Relationship Between Training Load and Injuries in Law Enforcement Recruits

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Canetti" }, { "@type": "Person", "name": "Evelyne Rathbone" }, { "@type": "Person", "name": "Ben Schram" }, { "@type": "Person", "name": "Joseph M. Dulla" }, { "@type": "Person", "name": "J. Jay Dawes" }, { "@type": "Person", "name": "Robert G. Lockie" }, { "@type": "Person", "name": "Robin M. Orr" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Law enforcement agencies typically conduct academy training to develop new officers. As these future officers are recruited from the general population, increases in physical workload during academy training can influence injury risk. This study explored the relationship between training load (TL) and injury risk among police officer recruits. Methods Data relating to injuries suffered, distance covered, physical fitness, and time spent in physical training were collected from 547 academy police recruits (431 male; 116 female). Course length varied between 20 and 22 weeks. A generalised linear mixed model was used to assess relationships between these variables and injury risk. The best fitting model was chosen using a stepwide approach with Akaike information criterion (AIC) and Bayesian information criterion (BIC) used for comparison. Results The best fitting model utilised weekly distance, week of training, and biological sex to predict injury (χ2= 38.3, p-value < 0.001). Higher weekly distances, earlier weeks of academy training, and female sex all resulted in higher probabilities of injury. Conclusions Rapid increases in TL (distance) during the transition from civilian to law enforcement recruit and lower fitness levels (resilience to TL) may lead to higher injury risk. The use of occupationally specific periodised, ability-based, training may lead to a more optimal TL for recruits, limiting overtraining while sufficiently developing fitness. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-840", "name": "Relationship Between Training Load and Injuries in Law Enforcement..." } } ] } Home Browse Relationship Between Training Load and Injuries in Law Enforcement... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Maupin D, Canetti EFD, Rathbone E et al. Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.12688/f1000research.168140.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] Danny Maupin https://orcid.org/0000-0002-0510-6627 1 , Elisa F.D. Canetti 2,3 , Evelyne Rathbone 2 , [...] Ben Schram 2,3 , Joseph M. Dulla 2 , J. Jay Dawes 3,4 , Robert G. Lockie 3,5 , Robin M. Orr 2,3 Danny Maupin https://orcid.org/0000-0002-0510-6627 1 , Elisa F.D. Canetti 2,3 , [...] Evelyne Rathbone 2 , Ben Schram 2,3 , Joseph M. Dulla 2 , J. Jay Dawes 3,4 , Robert G. Lockie 3,5 , Robin M. Orr 2,3 PUBLISHED 29 Aug 2025 Author details Author details 1 University of Surrey Faculty of Health and Medical Sciences, Guildford, England, UK 2 Bond University Faculty of Health Sciences and Medicine, Gold Coast, Queensland, Australia 3 Tactical Research Unit, Bond University, Gold Coast, Queensland, 4226, Australia 4 School of Kinesiology, Oklahoma State University, Stillwater, Oklahoma, 74078, USA 5 California State University Fullerton Department of Kinesiology, Fullerton, California, 92831, USA Danny Maupin Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Elisa F.D. Canetti Roles: Conceptualization, Formal Analysis, Investigation, Project Administration, Software, Supervision, Validation, Writing – Review & Editing Evelyne Rathbone Roles: Formal Analysis, Software, Writing – Review & Editing Ben Schram Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing Joseph M. Dulla Roles: Conceptualization, Data Curation, Project Administration, Resources, Writing – Review & Editing J. Jay Dawes Roles: Data Curation, Investigation, Project Administration, Resources, Writing – Review & Editing Robert G. Lockie Roles: Data Curation, Methodology, Project Administration, Resources, Writing – Review & Editing Robin M. Orr Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Law enforcement agencies typically conduct academy training to develop new officers. As these future officers are recruited from the general population, increases in physical workload during academy training can influence injury risk. This study explored the relationship between training load (TL) and injury risk among police officer recruits. Methods Data relating to injuries suffered, distance covered, physical fitness, and time spent in physical training were collected from 547 academy police recruits (431 male; 116 female). Course length varied between 20 and 22 weeks. A generalised linear mixed model was used to assess relationships between these variables and injury risk. The best fitting model was chosen using a stepwide approach with Akaike information criterion (AIC) and Bayesian information criterion (BIC) used for comparison. Results The best fitting model utilised weekly distance, week of training, and biological sex to predict injury (χ 2 = 38.3, p -value < 0.001). Higher weekly distances, earlier weeks of academy training, and female sex all resulted in higher probabilities of injury. Conclusions Rapid increases in TL (distance) during the transition from civilian to law enforcement recruit and lower fitness levels (resilience to TL) may lead to higher injury risk. The use of occupationally specific periodised, ability-based, training may lead to a more optimal TL for recruits, limiting overtraining while sufficiently developing fitness. READ ALL READ LESS Keywords police, cadet, academy, injury risk, tactical Corresponding Author(s) Danny Maupin ( [email protected] ) Close Corresponding author: Danny Maupin Competing interests: No competing interests were disclosed. Grant information: This research was supported by Australian Government Research Training Program that provided general living costs for the lead author. They made no contributions in the collection of data, analysis of results, or to the preparation of this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Maupin D et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Maupin D, Canetti EFD, Rathbone E et al. Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.12688/f1000research.168140.1 ) First published: 29 Aug 2025, 14 :840 ( https://doi.org/10.12688/f1000research.168140.1 ) Latest published: 29 Aug 2025, 14 :840 ( https://doi.org/10.12688/f1000research.168140.1 ) Introduction The tracking and optimisation of training load (TL) has recently grown in popularity in the sporting world as a strategy to decrease injury risk while improving fitness and performance. 1 This method encompasses a wide variety of tools to measure TL, which can be organised into external and internal loads. 1 External load (EL) is defined as “any external stimulus applied to the athlete that is measured independently of their internal characteristics”. 1 Measures of EL include variables such as distance run, volume of weight lifted, or number of accelerations as measured by devices such as Global Positioning System (GPS) units. 1 , 2 Internal load (IL) is any load that is “measurable by assessing internal response factors within the biological system, which may be physiological, psychological, or other”. 1 Variables of IL include heart rate or ratings of perceived exertion (RPE). 1 , 2 These variables can be measured using a variety of methods, such as a total value across one or multiple weeks or the change in values between weeks. 2 Rapid changes in EL and IL variables can be indicative of future injury risk or performance change. For example, Piggott 3 demonstrated that 40% of injuries in Australian Football League (AFL) players followed a change in TL compared to the previous week, while high running distances occurring over three weeks (73,721 – 86,662 meters) increased injury risk in the same population (odds ratio [OR] = 5.5). 4 TL has also been associated with performance changes. A study of rugby league players found that high IL, as measured by session RPE, was significantly ( p -value = 0.04) related to decreases in agility performance. 5 Though commonly used in sports, programs that optimise TL to mitigate injury and performance loss may be of benefit in other populations. Tactical populations are one such group that may see benefit from optimising load prescription given their high prevalence of injuries. 6 Tactical agencies, including law enforcement, employ periods of training within academies, which combine classroom lectures, physical training sessions, and occupational skill development. 7 – 9 Recruits who participate in academy training are often drawn from the general population, and academy life can represent a significant increase in both physical and mental stress. 10 , 11 This rapid increase in stress is one of the major contributing factors to musculoskeletal injuries suffered by recruits. 10 , 11 These injuries have multiple second order effects, such as personnel and financial costs for organisations. For example, injuries to recruits can result in separations or having to leave the academy, thus a loss of qualified personnel. 12 In addition, injured recruits elicit treatment and lost training time costs as well as costs associated with longer term care. 13 Given average costs to train a law enforcement recruit, with examples ranging from circa $64,000 USD ($104,000 USD when adjusted to 2023 values) 14 to $100,000 USD ($150,000 when adjusted to 2023 values), 15 injuries leading to separations can result in significant financial cost. Additionally, one of the biggest predictors of injury risk is having previously sustained an injury. 16 , 17 Therefore, reducing injuries during academy training may ensure healthier, and potentially longer, careers for tactical personnel. Research in military training has shown that training injuries are often overuse in nature, and may be the result of overtraining (e.g., excessive workloads). 10 Additionally, military recruits who complete high running distances (i.e., an EL variable) during an eight-week boot camp (>25 miles (40.2 km)) have been shown to be at an increased risk of injury with no further improvement in fitness. 18 While law enforcement and military encompass differing occupational tasks, law enforcement academies typically employ a similar physical training style, often noted as paramilitary training. 19 These training programs typically include body weight circuit training and long distance runs, 20 , 21 and are often considered to be a contributing factor to injuries in law enforcement recruits. 19 Crucially, it is still vital for these professions to engage in physical activity to improve physical fitness as various components of fitness have been found to significantly relate to occupational task performance. 22 – 24 Due to the impacts that injuries have on law enforcement, at both the individual and organisational level, it is vital that TL-related contributors to injuries are identified. Identifying these contributors can help inform specific and effective injury mitigation strategies. Though previous research in sports has explored the potential relationship between TL and injury, 2 , 4 , 5 little research has been performed within the tactical environment. Therefore, the aim of this study was to examine the relationship between injury risk and TL in a tactical population undergoing academy training to help inform injury reduction and training programs. It was hypothesized that high amounts of distance covered, and large weekly changes would contribute to injury risk in this population. Methods A mixture of prospective and retrospective methods was used to collect data for this study. This previously published methodology 25 was performed to validate a desktop analysis. The desktop analysis allowed for measuring variables of interest (e.g., distance covered per week, weekly change in distance), and applying to multiple classes ensuring adequate sample size and power. One class was followed prospectively to validate a desktop analysis which was then applied retrospectively to six other recruit classes. Full description of the methodology has previously been published. 25 The adequate sample size allowed the use of statistically valid modelling procedure to accurately examine the impact of dependent variables on injury risk. Subjects This retrospective data consisted of 547 participants (431 male; 116 female). It should be noted that demographic data (e.g., height, weight, and age) was only provided for five of the seven classes included. Research within tactical populations often lacks demographic data such as age, weight, or height due to security concerns and potential legal issues such as age or sex discrimination. 26 , 27 Therefore, the following shows the demographic data for a subsample of the studied populations: Male n = 349, age = 27 ± 6 yrs, height = 176.1 ± 11.4 cm, weight = 82.6 ± 13.1 kg; Female n = 81, age = 27 ± 5 yrs, height = 164.4 ± 7.0 cm, weight = 65.8 ± 12.8 kg. Prospectively collected data was from a subsample of 24 recruits, 9 female (age = 29.9 ± 6.4 years, height = 163.4 ± 6.5 cm, body mass = 68.2 ± 11.2 kg) and 15 male (age = 35.5 ± 11.9 years, height = 176.1 ± 9.8 cm, body mass = 82.6 ± 11.9 kg) recruits, randomly selected from one class, starting October 12 th , 2020 and ending November 6 th 2020. Informed consent was provided by the recruits in a written form and ethical approval was given by the Bond University Human Research Ethics Committee and by the California State Fullerton Institutional Review Board under HSR-17-18-370. Retrospective data was access starting October 18 th , 2020. Identifying information was present in the initial data. This research was originally conducted as part of a doctoral thesis by the lead author and has been modified for publication in its current form. 28 Procedures TL outcome measures Training and schedule data were provided from seven recruit classes from one United States law enforcement agency using a mix of prospective and retrospective methods. One class was followed prospectively with data collected to validate a desktop analysis (previously published 25 ). All classes took place in the same location, but under the supervision of various staff members. Course length did differ between classes, with one class lasting 20 weeks (prospectively followed), and the other six classes being 22 weeks in length. A desktop analysis was conducted on seven law enforcement recruit classes. The desktop analysis consisted of examining the documented schedule of a recruit class (classes, physical training sessions, and occupational drills among others) to estimate distance covered and time spent completing various activities. The desktop analysis of distance covered was previously validated through the use Polar Team Pro Sensors (Polar Electro Inc. Bethpage, New York, United States) implemented over the course of four weeks. 25 Estimations for the desktop analysis were based on a cohort, not an individual level, with outliers who did not participate in specific activities (e.g., due to injury) ignored for the duration of that activity only. As a desktop analysis constitutes an overall workload and not an individualised workload, this technique was used to estimate the workload of the average recruit. In situations where the class split into multiple groups, one group was followed and analysed. For example, during an academy session, one group might be assigned to complete scenario-based training while the rest of class completes physical fitness training. This procedure may affect timings of load experience by recruits, but over time the overall load experienced would be similar. This protocol has been used in previous research investigating the workloads of military 29 and law enforcement 25 personnel undergoing training. The analysis provided weekly total distance, weekly change in distance, and cumulative distance (the summation of all previous distances covered). Time spent on physical training or completing various activities was calculated by the lead author based on a desktop analysis and reports provided by academy staff. These activities were then assigned to one of the following categories: aerobic, anaerobic, muscular conditioning, multi-modal, class, and skills training. Definitions for these terms were taken in part from the National Strength and Conditioning Association 30 and these classifications are described in detail in previously published work. 25 Recruit physical fitness was utilised as a potential predictive variable and was assessed through the standardised tests known as the PT500 and Work Sample Test Battery (WSTB). In brief, these activities included maximal push-ups (performed in 120 s), sit-ups (performed in 120 s), mountain climbers (performed in 120 s), pull-ups (maximum of 20 repetitions), obstacle courses, medicine ball toss, body drag, 2.4 km run, 201 m run, and wall climbs. These standardised assessments have been previously described in detail in the literature. 9 , 22 , 31 Injury outcome measures Injury data were provided from the agency’s worker’s compensation database and limited to the included classes. Data provided, inclusion and exclusion criteria, as well as the classification process have been described in detail in previously published work. 32 In brief, records were included if related to a recruit, injury occurred during academy training, and excluded if the data were incomplete, a duplicate record, or in relation to illness or non-injury. Though some conceptual models recommend incorporating injuries that have a physiological rationale with variables being studied (e.g., distance run and stress fractures), 33 , 34 all injuries were included to increase sample size as well as to account for the possible effects that fatigue may have on injuries. 35 Statistical analysis Descriptive statistics are reported as frequencies and percentages for categorical variables and mean ± SD for normally distributed continuous variables. Normality and other assumption checks were completed (i.e., distribution plots, skewness, kurtosis, outliers, Shapiro-Wilk, and Levene’s tests) before analysis to determine the appropriateness of parametric or non-parametric analyses. A generalised linear mixed model (GLMM) with maximum likelihood estimation, based on an adaptive Gauss-Hermite approximation, was utilised to explore the relationship between distance, weekly change in distance, cumulative distance, time spent on physical training and associated categories as well as fitness measures (e.g., PT500, WSTB and their respective components), and the binomial outcome, injury. This was completed using a logit transformed model with a binomial distribution and weeks of training as a repeated measure. Due to the nature of the data collection (i.e., utilising a desktop analysis), each recruit within a class was assumed to have experienced the same training. Given variations in training staff and programs, individual training classes were treated as a random effect. All variables mentioned in the procedures section were explored for potential relationships with injury risk. Grand mean centering (the process of transforming a variable into deviations around a fixed point) was utilised for the variables cumulative distance, PT500, and WSTB scores to avoid multicollinearity and improve convergence. If a recruit separated, or left the academy, all further measures past the week of separation, were marked as zero. This was performed to continue to account for recruits that were intended to undergo, but failed to complete, the training. To choose the best fitting model, a stepwise approach was utilised, wherein each variable was individually modelled as a potential predictor of injury. Comparisons between the models’ Akaike information criterion (AIC) and Bayesian information criterion (BIC) scores were then conducted, with the lowest score suggesting the best fit. The best fitting model was carried forward and the remaining variables added individually as a predictor. With AIC and BIC as a reference, this process was repeated (the addition of a predictor resulting in the lowest AIC and BIC scores) until further additions of a predictor did not significantly improve model fit ( p < 0.05). All statistical analyses were conducted using R statistical software 36 (version 1.25.042) with packages tidyverse, 37 pander, 38 furniture, 39 texreg, 40 psych, 41 lme4, 42 gee, 43 effects, 44 performance, 45 interactions, 46 lattice, 47 patchwork, 48 and devtools. 49 Due to the complexity in analysing residuals of a GLMM, 50 model diagnostics were performed using the DHARMa package 51 ( http://florianhartig.github.io/DHARMa/ ) to more effectively examine residuals. Statistical significance was set at the 0.05 level. Results Data were available from 547 individuals, of which 431 (78.8%) were male and 116 (21.2%) were female, who participated in training during the research timeframe. A total of 76 injuries occurred across the seven classes, with injuries occurring most often during the beginning of the program (Week 2 to Week 4) and a second spike occurring around Week 13 ( Figure 1 ). Of these injuries, 23 (30.3%) occurred in female recruits, and 53 (69.7%) occurred in male recruits. This represents approximately 19% of female recruits and 12% of male recruits suffering an injury. Figure 1. Number of injuries that occurred per week of academy training. Figure 2 shows the average distance covered per week across the seven classes. Recruits covered approximately 15 to 23 km per week, except for four weeks (Weeks 1, 18, 21, and 22). The highest distances covered per week exceeded 30 km (Weeks 15, 17, and 20) for some classes. There was a large 10 km increase in the distance covered in Week 2 compared to Week 1. Figure 2. Average distance covered per week of academy training. Key: Error bars show the highest distance covered for that week. Comparisons of the GLMM models found the best fitting model to utilise weekly distance, week of training, and sex to predict injury (χ 2 = 38.3, p -value < 0.001). Supplementary Digital File 1 (Extended data) details the individual models made prior to the final, presented model. Referent values for these variables were as follows: distance – 0 km, week – Week 1, sex – male. Results of the model suggest that for every 0.08 km (80 m) covered, the odds of sustaining an injury were increased by a factor of 1.08 (95% CI 1.04, 1.12). As the academy progressed, injury risk decreased per week (OR = 0.94, 95% CI 0.91, 0.98). Lastly, biological sex was another significant factor, with males less likely to sustain a musculoskeletal injury (OR = 0.55, 95% CI 0.34, 0.91). The fixed effects estimate, z -value, odds ratio with 95% CI and statistical significance of each predictor can be seen in Table 1 . The addition of further variables either did not significantly improve model fit or suffered from issues of convergence or overfitting. Diagnostic checks were carried out on the final model and no issues were detected (Extended Data Supplementary Digital File 2). Table 1. Results of the described GLMM analysis and proposed injury odds in recruits during academy training. Predictor Estimate z -value p -value OR (95% CI) Intercept -5.52 -11.40 <0.001 0.00 (0.00, 0.01) Distance (km) 0.08 4.35 <0.001 1.08 (1.04, 1.12) Week -0.06 -2.84 0.005 0.94 (0.91, 0.98) Sex (male) -0.59 -2.34 0.019 0.55 (0.34, 0.91) The results of this model suggest that higher distances covered per week resulted in an increased probability of injury ( Figure 3 ). As this graph was not linear, it suggested that injury risk was compounded by further increases in distance. Figure 3. Predicted probability of injury by distance covered per week. The model also suggests that as the academy progresses, the probability of injury decreases. Higher distances occurring in earlier weeks are more likely to result in recruit injuries, with distances of approximately 30 km resulting in higher probabilities of injury. This probability continues to decrease as the academy training progresses ( Figure 4 ). Figure 4. Predicted probability of injury by week. Lastly, when analysing the impact of sex on injuries, female recruits were more likely to suffer injuries than their male counterparts, with female recruits almost twice as likely to suffer an injury as male recruits when compared across similar distances and weeks ( Figure 5 ). Figure 5. Predicted probability of injury by sex. Discussion The aim of this study was to investigate the impact of TL on injury risk in law enforcement recruits. The results of this study found distance covered per week, week of training, and biological sex were significant predictors of injury. Higher distances covered per week, and earlier weeks of training both increased injury risk, while female recruits were significantly more likely to be injured compared to male recruits. The current data has important implications for the training staff of law enforcement recruits with regards to reducing injury risk in their personnel. The results from this study indicate that decreasing the distance covered, particularly in the beginning of training, may be a method to decrease TL and therefore reduce injury risk. This follows research in other populations, such as runners and military recruits, that show an increasing injury incidence with higher running distances. 18 , 52 – 54 Research by Trank et al. 18 found that military recruits who completed over 25 miles (40.2 km) running over an eight-week period had a significantly higher injury rate than recruits who ran fewer than 25 miles (40.2 km). Further, research in AFL players suggested three-week running distances between 74 to 87 km were associated with an increased risk of injury. 4 Recruits in the law enforcement population informing this research covered up to 30 km per week, a similar distance to the aforementioned AFL players when averaged over a three-week period. However, a potential difference between these TL relates to the differences in the intensity at which the distances were covered. In AFL players, distances were covered mainly by running, potentially at higher speeds, with previous research identifying players ran between 6.8 and 7.5 km/hour on average during games. 55 However, though law enforcement recruits may be working at a lower intensity they are also likely to have lower levels of fitness. Elite AFL players have been found to average VO 2Max around 60 ml/kg/min, 56 compared to recruits in this population who have an average VO 2Max of 40.2 ml/kg/min. 31 Thus, while running speeds may have been slower in the law enforcement population, their relative intensity may still be high. Furthermore, previous research has shown that lower levels of aerobic fitness are associated with higher rates of injuries in both AFL 57 and tactical populations. 58 It has also been proposed that lower aerobic fitness may negatively impact the ability of AFL players to tolerate changes in TL and therefore present with an increased risk of injury. 59 Thus, the lower overall fitness of law enforcement recruits, itself a risk factor of injury, 60 – 63 may also limit the amount of TL that can be tolerated prior to injury. The higher distances covered may be leading to higher injury risk due to increased exposure, which has previously been shown to be a determinant of injury. 64 While decreasing total distance covered may be a valid strategy to minimise injury risk by limiting exposure, it is vital that recruits are sufficiently exposed to physical training in order to improve their physical fitness, which is crucial to the typical occupational tasks performed in law enforcement. 22 – 24 Alternate physical training strategies that improve physical fitness, while controlling for distance covered, warrant consideration, such as interval training and Ability Based Training whereby fitter individuals run further than less fit individuals. 19 , 26 As the law enforcement population informing this study often engages in long distance running as a means of training, 20 the use of alternative strategies to reduce distance covered may benefit injury risk, while still being sufficient to improve physical fitness. Studies have shown that interval training may reduce injury risk in military recruits, though the true effect of interval training is unknown due to presence of other injury mitigation strategies in these studies. 65 , 66 However, although interval training may reduce the number of loading cycles, the increase in intensity and ground reaction force may impact risk of injury. 64 The use of cross training is another method that has been proposed to limit injuries due to its ability to reduce repetitive stress on specific body parts, 67 though research will be needed to examine its impact on injury in this population. Utilising strength training may be another method to reduce distance covered, potentially reducing injury risk, while still improving fitness in recruits. Although this form of training will be more likely to improve aspects of muscular strength and power, these factors are still vital to the performance of occupational tasks 23 , 24 and have been related to injury risk in previous studies. 27 Applying a varied physical training program could lead to a more well-rounded fitness profile, and more importantly for the purposes of this study, reduce total distance which was a predictor of injury risk. Week of training was also a predictor of injury, with a higher probability of injuries occurring earlier in the academy. Higher risk of injury earlier in the program may be due to an increase in TL and stress as recruits transition from civilian life. Research on college students (mean age of 22.6 years) found that they could cover distances ranging from 1.9 to 3.2 km per day, or approximately 9.5 to 15.8 km across five days (the same length of the typical work week for recruits) as part of normal routine. 68 Recruits participating in this academy can be expected to cover approximately 20 km a week during the early stages of the academy. 25 This transition and increase in TL has previously been theorised to contribute to the higher levels of injuries experienced in recruit populations. 10 The use of strategies such as ABT and population-specific periodisation may ease this transition, resulting in a more optimal TL. Law enforcement academies tend to engage in a “one size fits all” training approach which, in reality, may fit no one. This approach imposes a standardised TL across a population with varying levels of fitness and potentially overloads unfit recruits and underloads fitter recruits. 19 The implementation of ABT, where recruits are trained at a more appropriate level given their ability, may reduce the TL for recruits at a higher risk of injury while ensuring fitter recruits receive sufficient stimulus to improve fitness. 19 Likewise, a population-specific periodised approach, targeted to the occupational demands, whereby periods of recovery are incorporated, may enable a graduated increase in TL. This concept can be seen by Ross and Allsopp, 69 who advocated for periods of strategic rest (termed “orthopaedic holidays”) that allow for sufficient recovery while not interfering with physical training. Though few weeks had distances covered greater than 30 km, it is possible that similar fitness improvements can be made while covering distances between the 16 and 24 km ranges, thus mitigating injuries related to overtraining. Although suboptimal TLs may be a factor in the relationship between training week and injury risk, this may also be explained by the healthy worker effect. The healthy worker effect refers to the idea that injury risk may decrease as academy training progresses due to the recruits at higher injury risk experiencing injuries earlier in the academy. 70 Future research will be necessary to study the impact of implementing cross training, strength training, and ABT on the distance covered, and more crucially, the injury risk and fitness improvements. Alternatively, physical training programs or sessions provided for recruits prior to their commencement of training may ease the transition into academy. Lastly, sex was also a significant predictor of injury with female recruits more likely to suffer injuries than males. Previous research in tactical populations, especially during periods of training, have also found that injuries are significantly higher in female personnel. 60 – 63 , 71 However, research in this area shows that this significant difference between male and female recruits is reduced when accounting for recruit fitness. 60 – 63 As the injury disparity between sexes is reduced when fitness is accounted for, a recruit’s fitness level may be a more important predictor in a recruit’s injury risk than sex. 60 – 63 This supposition is supported by research showing that female recruits, on average, are less fit than male recruits, 31 , 72 and that fitness has previously been tied to injury risk across a variety of tactical populations. 17 , 58 Despite these findings, this study was not able to adequately assess fitness (as measured by the PT500, WSTB, and associated assessments) as an injury predictor. This was due to the test models suffering from problems such as overfitting and convergence, likely to have been exacerbated because only initial values were used, meaning that each recruit had the same weekly fitness scores. As fitness is known to change during recruit training, 31 future research could attempt to measure fitness level at different stages of the training program. Limitations are present in this study. Firstly, the lack of variation in the desktop analysis limits the conclusions of this model. While academy training staff do consider academy programs by cohort models, more individualised data may have provided more information on the relationships between variables of interest and injuries. The only individualised variable in the data was sex and if other individualised variables (e.g., age or weight) were available for modelling, it is unknown if sex would still be a predictive factor. The lack of an IL measure is another limitation. The use of IL is important as different individuals may respond differently to the same training stimulus and is an adequate measure of intensity. Measuring internal TL and assessing its relationship to injury risk will be a vital future avenue of research, especially when understanding the impact of physical training strategies such as interval training or strength training that will reduce the distance covered but may result in higher intensities. Future research could compare the injury rates of utilising an interval training program versus a standard physical training program more effectively if it considered not only external loads (e.g., distances covered) but also internal loads (e.g., RPE or heart rate). Additionally, though the week of training was found to be a significant predictor of injury, this may be due, in part, to the healthy worker effect. 70 Injury risk may decrease as the academy progresses due to recruits that are likely to be injured suffering injuries and separating from the academy in the beginning. Despite the lack of individualised data measuring EL and IL being perceived as a limitation to this study, it is unlikely that law enforcement academies will have the resources to individually monitor recruits throughout the academy. For example, the average number of recruits per class, based on 547 recruits across seven classes, was 78, with multiple classes running concurrently across various locations. Supplying each recruit with a GPS and heart rate monitor device may prove to be too costly for the average law enforcement academy especially when combined with the human resource cost of needing to collect, analyse, and implement plans based on this data in real time. Academy staff are experts in the field of law enforcement but may have little experience or knowledge in the specialised field of data and sports science. Additionally, law enforcement academies rarely employ dedicated strength and conditioning or sports science professionals due to the added financial costs. Thus, noting the limitations, this research utilised a pragmatic approach that the average law enforcement organisation could implement at a cohort level (i.e., a desktop analysis with distances as the variable). Other cost-effective options, such as step counters/pedometers, may also warrant consideration to provide more individualised information. While future research may continue to explore the concept of individualised load monitoring, realistic and pragmatic strategies for these populations will be vital until individualisation is possible within the given academy constraints. Practical applications Large distances covered, earlier weeks of training, and female sex were significantly associated with injury risk of recruits undergoing training in this study, with large distances covered early in training associated with a higher likelihood of injury. Specifically, distances over 30 km during the first half of the academy program may needlessly increase injury risk. Rapid increase in TL (distance) during the transition from civilian to law enforcement recruit and lower fitness levels (resilience to training load) may also be likely causes of injury in law enforcement recruits. Practitioners and researchers need to explore the use of strategies such as occupation specific periodisation and ABT that may lead to a more optimal TL, thus limiting overtraining in recruits while sufficiently developing fitness. Future research will need to assess IL variables as these may also have a link to injury risk. Finally, given resource constraints, the use of a desktop analysis of academy training programs, can serve to provide insights into TL induced injury risk. Data availability Due to confidentiality agreements with the participating law enforcement agency, the dataset used in this study cannot be made publicly available. This restriction is in place to ensure the security and confidentiality of the agency and its operations. However, in the interest of transparency and to support best practices in Open Science, the data can be made available upon reasonable request including for purposes of verifying reproducibility. Interested researchers are encouraged to contact the corresponding author to request access. Any data shared will be provided under the condition that it is not further distributed or made publicly available. Extended data OSF: Relationship Between Training Load and Injuries in Law Enforcement Recruits DOI 10.17605/OSF.IO/C3R6D. 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Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 29 Aug 2025 ADD YOUR COMMENT Comment Author details Author details 1 University of Surrey Faculty of Health and Medical Sciences, Guildford, England, UK 2 Bond University Faculty of Health Sciences and Medicine, Gold Coast, Queensland, Australia 3 Tactical Research Unit, Bond University, Gold Coast, Queensland, 4226, Australia 4 School of Kinesiology, Oklahoma State University, Stillwater, Oklahoma, 74078, USA 5 California State University Fullerton Department of Kinesiology, Fullerton, California, 92831, USA Danny Maupin Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Elisa F.D. Canetti Roles: Conceptualization, Formal Analysis, Investigation, Project Administration, Software, Supervision, Validation, Writing – Review & Editing Evelyne Rathbone Roles: Formal Analysis, Software, Writing – Review & Editing Ben Schram Roles: Conceptualization, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing Joseph M. Dulla Roles: Conceptualization, Data Curation, Project Administration, Resources, Writing – Review & Editing J. Jay Dawes Roles: Data Curation, Investigation, Project Administration, Resources, Writing – Review & Editing Robert G. Lockie Roles: Data Curation, Methodology, Project Administration, Resources, Writing – Review & Editing Robin M. Orr Roles: Conceptualization, Data Curation, Investigation, Methodology, Project Administration, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This research was supported by Australian Government Research Training Program that provided general living costs for the lead author. They made no contributions in the collection of data, analysis of results, or to the preparation of this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (1) version 1 Published: 29 Aug 2025, 14:840 https://doi.org/10.12688/f1000research.168140.1 Copyright © 2025 Maupin D et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Maupin D, Canetti EFD, Rathbone E et al. Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.12688/f1000research.168140.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 29 Aug 2025 Views 0 Cite How to cite this report: Fallowfield JL. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431441 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431441 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 06 Jan 2026 Joanne L. Fallowfield , People Support Headquarters, HMS Temeraire, Navy Command, Portsmouth, Hampshire, UK Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185304.r431441 f1000res168140 Relationship Between Training Load and Injuries in Law Enforcement Recruits General Comments This paper serves to reinforce well-evidenced occupational training risk in uniformed services populations. That is, a step-wise (incident or acute) increase ... Continue reading READ ALL f1000res168140 Relationship Between Training Load and Injuries in Law Enforcement Recruits General Comments This paper serves to reinforce well-evidenced occupational training risk in uniformed services populations. That is, a step-wise (incident or acute) increase in training load at the onset of occupationally related training and/or (chronic) training exposure are associated with injury risk, which is moderated by biological sex, and this risk decreases in those trainees surviving for longer. As such, the conclusions are not new. As such, the value of this paper is evidencing these consistent occupational injury risk findings as also being present in law enforcement trainees during initial occupational training. Thus, the generation of occupation-specific models of injury risk for a uniformed workforce undertaking initial training—outside of the military—does bring some new insights specific to future injury mitigation. Previous published evidence in this workforce has focussed on in-service officers and/or injuries inflicted in the course of duty (e.g. road traffic collisions, injuries suffered in the line of duty). Indeed, some of the analytical approaches are the most interesting aspects of this paper. Limitations in injury outcome data (quality and precision), as well as the desk-based approach to determining (estimating) training load undermine the analyses – and, indeed, the ability of this paper to evidence specific action in addressing an important aim (i.e. reducing injury occurrence). The manuscript limitations do address part of the issue with respect to the desk-based training load determination (i.e. generic for the training population not specific to an individual), but does not address the potential limitations to the injury outcome data. These data were extracted from a workforce compensation database, which will therefore lack the necessary detail as well as introducing a threshold level of severity (i.e. sufficient to secure employer compensation). This important point is passed over very quickly in the manuscript Methods, without due consideration of the impacts of this data source to the ensuing modelling. The potential risk here is that the scientific method is taking precedence over input data veracity. Specific Comments Page Para Comment Introduction 3 3 An important point is that the majority of recruits are drawn from the general (civilian) population. Increasing poor health behaviours across many Western nations are associated with poorer health status of this recruiting pool. This is increasing risk during initial occupational training in addition to the increased external load and training exposure. It would be helpful to join these dots of this specific case example, where greater training risk is being taken at service entry, independent of the risk presented by training exposure. Methods 4 1 It is noted that the researchers encountered legal and security challenges to securing participant demographic data. However, given the point above with respect to the increasing risk presented by the recruitment pool, it would be helpful to provide some indication of health-related risk of the participants at the start of training. 2 Whilst there will be many core elements to law enforcement officer training, there are potential differences arising from location and likely scope of the jurisdiction. Noting, this study focussed on one US law enforcement agency, can the researchers provide any evidence to understand and/or support the generalisation of these findings to all/ most agencies. Intuitively this will be the case, but what evidence would support/uphold this view? Also to note the difference in training exposure (20 vs. 22 weeks). As there was a time factor for ‘injury occurrence’, how was this controlled for in the analyses? Also noted, where differences in delivery staff can be the biggest independent factor influencing individual training experience/exposure and hence outcome; did the researchers retrospectively analyse the impact of different training teams? Or, was this accepted as noise/error in the data? These points are mentioned in the Methods, which is really useful, but I would also like to understand any controlling or mitigating factors for their impact on the outcomes. 6 Injury Outcome Measures. Given the importance of these data to the model, this section is relatively vague in terms of the reported detail. It could be argued that if this injury count was limited to the workers’ compensation scheme, this would limit the recording of injuries to only the more severe injuries and miss the less severe (but potentially training interrupting) injuries. Moreover, this appears to be a count of ‘all’ injuries, which is increasingly seen as imprecise for determining specific occupational risk exposure. Noted, the researchers followed the recommendation for including injuries with a physiological rationale, but this relies on the detail collected from the compensation record, which—if not the original medical record—is likely to lack the necessary precision and detail. Whilst I understand that the researchers might have been limited by data protection regulations to access more accurate data, this is a significant limitation of this work. I would suggest that this section needs more explanation to ensure that the methods applied for the injury input data to the model are adequately robust. The risk here is both missing data (due to the bar set for compensation), and/or injury reporting imprecision. Both would present error in models of injury risk. 5 2 Statistical Analyses. I am not sure about the approach taken to dealing with trainees who left training, where their subsequent training data were assigned a value of ‘0’. Whilst this maximises data inclusion, does this approach not pull the aggregated data down towards zero for subsequent time points – where there would be a ‘time’ factor, as the number of non-completers would likely increase over time? Results 5 4 Injury data should also be reported relative to training exposure (e.g. training days). Moreover, as these appear to be all and any injury, this feels relatively imprecise. What assurances can the researchers provide that these analyses do not include spurious outcomes that would artificially improve the performance of the model? The lack of precision in the training input data (i.e. taken from a desk review), in addition to the method for determining injury outcomes, undermines the veracity of the Results. The researchers have made the best of the data that they have, which have not provided any surprising findings – which could be argued supports the approach. However, it has resulted in findings that are entirely predictable. That is: onset of occupational training is associated with increased injury risk; increases in training load increases training risk; ‘surviving’ longer during occupational training is protective; and female biological sex is a moderator of increased risk. Discussion 8 1 Agree, the findings from this study, in terms of being population-specific, should be important for training delivery staff. However, this information is known; the challenge is how do we change the behaviours of these staff and their organisations? I agree, there are better ways of managing the physical training transition from civilian to the occupational role (for law enforcement, emergency/ first responder services and the military), but employers have consistently ignored the science to change. The authors have made suggestions to improving the training programme, but none of these are specifically supported by the evidence presented in the paper, which describes the ‘problem’. I would suggest that this is due to the lack of specific detail on injury outcomes and the desk-based approach to determining training load. Thus, these suggestions must be regarded as speculation based on generalised observations from occupational and sports physiology. Whilst this is not a criticism per se, these limitations to the methods—with respect to addressing the paper’s aim—and hence the evidence limit specific practical application. 9-10 Limitations. Whilst the focus of the limitations is largely due to measurement of training load—and individualising the exposure and impact—it does not mention the limitations of the injury outcome data. This is a significant issue, and an important oversight in this section. The limitations of the injury outcome data should be addressed, or at least acknowledged, in these analyses. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Applied and occupational physiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Fallowfield JL. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431441 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431441 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Powell S. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431439 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431439 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 23 Dec 2025 Steven Powell , Applied Physiology, Institute of Naval Medicine (Ringgold ID: 71416), Gosport, England, UK Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185304.r431439 Overview This paper has examined the relationship between training load and injury risk in law enforcement recruits. I believe the research question is pertinent to provide evidence-based strategies to mitigating injury risk in such populations, where the ... Continue reading READ ALL Overview This paper has examined the relationship between training load and injury risk in law enforcement recruits. I believe the research question is pertinent to provide evidence-based strategies to mitigating injury risk in such populations, where the majority of previous wok has been completed in sport and military populations. The study includes a large sample size with a good split of males and females considering the typical demographic of tactical populations. Having undertaken similar research in tactical populations, I fully appreciate the challenges of capturing the necessary data to answer the question. In this instance, the researchers have used a desktop analysis of distance covered, of which they validated in one class and then applied retrospectively to the other six classes; this is a methodological limitation which is fully acknowledged, however does significantly impact the outcomes of the research. The results indicated that higher weekly distances, earlier weeks in academy training, and female sex all resulted in higher probabilities of injury. My general and specific comments are detailed below. Comments The background and research question is important to understand whether a relationship exists between training load and injury risk in police officer recruits. I think the introduction could include a greater number of references with regards to the previous work in this area. The novelty isn’t examining TL and injury risk in a tactical population; it is examining that relationship during law enforcement training. A desktop analysis has been undertaken to quantify TL variables. One class was used to validate the analysis and then that has been applied to the other six classes. Obviously, this is a big methodological limitation, although I do fully appreciate the difficulty in monitoring TL in such populations and environments. Unfortunately, it does mean you are assuming a homogenous load distribution across all classes and cannot consider individual variability, resulting in limited ability for causality. Large sample size and good split of male and females considering the typical demographic of such occupational groups. I’m not sure I fully understand why demographic information is limited, when this is routinely published across many tactical populations. Also, given the population being recruited from (and assuming all recruits have passed a pre-joining physical assessment and therefore are fairly homogenous), I’m not sure if adding the remaining participants’ data to the calculated averages would make a great deal of difference? A table showing the recruits’ fitness scores would be beneficial for the reader, as we have little information on the characteristics of the group. In addition to the model, given that baseline fitness data are available, it would be extremely informative in future work (or as an additional analysis) to examine time-to-injury using survival methods. For example, Kaplan–Meier curves stratified by fitness quartiles, or a Cox model with fitness, sex and weekly distance as covariates, could clarify how much of the observed sex and load effects are mediated by initial fitness and highlight early vulnerability in low-fitness recruits. You say rapid increases in TL during the transition between civilian to law enforcement recruit may lead to higher injury risk – did you ask about pre-training physical activity or inferring from fitness level? The absence of any internal load TL metrics and treating TL exposure for each recruit as having the same physiological effect is a significant limitation. Clearly the studied could be improved by using either some form of accelerometer (to measure individual EL) or include some form of IL monitoring. Again, fully appreciate how logistically challenging that can be, however it has been done in similar populations previously and would have provided a measure of intensity in addition to volume. May have been useful to include self-report injuries also, as we know often some injuries aren’t reported officially. The statistical approach was appropriate for the data collected, the limitation being the model being unable to determine causality due to the lack of individual-level data. Was biological sex treated as an independent predictor, or could baseline fitness be used as a covariate? The observed sex effect may be partially confounded as female recruits often begin with lower fitness, meaning the same absolute TL results in a greater relative IL. It would be good to see confidence intervals illustrated on the figures. Formatting of graphs should be more consistent. Figure 4 seems to have a title, but the others do not. The outline on Figure 4 is black, but Figure 3 is grey. Specific Comments Page 3, para1, line 1: Has TL ‘recently’ grown in popularity or is it just routine now? Could argue that TL monitoring has been routine for the past two decades – I suppose it depends on the definition of ‘recently’! Page 3, para 3, line 3: Often or always drawn from the general population? Page 3, para 4: I think the authors could provide more references on the current research around TL and injury risk in tactical/military populations. Page 3, para 5, line 4: I would argue there has been a lot of research looking at the relationship between TL and injury risk in tactical populations. However, less so in law enforcement. Subjects’ para: you have included a decimal point for age in your prospective demographic data but not in the subsample above. Discussion, para 2: you have focussed on research in AFL players to discuss the context of your findings. I would suggest focussing on tactical population research to make your discussion stronger and more relevant. Peer Review Form Q’s Selected partly for Q1 as I believe more relevant tactical literature could be referenced throughout Selected partly as, whilst the limitation is firmly acknowledged, there is a flaw in the methods with the lack of individual data Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Applied physiology in military populations. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Powell S. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431439 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431439 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Sutton VR. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431437 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431437 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Dec 2025 Vanessa R Sutton , Edith Cowan University, Joondalup, Western Australia, Australia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185304.r431437 Dear F1000 Editorial Team, Thank you for the opportunity to review this interesting submission. Further research into this topic is much needed and the authors should be commended for their commitment to improving the health and performance of tactical ... Continue reading READ ALL Dear F1000 Editorial Team, Thank you for the opportunity to review this interesting submission. Further research into this topic is much needed and the authors should be commended for their commitment to improving the health and performance of tactical athletes. I agree with the comments of Reviewer 1 (Dr Dhahbi Wissem) and addressing these would substantially improve the quality of the manuscript. In addition, without a measure of exposure to injury risk, and censoring tactical athletes when they were injured (or when they experience program attrition from other causes), the conclusions able to be made from the current dataset are limited. Is there any capacity to perform time-to-event survival analysis, as this would be considered best practice when modelling injury risk over time see (Ref 1) ? This would enable the reader to have much more confidence in the estimates Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly References 1. Nielsen R, Bertelsen M, Ramskov D, Møller M, et al.: Time-to-event analysis for sports injury research part 1: time-varying exposures. British Journal of Sports Medicine . 2019; 53 (1): 61-68 Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Injury prevention I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Sutton VR. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431437 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431437 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Wissem D. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r411093 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-411093 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Sep 2025 Dhahbi Wissem , Qatar Police College, Doha, Qatar; University of Jendouba, Jendouba, Tunisia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.185304.r411093 General Comments The study addresses a highly relevant and under-researched topic within a tactical population, which is a strength. The use of a large sample size (N=547) and a mixed-methods approach is commendable. The paper identifies significant ... Continue reading READ ALL General Comments The study addresses a highly relevant and under-researched topic within a tactical population, which is a strength. The use of a large sample size (N=547) and a mixed-methods approach is commendable. The paper identifies significant predictors of injury (weekly distance, week of training, and sex) and provides practical implications for training staff. However, several methodological limitations and a lack of individual-level data significantly weaken the conclusions. The core issue is the reliance on a "desktop analysis" to estimate a cohort's workload rather than measuring individual training loads, which fundamentally limits the study's ability to establish a causal relationship or provide actionable, individualized advice. Major Weaknesses Reliance on Cohort-Level Data: The study's primary methodological weakness is the use of a "desktop analysis" to estimate training load for an entire class rather than measuring individual-level data. The authors acknowledge this as a limitation, but it is a critical flaw. As stated, "each recruit within a class was assumed to have experienced the same training". This assumption is not scientifically sound and compromises the validity of the findings, as it ignores individual variations in physical activity and response to training. Lack of Internal Training Load (ITL) Measures: The study only considers external load (distance covered) and does not include any measures of internal load, such as heart rate or ratings of perceived exertion (RPE). The authors themselves note this is a limitation and that ITL is important because different individuals respond differently to the same external stimulus. The absence of ITL makes it impossible to fully understand the physiological stress and subsequent injury risk. Methodology and Data Collection Imprecision: The use of a "desktop analysis" to estimate variables like distance and time spent on activities is imprecise. The authors state that "estimations...were based on a cohort, not an individual level, with outliers who did not participate in specific activities (e.g., due to injury) ignored for the duration of that activity only". This introduces significant potential for error and misrepresentation of the actual training loads experienced by recruits. Minor Weaknesses Inadequate Demographic Data: The study only provides demographic data (age, height, weight) for a subsample of the participants (n=349 males, n=81 females). While the authors cite security concerns as the reason, the lack of this data for the full cohort (N=547) is a limitation, especially as individual characteristics can influence injury risk. Healthy Worker Effect: The discussion mentions the healthy worker effect as a potential explanation for the decrease in injuries over time. However, the study does not provide sufficient data or analysis to quantify or adjust for this effect, which is a key confounder. This makes it difficult to ascertain whether the decrease in injury risk is due to adaptation or simply the attrition of injured recruits. Inconsistent Referencing: There are inconsistencies and errors in the referencing throughout the paper, with some citations appearing to be out of order or incorrectly formatted (e.g., 20.2, 33.4, 24.5). Specific Comments Page 1, Line 737: The use of "Danny Maupin 101" is an unusual formatting choice for the author's name. Please clarify if the "101" is intentional, as it may be a typo. Page 3, Line 821: The sentence "This previously published methodology was performed to validate a desktop analysis" is unclear. It implies the methodology was published to validate the desktop analysis, but the next sentence states the desktop analysis was used to ensure adequate sample size. This needs to be rephrased for clarity. Page 4, Lines 833-834: The statement "This research was originally conducted as part of a doctoral thesis by the lead author and has been modified for publication in its current form" should be placed in the acknowledgements or grant information section, not the main body of the methods. Page 4, Line 845: The statement that outliers who did not participate "were ignored for the duration of that activity only" is a significant methodological weakness that needs further explanation. Ignoring these outliers may skew the cohort average and does not accurately represent the experience of all recruits. Page 4, Line 849: The assertion that "over time the overall load experienced would be similar" despite different training timings is an assumption that is not supported by data. This should be rephrased to reflect that this is a limitation, as the timing of load is critical for recovery and injury risk. Page 5, Lines 887-888: The percentages "30.3% occurred in female recruits, and 69.7% occurred in male recruits" are presented without context of the total number of female and male recruits. The next sentence, "This represents approximately 19% of female recruits and 12% of male recruits suffering an injury", is a key point that should be presented first for better context and clarity. Page 6, Line 936: The statement "for every 0.08 km (80 m) covered, the odds of sustaining an injury were increased by a factor of 1.08 (95% CI 1.04, 1.12)" presents a very small and potentially insignificant increase in odds that may not be clinically meaningful, despite being statistically significant. This needs to be discussed more thoroughly in the discussion section. Page 8, Line 1009: The comparison of law enforcement recruits to AFL players is a stretch, as the populations and training contexts are vastly different. While the authors attempt to justify it, the comparison feels forced and diminishes the specific context of the law enforcement recruits. Page 9, Line 622: The authors state that the study was "not able to adequately assess fitness...as an injury predictor". This is a significant limitation and undermines the conclusion that lower fitness levels may be a cause of injury, as this cannot be supported by the study's own data. Page 2, Line 765: The authors state the paper provides a "comprehensive understanding of the relationship between training load and injuries". Given the methodological limitations, especially the lack of individual data, this claim is too strong. Page 10, Lines 1032-1035: The discussion mentions the need for more research using “wearable technology to collect individual-level data”. Page 10, Line 1045: The discussion mentions that "factors such as poor sleep and hydration" could be linked to injury. Page 11, Lines 1049-1050: The paper's limitation section correctly identifies the use of "desktop analysis" and the lack of individual measures as a weakness. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Biomechanics and sports injury and rehabilitation I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Wissem D. Reviewer Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r411093 ) The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-411093 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 06 Oct 2025 Danny Maupin , University of Surrey Faculty of Health and Medical Sciences, Guildford, UK 06 Oct 2025 Author Response Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, ... Continue reading Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, though currently I am waiting for other review reports before the revision as recommended by the journal. Your criticisms of the methodology are valid, and though it would be ideal to measure these variables across individuals, it is not always feasible particularly in this context. The use of a desktop analysis is again not ideal but still provides value in this context. In addition to my previous work using this method, a similar methodology has also been used to estimate the risk of osteoarthritis in members of the Australian military (1). This is also likely to be more realistic and practical for these populations, which is described in the limitations section and is further detailed in an article I have written about the challenges in optimising training in these populations (2). Any changes these organisations make are not likely to be on an individual basis, but on a cohort basis. Our current challenge at the moment is working with the organisations to utilise smaller training groups based on fitness or other characteristics which is still proving to be a hard task considering the staff to recruit ratios. Further since training takes place on a cohort rather than individual level, individuals will be exposed to very similar levels of load as seen in my previous research comparing the desktop analysis to GPS units (3). While individualised data would be the gold standard, cohort level data provides a level of insight that is actionable and representative of the training programs in this population, particularly as actions taking by this population will be at the cohort rather than individual level. It is also accurate that causality is not appropriately addressed in this study. Great care was taken to avoid direct or indirect allusions to causality due to this (please do let me know if I have missed any!) and instead discuss it a relationship level. However, the issue of causality on this topic (training load and injury risk) is a consistent one throughout this literature where researchers often do not distinguish between associations and causes. The lack of internal training load is another weakness that was mentioned in the study and would be ideal to include. However, studies that utilise external training load only have been prevalent in the literature (4–6). The inclusion of internal training loads would provide further evidence on risk factors to injury. However, it is unlikely that this population will be able to manage internal training loads given their individualised nature and the barriers to individualised training load monitoring present. I agree also with your minor weaknesses. I see that I have not added demographics to the limitations of the study and will do so on the revised copy, along with correcting references. Regarding the healthy worker effect, this is a confounder and has been added to the limitations accordingly. However, being able to control for the healthy worker was not feasible in this situation. 1. Orr R, Schram B, Irving S, Pope N, Pope R. Measuring occupational exposures to osteoarthritis in the lower limb in Australian Defence Force job categories [Internet]. Bond Unviersty Tactical Research Unit; 2019. Available from: https://www.dva.gov.au/documents-and-publications/measuring-occupational-exposures-osteoarthritis-lower-limb-adf-job 2. Maupin DJ, Schram B, Dulla JM, Canetti EFD, Orr RM. Implementing Training Load Monitoring in Tactical Populations. Strength & Conditioning Journal. 2022 Mar 16;10.1519/SSC.0000000000000883. 3. Maupin D, Schram B, Canetti EF, Dulla JM, Dawes JJ, Lockie RG, et al. Profiling the typical training load of a law enforcement recruit class. International journal of environmental research and public health. 2022;19(20):13457. 4. Gabbett TJ, Ullah S. Relationship Between Running Loads and Soft-Tissue Injury in Elite Team Sport Athletes. The Journal of Strength & Conditioning Research. 2012 Apr;26(4):953. 5. Malone S, Roe M, Doran D, Gabbett T, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. Vol. 20, Journal of Science and Medicine in Sport. 2016. p. 250–4. 6. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017 Apr 1;51(8):645–50. Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, though currently I am waiting for other review reports before the revision as recommended by the journal. Your criticisms of the methodology are valid, and though it would be ideal to measure these variables across individuals, it is not always feasible particularly in this context. The use of a desktop analysis is again not ideal but still provides value in this context. In addition to my previous work using this method, a similar methodology has also been used to estimate the risk of osteoarthritis in members of the Australian military (1). This is also likely to be more realistic and practical for these populations, which is described in the limitations section and is further detailed in an article I have written about the challenges in optimising training in these populations (2). Any changes these organisations make are not likely to be on an individual basis, but on a cohort basis. Our current challenge at the moment is working with the organisations to utilise smaller training groups based on fitness or other characteristics which is still proving to be a hard task considering the staff to recruit ratios. Further since training takes place on a cohort rather than individual level, individuals will be exposed to very similar levels of load as seen in my previous research comparing the desktop analysis to GPS units (3). While individualised data would be the gold standard, cohort level data provides a level of insight that is actionable and representative of the training programs in this population, particularly as actions taking by this population will be at the cohort rather than individual level. It is also accurate that causality is not appropriately addressed in this study. Great care was taken to avoid direct or indirect allusions to causality due to this (please do let me know if I have missed any!) and instead discuss it a relationship level. However, the issue of causality on this topic (training load and injury risk) is a consistent one throughout this literature where researchers often do not distinguish between associations and causes. The lack of internal training load is another weakness that was mentioned in the study and would be ideal to include. However, studies that utilise external training load only have been prevalent in the literature (4–6). The inclusion of internal training loads would provide further evidence on risk factors to injury. However, it is unlikely that this population will be able to manage internal training loads given their individualised nature and the barriers to individualised training load monitoring present. I agree also with your minor weaknesses. I see that I have not added demographics to the limitations of the study and will do so on the revised copy, along with correcting references. Regarding the healthy worker effect, this is a confounder and has been added to the limitations accordingly. However, being able to control for the healthy worker was not feasible in this situation. 1. Orr R, Schram B, Irving S, Pope N, Pope R. Measuring occupational exposures to osteoarthritis in the lower limb in Australian Defence Force job categories [Internet]. Bond Unviersty Tactical Research Unit; 2019. Available from: https://www.dva.gov.au/documents-and-publications/measuring-occupational-exposures-osteoarthritis-lower-limb-adf-job 2. Maupin DJ, Schram B, Dulla JM, Canetti EFD, Orr RM. Implementing Training Load Monitoring in Tactical Populations. Strength & Conditioning Journal. 2022 Mar 16;10.1519/SSC.0000000000000883. 3. Maupin D, Schram B, Canetti EF, Dulla JM, Dawes JJ, Lockie RG, et al. Profiling the typical training load of a law enforcement recruit class. International journal of environmental research and public health. 2022;19(20):13457. 4. Gabbett TJ, Ullah S. Relationship Between Running Loads and Soft-Tissue Injury in Elite Team Sport Athletes. The Journal of Strength & Conditioning Research. 2012 Apr;26(4):953. 5. Malone S, Roe M, Doran D, Gabbett T, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. Vol. 20, Journal of Science and Medicine in Sport. 2016. p. 250–4. 6. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017 Apr 1;51(8):645–50. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 06 Oct 2025 Danny Maupin , University of Surrey Faculty of Health and Medical Sciences, Guildford, UK 06 Oct 2025 Author Response Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, ... Continue reading Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, though currently I am waiting for other review reports before the revision as recommended by the journal. Your criticisms of the methodology are valid, and though it would be ideal to measure these variables across individuals, it is not always feasible particularly in this context. The use of a desktop analysis is again not ideal but still provides value in this context. In addition to my previous work using this method, a similar methodology has also been used to estimate the risk of osteoarthritis in members of the Australian military (1). This is also likely to be more realistic and practical for these populations, which is described in the limitations section and is further detailed in an article I have written about the challenges in optimising training in these populations (2). Any changes these organisations make are not likely to be on an individual basis, but on a cohort basis. Our current challenge at the moment is working with the organisations to utilise smaller training groups based on fitness or other characteristics which is still proving to be a hard task considering the staff to recruit ratios. Further since training takes place on a cohort rather than individual level, individuals will be exposed to very similar levels of load as seen in my previous research comparing the desktop analysis to GPS units (3). While individualised data would be the gold standard, cohort level data provides a level of insight that is actionable and representative of the training programs in this population, particularly as actions taking by this population will be at the cohort rather than individual level. It is also accurate that causality is not appropriately addressed in this study. Great care was taken to avoid direct or indirect allusions to causality due to this (please do let me know if I have missed any!) and instead discuss it a relationship level. However, the issue of causality on this topic (training load and injury risk) is a consistent one throughout this literature where researchers often do not distinguish between associations and causes. The lack of internal training load is another weakness that was mentioned in the study and would be ideal to include. However, studies that utilise external training load only have been prevalent in the literature (4–6). The inclusion of internal training loads would provide further evidence on risk factors to injury. However, it is unlikely that this population will be able to manage internal training loads given their individualised nature and the barriers to individualised training load monitoring present. I agree also with your minor weaknesses. I see that I have not added demographics to the limitations of the study and will do so on the revised copy, along with correcting references. Regarding the healthy worker effect, this is a confounder and has been added to the limitations accordingly. However, being able to control for the healthy worker was not feasible in this situation. 1. Orr R, Schram B, Irving S, Pope N, Pope R. Measuring occupational exposures to osteoarthritis in the lower limb in Australian Defence Force job categories [Internet]. Bond Unviersty Tactical Research Unit; 2019. Available from: https://www.dva.gov.au/documents-and-publications/measuring-occupational-exposures-osteoarthritis-lower-limb-adf-job 2. Maupin DJ, Schram B, Dulla JM, Canetti EFD, Orr RM. Implementing Training Load Monitoring in Tactical Populations. Strength & Conditioning Journal. 2022 Mar 16;10.1519/SSC.0000000000000883. 3. Maupin D, Schram B, Canetti EF, Dulla JM, Dawes JJ, Lockie RG, et al. Profiling the typical training load of a law enforcement recruit class. International journal of environmental research and public health. 2022;19(20):13457. 4. Gabbett TJ, Ullah S. Relationship Between Running Loads and Soft-Tissue Injury in Elite Team Sport Athletes. The Journal of Strength & Conditioning Research. 2012 Apr;26(4):953. 5. Malone S, Roe M, Doran D, Gabbett T, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. Vol. 20, Journal of Science and Medicine in Sport. 2016. p. 250–4. 6. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017 Apr 1;51(8):645–50. Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, though currently I am waiting for other review reports before the revision as recommended by the journal. Your criticisms of the methodology are valid, and though it would be ideal to measure these variables across individuals, it is not always feasible particularly in this context. The use of a desktop analysis is again not ideal but still provides value in this context. In addition to my previous work using this method, a similar methodology has also been used to estimate the risk of osteoarthritis in members of the Australian military (1). This is also likely to be more realistic and practical for these populations, which is described in the limitations section and is further detailed in an article I have written about the challenges in optimising training in these populations (2). Any changes these organisations make are not likely to be on an individual basis, but on a cohort basis. Our current challenge at the moment is working with the organisations to utilise smaller training groups based on fitness or other characteristics which is still proving to be a hard task considering the staff to recruit ratios. Further since training takes place on a cohort rather than individual level, individuals will be exposed to very similar levels of load as seen in my previous research comparing the desktop analysis to GPS units (3). While individualised data would be the gold standard, cohort level data provides a level of insight that is actionable and representative of the training programs in this population, particularly as actions taking by this population will be at the cohort rather than individual level. It is also accurate that causality is not appropriately addressed in this study. Great care was taken to avoid direct or indirect allusions to causality due to this (please do let me know if I have missed any!) and instead discuss it a relationship level. However, the issue of causality on this topic (training load and injury risk) is a consistent one throughout this literature where researchers often do not distinguish between associations and causes. The lack of internal training load is another weakness that was mentioned in the study and would be ideal to include. However, studies that utilise external training load only have been prevalent in the literature (4–6). The inclusion of internal training loads would provide further evidence on risk factors to injury. However, it is unlikely that this population will be able to manage internal training loads given their individualised nature and the barriers to individualised training load monitoring present. I agree also with your minor weaknesses. I see that I have not added demographics to the limitations of the study and will do so on the revised copy, along with correcting references. Regarding the healthy worker effect, this is a confounder and has been added to the limitations accordingly. However, being able to control for the healthy worker was not feasible in this situation. 1. Orr R, Schram B, Irving S, Pope N, Pope R. Measuring occupational exposures to osteoarthritis in the lower limb in Australian Defence Force job categories [Internet]. Bond Unviersty Tactical Research Unit; 2019. Available from: https://www.dva.gov.au/documents-and-publications/measuring-occupational-exposures-osteoarthritis-lower-limb-adf-job 2. Maupin DJ, Schram B, Dulla JM, Canetti EFD, Orr RM. Implementing Training Load Monitoring in Tactical Populations. Strength & Conditioning Journal. 2022 Mar 16;10.1519/SSC.0000000000000883. 3. Maupin D, Schram B, Canetti EF, Dulla JM, Dawes JJ, Lockie RG, et al. Profiling the typical training load of a law enforcement recruit class. International journal of environmental research and public health. 2022;19(20):13457. 4. Gabbett TJ, Ullah S. Relationship Between Running Loads and Soft-Tissue Injury in Elite Team Sport Athletes. The Journal of Strength & Conditioning Research. 2012 Apr;26(4):953. 5. Malone S, Roe M, Doran D, Gabbett T, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. Vol. 20, Journal of Science and Medicine in Sport. 2016. p. 250–4. 6. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017 Apr 1;51(8):645–50. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 29 Aug 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 1 29 Aug 25 read read read read Dhahbi Wissem , Qatar Police College, Doha, Qatar; University of Jendouba, Jendouba, Tunisia Vanessa R Sutton , Edith Cowan University, Joondalup, Australia Steven Powell , Institute of Naval Medicine (Ringgold ID: 71416), Gosport, UK Joanne L. Fallowfield , Navy Command, Portsmouth, UK Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Fallowfield J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 06 Jan 2026 | for Version 1 Joanne L. Fallowfield , People Support Headquarters, HMS Temeraire, Navy Command, Portsmouth, Hampshire, UK 0 Views copyright © 2026 Fallowfield J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions f1000res168140 Relationship Between Training Load and Injuries in Law Enforcement Recruits General Comments This paper serves to reinforce well-evidenced occupational training risk in uniformed services populations. That is, a step-wise (incident or acute) increase in training load at the onset of occupationally related training and/or (chronic) training exposure are associated with injury risk, which is moderated by biological sex, and this risk decreases in those trainees surviving for longer. As such, the conclusions are not new. As such, the value of this paper is evidencing these consistent occupational injury risk findings as also being present in law enforcement trainees during initial occupational training. Thus, the generation of occupation-specific models of injury risk for a uniformed workforce undertaking initial training—outside of the military—does bring some new insights specific to future injury mitigation. Previous published evidence in this workforce has focussed on in-service officers and/or injuries inflicted in the course of duty (e.g. road traffic collisions, injuries suffered in the line of duty). Indeed, some of the analytical approaches are the most interesting aspects of this paper. Limitations in injury outcome data (quality and precision), as well as the desk-based approach to determining (estimating) training load undermine the analyses – and, indeed, the ability of this paper to evidence specific action in addressing an important aim (i.e. reducing injury occurrence). The manuscript limitations do address part of the issue with respect to the desk-based training load determination (i.e. generic for the training population not specific to an individual), but does not address the potential limitations to the injury outcome data. These data were extracted from a workforce compensation database, which will therefore lack the necessary detail as well as introducing a threshold level of severity (i.e. sufficient to secure employer compensation). This important point is passed over very quickly in the manuscript Methods, without due consideration of the impacts of this data source to the ensuing modelling. The potential risk here is that the scientific method is taking precedence over input data veracity. Specific Comments Page Para Comment Introduction 3 3 An important point is that the majority of recruits are drawn from the general (civilian) population. Increasing poor health behaviours across many Western nations are associated with poorer health status of this recruiting pool. This is increasing risk during initial occupational training in addition to the increased external load and training exposure. It would be helpful to join these dots of this specific case example, where greater training risk is being taken at service entry, independent of the risk presented by training exposure. Methods 4 1 It is noted that the researchers encountered legal and security challenges to securing participant demographic data. However, given the point above with respect to the increasing risk presented by the recruitment pool, it would be helpful to provide some indication of health-related risk of the participants at the start of training. 2 Whilst there will be many core elements to law enforcement officer training, there are potential differences arising from location and likely scope of the jurisdiction. Noting, this study focussed on one US law enforcement agency, can the researchers provide any evidence to understand and/or support the generalisation of these findings to all/ most agencies. Intuitively this will be the case, but what evidence would support/uphold this view? Also to note the difference in training exposure (20 vs. 22 weeks). As there was a time factor for ‘injury occurrence’, how was this controlled for in the analyses? Also noted, where differences in delivery staff can be the biggest independent factor influencing individual training experience/exposure and hence outcome; did the researchers retrospectively analyse the impact of different training teams? Or, was this accepted as noise/error in the data? These points are mentioned in the Methods, which is really useful, but I would also like to understand any controlling or mitigating factors for their impact on the outcomes. 6 Injury Outcome Measures. Given the importance of these data to the model, this section is relatively vague in terms of the reported detail. It could be argued that if this injury count was limited to the workers’ compensation scheme, this would limit the recording of injuries to only the more severe injuries and miss the less severe (but potentially training interrupting) injuries. Moreover, this appears to be a count of ‘all’ injuries, which is increasingly seen as imprecise for determining specific occupational risk exposure. Noted, the researchers followed the recommendation for including injuries with a physiological rationale, but this relies on the detail collected from the compensation record, which—if not the original medical record—is likely to lack the necessary precision and detail. Whilst I understand that the researchers might have been limited by data protection regulations to access more accurate data, this is a significant limitation of this work. I would suggest that this section needs more explanation to ensure that the methods applied for the injury input data to the model are adequately robust. The risk here is both missing data (due to the bar set for compensation), and/or injury reporting imprecision. Both would present error in models of injury risk. 5 2 Statistical Analyses. I am not sure about the approach taken to dealing with trainees who left training, where their subsequent training data were assigned a value of ‘0’. Whilst this maximises data inclusion, does this approach not pull the aggregated data down towards zero for subsequent time points – where there would be a ‘time’ factor, as the number of non-completers would likely increase over time? Results 5 4 Injury data should also be reported relative to training exposure (e.g. training days). Moreover, as these appear to be all and any injury, this feels relatively imprecise. What assurances can the researchers provide that these analyses do not include spurious outcomes that would artificially improve the performance of the model? The lack of precision in the training input data (i.e. taken from a desk review), in addition to the method for determining injury outcomes, undermines the veracity of the Results. The researchers have made the best of the data that they have, which have not provided any surprising findings – which could be argued supports the approach. However, it has resulted in findings that are entirely predictable. That is: onset of occupational training is associated with increased injury risk; increases in training load increases training risk; ‘surviving’ longer during occupational training is protective; and female biological sex is a moderator of increased risk. Discussion 8 1 Agree, the findings from this study, in terms of being population-specific, should be important for training delivery staff. However, this information is known; the challenge is how do we change the behaviours of these staff and their organisations? I agree, there are better ways of managing the physical training transition from civilian to the occupational role (for law enforcement, emergency/ first responder services and the military), but employers have consistently ignored the science to change. The authors have made suggestions to improving the training programme, but none of these are specifically supported by the evidence presented in the paper, which describes the ‘problem’. I would suggest that this is due to the lack of specific detail on injury outcomes and the desk-based approach to determining training load. Thus, these suggestions must be regarded as speculation based on generalised observations from occupational and sports physiology. Whilst this is not a criticism per se, these limitations to the methods—with respect to addressing the paper’s aim—and hence the evidence limit specific practical application. 9-10 Limitations. Whilst the focus of the limitations is largely due to measurement of training load—and individualising the exposure and impact—it does not mention the limitations of the injury outcome data. This is a significant issue, and an important oversight in this section. The limitations of the injury outcome data should be addressed, or at least acknowledged, in these analyses. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Applied and occupational physiology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Fallowfield JL. Peer Review Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431441) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431441 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Powell S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 23 Dec 2025 | for Version 1 Steven Powell , Applied Physiology, Institute of Naval Medicine (Ringgold ID: 71416), Gosport, England, UK 0 Views copyright © 2025 Powell S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Overview This paper has examined the relationship between training load and injury risk in law enforcement recruits. I believe the research question is pertinent to provide evidence-based strategies to mitigating injury risk in such populations, where the majority of previous wok has been completed in sport and military populations. The study includes a large sample size with a good split of males and females considering the typical demographic of tactical populations. Having undertaken similar research in tactical populations, I fully appreciate the challenges of capturing the necessary data to answer the question. In this instance, the researchers have used a desktop analysis of distance covered, of which they validated in one class and then applied retrospectively to the other six classes; this is a methodological limitation which is fully acknowledged, however does significantly impact the outcomes of the research. The results indicated that higher weekly distances, earlier weeks in academy training, and female sex all resulted in higher probabilities of injury. My general and specific comments are detailed below. Comments The background and research question is important to understand whether a relationship exists between training load and injury risk in police officer recruits. I think the introduction could include a greater number of references with regards to the previous work in this area. The novelty isn’t examining TL and injury risk in a tactical population; it is examining that relationship during law enforcement training. A desktop analysis has been undertaken to quantify TL variables. One class was used to validate the analysis and then that has been applied to the other six classes. Obviously, this is a big methodological limitation, although I do fully appreciate the difficulty in monitoring TL in such populations and environments. Unfortunately, it does mean you are assuming a homogenous load distribution across all classes and cannot consider individual variability, resulting in limited ability for causality. Large sample size and good split of male and females considering the typical demographic of such occupational groups. I’m not sure I fully understand why demographic information is limited, when this is routinely published across many tactical populations. Also, given the population being recruited from (and assuming all recruits have passed a pre-joining physical assessment and therefore are fairly homogenous), I’m not sure if adding the remaining participants’ data to the calculated averages would make a great deal of difference? A table showing the recruits’ fitness scores would be beneficial for the reader, as we have little information on the characteristics of the group. In addition to the model, given that baseline fitness data are available, it would be extremely informative in future work (or as an additional analysis) to examine time-to-injury using survival methods. For example, Kaplan–Meier curves stratified by fitness quartiles, or a Cox model with fitness, sex and weekly distance as covariates, could clarify how much of the observed sex and load effects are mediated by initial fitness and highlight early vulnerability in low-fitness recruits. You say rapid increases in TL during the transition between civilian to law enforcement recruit may lead to higher injury risk – did you ask about pre-training physical activity or inferring from fitness level? The absence of any internal load TL metrics and treating TL exposure for each recruit as having the same physiological effect is a significant limitation. Clearly the studied could be improved by using either some form of accelerometer (to measure individual EL) or include some form of IL monitoring. Again, fully appreciate how logistically challenging that can be, however it has been done in similar populations previously and would have provided a measure of intensity in addition to volume. May have been useful to include self-report injuries also, as we know often some injuries aren’t reported officially. The statistical approach was appropriate for the data collected, the limitation being the model being unable to determine causality due to the lack of individual-level data. Was biological sex treated as an independent predictor, or could baseline fitness be used as a covariate? The observed sex effect may be partially confounded as female recruits often begin with lower fitness, meaning the same absolute TL results in a greater relative IL. It would be good to see confidence intervals illustrated on the figures. Formatting of graphs should be more consistent. Figure 4 seems to have a title, but the others do not. The outline on Figure 4 is black, but Figure 3 is grey. Specific Comments Page 3, para1, line 1: Has TL ‘recently’ grown in popularity or is it just routine now? Could argue that TL monitoring has been routine for the past two decades – I suppose it depends on the definition of ‘recently’! Page 3, para 3, line 3: Often or always drawn from the general population? Page 3, para 4: I think the authors could provide more references on the current research around TL and injury risk in tactical/military populations. Page 3, para 5, line 4: I would argue there has been a lot of research looking at the relationship between TL and injury risk in tactical populations. However, less so in law enforcement. Subjects’ para: you have included a decimal point for age in your prospective demographic data but not in the subsample above. Discussion, para 2: you have focussed on research in AFL players to discuss the context of your findings. I would suggest focussing on tactical population research to make your discussion stronger and more relevant. Peer Review Form Q’s Selected partly for Q1 as I believe more relevant tactical literature could be referenced throughout Selected partly as, whilst the limitation is firmly acknowledged, there is a flaw in the methods with the lack of individual data Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Applied physiology in military populations. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Powell S. Peer Review Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431439) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431439 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Sutton V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Dec 2025 | for Version 1 Vanessa R Sutton , Edith Cowan University, Joondalup, Western Australia, Australia 0 Views copyright © 2025 Sutton V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear F1000 Editorial Team, Thank you for the opportunity to review this interesting submission. Further research into this topic is much needed and the authors should be commended for their commitment to improving the health and performance of tactical athletes. I agree with the comments of Reviewer 1 (Dr Dhahbi Wissem) and addressing these would substantially improve the quality of the manuscript. In addition, without a measure of exposure to injury risk, and censoring tactical athletes when they were injured (or when they experience program attrition from other causes), the conclusions able to be made from the current dataset are limited. Is there any capacity to perform time-to-event survival analysis, as this would be considered best practice when modelling injury risk over time see (Ref 1) ? This would enable the reader to have much more confidence in the estimates Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly References 1. Nielsen R, Bertelsen M, Ramskov D, Møller M, et al.: Time-to-event analysis for sports injury research part 1: time-varying exposures. British Journal of Sports Medicine . 2019; 53 (1): 61-68 Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Injury prevention I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Sutton VR. Peer Review Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r431437) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-840/v1#referee-response-431437 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Sep 2025 | for Version 1 Dhahbi Wissem , Qatar Police College, Doha, Qatar; University of Jendouba, Jendouba, Tunisia 0 Views copyright © 2025 Wissem D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions General Comments The study addresses a highly relevant and under-researched topic within a tactical population, which is a strength. The use of a large sample size (N=547) and a mixed-methods approach is commendable. The paper identifies significant predictors of injury (weekly distance, week of training, and sex) and provides practical implications for training staff. However, several methodological limitations and a lack of individual-level data significantly weaken the conclusions. The core issue is the reliance on a "desktop analysis" to estimate a cohort's workload rather than measuring individual training loads, which fundamentally limits the study's ability to establish a causal relationship or provide actionable, individualized advice. Major Weaknesses Reliance on Cohort-Level Data: The study's primary methodological weakness is the use of a "desktop analysis" to estimate training load for an entire class rather than measuring individual-level data. The authors acknowledge this as a limitation, but it is a critical flaw. As stated, "each recruit within a class was assumed to have experienced the same training". This assumption is not scientifically sound and compromises the validity of the findings, as it ignores individual variations in physical activity and response to training. Lack of Internal Training Load (ITL) Measures: The study only considers external load (distance covered) and does not include any measures of internal load, such as heart rate or ratings of perceived exertion (RPE). The authors themselves note this is a limitation and that ITL is important because different individuals respond differently to the same external stimulus. The absence of ITL makes it impossible to fully understand the physiological stress and subsequent injury risk. Methodology and Data Collection Imprecision: The use of a "desktop analysis" to estimate variables like distance and time spent on activities is imprecise. The authors state that "estimations...were based on a cohort, not an individual level, with outliers who did not participate in specific activities (e.g., due to injury) ignored for the duration of that activity only". This introduces significant potential for error and misrepresentation of the actual training loads experienced by recruits. Minor Weaknesses Inadequate Demographic Data: The study only provides demographic data (age, height, weight) for a subsample of the participants (n=349 males, n=81 females). While the authors cite security concerns as the reason, the lack of this data for the full cohort (N=547) is a limitation, especially as individual characteristics can influence injury risk. Healthy Worker Effect: The discussion mentions the healthy worker effect as a potential explanation for the decrease in injuries over time. However, the study does not provide sufficient data or analysis to quantify or adjust for this effect, which is a key confounder. This makes it difficult to ascertain whether the decrease in injury risk is due to adaptation or simply the attrition of injured recruits. Inconsistent Referencing: There are inconsistencies and errors in the referencing throughout the paper, with some citations appearing to be out of order or incorrectly formatted (e.g., 20.2, 33.4, 24.5). Specific Comments Page 1, Line 737: The use of "Danny Maupin 101" is an unusual formatting choice for the author's name. Please clarify if the "101" is intentional, as it may be a typo. Page 3, Line 821: The sentence "This previously published methodology was performed to validate a desktop analysis" is unclear. It implies the methodology was published to validate the desktop analysis, but the next sentence states the desktop analysis was used to ensure adequate sample size. This needs to be rephrased for clarity. Page 4, Lines 833-834: The statement "This research was originally conducted as part of a doctoral thesis by the lead author and has been modified for publication in its current form" should be placed in the acknowledgements or grant information section, not the main body of the methods. Page 4, Line 845: The statement that outliers who did not participate "were ignored for the duration of that activity only" is a significant methodological weakness that needs further explanation. Ignoring these outliers may skew the cohort average and does not accurately represent the experience of all recruits. Page 4, Line 849: The assertion that "over time the overall load experienced would be similar" despite different training timings is an assumption that is not supported by data. This should be rephrased to reflect that this is a limitation, as the timing of load is critical for recovery and injury risk. Page 5, Lines 887-888: The percentages "30.3% occurred in female recruits, and 69.7% occurred in male recruits" are presented without context of the total number of female and male recruits. The next sentence, "This represents approximately 19% of female recruits and 12% of male recruits suffering an injury", is a key point that should be presented first for better context and clarity. Page 6, Line 936: The statement "for every 0.08 km (80 m) covered, the odds of sustaining an injury were increased by a factor of 1.08 (95% CI 1.04, 1.12)" presents a very small and potentially insignificant increase in odds that may not be clinically meaningful, despite being statistically significant. This needs to be discussed more thoroughly in the discussion section. Page 8, Line 1009: The comparison of law enforcement recruits to AFL players is a stretch, as the populations and training contexts are vastly different. While the authors attempt to justify it, the comparison feels forced and diminishes the specific context of the law enforcement recruits. Page 9, Line 622: The authors state that the study was "not able to adequately assess fitness...as an injury predictor". This is a significant limitation and undermines the conclusion that lower fitness levels may be a cause of injury, as this cannot be supported by the study's own data. Page 2, Line 765: The authors state the paper provides a "comprehensive understanding of the relationship between training load and injuries". Given the methodological limitations, especially the lack of individual data, this claim is too strong. Page 10, Lines 1032-1035: The discussion mentions the need for more research using “wearable technology to collect individual-level data”. Page 10, Line 1045: The discussion mentions that "factors such as poor sleep and hydration" could be linked to injury. Page 11, Lines 1049-1050: The paper's limitation section correctly identifies the use of "desktop analysis" and the lack of individual measures as a weakness. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Biomechanics and sports injury and rehabilitation I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 06 Oct 2025 Danny Maupin, University of Surrey Faculty of Health and Medical Sciences, Guildford, UK Hi Dhahbi, Thank you very much for taking the time out of your schedule to review my article. I will ensure that your comments are noted in a revised paper, though currently I am waiting for other review reports before the revision as recommended by the journal. Your criticisms of the methodology are valid, and though it would be ideal to measure these variables across individuals, it is not always feasible particularly in this context. The use of a desktop analysis is again not ideal but still provides value in this context. In addition to my previous work using this method, a similar methodology has also been used to estimate the risk of osteoarthritis in members of the Australian military (1). This is also likely to be more realistic and practical for these populations, which is described in the limitations section and is further detailed in an article I have written about the challenges in optimising training in these populations (2). Any changes these organisations make are not likely to be on an individual basis, but on a cohort basis. Our current challenge at the moment is working with the organisations to utilise smaller training groups based on fitness or other characteristics which is still proving to be a hard task considering the staff to recruit ratios. Further since training takes place on a cohort rather than individual level, individuals will be exposed to very similar levels of load as seen in my previous research comparing the desktop analysis to GPS units (3). While individualised data would be the gold standard, cohort level data provides a level of insight that is actionable and representative of the training programs in this population, particularly as actions taking by this population will be at the cohort rather than individual level. It is also accurate that causality is not appropriately addressed in this study. Great care was taken to avoid direct or indirect allusions to causality due to this (please do let me know if I have missed any!) and instead discuss it a relationship level. However, the issue of causality on this topic (training load and injury risk) is a consistent one throughout this literature where researchers often do not distinguish between associations and causes. The lack of internal training load is another weakness that was mentioned in the study and would be ideal to include. However, studies that utilise external training load only have been prevalent in the literature (4–6). The inclusion of internal training loads would provide further evidence on risk factors to injury. However, it is unlikely that this population will be able to manage internal training loads given their individualised nature and the barriers to individualised training load monitoring present. I agree also with your minor weaknesses. I see that I have not added demographics to the limitations of the study and will do so on the revised copy, along with correcting references. Regarding the healthy worker effect, this is a confounder and has been added to the limitations accordingly. However, being able to control for the healthy worker was not feasible in this situation. 1. Orr R, Schram B, Irving S, Pope N, Pope R. Measuring occupational exposures to osteoarthritis in the lower limb in Australian Defence Force job categories [Internet]. Bond Unviersty Tactical Research Unit; 2019. Available from: https://www.dva.gov.au/documents-and-publications/measuring-occupational-exposures-osteoarthritis-lower-limb-adf-job 2. Maupin DJ, Schram B, Dulla JM, Canetti EFD, Orr RM. Implementing Training Load Monitoring in Tactical Populations. Strength & Conditioning Journal. 2022 Mar 16;10.1519/SSC.0000000000000883. 3. Maupin D, Schram B, Canetti EF, Dulla JM, Dawes JJ, Lockie RG, et al. Profiling the typical training load of a law enforcement recruit class. International journal of environmental research and public health. 2022;19(20):13457. 4. Gabbett TJ, Ullah S. Relationship Between Running Loads and Soft-Tissue Injury in Elite Team Sport Athletes. The Journal of Strength & Conditioning Research. 2012 Apr;26(4):953. 5. Malone S, Roe M, Doran D, Gabbett T, Collins K. High chronic training loads and exposure to bouts of maximal velocity running reduce injury risk in elite Gaelic football. Vol. 20, Journal of Science and Medicine in Sport. 2016. p. 250–4. 6. Windt J, Gabbett TJ, Ferris D, Khan KM. Training load--injury paradox: is greater preseason participation associated with lower in-season injury risk in elite rugby league players? Br J Sports Med. 2017 Apr 1;51(8):645–50. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Wissem D. Peer Review Report For: Relationship Between Training Load and Injuries in Law Enforcement Recruits [version 1; peer review: 4 approved with reservations] . F1000Research 2025, 14 :840 ( https://doi.org/10.5256/f1000research.185304.r411093) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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