Lower Limb Joint Coordination and Coordination Variability During Landing: A Scoping Review

Abstract

Landing requires precise coordination among lower limb joints to absorb impact forces and maintain dynamic stability. Coordination and its variability during landing are influenced by factors such as injury status, training, sex, age, fatigue, and task complexity. Altered coordination patterns may compromise impact absorption and increase injury risk, highlighting the importance of understanding these movement strategies across populations and conditions. This scoping review aimed to map and synthesize the existing literature on lower limb joint coordination and coordination variability during landing tasks across different populations and task conditions. A comprehensive search was conducted across four databases (PubMed, Web of Science, Scopus, SPORTDiscus) through November 2024, with additional articles identified through reference screening. Peer-reviewed studies were included if they assessed joint or segmental coordination and/or coordination variability using time-series analyses (such as vector coding, continuous relative phase, and discrete relative phase) during landing tasks in human participants. Formal critical appraisal was not performed, consistent with PRISMA-ScR guidelines. Eighteen studies were thematically grouped into five focus areas: injured/at-risk individuals, training/fatigue interventions, gender differences, age differences, and healthy populations under varied landing conditions. Injured individuals exhibited altered coordination patterns, often showing either rigid or erratic strategies with excessive or reduced variability. Training interventions generally improved coordination stability, whereas fatigue increased variability and disrupted control. Females displayed more constrained patterns and lower coordination variability compared to males, particularly at the knee joint. Children demonstrated greater variability and less refined coordination than adults. Healthy individuals typically showed symmetric adaptable variability. Lower limb joint coordination and its variability during landing are shaped by injury status, fatigue, training, sex, age, and task complexity. These findings highlight the need for consistent methodologies and suggest that coordination analysis can inform injury prevention, rehabilitation, and targeted training strategies to optimize landing performance and safety.

1. Introduction

Landing from a jump is a high-impact task requiring precise coordination of the lower limb joints to safely dissipate forces [1]. During landing, the hip, knee, and ankle must move synchronously to control deceleration and maintain stability; how these joints coordinate their motions, and how consistent that coordination is across repeated landings, can influence both performance and injury risk [2]. In biomechanics, joint coordination refers to the timing and pattern of multi-joint movements (often quantified via coupling angles or phase relationships between joint motions), whereas coordination variability refers to the trial-to-trial fluctuations in joint coupling patterns, typically quantified using the standard deviation or angular dispersion of coupling angles over multiple trials [3]. Coordination variability is rooted in dynamic systems theory, which posits that healthy movement may exhibit a certain degree of flexibility or “variability” as the neuromuscular system explores different solutions for a task [4]. Sufficient variability can indicate adaptable motor control, while too little or too much variability might signify a less stable or less optimized coordination strategy [3]. Landing mechanics are often studied within this framework to understand why some individuals (or populations) are more injury-prone than others despite performing the same task [5]. Notably, non-contact injuries such as anterior cruciate ligament (ACL) ruptures frequently occur during landings; abnormal coordination pattern (for instance, poor hip–knee synchronization or excessive joint stiffness) is thought to elevate ligamentous loads and injury risk [6]. Therefore, examining how joints move together (coordination patterns) and how consistently they do so (coordination variability) provides insight beyond traditional discrete measures like peak angles or forces.

Individual differences in coordination may arise from prior injuries, training background, sex, age, or the specific demands of the landing task [5]. Athletes recovering from ACL reconstruction or those with chronic ankle instability (CAI) often exhibit altered movement strategies during landing as part of protective or compensatory mechanisms, potentially leading to either unusually rigid couplings or excessive variability [7,8]. Training interventions and fatigue can also modulate coordination; a well-conditioned athlete might display a refined, repeatable joint synergy, whereas fatigue may alter normal timing between joints and increase variability [9]. Sex differences have been documented as well; female athletes, who suffer higher rates of ACL injury, sometimes demonstrate different lower limb coordination strategies than males during landing and cutting manoeuvers [3]. Age and developmental stage are other factors: children and adolescents may not coordinate joints during landing with the same consistency or technique as adults due to neuromuscular maturation and skill learning [10]. Finally, the nature of the landing task itself (i.e., single-leg versus double-leg landings, expected versus unexpected landings, different directions or heights) strongly influences coordination patterns. A change in landing direction or the addition of a second landing in a sequence can alter how joints work together and how variable that coordination is.

Given the multifactorial influences on landing coordination, this scoping review was conducted to map the literature on lower limb joint coordination and coordination variability during landing tasks. This review synthesizes findings from diverse populations and landing conditions, highlighting how injury status, training/fatigue, gender, age, and task demands affect inter-joint coordination. Understanding these patterns is crucial for biomechanists and clinicians aiming to identify at-risk movement profiles and to design interventions (training, rehabilitation, or technique modification) that promote safer and more effective landing mechanics. In the following sections, we present the methods of our scoping review and then detail the results in five focus areas: (1) injured or at-risk individuals, (2) training and fatigue interventions, (3) gender differences, (4) age differences between children and adults, and (5) healthy individuals under varying landing conditions. A comparative synthesis and discussion then integrate these findings, with implications for injury prevention, training, youth development, and theoretical frameworks in biomechanics.

2. Methods

This scoping review was conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews—Appendix A) guidelines [11].

Protocol Registration: Although no external registration was completed, an internal protocol was developed to guide the review process, including eligibility criteria, database selection, and charting strategy. At the time of writing, registration on PROSPERO was not feasible as it does not accept scoping reviews not focused on direct health outcomes. We acknowledge that the absence of protocol registration limits external reproducibility and transparency.

Search Strategy: The following search strategy was used in PubMed and adapted for other databases (i.e., Web of Science, Scopus, SPORTDiscus) using equivalent field tags: (“joint coordination” OR “coordination variability” OR “inter-joint coordination” OR “movement coordination”) AND (landing OR “drop jump” OR “jump landing” OR “landing mechanics”). No restrictions were placed on publication year, and results were limited to peer-reviewed journal articles published in English. The final search was conducted in March 2024.

Inclusion Criteria: We included peer-reviewed original research articles that (1) involved human participants and reported biomechanical measures of lower limb joint/segment coordination during a landing task, and (2) quantified coordination patterns and/or coordination variability. Both bilateral (two-leg) and unilateral (single-leg) landing tasks were considered (including drop landings, stop-jumps, drop vertical jumps, and similar deceleration/jump-landing tasks). Studies needed to report a coordination metric such as coupling angle analyses (vector coding (VC), continuous relative phase (CRP), and discrete relative phase (DRP)) or variability of coordination (standard deviation or angular dispersion of coupling angles across trials). We included studies examining healthy individuals as well as those focusing on specific groups (athletes with injury history, trained versus untrained, male versus female, and different age groups). There were no restrictions on the type of landing direction (vertical, diagonal, etc.) or height. We excluded studies that only analyzed temporal events or discrete joint angles without examining joint coupling relationships, as well as studies of gait or running (as opposed to landing) unless a landing component was explicitly analyzed. Only articles in English were included.

Study Selection: Two reviewers independently screened titles and abstracts for relevance. Full texts of potentially relevant papers were then assessed against the inclusion criteria. Disagreements were resolved through discussion. A PRISMA flow diagram (Figure 1) was used to document the selection process, yielding a total of 18 studies that met the inclusion criteria.

Data Charting and Synthesis: Data from included studies were charted using a standardized form. Extracted information included author/year, participant characteristics (sample size, population or group types such as ACL-injured, male/female, and age), landing task description (i.e., single-leg drop from 30 cm, double-leg drop jump with subsequent jump, and anticipated versus unanticipated conditions), coordination analysis methods (VC, CRP, and DRP), and key findings regarding coordination patterns and variability. Rather than conducting a meta-analysis (not appropriate due to the heterogeneity of methods and outcomes), we performed a descriptive synthesis. Consistent with scoping review objectives, we organized the results to map the evidence by thematic focus. Five focus groups were established based on recurring themes in the literature: (1) injured or at-risk individuals (those with ACL reconstruction, chronic ankle instability, or known risk factors), (2) effects of training interventions or fatigue on landing coordination, (3) gender differences, (4) age differences (children versus adults), and (5) healthy individuals under different landing task conditions. We then summarized and compared findings within and across these categories. Summary tables were prepared to provide an overview of study characteristics and results (see Table 1). Where applicable, we calculated or reported effect sizes or statistical significance as given in the original studies to comment on the magnitude of observed differences. Throughout the review, we use consistent terminology for coordination patterns (in-phase versus anti-phase motion, or proximal versus distal joint dominance in a coupling) and coordination variability (often reported as the standard deviation or angular variance of coupling angle over multiple trials) [12]. All results are reported with appropriate citations to the source studies. Finally, we collated overarching insights in Section 4, examining how coordination strategies differ by population and task, the implications of variability and symmetry findings, and directions for future research.

Risk of Bias Mitigation: To mitigate potential sources of bias in this scoping review, a series of structured methodological steps were employed [13]. A broad and systematic search strategy was conducted across multiple databases using clearly defined eligibility criteria focused on studies analyzing lower limb joint coordination or coordination variability during landing tasks. Independent screening of titles, abstracts, and full texts was performed by two reviewers to reduce selection bias, with discrepancies resolved through discussion. By categorizing included studies into five distinct focus areas (injury, training/fatigue, gender, age, and healthy populations), thematic synthesis was conducted in a balanced and transparent manner, minimizing interpretation bias. Although critical appraisal of study quality was not performed, in accordance with PRISMA-ScR guidelines, limitations in study designs and variability in methods were acknowledged and discussed [11]. Conflicting findings were retained and interpreted in context to avoid confirmation bias. A PRISMA-ScR flow diagram and checklist were also included to ensure transparency and reproducibility of the review process.

Table 1. Summary of included studies.

Authors/Year	Country	Gender	Target Population	Landing Task	Analysis Method	Key Findings
Blache et al., 2017 [14]	France	Male (24)	ACL-R and Healthy	Double-leg DVJ	CRP	Altered hip–knee coordination in ACLR athletes
Kazemi et al., 2023 [15]	Iran	Male (60)	Soccer players	Single-leg drop	VC	Increased variability in ankle joint coupling
Sarvestan et al., 2020 [7]	Czechia	Females (11) and Males (19)	Collegiate athletes with CAI	Drop landing	VC	Lower coordination variability post-ACL reconstruction
Herb et al., 2020 [16]	USA	Both (28)	CAI and Healthy	DVJ	VC	Higher lower limb joint variability
Li et al., 2021 [17]	Georgia	Females (21)	Unilateral CAI	Stop jump	DRP	Abnormal coupling in transverse plane
Fakhraei et al., 2024 [18]	Iran	Females (84)	Soccer, Basketball and Volleyball players	Balance training (pre-post)	VC	Reduced coordination variability after training
Hughes et al., 2008 [19]	England	Females (5) and Males (5)	Volleyball Players	DVJ (trained versus untrained)	CRP	Untrained showed higher coordination variability
Sinsurin et al., 2020 [2]	Thailand	Females (21)	Healthy	Single-leg landings (varied directions)	Angular Velocity-Based	Worse coordination in lateral landings
Krajewski et al., 2020 [20]	Scotland	Males (14)	Elite Rugby Athletes	Drop jump (pre-post fatigue)	CRP	Fatigue increased variability and altered coordination
Dennis et al., 2023 [21]	USA	Females (28) and Males (28)	Healthy	Single-leg drop	VC	Females had lower CRP variability than males
Dennis et al., 2024 [22]	USA	Females (28) and Males (28)	Healthy	Side-step landing	VC	Females more rigid in frontal plane coupling
Alirezaei et al., 2017 [23]	Iran	Females (10) and males (10)	Athletes	Drop jump (insole conditions)	CRP	Females less variable; cushioned insoles reduced difference
Koshino et al., 2017 [24]	Japan	Females (11) and Males (11)	Healthy	Double-leg drop	VC	Females showed reduced frontal-plane coordination
Monfort-Torres et al., 2024 [25]	Spain	Females (15) and Males (15)	Gymnasts, Volleyball players and Healthy	Single-leg drop	VC	Non-sport children had more anti-phase motion
Raffalt et al., 2016 [10]	Denmark	Males (20)	Athletes	Countermovement jump	CRP	Children had higher intra- and inter-subject variability
Dadfar et al., 2021 [26]	Iran	Females (26)	Active players	Double-leg DVJ	VC	Symmetric, in-phase coordination in healthy subjects
Zhang et al., 2021 [27]	China	Male (20)	Healthy	Single-leg drop	CRP	Consistent hip–knee flexion pattern
Wang et al., 2024 [28]	China	Both (18)	Resistance training group	DVJ (1st versus 2nd landing)	VC	Second landing had higher variability and less in-phase coupling

CRP = Continuous relative phase; VC = Vector coding; DVJ = Drop vertical jump landing; ACL = Anterior cruciate ligament; CAI = Chronic ankle instability.

Table 1. Summary of included studies.

Authors/Year	Country	Gender	Target Population	Landing Task	Analysis Method	Key Findings
Blache et al., 2017 [14]	France	Male (24)	ACL-R and Healthy	Double-leg DVJ	CRP	Altered hip–knee coordination in ACLR athletes
Kazemi et al., 2023 [15]	Iran	Male (60)	Soccer players	Single-leg drop	VC	Increased variability in ankle joint coupling
Sarvestan et al., 2020 [7]	Czechia	Females (11) and Males (19)	Collegiate athletes with CAI	Drop landing	VC	Lower coordination variability post-ACL reconstruction
Herb et al., 2020 [16]	USA	Both (28)	CAI and Healthy	DVJ	VC	Higher lower limb joint variability
Li et al., 2021 [17]	Georgia	Females (21)	Unilateral CAI	Stop jump	DRP	Abnormal coupling in transverse plane
Fakhraei et al., 2024 [18]	Iran	Females (84)	Soccer, Basketball and Volleyball players	Balance training (pre-post)	VC	Reduced coordination variability after training
Hughes et al., 2008 [19]	England	Females (5) and Males (5)	Volleyball Players	DVJ (trained versus untrained)	CRP	Untrained showed higher coordination variability
Sinsurin et al., 2020 [2]	Thailand	Females (21)	Healthy	Single-leg landings (varied directions)	Angular Velocity-Based	Worse coordination in lateral landings
Krajewski et al., 2020 [20]	Scotland	Males (14)	Elite Rugby Athletes	Drop jump (pre-post fatigue)	CRP	Fatigue increased variability and altered coordination
Dennis et al., 2023 [21]	USA	Females (28) and Males (28)	Healthy	Single-leg drop	VC	Females had lower CRP variability than males
Dennis et al., 2024 [22]	USA	Females (28) and Males (28)	Healthy	Side-step landing	VC	Females more rigid in frontal plane coupling
Alirezaei et al., 2017 [23]	Iran	Females (10) and males (10)	Athletes	Drop jump (insole conditions)	CRP	Females less variable; cushioned insoles reduced difference
Koshino et al., 2017 [24]	Japan	Females (11) and Males (11)	Healthy	Double-leg drop	VC	Females showed reduced frontal-plane coordination
Monfort-Torres et al., 2024 [25]	Spain	Females (15) and Males (15)	Gymnasts, Volleyball players and Healthy	Single-leg drop	VC	Non-sport children had more anti-phase motion
Raffalt et al., 2016 [10]	Denmark	Males (20)	Athletes	Countermovement jump	CRP	Children had higher intra- and inter-subject variability
Dadfar et al., 2021 [26]	Iran	Females (26)	Active players	Double-leg DVJ	VC	Symmetric, in-phase coordination in healthy subjects
Zhang et al., 2021 [27]	China	Male (20)	Healthy	Single-leg drop	CRP	Consistent hip–knee flexion pattern
Wang et al., 2024 [28]	China	Both (18)	Resistance training group	DVJ (1st versus 2nd landing)	VC	Second landing had higher variability and less in-phase coupling

CRP = Continuous relative phase; VC = Vector coding; DVJ = Drop vertical jump landing; ACL = Anterior cruciate ligament; CAI = Chronic ankle instability.

3. Results

3.1. Methodological Considerations in Coordination Analysis

Studies in this review employed various methods to assess joint coordination, primarily VC, CRP, and DRP. Each of these approaches offers unique insights, but they also differ in sensitivity, data requirements, and interpretability. VC is a geometric method using angle–angle plots to assess directional relationships between joints. It is relatively simple and widely used but may not fully capture temporal coupling. CRP, in contrast, incorporates angular velocity to assess phase relationships across time and is better suited to capturing dynamic in-phase versus anti-phase patterns. DRP focuses on discrete timing between key motion events (i.e., peak flexion), which can be useful for task segmentation but may miss continuous coordination patterns.

Table 2. Methodological differences, strengths, and limitations of various coordination analysis methods.

Method	Measures	Key Input	Strengths	Limitations
VC	Angular relationship between two joints	Angle–angle plot	Simple visualization; sensitive to directional changes	Less sensitive to phase/timing; planar
CRP	Temporal phase relationship (i.e., in-phase versus anti-phase)	Angle + angular velocity	Captures both magnitude and timing; suitable for cyclical motions	Sensitive to signal noise; requires smoothing
DRP	Relative timing at key events	Peak or event-based timing	Useful for task segments; straightforward to compute	Limited temporal resolution; ignores continuous behavior

summarizes these distinctions. The use of different methods across studies may partly explain variability in findings; thus, readers should interpret coordination outcomes within the methodological context of each study.

For better comprehension and reducing repetitive explanations within the main text,

Table 3. Key coordination terminology for coordination analysis and interpretation.

Term	Definition
In-phase	Joints move in the same direction and at similar timing during a task (i.e., simultaneous hip and knee flexion).
Anti-phase	Joints move in opposite directions or out of sync (i.e., hip flexion paired with knee extension).
Proximal dominance	Greater movement or control observed at the more proximal joint (i.e., hip over knee).
Distal dominance	Greater movement or control observed at the more distal joint (i.e., ankle over knee).
Coordination variability	The trial-to-trial variation in joint coupling or movement strategy, indicating movement consistency or adaptability.

provides definitions for key coordination terminology used throughout the review. These terms, including in-phase, anti-phase, proximal dominance, and coordination variability, are central to understanding joint coupling behavior during landing. By consolidating these definitions in a single location, we aim to improve clarity and flow while maintaining accessibility for readers who may be less familiar with biomechanical coordination analysis.

3.2. Injured or At-Risk Individuals

Several studies have examined lower limb coordination during landing in individuals with a history of injury or those considered at elevated risk. A common theme across these populations is disrupted joint synergy, often reflected as either rigid, low-variability strategies or excessive, erratic variability, depending on the injury type.

ACL Reconstruction (ACLR): Following ACL injury and reconstruction, individuals often display persistent alterations in joint coordination. Blache, de Fontenay [14], and Park and Yoon [29] observed that ACLR patients commonly adopt a rigid coordination strategy with reduced variability, particularly in hip–knee couplings. These patterns, often interpreted as protective or compensatory, suggest constrained joint motion and reduced neuromuscular flexibility. Frontal-plane coordination, for instance, may shift toward greater hip control to compensate for knee instability [30]. These deficits can persist well beyond clinical recovery, underscoring the limitations of standard rehabilitation in fully restoring dynamic inter-joint control.

Chronic Ankle Instability (CAI): Individuals with chronic ankle instability, typically due to recurrent lateral ankle sprains, also display unique coordination adaptations [7,31]. In contrast to ACLR, where reduced variability is common, CAI individuals have been reported to show increased joint coupling variability during landing [32]. Herb, Blemker [16] examined drop vertical jumps in a cohort with chronic ankle instability and found significantly higher variability in lower extremity joint coupling compared to healthy controls. In that study, trial-to-trial coordination variability was elevated particularly in couplings involving the injured ankle, suggesting inconsistency in how the ankle and more proximal joints work together on each landing. The authors interpreted higher coupling variability as a possible sensorimotor deficit (the unstable ankle may lead to erratic motor patterns as the body struggles to stabilize, resulting in less repeatable coordination). Interestingly, this finding is the opposite of what is often seen in high-risk ACL mechanisms; instead of being too rigid, CAI landing patterns might be too variable or poorly controlled. Coordination and symmetry analyses have also revealed that individuals with CAI can exhibit altered inter-limb coordination [33]. As an example, comparisons of drop jump landings between the injured ankle side and the uninjured (or between the CAI group and “copers” who had ankle sprains but no instability) indicate that CAI is associated with asymmetric coordination patterns (the unstable limb may not mirror the movement strategy of the other limb) [34,35].

Other At-Risk Populations: Some studies have looked at coordination in individuals predisposed to injury even if they have no current injury. For instance, females are often considered “at-risk” for ACL injury (to be discussed in detail in Section 3.4 on gender differences), and certain coordination traits have been identified in high-risk female athletes (like those with valgus knee kinematics) [36]. There is evidence that individuals with patellofemoral pain (PFP) may exhibit coordination changes during high-impact tasks. While not landing-focused, a few researchers noted that PFP and CAI can be associated with decreased coupling variability in walking or running [37], implying a stiffening strategy [17]; however, during a more demanding task like a drop landing, PFP patients instead offload painful ranges, potentially leading to atypical phase relationships. In the context of drop landings, it is plausible that at-risk athletes (i.e., those identified via screening as having poor jump-landing mechanics) show altered hip–knee–ankle coordination even before an injury occurs [38]. One example is a study of medial knee collapse risk: individuals who exhibit dynamic knee valgus during landings often have distinct coordination patterns, such as a dominance of hip internal rotation with knee abduction coupling [39]. This aligns with dynamic systems theory in motor control, which posits that inefficient or constrained coordination can emerge in response to injury risk factors or neuromuscular immaturity [4,37]. In summary, injured and at-risk populations tend to demonstrate either abnormally low or high coordination variability and modified coupling patterns as a result of their injury history or intrinsic risk factors. These changes can manifest as side-to-side asymmetries, shifts in joint dominance (i.e., relying on hip versus knee motion), or timing alterations that could impair the effectiveness of energy dissipation during landing.

3.3. Training and Fatigue Interventions

Dynamic coordination of the lower limbs during landing is not static; it can be altered through training interventions or acutely affected by fatigue. Several studies in the review addressed whether purposeful training or the presence of fatigue modulates joint coordination and variability, with implications for injury prevention programs and athletic conditioning.

Neuromuscular Training Effects: Interventions such as balance training, plyometrics, or technique training may influence coordination patterns. In patients with chronic ankle instability, rehabilitation programs have aimed to restore more stable coordination. For example, a recent study evaluated the effects of a balance training program on joint coupling in CAI patients [40]. Before training, CAI individuals showed the aforementioned high coupling variability and atypical shank–foot coordination. After a period of targeted neuromuscular training (using unstable surfaces and exercises to challenge ankle control), the participants demonstrated reduced variability in joint coupling (approaching the consistency seen in healthy individuals) and more symmetric coordination between lower limb segments.

Another quasi-intervention explored in the literature is equipment modification. A study by Alirezaei et al. examined how changing shoe insole stiffness affected coordination in male and female athletes during landing [23]. While the primary finding was related to gender (females initially had lower coupling variability than males; see Section 3.4), it also showed that adding cushioning (soft or stiff insoles) did not significantly change coordination patterns or variability overall (p > 0.05). In other words, simply altering shoe insole properties did not perturb the coordination strategy of landing in a consistent way. However, one interesting outcome was that with insoles (regardless of stiffness), female athletes were able to achieve similar coupling variability as males. This could imply that a more comfortable or supported landing surface (via insoles) might encourage females to utilize a slightly more varied movement strategy (increasing variability from their normally lower baseline to match males), or conversely, that the insoles provided no additional challenge so both sexes landed with their natural patterns.

Fatigue and Exhaustive Exercise: Fatigue is known to degrade neuromuscular performance and has been hypothesized to alter coordination control. In landing tasks, fatigue (whether from repetitive jumps, running, or targeted muscle exhaustion) can lead to changes in joint kinematics and potentially coordination variability. Dickin, Johann [41] explored the combined effects of drop height and fatigue on landing mechanics in active females. They observed that higher drop heights and a fatigued state each independently tended to increase risky biomechanical features (such as greater knee valgus and ground reaction forces). While their study reported traditional kinetic/kinematic metrics, the implications for coordination can be inferred: as athletes fatigued, the fine-tuned timing between joints deteriorates, possibly increasing coordination variability or causing shifts to less favourable coordination patterns (including more reliance on passive structures as muscles tire). Another investigation specifically looked at the effect of isolated muscle fatigue on coordination: Samaan, Hoch [42] induced hamstring fatigue and measured hip–knee joint coordination variability during a subsequent sidestep cutting manoeuvre. They found that hamstring fatigue led to significant changes in coordination variability—in that sidestep task, variability increased, indicating a loss of the usual coordination control once the hamstrings were weakened.

A practical example of fatigue’s impact is seen when comparing the first versus second landing in a drop jump sequence. Wang and Liu [28] noted that after performing a maximal jump (i.e., entering the second landing phase of a drop jump task), individuals showed higher coordination variability in multiple joint couplings compared to the initial landing. Specifically, during the second landing (which occurs after the muscle effort of a rebound jump), variability in hip–knee sagittal motion and knee–ankle couplings was greater than in the first landing. This aligns with the notion that even short-term fatigue or task repetition can increase variability. Notably, in that study, the coordination pattern also shifted: the proportion of in-phase (synchronized) motion between certain joints decreased in the later landing, while anti-phase or joint-dominant patterns increased. The authors suggested this change could be due to fatigue or reduced preparation in the second landing, and that the increased variability and loss of in-phase coordination might elevate injury risk in the knees and ankles. Despite these within-session fatigue effects, it was also reported that bilateral symmetry of coordination did not significantly worsen in the second landing—that is, both legs were affected similarly by the fatigue, maintaining symmetry even as overall variability increased.

3.4. Gender Differences

A prominent theme in landing biomechanics is the difference between male and female movement patterns, motivated in part by the disproportionately higher incidence of ACL injuries in female athletes. Several studies in this review explicitly compared coordination patterns and variability between genders during landing or cutting tasks to see if sex-specific strategies exist that might explain different injury risks.

Coupling Variability Differences: A foundational study by Pollard, Heiderscheit, van Emmerik, and Hamill examined gender differences in lower extremity coupling variability during an unanticipated cutting manoeuvre (a task closely related to lateral jump landing) [3]. Using a vector coding analysis of coordination and focusing on the initial 40% of stance (deceleration phase), they found that women exhibited significantly lower coordination variability than men in multiple joint couplings. Specifically, female athletes had 32–46% less variability than males in four key couplings (including thigh rotation versus leg rotation, thigh adduction/abduction versus leg adduction/abduction, knee flexion-extension versus knee rotation, and knee flexion-extension versus hip rotation). In practical terms, the women tended to use a more repeatable, constrained pattern (less trial-to-trial deviation) in how their hip, knee, and ankle segments moved relative to each other during the cut. The authors postulated that these gender differences in coordination variability could be linked to injury risk: a less flexible coordination strategy in females might reduce their ability to adapt to sudden perturbations, such as uneven ground or an opponent’s contact, thereby potentially increasing ACL injury likelihood if an unexpected load is applied. Women’s lower variability might reflect a neuromuscular control strategy that prioritizes consistency (perhaps due to training or neuromechanics) but at the cost of adaptability. This landmark finding aligns with the idea that an optimal amount of variability is healthy; too little (rigidity) may be as problematic as too much. It is worth noting that these differences were noted in a cutting task, primarily a horizontal deceleration with a change in direction, but similar analyses have been extended to jump landings as well.

Joint Coordination Patterns: Beyond variability magnitude, do males and females coordinate joints differently during landing? Some studies indicate yes. One investigation into drop jump landings found that males and females may adopt different inter-joint coordination strategies to achieve shock absorption. For example, males might utilize a greater contribution of hip flexion with relatively synchronous knee motion, whereas females might exhibit a different phasing, possibly with the knee dominating the motion more than the hip or with different timing in pronation–supination at the ankle. In a study on adolescent athletes performing unanticipated single-leg drop jumps, researchers observed sex-specific kinematic adaptations: female adolescents landed with greater knee valgus angles and tended to show a coordination pattern indicating more reliance on the knee joint (less hip engagement) compared to males. Although that study was focused on joint angles and energy absorption, it implies underlying coordination differences—i.e., females coupling hip and knee motions in a way that results in more frontal-plane knee motion (a potentially high-risk pattern) while males perhaps engage more sagittal-plane hip motion in coordination with the knee to buffer loads. Another study using CRP analysis explicitly compared male and female coupling on a jump-landing task [23]. Without any special insole, women in that study had a lower CRP and lower CRP variability for the foot–shank coupling than men (consistent with Pollard’s findings, showing less variability in females). Interestingly, when cushioned insoles were introduced, these gender differences diminished, meaning females were capable of achieving similar coordination patterns and variability as males under both soft and stiff insole conditions. This suggests that the inherent difference (women’s lower variability in the ankle/foot coupling) is not a fixed trait, given a slightly different context (like a softer landing surface), women adjusted their coordination to match that of men.

Implications of Gender Differences: The consistent observation is that females often show more synchronized and repeatable joint coordination (lower variability) in certain couplings during landing-like manoeuvres, whereas males show more variable or flexible coordination. However, the specific couplings where females were less variable (notably involving transverse plane rotations and frontal plane motions of the knee are those associated with knee injury mechanisms [21,22]. Lower variability there might indicate that female athletes have difficulty altering their movement pattern when needed; for instance, if a landing perturbation forces a deviation, a female athlete’s neuromuscular system may not explore alternative coordination quickly enough, leading to ligament strain [24]. Males’ higher variability might mean a greater ability to distribute loads differently across trials (which could be protective) or it could simply reflect looser control (which might have other drawbacks like less consistency in performance). It has also been suggested that hormonal or anatomical differences could influence muscle activation patterns and thus coordination, but the studies in this review highlight functional outcomes rather than causes [43].

3.5. Age Differences: Children Versus Adults

Age and development level introduce another dimension to landing coordination. Young children, adolescents, and adults differ in musculoskeletal maturity, motor control experience, and strength—all factors that can shape how joints coordinate during a landing. Several studies compared children to adults (or examined children of different ages) to understand how coordination patterns and variability evolve with development.

Coordination Variability in Children: Research indicates that children exhibit higher coordination variability than adults during complex motor tasks, reflecting a learning process and less stable neuromuscular control. Raffalt, Alkjær [10] investigated countermovement jump landings in children versus adults and found that intra-subject coordination variability was higher in children, especially for couplings of proximal segments. Practically, on each landing attempt, a given child showed more fluctuation in how their hip and knee moved relative to each other (or how the thigh and shank segments were coordinated) compared to an adult performing the same task. Children also showed less consistency in the specific coordination pattern they “chose”, implying they were still exploring different movement solutions rather than having a well-ingrained strategy. Adults, in contrast, tended to exhibit a repeatable coordination pattern for countermovement landings, indicative of a more refined and stable motor solution developed through practice and neuromuscular maturation.

A recent study by Monfort-Torres, García-Massó [25] specifically looked at single-leg drop landings in children who practice sports (gymnastics or volleyball) versus non-sporting children. They found that coordination patterns during the landing’s impact phase differed by group: for example, in the frontal plane, non-athletic children more frequently displayed an anti-phase with proximal dominance pattern (meaning the hip and knee were moving in opposite directions or out of sync, with the hip movement dominating) compared to sport-trained children. Moreover, they noted differences in variability: during certain phases of landing (like the second peak of ground reaction force), children in different sports groups had different variability levels (in this case. volleyball players showed higher coordination variability than gymnasts in one phase). This could relate to the nature of their training; volleyball involves varied jump-landing scenarios (potentially encouraging adaptability), whereas gymnastics often emphasizes sticking landings (perhaps encouraging a consistent strategy). Another key observation was that the control (non-athlete) children had a greater frequency of less coordinated patterns (like the anti-phase mentioned) than the athletic children, underscoring how practice and skill development in youth can refine coordination.

Movement Strategies among Children versus Adults: Beyond variability, the absolute coordination patterns can differ with age. Young children, especially those under ~10 years old, tend to land “stiffer”—characterized by less hip and knee flexion and shorter landing phases. This can correlate with a coordination pattern that is not optimized for absorbing force (i.e., more in-phase ankle-knee motion meaning both joints lock up somewhat). As children get older and stronger, they generally increase their knee and hip flexion usage during landing, which is accompanied by a shift in coordination: more pronounced in-phase hip–knee flexion (both joints flex together smoothly) and better timing with the ankle. Essentially, older children and adults show a coordinated flexion strategy (often called a soft-landing technique) where joints flex in harmony to decelerate the body. Younger children often have not mastered that, resulting in either asynchrony (one joint moving too much relative to another) or highly variable patterns from trial to trial. One study conducted by Angulo-Barroso, Ferrer-Uris [44] on young children’s drop landing strategies found that the youngest children had the least efficient landing patterns, such as co-contracting or moving joints less (leading to high impacts), whereas by age ~9, children’s patterns started to resemble small adults with more coordination between hip and knee motion. The authors noted that improvements in coordination with age were a major factor in better load attenuation, rather than just strength.

Inter-Subject versus Intra-Subject Variability: It is also informative to distinguish variability within one person’s repeated trials (intra-subject variability) and variability across individuals (inter-subject). Children typically show greater variability in both senses. Raffalt, Alkjær [10] reported not only higher intra-child variability but also that one child’s preferred coordination pattern might differ substantially from another child’s, indicating multiple movement solutions being used across the group of children (high inter-subject variability). Adults, presumably through years of convergence, tend to have more similar coordination strategies (lower inter-subject variability) and each adult internally is more consistent (low intra-subject variability). These findings align with classic motor development theory (Bernstein’s “degrees of freedom” problem), where novice learners (children) initially freeze or unpredictably vary joint motions, and with practice, they learn to coordinate joints more efficiently and consistently [45].

3.6. Healthy Individuals Under Different Landing Conditions

The final category of studies focused on healthy individuals (typically young adult athletes or recreationally active people) and how their lower limb coordination varies with different landing task conditions. These works provide insight into the “normal” coordination strategies and how factors like task type, direction, and sequence can influence those strategies.

Double-Leg versus Single-Leg Landings: Coordinating two limbs in a bilateral landing can be different from a unilateral landing. While none of the included studies directly compared single versus double in the same paper, we can draw from multiple sources. Generally, single-leg landings are more challenging, often leading to greater variability and different coordination emphasis than double-leg landings (because one limb must absorb all the load). For instance, in single-leg drop landings, Sinsurin, Vachalathiti [2] observed that even among trained female volleyball athletes, coordination quality could differ between the landing leg sides. They found that the non-dominant limb had better coordination (smoother knee–hip kinematic coupling) than the dominant limb when landing on one leg during multi-directional jumps. This somewhat counterintuitive result (the “weaker” or non-preferred leg coordinating more effectively) might be due to the dominant leg being used more forcefully and thus exhibiting more perturbations in motion. In any case, it suggests that symmetry is not a given: healthy individuals can have asymmetrical coordination patterns between limbs, with one leg showing a more stable intra-limb coordination than the other. In double-leg landings, on the other hand, studies like Wang and Liu [28] noted no major side-to-side coordination asymmetry, where both legs tend to move nearly simultaneously and symmetrically in healthy subjects during a normal drop landing. Thus, a normal healthy strategy in a bilateral landing is a symmetric one (each leg mirroring coordination), whereas unilateral landings highlight the unique coordination demands on each limb.

Landing Direction and Anticipation: The direction of landing (or subsequent movement) can alter coordination. Sinsurin, Vachalathiti [2] had athletes perform single-leg landings in multiple directions: forward (0°), diagonal (30°, 60°), and lateral (90°). They reported significant differences in knee coordination depending on direction. In particular, lateral landings (90° to the side) showed poorer knee joint coordination in the early phase of landing compared to forward or diagonal landings. “Poorer coordination” in their context was defined by a velocity–angle plot analysis, implying that the knee’s angular velocity and angle did not follow the typical relationship seen in safer landings, likely indicating a timing mismatch between hip and knee or a more erratic knee motion when landing to the side. This makes intuitive sense: a pure lateral landing may impose more challenge on frontal-plane stability (hip abduction/adduction control, knee varus-valgus), disrupting the usual sagittal-plane flexion synergy. Diagonal and forward landings allowed more normal coordination (with the knee flexing in a controlled manner). The practical outcome is that as the landing direction deviates from straightforward, the coordination task becomes more complex and variability may increase. On a related note, whether a landing is anticipated or unanticipated can also influence coordination, though direct measures of coupling under these conditions are less common. Typically, unanticipated landings (when an athlete does not know which direction or when they will land) lead to stiffer, more cautious strategies, likely reducing variability in an attempt to stabilize, or sometimes causing atypical joint coupling because reflexes, rather than pre-planned muscle synergies, dominate the motion. While none of the 18 studies explicitly gave an unanticipated versus anticipated landing comparison focusing on coordination, the general biomechanics literature suggests only modest changes in landing mechanics with unanticipated conditions. Those changes (i.e., slightly increased knee valgus or different muscle timing) could correspond to slight shifts in coordination pattern (perhaps a bit more out-of-phase motion due to surprise). In summary, healthy individuals adapt their inter-joint coordination depending on what the landing task demands: straightforward tasks yield consistent in-phase joint motions, whereas unusual directions or uncertain landings can perturb that coordination.

Sequential Landings and Task Complexity: The DVJ task, which includes a drop landing followed by an immediate jump and then a second landing, provides a scenario to study how coordination might change from one landing to the next. Wang and Liu [28] showed that the second landing (after the jump) elicited different coordination outcomes than the first. They found reductions in the proportion of in-phase coordination for certain joint couplings (hip sagittal–knee sagittal, knee frontal–ankle frontal, knee sagittal–ankle sagittal) in the second landing, with corresponding increases in joint-dominant or anti-phase patterns. Essentially, the joints were less synchronized in their movements the second time around, possibly because the intervening jump altered landing mechanics (the athlete might land more upright or with different muscle pre-activation). As noted earlier, coordination variability was higher in that second landing. This demonstrates that even in the same individual, performing a landing under a slightly altered context (here, a rebound jump in between) can change coordination. Another example of task complexity is height: a higher drop likely means greater impact, which could influence coordination. Although not directly measured as coupling variability in the included studies, one can hypothesize that at higher drop heights, some individuals might shift to a more synchronous, rigid pattern to resist the large impact (reducing variability as they brace), or conversely, others might buckle differently each time (increasing variability). The interplay is not fully clear without direct data, but task difficulty (height, single versus double leg, one landing versus repeated landings) definitely modulates how joints coordinate.

Coordination and “Normal” Variability: It is important to highlight what a normal coordination pattern looks like in healthy young adults during a standard landing. Generally, during the initial impact phase of a drop landing, an in-phase pattern of hip and knee flexion is expected, where both hip and knee flex (and ankle dorsiflexes) together to cushion the landing. Vector coding analyses often find this in-phase (synchronized flexion) occupies a large percentage of the stance phase in healthy athletes. As the motion continues, some joint dominance may occur; for example, later in the eccentric phase, the knee might slow its flexion while the hip continues to flex (a proximal dominance phase), or the ankle may plantarflex while the knee is still flexing (distal dominance), depending on the task. These subtle shifts are part of a normal pattern to redistribute loads. Coordination variability in healthy individuals is typically moderate: not zero (because no two landings are identical, and some adaptability is present) but not extremely high either (because skilled individuals have a practiced strategy). One study found coordination variability (standard deviation of coupling angle) on the order of a few degrees for experienced athletes in controlled landings, whereas novices or perturbed conditions yielded much larger values.

Healthy individuals also demonstrate symmetric coordination between limbs in bilateral tasks. If significant asymmetry is observed (one limb uses a different coordination strategy than the other), it could indicate a hidden deficit or limb dominance issue. In the context of our review, aside from the dominant versus non-dominant differences in Sinsurin’s single-leg landings, most healthy participants exhibited symmetry and consistent patterns [2]. This provides a baseline expectation: interventions or pathologies that break this symmetry or consistency are thus notable.

In closing this section, healthy individuals provide a reference model of coordinated landing: generally, in-phase, coordinated joint flexion to absorb impact, with some variability and adaptability to meet task demands. When tasks become more challenging (unexpected, multi-directional, repetitive, or unilateral), coordination can shift—often becoming less optimal (i.e., reduced synchronization or increased variability). These findings underscore why training in varied landing scenarios is beneficial: it can broaden an athlete’s repertoire of coordination strategies and potentially improve their ability to handle non-ideal situations (like an awkward land) without getting injured. They also highlight that even in healthy people, fatigue and complexity can transiently induce patterns reminiscent of at-risk profiles (i.e., the second landing’s increased variability). Thus, assessing coordination across different conditions is important in research and practical assessments (i.e., jump tests might include a fatigue element to reveal how coordination breaks down). Figure 2 portrays the effects of gender, injury, age, training, and fatigue on lower limb coordination and coordination variability during landing.

4. Discussion

This scoping review synthesized findings from 18 studies investigating lower limb joint coordination and variability during landing across various populations and task conditions. Key findings indicate that coordination strategies differ based on injury status, training exposure, sex, age, fatigue, and task complexity (among healthy individuals). Injured populations, such as individuals with ACL reconstruction or chronic ankle instability, displayed either overly rigid or highly variable movement patterns, respectively. Training interventions improved coordination stability, while fatigue disrupted it. Female athletes tended to show more constrained coordination than males, and children exhibited higher variability and less refined strategies than adults. Across healthy populations, a moderate level of coordination variability, coupled with in-phase joint motion (both joints portray similar movement—see Table 3), emerged as a characteristic of adaptive and stable landing strategies. These findings collectively underscore the role of coordination analysis in identifying movement deficiencies and informing intervention design.

Coordination Strategies and Injury Risk: A unifying observation across many studies is that individuals at elevated injury risk often display either unusually rigid coordination or overly variable coordination. When synthesizing coordination patterns across populations, clear distinctions emerge. ACL-reconstructed individuals tend to display rigid, low-variability coupling, particularly at the knee, likely as a protective strategy, whereas those with chronic ankle instability often exhibit overly variable, inconsistent coordination, especially at the ankle and its coupling with proximal joints. In contrast, healthy individuals show moderate variability and in-phase motion, particularly during double-leg landings, representing a more adaptive control strategy. These contrasts suggest that both extremes, rigid or erratic coordination, may reflect compromised neuromuscular control, albeit with different underlying mechanisms. Furthermore, whereas ACL-injured athletes often show reduced inter-limb symmetry, CAI individuals exhibit more intra-limb inconsistencies. This comparison helps define a “healthy variability range”, reinforcing that both over-constrained and overly variable coordination can elevate injury risk, albeit through distinct pathways.

From a dynamic systems perspective, both extremes can be suboptimal [37]. Female athletes and ACL-reconstructed individuals frequently showed rigid or low-variability coordination patterns (especially involving knee motions), whereas those with chronic ankle instability showed excessive variability and inconsistent couplings. These patterns likely represent different mechanisms of risk. A rigid coordination pattern (low variability) may fail to accommodate perturbations—if something unexpected happens during landing, a person who always uses the exact same joint strategy might not be able to adjust in time, leading to ligament overload (as hypothesized for women in cutting tasks [3]. On the other hand, very high coordination variability (as in CAI) suggests a lack of reliable motor control—the person cannot produce the same coordination twice, hinting at neuromuscular deficits that could cause unpredictable force distributions and possible injury in any given landing [18]. Notably, the healthy, trained populations seem to reside in the middle ground: they have a predominant coordination pattern (in-phase flexion synergy) but with enough variability to adapt to slight changes, and they exhibit neither stark rigidity nor uncontrolled randomness. This aligns with the concept of an optimal variability window for skilled movement [3,10]. To clarify, coordination variability reflects the consistency (or lack thereof) of how joints move relative to each other across repeated trials. This variability can be quantified through standard deviation or angular dispersion of coupling angles. It is neither inherently good nor bad; rather, its implications depend on the context. In skilled, healthy individuals, moderate variability is often seen as beneficial, indicating flexibility and adaptability in movement control. However, excessive variability (as seen in chronic ankle instability) may indicate poor neuromuscular control, while very low variability (as in some ACL-reconstructed athletes) can suggest a rigid or overly constrained strategy that lacks adaptability. This supports the idea of an “optimal variability window”, where coordination is both stable and responsive to changing task demands.

Although this review did not conduct a formal critical appraisal of the included studies, as per the PRISMA-ScR guidelines, we acknowledge that the strength of the synthesized evidence varies across studies. Many included studies were cross-sectional in design, with small sample sizes and diverse participant characteristics. Additionally, methodological heterogeneity was common, particularly in coordination analysis techniques (vector coding, CRP, DRP), outcome definitions, and landing protocols. These differences may limit direct comparisons and the generalizability of findings. Nevertheless, most studies employed appropriate biomechanical methods and clearly reported their analytic frameworks. To help readers contextualize our synthesis, we highlight these methodological considerations as part of a qualitative assessment of evidence quality.

Influence of Task Demands: Landing biomechanics are highly context-dependent. Task factors—such as single versus double-leg landings, landing direction, presence of subsequent movements (a rebound jump), or fatigue—significantly influence coordination. The evidence shows that when a task is made more difficult or novel, coordination tends to shift in ways that could challenge the neuromuscular system. For example, introducing a lateral component to a landing made knee–hip coordination less effective (poorer synchronization) in healthy athletes. Fatigue, whether over many jumps or induced by a prior movement, generally increased coordination variability and sometimes pushed joints out of their usual in-phase relationship. These findings have practical implications: training programs should expose athletes to varied landing scenarios (different directions, unpredictable landings, landings when fatigued) so that they develop robust coordination strategies. An athlete who only ever practices pristine, straightforward drop landings might have a very low-variability, optimized pattern for that scenario, but once in a game situation with chaos, that pattern might not suffice. By training with variability (i.e., random drop heights, multi-direction jumps), one can potentially increase the adaptive capacity of the coordination—essentially teaching the system to handle perturbations gracefully. This is supported by studies in other domains showing variability training can improve motor learning and adaptability.

Coordination Variability and Symmetry: A recurring measure in these studies was coordination variability, often quantified as the standard deviation of coupling angles (via vector coding or CRP) over repeated trials. A nuanced understanding of this measure is important. High coordination variability is not inherently bad—in some contexts (like early learning or exploration), it reflects a healthy search for a better solution. Low variability is not inherently good—it might reflect skill, or it might reflect a rigid strategy. The optimal level likely depends on the task and individual. That said, trends emerged: (1) Skilled, uninjured adults performing familiar tasks tend to have relatively low variability (a stable solution) with small deviations trial-to-trial. (2) Novices, children, or those performing new/challenging tasks have higher variability as the system searches for stability. (3) Injuries or deficits can push variability to either end—some injuries cause a protective consistency (low var.) while others cause erratic movement (high var.). This U-shaped concept—that both too little and too much variability can be signs of trouble—is supported by multiple studies. Clinically, this suggests that simply “reducing variability” is not always the goal; rather, appropriate variability is the goal. A practical example is ACL injury prevention programs: some coaches initially tried to make athletes land the exact same “perfect” way every time (minimizing variability), but the research here indicates it may be beneficial for athletes to practice varying their landings (within safe technique bounds) to build resilience. Regarding symmetry, most healthy individuals land with symmetric coordination between limbs (especially in double-leg tasks), whereas injury can introduce asymmetry. No marked asymmetries were seen between dominant and non-dominant legs in standard tasks (dominant versus non-dominant differences only showed up under specific conditions like lateral landings). However, after an injury like ACLR, even if not explicitly measured in the reviewed studies, one can expect inter-limb asymmetry in coordination—for instance, the ACLR limb might show a different coupling strategy or variability than the intact limb. Rehabilitation should therefore aim not just to restore strength, but also to restore symmetric and normal coordination patterns between limbs. Some researchers have begun to use coordination variability as a return-to-sport metric, under the idea that an athlete who still shows aberrant variability or asymmetry in coordination is not fully rehabilitated, even if jump height or strength is back to normal.

Implications for Injury Prevention and Training: Understanding these coordination differences can inform injury prevention. For ACL injuries, training might focus on increasing coordination variability in females (i.e., by teaching multiple landing strategies or increasing hip involvement to avoid a singular knee-dominant pattern). Conversely, for CAI, training focuses on decreasing excessive variability (i.e., through balance training to stabilize the ankle and produce a more reliable neuromuscular pattern). Coaches can incorporate drills that challenge coordination (like perturbation training: slight pushes during landing or landing on uneven surfaces) to enhance adaptability. Importantly, any intervention should be monitored to see if it moves the athlete’s coordination metrics toward a healthier range (i.e., is variability decreasing or increasing as desired, are couplings becoming more in-phase when they should?). The review also highlights the importance of neuromuscular fatigue: late-game injuries might be explained in part by coordination breakdown. Strength and conditioning programs thus should also target muscular endurance and neurocognitive factors to maintain coordination quality under fatigue.

Youth Development: For younger athletes and children, the findings underscore patience and proper progression. Children naturally have high variability; demanding perfect technique too early might hinder their self-optimization process. Instead, providing a variety of movement experiences and gradually increasing difficulty allows children to refine their coordination with age. The differences observed between sport-trained and non-trained children suggest that early engagement in jumping sports can accelerate coordination development—but those sports should ideally teach sound fundamentals (i.e., encourage the use of hip and knee flexion rather than landing stiff-legged). Injury risk in youth is lower for ACL (compared to teens/adults) partly because their landings are less forceful, but as they grow, if they carry forward a poor coordination habit (like always landing knee-straight due to lack of training), that becomes risky. So, the integration of coordination training in late childhood and adolescence is key to bridging the gap in adult movement patterns.

Biomechanical Frameworks and Future Directions: This review’s findings can be framed in the context of the systems approach to biomechanics. Rather than viewing joints in isolation, it is clear that the coordination among joints is a critical determinant of outcome (injury or performance). Methods like vector coding and CRP have proven useful in quantifying these interactions. However, they also simplify complex 3D reality into planar couplings. Future research might expand into multi-planar or network analyses of coordination (treating the limb as a connected system rather than pairwise couplings only). Moreover, prospective studies are needed: while cross-sectional differences (i.e., between injured and healthy, or male vs. female) are shown here, we need to know if those differences cause injuries or are just correlated. Longitudinal studies could track coordination variability in athletes and see if certain patterns predict future injury. Early results in cutting manoeuvres suggest they might (i.e., women with very low coupling variability could be at higher ACL risk), but confirmation is needed in landing scenarios. Another area for future work is intervention efficacy: for example, does deliberately increasing landing coordination variability in a female soccer team lead to fewer ACL injuries? Or does balance training that normalizes coupling in CAI actually translate to fewer reinjuries? These practical links must be established.

Finally, wearable technology (inertial sensors) and real-time biofeedback may soon allow coaches to monitor coordination patterns outside the lab [46,47]. Imagine a system that detects if an athlete is getting too stiff in their landing coordination and prompts them or the coach to adjust training. The foundation laid by the studies in this review points toward such applications. Coordination and variability metrics provide a richer description of movement quality than single-angle measures, capturing the interaction between joints that is ultimately what defines a fluid, safe landing.

Limitations: This scoping review has several potential limitations. First, as a scoping review, it does not include a meta-analysis or quantitative synthesis, limiting the ability to draw conclusions about effect sizes or intervention efficacy. Additionally, the included studies are highly heterogeneous in terms of participant populations, tasks performed (i.e., drop jumps, sidestep cutting, single-leg landings), coordination measures used (i.e., CRP, vector coding, Lyapunov exponent), and experimental conditions such as fatigue or taping protocols. This variability presents challenges in directly comparing results. Furthermore, many studies emphasized functional outcomes without addressing underlying neuromuscular, anatomical, or hormonal mechanisms that may influence coordination. The review may also be affected by language and publication bias, as only English-language, peer-reviewed articles were included. Age and sex influences were not consistently reported across studies, despite being relevant factors in motor coordination. The predominance of cross-sectional studies also limits the understanding of longitudinal changes. Lastly, variability in motion capture technology, signal processing, and normalization techniques across studies may introduce inconsistencies in coordination metrics. To mitigate these limitations, a comprehensive and systematic search strategy was employed across multiple databases, detailed data extraction was performed using a structured template, and findings were synthesized thematically to highlight patterns and gaps without overinterpreting heterogeneous data. The decision to clearly distinguish time-series from non-time-series studies further allowed a more nuanced interpretation of coordination variability outcomes. Finally, although an internal protocol guided the review process, its lack of formal registration (such as PROSPERO or OSF) represents a limitation in terms of transparency and reproducibility.

5. Conclusions

This scoping review identified key patterns in lower limb joint coordination and coordination variability during landing across various populations and conditions. Injured individuals often displayed either rigid or inconsistent coordination strategies, while healthy individuals exhibited adaptable, moderate variability. Factors such as sex, age, fatigue, and training also influenced coordination patterns. These findings emphasize the importance of considering coordination metrics in injury risk assessment, rehabilitation, and training design. Future studies should adopt standardized methods and longitudinal designs to better understand how coordination evolves over time and responds to targeted interventions.