Improving Balance and Technical Skills of Young Alpine Skiers: Outcomes of a 10-Week Complex Dry-Land Training Program

Abstract

Limited research has explored the impact of balance training on young alpine skiers, despite its recognized importance in the sport. This study evaluated the effects of a 10-week dry-land balance training program on the balance and skiing technique of skiers aged 9–11 years. The program employed five training modalities: neuromuscular, plyometric, core stability, proprioceptive, and equipment-based balance training. Thirty participants were divided into experimental and control groups based on their initial SKI IQ scores. The assessments included static balance (BTS P-Walk), explosive power (BTS G-Walk), and on-snow metrics (SKI IQ; balance, pressure, and edging) using CARV technology. The statistical analysis revealed significant improvements in the experimental group for the SKI IQ (F = 13.239; p = 0.001; η² = 0.321) in terms of the balance metric (F = 4.800; p = 0.037) and pressure metric (F = 8.084; p = 0.008), and for the static balance parameters, such as mediolateral stability in a two-legged stance (F = 4.304; p = 0.047; η² = 0.133) and anteroposterior stability in eyes-closed conditions (F = 14.249; p = 0.001; η² = 0.337). The single-leg stance stability and explosive force (F = 6.08; p = 0.02) also showed marked enhancements. The edging performance, however, showed no significant change. This study underscores the value of balance training for enhancing young skiers’ performance metrics and highlights wearable technology as an effective tool for real-time feedback. Integrating balance-focused programs into alpine skiing preparation can boost performance.

1. Introduction

Skiing has been a cornerstone of the Winter Olympic Games, representing a diverse array of competitions, including alpine skiing, cross-country skiing, ski jumping, Nordic combined, and snowboarding [1]. Among these disciplines, alpine skiing stands out as a complex and multifaceted sport.

Alpine skiing performance is driven by a unique combination of physiological, functional, and coordinative factors, requiring muscular strength, aerobic and anaerobic capacity, coordination, agility, and flexibility [2,3,4]. In addition, the environmental challenges of high altitudes and low temperatures during training and competition further complicate the demands placed on athletes [5].

The physiological requirements of alpine skiing reflect its intense physical demands. Characterized by efforts lasting between 45 s and 2.5 min, alpine skiing places considerable strain on an athlete’s body, necessitating well-developed motor skills, aerobic and anaerobic capacities, and physical endurance [6,7]. Variations in performance over time may stem from changes in techniques, regulations, equipment, or training regimens, emphasizing the dynamic nature of the sport [3]. Consequently, optimizing performance requires a comprehensive approach to physical training that focuses on aerobic and anaerobic capacity, muscular strength, and advanced motor skills [8,9,10,11].

In recent decades, alpine skiing has undergone substantial material and infrastructure advancements. These include firmer and more compact artificial snow surfaces and modern ski designs that have elevated the sport’s technical and physical demands [8]. Additionally, the introduction of “rapid gate” poles has contributed to increased velocity and dynamism, resulting in significant morpho-functional implications for athletes [8]. As a result, earlier research may not fully capture the relevance of modern technologies and equipment, warranting updated studies that align with current conditions.

An enhanced understanding of the factors influencing performance in alpine skiing can serve as a foundation for improving athlete readiness [9]. While our knowledge of skiing biomechanics has advanced significantly in recent years [10], the physical, technical, and tactical aspects determining skiing performance still remain partially uncertain [11].

To meet the extensive physical demands of alpine skiing, athletes undergo comprehensive training regimens that focus on core strength and stability, power, aerobic and anaerobic endurance, coordination, motor skills, and mobility. Supplementary exercises often derive from other sports, illustrating the breadth of the methods used to enhance overall performance [12], as cited by Gilgien et al. [13]. This multifaceted approach underscores the sport’s rigorous requirements and the need for a holistic training strategy.

Although motor control and balance are acknowledged as critical for performance, the evidence regarding their direct impact on skiing success is not always consistent [14,15,16,17]. Several recent studies have sought to clarify this relationship; however, the differences in measurement tools and testing protocols have led to conflicting results [12,18,19]. In particular, variations in methodology—such as laboratory-based tests vs. on-snow conditions—may account for these inconsistencies [12,20]. Additionally, the absence of standardized, sport-specific balance tests often hinders the robust comparisons of findings across different studies [19,21].

In alpine skiing, as in other sports, technical ability is a crucial factor for performance, but it cannot be fully expressed without adequate physical and psychological skills [12,13,22].

The development of technical ability is closely related to neurogenic activity, while physical ability is associated with energetic and metabolic aspects [23,24].

One of the key technical aspects is the ability to maintain a carved turn, resisting the generated G-forces while effectively controlling one’s edges and balance [12]. In competitive contexts, skiers aim to execute carved turns with minimal lateral skids and reduced friction, thereby achieving the fastest possible run times [25]. LeMaster further emphasized that in an ideal carved turn, the ski should not move laterally while engaging the snow [26]. Turns are therefore frequently examined to capture the key parameters, such as edge angle, symmetry, and turn phases [22,27,28].

Consistently adapting one’s turning technique to accommodate the changes in terrain, slope, gate setup, and snow conditions is a technically complex process that necessitates evaluating the performance parameters that are more comprehensive and subtle than merely assessing the overall race time [29,30,31,32], as cited in Supej et al. [33].

In such dynamic conditions, athletes must continuously adapt to high internal and external forces to maintain postural control, which is crucial [18,34]. Biomechanical studies have shown that any deficit in the controlling posture can trigger compensatory body movements, potentially reducing force production and overall skiing technique [20,27].

Another significant inconsistency in the field is the diverse terminology used to describe the concept of balance and the associated exercises for developing postural stability. Recent work has highlighted multiple terms—“balance training”, “sensorimotor training”, “proprioceptive training”, and “neuromuscular training”—all referring to similar or overlapping concepts [35,36,37,38,39,40]. This terminological diversity can lead to interpretive challenges, underscoring the need for standardization in both research and practice. While various terminologies exist in the literature, we use ‘balance training’ in this study to collectively refer to interventions that target neuromuscular coordination, proprioceptive feedback, and postural control.

The increasing recognition of balance as a critical factor in skiing performance has led to calls for more focused research in this area. Despite its significance, balance optimization in skiing remains an underexplored topic. Researchers, such as Brachman et al. [21], Male et al. [15], Noe and Paillard [17], and Słomka et al. [41], have highlighted the notable lack of studies focused on this aspect of skiing. This issue is further compounded by the absence of standardized tests specifically designed for on-snow conditions, making it difficult to accurately assess balance and its effects on performance [19,21].

While some of the conflicting results observed in previous studies regarding skiers’ postural control have arisen from unspecific testing protocols, such as laboratory-based tests that fail to differentiate between skiers of varying skill levels [20], recent investigations using more challenging, sport-specific protocols (e.g., wearing ski boots in dynamic balance tests) have suggested that experienced skiers employ hip strategies that may not be captured by standard laboratory testing [42]. Consequently, when applying more specific balance tests tailored for alpine skiers, the results appear reliable and sensitive, even for elite skiers [20].

In outdoor sports such as alpine skiing, the need for valid and reliable on-field performance assessments is particularly critical due to the complex environmental factors [33]. Standardized procedures employing advanced wearable technologies are increasingly valuable, as they enable an evaluation of biomechanical parameters and uncover subtle yet critical details about skiing performance [33]. For example, tools, such as GNSS systems, can provide real-time or near-real-time data on an athlete’s performance [33]. However, ensuring the accuracy and validity of these technologies are crucial to avoid drawing incorrect conclusions [33].

In this regard, reliable feedback is essential; however, the majority of coaches must rely on their own visual evaluation, which might not be sufficiently sensitive to discern the subtle yet significant differences in technique [43]. As a result, the recent advancements in wearable technology offer a promising solution by providing detailed, quantitative data on skiing technique, facilitating more precise and informed interventions.

This research seeks to address three interrelated hypotheses, aiming to contribute to the understanding and optimization of the performance of junior alpine skiers. Firstly, we hypothesize that targeted balance training can result in measurable improvements in balance metrics and skiing technique. This hypothesis arises from the recognition that despite often being underemphasized in traditional skiing training, balance is a fundamental determinant of overall performance.

Secondly, we propose that wearable technology can provide distinctive insights into the performance and technique of junior alpine skiers. These technologies are expected to deliver immediate data, enhancing conventional coaching strategies. By facilitating more tailored and precise training methodologies, wearable technology has the potential to significantly optimize athlete performance.

Thirdly, we hypothesize that a balance development protocol can have a positive impact on static balance values and explosive force. This aspect highlights the interconnected nature of balance training and its broader implications for athletic performance.

By integrating these hypotheses, this research aims to validate a comprehensive approach to improving junior skiers’ performance. Focusing on the combination of balance training and advanced wearable technology, this study seeks to bridge the existing gaps in skiing training methods and foster a deeper understanding of performance optimization in this highly demanding sport.

2. Materials and Methods

2.1. Study Design

Our research employed a mixed two-factor experimental design with both an experimental and a control group. Specifically, the independent variable was a structured balance development program, while dependent variables included on-snow performance (via CARV), static balance (via BTS P-Walk), and explosive force (via BTS G-Walk). This approach allowed us to isolate the effect of balance training from other factors, thus providing insights into its efficacy for enhancing junior skiers’ performance.

2.2. Participant Selection

Participants in this research study were chosen based on their willingness to partake in the investigation, having been recruited from two sports clubs in Cluj-Napoca. A total of 30 junior alpine skiers (aged 9–11 years) were recruited, reflecting both the limited availability of specialized athletes in this age range and feasibility constraints related to training schedules and resources. Although a larger sample would generally increase statistical power, the research objective focused on a highly specific population (young, competitive alpine skiers), which restricted our pool of potential participants.

Inclusion criteria were (1) age between 9 and 11 years, (2) at least two years of organized ski training experience, and (3) no history of lower-limb injury in the six months preceding this study. Exclusion criteria were (1) unwillingness to participate or inability to complete the full protocol, and (2) any musculoskeletal or neurological condition affecting balance or jumping ability.

Before inclusion, parents of potential participants received an informational and consent form outlining this study’s objectives, assessment procedures, and potential risks. Participation was voluntary and could be withdrawn at any time. This study was approved by the Ethics Committee of Babeș-Bolyai University, and all procedures adhered to the Declaration of Helsinki.

2.3. Demographic Characteristics

As shown in

Table 1. Participants’ baseline demographic information.

Group	n	Age (Years)	Height (cm)	Weight (kg)	Muscle Mass (kg)	Fat Mass (%)
Experimental	15	9.86 (SD = 0.83)	143.93 (SD = 8.66)	38.87 (SD = 6.78)	29.25 (SD = 3.67)	19.81 (SD = 7.41)
Control	15	9.66 (SD = 0.61)	143.7467 (SD = 8.23)	35.17 (SD = 9.30)	26.83 (SD = 4.74)	15.78 (SD = 7.26)

, the baseline demographic information of the participants is summarized below.

2.4. Randomization Procedure

Participants were matched–paired based on their SKI IQ scores—a composite index (weighted average of balance, edging, and pressure) obtained during the initial on-snow assessments. Each pair was then randomly assigned to either the experimental group (EG) or the control group (CG), ensuring a balanced distribution of critical characteristics while minimizing confounding variables.

2.5. Testing Protocol

2.5.1. On-Snow Test Protocol

To evaluate the athletes’ performance, we used a giant slalom course (20 gates, with a distance of 18 m between gates) for both the initial and final on-snow testing. Participants were instructed to use giant slalom skis (radius > 16 m) and completed the tests between 2:00 PM and 7:00 PM on the blue slope at Mărișel ski resort, Romania.

Before testing, athletes performed a standard warm-up and a course inspection run. Five members of the research team, plus the research leader, were positioned along the course to verify proper gate passage.

2.5.2. Instruments for On-Snow Assessments

We selected the CARV system [44] (Carv Digital Ski Coach, London, UK) because it offers real-time biomechanical feedback relevant to balance, edging, and pressure—key aspects of skiing performance. The smart insoles (dimensions: 90 × 45 × 18 mm; weight: 27 g) contain an accelerometer, a gyroscope, a magnetometer, a barometer, and 36 pressure sensors, collectively sampling at 25 Hz. The insole was placed inside the ski boot, between the shell and the liner, while a small data logger was attached to the boot’s exterior. Data were transmitted via Bluetooth Low Energy to the accompanying smartphone app. In total, 35 metrics were processed, aligning with our goal to assess technique improvements following balance training. The system’s battery (Li-Po) lasts up to 8 h, allowing for comprehensive on-snow evaluations.

2.5.3. Dry-Land Test Protocols

Static Balance Test Protocols

Static balance was assessed pre- and post-intervention using the BTS P-Walk device on a stable surface with athletes barefoot.

• Bipedal test: participants stood with their feet on designated markers, hands on hips, eyes closed, maintaining balance for 30 s.
• Unipedal test: Participants stood on one leg for 15 s, with the free foot resting at the ankle. This protocol mimics unilateral weight distribution demands of skiing.

Counter-Movement Jump (CMJ) Protocol

The Counter-Movement Jump (CMJ) test was used to evaluate explosive force, with jump height measured via the G-walk system. The test involved an initial eccentric phase, where the leg extensor muscles were stretched through a preparatory contraction, followed by a concentric phase, characterized by an explosive extension in the opposite direction.

Participants performed 15 consecutive CMJs with hands on hips to eliminate the influence of arm swing, and the G-walk system calculated the average jump height. We selected the Counter-Movement Jump (CMJ) to measure explosive force because it is a well-established, valid indicator of lower-limb power, requiring minimal technical skill and demonstrating high reliability in adolescent populations [45]. This aligns directly with this study’s goal of evaluating improvements in lower-limb power for alpine skiing, where rapid force generation is critical for propulsion and quick directional changes.

2.6. Instruments for Dry-Land Performance Assessments

2.6.1. BTS G-Walk

The BTS G-Walk (BTS Bioengineering, Garbagnate Milanese, Italy) is an inertial measurement unit (IMU) used to measure explosive force during Counter-Movement Jump (CMJ) tests. The sensor (weight: 37 g; dimensions: 70 × 40 × 18 mm) is placed at L5 via a semi-elastic belt and transmits data via Bluetooth 3.0 to G-Studio software (Available at: https://www.btsbioengineering.com/en/ (accessed on 20 December 2024)) at 100 Hz. This device combines an accelerometer, gyroscope, magnetometer, and GPS receiver, providing a detailed assessment of jump dynamics and average jump height. Its reliability for assessing lower-limb power makes it ideally suited for evaluating skiing-specific explosive demands [46].

2.6.2. BTS P-Walk

The BTS P-Walk (FM12050 BTS-Bioengineering, Garbagnate Milanese, Italy) is a pressure-plate system capable of making stabilometric assessments by measuring center-of-pressure (COP) parameters at up to 100 Hz. We used it for static balance tests (bipedal and unipedal) with athletes barefoot. By quantifying COP distance and average X/Y positions (anteroposterior and mediolateral axes), it provides sensitive insights into postural sway and stability—particularly relevant when translating edging and balance control to alpine skiing. Its design incorporates over 9000 resistive pressure sensors, enabling high-resolution detection of subtle shifts in COP.

2.7. Balance Training Protocol

This study integrated diverse balance development strategies, such as neuromuscular, core, and upper-body stability; proprioceptive balance training with equipment; and plyometric training. Balance development exercises were selected and adapted from an assortment of studies, all of which reported beneficial outcomes following balance interventions that incorporated diverse training approaches.

The balance development training occurred twice a week for 10 weeks between September and November, with each session lasting 30–40 min, excluding the warm-up exercises. These warm-up exercises consisted of basic warm-up exercises commonly used in physical education lessons. In addition to the research leader, a specialist was present to guide correct exercise execution. An example of the training can be observed in

Table 2. Example of neuromuscular training.

Exercise	Sets	Reps	Rest Between Sets (s)
Plank position, alternating single-leg raise	1	10L–10R	P 30
Side-lying forearm support with single-leg raise	1	15L–15R	P 30
Supine position, arms alongside the body, one knee bent at 45 degrees, heel contact with the ground, hip lifted off the ground with leg extension	1	10L–10R	P 30
Bulgarian split squat followed by jump	1	5L–5R	P 30
Lunge step with trunk twist	1	10L–10R	P 30
Bicycle crunches	1	20	P 30
Skier’s jump with hold	1	15	P 30

Note: P = passive; A = active; L = left limb; R = right limb.

.

The balance development protocol was conducted in a fitness room, with participants in the experimental group divided into two groups: the first group trained between 17:00 and 18:00, and the second group trained between 18:00 and 19:00 on Mondays and Wednesdays. Materials used in the balance development protocol included fitness mats, exercise balls, balance boards, balls of various sizes, and hurdles.

In this study, while the experimental group engaged in a structured protocol targeting the development of both static and dynamic balance, the control group continued to adhere to its standard dry-land training regimen, which was tailored to the specific requirements of the corresponding training period.

2.8. Statistical Analyses

All statistical analyses were performed using SPSS software Version 29 (IBM Corp., Armonk, NY, USA). First, we compared baseline parameters (age, weight, muscle mass, fat percentage, and height) between groups using independent-samples t-tests. All t-values were non-significant, indicating no major baseline disparities between the experimental and control groups.

Normality was tested using the Kolmogorov–Smirnov test, skewness, and kurtosis. Although minor deviations were detected, ANOVA was robust to these violations [47]. Additionally, skewness and kurtosis values fell within acceptable ranges, supporting the use of parametric methods.

Levene’s test was performed to check homogeneity of variance, which was non-significant for all measures, indicating similar variance across groups. Outliers were identified via z-scores exceeding ±3 and excluded if they substantially influenced results. In this study, outlier removal did not significantly affect outcomes. Variance ratios also met Cohen’s [48] criterion (below 4), further supporting homogeneity.

Because variance analysis methods were robust to minor normality violations [49], we concluded that all assumptions for ANOVA and MANOVA were adequately met, validating our overall results. Unless otherwise stated, an alpha level of p < 0.05 was considered significant.

3. Results

3.1. Comparisons Between the Pre-Test and Post-Test of Control and Experimental Groups (ON-SNOW Assessment)

In this study, the SKI IQ, edging, balance, and pressure were recorded as composite scores rather than measurements in conventional physical units (e.g., meters, kilograms). Each of these indices aggregates data from multiple submetrics. For instance, the balance metric on the CARV system combines early forward movement (%) and mid-turn balance (%) into a dimensionless index. Consequently, these values do not represent direct measurements of length, mass, or force, but rather scaled performance scores derived from device-specific algorithms.

However, the final composite scores are best understood as relative indicators of technique or skill rather than measures in standard International System (SI) units. This approach enables a more nuanced assessment of skiing performance, capturing factors (e.g., weight shifting, timing) that are difficult to quantify with a single physical dimension.

In

Table 3. Descriptive statistics of differences between pre-test and post-test.

Variables	EG vs. CG	Mean	Std. Deviation	N
Ski IQ difference	EG	6.0667	3.63449	15
	CG	1.7333	2.84019	15
	Total	3.9000	3.88942	30
Edging difference	EG	2.6000	3.73784	15
	CG	1.6000	6.97751	15
	Total	2.1000	5.52330	30
Pressure difference	EG	5.6000	8.27906	15
	CG	−2.3333	6.94537	15
	Total	1.6333	8.52373	30
Balance difference	EG	3.5333	3.27036	15
	CG	−0.5333	6.40164	15
	Total	1.5000	5.40594	30

, we present the descriptive statistics regarding the difference from the pre-test to the post-test for the control and experimental groups.

Moreover, in

Table 4. Descriptive statistics of variables tested.

Variables	EG vs. CG	Mean	Std. Deviation	N
Ski IQ pre-test	EG	95.20	9.167	15
	CG	92.20	9.120	15
	Total	93.70	9.113	30
Ski IQ post-test	EG	101.27	9.982	15
	CG	93.93	10.361	15
	Total	97.60	10.669	30
Edging pre-test	EG	46.20	11.143	15
	CG	43.13	13.410	15
	Total	44.67	12.215	30
Edging post-test	EG	48.80	9.088	15
	CG	44.73	11.411	15
	Total	46.77	10.345	30
Pressure pre-test	EG	49.73	6.703	15
	CG	49.33	5.434	15
	Total	49.53	5.998	30
Pressure post-test	EG	55.33	9.394	15
	CG	47.00	7.464	15
	Total	51.17	9.352	30
Balance pre-test	EG	43.80	7.103	15
	CG	43.80	8.170	15
	Total	43.80	7.522	30
Balance post-test	EG	47.33	5.525	15
	CG	43.27	6.100	15
	Total	45.30	6.081	30

, we present the descriptive statistics at the pre-test and post-test, such as the mean and standard deviation of the calculated variables.

We utilized a multivariate analysis of variance (MANOVA) to investigate the potential differences between the control and experimental groups in terms of variables, such as the SKI IQ, balance, edging, and pressure. The dependent variable was determined by calculating the difference between the post-test and pre-test scores.

Following the analysis, a notable distinction was observed between the control and experimental groups in terms of the SKI IQ (F = 13.239; p = 0.001; η² = 0.321). This difference possesses a large magnitude, signifying a significant effect size. Additionally, we observed statistically significant variations in the balance metric (F = 4.800; p = 0.037) and the pressure metric (F = 8.084; p = 0.08), as indicated in

Table 5. Differences between pre-test and post-test of control and experimental groups.

Dependent Variable	Sum of Squares	Degrees of Freedom	F	p-Value	η2
SKI IQ Difference	140.833	1	13.239	0.001 *	0.321
Balance Difference	124.033	1	4.800	0.037 *	0.146
Pressure Difference	472.033	1	8.084	0.008 *	0.224
Edging Difference	7.500	1	0.239	0.628	0.008

Note: * indicates p < 0.005.

. Overall, the experimental group demonstrated larger improvements in the SKI IQ, balance, and pressure compared to the control group.

Notably, the edging difference did not reach statistical significance (p = 0.628). This could be due to the relatively short duration of the intervention, the complexity of edging skills, or the possibility that both groups already possessed competent edging ability. Future research could employ a longer training period or a larger sample size to clarify these findings.

In Figure 1, we present the comparative distributions of the pre-test vs. post-test Ski IQ for both groups. The figure highlights a larger shift in the experimental group, illustrating the positive impact of the intervention on the Ski IQ.

Similarly, the balance variable also exhibited an increase from the pre-test to the post-test in the experimental group, in contrast to a minor decline in the values in the control group, as can be observed in Figure 2.

3.2. Differences Between Pre-Test and Post-Test in Control and Experimental Groups for Dry-Land Testing Variables

(a)

Analysis of Results for Balance Testing on Two Legs with Eyes Open

Variable Codes:

• CMJ Height: height of the Counter-Movement Jump.
• OD2P AP: balance on two legs with eyes open in the anteroposterior direction.
• OD2P ML: balance on two legs with eyes open in the mediolateral direction.
• OD2P COP: balance on two legs—center-of-pressure movement distance.

The OD2P_AP, OD2P_ML, and OD2P_COP were measured in millimeters (mm), indicating the distance-based center-of-pressure shifts in a two-legged stance with eyes open. In contrast, the CMJ Height was recorded in centimeters (cm).

To examine the differences between the control and experimental groups based on several relevant variables, we implemented a multivariate analysis of variance (MANOVA). Consequently, we evaluated the differences by grouping the dependent variables into sets of three.

The dependent variable was determined by calculating the difference between the final and initial scores. Thus, the three OD2P variables were included in the same analysis. The dependent variable was established by computing the difference between the post-test and pre-test scores.

Analyzing the results shown in

Table 6. The difference between the pre-test and post-test results for the experimental group (EG) versus the control group (CG) on the two-legged balance test.

Dependent Variable (mm)	Sum of Squares	Degrees of Freedom	F	p-Value	η2
OD2P_AP_difference	1.661	1	1.584	0.219	0.054
OD2P_ML_difference	7.223	1	4.304	0.047 *	0.133
OD2P_COP_difference	77,376.534	1	5.212	0.030 *	0.157

Note: * indicates p < 0.005.

for the variables OD2P_AP, OD2P_ML, and OD2P_COP, the following conclusions can be drawn:

• For the variable OD2P_AP, the value of F (1584) does not indicate a statistically significant difference between the groups, as the p-value is greater than 0.05 (p = 0.219). The effect size, represented by η², is 0.054, suggesting a small effect.
• For the variable OD2P_ML, the value of F (4304) indicates a statistically significant difference between the groups, as the p-value is less than 0.05 (p = 0.047). The effect size, represented by η², is 0.133, suggesting a medium effect.
• For the variable OD2P_COP, the value of F (5212) indicates a statistically significant difference between the groups, with the p-value being less than 0.05 (p = 0.030). The effect size, represented by η², is 0.157, suggesting a medium-to-large effect.

In conclusion, the balance development protocol had a significant effect on the variables OD2P_ML and OD2P_COP. Although OD2P_AP did not show significance, this non-significant result may reflect the test’s inherent lower sensitivity in the anteroposterior plane or variability in the participants’ skill levels. Overall, the protocol appears effective at improving some two-legged balance dimensions.

(b)

Analysis of Results for Balance Testing on Two Legs with Eyes Closed

Variable Codes:

• OI2P_AP: eyes closed on two legs in the anteroposterior direction.
• OI2P_ML: eyes closed on two legs in the mediolateral direction.
• OI2P_COP: eyes closed on two legs—center-of-pressure (COP) movement distance.

All the OI2P_AP, OI2P_ML, and OI2P_COP variables were measured in millimeters (mm), reflecting the distance of the center-of-pressure shifts in the anteroposterior and mediolateral directions under an eyes-closed, two-legged stance (

Table 7. Analysis of the results of two-leg balance testing with eyes closed.

Dependent Variable (mm)	Sum of Squares	Degrees of Freedom	F	p-Value	η2
OI2P_AP_difference	9.296	1	14.249	0.001 *	0.337
OI2P_ML_difference	4.961	1	1.645	0.210	0.055
OI2P_COP_difference	2268.612	1	0.208	0.652	0.007

Note: * indicates p < 0.005.

).

The analysis indicates that for the dependent variable “OI2P_AP_difference”, the results are statistically significant (F = 14.249; p = 0.001), with a large effect size (η² = 0.337), demonstrating the strong influence of the independent variable. Conversely, no statistically significant effects were observed for “OI2P_ML_difference” or “OI2P_COP_difference”.

These results indicate a strong improvement in the anteroposterior dimension with eyes closed, while the mediolateral and COP measures remained non-significant. Such non-significance could reflect the inherent variability under eyes-closed conditions or an insufficient training duration for robust improvements of all the balance dimensions.

(c)

Analysis of Results for Single-Leg Balance Testing—Right Leg

Variable Codes:

• SP1PD_AP: single-leg stance on the right leg—anteroposterior direction.
• SP1PD_ML: single-leg stance on the right leg—mediolateral direction.
• SP1PD_COP: single-leg stance on the right leg—center-of-pressure (COP) movement distance.

All the SP1PD_AP, SP1PD_ML, and SP1PD_COP variables were measured in millimeters (mm).

The statistical analysis, as shown in

Table 8. Differences between EG and CG in single-leg balance values on the right foot.

Dependent Variable (mm)	Sum of Squares	Degrees of Freedom	F	p-Value	η2
SP1PD_AP_diffrence	3.874	1	2.763	0.108	0.090
SP1PD_ML_diffrence	26.152	1	8.966	0.006 *	0.243
SP1PD_COP_diffrence	53,602.587	1	4.344	0.046 *	0.134

Note: * indicates p < 0.005.

, reveals that for the dependent variable “SP1PD_ML_difference”, the results are statistically significant (F = 8.966; p = 0.006), with a partial η² = 0.243, indicating a moderate-to-large effect size.

For the dependent variable “SP1PD_COP_difference”, the analysis, presented in Table 8, yields a statistically significant result (F = 4.344; p = 0.046), with a partial η² = 0.134, reflecting a moderate effect size. No statistically significant differences were observed for the variable “SP1PD_AP_difference”.

(d)

Analysis of Results for Single-Leg Balance Testing—Left Leg

Variable Codes:

• SP1PS_AP: single-leg stance on the left leg—anteroposterior direction.
• SP1PS_ML: single-leg stance on the left leg—mediolateral direction.
• SP1PS_COP: single-leg stance on the left leg—center-of-pressure (COP) movement distance.

All the SP1PS_AP, SP1PS_ML, and SP1PS_COP variables were measured in millimeters (mm), representing the distance of the center-of-pressure shifts in the anteroposterior and mediolateral planes for a single-leg stance on the left leg.

The statistical analysis, as shown in

Table 9. Differences between the EG and the CG in single-leg balance values on the left leg.

Dependent Variable (mm)	Sum of Squares	Degrees of Freedom	F	p-Value	η2
SP1PS_AP_difference	1699	1	2.185	0.151	0.072
SP1PS_ML_difference	8694	1	6.396	0.017 *	0.186
SP1PS_COP_difference	18,595,320	1	3.322	0.079	0.106

Note: * indicates p < 0.005.

, reveals that for the dependent variable “SP1PS_ML_difference”, the results are statistically significant (F = 6.396; p = 0.017), with a partial η² = 0.186, indicating a moderate effect size.

For the dependent variable “SP1PS_COP_difference”, the analysis, presented in Table 8, reports a result close to statistical significance (F = 3.322; p = 0.079), suggesting a potential difference between the groups, though not definitive. The partial η² = 0.106 reflects a small-to-moderate effect size. No statistically significant differences were observed for the variable “SP1PS_AP_difference”. While improvements were evident in the mediolateral control on the left leg, the anteroposterior direction did not reach significance. Future studies may employ more targeted single-leg interventions or longer protocols to determine whether stronger effects emerge for all the axes of balance.

(e)

Analysis of Results for Explosive force

The ANOVA analysis for the Counter-Movement Jump (CMJ) height values, as illustrated in Figure 3, indicates minimal changes in the CMJ height from the pre-test to the post-test for the control group. However, the experimental group shows a significant increase, with the test results being statistically significant (F = 6.08; p = 0.02).

In summary, the non-significant findings for certain balance axes (e.g., OD2P_AP and SP1PD_AP) may reflect methodological constraints, group heterogeneity, or the relatively short duration of the training. Additionally, a moderate sample size could limit the statistical power to detect smaller effects. Nevertheless, the significant improvements in the SKI IQ, specific balance measures, pressure, and CMJ height highlight the potential impact of the balance development protocol.

4. Discussion

The primary aim of this research was to address inconsistencies and gaps in the literature regarding the effects of balance training on alpine skiing performance. Specifically, this study sought to evaluate the impact of a structured balance development protocol for improving balance metrics, skiing technique, and overall athletic performance in young skiers. A secondary objective was to integrate wearable technology into the training process, leveraging real-time feedback and precise data to complement traditional methods and optimize performance outcomes. Furthermore, a tertiary focus was investigating the impact of the balance development protocol on static balance values and explosive force.

A multitude of studies have emphasized the importance of dry-land training as a crucial component of skiers’ overall preparation, yet the majority have focused on improving aerobic capacity, anaerobic capacity, and muscular strength. Investigations into the significance of balance in alpine skiing remain limited and have frequently yielded contradictory results. Some researchers have argue that incorporating specific balance training may contribute to improved performance [18,19].

Several studies have indicated enhancements in both static and dynamic balance, along with improved postural stability [41,50,51] following different balance intervention protocols. These improvements align with our results by reinforcing the idea that targeted balance work can enhance certain performance markers. In contrast, other studies have suggested that experimental and control groups have demonstrated either similar improvements or failed to show significant enhancements in balance performance [52,53]. These conflicting outcomes highlight the role of individual variability, differing training protocols, and sample characteristics in shaping study conclusions.

The direct correlation of balance, as a critical motor skill in alpine skiing, with performance outcomes has been debated [12,15,16,17]. Despite its importance in maintaining postural control under dynamic and unpredictable conditions, many training programs emphasize muscular strength and aerobic/anaerobic development. Our data suggest that balance training can be a worthwhile addition to existing protocols, but its overall impact may vary depending on factors, such as baseline ability, training history, and terrain conditions [22,28].

Existing studies examining balance development in skiers under the age of 16 are scarce. A notable example is an investigation of an 8-week complex balance training program for 16-year-olds, which demonstrated improvements in dynamic stability and reduced asymmetry between the lower extremities [41]. However, the absence of a control group limits the generalizability and validity of these findings. Similarly, an 8-week neuromuscular training program for elite junior skiers [51] improved dynamic balance but failed to impact vertical jump performance. These discrepancies underscore the need for methodologically robust studies that elucidate the role of balance training in alpine skiing.

Simultaneously, injury prevention is crucial and necessitates the appropriate strength development and physical conditioning programs. Although injury prevention and the mitigation of neuromuscular fatigue are central concerns in alpine skiing, the present study does not directly assess these outcomes. Future research could investigate whether improvements in balance translate to reduced injury risk or enhanced fatigue management, as suggested elsewhere [12]. Such links remain speculative in the context of our data and should be addressed in studies incorporating biomechanical or physiological measures specific to injury risk and fatigue.

In our study, the experimental group followed various balance development methods, including neuromuscular training, core and upper-body stability training, proprioceptive training, balance equipment training, and plyometric training. These choices were informed by previous research [41,54,55,56,57,58,59,60,61,62,63,64,65] indicating that multifaceted interventions can positively affect various aspects of motor control and stability. Nevertheless, we acknowledge that the effects of these interventions may differ among individuals based on their unique physiological, biomechanical, and skill profiles.

The analysis presented in this study highlights that the experimental group, which underwent a comprehensive 10-week balance training program, exhibited markedly superior improvements when compared to the control group. These enhancements were substantiated by statistically significant differences in three key variables for on-snow assessment: the Ski IQ (F = 13.239; p = 0.001; η² = 0.321), balance metric (F = 4.800; p = 0.037), and pressure metric (F = 8.084; p = 0.08). While these findings suggest that balance training can positively affect multiple facets of skiing, care must be taken not to overgeneralize. An individual’s baseline skills, the length of the intervention, and specific training components may influence the degree of improvement.

The Ski IQ, a variable representing a key aspect of a skier’s technique, has been demonstrated to undergo substantial improvement through targeted dry-land training interventions, thereby underscoring its pivotal role in enhancing alpine skiing performance and emphasizing the importance of incorporating such training approaches for the development of young skiers.

Conversely, we found a statistically significant difference in the balance variable from the pre-test to the post-test on the EG. This indicates that the implemented training intervention might have had a substantial impact on this particular aspect of the skiers’ performance. This finding underscores the efficacy of the selected training strategies for enhancing balance development in alpine skiing. We also noted that edging did not reach significance, highlighting the complexity of skill acquisition in alpine skiing. This might indicate that edging requires more specific or prolonged training stimuli, or that technical adaptations develop on different timescales than core balance and pressure skills.

The further examination of the dry-land assessments revealed that the balance development protocol had a pronounced positive influence on the static balance parameters and trunk stability. Specifically, significant intergroup differences were identified for OD2P_ML (F = 4.304; p = 0.047; η² = 0.133) and OD2P_COP (F = 5.212; p = 0.030; η² = 0.157), signifying the protocol’s substantial impact on these variables.

For the OI2P_AP variable, a moderate-to-large effect size was observed (F = 14.249; p = 0.001; η² = 0.337). However, no statistically significant effects were noted for OI2P_ML or OI2P_COP, suggesting that the training protocol’s impact may vary across the different dimensions of static balance.

In terms of unilateral balance, the SP1PD_ML variable demonstrated a statistically significant intergroup difference (F = 8.966; p = 0.006; η² = 0.243), as did SP1PD_COP (F = 4.344; p = 0.046; η² = 0.134). Meanwhile, SP1PD_AP did not display significant differentiation. Similarly, SP1PS_ML exhibited significant improvement in the experimental group (F = 6.396; p = 0.017; η² = 0.186).

Explosive force, assessed through the Counter-Movement Jump (CMJ) performance, showed a statistically significant enhancement in the experimental group (F = 6.08; p = 0.02). Although this suggests that balance training might positively influence lower-limb explosiveness, we recommend further studies incorporating direct measures of muscle power and biomechanics to confirm the robustness of these effects under actual skiing conditions.

Finally, our findings indicate that a systematic balance development protocol can enhance several performance indicators in alpine skiing, yet some variables (e.g., edging, certain axes of balance) did not show significant changes. These mixed outcomes demonstrate that balance training should be tailored to an individual’s needs and integrated with other sport-specific practices for maximal benefit. Future research should directly assess injury prevention and fatigue dynamics to validate whether these potential benefits extend beyond the observed performance improvements.

Limitations

While this study provides valuable insights, several limitations should be acknowledged when interpreting the findings. Firstly, the relatively small sample size (30 participants) may limit the generalizability of the results, suggesting the need for larger, more representative samples in future research. Secondly, only 4 out of the 30 participants were female, resulting in a gender imbalance that might affect the applicability of our conclusions to female skiers. Future investigations should aim for more balanced cohorts or conduct subgroup analyses to explore potential sex-specific responses.

Additionally, the accuracy of wearable technology, which can be influenced by device calibration, user error, and external conditions, may have affected the precision of our measurements. Furthermore, external factors, such as snow conditions, slope variability, and skier fatigue, were not fully controlled for, potentially introducing additional variance into the on-snow performance measures. Finally, the possibility of uncontrolled confounding variables—such as prior training experience, nutritional habits, or physiological characteristics—could have influenced the outcomes. These considerations should guide subsequent research efforts to refine study designs and enhance the robustness of the findings in this domain.

5. Conclusions

This investigation examined three interrelated hypotheses regarding the integration of balance training and wearable technology for improving the performance of junior alpine skiers.

Firstly, the findings indicate that targeted balance training yields significant improvements in several balance metrics and in skiing technique, supporting the notion that balance is an important determinant of athletic performance in alpine skiing. Nevertheless, the extent of these improvements may vary depending on an individual’s baseline skills, training duration, and other contextual factors.

The second hypothesis, which proposed that wearable technology would provide actionable insights to complement traditional methods, is largely substantiated. In particular, the utilization of advanced monitoring systems facilitated biomechanical assessments and real-time feedback, enabling data-driven refinements to specific methodologies.

The third hypothesis, concerning the impact of balance training on explosive force production, is only partially confirmed. Although statistically significant improvements in the Counter-Movement Jump (CMJ) performance were observed (F = 6.08; p = 0.02), suggesting a possible link between neuromuscular control and balance interventions, not all the analyzed variables demonstrated significant changes. This outcome indicates that, while balance training may enhance lower-limb power to some extent, other considerations—such as training volume or individual variation—can influence explosive force results.

The statistically significant enhancements in the Ski IQ (F = 13.239; p = 0.001; η² = 0.321), balance (F = 4.800; p = 0.037), and pressure metrics (F = 8.084; p = 0.08) within the experimental group underscore the efficacy of the implemented balance training protocol. These findings align with some existing studies, but contrast with those reporting limited connections between balance interventions and performance.

However, given the mixed findings across studies and the individualized responses observed, further investigations are needed to strengthen these results, identify the optimal balance training protocols across various age groups and skill levels, and clarify whether additional outcomes, such as injury prevention, can be directly attributed to balance interventions. Such research will contribute to a more comprehensive understanding of balance’s role in alpine skiing, facilitating more tailored and effective training programs.