Influence of exercise interventions on functional movement screen scores in athletes: a systematic review and meta-analysis

Abstract

The functional movement screen (FMS) is a widely recognized tool for evaluating movement patterns and identifying potential injury risks in athletes. This systematic review and meta-analysis aimed to assess the effectiveness of various exercise interventions in enhancing FMS scores, thus shedding light on their potential roles in optimizing movement quality and reducing injury risk. We conducted comprehensive searches of PubMed, Web of Science, Scopus, and Embase (through May 2025) to identify experimental studies reporting pre–post changes in FMS after structured exercise interventions. Two reviewers independently screened and extracted data, assessed study quality using the PEDro scale and ROBINS-I tool, rated evidence certainty via GRADE, and then performed random-effects meta-analyses in RevMan 5.4 to calculate pooled mean differences (MD) with 95% confidence intervals (CI). Forty trials encompassing 1,604 athletes met the inclusion criteria. Pooled analyses revealed moderate-certainty improvements in FMS scores for resistance training (MD = 2.21; 95% CI 1.82–2.61; P < 0.001; I² = 18%), integrated training (MD = 1.92; 1.57–2.27; P < 0.001; I² = 13%), neuromuscular training (MD = 2.31; 1.64–2.98; P < 0.001; I² = 0%), core stability training (MD = 2.89; 2.46–3.32; P < 0.001; I² = 45%), and functional training (MD = 1.74; 1.34–2.13; P < 0.001; I² = 0%). In contrast, the FIFA 11 + warm-up had a smaller effect (MD = 1.69; 0.79–2.60; P < 0.001), with high heterogeneity (I² = 84%) and very low certainty. Targeted exercise interventions, particularly those focused on resistance, neuromuscular, and core stability training, effectively increase FMS scores, which may improve movement quality and lower injury risk. However, caution is warranted when linking FMS improvements directly to performance outcomes, and further research is needed to determine how these interventions benefit specific athletic populations, ranging from youth to professional athletes.

Introduction

In recent years, the demands placed on athletes have intensified as the drive for peak performance and injury prevention has become paramount in sports science¹. One widely used tool for assessing the quality of movement and identifying potential injury risks is the functional movement screen (FMS)^2,3. The FMS is a standardized assessment protocol comprising seven fundamental movement patterns, such as the deep squat, hurdle step, inline lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability, which are scored on a 0–3 scale⁴. These tests play crucial roles in athletic performance by identifying movement limitations and asymmetries that can hinder athletic potential and increase injury risk. By pinpointing these movement dysfunctions, FMS allows for targeted corrective exercises, optimizes movement patterns, and ultimately enhances athletic performance⁵.

The FMS not only predicts injury risk, where lower composite scores correlate with up to a 2.7‐fold higher likelihood of lower-limb injury in athletes⁵—but also shows moderate associations with sprint and jump performance in soccer and basketball players⁶. However, no meta‐analysis to date has synthesized how structured exercise interventions modify FMS in diverse athletic cohorts, making our work the first to quantify training-induced improvements on this clinically relevant 21-point FMS scale.

Despite its extensive use both in clinical practice and in sports settings, the optimal methods for enhancing FMS scores remain unclear. Numerous studies have investigated the effects of various exercise interventions, ranging from resistance and integrated training to neuromuscular training, core stability training, and functional training, on movement quality as measured by the FMS^7–10. However, previous reviews—such as Clark et al. (2022), who qualitatively summarized five ‘high‐risk’ athlete studies without pooling effect sizes, and Kraus et al. (2014), who reviewed eight interventions but lacked sport‐specific subgroup analyses—have been limited by small article numbers, the absence of quantitative meta‐analyses, and the minimal exploration of between‐sport or age‐group differences^11,12, leaving a critical gap in our understanding regarding which specific modalities offer the greatest benefit. There is a particular need for a comprehensive meta-analytical approach that not only synthesizes data across diverse training programs but also critically examines methodological discrepancies and highlights the practical implications for designing targeted interventions.

The current review seeks to address these gaps by systematically evaluating and quantitatively synthesizing the evidence on how different exercise interventions impact FMS scores in athletic populations. In doing so, this study aims to clarify the relative effectiveness of interventions that improve movement quality, thereby offering practical guidance for coaches, rehabilitation professionals, and sports scientists. By differentiating between modalities, such as integrated training that combines strength, flexibility, and balance; neuromuscular training focused on proprioception and coordination; and functional training that replicates the demands of specific sports, focusing on this aspect is a valuable contribution. This meta-analysis endeavors to inform evidence-based practice and optimize training protocols for injury prevention and performance enhancement.

Method

This systematic review was conducted according to the PRISMA guidelines for systematic reviews¹³ and was prospectively registered in the International Prospective Register of Systematic Reviews with Registration Number: CRD42024590981.

Search strategy

A comprehensive search was conducted across multiple scientific literature databases, including PubMed, Web of Science, Scopus, and Embase, from their inception until May 2025. The search strategy utilized the terms for the FMS component: "Functional Movement Screen" OR FMS.

For the exercise intervention component, exercise OR interventions OR training OR programs OR “resistance training” OR “integrated training” OR “neuromuscular training” OR "core stability training" OR “functional training” OR "FIFA 11 + ".

For the athlete component: athlete OR players.

Furthermore, reference lists from previously related studies examining the impact of exercise interventions on FMS were manually reviewed to ensure the identification of all relevant research. The detailed search strings for each database are provided in Table S5.

Eligibility criteria

We defined our eligibility via the PICOS framework¹⁴:

Inclusion criteria

• • Population: Athletes of any age or sport.

• • Intervention: Any organized exercise program (e.g., resistance, integrated, neuromuscular, core‐stability, functional, etc.)

• • Comparator: Pre‐ versus post‐intervention assessment.

• • Outcomes: Fundamental Movement Skill (FMS) scores.

• • Study Design: Experimental trials (randomized or nonrandomized controlled) published in English.

Exclusion criteria

• • Studies limited to non‐exercise treatments (e.g., manual therapy alone).

• • Non‐athlete samples.

• • Retrospective or cross‐sectional designs.

• • Non‐English publications.

• • Insufficient reporting of FMS outcomes.

Study selection

The titles, abstracts, and full texts of the studies meeting the inclusion criteria were evaluated by AAM and MAB. In instances of disagreement, the two authors discussed the matter to reach a resolution. If they could not reach a consensus, a third reviewer (SHM) was consulted for assistance.

Quality assessment

Two independent reviewers assessed the quality of the studies via the PEDro scale, which is based on ten criteria. Any disagreements in the ratings were resolved through discussion or, if necessary, adjudicated by a third reviewer¹⁵. Studies scoring 9 or 10 were categorized as “high” quality, those with scores between 6 and 8 were considered "moderate," those with scores of 4 or 5 were labeled "fair," and those with scores below 4 were deemed "poor." We selected the PEDro scale because it is one of the most widely used and validated instruments for assessing the methodological quality of randomized controlled trials in physical therapy and sports medicine^15,16. For non-randomized intervention studies, we applied the ROBINS-I tool—comprising seven domains and 34 signaling questions—to gauge bias, with each domain and the overall study rated as low, moderate, serious, critical, or lacking sufficient information¹⁷.

Data collection

Data collection was carried out through a thorough process in which the AAM extracted all pertinent data from the selected studies, whereas the SHM verified the accuracy of this information. Specifically, FMS scores were gathered and organized on the basis of the type of intervention to maintain consistency in both the results and discussion sections. Additionally, the systematic extraction included details related to the type of study, type of sport, training interventions, training duration, number of sessions, participant demographics, and characteristics such as age and sex from the included studies.

Handling of missing or incomplete data

During data extraction, studies with missing or incomplete information were noted. When key outcome data were missing, attempts were made to contact the corresponding authors to obtain the necessary details. If no response was received, specific analytical adjustments were performed, such as sensitivity analyses or, where appropriate, imputation via available statistical methods.

Synthesis of results

All analyses were carried out in RevMan 5.4 via two-tailed random effects models to accommodate anticipated variability in intervention protocols, participant characteristics, and assessment timing. We pooled raw mean differences (MD) with 95% confidence intervals (CI) to preserve the original 0–21 FMS units and maximize clinical interpretability. Prior to pooling, we confirmed that all studies employed the same FMS scoring scale, obviating the need for standardized mean differences; meta-analysis was restricted to outcomes reported by at least two studies with comparable methods¹⁸.

Results Statistical heterogeneity was quantified with the I² statistic and its chi-square p value using conventional thresholds (< 25% minimal, 25–50% low, 50–75% moderate, > 75% high). In cases of moderate or greater inconsistency (I² > 50%), we conducted predefined subgroup analyses by sport—most notably isolating soccer cohorts—to explore sources of variance. Publication bias was evaluated for any outcome with ≥ 10 studies via funnel-plot symmetry; if asymmetry suggested small-study effects, we applied the trim-and-fill method to generate bias-adjusted MD estimates.

Each pooled effect was then rated under the GRADE framework across five domains—risk of bias (PEDro scores), inconsistency (I²² and CI overlap), indirectness (PICO alignment), imprecision (CI width and total N), and publication bias—resulting in certainty ratings ranging from “high” to “very low”^19–21 (see Supplementary Table S4 for detailed justifications).

Study selection

An initial search across four databases retrieved 740 records (83 from PubMed, 256 from Web of Science, 309 from Scopus, and 92 from Embase). Once duplicates were removed, 308 unique records remained. Screening against our inclusion criteria led to the exclusion of 268 studies, leaving 40 English-language articles that satisfied all eligibility requirements.

Figure 1 shows the flow diagram of the selection process and the number of excluded studies at each stage.

Fig. 1.

PRISMA flow diagram.

Characteristics of the included studies

The total sample size across the included studies was 1,604 participants. These studies were published between 2011 and 2025, with a notable concentration in recent years; 2019 was the most prominent year at 18.4%, and a substantial 50% of the studies occurred between 2020 and 2025. A total of 52.5% of the studies used the RCT methodology. The demographic analysis revealed that the average age of the athletes was 19 years, with males comprising 66.7% of the participants. Among these athletes, soccer players made up 29%, and basketball players accounted for 23.7%. In terms of training methods, functional training was the most prevalent at 20%. The typical duration of these training interventions was approximately 9 weeks. The details of all the studies are presented in Table S1.

Quality assessment

Applying the PEDro scale to randomized trials, 21 studies fell into the moderate-quality bracket (scores 6–8), and one study achieved high quality (scores 9–10). The mean PEDro score was 7.1, and interrater reliability was substantial (Cohen’s κ = 0.82). For non‐randomized studies, the ROBINS-I tool indicated an overall moderate risk of bias. Full item-level ratings for all studies are provided in Tables S2 and S3.

Publication bias assessment

To evaluate the potential influence of publication bias on our meta-analytic findings, we generated a funnel plot (see Fig. S1) that displays the effect sizes (expressed as the mean differences in FMS scores) against their corresponding standard errors. Visual inspection of the funnel plot revealed a symmetrical distribution of studies around the overall pooled effect size. This symmetry suggests that smaller studies, which typically have greater variability, are not disproportionately skewed toward positive results; therefore, the risk of publication bias appears minimal.

Types of interventions

Seven studies implemented resistance training^8,22–27, whereas five studies utilized integrated training^28–32. Five studies focused on the FIFA 11 + program^33–37, and three studies incorporated neuromuscular training^10,38,39. Additionally, seven studies employed core stability training^7,40–45, eight studies used functional training programs^9,46–52, and five studies featured other exercise interventions^53–57.

Effects of resistance training on FMS

Seven studies^8,22–27 investigated the influence of resistance‐based protocols on FMS scores in mixed martial artists, soccer, basketball, and kho-kho athletes over intervention periods spanning 2–15 weeks (mean 7 weeks). Across these studies, 194 athletes (mean age 19.3 years) were assessed. Every trial reported statistically significant post-training increases in composite FMS scores. Pooled analysis (Fig. 2) yielded a mean difference (MD) of 2.21 (95% CI 1.82–2.61; p < 0.001) with low heterogeneity (I² = 18%), supporting moderate‐certainty evidence.

Fig. 2.

Results of meta-analysis.

Effects of integrated training on FMS

Five studies^28–32 examined combined strength, flexibility, and balance interventions in Gaelic footballers, basketballers, soccer players, and netball players over 4–20 weeks (mean 8 weeks). The cumulative sample comprised 214 athletes (mean age 18.6 years). All investigations demonstrated significant FMS improvements following integrated training. The meta-analysis (Fig. 2) produced MD = 1.92 (95% CI 1.57–2.27; p < 0.001; I² = 13%), indicating moderate‐certainty support.

Effects of the FIFA 11 + program on FMS

Five studies^33–37 assessed the standardized FIFA 11 + warm-up over 6–8 weeks in soccer players (n = 163; mean age 18.3 years). Two reported significant FMS gains^34,35, whereas three reported no meaningful change^33,36,37. The aggregated effect size was MD = 1.69 (95% CI 0.79–2.60; p < 0.001), but there was high inconsistency (I² = 84%) and very-low GRADE rating temper confidence in these findings (Fig. 2).

Effects of neuromuscular training on FMS

Three studies^10,38,39 that targeted proprioception and coordination in volleyball and badminton athletes (n = 80; mean age 16 years; durations 10 days to 8 weeks) reported uniformly improved FMS scores. Random-effects synthesis (Fig. 2) indicated MD = 2.31 (95% CI 1.64–2.98; p < 0.001) with no heterogeneity (I² = 0%), providing moderate-certainty evidence of efficacy.

Effects of core stability training on FMS

Seven studies^7,40–45 involving basketball, tennis, and swimming athletes (n = 437; mean age 18 years; 6–16 weeks) employed trunk-stabilization regimens. All reported significant post-intervention FMS enhancements. The meta-analysis (Fig. 2) revealed the largest pooled effect (MD = 2.89; 95% CI 2.46–3.32; p < 0.001; I² = 45%), which is consistent with moderate-certainty evidence.

Effects of functional training on FMS

Eight studies^9,46–52 focused on soccer, basketball, tennis, and dragon boat athletes. (n = 326; mean age 16.5 years; 3–12 weeks). All trials demonstrated FMS score gains, although one study⁴⁷ noted intersport variability. Restricting the meta-analysis to soccer cohorts produced MD = 1.74 (95% CI 1.34–2.13; p < 0.001; I² = 0%), reflecting moderate evidence strength (Fig. 2).

Effects of other exercise interventions on FMS

Five studies^53–57 explored the effects of different exercise interventions, including the Lumbopelvic-Hip Complex, suspension training, Foot Muscle Strengthening, Foam Roller exercises, Pilates, and yoga, on FMS. Among these, three studies reported that suspension training, Pilates, and yoga led to statistically significant improvements in FMS scores (p < 0.05)^54,55,57. In contrast, two studies^53,56 reported that interventions focused on the Lumbopelvic-Hip Complex and Foam Roller exercises did not produce significant changes in FMS scores (p > 0.05).

Discussion

In this systematic review and meta-analysis of 40 trials involving 1,604 athletes, targeted exercise interventions—particularly resistance training, neuromuscular training, core stability training, integrated training, and functional training —consistently increased FMS scores. These findings align with and extend the literature on the role of structured exercise modalities in optimizing movement quality and preventing injury.

Resistance training and neuromuscular training resulted in moderate improvements in FMS (MD: 2.21 and MD: 2.31, respectively). Prior investigations have demonstrated that resistance protocols elicit neural adaptations, including increased motor unit recruitment and synchronization, which translate to enhanced proprioceptive acuity and movement control^58,59. Similarly, neuromuscular training has been shown to improve dynamic joint stability through feedforward and feedback mechanisms, facilitating smoother movement patterns^60,61. These mechanisms plausibly account for the observed gains in FMS scores, as improved neuromuscular coordination underpins successful completion of FMS tasks⁶².

Core stability training produced the largest effect size (MD: 2.89), underscoring the centrality of trunk control in terms of biomechanical efficiency. Research by Kibler and colleagues highlights how a stable core serves as a proximal base for force transmission, reducing compensatory movements and aberrant loading downstream⁶³. By minimizing unwanted motion at the lumbopelvic-hip complex, core-focused interventions likely enhance performance on deep squats, trunk stability push-up, and rotary stability components of the FMS^63,64. These mechanistic insights reinforce why athletes engaging in systematic core protocols achieve superior movement quality.

Integrated training models, which combine flexibility, strength, and balance exercises, yielded significant but smaller improvements (MD: 1.92). This multidimensional approach reflects contemporary paradigms in athletic conditioning, such as the Integrated Performance Triangle, which posits that isolated modalities synergize to produce holistic gains in movement competence^28,29. The low heterogeneity (I² = 13%) across these studies suggests that balanced programs, when standardized, reliably enhance multiple domains of movement without overemphasizing a single physiological attribute.

Conversely, the FIFA 11 + program produced heterogeneous outcomes (I² = 84%) and very low certainty of evidence, despite its widespread adoption in soccer. This variability may stem from differences in implementation fidelity, athlete compliance, and session intensity across studies⁶⁵. Some trials reported significant improvements, likely reflecting high adherence and individualized progression, whereas others reported negligible effects, possibly due to underdosing or a lack of emphasis on core and resistance elements. Harmonizing program delivery and dose‒response characteristics will be essential for clarifying the true impact of FIFA 11 + on functional movement.

Functional training interventions also demonstrated consistent FMS improvements, although effect sizes varied by sport. For example, soccer players experienced moderate gains (MD: 1.74), echoing findings by Formenti et al. that field-based functional drills enhance sport-specific movement patterns⁶⁶. However, sports demanding unique neuromuscular demands (e.g., dragon boat racing) may require more tailored functional protocols to fully translate to FMS performance.

The relatively modest or null effects observed in interventions focusing on foam rolling without dynamic progression underscore the importance of active loading and movement specificity. Passive modalities may transiently improve tissue pliability but lack the neuromuscular training engagement necessary for lasting movement quality adaptations^67,68.

Limitations

Despite the strengths of our comprehensive literature search and the rigorous application of both the PEDro scale and the GRADE framework, several limitations must be acknowledged. First, the included studies exhibited a considerable range of methodological quality. Most studies were of moderate quality, and the absence of blinding in many trials might have introduced performance and detection biases. Second, the nature of FMS as an outcome measure, while widely used, does not capture all dimensions of athletic performance or injury incidence, and improvements in FMS scores may not directly equate to reduced injury risk in real-world competitive settings. Moreover, the relatively short durations of many interventions (typically approximately nine weeks) limit our ability to assess the long-term sustainability of the observed benefits. Finally, the predominance of younger, male athletes in the sample restricts the generalizability of our findings across genders, age groups, and sport-specific contexts.

Practical Implications and future directions

From a practical standpoint, these findings have significant implications for sport scientists, strength and conditioning coaches, and rehabilitation professionals. Designing conditioning programmes that incorporate resistance, core stability, and neuromuscular components may not only optimize functional movement but also serve as a prophylactic measure against musculoskeletal injuries^69–71. In an era where athlete longevity and performance enhancement are paramount, the evidence supporting these targeted interventions provides a clear rationale for integrating them into routine training regimens. Simultaneously, caution is warranted when interpreting the benefits of programs such as the FIFA 11 + or more generic functional training approaches until further high-quality studies elucidate their optimal implementation.

Future studies should focus on reducing methodological heterogeneity by adopting uniform intervention protocols, standardizing outcome measures, and exploring the dose‒response relationships inherent in these training modalities. Longitudinal investigations addressing the impact of these interventions on injury incidence, performance metrics, and long-term athletic development are essential. Additionally, research that stratifies findings by sport, age, and baseline movement deficits will be invaluable in developing personalized, sport-specific conditioning programmes.

Conclusion

This systematic review and meta-analysis substantiates that targeted exercise interventions, particularly those emphasizing core stability, resistance training, and neuromuscular control, offer a robust strategy for enhancing FMS scores among athletes. Although these improvements in movement quality suggest potential benefits for athletic performance and injury risk reduction, caution is warranted when directly extrapolating FMS score enhancements to overall performance gains or definitive injury prevention. Notably, the majority of the evidence is derived from studies involving primarily youth and mixed-gender cohorts; thus, additional research is necessary to determine whether these findings extend to professional athletes and other underrepresented populations. As our understanding of the interplay between training modalities and functional movement evolves, future studies should aim to refine these protocols further, ensuring that interventions are tailored to the unique demands of various sports disciplines and individual athlete profiles.

Author contributions

AAM was responsible for the conceptualization, investigation, methodology, and writing of the original draft, as well as software development and formal analysis. SHM contributed to the investigation, project administration, supervision, validation, visualization, and writing—review and editing. MAB handled data curation, software development, and writing—review and editing. HM focused on formal analysis, resource management, and writing—review and editing.

Data availability

All data related to the present study are available in this manuscript and the supplementary file.

Declarations

Competing interests

The authors declare no competing interests.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-12371-2.