoring protocol as Low (\u0026lt;\u0026thinsp;600 MET-min/week), Moderate (600\u0026ndash;2999 MET-min/week) or High (\u0026ge;\u0026thinsp;3000 MET-min/week).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.4.5 Physiological Measurements\u003c/h2\u003e\u003cp\u003eResting heart rate and blood pressure were measured using an automated sphygmomanometer (Omron HEM-7120, Kyoto, Japan), after 5 minutes of seated rest. Oxygen saturation was measured using a pulse oximeter (Nonin 9590, Plymouth, MN). All measurements were taken in triplicate, and the average value used for analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data Collection Procedure\u003c/h2\u003e\u003cp\u003eAll data collection sessions were conducted in the university's Human Performance Laboratory under standardized environmental conditions (temperature 20\u0026ndash;22\u0026deg;C, humidity 40\u0026ndash;60%). Participants attended a single 2-hour assessment session during which all measurements were completed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eData analysis was performed using IBM SPSS version 28.0 (IBM Corp., Armonk, NY). Normality of continuous variables was assessed using the Shapiro-Wilk test and visual inspection of histograms and Q-Q plots. Descriptive statistics were calculated as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed variables and median (interquartile range) for non-normally distributed variables. Pearson correlation coefficients were calculated to examine bivariate relationships between predictor variables and FMS scores. Variables showing significant correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with FMS scores were entered into a stepwise multiple linear regression model with FMS total score as the dependent variable. Moderation analysis was conducted using the PROCESS macro for SPSS (Model 1) to examine whether BMI moderates the relationship between significant predictors and FMS scores[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. All continuous variables were mean-centered to reduce multicollinearity and improve interpretation of interaction terms. Simple slope analysis was performed to probe significant interactions at \u0026plusmn;\u0026thinsp;1 SD from the mean moderator value.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Participant Characteristics\u003c/h2\u003e\u003cp\u003eA total of 111 male healthcare students completed all assessments (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with a mean age of 20.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16 years (range: 18\u0026ndash;23 years). The mean BMI was 28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 kg/m\u0026sup2;, with 75 participants (67.6%) classified as overweight (BMI 25.0-29.9) and 36 participants (32.4%) classified as obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30.0).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipant Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNormal Values/References\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e20.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18\u0026ndash;23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeight (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e165.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148\u0026ndash;182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWeight (kg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e78.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58\u0026ndash;108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e28.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.0-36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.5\u0026ndash;24.9 (normal)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e89.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72\u0026ndash;106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;94 cm (men), \u0026lt;\u0026thinsp;80 cm (women)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHip circumference (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e105.8\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88\u0026ndash;124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist-to-Hip Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72\u0026ndash;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.90 (men), \u0026lt;\u0026thinsp;0.85 (women)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWaist-to-Height Ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.45\u0026ndash;0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.50 (normal)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e118.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95\u0026ndash;142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e90\u0026ndash;120 mmHg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiastolic BP (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e76.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60\u0026ndash;95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u0026ndash;80 mmHg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResting HR (bpm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55\u0026ndash;105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60\u0026ndash;100 bpm\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpO₂ (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95\u0026ndash;100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6MWD (meters)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e542.8\u0026thinsp;\u0026plusmn;\u0026thinsp;78.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e380\u0026ndash;720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u0026ndash;700 m (age-matched)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Activity (MET-min/week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2847\u0026thinsp;\u0026plusmn;\u0026thinsp;1205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420\u0026ndash;6180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;600 (active)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFMS Total Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u0026ndash;19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;14 (low injury risk)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Movement Quality Assessment\u003c/h2\u003e\u003cp\u003eThe mean FMS total score was 14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 (range: 9\u0026ndash;19). The individual test component scores of the FMS were, deep squat 2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, hurdle step 2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, in-line lunge 2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6, shoulder mobility 2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7, active straight leg raise 2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7, trunk stability push-up 1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8, and rotary stability 1.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9.\u003c/p\u003e\u003cp\u003eBased on established FMS cutoff scores, 23 participants (20.7%) scored\u0026thinsp;\u0026le;\u0026thinsp;13, indicating high injury risk, while 15 participants (13.5%) scored\u0026thinsp;\u0026ge;\u0026thinsp;17, indicating optimal movement quality. The majority (n\u0026thinsp;=\u0026thinsp;73, 65.8%) scored between 14\u0026ndash;16, representing moderate movement quality with room for improvement.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Functional Exercise Capacity and Physical Activity\u003c/h2\u003e\u003cp\u003eThe mean 6MWD was 542.8\u0026thinsp;\u0026plusmn;\u0026thinsp;78.9 meters (range: 380\u0026ndash;720 meters), which is below age-predicted normal values (typically 600\u0026ndash;700 meters for this age group). Peak heart rate during the test averaged 142.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.7 bpm (70.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2% of age-predicted maximum), and oxygen saturation remained stable throughout testing (pre-test: 98.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4%, post-test: 97.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6%).\u003c/p\u003e\u003cp\u003ePhysical activity levels varied considerably, with a mean of 2847\u0026thinsp;\u0026plusmn;\u0026thinsp;1205 MET-minutes per week. According to IPAQ classifications, 28 participants (25.2%) were classified as having low physical activity (\u0026lt;\u0026thinsp;600 MET-min/week), 57 participants (51.4%) as moderate (600\u0026ndash;2999 MET-min/week), and 26 participants (23.4%) as high (\u0026ge;\u0026thinsp;3000 MET-min/week).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Correlation Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFMS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWHtR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6MWD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePA Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSpO₂\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFMS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.445*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.321*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.742***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.589**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.312*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.445*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.823***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.756***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.512**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWHtR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.823***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.698**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.456*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.345*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWHR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.321*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.756***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.698**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.389*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6MWD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.742***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.512**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.456*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.389*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.634**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.345*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.398*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePA Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.589**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.345*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.634**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.345*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.234\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpO₂\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.312*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.000***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFMS total score showed significant correlations with multiple variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The strongest positive correlations were observed with 6MWD (r\u0026thinsp;=\u0026thinsp;0.742, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = [0.668, 0.802]) and physical activity level (r\u0026thinsp;=\u0026thinsp;0.589, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = [0.484, 0.678]).\u003c/p\u003e\u003cp\u003eModerate positive correlations were observed with SpO₂ (r\u0026thinsp;=\u0026thinsp;0.312, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% CI = [0.176, 0.436]). Age showed no significant correlation with FMS scores (r\u0026thinsp;=\u0026thinsp;0.089, p\u0026thinsp;=\u0026thinsp;0.356), indicating that movement quality differences were not attributable to age variations within this narrow age range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Multiple Linear Regression Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB (Unstandardized)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI for B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eβ (Standardized)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI for β\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eEffect Size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAdditional Info\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6MWD (meters)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.009, 0.039]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.312, 0.676]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePrimary predictor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhysical Activity Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.0015, 0.0055]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[0.156, 0.440]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSecondary predictor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[-0.567, -0.029]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[-0.345, -0.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSmall-Medium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSignificant negative\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel Statistics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eVery Large (f\u0026sup2; = 4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR\u0026sup2; = 0.800, Adj R\u0026sup2; = 0.794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe effect size for the overall model was very large (Cohen's f\u0026sup2; = 4.0), indicating exceptional practical significance. Of the individual predictors, the 6MWD (β\u0026thinsp;=\u0026thinsp;0.494, p\u0026thinsp;=\u0026thinsp;0.002) was the strongest predictor, with a 95% CI of [0.312, 0.676]. For every 100-meter increase in 6MWD, FMS scores increased by approximately 2.5 points, representing a large individual effect. Physical Activity Level (β\u0026thinsp;=\u0026thinsp;0.298, p\u0026thinsp;=\u0026thinsp;0.008) was the second strongest predictor, with a 95% CI of [0.156, 0.440]. Higher physical activity levels were associated with better movement quality, representing a medium individual effect. BMI (β = -0.203, p\u0026thinsp;=\u0026thinsp;0.026) was shown to be a significant negative predictor, with a 95% CI of [-0.345, -0.061].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6Moderation Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModeration Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalysis Component\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (Standardized)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMain Effect: 6MWD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.312, 0.676]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStrong positive predictor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMain Effect: BMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[-0.345, -0.061]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNegative predictor\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInteraction: 6MWD \u0026times; BMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.028, 0.346]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant moderation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel R\u0026sup2;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80.0% variance explained\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eΔR\u0026sup2; (Interaction)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3% additional variance\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSimple Slopes Analysis\u003c/b\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEffect decreases with BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow BMI (-1 SD\u0026thinsp;=\u0026thinsp;25.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.025, 0.057]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStrongest effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean BMI (28.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.021, 0.045]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModerate effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh BMI (+\u0026thinsp;1 SD\u0026thinsp;=\u0026thinsp;31.9)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e[0.003, 0.031]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeakest effect\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBMI significantly moderated the relationship between 6MWD and FMS scores (β\u0026thinsp;=\u0026thinsp;0.187, 95% CI = [0.028, 0.346], p\u0026thinsp;=\u0026thinsp;0.021, ΔR\u0026sup2; = 0.023). The interaction term added 2.3% additional variance to the model, representing a small but meaningful improvement in prediction.\u003c/p\u003e\u003cp\u003eSimple slope analysis revealed that the positive relationship between 6MWD and FMS varied significantly across BMI levels. For Low BMI (-1 SD\u0026thinsp;=\u0026thinsp;25.5 kg/m\u0026sup2;), the values were β\u0026thinsp;=\u0026thinsp;0.041, 95% CI = [0.025, 0.057], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, for Mean BMI (28.7 kg/m\u0026sup2;), they were β\u0026thinsp;=\u0026thinsp;0.033, 95% CI = [0.021, 0.045], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and for High BMI (+\u0026thinsp;1 SD\u0026thinsp;=\u0026thinsp;31.9 kg/m\u0026sup2;), β\u0026thinsp;=\u0026thinsp;0.017, 95% CI = [0.003, 0.031], p\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results indicate that the positive association between functional exercise capacity and movement quality was strongest in participants with lower BMI values and diminished as BMI increased. The Johnson-Neyman technique identified the region of significance for the moderation effect between BMI values of 24.8 and 33.2 kg/m\u0026sup2;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Post-hoc Power Analysis\u003c/h2\u003e\u003cp\u003eWith the final sample size of 111 participants and the observed large effect size (f\u0026sup2; = 4.0), the achieved power for the multiple regression analysis was \u0026gt;\u0026thinsp;0.99, well exceeding the planned 80% power. This indicates that the study was adequately powered to detect the observed relationships and that the results are statistically robust.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Principal Findings\u003c/h2\u003e\u003cp\u003eThis study identified functional exercise capacity, as measured by the 6MWD test, as the primary determinant of movement quality in overweight and obese healthcare students. The finding that 6MWD explained nearly 50% of the variance in FMS scores (β\u0026thinsp;=\u0026thinsp;0.494) represents a large effect size and highlights the fundamental importance of cardiovascular fitness and functional capacity for optimal movement patterns. Additionally, BMI significantly moderated this relationship, with the association between functional exercise capacity and movement quality being strongest in participants with lower BMI values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Physiological Mechanisms\u003c/h2\u003e\u003cp\u003eThe strong predictive value of 6MWD for movement quality likely reflects several underlying physiological mechanisms. The 6MWD test simultaneously assesses cardiorespiratory fitness, muscular endurance, and neuromuscular coordination- all components essential for optimal movement patterns[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Individuals with better functional exercise capacity typically demonstrate superior motor control, proprioceptive awareness, and muscle activation patterns, which are fundamental to achieving high FMS scores[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHealthcare students with higher functional exercise capacity likely possess better cardiovascular health, enhanced oxygen delivery to working muscles, improved fatigue resistance, and more efficient movement patterns\u0026mdash;all factors that contribute to maintaining proper movement mechanics during functional tasks[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a biomechanical perspective, the 6MWD test requires sustained activation of multiple muscle groups while maintaining postural control and movement efficiency. These same neuromuscular demands are assessed by the FMS, albeit through different movement patterns. The strong correlation suggests that individuals who can efficiently coordinate multiple systems during prolonged walking are also capable of demonstrating quality movement patterns in the fundamental movements assessed by the FMS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.3 BMI Moderation Effect: Clinical Significance\u003c/h2\u003e\u003cp\u003eThe significant moderation effect of BMI on the 6MWD-FMS relationship represents a novel and clinically important finding. Contrary to our initial hypothesis, the results demonstrate that the positive association between functional exercise capacity and movement quality becomes weaker as BMI increases, not stronger. This finding has several important implications for understanding movement quality in overweight and obese populations.\u003c/p\u003e\u003cp\u003eSeveral mechanisms may explain this moderation effect. First, individuals with higher BMI face greater biomechanical constraints during movement, which may limit the extent to which improved cardiovascular fitness can translate into better movement quality[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The additional mass creates altered joint loading patterns, changed center of gravity, and increased metabolic demands that may persist regardless of fitness level[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, excess adipose tissue, particularly central adiposity, may create physical barriers to optimal movement patterns. Even individuals with good cardiovascular fitness may struggle to achieve ideal movement mechanics when excess body mass interferes with joint range of motion or creates compensatory movement patterns[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This suggests that for individuals with higher BMI, movement quality interventions may need to address body composition in addition to cardiovascular fitness.\u003c/p\u003e\u003cp\u003eThird, the diminishing returns observed at higher BMI levels may reflect a threshold effect, where the benefits of improved fitness become progressively smaller as biomechanical constraints increase. This finding suggests that weight management strategies may be particularly important for optimizing movement quality in obese healthcare students, even when cardiovascular fitness is adequate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Physical Activity as a Secondary Predictor\u003c/h2\u003e\u003cp\u003ePhysical activity level emerged as the second strongest predictor of movement quality (β\u0026thinsp;=\u0026thinsp;0.298), which aligns with previous research demonstrating the importance of regular physical activity for maintaining optimal movement patterns[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the fact that 6MWD remained a stronger predictor suggests that functional exercise capacity may be more important than overall physical activity volume for movement quality.\u003c/p\u003e\u003cp\u003eThis distinction is clinically relevant because it suggests that interventions should focus on improving functional fitness rather than simply increasing overall activity levels. Healthcare students may benefit more from structured exercise programs that specifically target cardiovascular fitness and functional movement patterns rather than general recommendations to \"be more active.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Comparison with Previous Literature\u003c/h2\u003e\u003cp\u003eOur findings align with previous research demonstrating relationships between cardiovascular fitness and movement quality in various populations. Studies in athletic populations have consistently shown positive correlations between aerobic fitness and FMS scores, with correlation coefficients ranging from 0.45 to 0.68[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Our observed correlation of 0.742 is at the higher end of this range, suggesting that the relationship may be particularly strong in overweight and obese populations.\u003c/p\u003e\u003cp\u003eThe mean FMS score of 14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 observed in our sample is comparable to values reported in other young adult populations (13.8\u0026ndash;15.2) but lower than those typically seen in athletic groups (16.1\u0026ndash;17.8)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e][\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This suggests that movement quality may indeed be compromised in overweight and obese healthcare students, supporting the need for targeted interventions.\u003c/p\u003e\u003cp\u003eThe 6MWD results (542.8\u0026thinsp;\u0026plusmn;\u0026thinsp;78.9 meters) are below age-predicted normal values for this population, which typically range from 600\u0026ndash;700 meters for healthy young adults[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This finding indicates that functional exercise capacity is indeed reduced in overweight and obese healthcare students, which may contribute to their compromised movement quality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Clinical and Educational Implications\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eHealthcare education programs should consider implementing both functional movement assessments (FMS) and fitness evaluations (6MWD) as part of comprehensive student health monitoring. Early identification of students with poor movement quality or low functional capacity could enable timely intervention before clinical rotations begin, potentially preventing work-related injuries and improving clinical performance.\u003c/p\u003e\u003cp\u003eThe strong predictive value of 6MWD suggests that interventions should prioritize cardiovascular fitness improvement as a primary strategy for enhancing movement quality. Students with lower 6MWD scores should be prioritized for structured exercise programs that combine cardiovascular training with movement quality exercises. The moderation effect of BMI indicates that intervention strategies should be tailored based on individual body composition. Students with higher BMI may require more comprehensive programs that address both cardiovascular fitness and weight management to achieve optimal movement quality improvements.\u003c/p\u003e\u003cp\u003ePhysical fitness and movement quality training should be integrated into healthcare curricula to prepare students for the physical demands of their future professions. This integration could include mandatory fitness courses focusing on functional exercise capacity development, movement screening programs conducted at program entry and annually thereafter, workplace ergonomics training emphasizing proper body mechanics for clinical tasks, injury prevention education based on individual FMS and fitness assessment results.\u003c/p\u003e\u003cp\u003eEarly identification and intervention for movement quality issues may prevent work-related musculoskeletal disorders, reduce healthcare costs, and improve career longevity for healthcare professionals. Given the high rates of injury and burnout in healthcare professions, proactive approaches to optimizing physical preparedness during education may yield significant long-term benefits.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e4.7 Study Limitations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe study was conducted at a single institution on only male healthcare students, which may limit generalizability to female healthcare students or students from different educational settings. Longitudinal or intervention studies are needed to establish causality and determine the effectiveness of targeted interventions. Physical activity data were self-reported using the IPAQ, which may be subject to recall bias and social desirability effects. The FMS, while widely used and validated, may not capture all aspects of movement quality relevant to healthcare practice, and finally, some factors like previous injury history, sleep quality and duration, stress levels, years of healthcare training, specific healthcare discipline demands and nutritional status were not assessed. These parameters are potentially able to influence movement quality and fitness levels, physical performance and movement efficiency.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e4.8 Future Research Directions\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFuture studies should longitudinally track changes in exercise capacity, BMI, and movement quality to establish causality and optimal intervention timing. Randomized controlled trials examining exercise interventions on movement quality would guide healthcare education programs. Including diverse healthcare disciplines, institutions, and cultural contexts would improve generalizability. Objective assessment of physical activity using wearables and exploration of underlying physiological and biomechanical mechanisms are recommended to inform targeted interventions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that functional exercise capacity, as measured by the 6MWD test, is the primary determinant of movement quality in overweight and obese healthcare students. The exceptional effect size achieved (Cohen\u0026apos;s f\u0026sup2; = 4.0) indicates very strong practical significance and suggests that interventions targeting functional fitness could yield substantial improvements in movement quality. The significant moderation effect of BMI reveals that the relationship between fitness and movement quality is complex and varies according to body composition. Specifically, the benefits of improved functional exercise capacity for movement quality are greatest in individuals with lower BMI and diminish as BMI increases. This finding has important implications for tailoring interventions based on individual characteristics. The strong relationships observed between fitness, body composition, and movement quality highlight the importance of addressing physical health as a core component of healthcare professional development. By identifying and addressing movement quality deficits early in training, healthcare education programs can potentially reduce future injury risk, improve job performance, and enhance career longevity for their graduates.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on longitudinal studies and intervention trials to establish causal relationships and develop evidence-based programs for optimizing movement quality in healthcare students. The ultimate goal should be preparing healthcare professionals who not only possess the clinical knowledge and skills necessary for patient care but also maintain the physical capacity to deliver that care safely and effectively throughout their careers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol was reviewed and approved by the Ethical Research Committee of Shaqra University (Approval number \u0026ndash; ERC_SU_F_202300004). The study was reported in accordance with the Standards for Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be available by the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the project (SU-ANN-202303).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, A.S., A.H. and K.A.; Data Collection, A.S., B.A., A.Z and H.M.; Methodology, A.S., A.H., K.A. and B.A.; Writing- Original Draft Preparation, A.S., K.A. and A.H.; Writing- Review \u0026amp; Editing, A.S., H.M., K.A. and B.A.; Supervision, A.Z. and H.M.; Project Administration, A.S. and K.A.; Funding Acquisition, A.S., K.A., A.Z. and B.A.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors extend their appreciation to the Deanship of Scientific Research at Shaqra University for funding this research work through the project (SU-ANN-202303).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKugler HL, Taylor NF, Brusco NK (2024) Patient handling training interventions and musculoskeletal injuries in healthcare workers: Systematic review and meta-analysis. Heliyon 10:e24937. https://doi.org/10.1016/J.HELIYON.2024.E24937\u003c/li\u003e\n\u003cli\u003eZaitoon RA, Said NB, Snober RH, et al (2024) Low back pain prevalence and associated factors among nurses: cross sectional study from Palestine. BMC Public Health 24:3076. https://doi.org/10.1186/S12889-024-20481-1\u003c/li\u003e\n\u003cli\u003eMakkawy E, Alrakha AM, Al-Mubarak AF, et al (2021) Prevalence of overweight and obesity and their associated factors among health sciences college students, Saudi Arabia. J Fam Med Prim Care 10:961. https://doi.org/10.4103/JFMPC.JFMPC_1749_20\u003c/li\u003e\n\u003cli\u003eDiani YH, Novelyn S, Cing JM, et al (2023) Prevalence of Overweight and Obesity among Medical Students across Indonesia: A Literature Review. Asian J Biol 18:34\u0026ndash;45. https://doi.org/10.9734/AJOB/2023/V18I2341\u003c/li\u003e\n\u003cli\u003eShafiee A, Nakhaee Z, Bahri RA, et al (2024) Global prevalence of obesity and overweight among medical students: a systematic review and meta-analysis. BMC Public Health 24:1673. https://doi.org/10.1186/S12889-024-19184-4\u003c/li\u003e\n\u003cli\u003eCook G, Burton L, Hoogenboom BJ, Voight M (2014) FUNCTIONAL MOVEMENT SCREENING: THE USE OF FUNDAMENTAL MOVEMENTS AS AN ASSESSMENT OF FUNCTION ‐ PART 1. Int J Sports Phys Ther 9:396\u003c/li\u003e\n\u003cli\u003eSousa AD, Baixinho CL, Presado MH, Henriques MA (2023) The Effect of Interventions on Preventing Musculoskeletal Injuries Related to Nurses Work: Systematic Review. J Pers Med 13:185. https://doi.org/10.3390/JPM13020185\u003c/li\u003e\n\u003cli\u003eNgan K, Drebit S, Siow S, et al (2010) Risks and causes of musculoskeletal injuries among health care workers. Occup Med (Chic Ill) 60:389\u0026ndash;394. https://doi.org/10.1093/OCCMED/KQQ052\u003c/li\u003e\n\u003cli\u003eAl-Rethaiaa AS, Fahmy AEA, Al-Shwaiyat NM (2010) Obesity and eating habits among college students in Saudi Arabia: A cross sectional study. Nutr J 9:1\u0026ndash;10. https://doi.org/10.1186/1475-2891-9-39/TABLES/9\u003c/li\u003e\n\u003cli\u003eAhmad A, Elbadawi NE, Osman MS, Elmahdi EM The Prevalence and Risk Factors of Obesity among Medical Students at Shaqra University, Saudi Arabia. Ann Med Health Sci Res\u003c/li\u003e\n\u003cli\u003eBonazza NA, Smuin D, Onks CA, et al (2017) Reliability, Validity, and Injury Predictive Value of the Functional Movement Screen: A Systematic Review and Meta-analysis. Am J Sports Med 45:725\u0026ndash;732. https://doi.org/10.1177/0363546516641937\u003c/li\u003e\n\u003cli\u003eKiesel K, Plisky PJ, Voight ML (2007) Can Serious Injury in Professional Football be Predicted by a Preseason Functional Movement Screen? N Am J Sports Phys Ther 2:147\u003c/li\u003e\n\u003cli\u003eChapman RF, Laymon AS, Arnold T (2014) Functional Movement Scores and Longitudinal Performance Outcomes in Elite Track and Field Athletes. Int J Sports Physiol Perform 9:203\u0026ndash;211. https://doi.org/10.1123/IJSPP.2012-0329\u003c/li\u003e\n\u003cli\u003eDelfa-de-la-Morena JM, Paes PP, J\u0026uacute;nior FC, et al (2025) Relationship of Physical Activity Levels and Body Composition with Psychomotor Performance and Strength in Men. Healthcare 13:1789. https://doi.org/10.3390/HEALTHCARE13151789\u003c/li\u003e\n\u003cli\u003eEnright PL, McBurnie MA, Bittner V, et al (2003) The 6-min walk test: A quick measure of functional status in elderly adults. Chest 123:387\u0026ndash;398. https://doi.org/10.1378/chest.123.2.387\u003c/li\u003e\n\u003cli\u003eRoss R, Blair SN, Arena R, et al (2016) Importance of Assessing Cardiorespiratory Fitness in Clinical Practice: A Case for Fitness as a Clinical Vital Sign: A Scientific Statement From the American Heart Association. Circulation 134:e653\u0026ndash;e699. https://doi.org/10.1161/CIR.0000000000000461\u003c/li\u003e\n\u003cli\u003eCzernichow S, Kengne A-P, Stamatakis E, et al (2011) Body mass index, waist circumference, and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk? Evidence from an individual-participant meta-analysis of 82,864 participants from nine cohort studies Europe PMC Funders Group. Obes Rev 12:680\u0026ndash;687. https://doi.org/10.1111/j.1467-789X.2011.00879.x\u003c/li\u003e\n\u003cli\u003eCook G, Burton L, Hoogenboom B (2006) Pre-Participation Screening: The Use of Fundamental Movements as an Assessment of Function \u0026ndash; Part 1. N Am J Sports Phys Ther 1:62\u003c/li\u003e\n\u003cli\u003eGulgin H, Hoogenboom B (2014) THE FUNCTIONAL MOVEMENT SCREENING (FMS)\u003csup\u003eTM\u003c/sup\u003e: AN INTER‐RATER RELIABILITY STUDY BETWEEN RATERS OF VARIED EXPERIENCE. Int J Sports Phys Ther 9:14\u003c/li\u003e\n\u003cli\u003eCrapo RO, Casaburi R, Coates AL, et al (2002) ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med 166:111\u0026ndash;117. https://doi.org/10.1164/AJRCCM.166.1.AT1102\u003c/li\u003e\n\u003cli\u003eCraig CL, Marshall AL, Sj\u0026ouml;str\u0026ouml;m M, et al (2003) International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 35:1381\u0026ndash;1395. https://doi.org/10.1249/01.MSS.0000078924.61453.FB\u003c/li\u003e\n\u003cli\u003eHayes, A. F. (2018). Introduction to Mediation, Moderation, and Conditional Process Analysis A Regression-Based Approach (Methodology in the Social Sciences) (2nd ed.). New York, NY The Guilford Press. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=3016713. Accessed 20 Oct 2025\u003c/li\u003e\n\u003cli\u003eHulens M, Vansant G, Lysens R, et al (2001) Study of differences in peripheral muscle strength of lean versus obese women: an allometric approach. Int J Obes Relat Metab Disord 25:676\u0026ndash;681. https://doi.org/10.1038/SJ.IJO.0801560\u003c/li\u003e\n\u003cli\u003eWearing SC, Hennig EM, Byrne NM, et al (2006) The biomechanics of restricted movement in adult obesity. Obes Rev 7:13\u0026ndash;24. https://doi.org/10.1111/J.1467-789X.2006.00215.X\u003c/li\u003e\n\u003cli\u003eLafortuna CL, Maffiuletti NA, Agosti F, Sartorio A (2005) Gender variations of body composition, muscle strength and power output in morbid obesity. Int J Obes (Lond) 29:833\u0026ndash;841. https://doi.org/10.1038/SJ.IJO.0802955\u003c/li\u003e\n\u003cli\u003eCorbeil P, Simoneau M, Rancourt D, et al (2001) Increased risk for falling associated with obesity: mathematical modeling of postural control. IEEE Trans Neural Syst Rehabil Eng 9:126\u0026ndash;136. https://doi.org/10.1109/7333.928572\u003c/li\u003e\n\u003cli\u003eMaffiuletti NA, Agosti F, Proietti M, et al (2005) Postural instability of extremely obese individuals improves after a body weight reduction program entailing specific balance training. J Endocrinol Invest 28:2\u0026ndash;7. https://doi.org/10.1007/BF03345521\u003c/li\u003e\n\u003cli\u003ePataky Z, Armand S, M\u0026uuml;ller-Pinget S, et al (2014) Effects of obesity on functional capacity. Obesity (Silver Spring) 22:56\u0026ndash;62. https://doi.org/10.1002/OBY.20514\u003c/li\u003e\n\u003cli\u003eWarburton DER, Nicol CW, Bredin SSD (2006) Health benefits of physical activity: the evidence. CMAJ 174:801\u0026ndash;809. https://doi.org/10.1503/CMAJ.051351\u003c/li\u003e\n\u003cli\u003eParchmann CJ, McBride JM (2011) Relationship between functional movement screen and athletic performance. J Strength Cond Res 25:3378\u0026ndash;3384. https://doi.org/10.1519/JSC.0B013E318238E916\u003c/li\u003e\n\u003cli\u003eLockie RG, Schultz AB, Callaghan SJ, et al (2015) A preliminary investigation into the relationship between functional movement screen scores and athletic physical performance in female team sport athletes. Biol Sport 32:41\u0026ndash;51. https://doi.org/10.5604/20831862.1127281\u003c/li\u003e\n\u003cli\u003eSchneiders DAG, Davidsson \u0026Aring;, H\u0026ouml;rman E, Sullivan PSJ (2011) FUNCTIONAL MOVEMENT SCREENTM NORMATIVE VALUES IN A YOUNG, ACTIVE POPULATION. Int J Sports Phys Ther 6:75\u003c/li\u003e\n\u003cli\u003eAbraham A, Sannasi R, Nair R (2015) NORMATIVE VALUES FOR THE FUNCTIONAL MOVEMENT SCREENTM IN ADOLESCENT SCHOOL AGED CHILDREN. Int J Sports Phys Ther 10:29\u003c/li\u003e\n\u003cli\u003eCasanova C, Celli BR, Barria P, et al (2011) The 6-min walk distance in healthy subjects: reference standards from seven countries. Eur Respir J 37:150\u0026ndash;156. https://doi.org/10.1183/09031936.00194909\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Movement quality, healthcare students, obesity, functional movement screen, exercise capacity","lastPublishedDoi":"10.21203/rs.3.rs-8101901/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8101901/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHealthcare students demonstrate high prevalence of overweight and obesity, which may compromise movement quality essential for their professional demands. Poor movement patterns in healthcare professionals are associated with increased injury risk and reduced work performance. This study aimed to investigate factors influencing movement quality in overweight and obese healthcare students in order to identify potential candidates for intervention.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross- sectional study was carried out on 111 young male healthcare students with BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u0026sup2;. Participants underwent comprehensive assessments including anthropometric measurements, Functional Movement Screen (FMS), Six-Minute Walk Distance (6MWD) test, and physical activity evaluation using the International Physical Activity Questionnaire (IPAQ).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean FMS score was 14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1. The final regression model explained 80% of variance in FMS scores (R\u0026sup2; = 0.800, F (3,107)\u0026thinsp;=\u0026thinsp;142.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing a very large effect size (Cohen's f\u0026sup2; = 4.0). 6MWD emerged as the strongest predictor, followed by physical activity level, and BMI. BMI significantly moderated the relationship between 6MWD and FMS (β\u0026thinsp;=\u0026thinsp;0.187, p\u0026thinsp;=\u0026thinsp;0.021), with stronger associations observed in participants with lower BMI values.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eFunctional exercise capacity, as measured by 6MWD, is the primary determinant of movement quality in overweight and obese healthcare students. The moderating effect of BMI suggests that targeted interventions should prioritize cardiovascular fitness improvement, particularly for students with higher BMI. These findings support the implementation of fitness and movement quality programs in healthcare education to better prepare students for their professional demands.\u003c/p\u003e","manuscriptTitle":"Determinants of Movement Quality in Obese and Overweight Healthcare Students: A Cross-Sectional Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 11:47:52","doi":"10.21203/rs.3.rs-8101901/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-13T05:49:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T01:00:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T04:57:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T13:32:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T06:54:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100487053610795628414089442106332747666","date":"2025-12-30T17:06:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284853353905360128937057902744280286411","date":"2025-12-29T17:49:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159753221080123108449713120761574300220","date":"2025-12-27T13:59:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115769332708436410398239501866742913280","date":"2025-12-27T13:22:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195013260252605573099973869978229637140","date":"2025-12-27T12:24:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T21:00:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-15T07:37:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-15T07:35:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-11-13T05:50:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98d69757-cfc5-41ce-a217-7c2ee0409113","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:04:59+00:00","versionOfRecord":{"articleIdentity":"rs-8101901","link":"https://doi.org/10.1186/s12982-026-01585-y","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2026-02-22 15:59:22","publishedOnDateReadable":"February 22nd, 2026"},"versionCreatedAt":"2025-12-03 11:47:52","video":"","vorDoi":"10.1186/s12982-026-01585-y","vorDoiUrl":"https://doi.org/10.1186/s12982-026-01585-y","workflowStages":[]},"version":"v1","identity":"rs-8101901","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8101901","identity":"rs-8101901","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}