Effectiveness of a Home-Based Telehealth Exercise Program Using the Physitrack^® App on Adherence and Vertical Jump Performance in Handball Players: A Randomized, Controlled Pilot Study

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Physitrack^® facilitates the implementation of individualized home-based training programs for athletes, optimizing the continuity of the work prescribed by the physiotherapist.

Abstract

Objective: To evaluate the effect of Physitrack^® on jump performance in handball players through performance, kinematic, and kinetic variables. Material and Methods: A pilot, randomized clinical trial was conducted with male handball players (n = 28). Participants were allocated to either an intervention group (IG), which completed a specific jump-training program, or a control group (CG), which followed a general strengthening program. Both programs were delivered via Physitrack^® over an 8-week period. Vertical jump variables were assessed using force platforms (Hawkin Dynamics^®), along with adherence questionnaires, the Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ), and the System Usability Scale (SUS). Results: Both groups showed significant improvements in jump height, flight time, and peak velocity (p < 0.05), without differences between groups. The IG, additionally, demonstrated improvements not statistically significant in the modified Reactive Strength Index (mRSI), Rate of Force Development (RFD), and power. Mean adherence was moderate, slightly higher in the IG (52.13% vs. 48.98%), with no significant differences between groups (p = 0.74). Physitrack^® received an excellent usability rating (SUS: 83.3/100) and good satisfaction (TSUQ: 3.68/5). These findings should be interpreted with caution given the pilot nature of the study and the limited sample size, which restrict statistical power and the generalizability of results. Conclusions: Physitrack^® is a feasible tool for prescribing home-based exercises and is well rated by users. It does not directly improve adherence but facilitates the implementation of effective programs although the content of the program has a greater influence on performance improvements than the platform itself.

1. Introduction

In recent years, telemedicine has gained significant importance in the field of health, especially in physiotherapy and rehabilitation, by enabling remote access and continuous monitoring of patients by healthcare professionals. This development has been largely driven by the advancement of digital technologies, which have facilitated patient contact and follow-up regardless of geographical barriers [1]. The use of telemedicine tools and tele-rehabilitation applications has been established as a practical solution for ensuring continuity of treatment, offering access to therapies and detailed monitoring of each patient’s progress.

Among the available digital tools, the Physitrack^® (Physitrack PLC, London, UK) platform has proven to be effective in the digital prescription of personalized exercise programs and in enabling real-time consultations—functions that have increased patient adherence and facilitated the achievement of their rehabilitation goals [2].

Handball, a sport characterized by its high intensity and the combination of physical and tactical skills, places great physical demands on its players due to constant jumping, changes of direction, and physical contact. The nature of the game, involving high-speed movements and a strong demand for motor skills, generates a considerable risk of injuries, especially in the lower limbs, which bear the constant jumping and abrupt changes of direction. Jumping ability is fundamental in handball, as it directly influences the player’s effectiveness during shooting and in other important actions within the game [3].

In this context, the countermovement jump (CMJ) has become an essential tool for assessing the muscular power and neuromuscular function of players. The CMJ is a simple and reliable test that allows the measurement of the explosive capacity of the leg extensor muscles, providing objective data on the athletes’ physical condition and their response to different training programs [4].

In addition to being essential for game performance, vertical jumping and explosive strength are factors that can contribute to injury prevention when properly trained. Several studies have shown that plyometric training can improve these abilities in elite handball players, enhancing both their performance and their resistance to high-frequency injuries common in this sport. However, achieving consistent adherence to specific programs aimed at improving jumping ability remains a major challenge in the field of sports physiotherapy [5].

In this context, Physitrack^® emerges as an innovative tool that enables physiotherapists and sports trainers to monitor athletes’ progress, provide continuous support, and adjust exercises according to each player’s needs. During the COVID-19 pandemic, the demand for and use of telemedicine tools increased significantly, proving to be effective in the treatment and monitoring of a wide range of conditions, from musculoskeletal injuries to postoperative rehabilitation [6].

Adherence is a key determinant of treatment effectiveness in physiotherapy. Historically, home-exercise programs show low adherence (30–40%), compromising outcomes, and delaying recovery [7]. Telemedicine platforms may counteract this issue by providing reminders, monitoring, instructional videos, and feedback, all of which enhance patient engagement and adherence [7,8].

In addition, these digital platforms allow dynamic personalization of treatment and continuous supervision by the professional without the need for travelling, which improves the integration of exercise into the patient’s daily routine and reduces barriers such as lack of time or accessibility [8]. These advantages are especially relevant in sports contexts, where consistency in training is essential for performance and injury prevention.

In recent years, the development of advanced technologies has facilitated the precise and accessible assessment of the CMJ and other performance indicators. For example, force platforms provide detailed data on the applied force, contact time, and jump height, allowing for a comprehensive analysis of neuromuscular performance [9].

Hawkin Dynamics^® is a company specialized in advanced physical performance and biomechanics assessment, primarily through its portable force platforms. These platforms measure key parameters such as force distribution, inter-limb asymmetry, rate of force development, and other variables related to muscular power. The measurements provide accurate, real-time data on jumping ability, movement strategies, and functional imbalances, making them an essential tool for optimizing performance and preventing injuries [10].

These platforms stand out for their ease of use and technological integration. The platforms connect mobile devices and computers through intuitive applications, allowing coaches, physiotherapists, and other professionals to analyze data immediately and make informed decisions to adjust training or rehabilitation programs. In addition, their ability to perform quick and non-invasive tests makes them ideal for use in high-demand settings such as elite sports, rehabilitation clinics, and research laboratories.

In the study by Anicic et al. [4], however, out of the 45 CMJ variables analyzed, only 24 demonstrated acceptable levels of reliability. Among them are performance parameters such as jump height, flight time, and take-off velocity, which are essential for analyzing the athlete’s explosive capacity.

The detailed analysis of these variables provides a comprehensive view of jump performance and allows for the optimization of training programs aimed at improving explosiveness, stability, and injury prevention in handball players [11].

According to Bishop et al., although numerous studies discuss the usability of different metrics in this test, this is often done within the context of a specific objective [12]. This article examines how the choice of metrics used during CMJ testing varies depending on its purpose. When applied to sports performance, the focus is typically on output-based variables such as jump height, peak power, and force production, which help relate CMJ performance to broader physical qualities. In contrast, when CMJ is used to monitor neuromuscular fatigue, practitioners rely more on time-derived variables, such as modified RSI, time to take-off, and propulsive phase duration, to identify fatigue-related alterations. Finally, when CMJ forms part of a return-to-performance test battery for injured athletes, priority is given to metrics such as landing and take-off forces, impulse, and inter-limb asymmetries to track recovery and readiness to resume sport.

Therefore, the primary objective of this study is to evaluate whether the use of the digital exercise-prescription platform contributes to the improvement of jump ability in handball players through the analysis of performance, kinematic, and kinetic variables. In addition, as a secondary objective it will be assessed whether Physitrack^® improves players’ adherence to exercise programs and the usability of the application will be analyzed from the users’ perspective, with special attention to ease of use and the perceived level of satisfaction.

It is hypothesized that prescribing a home-based exercise program through the Physitrack^® app will improve jump performance in handball players, as reflected by increases in performance, kinematic, and kinetic CMJ variables, and that this tool will promote greater adherence compared to a general strengthening program.

2. Materials and Methods

2.1. Study Type and Design

This is a pilot study with a randomized and controlled clinical trial design, conducted in the 2nd National and 2nd Regional Male divisions of the Móstoles Handball Club (Madrid, Spain). It has been proposed as a pilot study in order to obtain the effect size of the main variables, which will allow for an appropriate sample size calculation. Due to the nature of the intervention, participant blinding was not feasible, and therefore the study could not be conducted as a blinded trial. The study was submitted to the Ethics and Research Committee of Universidad San Pablo CEU, which approved the development of this research and assigned it the code 904/24/TFG. The clinical trial has been registered on “https://clinicaltrials.gov (accessed on 27 March 2025)” with the ID NCT06900309.

2.2. Inclusion/Exclusion Criteria

Players over 18 years of age, without active injuries, who were part of the club’s roster during the 2024–2025 season and had not previously used Physitrack^®, were included in the study. After the initial assessment, 28 players were randomized into an intervention group (n = 14) or a control group (n = 14). Three participants were excluded from the final analysis due to withdrawal or injury (IG: n = 13; CG: n = 11). For participant randomization, a random sequence was generated using the tool free version from https://random.org (accessed on 27 March 2025), assigning “0” to the control group and “1” to the intervention group. This method ensured random allocation, minimized selection bias, and guaranteed the internal validity of the study.

Participants were included if they were 18 years of age or older. In addition, participants were included if they were registered with the Móstoles Handball Club and were part of the 2024–2025 roster. Their participation was voluntary. Players who were injured at the time of the study were excluded. Players who were already familiar with the Physitrack^® application and had used it previously were also excluded.

2.3. Test Description

A warm-up was performed prior to the test (20 jumping jacks). The bilateral countermovement jump test was carried out on an instrumented force platform (Hawkin Dynamics, Inc., Westbrook, ME, USA). Participants were instructed to perform the jump continuously, focusing on achieving maximum height and force while keeping their hands on their hips. All subjects were given the same verbal cue before each attempt: “jump as fast and as high as you can.” The test was repeated three times (Figure 1).

Both groups completed an 8-week home exercise program through Physitrack^®. The control group performed a general strengthening program, based on a progressive bodyweight strengthening routine focused on improving core stability, mobility, and general functional endurance, with increasing volume every two weeks (Appendix A,

Table A1. General strengthening program, based on a progressive bodyweight strengthening routine focused on improving core stability, mobility, and general functional endurance, with increasing volume every two weeks.

Week	Exercise	Series	Repetition	Rest
Weeks 1–2: Initial adaptation	Core plank	3	20–30 s	30–60 s
	Hip 90/90	3	6 reps	30–60 s
	Push-ups	3	6–8 reps	30–60 s
	L-shape raise	3	8 reps	30–60 s
	Bodyweight lunges	3	8 reps per side	30–60 s
	Bodyweight squat	3	8–10 reps	30–60 s
Weeks 3–4: Moderate increase	Core plank	3	30–40 s	30–60 s
	Hip 90/90	3	8 reps per side	30–60 s
	Push-ups	3	8–10 reps	30–60 s
	L-shape raise	3	10 reps	30–60 s
	Bodyweight lunges	3	10 reps	30–60 s
	Bodyweight squat	3	10–12 reps	30–60 s
Weeks 5–6: High intensity	Core plank	4	40–50 s	30–60 s
	Hip 90/90	4	10 reps per side	30–60 s
	Push-ups	4	10–12 reps	30–60 s
	L-shape raise	4	12 reps	30–60 s
	Bodyweight lunges	4	12 reps per side	30–60 s
	Bodyweight lunges	4	12–15 reps	30–60 s
Weeks 7–8: Maximum power	Core plank	5	50–60 s	30–60 s
	Hip 90/90	5	12 reps per side	30–60 s
	Push-ups	5	12–15 reps	30–60 s
	L-shape raise	5	12–15 reps	30–60 s
	Bodyweight lunges	5	15 reps per side	30–60 s
	Bodyweight squat	5	15–20 reps	30–60 s

), while the intervention group followed a plyometric jump protocol designed to progressively enhance explosive lower-limb power, reactivity, and unilateral control through hurdle jumps, squat jumps, stride jumps, and increasingly complex drop (Appendix B,

Table A2. Plyometric jump program designed to enhance explosive lower-limb power, reactivity and unilateral control, with increasing volume every two weeks.

Week	Exercise	Series	Repetition	Rest
Weeks 1–2: Initial adaptation	Hurdles jumps	3	8	60–90 s
	Narrow squat jump	3	8	60–90 s
	Stride jump	3	8	60–90 s
	Plyometrics—drop jump—two feet on the ground and jump (with arm swing)	3	6	60–90 s
	Counter move—drop jump—landing on one leg and jumping to the left on two legs (with arm swing)	3	6 (per side)	60–90 s
	Countermovement—drop jump—landing on two feet and jumping over hurdles	3	8	60–90 s
Weeks 3–4: Moderate increase	Hurdles jumps	4	10	60–90 s
	Narrow squat jump	4	10	60–90 s
	Stride jump	4	10	60–90 s
	Plyometrics—drop jump—two feet on the ground and jump (with arm swing)	4	8	60–90 s
	Counter move—drop jump—landing on one leg and jumping to the left on two legs (with arm swing)	4	8 (per side)	60–90 s
	Countermovement—drop jump—landing on two feet and jumping over hurdles	4	10	60–90 s
Weeks 5–6: High intensity	Hurdles jumps	4	12	90 s
	Narrow squat jump	4	12	90 s
	Stride jump	4	12	90 s
	Plyometrics—drop jump—two feet on the ground and jump (with arm swing)	4	10	90 s
	Counter move—drop jump—landing on one leg and jumping to the left on two legs (with arm swing)	4	10 (per side)	90 s
	Countermovement—drop jump—landing on two feet and jumping over hurdles	4	12	90 s
Weeks 7–8: Maximum power	Hurdles jumps	5	10	90–120 s
	Narrow squat jump	5	10	90–120 s
	Stride jump	5	10	90–120 s
	Plyometrics—drop jump—two feet on the ground and jump (with arm swing)	5	12	90–120 s
	Counter move—drop jump—landing on one leg and jumping to the left on two legs (with arm swing)	5	12	90–120 s
	Countermovement—drop jump—landing on two feet and jumping over hurdles	5	10	90–120 s

). All players were provided with an instructional video on how to use the app. Regular club training sessions were maintained throughout the study. Immediately after the 8-week exercise program, the CMJ test was repeated to collect the post-program variables.

2.4. Countermovement Jump (CMJ) Variables

The CMJ is a widely used test to assess neuromuscular performance, consisting of 5 well-defined phases: unloading phase, braking or eccentric phase, propulsion or concentric phase, flight phase, and landing phase (Figure 2). In this study, the following key variables were used, with special attention to those reflecting improvements after an exercise program:

• Jump Height (JH): Main indicator of explosive power; greater height reflects better force application.
• Take-off Velocity: More sensitive to strength improvements than height; reflects efficiency in force-to-movement conversion.
• Peak Power: Calculated from force and velocity of the center of mass; key in strength and explosiveness programs.
• Time to Take-off: Time from the beginning of movement to take-off; a shorter time with equal or greater jump height indicates faster force application.
• Rate of Force Development (RFD): Speed at which force is generated during the concentric phase.
• Flight Time: Directly related to jump height; used in combination with other metrics.
• Modified Reactive Strength Index (mRSI): Ratio between jump height and time to take-off; measures the efficiency of the stretch-shortening cycle. Low values may indicate fatigue or reactive deficits, whereas high values suggest neuromuscular efficiency and reduced joint load.
• Stiffness: Resistance of the muscle-tendon system; excessive or insufficient levels may be associated with a higher risk of injury.
• Time to Stabilization (TTS): Time an athlete takes to regain balance after landing; high values indicate possible neuromuscular deficits, fatigue, or imbalances.
• Peak Propulsive Force: Maximum force generated during the propulsion phase; reflects the potential for maximal force production.
• Propulsive Impulse: Integration of the applied force over time during the propulsion phase; an increase indicates a greater capacity for sustained force application.

Finally, to analyze individual progression throughout the intervention, three improvement variables were defined:

• Jump height improvement: Change in jump height.
• Flight time improvement: Change in flight time.
• Peak velocity improvement: Change in peak velocity.

2.5. Adherence Variables and Satisfaction and Usability Questionnaires

Adherence to the exercise program was evaluated using a multidimensional approach based on the recommendations of Collado-Mateo et al. [8]. To obtain a comprehensive estimate, three key dimensions were combined: attendance at sessions, compliance with the program in terms of intensity and volume, and the psychosocial factors associated with the platform used.

First, attendance was calculated through the automatic record provided by Physitrack^®, expressing the percentage of sessions completed in relation to the total number of sessions scheduled.

Secondly, the degree of compliance was analyzed by comparing the number of sessions carried out with those established in the intervention schedule, allowing for an assessment of the program’s temporal adherence. This indicator made it possible to evaluate both the temporal follow-up and the fidelity to the prescribed exercise program.

To incorporate the psychosocial dimension, the results from two questionnaires administered via Microsoft Forms were used: the Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) and the System Usability Scale (SUS), both validated in Spanish. To enable their combination, the mean scores obtained from each questionnaire were normalized to a 0–100 scale.

Finally, to obtain an overall measure of adherence, a composite formula was applied that weighted the three previously calculated dimensions, following the recommendations of Collado-Mateo et al. [8] 40% for attendance, 40% for compliance, and 20% for psychosocial factors:

Total Adherence = (Attendance × 0.4) + (Compliance × 0.4) + (Questionnaires × 0.2)

2.6. Statistical Analysis

Statistical analysis was performed using IBM SPSS Statistics^® software (version 29.0.2.0(20)). A statistical significance level of α = 0.05 was established, with results considered significant when p < 0.05.

Initially, a descriptive analysis of the players’ sociodemographic and anthropometric variables was performed, expressing quantitative variables as means and standard deviations. Independent samples of Student’s t-tests and the non-parametric Mann–Whitney U test were used to compare the means of these variables before the intervention to ensure the homogeneity of the two groups. The normality of the distribution of continuous variables was assessed using the Shapiro–Wilk test, which is appropriate for small sample sizes.

Independent samples of Student’s t-tests and the non-parametric Mann–Whitney U test were used to compare the means of the Hawkin Dynamics^® variables before the intervention to ensure the homogeneity of the two groups, and after the intervention. To compare the variables derived from the vertical jump before and after the intervention within each group (control and intervention), the paired samples Student’s t-test was used, or, in cases of non-normality, the Wilcoxon test. Effect sizes were also calculated. Furthermore, individual improvement variables were calculated for those physical performance measures that showed significant differences (p < 0.05), by subtracting the pre-intervention value from the post-intervention value.

Total adherence was compared between the two groups using an independent samples t-test, and subsequently, correlations between adherence to the exercise program and the improvement variables were analyzed using Pearson’s bivariate correlation test. When the assumption of normality was not met, Spearman’s rank correlation coefficient was used as an alternative. These tests allowed for the exploration of the linear or monotonic relationship between adherence and the changes in physical performance obtained after the intervention.

Participants’ satisfaction and perceived usefulness regarding the use of Physitrack^® were evaluated using the Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ). This instrument, validated for telemedicine contexts, allows for the collection of information about the user’s experience in terms of ease of use, perceived usefulness, and overall satisfaction.

Finally, the usability of the Physitrack^® application was evaluated using the overall score of the System Usability Scale (SUS) questionnaire.

3. Results

3.1. Sociodemographic and Anthropometric Variables

Table 1. Characteristics sociodemographic and anthropometric of the players.

	Control Group	Intervention Group	p-Value
Characteristics of the Players	n = 14 Mean ± SD	n = 14 Mean ± SD
Age (years)	22.8 ± 3.2	23.71 ± 4.51	0.56
Height (cm)	1.80 ± 0.06	1.79 ± 0.04	0.63
Weight (kg)	82.6 ± 16.1	78.6 ± 9.5	0.12
BMI	26.47 ± 3.91	24.37 ± 2.27	0.99
Years playing	12.5 ± 2.8	14.2 ± 6.2	0.36

Mean ± standard deviation and p-value (independent samples Student’s t-test).

shows the sociodemographic and anthropometric data of both groups and the p-value of the comparison between them prior to the study. It can be observed that there are no statistically significant differences between the two groups.

3.2. Jump Performance (CMJ)

The comparison between groups prior to the exercise program indicated that there were no differences between the groups in any of the variables derived from the vertical jump test (

Table 2. Comparison between Intervention Group and Control Group in PRE Hawkin Dynamics^® Variables.

Variable	Control PRE	Intervention PRE	p
Jump Height (m)	0.35 ± 0.05	0.36 ± 0.06	0.970
Peak Propulsive Force (N) *	2215.4 ± 429.9	1935.9 ± 281.6	0.777
Flight Time (s) *	0.53 ± 0.04	0.53 ± 0.05	0.991
Propulsive Impulse (N·s)	444.9 ± 101.7	393.6 ± 51.1	0.129
Time to Takeoff (s) *	0.75 ± 0.13	0.76 ±0.10	0.448
mRSI (Reactive Strength Index)	0.48 ± 0.10	0.48 ± 0.11	0.599
Time to Stabilization (s)	725.5 ± 134.3	768.7 ± 180.4	0.511
Peak Propulsive Power (W)	4548.8 ± 737.7	4090.8 ± 697.7	0.542
Stiffness (N/m/kg)	−7408 ± 2132	−6331 ± 1295	0.130
Braking RFD (N/s) *	8628 ± 4414	7436 ± 3080	0.701
Peak Velocity (m/s)	2.74 ± 0.19	2.76 ± 0.19	0.953

Mean ± standard deviation and p-value, independent samples Student’s t-test or Mann–Whitney U test (asterisk-marked variables).

). This comparison, like the previously mentioned one of the sociodemographic and anthropometric variables, indicates that the random distribution into the two groups was homogeneous.

Both groups showed significant improvements in jump height, flight time, and peak velocity after the intervention (

Table 3. Pre-post comparison of control and intervention groups using HD platforms.

Variable	Control PRE	Control POST	p	d Cohen	Intervention PRE	Intervention POST	p	d Cohen
Jump Height (m)	0.35 ± 0.05	0.37 ± 0.05	0.008	0.02	0.36 ± 0.05	0.38 ± 0.06	0.037	0.03
Peak Propulsive Force (N) *	2215.4± 429.9	2199.6 ± 374.6	0.722	156.49	1935.2 ± 281.6	1977.7 ± 277.2	0.316	120.32
Flight Time (s) *	0.53 ± 0.04	0.55 ± 0.04	0.011	0.02	0.53 ± 0.04	0.56 ± 0.04	0.016	0.02
Propulsive Impulse (N·s)	444.9 ± 101.7	448.5± 95.5	0.577	22.49	393.6 ± 51.1	398.4 ± 42.6	0.575	25.43
Time to Takeoff (s)	0.75 ± 0.13	0.76 ± 0.14	0.674	0.08	0.76 ± 0.10	0.74 ± 0.09	0.543	0.11
mRSI (Reactive Strength Index)	0.48 ± 0.10	0.50 ± 0.11	0.176	0.04	0.48 ± 0.12	0.53 ± 0.12	0.094	0.07
Time to Stabilization (s)	725.5± 134.3	717.2 ± 128.6	0.914	160.55	768.7 ± 180.4	747.4 ± 227.7	0.894	201.97
Peak Propulsive Power (W) *	4548.8 ± 737.7	4646.1 ± 615.9	0.136	219.36	4090.8 ± 697.7	4277.3 ± 747.7	0.189	341.05
Stiffness (N/m/kg)	−7408 ± 2132	−7306 ± 2614	0.809	1496.11	−6331 ± 1295	−6351 ± 1151	0.986	1103.96
Braking RFD (N/s) *	8628 ± 4414	8890 ± 4618	0.713	2508.62	7436 ± 3080	8151 ± 2885	0.186	1774.36
Peak Velocity (m/s)	2.74 ± 0.19	2.80 ± 0.18	0.004	0.06	2.76 ± 0.19	2.85 ± 0.22	0.023	0.09

Mean ± standard deviation and p-value [paired samples mean comparison tests, Stdent t-test or Wilcoxon test (asterisk-marked varaibles)] * p < 0.05: statistically significant difference, (value in bold)

), with a p-value < 0.05.

In the intervention group, statistically significant improvements were observed in jump height (p = 0.037), flight time (p = 0.016), and peak velocity (p = 0.023). Other variables did not show statistically significant changes, although better results were observed in the reactive strength index, peak propulsive power, peak propulsive force, and braking phase RFD.

No improvements were detected in take-off time, stabilization, propulsive impulse, or stiffness.

In the control group (Table 3), statistically significant differences were observed in some variables after the intervention, specifically in jump height (p = 0.008), flight time (p = 0.011), and peak velocity (p = 0.004).

When comparing the two groups after the intervention, no statistically significant differences were found between them (

Table 4. Comparison between Intervention Group and Control Group in POST Hawkin Dynamics^® Variables.

Variable	Control POST	Intervención POST	p
Jump Height (m)	0.37 ± 0.05	0.38 ± 0.06	0.521
Peak Propulsive Force (N) *	2199.6 ± 374.6	1977.7 ± 277.2	0.277
Flight Time (s) *	0.55 ± 0.04	0.56 ± 0.04	0.586
Propulsive Impulse (N·s)	448.5 ± 95.5	398.4 ± 42.6	0.109
Time to Takeoff (s) *	0.76 ± 0.14	0.74± 0.09	0.870
mRSI (Reactive Strength Index)	0.50 ± 0.11	0.52 ± 0.12	0.565
Time to Stabilization (s)	717.2 ± 128.6	747.4 ± 227.7	0.688
Peak Propulsive Power (W)	4646 ± 615	4277 ± 747	0.190
Stiffness (N/m/kg)	−7306 ± 2614	−6351 ± 1151	0.257
Braking RFD (N/s) *	8890 ± 4618	8151 ± 2 885	0.639
Peak Velocity (m/s)	2.80 ± 0.18	2.85 ± 0.22	0.526

Mean ± standard deviation and p-value, independent samples Student’s t-test or Mann-Whitney U test (asterisk-marked variables).

); both groups improved equally and in the same variables.

3.3. Adherence

Overall adherence was moderate (total mean: 50.56%), with a slight difference between groups: 52.13% in the intervention group versus 48.98% in the control group (p = 0.74) (

Table 5. Summary of total adherence in the intervention and control groups.

Variable	Control	Intervention	p-Value
Attendance (%)	46.8± 32.31	41.9 ± 31.81	0.68
Compliance (%)	41.29 ± 31.07	44.25 ± 30.12	0.8
Psychosocial (%)	78.4 ± 6.85	78.4 ± 6.85	1
Total adherence (%)	48.98 ± 25.14	53.13 ± 24.83	0.74

Mean ± standard deviation and p-value (Student’s t-test).

). A decreasing trend was observed over the weeks, reaching the lowest attendance rate in week 8 (23%), as shown in Figure 3.

No statistically significant correlations were found between mean adherence and individual improvement variables in jumping (r = 0.327 for jump height; ρ = 0.174 for flight time; r = 0.296 for peak velocity), although the associations were positive (Figure 4, Figure 5 and Figure 6).

3.4. Satisfaction and Usability Questionnaires

Physitrack^® was rated as an excellent usability tool (SUS: 83.3 ± 10.5 out of 100). The TSUQ showed a good level of satisfaction (3.68/5), highlighting items such as ease of use (4.45) and privacy protection (4.6). The lowest-rated items were related to technical aspects and reduced contact with the physiotherapist.

4. Discussion

The objective of this study was to evaluate whether the use of the Physitrack^® application contributes to the improvement of jumping ability in handball players through the analysis of performance, kinematic, and kinetic variables. In addition, it was assessed whether Physitrack^® improved players’ adherence to exercise programs. The usability of the application was analyzed from the users’ perspective, with special attention to ease of use and perceived level of satisfaction.

Regarding performance and using the vertical jump (CMJ) as the test, both groups showed significant improvements in key variables such as jump height, flight time, and peak velocity, as measured by force platforms. In the intervention group, significant increases were observed in jump height (p = 0.037), flight time (p = 0.016), and peak velocity (p = 0.023). Similarly, the control group also showed improvements in jump height (p = 0.008), flight time (p = 0.011), and peak velocity (p = 0.004).

This finding suggests that both plyometric and general strengthening training can induce neuromuscular adaptations in this population, even without in-person supervision. However, although the plyometric program was expected to generate superior gains due to the principle of training specificity, this was not observed. Possible explanations include insufficient stimulus differentiation between programs, as the general strengthening program also included multi-joint, power-oriented exercises that can indirectly improve explosive performance [14]; the relatively short training period, which may not have allowed specific adaptations to emerge distinctly; and the con-current training load from regular team sessions, which may have homogenized responses across groups.

The presence of improvements in some broader variables in the intervention group (such as mRSI, peak propulsive power, or braking phase RFD) suggests that specific training might have a more targeted impact on qualitative components of explosive performance, but given the lack of statistical significance, these observations should be interpreted cautiously (mRSI: p = 0.098; RFD braking: p = 0.059; Propulsive Peak Power: p = 0.091).

This pattern is consistent with what has been described in previous studies on plyometric training in handball players. In a recent review by Jaksic D et al., it was concluded that after six weeks of training, with two sessions per week, performance improvements could be induced, with the optimal period being eight weeks, as used in the present study [5].

Nonetheless, the review also highlights that the magnitude of improvement depends on the athletes and on the contrast between intervention and control stimuli, an aspect that might have been attenuated in this study, potentially explaining the absence of between-group differences.

The simultaneous improvement in both groups could be explained by the novelty effect, or by the fact that any systematic intervention based on the principle of progressive overload can generate improvements in recreational or semi-competitive players [15,16,17].

On the other hand, the simultaneous improvement in both groups (no differences were found between groups) could be explained by the novelty effect or by the fact that any systematic intervention based on the principle of progressive overload can lead to improvements in recreational or semi-competitive players. In the program designed for this study (Annex A), the progression of loads in the different exercises was carefully considered; every two weeks, the number of repetitions and sets for each exercise proposed in both groups was increased, thus adhering to the principle of progressive overload [15].

It is also possible that these improvements were due to the participants continuing their regular team training (three sessions per week), which may have enhanced the effects of the prescribed programs. Furthermore, the effects could be influenced by factors such as playing position, previous experience, or the degree of individual engagement [15,17,18,19].

Likewise, the findings of this study highlight the importance of using objective assessment tools with high reliability for measuring neuromuscular performance. In this regard, the Hawkin Dynamics^® force platforms are established as a reference resource for the evaluation of vertical jump performance, as they provide precise data on key kinetic and kinematic variables. Their use allows not only for the identification of significant changes after a training program but also for the detection of trends in more sensitive parameters such as the rate of force development or propulsive power, which are fundamental in the analysis of explosive performance in handball.

The appropriate selection and analysis of these metrics allow professionals to accurately monitor progress, identify residual deficits, and optimize training or recovery strategies. According to Bishop C et al., there is no single universal metric; rather, its selection must be aligned with the purpose of the test to obtain meaningful and contextually applicable results [12].

On the other hand, the results of this pilot study indicate that both the general strengthening program and the specific plyometric program, both prescribed through the Physitrack^® platform, were feasible and generated positive effects on adherence and the overall physical performance of the handball players.

The mean adherence to the program was moderate (50.56%), with a slight advantage for the intervention group (52.1%) compared to the control group (48.9%). Although suboptimal, this rate falls within the usual range for unsupervised home-based programs (30–60%), as reported by Argent et al. and Collado-Mateo et al. [7,8]. The high standard deviation (24.57%) indicates considerable interindividual variability, with some participants completing 100% of the sessions and others not performing any.

To interpret these results within a theoretical framework, the Self-Determination Theory (SDT) [20] provides a useful lens: adherence is strongly influenced by the fulfillment of autonomy, competence, and relatedness needs. Several barriers reported by participants-such as lack of motivation, boredom, limited feedback, and reduced human contact, directly relate to unmet psychological needs, particularly competence and relatedness, which may explain the moderate adherence observed. The main barriers identified in this study that may have influenced the data, according to the questionnaires completed by participants, were motivation—due to the absence of enjoyable experiences or boredom—lack of social support or absence of in-person professional guidance, and a low perception of their own skills or self-efficacy. These barriers had already been mentioned by Collado-Mateo et al. in their review and represent a clear target to address when prescribing home-based exercise programs [8].

Alternatively, although no statistically significant correlations were found between adherence and performance improvements (r = 0.314 for Jump Height; r = 0.272 for Flight Time; r = 0.176 for Peak Velocity), the direction of the relationship suggests that greater engagement could translate into better results, but these trends must be interpreted conservatively given the non-statistically significant results. This pattern, although not conclusive, is consistent with what was reported by Sladeckova et al. who emphasized the importance of adherence in exercise-based telerehabilitation programs [18]. This review underscores the relevance of adherence for improving outcomes but also points out that it does not always guarantee program effectiveness. Such effectiveness also depends on other factors, such as the suitability of the program to individual needs, the quality of the program design, and the technological tools employed. Previous studies have also shown that adherence tends to decrease in home-based programs, especially when there is no continuous supervision or constant feedback [7]. However, digital tools such as Physitrack^® have proven useful for maintaining patient engagement through features like automated reminders, intuitive interfaces, and multimedia content that facilitate exercise understanding. This type of tool has been effective even in populations with chronic neck pain and multiple sclerosis, reinforcing its applicability beyond the sports field [18,19].

The usability of Physitrack^® was rated highly, with an average score of 83.3/100 on the SUS questionnaire, placing it within the excellence range (SUS > 80) according to Kortum et al. [21]. Participants considered the platform intuitive, efficient, and easy to integrate into their routine. The TSUQ also reflected a good overall perception of the system (3.67/5), with particularly high scores in the items related to privacy, ease of use, and perceived usefulness. However, some players expressed concern about reduced human contact and the feeling of insufficient therapeutic follow-up, which aligns with the findings of studies such as that of Özden et al. [19], these concerns again align with SDT, particularly the need for relatedness, suggesting that telerehabilitation systems should integrate mechanisms that foster a stronger sense of connection with professionals.

Finally, the methodological limitations inherent to this pilot study must be acknowledged. The small sample size, the limited duration of the intervention, and the absence of medium- and long-term follow-up constrain the generalizability of the results. Furthermore, the fact that the players continued their regular club training, as well as individual variability in motivation and compliance, may have influenced the improvements observed.

Future research should test specific, measurable hypotheses for example: integrating real-time feedback within Physitrack^® to enhance competence and engagement; incorporating periodic synchronous check-ins to reinforce relatedness; and personalizing progression algorithms based on individual performance trends. These experimentally testable approaches may help overcome adherence barriers and potentiate the effects of home-based training programs.

5. Conclusions

After eight weeks of training with programs prescribed through a digital platform, both groups showed statistically significant improvements in jump height, flight time, and peak velocity, according to the data collected with Hawkin Dynamics^®. Importantly, no statistically significant differences were found between the intervention and control groups, as both demonstrated similar improvements across these performance variables.

Both groups showed moderate adherence, with an overall average of 50% and minimal differences between the intervention group (52%) and the control group (48%). Although high interindividual variability was observed, no statistically significant correlations were found between adherence and performance improvement.

The results of the SUS questionnaire showed excellent usability (mean: 83.3/100), while the TSUQ reflected good overall satisfaction (mean: 3.67/5). Users rated the platform positively in terms of ease of use, privacy, and usefulness, although some expressed the need for more follow-up and human contact, key aspects for improving engagement.

As this is a pilot study, these findings should be interpreted with caution. To draw more conclusive inferences and better understand the true magnitude of the effects observed, a full-scale trial with an adequately powered sample size will be necessary.