Open Access

IMPACT OF REHABILITATION TRAINING COMBINED WITH VISUAL FEEDBACK BALANCE TRAINING ON MOTOR FUNCTION IN PATIENTS WITH SPORTS-RELATED KNEE INJURIES: A BIOMECHANICAL APPROACH

QINGFEN JI

https://orcid.org/0009-0003-1347-8783

Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P. R. China

Lishui People’s Hospital, The First Affiliated Hospital of Lishui University, Lishui 323000, P. R. China

E-mail Address: 563634394@qq.com

Search for more papers by this author

XIAOFEN LI

https://orcid.org/0009-0009-6700-4034

Lishui People’s Hospital, The First Affiliated Hospital of Lishui University, Lishui 323000, P. R. China

E-mail Address: 295399413@qq.com

Search for more papers by this author

GUANGYI ZOU

https://orcid.org/0009-0009-1579-6228

Lishui People’s Hospital, The First Affiliated Hospital of Lishui University, Lishui 323000, P. R. China

E-mail Address: 18957093502@163.com

Search for more papers by this author

, and

HONGYING PAN

https://orcid.org/0009-0007-3006-0855

Nursing Department, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P. R. China

E-mail Address: 3191016@zju.edu.cn

Corresponding author.

Search for more papers by this author

https://doi.org/10.1142/S0219519424400839Cited by:0 (Source: Crossref)

This article is part of the issue:

Special Issue: Application and Progress of Biomechanics in Medicine — Part III
Guest Editors: Esteban Peña Pitarch and Eddie Y. K. Ng

Abstract

Objective: This study aimed to evaluate the effect of rehabilitation training combined with visual feedback balance training on motor functions in patients with knee injuries. Methods: A total of 80 patients with sports-related knee injuries from the outpatient and inpatient departments of Lishui City People’s Hospital were randomly divided into control and observation groups, with 40 patients in each group. The control group underwent rehabilitation training based on biomechanical principles, and the observation group underwent additional visual feedback balance training. Trunk control ability, limb motor function, and walking stability were compared between the two groups before, 1 month after, and 3 months after training. Results: After 1 month of training, the lower-limb function scores, trunk control ability scores in activities such as turning to the healthy side and the affected side on the bed, sitting and standing balance, and scores for Dynamic Gait Index and Berg balance scale were all higher in the observation group compared with the control group ( $P < 0.05$ ). After 3 months of training, the differences in scores between the two groups became more pronounced ( $P < 0.05$ ). Conclusions: Rehabilitation training based on biomechanical principles combined with visual feedback balance training effectively improved the limb motor function, enhanced the trunk control ability, and maintained the body balance and walking stability in patients with sports-related knee injuries. This study provided a more effective novel rehabilitation approach for the postoperative recovery of patients with knee injuries.

Keywords:

1. Introduction

Sports-related knee injuries, including ligament tears, meniscal damage, and patellar dislocations, are common worlwide.^1,2,3 According to recent epidemiological data, annually over 3 million individuals require treatment for sports-related knee injuries. The knee joint is one of the most complex and weight-bearing joints in the human body, which is critical to walking, running, jumping, and other daily and athletic activities.⁴ Severe pain, joint instability, and restricted movement are some common symptoms of knee injury, which can further lead to a range of long-term health issues.⁵

First, acute knee injuries can cause damage to the joint’s soft tissues such as ligament tears,⁶ cartilage injuries,⁷ and synovitis. If these issues are not timely addressed or improperly managed, they can result in chronic pain and functional impairment.⁸ Second, given the knee’s significant role in supporting body movement, knee injuries may lead to altered gait, muscle strength imbalances, and postural abnormalities, increasing the risk of secondary injuries.⁹ Additionally, decreased knee stability and functional impairment can impact the mental health of patients, leading to anxiety, depression, and other psychological problems.¹⁰

Studies have shown that within a year of a knee injury, about 60% of patients do not fully return to their pre-injury activity levels.¹¹ Additionally, 10–20% of patients still experience knee instability or functional limitations after completing rehabilitation. Long-term functional impairments can also lead to reduced physical activity, further increasing the risks of obesity, cardiovascular diseases, and metabolic syndrome.¹² Therefore, effectively restoring knee joint function and preventing long-term health issues are a significant challenge in treating knee injuries.

In recent years, an increasing number of studies have reported on rehabilitation training based on biomechanical principles. Many studies have explored the impact of different rehabilitation methods on knee joint function.¹³ Rehabilitation methods incorporating biomechanical analysis have been found to significantly enhance rehabilitation efficiency. For instance, the movement trajectory, force distribution, and stability changes of the knee joints can be accurately assessed through motion capture technology and mechanical analysis, allowing the development of personalized rehabilitation programs. Additionally, visual feedback balance training,¹⁴ as an emerging rehabilitation method, has been shown to significantly improve proprioception and movement stability in the knee joint. A few studies also suggested that visual feedback balance training can reduce rehabilitation time and improve functional recovery; however, further large-scale randomized controlled trials are needed to confirm its long-term effects.¹⁵

Despite preliminary evidence showing the potential of visual feedback balance training in knee injury rehabilitation, existing research largely focused on evaluating single training methods, with limited exploration of the integrated application and synergistic effects of multiple rehabilitation approaches.¹⁶ Therefore, this study aimed to evaluate the impact of rehabilitation training based on biomechanical principles combined with visual feedback balance training on motor function in patients with sports-related knee injuries. It sought to explore the potential of this combined approach in improving knee stability, reducing the risk of secondary injuries, and enhancing overall rehabilitation outcomes. This study provided a more scientific and effective rehabilitation treatment plan for patients with knee injury, ultimately improving their functional recovery and quality of life, and offering theoretical and empirical support for future clinical practice.

2. Participants and Methods

2.1. Study participants

A total of 80 patients with sports-related knee injuries treated in the orthopedic outpatient and inpatient departments of our hospital from January 2021 to January 2022 were selected. They were randomly divided into observation and control groups, with 40 patients in each group. The observation group comprised 16 female and 24 male patients, aged 22–54 years, with an average age of $35.31 \pm 13.41$ years. The control group comprised 14 female and 26 male individuals, aged 21–50 years, with an average age of $37.51 \pm 12.34$ years. No statistically significant differences were noted in terms of age, sex, diagnosis, disease duration, ligament or meniscal injuries and severity between the two groups ( $P > 0.05$ ) (Table 1). Diagnosis and severity criteria were based on the American College of Rheumatology’s 1995 standards for sports-related knee injuries. The sample size for this study was calculated using the following formula: $n = 2 σ^{2} {(Z_{α / 2} + Z_{β})}^{2} / {(μ_{1} - μ_{2})}^{2}$ , where $α = 0.05$ (two-sided) and $β = 0.20$ . The calculation resulted in an estimated sample size of approximately 40 participants per group. The study was approved by the hospital ethics committee, and informed consent was obtained from patients or their families.

**Table 1. Demographic characteristics of patients in control and observation groups participating in the experiment.**
	Age	Sex^a	Disease duration^b	Severity^c	Ligament or meniscal injuries^d
Control group ( $n = 40$ )	$37.51 \pm 12.34$	26/14	31/9/0	0/18/22	25/15
Observation group ( $n = 40$ )	$35.31 \pm 13.41$	24/16	33/7/0	0/17/23	27/13

Notes: ^aNumber of male patients/female patients.

^bNumber of patients in acute phase/subacute phase/chronic phase.

^cNumber of patients with level 1 (mild) injury/level 2 (moderate) injury/level 3 (severe) injury.

^dNumber of patients with ligament/meniscal injury.

2.2. Inclusion and exclusion criteria

The inclusion criteria were as follows:

(1)	Patients with unilateral knee ligament or meniscal injuries caused by sports, diagnosed according to the aforementioned diagnostic criteria.
(2)	Patients willing to undergo surgical treatment and have undergone surgery.
(3)	Patients aged 20–55 years.

The exclusion criteria were as follows:

(1)	Patients not meeting the diagnostic and inclusion criteria.
(2)	Patients with congenital skeletal and muscular abnormalities.
(3)	Patients with joint diseases caused by non-sports injuries such as osteoarthritis or other rheumatic conditions.
(4)	Patients with mental disorders or serious cardiovascular, liver, or kidney diseases.
(5)	Patients with knee joint dysfunction caused by bone structural damage or bone tumors.

2.3. Methods

Patients in the control group underwent a rehabilitation training program based on biomechanical principles. This program integrated Micro-Electro-Mechanical System (MEMS) motion sensors and wearable systems to automatically and objectively assess the motor performance of patients with sports-related knee injuries, guiding them through a series of standardized exercises. During training, inertial sensors, electromyography sensors, and infrared sensors were used to enhance rehabilitation outcomes. The video sensors tracked specific movements of the patients, and inverse dynamic analysis of pedal movement was used to quantify hip joint biomechanical parameters. The rehabilitation training was designed based on the range of motion of the lower-limb joints of patients, including hip flexion ( $13 0^{\circ}$ – $15 0^{\circ}$ ), extension ( $1 0^{\circ}$ – $1 5^{\circ}$ ), abduction ( $3 0^{\circ}$ – $4 5^{\circ}$ ), and adduction ( $2 5^{\circ}$ – $3 0^{\circ}$ ); knee extension (no more than $1 0^{\circ}$ ) and flexion (up to $13 0^{\circ}$ ), with $4 0^{\circ}$ internal and external rotation; ankle dorsiflexion ( $1 0^{\circ}$ – $2 0^{\circ}$ ), abduction ( $2 0^{\circ}$ – $3 0^{\circ}$ ), adduction ( $1 0^{\circ}$ – $2 0^{\circ}$ ), and $3 0^{\circ}$ – $5 0^{\circ}$ internal and external rotations. Based on the range of motion of each joint, patients performed rehabilitation exercises twice daily for 20–30min per session, including leg lifts, lateral leg lifts, and heel raises. Additionally, patients in the control group were required to participate in a daily standard rehabilitation program designed by the rehabilitation department of our hospital. The training included standing balance exercises, single-leg standing exercises, joint control exercises, stepping exercises, and stair climbing exercises, with each session lasting 1h and conducted twice daily.

In addition, to the rehabilitation program based on biomechanical principles used in the control group, the observation group underwent visual feedback balance training under the guidance of a therapist using the Doctor Kinetic interactive system. This equipment included a display, sensors, and a mobile cart. Sensors monitored the patient’s posture in real time and provided feedback on display using reference objects such as balls and submarines. The patients were required to maintain upright and dynamic postures during training. The specific steps were as follows:

(1)	Stability limit training: The patient stood barefoot facing the screen with feet shoulder-width apart and arms naturally at their sides, fully relaxed. The therapist stood beside the patient, who slowly leaned forward until reaching their maximum stability limit, holding for 5s, gradually increasing to 20s. During training, the patient used a mirror to self-correct pelvic tilt, with guidance from the therapist if needed. Each session lasted 15–20min, twice daily.
(2)	Trunk strengthening training: The patient performed the Bobath handshake with arms extended to $9 0^{\circ}$ , completing virtual building tasks through rotational movements. The therapist may guide the patient by tapping their trunk or holding their hands, gradually adding resistance. Each session lasted 15–20min, twice daily.
(3)	Center-of-gravity transfer training: The screen shows a meadow, with the patient represented as a bright spot at the center. The patient adjusted body posture and moved the spot to randomly appearing black holes and back to the center. The patient had to control the pelvis and knee joints, resisting the force toward the affected side. Each session lasted 15–20min, twice daily.
(4)	Sit-to-stand transfer training: The patient, acting as a submarine, transitioned from sitting to standing to touch floating coins on the screen. Initial training should not be too fast to maintain balance. Each session lasted 15–20min, twice daily.
(5)	Walking training: The patient simulated walking on flat ground on the screen, avoiding obstacles. As recovery progressed, the training was adjusted to walking uphill or downhill. Initial sessions involved parallel bars or caregiver assistance, with intensity adjusted based on scores. Training intervals were 30s, with a total duration of 30–45min, twice daily.

2.4. Evaluation indicators

Lower-limb function: Before, 1 month after, and 3 months after training, the responsible nurse assessed the lower-limb function using the Fugl–Meyer motor function assessment (FMA), which included two dimensions: upper limb (0–34 points) and lower limb (0–66 points), with a total of 50 items. Each item had a maximum score of 2 points, with a total score ranging from 0 to 100 points. We only used the lower-limb scoring portion of the assessment. Higher scores indicated better limb motor function1. Studies showed an internal consistency reliability (Cronbach’s alpha) of over 0.90 and intraclass correlation coefficient (ICC) of 0.95, indicating strong stability. In terms of validity, the FMA demonstrated good criterion validity with a significant correlation to the Barthel index (BI) ( $r = 0.79$ ).¹⁷

Trunk control ability: Before, 1 month after, and 3 months after training, the responsible nurse assessed trunk control ability using the Sheikh Trunk Control Scale. This scale evaluated four dimensions: turning to the healthy side in bed, turning to the affected side in bed, sitting up from a lying position, and maintaining balance while sitting in a chair without armrests. Each dimension had a maximum score of 25 points, with a total score ranging from 0 to 100 points. Higher scores indicated better trunk control ability. An internal consistency reliability (Cronbach’s alpha) was approximately 0.80 and ICC was 0.85. In terms of validity, the scale showed a correlation of more than 0.70 with the Trunk Impairment Scale, indicating strong construct validity.¹⁸

Walking stability: This evaluation included balance and walking function. Before, 1 month after, and 3 months after training, the responsible nurse assessed balance using the Berg balance scale (BBS), which included 14 items. Each item had a maximum score of 4 points, with a total score ranging from 0 to 56 points. Higher scores indicated better balance ability. The internal consistency reliability (Cronbach’s alpha) exceeded 0.90, and ICC was 0.98. Regarding validity, BBS showed a negative correlation with the Timed Up and Go Test ( $r = - 0.76$ ) and a positive correlation with the Functional Reach Test ( $r = 0.72$ ), supporting both the criterion and construct validity of walking stability.¹⁹ The walking function was assessed using the Dynamic Gait Index (DGI), which included eight items. Each item had a maximum score of 3 points, with a total score ranging from 0 to 24 points. Higher scores indicated better walking ability. The internal consistency reliability (Cronbach’s alpha) was more than 0.85 and ICC was more than 0.90. In terms of validity, DGI was correlated with the BBS ( $r = 0.67$ ) and showed a high correlation with the functional gait assessment ( $> 0.80$ ), supporting its criterion and construct validity.¹⁹

2.5. Statistical analysis

The data were processed using SPSS 26.1. Continuous variables were expressed as $mean \pm standard$ deviation (SD). Independent t tests were used to compare between the two groups. Before performing t tests, normality and homogeneity of variances were assessed. If the assumptions of normality and homogeneity of variances were met, independent t tests were performed. If the assumptions were violated, nonparametric tests such as the Mann–Whitney U test were used as an alternative. Statistical significance was set at $P < 0.05$ .

3. Results

3.1. Comparison of lower-limb function scores before and after training

No statistically significant differences in pre-training lower-limb function scores were observed between the two groups ( $P > 0.05$ ). Post-training, both groups showed significant improvements in limb-lower function scores ( $P < 0.05$ ), with the observation group outperforming the control group ( $P < 0.05$ ), as shown in Table 2.

**Table 2. Comparison of lower-limb function scores before and after training in both control and observation groups (score, $mean \pm SD$ ).**
	Before training	After 1 month of training	After 3 months of training
Control group ( $n = 40$ )	$46.35 \pm 3.05$	$49.15 \pm 3.05$	$55.98 \pm 2.99$
Observation group ( $n = 40$ )	$47.28 \pm 3.02$	$51.98 \pm 2.50$	$59.65 \pm 3.03$
P	0.177	$< 0.001$	$< 0.001$

3.2. Comparison of trunk control ability before and after training in both control and observation groups

No statistically significant differences in trunk control ability scores were observed between the two groups before training ( $P > 0.05$ ), as shown in Table 3. However, the scores for trunk control abilities such as turning to the healthy side in bed (Item A), turning to the affected side in bed (Item B), sitting up from a lying position (Item C), and maintaining balance while sitting without armrests (Item D) were significantly higher after 1 month and 3 months of training than before training ( $P < 0.05$ ) in both control and observation groups. Furthermore, the observation group showed significantly higher scores than the control group ( $P < 0.05$ ), as shown in Table 3.

**Table 3. Comparison of trunk control ability scores between the control and observation groups (score, $mean \pm SD$ ).**
Before training	Item A	Item B	Item C	Item D	Total score
Control group ( $n = 40$ )	$17.53 \pm 2.42$	$16.53 \pm 3.11$	$17.55 \pm 1.77$	$20.55 \pm 1.75$	$72.15 \pm 4.86$
Observation group ( $n = 40$ )	$17.15 \pm 2.35$	$16.78 \pm 2.96$	$17.18 \pm 1.71$	$20.58 \pm 1.72$	$71.68 \pm 4.14$
P	0.463	0.846	0.345	0.948	0.639
After 1 month of training	Item A	Item B	Item C	Item D	Total score
Control group ( $n = 40$ )	$18.75 \pm 2.33$	$18.48 \pm 2.89$	$19.48 \pm 1.77$	$21.38 \pm 1.64$	$78.08 \pm 4.51$
Observation group ( $n = 40$ )	$20.03 \pm 1.79$	$20.23 \pm 2.49$	$20.83 \pm 1.66$	$22.68 \pm 1.61$	$83.75 \pm 3.79$
P	0.008	0.019	$< 0.001$	0.002	$< 0.001$
After 3 months of training	Item A	Item B	Item C	Item D	Total score
Control group ( $n = 40$ )	$21.05 \pm 2.47$	$20.48 \pm 2.66$	$21.60 \pm 1.85$	$22.95 \pm 1.65$	$86.08 \pm 4.42$
Observation group ( $n = 40$ )	$22.50 \pm 1.85$	$22.60 \pm 2.04$	$23.20 \pm 1.54$	$24.53 \pm 0.75$	$92.83 \pm 3.06$
P	0.004	$< 0.001$	$< 0.001$	$< 0.001$	$< 0.001$

Notes: Item A, turning to the healthy side in bed; Item B, turning to the affected side in bed; Item C, sitting up from a lying position; Item D, maintaining balance while sitting without armrests.

3.3. Comparison of walking stability scores before and after training in both control and observation groups

No statistically significant differences in BBS and DGI scores were noted between the two groups before training ( $P > 0.05$ ). BBS and DGI scores were significantly higher in both groups after 1 month and 3 months of training than before training ( $P < 0.05$ ), and the observation group scored significantly higher than the control group ( $P < 0.05$ ), as shown in Table 4.

**Table 4. Comparison of BBS and DGI scores for walking stability before and after training in the control and observation groups of patients (score, $mean \pm SD$ ).**
	BBS scores^a			DGI scores^b
	Before training	After 1 month of training	After 3 months of training	Before training	After 1 month of training	After 3 months of training
Control group ( $n = 40$ )	$41.95 \pm 3.30$	$45.38 \pm 3.04$	$50.53 \pm 2.67$	$17.48 \pm 2.35$	$18.80 \pm 2.94$	$20.95 \pm 1.99$
Observation group ( $n = 40$ )	$40.93 \pm 3.36$	$48.03 \pm 3.22$	$53.28 \pm 2.47$	$17.68 \pm 2.22$	$20.65 \pm 1.85$	$22.93 \pm 1.42$
P	0.153	$< 0.001$	$< 0.001$	0.723	$< 0.001$	$< 0.001$

Notes: ^aDGI scores: scores for Dynamic Gait Index.

^bBBS scores: scores for Berg balance scale.

4. Discussion

The development of the social economy and the enhancement of public health awareness have significantly increased the participation rate in sports activities. Nevertheless, this has also led to an increase in sports-related knee injuries.²⁰ The frequent occurrence of knee injuries has garnered widespread attention from the public and the medical community. Preventing such injuries and extending the healthy lifespan of the knee joint have become a hot topic of research and discussion. The knee joint is as an important part of the lower limb, which is composed of the femur, tibia, and patella. Its structure is complex and delicate. The muscle groups around the knee joint, through antagonistic and synergistic actions, not only support the activities of the knee joint but are also key to its stability. During high-intensity or improper exercise, these muscle groups play a buffering and protective role. Through timely muscle contraction, they can absorb impact forces and reduce direct damage to the knee joint. Additionally, proper muscle strength and balance ensure the stability of the knee joint during exercise, preventing injuries caused by excessive twisting or stretching.²¹ Drug therapy and rehabilitation therapy are the main treatments for sports-related knee injuries. Studies have pointed out that formulating appropriate rehabilitation training programs for patients can significantly enhance their gait stability and help restore balance function.²² Conventional biomechanics-based rehabilitation training measures the joint range of motion of the lower limbs using instruments and implements training based on the measurements. Although it can improve the motor function of patients to some extent and ensure the safety of rehabilitation training, it lacks targeted measures for postural imbalance, resulting in suboptimal rehabilitation effects.^23,24

Recently, visual feedback training for self-posture control has been widely used, which mainly focuses on the combination of balance training and trunk strengthening training, achieving good results. Feedback is the basis for individual motor skill learning and performance and is related to functional improvement and training motivation. Movement is achieved through the connection between the upper limbs, lower limbs, and trunk. Therefore, the recovery effect of trunk control ability is key to improving the balance function and motor function.²⁵ In visual feedback balance training, quantified posture information is provided to patients through computer programs, which corrects incorrect postures during training in a timely manner as well as encourages patients to engage better in training, reducing their tendency to tilt. Moreover, visual feedback enables patients to feel their center of gravity. Once they detect a shift in their center of gravity, they can make timely adjustments, improving their trunk balance in both standing and sitting positions.^26,27 This study showed that after 1 month and 3 months of training, the total scores for the limb motor function, including lower and upper limbs, were higher than those before training ( $P < 0.05$ ), and the scores were higher in the observation group than in the control group. This indicated that biomechanics-based rehabilitation training combined with visual feedback balance training effectively improved the motor function of patients with sports-related knee injuries.

This study demonstrated that the limb motor function, trunk control, and gait stability of patients after 1 month and 3 months of training were significantly better in the observation group compared with the control group. This suggested that biomechanics-based rehabilitation training combined with visual feedback balance training can enhance trunk stability and improve walking ability. The reasons for this are as follows: in visual feedback balance training, trunk control training promoted simultaneous contraction of agonist and antagonist muscle groups, helping to establish proximal stability for limb functions and walking. The center-of-gravity transfer training targeted tilted postures of patients in other anatomical planes, helping them to recover to an upright position. Sit-to-stand transfer training improved ankle joint control ability, enhancing dynamic and static balance and promoting trunk stability.

This study had several limitations and areas for improvement. First, although the Lysholm Knee Scoring Scale is evidently a more suitable assessment tool for evaluating knee joint function in patients, we did not consider this scale in the initial experimental design. This is because the FMA and Sheikh Trunk Control Scale used in this study could partially reflect important indicators such as range of motion, balance function, and knee joint functionality. Second, pain scoring is a significant factor influencing postoperative rehabilitation outcomes. This study lacked relevant evaluation metrics for pain. Future research should aim to enhance the evaluation system. Furthermore, although the sample size in this study met the basic requirements for experimental design, it remained relatively small, and a larger sample size is necessary for more in-depth research. Additionally, we found that although most patients showed significant improvement in knee joint function after 3 months of rehabilitation training, they did not reach satisfactory normal functional levels. The intervention period in the experiment was relatively short.

In conclusion, biomechanics-based rehabilitation training combined with visual feedback balance training could effectively enhance trunk control ability and walking stability, and reduce the degree of body tilt in patients with sports-related knee injuries. It positively promoted the improvement of motor function and is worthy of clinical reference.

5. Ethics and Clinical Registration

This study was approved by the ethics committee, with the Institutional Review Board number LLW-FO-403. The study has been registered and the relevant information has been filed in the National Medical Research Registration and Filing System.

Acknowledgment

This study was supported by the Zhejiang Medicine and Health Science and Technology Project (No. 2024KY1867).

ORCID

Qingfen Ji https://orcid.org/0009-0003-1347-8783

Xiaofen Li https://orcid.org/0009-0009-6700-4034

Guangyi Zou https://orcid.org/0009-0009-1579-6228

Hongying Pan https://orcid.org/0009-0007-3006-0855

References

1. Logerstedt DS et al., Knee pain and mobility impairments: Meniscal and articular cartilage lesions revision, J Orthop Sports Phys Ther 48(2) :A1–A50, 2018. Crossref, Web of Science, Google Scholar
2. Siegel L, Vandenakker-Albanese C, Siegel D, Anterior cruciate ligament injuries: Anatomy, physiology, biomechanics, and management, Clin J Sport Med 22(4) :349–355, 2012. Crossref, Web of Science, Google Scholar
3. Andreoli CV et al., Epidemiology of sports injuries in basketball: Integrative systematic review, BMJ Open Sport Exerc Med 4(1) :e000468, 2018. Crossref, Google Scholar
4. Gimigliano F et al., Epidemiology of musculoskeletal injuries in adult athletes: A scoping review, Medicina 57(10) :1118, 2021. Crossref, Google Scholar
5. Maniar N et al., Trends in Australian knee injury rates: An epidemiological analysis of 228,344 knee injuries over 20 years, Lancet Reg Health West Pac 21 :100409, 2022. Google Scholar
6. Creamer P, Lethbridge-Cejku M, Hochberg MC, Factors associated with functional impairment in symptomatic knee osteoarthritis, Rheumatology 39(5) :490–496, 2000. Crossref, Web of Science, Google Scholar
7. Park C, Son H, Yeo B, The effects of lower extremity cross-training on gait and balance in stroke patients: A double-blinded randomized controlled trial, Eur J Phys Rehabil Med 57(1) :4–12, 2021. Crossref, Web of Science, Google Scholar
8. Logerstedt DS et al., Knee pain and mobility impairments: Meniscal and articular cartilage lesions revision 2018: Clinical practice guidelines linked to the International classification of Functioning, disability and health from the Orthopaedic section of the American Physical Therapy Association, J Orthop Sports Phys Ther 48(2) :A1–A50, 2018. Crossref, Web of Science, Google Scholar
9. Simoneau GG, Knee pain and mobility impairments: Meniscal and articular cartilage lesions revision 2018 using the evidence to guide physical therapist practice, J Orthop Sports Phys Ther 48(2) :123–124, 2018. Crossref, Web of Science, Google Scholar
10. Patankar AG, Christino MA, Milewski MD, Psychological aspects of adolescent knee injuries, Clin Sports Med 41(4) :595–609, 2022. Crossref, Web of Science, Google Scholar
11. Michalitsis S et al., Meniscal and articular cartilage lesions in the anterior cruciate ligament-deficient knee: Correlation between time from injury and knee scores, Knee Surg Sports Traumatol Arthrosc 23 :232–239, 2015. Crossref, Web of Science, Google Scholar
12. Brambilla L et al., Prevalence of associated lesions in anterior cruciate ligament reconstruction: Correlation with surgical timing and with patient age, sex, and body mass index, Am J Sports Med 43(12) :2966–2973, 2015. Crossref, Web of Science, Google Scholar
13. Buckthorpe M et al., Optimising the early-stage rehabilitation process post-ACL reconstruction, Sports Med 54(1) :49–72, 2024. Crossref, Web of Science, Google Scholar
14. Zhang T et al., Effects of visual feedback during balance training on knee function and balance ability in postoperative patients after knee fracture: A randomized controlled trial, J Rehabil Med 41 :54, 2022. Google Scholar
15. Domínguez-Navarro F et al., Effects of balance and proprioceptive training on total hip and knee replacement rehabilitation: A systematic review and meta-analysis, Gait Posture 62 :68–74, 2018. Crossref, Web of Science, Google Scholar
16. De Fazio R, Mastronardi VM, De Vittorio M, Visconti P, Wearable sensors and smart devices to monitor rehabilitation parameters and sports performance: An overview, Sensors (Basel) 23(4) :1856, 2023. Crossref, Web of Science, Google Scholar
17. Sanford J et al., Reliability of the Fugl-Meyer assessment for testing motor performance in patients following stroke, Phys Ther 73(7) :447–454, 1993. Crossref, Web of Science, Google Scholar
18. Sui Y et al., Effects of trunk training using motor imagery on trunk control ability and balance function in patients with stroke, BMC Sports Sci Med Rehabil 15(1) :142, 2023. Crossref, Web of Science, Google Scholar
19. Godi M et al., Comparison of reliability, validity, and responsiveness of the mini-BESTest and Berg Balance Scale in patients with balance disorders, Phys Ther 93(2) :158–167, 2013. Crossref, Web of Science, Google Scholar
20. Leong SC, Tang YM, Toh FM, Fong KNK, Examining the effectiveness of virtual, augmented, and mixed reality (VAMR) therapy for upper limb recovery and activities of daily living in stroke patients: A systematic review and meta-analysis, J Neuroeng Rehabil 19(1) :93, 2022. Crossref, Web of Science, Google Scholar
21. Parlak Demir Y, Yildirim SA, Reliability and validity of Trunk Control Test in patients with neuromuscular diseases, Physiother Theory Pract 31(1) :39–44, 2015. Crossref, Google Scholar
22. Beck Jepsen D et al., Correction: Predicting falls in older adults: An umbrella review of instruments assessing gait, balance, and functional mobility, BMC Geriatr 22(1) :780, 2022. Crossref, Google Scholar
23. Delafontaine A et al., Effects of manual therapy on Parkinson’s gait: A systematic review, Sensors (Basel) 24 :7, 2024. Crossref, Web of Science, Google Scholar
24. Montalvo AM et al., “What’s my risk of sustaining an ACL injury while playing sports?” A systematic review with meta-analysis, Br J Sports Med 53(16) :1003–1012, 2019. Crossref, Google Scholar
25. Logerstedt DS, Ebert JR, MacLeod TD, Heiderscheit BC, Gabbett TJ, Eckenrode BJ, Effects of and response to mechanical loading on the knee, Sports Med 52(2) :201–235, 2022. Crossref, Web of Science, Google Scholar
26. Park C, Son H, Yeo B, The effects of lower extremity cross-training on gait and balance in stroke patients: A double-blinded randomized controlled trial, Eur J Phys Rehabil Med 57(1) :4–12, 2021. Crossref, Web of Science, Google Scholar
27. Diekfuss JA et al., Real-time biofeedback integrated into neuromuscular training reduces high-risk knee biomechanics and increases functional brain connectivity: A preliminary longitudinal investigation, Psychophysiology 57(5) :e13545, 2020. Crossref, Web of Science, Google Scholar

Vol. 24, No. 09

Metrics

Downloaded 81 times

History

Received 22 April 2024

Accepted 27 August 2024

Published: 19 November 2024

Information

This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords

PDF download