Epidemiological attribution of knee and ankle injuries in firefighters

Abstract

This study aimed to evaluate the main factors of knee and ankle injuries among Chinese firefighters, focusing on the role of training arrangements, training conditions, and knowledge of training injuries. A questionnaire survey was conducted among 963 firefighters to collect data on training content, duration, load, protective equipment, rehabilitation measures and fatigue after training. Multiple regression analysis was used to explore the relationship between each factor and knee and ankle injuries. Logistic regression analysis revealed that training load parameter (A2) significantly reduced knee injury risk (OR = 0.784,95% CI = 0.642–0.958, p < 0.05), while personal protective equipment (B4) also showed a significant negative association (OR = 0.816,95% CI = 0.679–0.982, p < 0.05). Rehabilitation measures (C4) demonstrated marked protective effects against both knee (OR = 0.846,95% CI = 0.723–0.990, p < 0.05) and ankle injuries (OR = 0.697,95% CI = 0.606–0.802, p < 0.01). Conversely, post-training fatigue or discomfort (A6) was associated with increased ankle injury risk (OR = 1.251,95% CI = 1.119–1.398, p < 0.01). Likelihood ratio tests confirmed statistically significant models for knee (χ²=65.580, df = 15, p < 0.001) and ankle injuries (χ²=79.663, df = 15, p < 0.001), with ROC curves showing AUC values of 0.71 (95% CI = 0.67–0.75) and 0.75 (95% CI = 0.71–0.79) respectively, indicating excellent predictive performance.This study shows that training load parameters, personal protective equipment, rehabilitation measures and fatigue or discomfort factors after training play a significant role in the occurrence of knee and ankle injuries. Based on this, the study proposed a two-stage injury occurrence analysis and three-level intervention method to reduce training injuries based on data analysis of firefighters’knee and ankle joint injuries, including “training load monitoring-dynamic protection adaptation-accurate rehabilitation intervention”.

Introduction

Firefighters are a globally recognized dangerous occupation¹, they are often accompanied by musculoskeletal disorders (MSD) in the training of reserve physical ability and skills, which seriously hinders the development of firefighters’ ability. MSD is pain in one or more areas of the body². These include injuries to the shoulder, lower back, knee, and ankle³. But studies have found that MSD has become the most common type of injury among firefighters around the world³. Its incidence among firefighters is as high as 85%⁴. And the incidence during training (55%) is higher than in the workplace³. The occurrence of MSD in firefighter training will bring a series of adverse effects、lead to serious financial losses and casualties⁵、decreased muscle strength and endurance⁶、reduced joint range of motion and flexibility⁷.As a result, the overall performance and responsiveness of firefighters are reduced when completing training and executing tasks⁸.

In order to reduce the impact of MSD on firefighters, scholars have studied different injury sites and found that the knee and ankle joints are at serious risk of injury. The study found that 77% of injuries among firefighters occurred in the lower limbs⁹. Among them, ankle and knee joint injuries occur most frequently³.And knee injuries were about twice as common as ankle injuries (22.6% vs. 10.7%, respectively)³. The factors that lead to knee and ankle injuries of firefighters are insufficient preparation^10,11, technical or equipment limitations¹², overload or fatigue^13,14etc. It has also been studied in depth among occupational groups that experience training-related injuries, such as male police cadets¹⁵.However, there is no large sample data study on knee and ankle joint injuries of Chinese firefighters.Therefore, this study conducted a quantitative analysis of the influence of various factors on firefighters’ knee and ankle joint injuries through multi-class logistic regression analysis, and explored the main influencing factors of firefighters’ knee and ankle joint injuries through a cross-sectional study of Chinese firefighters, in order to provide a reference for reducing the current situation of firefighters’ knee and ankle joint injuries worldwide.

This study deriving the two-stage etiological factors of injury occurrence— “biomechanical compensation and micro-damage accumulation” —from the results of multi-category logistic regression analysis. It proposes a three-tier intervention approach to reduce firefighter injuries, providing a replicable solution for musculoskeletal disease prevention in high-risk occupational groups. The findings offer significant reference value for improving occupational health management systems.

Research objects and methods

The study protocol was approved by the Ethical Review Committee of Xuzhou First People’s Hospital(xyy11[2025]0564), and informed consent was obtained from all subjects before filling out the survey questions. Participants were all informed that their personal information was not identified and collected and that all data was anonymized. The study was complied with the Declaration of Helsinki. We distributed questionnaires through the Wenjuanxing APP using simple random sampling, covering firefighters from 24 provincial-level administrative regions of China, including Zhejiang, Yunnan, Hunan, Fujian, Guizhou, Guangdong, Shanghai, Liaoning, Chongqing, Hainan, Sichuan, Jiangsu, Gansu, Inner Mongolia, Shaanxi, Jilin, Ningxia, Heilongjiang, Shandong, Hebei, Anhui, Guangxi, Henan, and Tianjin. A total of 1,043 questionnaires were distributed, and after excluding 80 invalid questionnaires with missing data, 963 valid questionnaires were ultimately obtained.

This study first developed a questionnaire using the Delphi method. Widely applied in recent decades, this approach has been recognized as an effective means to enhance data quality when empirical evidence is insufficient or controversial¹⁶. The Delphi consensus identified that knee and ankle injuries are relatively common among firefighters, yet there remains no unified standard for injury data collection. Based on this, the research collected relevant injury data through the Delphi method. Expert selection criteria included: (1) Working at domestic fire emergency command centers or university sports colleges; (2) Professional background in sports science, physical training, sports education, or fire science; (3) Three years of work experience with associate senior professional title or higher; (4) No more than five experts per institution. Below are the basic profiles of participating experts (see Table 1). After two rounds of expert feedback, the final questionnaire framework was established. The Cronbach’s α-coefficient exceeded 0.65, with a KMO value reaching 0.892 (Table 2).

Table 1.

Basic information of participating experts (N = 11).

Name	Age	Unit	Years of Work	Professional Title/Position	Education Level	Research Direction
Zhu**	56	China University of Mining and Technology	32	Professor	Doctor	Fire Engineering Research
Zhu**	37	China University of Mining and Technology	6	Associate Professor	Doctor	Fire Engineering Research
Zhang**	66	Beijing Sport University	35	Professor	Doctor	Sports Education and Training
Zhai*	55	China University of Mining and Technology	33	Professor	Doctor	Sports Education and Training
Chen**	40	China University of Mining and Technology	13	Associate Professor	Doctor	Physical Fitness Training
Zhu**	50	Shandong Sport University	26	Associate Professor	Doctor	Physical Fitness Training
Zhang**	51	China University of Mining and Technology	24	Associate Professor	Master	Sports Teaching Theory
Liu**	60	South China Normal University	37	Professor	Doctor	Sports Rehabilitation
Lu*	37	Tongshan District Fire and Rescue Brigade, Xuzhou City	15	Captain	Bachelor	Firefighting Command and Training
Ma*	42	Suining County Fire and Rescue Brigade, Xuzhou City	17	Captain	Bachelor	Firefighting Command and Training
Qiu **	45	Yunlong Lake Scenic Area Fire and Rescue Brigade, Xuzhou City	20	Captain	Bachelor	Firefighting Command and Training

Table 2.

The KMO and bartlett’s Test.

Kaiser-Meyer-Olkin Measure of Sampling Adequacy		0.892
Bartlett’s test of sphericity	Approximate Chi-square	17808.752
	Degree of freedom	276
	Significance	0.000

Secondly, in order to further obtain the relevant information of front-line firefighters, the interview method was used to randomly select some in-service firefighters from the fire rescue teams in each region of Xuzhou city for face-to-face interviews. According to the interview results, the current situation of firefighter training and the risk factors of training injury were summarized, which provided the basis for the sorting out of follow-up problems and countermeasures of this study. Finally, GraphPad Prism10.1 and SPSS 27.0 software were used for correlation analysis and multi-class logistic regression analysis to study the factors of firefighter knee and ankle joint injury.

Basic information of respondents

Among the 963 firefighters surveyed, all of them were male, with age ranging from 18 to 57 years old, weight ranging from 52 to 110 kg, and average height of 1.73 ± 0.052 m. 90% of them had a bachelor’s degree or below, 10% had a bachelor’s degree or above, and 41.3% of them had less than two years of service. Among them, A1-A7 are training arrangement related factors, B1-B4 are training condition related factors, and C1-C4 are training injury knowledge related factors.Among them, the sample sizes of each variable are complete, reflecting the broad representativeness of the research subjects.

After compiling the first round of survey questionnaires and conducting analysis, we made appropriate adjustments to specific items based on expert feedback while adding supplementary items, resulting in the second-round firefighter training injury risk assessment questionnaire. The mean scores from expert evaluations in the second round screening all exceeded 3.5 with a standard deviation below 1, indicating validated indicator selection. This confirmed the completion of investigation and screening processes for all indicators. Ultimately, 24 risk assessment indicators for firefighter training injuries were established, categorized into four types: preventive factors, training schedule factors, unhealthy habit factors, and training condition factors. These categories were further coded to identify influencing factors (Table 3).

Table 3.

Score results of each indicator in the first and second round of Delphi index screening.

Classification	Damage influencing factors	Encoding	First-round averages	First standard deviation	Second-round averages	Second round of standard deviation
Training perception and daily habits	Targeted Training Arrangement	A1	4.702	0.453	4.316	0.432
	Training Load Parameters	A2	4.654	0.474	4.751	0.531
	Training Load	A3	4.535	0.512	4.583	0.562
	Pre - training Warm - up	A4	4.332	0.423	4.423	0.493
	Post - training Relaxation	A5	4.369	0.425	4.648	0.593
	Post - training Fatigue or Discomfort	A6	4.534	0.456	3.654	0.379
	Training in accordance with the Outline	A7	4.329	0.438	4.563	0.483
Training schedule	Training Venue and Facilities	B1	4.726	0.546	4.574	0.541
	Shoes Worn during Training	B2	3.454	0.368	4.564	0.486
	Load during Training	B3	4.613	0.486	4.236	0.456
	Personal Protective Equipment	B4	4.493	0.556	4.544	0.457
	Knowledge of Injury Prevention and Treatment	B5	4.603	0.446	4.422	0.449
	Injury Prevention and Treatment Measures	B6	4.301	0.434	4.227	0.368
	Understanding Training Injury Knowledge through Lectures and Cases	B7	4.203	0.413	3.792	0.393
Training conditions	Knowledge of Trauma Prevention and Treatment	C1	4.452	0.42	4.454	0.456
	Targeted Training Arrangement	C2	4.327	0.646	4.536	0.521
	Training Load Parameters	C3	3.256	0.369	4.454	0.52
	Rehabilitation Measure	C4	4.113	0.335	4.412	0.394

Research results

Damage rate and related analysis

Table 4 is a descriptive statistic of 963 data.This table presents the descriptive statistical results of 20 indicators in 4 core dimensions, including basic information, training cognition and daily habits, training arrangement and training conditions, of 963 firefighter samples. It covers sample size, mean value and standard deviation, and provides basic data support for subsequent analysis of factors affecting knee and ankle joint injuries.

Table 4.

Descriptive statistics of variables (N = 963).

	Name	Sample Size	Mean	Standard Deviation
Essential information	Age (years old)	963	27.278	7.160
	Height (cm)	963	173.716	5.240
	Weight (kg)	963	69.729	9.021
	Years of Service	963	5.849	6.744
	Ankle Injury	963	0.323	0.468
	Knee Injury	963	0.464	0.499
Training perception and daily habits Training conditions	A1 (Targeted Training Arrangement)	963	3.033	1.403
	A2 (Training Load Parameters)	963	3.590	1.219
	A3 (Training Load)	963	3.429	1.246
	A4 (Pre - training Warm - up)	963	3.913	1.117
	A5 (Post - training Relaxation)	963	3.811	1.153
	A6 (Post - training Fatigue or Discomfort)	963	3.147	1.257
	A7 (Training in accordance with the Outline)	963	3.972	1.099
Training schedule	B1 (Training Venue and Facilities)	963	3.327	1.291
	B2 (Shoes Worn during Training)	963	3.340	1.339
	B3 (Load during Training)	963	3.385	1.229
	B4 (Personal Protective Equipment (PPE))	963	3.744	1.139
	C1 (Knowledge of Injury Prevention and Treatment)	963	3.925	1.074
	C2 (Injury Prevention and Treatment Measures)	963	3.984	1.065
	C3 (Understanding Training Injury Knowledge through Lectures and Cases)	963	3.886	1.099
	C4 (Rehabilitation Measures)	963	3.682	1.168

Our research hypothesis is based on two assumptions. First, the sample has an average age of 27.278 years (standard deviation 7.160), an average Length of service of 5.849 years (standard deviation 6.744), and predominantly bachelor’s degree or lower education (90%). The sample is sourced from 24 provincial-level regions across China, assuming it serves as a random representative of the Chinese firefighter population with characteristics consistent with the actual group. The Cronbach’s Alpha > 0.65, KMO = 0.892, and Bartlett’s test of sphericity p < 0.01 further validate the reliability of the sample data, providing empirical support for the hypotheses.

Secondly, the included risk factors such as “training load (A2)”, “Personal protective equipment (B4)”, and “rehabilitation measures (C4)” are assumed to have a stable impact on injuries during the study period (questionnaire survey period), unaffected by short-term training program adjustments or temporary equipment changes. The rationale for this assumption lies in the study’s use of the “training according to the program (A7)” indicator (mean 4.563, standard deviation 0.483) to confirm the consistency between the sample’s training content and the official program, thereby reducing interference from “sudden changes in the training system” on variable validity.

Among the 963 valid samples recovered, it was found that firefighters had the highest rate of knee and ankle injuries, with 447 having knee injuries and 311 having ankle injuries. Figure 1 shows the distribution of the specific number of injured people.Table 5 shows the incidence of knee and ankle injuries in firefighters and their 95% confidence intervals.

Fig. 1.

Distribution of knee and ankle joint injury sites (n = 963).

Table 5.

Prevalence rate and 95% confidence interval of knee and ankle injuries in firefighters.

Injury site	Total sample size (n)	Number of injury cases (n)	Prevalence rate (%)	95% confidence interval (%)
knee joint	963	447	46.4	43.2–49.6
Ankle joint	963	311	32.3	29.3–35.3

Table 6 presents the multicollinearity evaluation results of independent variables. All variables exhibit Variance Inflation Factors (VIF) < 5, with Pearson correlation coefficients between variables all below 0.7, fully meeting the “no collinearity” criterion and eliminating information redundancy. This allows direct inclusion of all variables into regression models (e.g., logistic regression, multiple linear regression) without requiring dimensionality reduction techniques like variable elimination or principal component analysis, thereby preserving comprehensive multi-dimensional information. Although training plan-related variables (A2, A3), training condition variables (B2, B4), and injury knowledge variables (C1, C3) belong to the same dimension, their absence of collinearity enables their full retention for analyzing independent effects of each sub-factor.

Table 6.

Evaluation results of independent variable Multicollinearity.

Variable category	Variable code	Variable name	VIF value	Pearson correlation coefficient (with highly correlated variables)	Collinearity diagnostic criteria	Judgment of results
Training Schedule	A2	Training load parameters	1.85	Correlation with A3 (training load) r = 0.42	VIF < 5, r < 0.7 No collinearity	No collinearity
	A3	Training load	1.92	Correlates with A2 (training load parameter) r = 0.42	VIF < 5, r < 0.7 No collinearity	No collinearity
Training conditions	B2	Training shoes	1.78	With B4 (a set personal protective equipment) r = 0.38	VIF < 5, r < 0.7 No collinearity	No collinearity
	B4	Personal protective equipment	1.81	With B2 (training footwear) r = 0.38	VIF < 5, r < 0.7 No collinearity	No collinearity
Damage knowledge	C1	Knowledge of injury prevention and treatment	1.67	Correlation with C3 (Lecture/Case Understanding of Injury) r = 0.45	VIF < 5, r < 0.7 No collinearity	No collinearity
Other variables (A1, A4, A5, A6, A7, B1, B3, C2, C4)	-	-	1.52–1.89	r < 0.40 (with all variables)	VIF < 5, r < 0.7 indicates no collinearity.	No collinearity

Table 7 validates the effectiveness of the knee and ankle injury models: Both classifications showed no overlapping samples (knee: 447 cases vs. 516 controls, ankle: 311 cases vs. 652 controls), demonstrating rigorous logic. The minimum expected frequencies (223.5 and 155.5) were significantly higher than the threshold of 5, meeting modeling requirements. Linear correlations were observed between training load (A2), age, service years, and injury probability logit (P) (p < 0.1), with unbiased parameter estimation. The model exhibited strong resistance to extreme sample interference, with OR values fluctuating below 5%, ensuring stable results suitable for injury risk analysis and probabilistic prediction.

Table 7.

Results of rationality and hypothesis testing of outcome variables (knee and ankle joint injury).

Inspection items	injury of knee (yes/no)	injury of ankle (yes/no)	Inspection standard	Judgment of results
Binary classification mutual exclusivity (cross-frequency)	Yes = 447 cases, No = 516 cases, no overlap	Yes = 311 cases, No = 652 cases, no overlap	Without “both is and is not” contradictory samples	Reasonable classification
Minimum expected frequency (chi-square test)	Minimum expected frequency = 223.5 (> 5)	Minimum expected frequency = 155.5 (> 5)	Expected frequency > 5 is suitable for binary classification models	Meets model requirements
Continuous independent variable linearity assumption (Linearity in Logit))	Training load parameter (A2): β = 0.21, t = 2.03, p = 0.042; Age: β = 0.15, t = 1.89, p = 0.059; Years of service: β = 0.18,t = 2.11, p = 0.035	Training load parameter (A2): β = 0.19, t = 1.92, p = 0.056; Age: β = 0.14, t = 1.78, p = 0.075; Years of service: β = 0.17,t = 2.05, p = 0.041	Continuous independent variables showed a significant linear association with logit(P) (p < 0.1 accepted)	Conforms to the linear hypothesis
Conventional value and high leverage value test	Normal values = 4 cases (boxplot method), High leverage value samples = 1 case (Cook’s distance > 0.01), Sensitivity analysis OR fluctuation = 0.784→0.791 (< 5%)	Constant value = 2 cases (boxplot method), high leverage value samples = 0 cases (Cook’s distance ≤ 0.01), sensitivity analysis OR fluctuation = 1.251→1.248 (< 5%)	Remains stable when outliers are removed OR fluctuations are < 10%	Remains stable when outliers are removed OR fluctuations are < 10%

Results of regression analysis

Table 8 presents the model fitting statistics. In terms of overall model validity, the likelihood ratio test results show that both the injury of knee model (χ²=65.580, df = 15, p < 0.001) and the injury of ankle model (χ²=79.663, df = 15, p < 0.001) reject the null hypothesis of “no predictive effect of independent variables,” indicating that the incorporated training arrangements, training conditions, and injury-related knowledge variables collectively have significant predictive value for injury occurrence, and the model construction is statistically meaningful.

Table 8.

Model fitting statistics.

Model fitting index	likelihood ratio test	McFadden R²	Cox & Snell R²	Nagelkerke R²	Hosmer-Lemeshow test	ROC curve AUC value (discrimination)	Classification accuracy (predictive accuracy)
Knee joint injury model (dependent variable: knee joint injury)	χ²=65.580, df = 15, p < 0.001	0.049	0.066	0.088	χ²=9.25, df = 8, p = 0.327	0.71(95%CI: 0.67–0.75)	72.3%(95%CI: 69.2%−75.4%)
Ankle joint injury model (dependent variable: ankle joint injury)	χ²=79.663, df = 15, p < 0.001	0.066	0.079	0.111	χ²=7.81, df = 8, p = 0.453	0.75(95%CI: 0.71–0.79)	76.5%(95%CI: 73.6%−79.4%)

Regarding model explanatory power, the coefficients of determination for both models are at relatively low levels. The injury of knee model shows McFadden R²=0.049, Cox & Snell R²=0.066, and Nagelkerke R²=0.088, while the injury of ankle model shows McFadden R²=0.066, Cox & Snell R²=0.079, and Nagelkerke R²=0.111. This aligns with common characteristics of epidemiological risk models, as injuries are influenced by multiple unincorporated factors such as individual constitution and task scenarios, limiting the explanatory power of a single model for injury. However, the injury of ankle model exhibits slightly higher R² values, suggesting relatively better explanatory performance for injury variation.

In terms of model goodness-of-fit, the Hosmer-Lemeshow test results show that both the injury of knee model (χ²=9.25, df = 8, p = 0.327) and the injury of ankle model (χ²=7.81, df = 8, p = 0.453) have p-values greater than 0.05, indicating no significant difference between the predicted probabilities and actual injury observations, and the models fit well.

From a practical perspective, the discrimination metrics (ROC curve AUC values) show that the injury of knee model has an AUC = 0.71 (95% CI: 0.67–0.75), while the injury of ankle model has an AUC = 0.75 (95% CI: 0.71–0.79), both exceeding 0.7. This demonstrates that the models perform well in distinguishing between “injury groups” and “non-injury groups,” with the injury of ankle model exhibiting superior discrimination.

In terms of classification accuracy, the injury of knee model achieves 72.3% (95% CI: 69.2%–75.4%), while the injury of ankle model achieves 76.5% (95% CI: 73.6%–79.4%), suggesting moderate predictive correctness in practical applications. The injury of ankle model shows slightly higher predictive accuracy than the injury of knee model, making it generally suitable for risk screening and preliminary prevention of firefighters’ knee injury of ankle.

Table 9 shows the summary of multivariate logistic regression analysis results for the knee joint and ankle joint. The data indicates that three variables are significantly associated with injury of knee (OR ≠ 1 and 95% CI does not include 1, p < 0.05), namely A2, B4, and C4. Two variables are significantly associated with injury of ankle, namely A6 and C4.

Table 9.

Summary of multiple logistic regression analysis results for knee and ankle joints.

	Knee injury model (Reference group: no knee injury)	OR value	95%CI	Ankle injury model (Reference group: no ankle injury)	OR value	95%CI
A1	0.015 (0.259)	1.015	(0.905, 1.138)	0.059 (0.948)	1.061	(0.942,1.195)
A2	−0.243* (−2.328)	0.784	(0.642,0.958)	−0.180 (−1.574)	0.834	(0.675,1.029)
A3	−0.014 (−0.146)	0.986	(0.831,1.172)	0.064 (0.593)	1.066	(0.942,1.206)
A4	−0.002 (−0.015)	0.998	(0.855,1.125)	0.001 (0.005)	1.001	(0.671,1.492)
A5	−0.084 (−0.676)	0.919	(0.783,1.081)	−0.177 (−1.350)	0.838	(0.692,1.014)
A6	0.094 (1.477)	1.100	(0.970,1.247)	0.224** (3.187)	1.251	(1.119,1.398)
A7	0.019 (0.203)	1.019	(0.893,1.153)	0.019 (0.195)	1.019	(0.889,1.158)
B1	−0.017 (−0.089)	0.983	(0.836,1.157)	0.139 (0.620)	1.149	(0.976,1.353)
B2	0.064 (1.047)	1.066	(0.940,1.207)	−0.006 (−0.086)	0.994	(0.843,1.172)
B3	0.089 (0.417)	1.093	(0.926,1.288)	−0.048 (−0.196)	0.953	(0.813,1.117)
B4	−0.203* (−2.161)	0.816	(0.679,0.982)	−0.182 (−1.832)	0.833	(0.693,1.001)
C1	−0.098 (−0.813)	0.907	(0.777,1.059)	0.088 (0.684)	1.092	(0.951,1.225)
C2	0.247 (1.895)	1.280	(1.008,1.623)	0.215 (1.573)	1.240	(0.985,1.559)
C3	0.125 (1.207)	1.133	(0.981,1.309)	0.121 (1.111)	1.129	(0.973,1.306)
C4	−0.167* (−2.077)	0.846	(0.723,0.990)	−0.361** (−4.297)	0.697	(0.606,0.802)
Intercept	0.514 (1.509)			−0.618 (−1.710)

It can be seen from Table 4 that “0.0 (no injury)” of knee joint injury was used as the reference item for multi-classification Logistic regression analysis, and the model formula is as follows:

1

In Formula (1) :

In Eq. (1), α1-α7 correspond to A1-A7, β1-β4 correspond to B1-B4, and γ1-γ4 correspond to C1-C4. The results showed that A2 (content and duration of training), B4 (personal protective equipment used in training) and C4 (rehabilitation measures) had a significant negative impact on the occurrence of knee joint injuries. That is, the more unreasonable training and duration, the less personal protective equipment is worn, and the less rehabilitation measures are taken after injury, the more likely it is to lead to knee joint injury.

The “0.0” of ankle joint injury was used as the reference item for multi-classification Logistic regression analysis, and the model formula was as follows:

2

In Formula (2) :

In Eq. (2), α1-α7 correspond to A1-A7, β1-β4 correspond to B1-B4, and γ1-γ4 correspond to C1-C4. The results showed that A6 (fatigue or discomfort at the end of training) had a significant positive effect on ankle injury, while C4 (rehabilitation measures after training injury) had a significant negative effect. That is, the higher the degree of fatigue and discomfort after training, the more prone to ankle injury; Taking appropriate rehabilitation measures after training injury will reduce the occurrence of ankle joint injury.

Discussion

In the injury research of Chinese firefighters, the knee and ankle injuries are particularly prominent. The results show that multiple factors have positive and negative effects on knee and ankle joint injuries. Combined with the results of field interviews with firefighters, the mechanism of each influencing factor is discussed. At the same time, the two-stage theory of firefighter knee and ankle joint injury and the three-level intervention model of “training load monitoring, dynamic protection adaptation, and precision rehabilitation intervention” are proposed after in-depth analysis.At the same time, through in-depth analysis, the two-stage injury causes of firefighters’ knee and ankle joints were proposed, and the three-level intervention of “training load monitoring, dynamic protection adaptation and accurate rehabilitation intervention” was proposed to reduce the injury.

Mechanism of training load monitoring

Research findings indicate that high-volume, high-intensity training combined with insufficient rest can lead to musculoskeletal and joint injuries. Interviews with frontline firefighters revealed a pressing need to “decrease training intensity” and “reduce the frequency of high-intensity training sessions”. A study on how training load affects runners’ gait demonstrated that appropriate training loads provide energy support for neuromuscular system stability, regulate lower limb movement rhythms to minimize biomechanical imbalance-induced gait variability, and enhance central nervous system control over lower limbs, thereby reducing “neuromuscular signal noise interference“¹⁷. Therefore, gait analysis can serve as a reliable indicator of training load for firefighters. When training loads remain within the “optimal range”, reduced gait variability suggests effective adaptation. Conversely, excessive training loads may increase gait variability, indicating potential risks of non-functional overtraining or injury.

Chronic overuse injuries not only result from prolonged high-intensity training but may also accelerate the onset of chronic damage¹⁸. This is particularly evident in the knee joint, where repetitive stress and impact frequently lead to soft tissue injuries (e.g., ligament sprains, meniscus tears) and bone joint disorders like patellar chondromalacia and arthritis¹⁹. For firefighters with knee injuries, the Joint-by-Joint Training Approach (JBJTA) offers a rehabilitation solution. Unlike traditional knee-focused approaches, JBJTA emphasizes the interconnectedness of the kinetic chain (trunk, hips, ankles, feet). In human movement, knee mechanics are influenced by adjacent joints: limited hip mobility can cause knee valgus, while poor trunk control amplifies ground reaction forces. By improving hip/ankle mobility, enhancing trunk/foot stability, and optimizing proprioception, JBJTA optimizes lower limb force distribution, reduces peak knee loads, and corrects abnormal biomechanical patterns (like knee valgus), thereby fundamentally lowering the risk of ACL injuries²⁰.

Based on this, further research should focus on how different training loads affect firefighters’ knee joints. By monitoring gait changes, we can predict injury risks. When knee injuries occur, the JBJTA rehabilitation method can be applied to develop more scientific training plans that optimize both exercise content and intensity.

Mechanism of precision rehabilitation intervention

Post-exercise discomfort, particularly muscle tension, soreness, and joint stiffness, serves as a potential injury indicator. Research indicates that when post-training discomfort isn’t promptly alleviated, fatigue-induced changes in movement patterns and overexertion can lead to soft tissue injuries²¹, especially around the ankle’s ligaments and tendons²². These fatigue-related injuries typically develop cumulatively and progressively, with prolonged exposure potentially causing chronic damage and functional impairment²³. Studies show preventive rehabilitation measures play a crucial role in reducing occupational injuries²⁴. Dhillon H et al. emphasize that early intervention through physical therapists, sports medicine specialists, and orthopedic doctors helps prevent secondary joint injuries while accelerating athletes’ full recovery²⁵. For weight-bearing joints like knees and ankles, proper rehabilitation training effectively reduces recurrence risks while enhancing joint stability and functionality²⁶.

Therefore, reducing post-training fatigue and discomfort—particularly through proper recovery measures like stretching, massage, and cold/hot compresses—can significantly lower the risk of ankle injuries. Additionally, strategically managing training volume and intensity, implementing effective fatigue management, and enhancing physical conditioning during recovery phases are crucial for injury prevention. Developing scientific training and recovery strategies, coupled with optimized rehabilitation protocols, can effectively reduce ankle injury rates while improving firefighters ‘overall health. For ankle injuries among firefighters, the Alfredson treatment protocol—a gold-standard approach for chronic mid-portion Achilles tendinitis—provides a reliable solution. This evidence-based method stimulates tendon repair through targeted exercises and training parameters, effectively alleviating pain and restoring function²⁷. Future research should further investigate how different rehabilitation interventions impact firefighters’ long-term health, providing more precise guidance for occupational training and health protection strategies.

Mechanism of dynamic protection adaptation

The results show that weight-bearing joints such as the knee are more vulnerable when firefighters’ Protective equipment cannot effectively provide sufficient support and protection²⁸.In the interview with frontline firefighters, firefighters also mentioned many times the practical needs of “distributing more targeted training equipment or auxiliary training equipment”, “increasing protective measures in high-altitude skill training” and “proposing to update personal protective equipment”.

Firstly, inadequate and improper use of protective equipment often increases the risk of injury to firefighters in extreme conditions, especially when performing high-impact activities such as jumping, fast walking, and weight training. Secondly, the knee injuries of firefighters are closely related to the design and suitability of protective equipment. Protective equipment, such as knee pads and leg pads, should have sufficient comfort and flexibility to avoid local discomfort or excessive compression due to inappropriate design. This leads to increased joint burden²⁹.Finally, the design, material and way of wearing protective equipment can have a significant impact on the effectiveness of protection, especially in strenuous sports and high-intensity training. Good protective equipment can significantly reduce the incidence of knee injuries³⁰.Therefore, the protective equipment of firefighters should not only meet the standards, but also be appropriately adjusted according to the individual body shape and use needs to provide the most effective protection. Further research can explore the differences in the prevention effects of different types and materials of protective equipment (such as knee pads, leg warmers, elastic bandages, etc.) on knee and ankle joint injuries, and provide more targeted equipment use suggestions for firefighters. In addition, firefighters and training managers should also increase training on the use of protective equipment to ensure that it is properly worn and used to minimize injury occurrence.

Two stages of analysis of lower limb injury in firefighters

The two-stage interaction mechanism is as follows: microdamage leads to pain → further compensation of movement mode → formation of new damage foci. When the accumulation of microdamage reaches the critical value in the compensation period, acute injury may occur suddenly.

The first stage involves biomechanical compensation, characterized by increased mobility in adjacent joints or body segments³¹. For example, excessive adduction and internal rotation of the hip joint can cause the knee center to shift inward relative to the foot³². As the foot remains fixed on the ground, this inward movement of the knee leads to tibial abduction and plantar preversion, ultimately resulting in dynamic genu valgum³². Excessive knee valgus has been associated with weakened hip muscle strength^33,34, potentially leading to recurrent knee injuries including anterior cruciate ligament (ACL) damage³⁵ and patellofemoral joint dysfunction³⁶. However, such compensatory mechanisms hinder long-term recovery and increase risks of other orthopedic issues by reducing functional engagement of affected limbs³⁷. Previous research suggests that minimizing compensatory actions during rehabilitation may facilitate functional recovery^38,39. The core mechanisms include: (1) Muscle activation imbalance. For instance, firefighters climbing stairs may overactivate their quadriceps to compensate for hamstring fatigue, increasing stress on the patellofemoral joint. (2) Energy expenditure redistribution. Leg length discrepancy inevitably alters pelvic positioning, causing sacral base imbalance and spinal curvature. As the spine adapts, compensatory movements may lead to cervical-cervicofacial junction joint dysfunction, triggering compensatory activities of the occipitalis minor muscle and altered head positioning. Most neck muscles subsequently engage in further compensatory actions, some involving increased muscle tension and potential spasms. For example, when the load on the knee is exceeded, the lumbar forward tilt Angle increases to share the pressure, which leads to the risk of lower back pain.

The second phase involves microtrauma accumulation. When repetitive microtraumas (such as collagen fiber fractures and cartilage microcracks) caused by biomechanical compensation exceed the body’s repair capacity, they gradually develop into chronic inflammation, structural degeneration, or functional impairment. This manifests as pain, loss of functional mobility, altered strength patterns, or changes in endurance patterns, indicating that the body has exceeded the physiological limits of relatively healthy tissues or shown “gradual decompensation” – a sign of slowly exhausted tissue adaptability. As this microtrauma adaptation process progresses, time-dependent postural adaptations may be further compromised by subsequent trauma, overwhelming repair potential and leading to exhaustion of adaptive capacity, resulting in functional disorders and various symptoms. Over time, adaptive modifications may evolve from the onset of functional impairments (e.g., low back pain) to actual pathological changes.

Third level intervention to reduce injury

Based on a two-stage analysis, we propose a three-tier intervention approach: “Training Load Monitoring-Dynamic Protection Adaptation-Precise Rehabilitation Intervention”. Designed for comprehensive prevention and control of knee and ankle injuries in firefighters, this system utilizes advanced technologies from sports biomechanics such as wearable technology, motion analysis, and injury prevention⁴⁰, enabling closed-loop risk management through digital and personalized methods. The first tier focuses on training load monitoring–biomechanical compensation prevention. In firefighter training practices, intelligent knee guards integrated with IMU sensors can monitor real-time knee flexion angles and impact acceleration. Foot pressure monitoring insoles provide dynamic feedback on sole pressure distribution. Load thresholds are set based on these data points, with red, yellow, and green alerts indicating different levels of training intensity. The second tier involves dynamic protection adaptation–blocking compensatory injuries. Targeted interventions can be implemented through dynamic gradient compression knee braces, carbon fiber hinge orthoses, 3D-printed custom ankle braces, and smart shock-absorbing boots. Simultaneously, a biomechanical database of firefighters is established to recommend personalized protective solutions via machine learning models. The third tier focuses on precision rehabilitation intervention–reversing micro-damage accumulation. Rehabilitation robots provide isokinetic strength training with precise torque feedback, while digital twin platforms construct three-dimensional joint models to simulate biomechanical effects of different rehabilitation protocols. The training load monitoring layer tracks joint mechanics in real-time through wearable devices, the dynamic protection adaptation layer automatically adjusts protective equipment parameters based on monitoring data, and the precision rehabilitation intervention layer delivers targeted treatment plans according to injury progression stages.

Limitations

While this study investigates the epidemiological causes of knee and ankle injuries among firefighters through questionnaire surveys, three limitations remain. First, self-report bias may compromise data accuracy. Core findings derived from participants ‘responses could be influenced by social desirability bias and recall bias, where some individuals might adjust answers to “avoid negative evaluations” or “remember inaccurately”. This may distort variables like “subjective injury severity” and weaken the validity of correlation analyses. Second, the cross-sectional design limits causal inference. Data collected at a single time point only reveals correlations but fails to capture temporal dynamics, limiting the conclusions’ generalizability and timeliness. Third, the absence of objective injury verification reduces reliability. The reliance on subjective reporting for “injuries” introduces measurement errors that affect conclusion credibility. Given variations in training practices, equipment, and healthcare systems, these findings may not fully apply to other regions or countries.

Conclusion

Through statistical modeling, this study systematically analyzed the key risk pathways and prevention strategies for firefighter knee-ankle joint injuries, proposing a two-stage etiological framework for lower limb damage: biomechanical compensation and microtrauma accumulation. The theory reveals the dynamic evolution from functional compensation to organic lesions in occupational injuries, providing a scientific “early detection and early intervention” approach for high-risk groups. Future research should further explore the impact of genetic-environmental interactions on individual compensatory capacity to achieve personalized protection. Based on the two-stage theory, a three-tier intervention strategy of “training load monitoring-dynamic protective adaptation-precision rehabilitation” is proposed. This approach achieves comprehensive precision prevention of occupational musculoskeletal injuries through the triad of “monitoring-protection-rehabilitation”.

Author contributions

Huiyu Wang: Writing - Review & Editing, Methodology, Validation, Formal analysis, Investigation, SupervisionGuoqing Zhu: Writing - Review & Editing, Funding acquisition.

Funding

This study was supported by the National Fire rescue Bureau science and technology plan key research projects(2020XFLR43).

Data availability

Data are available upon reasonable request. All data relevant to the study are included in the article. Any additional data may be available from the corresponding author (Huiyu Wang) on reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval

The ethical approval has been granted by the Ethics Review Committee of Xuzhou First People’s Hospital. Approval number: xyy11[2025]0564.

Patient and public involvement

Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review

Not commissioned; externally peer reviewed.