Relationships between body composition and firefighter occupational performance: A systematic review and meta-analysis

Abstract

Background

Firefighters face substantial physical demands in their profession, executing a range of challenging tasks under variable and high-stress conditions. Understanding the relationship between body composition and occupational performance tests (OPTs) can help to prepare both current and aspiring firefighters for the physical demands of the job.

Objective

To evaluate the relationship between body composition and the completion of firefighting OPTs.

Methods

The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies utilized career or firefighter recruits and examined the association of measures of body composition, body fat percentage (BF%), fat-free mass (FFM), fat mass (FM) and body mass index (BMI), with OPT outcomes. The meta-analysis synthesized correlation coefficients via a random-effects model.

Results

The systematic review included 26 studies. Body composition was assessed through a mix of laboratory and field tests. Twenty studies featured an OPT circuit with simulated fire suppression and rescue tasks. Pooled correlations between time to complete an OPT with fat free mass, body fat%, and body mass index were r̅ = −0.61, r̅ = 0.46, and r̅ = 0.10, respectively. For individual firefighting tasks, pooled correlations were generally strongest for FFM, followed by BF%, with weakest correlations observed for BMI.

Conclusions

The literature indicates that greater FFM and lower BF% are beneficial for the performance of firefighting OPTs, highlighting the importance of focusing on specific body composition measures for firefighter capabilities. The findings can be used to inform body composition screening and training programs designed to optimize firefighters’ ability to perform occupational duties.

Keywords

occupational health emergency responders firefighters body composition adiposity anthropometry fat free mass

Introduction

Firefighters play a vital role in promoting community safety by responding to various emergency situations, including structural fires, medical emergencies, car accidents, and hazardous spills.¹ Unfortunately, firefighters are well-documented to incur work-related injuries^2,3 and have high prevalence of other chronic health conditions, such as cardiovascular disease.^4–6 A common risk factor associated with both chronic health issues and musculoskeletal injuries (MSKIs) is obesity.^7,8 High obesity rates of firefighters have been documented in several previous studies.^5,7,9,10 Additionally, prior research has focused on the association between body composition, body mass index (BMI), and absenteeism, including missed workdays, in both firefighter⁷ and non-fighter populations.^11–13 A consistent finding across these studies is the connection between obesity and increased absenteeism across all worker groups.^7,11–14 For example, Poston et al.⁷ found that firefighters classified as having class II and III obesity were five times more likely to miss workdays due to injuries compared to their normal-weight counterparts. Similarly, Jahnke et al.⁹ reported that obese firefighters were 5 times more likely to suffer MSKIs than those who were not obese.

To fulfill their duties, firefighters perform a range of occupational tasks (Table 1), such as lifting equipment (e.g., ladders), carrying or dragging casualties and victims, pushing obstacles (e.g., fallen roof beams), pulling objects (e.g., hose hoists), and manipulating firefighting tools.^15–17 In real-world emergency scenarios, these tasks are not performed in an isolated manner but rather consecutively, with minimal pauses, often over extended periods.^18,19 Furthermore, safe and effective task execution requires firefighters to wear personal protective equipment (PPE) and self-contained breathing apparatus (SCBA),^17,20 which together can weigh over 22 kg and significantly increase the physiological demands of firefighting tasks. Given the diverse range of tasks and environments, coupled with the use of PPE and SCBA, understanding the modifiable factors that support safe and effective task execution is crucial for designing firefighter-specific training programs that optimize occupational performance as well as promote safety, and decrease risk of injury.

Table 1.

Definitions of firefighting occupational tasks.

Task	Definition	Task	Definition
Occupational Circuit	Completing a sequence of firefighting occupational tasks in series with the instruction, or goal, to complete the series of tasks as quickly and efficiently as possible.	Stair Climb	Ascending stairs, with or without equipment.
Ladder Raise	Placing and extending a ladder to access higher floors, roofs, or other elevated areas	Victim drag	Moving a person or dummy a specific distance to simulate extrication from unsafe environment.
Ladder Climbing	Ascending a ladder.	Hose Pulling or Dragging	Manual movement of hose across surface (not lifting and carrying)
Equipment Carry	Lifting and carrying equipment, including a ladder, a specific distance (not dragging or pulling). If ascending stairs then the task is classified as stair climb.	Crawl	Moving on hands, knees, hands, and feet to navigate through confined spaces, low-visibility environments, or areas with restricted vertical clearance.
Forcible Entry	Requires firefighters to apply repeated high force against a solid object, such as doors, windows, or barriers, to create an entry point. Tools commonly used for forcible entry include axes, halligans, sledgehammers, and battering rams. Firefighters may also use a Kieser sled, which simulates the force needed for such tasks.	Other	Another firefighting occupational task not defined above.

Note: Generative artificial intelligence (ChatGPT, OpenAI) was used to create the icons in the table.

Job task analysis is used to deconstruct physically demanding jobs into their component tasks, analyzing the frequency, intensity, and duration of each. This process helps identify critical tasks that can serve as occupational performance tests or physical employment standards, essential for assessing and training individuals in these roles.^20,21 Identifying critical job tasks allows these tasks to serve as occupational performance tests (OPTs) or physical employment tests, which can then contribute to physical employment standards.²¹ These tests can be administered as pre-employment screening or annual performance assessments.²¹ The physical fitness requirements of a physically demanding job are estimated through a needs analysis,^15,22 similar to a physical demands analysis.^20,21 A needs-analysis contains elements which inform an understanding of the physiological (e.g., physical fitness), biomechanical, and injury prevention demands for a physically demanding job.^15,16,22 For instance, Gledhill and Jamnik¹⁶ used a needs assessment to highlight the importance of understanding the physical fitness requirements for safe and effective firefighter OPT performance. Subsequently, several reviews have highlighted the diverse physical fitness requirements of firefighting OPTs.^15,23

One component of physical fitness is body composition. Body composition refers to the proportions of various tissues within the body, such as fat, muscle, and bone. Body composition is of interest because previous studies have reported high rates of obesity rates amongst firefighters.^5,7,9,10 A common metric of obesity is body fat percentage (BF%), which is the proportion of total body mass composed of fat mass (FM). The remaining body mass is termed fat-free mass (FFM) and includes muscle, bone, organ, and other soft tissues. BMI is another common metric used to classify obesity by categorizing individuals based on their height and body mass.^24,25 However, BMI's validity is limited, especially for individuals with high muscle mass; this is because it does not differentiate between muscle and fat proportions.^24,25 This limitation is particularly relevant for firefighters, where BMI may not accurately identify obesity.⁵ Despite the quick and easy calculation of BMI, its limitations, therefore, necessitate more accurate body composition analysis tools in firefighters. However, the validity, reliability, financial cost, and expertise required to administer various body composition measurement techniques presents barriers to implementing annual body composition testing in the fire service.

In terms of ability to perform firefighting OPTs, evidence suggests that body composition can influence firefighter occupational performance.^19,26,27 For instance, research has demonstrated that higher BF% and BMI are linked to reduced work efficiency when performing firefighting tasks.^19,26,27 Langford et al.¹⁹ observed that firefighters with greater BF% and BMI had lower work efficiency ratings during an air consumption drill. Similarly, Norris et al.²⁶ reported that greater BF% and FM were associated with lower work efficiency. Interestingly, Ras and colleagues found that while higher BF% and BMI were associated with longer times to complete simulated stair climbs, these factors were not associated with completion times of other firefighting tasks.²⁷ An important practical implication of these findings is that firefighters with higher BF% or BMI often exhibit greater exertion levels when performing tasks.^28,29 Additionally, there is a clear logic for the benefit of greater FFM in performing firefighting OPTs, as FFM largely consists of skeletal muscle, the tissue responsible for generating the forces required to lift, carry, push, and pull heavy objects.

Considering the obesity related problems experienced by firefighters^5,7,9,10 it is necessary to understand how body composition is related to the ability to perform OPTs. This knowledge may be critical for efforts to maintain operational readiness in the fire service. Additionally, for aspiring firefighters, who may be required to pass an OPT to enter the profession, knowledge of preferred body composition can inform physical fitness training and dietary intake in preparation for OPTs. To date, there is no comprehensive synthesis of the relationship between firefighter body composition and the ability to complete OPTs. Thus, the objective of this review is to quantify how specific body composition metrics influence the performance of specific occupational tasks (e.g., hose dragging, stair climbing, simulated victim extraction, load carriage, forcible entry) and the timed completion of OPTs routinely performed in the fire service. The outcomes of this systematic review and meta-analysis aim to provide actionable insights for professionals involved in firefighter training, exercise prescription, and the establishment of body composition recommendations for firefighters.

Methods

Review registration

The study selection, search criteria, data extraction, and assessment of study quality were defined and informed by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.³⁰ The protocol of this systematic review was registered a priori with PROSPERO (registration #: CRD42024504382). The systemic review reported in the present paper was part of a larger literature search to identify all peer-reviewed studies that reported a relationship between different components of physical fitness and firefighting OPTs.

Eligibility criteria

Included studies were required to meet the following criteria: (1) participants described as recruits, career or volunteer firefighters to mitigate experience bias with firefighting OPTs performed by non-firefighters, (2) examined associations of measures of physical fitness relative to timed performance of firefighting OPTs, (3) conducted via empirical study designs (cross-sectional,), (4) written in English, and (5) published in the last 50 years (1973 to 2023). Studies were excluded for the following: (1) peer-reviewed full-text articles were not able to be retrieved, (2) not written in the English language, or (3) the article was a single case study, review, commentary, or opinion article excluding original data.

Information sources

Based on recommendations for comprehensive literature coverage and recall,³⁰ three databases (PubMed, CINAHL, SPORTDiscus) were searched. The original search was conducted between January and February 2024, with a follow up in July 2024 to ensure no new articles were published. Additionally, the reference lists of the retrieved articles were reviewed to identify any further potentially relevant studies not captured by our initial search strategy.

Search strategy and study selection

Prior systematic reviews^31,32 and pilot testing informed the development of targeted search terms for this study. Search terms for 1) sample of firefighters were: ‘firefighter’ OR ‘firefighters’ OR ‘fire service’ OR ‘fire personnel’ OR ‘firefighting’; 2) body composition were: ‘fitness’ OR ‘body composition’ OR ‘body fat’ OR ‘fat mass’ OR ‘fat free mass’ OR ‘muscle’ OR ‘fat’ OR ‘body mass index’ OR ‘adipos*’; and 3) occupational performance were: ‘job task’ OR ‘occupational task’ OR ‘job performance’ OR ‘work-related tasks’ OR ‘physical demands’. The search strategy used boolean operators such that the sample of firefighters (A) and measure of body composition (B) was combined with occupational performance (C) terms [i.e., (A) AND (B) AND (C)].

The search results were exported to a Microsoft Excel database (Microsoft Excel, Redmond, WA, USA), where duplicates were removed. Subsequently, two authors (JM, KN) screened all titles and abstracts independently for relevance and eligibility. The full texts of the articles retained after screening (n = 58) were then independently assessed against the selection criteria by the same two authors (JM, KN).

Data extraction

For all eligible studies, data were extracted and tabulated independently by two authors (JM, KN). A data extraction spreadsheet to describe the key characteristics of the included studies was developed based on the Cochrane Consumers and Communication Review Group's data extraction template. Extracted information included author names, title and year of publication, sample size, description of participants, methodology to assess body composition, OPTs completed, detail on PPE and SCBA worn (if any) while performing OPTs, and study results. The extraction of correlation coefficients from individual studies to facilitate subsequent meta-analysis was performed first by one author (JM), then checked by a second author (KN) to ensure accuracy. Only the correlations between measures of body composition and the ability to perform firefighting OPTs, whether an entire battery or single task, were extracted. Any discrepancies in the extracted data were resolved through discussion and consensus of the researchers, with a third researcher (MFM) available if needed.

Risk of bias and quality assessment

The risk of bias of the included studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal tool for cross-sectional studies.³³ To mitigate bias, two authors (JM, KN) independently evaluated the risk of bias in the studies. Interrater agreement was measured through a calculation of Cohen's Kappa coefficient (k). Agreement scores were categorized as fair (k = 0.21–0.40), moderate (k = 0.41–0.60), substantial (k = 0.61–0.80), almost perfect (k = 0.81–0.99).³⁴ The JBI for cross-sectional studies included eight items. Each study's outcome on the JBI, answered with “Yes”, was denoted with a ‘1’, “No” with ‘0’, any inapplicable question with ‘N/A’, and unclear with ‘UN’. Percentages were assigned based on the total included items, divided by the total, and rounded to the nearest whole number. The risk of bias for individual studies was assessed based on the following criteria: a low risk of bias if 70% or more of the answers scored “Yes”, a moderate risk if 50% to 69% of questions scored “Yes”, and a high risk of bias if “Yes” scores were below 50.³⁵ Although risk of bias is preferred, five items from the strengthening the reporting of observational studies in epidemiology (STROBE) checklist were included to assess additional quality aspects of the studies,³⁶ which the authors believed to be relevant for the purposes of the review. The STROBE items added were item 2 (‘Explain the scientific background and rationale for the investigation being reported’), item 3 (‘State specific objectives, including any prespecified hypotheses’), item 14a (‘Give characteristics of study participants [e.g., demographic, clinical, social] and information on exposures and potential confounders’), item 19 (‘Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias’), and item 22 (‘Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based’). Agreement scores for the five STROBE items and the combined JBI and STROBE items (n = 13) were computed.

Study synthesis and meta-analysis

For the meta-analysis overall effect estimates were computed for the correlation between 1) any measure of body composition (e.g., BF%, FFM, FM, BMI) with performance of a firefighter OPT and, 2) any measure of body composition (e.g., BF%, FFM, FM, BMI) with performance of individual firefighting tasks. Firefighting tasks were categorized using a scheme like that used by Hauschild and colleagues.³¹

The presence of heterogeneity was assessed with the Q and I² statistic.³⁷ Heterogeneity among studies was classified using the I² statistic, where I² values of 25%, 50%, and 75% represent low, moderate, and high heterogeneity, respectively.³⁸ In addition to the heterogeneity statistics, the goals of the statistical inference and number of studies were considered when determining whether to use a fixed- or random-effects meta-analysis model.³⁹ Because the goal of the statistical inference was to generalize beyond the included studies a random-effects model was selected for the review.³⁹ For the meta-analyses, the correlation coefficients were pooled using a random-effects meta-analysis model and the Hunter and Schmidt method.⁴⁰ To compute the effect size, correlation coefficients obtained from individual studies were transformed into Fisher's z scores⁴¹ for computing the pooled correlation, then the inverse transformation was applied to obtain back the original correlation coefficients to present the results. Standard errors of the pooled correlations and 95% confidence intervals were computed. Meta-analyses were performed when correlations from at least 2 studies were available.³¹ To interpret the strength of the pooled correlations a 5-level scale was used³¹ with the following classifications: ‘very strong’ (0.7 ≤ r), ‘strong’ ((0.50 ≤ r < 0.70), ‘moderate’ (0.40 ≤ r < 0.50), ‘fair’ (0.30 ≤ r < 0.40), and ‘weak’ (r < 0.30).³¹

In most cases performance of firefighting OPTs was based on time to complete the individual or circuit of OPTs. Thus, a lower time reflected a superior work rate. However, for 3 studies which the OPT was a ‘work efficiency’ assessment,^19,26,28 a higher work efficiency score reflected superior performance. In these cases, the correlations between the body composition measure and work efficiency performance had a reverse direction than OPTs which were timed (e.g., positive correlation instead of negative correlation or vice versa). Additionally, for a timed OPT the instruction to firefighters was commonly reported as ‘perform as fast as possible’ and for work efficiency the instruction was ‘perform as fast and efficiently as possible’. Thus, the firefighters may perform the tasks differently that would alter relationships between body composition measures and OPT performance. For these reasons, it was decided to not pool the work efficiency correlations with those for timed OPTs. Additionally, Jagim et al.²⁸ examined work efficiency based on work completed per air cylinder (vs. standardized workload) and reported 3 outcomes: a work efficiency metric, time on the course and total distance traveled. For pooled correlations only body composition correlations with the work efficiency metric reported by Jagim and colleagues²⁸ were included with the two other studies^19,26 that reported a similar work efficiency metric.

Publication bias was evaluated visually by inspecting the funnel plot, the trim and fill method, Egger's test and the fail-safe N using the Rosenthal method.⁴² Sensitivity analysis was conducted to identify any highly influential studies which might bias the analysis by removing one study at a time and reviewing the change in the overall pooled correlation and heterogeneity. The meta-analyses were performed using packages ‘psychmeta’ and ‘metafor’ in R (version 4.2.1 R Core Team, Vienna, Austria). Statistical significance level was set to p < 0.05.

Results

Search results

The literature search initially identified 814 studies. Based on eligibility criteria, 796 studies were eliminated. During the data extraction process, 8 additional studies were retrieved through forward and backward citation searches. Twenty-six studies were included in the systematic review, with 23 in the meta-analysis (Figure 1).

Figure 1.

Search flow diagram.

Characteristics of included studies

Sample sizes of the included studies ranged from 19²⁶ to 306⁴³ firefighters. The review included a total of 2224 firefighters (1900 male, 324 female), with the majority being career firefighters (n = 1676). Recruits (n = 532) and volunteer (n = 16) firefighters were also represented. The studies were conducted across 6 countries, predominantly in the United States with 18 studies. The average JBI risk of bias score was 83.8 ± 15.2%, indicating a generally ‘low’ risk across studies (Table 2). Five studies^44–48 had moderate risk of bias, and none had a high risk of bias. Frequently, studies marked as ‘0’ for items related to unaddressed confounding variables (items 5 and 6). Similar to a prior review,⁴⁹ a study was marked ‘1’ for item 5 if at least one confounding variable was acknowledged, and ‘1’ for item 6 if methodological efforts were made to control for these variables. The studies also scored well on the additional STROBE checklist items, with an average score of 89.0 ± 12.8% (Table 2). It was noted that several studies did not disclose funding sources, hence were marked ‘UN’ for unclear. The kappa scores for the JBI, STROBE, and combined assessments were 0.89, 0.96, and 0.91 respectively, indicating an almost perfect level of agreement.³⁴

Table 2.

Study risk of bias and quality assessment.

	JBI Items									STROBE Items
Author(s)	1	2	3	4	5	6	7	8	JBI Score	2	3	14a	19	22	STROBE Score	Total Score
Blacker et al., 2016	1	0	1	1	1	1	1	1	88%	1	1	0	1	1	80%	85%
Chizewski et al., 2021	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Davis et al., 1982	1	1	1	1	0	1	1	1	88%	1	0	1	0	1	60%	77%
Harbison et al., 2023	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Henderson et al., 2007	1	0	1	1	1	1	1	1	88%	1	1	1	0	UN	83%	83%
Jagim et al., 2023	1	1	1	1	0	0	1	1	75%	1	1	1	1	1	100%	85%
Langford et al., 2023	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Marcel-Millet et al., 2023	1	0	1	1	1	1	1	1	88%	1	1	1	1	1	100%	92%
Michaelides et al., 2008	0	0	1	1	0	0	1	1	50%	1	1	1	0	UN	75%	58%
Michaelides et al., 2011	0	1	1	1	0	0	1	1	63%	1	1	1	1	UN	100%	75%
Misner et al., 1987	0	1	1	1	0	0	1	1	63%	1	1	1	0	UN	75%	67%
Misner et al., 1988	1	1	1	1	0	0	1	1	75%	1	1	1	0	UN	75%	75%
Misner et al., 1989	0	1	1	1	0	0	1	1	63%	1	1	1	0	UN	75%	67%
Mota et al., 2018	1	1	1	1	1	1	1	1	100%	1	1	0	1	1	80%	92%
Norris et al., 2021	1	0	1	1	1	1	0	1	75%	1	1	1	1	1	100%	85%
Ras et al., 2022	1	1	1	1	0	0	1	1	75%	1	1	1	1	1	100%	85%
Ras et al., 2023	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Ras et al., 2024	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Rhea et al., 2004	1	1	1	1	0	0	1	1	75%	1	1	1	0	UN	75%	75%
Ryan et al., 2022	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Saari et al., 2020	1	1	1	1	1	0	1	1	88%	1	1	1	1	UN	100%	92%
Schmidt et al., 2012	1	1	1	1	1	1	1	1	100%	1	1	1	0	UN	80%	92%
Sheaff et al., 2010	1	1	1	1	1	1	1	1	100%	1	1	1	1	1	100%	100%
Skinner et al., 2020	1	1	1	1	1	0	1	1	88%	1	1	1	1	1	100%	92%
Williford et al., 1999	0	1	1	1	0	0	1	1	63%	1	1	1	0	UN	75%	67%
Xu et al., 2020	1	0	0	1	1	1	1	1	75%	1	1	0	1	1	80%	77%

JBI: Johanna Briggs Institute; STROBE: strengthening the reporting of observational studies in epidemiology.

Body composition measures

Various methods were used to assess body composition, as detailed in Supplementary Table 1. Skinfold calipers^{43,44,47,48,50–52} and bioelectric impedance analysis (BIA)^{19,26–28,45,46,53–56} were the most frequently used methods. Additionally, several studies used dual-energy x-ray absorptiometry (DEXA)^56–59 and air plethysmography⁶⁰ to assess body composition.

Occupational tasks

Twenty studies incorporated firefighting OPTs, where firefighters sequentially complete various tasks (Supplementary Table 1). The duration to complete firefighting OPTs varied, with the shortest average time being 106 s⁵⁵ and the longest being 1510 s (∼25.2 min).²⁸ Common tasks in OPTs included stair climbing, equipment carrying, ladder raising and climbing, victim dragging, forcible entry, and crawling. Additionally, 7 studies utilized discrete testing of firefighting tasks, allowing rest between tasks^{43,44,51,52,60–62} or assessed only a single task, which was a stair climb in both instances.^51,63 Two studies^52,62 used both OPT and discrete task testing. Typically, the firefighters performed OPTs wearing PPE and SCBA, which had a mass between 20–25 kg (Supplementary Table 1). However, not all studies had firefighters perform the occupational tasks ‘on air’, although a SCBA may have been worn. Details of whether participants were ‘on air’ are included in Supplementary Table 1. Notably, 3 studies^19,26,28 examined data from firefighter air consumption drills, where the amount of consumed compressed air was recorded to assess work efficiency.

Relationship between body composition and performance of occupational tasks

The frequency of all correlations per body composition measure is summarized in Table 3. Pooled correlations between body composition measures and performance of OPTs are provided in Figure 2(a)–(c) and Table 4 for OPTs and individual firefighting tasks.

Figure 2.

Forest plots of pooled correlations for body composition measures with timed circuits of firefighting occupational performance tests (longer times represent deficient performance). (a) Body fat percentage, (b) Fat-free mass, (c) Body Mass Index.

Table 3.

Frequency and strength of correlations (n = 159) between body composition measures and firefighting occupational performance tests and individual tasks.

	BF% (n = 68)		Fat-free mass (n = 52)		Fat mass (n = 6)		BMI (n = 35)
Strength of Correlations	Circuit of OPTs(n = 9)	Singletasks(n = 59)	Circuit of OPTs(n = 5)	Singletasks(n = 47)	Circuit of OPTs(n = 1)	Singletasks(n = 5)	Circuit of OPTs(n = 6)	Singletasks(n = 29)
Very strong (r ≥ 0.70)	0 (0.0%)	1 (1.7%)	1 (20.0%)	4 (8.5%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
Strong (r = 0.50–0.69)	1 (9.1%)	7 (11.8%)	2 (40.0%)	16 (34.0%)	0 (0.0%)	1 (20.0%)	0 (0.0%)	0 (0.0%)
Moderate (r = 0.40–0.49)	2 (18.2%)	7 (11.8%)	2 (40.0%)	7 (14.9%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	1 (3.4%)
Fair (r = 0.30–0.39)	3 (45.5%)	12 (20.3%)	0 (0.0%)	5 (10.6%)	1 (100%)	0 (0.0%)	0 (0.0%)	3 (10.3%)
Weak (r < 0.30)	0 (0.0%)	6 (10.2%)	0 (0.0%)	4 (8.5%)	0 (0.0%)	0 (0.0%)	1 (16.7%)	5 (17.2%)
Non-Significant Correlations	3 (27.3%)	26 (44.1%)	0 (0.0%)	11 (23.4%)	0 (0.0%)	4 (80.0%)	5 (83.3%)	20 (69.0%)

Note: 1) Abbreviations: OPT, occupational performance test; BF%, body fat percentage.2) Percentages in parentheses are computed per column and represent the percentage of statistically significant correlations for each strength level. 3) Classification of strength of correlations was based on absolute magnitude of correlations due to some relationships being both positive (e.g., BF% with OPTs) and negative (e.g., fat-free mass with OPTs). 4) Correlations between work efficiency and body composition metrics are included in the single tasks columns.

Table 4.

Pooled correlations for individual firefighting tasks.

Body Fat %
Task	k	N	r̅	SE	95% CI	Q (dof)p-value	I²
Stair Climb	13	1209	0.30	0.07	[0.15, 0.44]	74.412 (12) <0.001	82.26
Victim Drag	9	796	0.17	0.08	[−0.02, 0.36]	46.101 (8) <0.001	73.58
Hose Pull	6	536	0.21	0.08	[0.01, 0.41]	17.464 (5) 0.004	88.92
Forcible Entry	5	536	0.24	0.06	[0.07, 0.40]	8.001 (4) 0.092	44.46
Equipment Carry	6	653	0.25	0.09	[0.03, 0.48]	28.101 (5) <0.001	92.62
Ladder Raise	4	414	0.20	0.06	[0.02, 0.38]	4.347 (3) 0.226	42.42
Hose Hoist	0	—	—	—	—	—	—
Work Efficiency	3	155	−0.43	0.03	[−0.57, −0.30]	0.444 (2) 0.801	0.00
All OPTs	56	5172	0.21	0.03	[0.14, 0.27]	283.881 (55) <0.001	81.06

Fat-Free Mass
	k	N	r̅	SE	95% CI	Q (dof)p-value	I²
Stair Climb	10	1069	−0.33	0.06	[−0.47, −0.19]	48.418 (9) <0.001	65.79
Victim Drag	7	697	−0.53	0.07	[−0.71, −0.35]	43.028 (6) <0.001	65.79
Hose Pull	4	437	−0.56	0.12	[−0.93, −0.18]	38.191 (3) <0.001	65.79
Forcible Entry	4	457	−0.53	0.01	[−0.57, −0.48]	0.517 (3) 0.915	0.00
Equipment Carry	5	574	−0.53	0.08	[−0.76, −0.29]	30.372 (4) <0.001	86.83
Ladder Raise	4	414	−0.52	0.04	[−0.64, −0.39]	3.309 (3) 0.346	9.34
Hose Hoist	1	—	—	—	—	—	—
Work Efficiency	1	—	—	—	—	—	—
All OPTs	43	4529	−0.39	0.05	[−0.48, −0.28]	619.024 (42) <0.001	87.5

Fat mass
	k	N	r̅	SE	95% CI	Q (dof)p-value	I²
Stair Climb	0	—	—	—	—	—	—
Victim Drag	1	—	—	—	—	—	—
Hose Pull	1	—	—	—	—	—	—
Forcible Entry	0	—	—	—	—	—	—
Equipment Carry	0	—	—	—	—	—	—
Ladder Raise	0	—	—	—	—	—	—
Hose Hoist	0	—	—	—	—	—	—
Work Efficiency	1	—	—	—	—	—	—
All OPTs	4	168	0.16	0.04	[0.04, 0.29]	0.745 (3) 0.863	0.00

Body Mass Index
	k	N	r̅	SE	95% CI	Q (dof)p-value	I²
Stair Climb	4	394	−0.24	0.19	[−0.86, 0.37]	49.164 (3)<0.001	63.45
Victim Drag	5	483	−0.05	0.04	[−0.16, 0.06]	3.120 (4)0.538	0.00
Hose Pull	4	435	0.08	0.01	[−0.01, 0.18]	0.100 (3)0.992	0.00
Forcible Entry	3	393	−0.01	0.02	[−0.13, 0.10]	0.457 (2)0.796	0.00
Equipment Carry	4	441	0.06	0.03	[−0.04, 0.16]	0.334 (3)0.954	0.00
Ladder Raise	4	441	−0.04	0.04	[−0.17, 0.10]	1.878 (3)0.598	0.00
Hose Hoist	0	—	—	—	—	—	—
Work Efficiency	2	98	−0.34	0.05	[−0.94, 0.27]	0.269 (1)0.604	0.00
All OPTs	27	2864	−0.01	0.04	[−0.09, 0.06]	95.687 (26)<0.001	55.48

A total of 68 correlations were reported between BF% and performance of OPTs (n = 9), work efficiency (n = 5),^19,26,28 or single firefighting tasks (n = 54; Table 3). Notably, correlations between BF% and performance of firefighting OPTs ranged from strong,⁴⁶ to moderate,^45,62 to fair,^52,53 to weak,⁴⁸ while 3 correlations^54,55,60 were found to be non-significant (p > 0.05). The pooled correlation between BF% and performance of OPTs was r̅ = 0.46 (95%CI: [0.34, 0.57]; Q(8) = 29.008, p = 0.002; I² = 67.51; Figure 2(a)). For single firefighting tasks, many (n = 26) of the correlations were not statistically significant. For 6 of the 7 correlations in which a ‘strong’ effect was reported, the task studied was a stair climb.^{44,46,48,53,63} There were no strong or very strong pooled correlations and only work efficiency (r̅ = −0.43, 95%CI: [−0.57, −0.30]) was of moderate strength (Table 4).

A total of 52 correlations were reported between FFM and performance of OPTs (n = 5),^{48,52–54,62} work efficiency (n = 4),^28,48 or single firefighting tasks (n = 43; Table 3). The correlations between FFM and performance of firefighting OPTs were very strong (n = 1),⁵³ strong (n = 2),^52,54 and moderate (n = 2),^48,62 with a pooled correlation of r̅ = −0.61 (95%CI: [−0.72, −0.49]; Q(4) = 13.267, p = 0.010; I² = 69.85; Figure 2(b)). For single firefighting tasks or work efficiency, 20 of 47 (45.4%) correlations were strong or very strong; however, there were 11 (23.4%) non-significant correlations (Table 3). Pooled correlations were all strong between FFM and tasks utilizing a victim drag (r̅ = −0.53, 95%CI: [−0.71, −0.35]), hose pull (r̅ = −0.56, 95%CI: [−0.93, −0.18]), forcible entry (r̅ = −0.53, 95%CI: [−0.57, 0.48]), equipment carry (r̅ = −0.53, 95%CI: [−0.76, −0.29]), and ladder raise (r̅ = −0.52, 95%CI: [−0.64, −0.39]).

Only 6 correlations were reported between FM and performance of occupational circuits of OPTs (n = 2) or single firefighting tasks (n = 4; Table 3). However, one of the occupational circuits of OPTs was a work efficiency test.²⁶ Due to the limited data, with no OPT having more than one correlation reported, it was not possible to compute pooled correlations for either the circuits or the single firefighting tasks. Of the 6 reported correlations, Skinner et al.⁶² reported 5 with Norris et al.²⁶ reporting the remaining correlation. For occupational circuits of firefighting OPTs, Skinner et al.⁶² found a correlation of r = 0.32 between FM with an occupational circuit. Regarding individual firefighting OPTs, 4 of 5 correlations were not statistically significant (Table 3). The only significant correlation was that reported by Norris et al.,²⁶ which was strong (r = 0.51) for the relationship between FM and work efficiency.

A total of 35 correlations were reported between BMI and performance of OPTs (n = 6),^{52–54,62,64,65} work efficiency (n = 2),^19,26 or single firefighting tasks (n = 27; Table 3). Notably, 5 of the 6 correlations between BMI and performance of an occupational circuit of firefighting OPTs were non-significant^{52–54,62,65} and the only other correlation was weak.⁶⁴ The pooled correlation between BMI and performance of occupational circuits was r̅ = 0.10 (95%CI: [0.02, 0.19]; Q(5) = 4.599, p = 0.467; I² = 0.00; Figure 2(c)). For single firefighting tasks, 20 of 29 (69.0%) correlations were non-significant with another 5 (17.2%) correlations being weak (Table 3). Pooled correlations were all weak between BMI and firefighting tasks, with 95% confidence intervals that included 0.

Publication bias and sensitivity analysis

Publication bias and sensitivity analyses focused on correlations for occupational circuits as these were most similar to OPTs utilized within the fire service. Funnel plots for BF%, FFM, and BMI are displayed in Figure 3(a)–(c), respectively. For BF%, the trim and fill analysis indicated no unpublished studies (p = 0.500), while Egger's test suggested the presence of funnel plot asymmetry (b = 0.86 [95%CI: 0.55, 1.18], p = 0.020). The fail-safe N analysis indicated 449 missing studies would be required to change the BF% pooled correlation findings. There was a study found to be influential on the pooled correlation, which was the 2023 study by Ras et al.⁵⁴ Regarding FFM, trim and fill analysis indicated no unpublished studies (p = 0.500), and Egger's test showed no funnel plot asymmetry (b = 0.74 [95% CI: 0.19, 1.29], p = 0.748). The fail-safe N analysis indicated 564 missing studies would be required to change the FFM pooled correlation findings. There were no studies found to be influential on the pooled FFM correlation. For BMI, the trim and fill analysis indicated there were no unpublished studies (p = 0.063), and Egger's test (b = 0.253 [95%CI: 0.04, 0.47], p = 0.132) did suggested that funnel plot asymmetry was present. The 2023 study by Ras et al.⁵⁴ and the study of Chizewski et al.⁶⁴ were found to be influential on the pooled BMI correlation.

Figure 3.

Funnel plots of correlations between body composition measures with timed circuits of firefighting occupational performance tests. (a) Body fat percentage, (b) Fat-free mass, (c) Body Mass Index.

Discussion

This systematic review and meta-analysis aimed to examine the relationship between body composition and firefighters’ performance of OPTs. The synthesis of the 26 included studies indicates that FFM is the most influential body composition metric impacting firefighters’ capabilities to perform OPTs; as FFM increases, the duration required to complete timed OPTs decreases (Figure 2(b)), thus reflecting a greater work rate per standardized work load. BF% emerged as the second most influential body composition measure in terms of circuits of OPTs; as BF% increases, the duration required to complete OPTs also increases (Figure 2(a)), reflecting a lesser work rate per standardized work load. Importantly, shorter durations to perform timed OPTs is favorable because it represents more effective and efficient firefighter capability in emergency situations. Lastly, the literature consistently demonstrates that BMI has a weak to no significant correlation with performance on firefighting OPTs.^66,67

The importance of FFM for performing firefighting OPTs was expected.^15,16 A seminal study in 1992 by Gledhill and colleagues evaluated the physical demands of firefighting tasks and found that many occupational tasks involved producing forces greater than 45 kg, often repeatedly.¹⁶ Subsequent biomechanical analyses confirmed that firefighting tasks require force production from both lower and upper body musculature.¹⁵ Therefore, greater FFM, largely comprised of skeletal muscle tissue, would be advantageous for performing many firefighting tasks. Possessing greater absolute FFM decreases the proportion of external load relative to FFM, thus yielding a favorable physiological profile for lifting, carrying and dragging heavy equipment or objects. An example is the simulated victim drag, used in many of the reviewed studies (see Table 3), commonly required dragging a ∼80 kg dummy 15–20 m. Thus, for some firefighting OPTs firefighters may have an additional 100 kg of mass (80 kg dummy + 20 kg PPE) to contend with, and greater FFM would be highly beneficial for force production related to lifting objects or victims, moving heavy equipment all while wearing PPE. In regard to the physiological strain induced by body-worn load carriage (e.g., PPE) related to employment standards, readers are encouraged to consult the review by Taylor and colleagues.⁶⁸

Interestingly, BF% was not found to have a strong relationship with the performance of firefighting OPTs. However, the pooled correlations supports that lower levels of BF% are likely beneficial for performing firefighting OPTs (Figure 2(a)). Findings from individual studies provide more insight into specific scenarios where BF% may be a more important determinant of occupational task performance. For instance, Michaelides et al. (2011)⁴⁶ found a strong influence of BF% on the time to complete an occupational circuit of firefighting OPTs. In this study,⁴⁶ BF% had the strongest correlation with time to complete the circuit (r = 0.57) of all the physical fitness assessments, which included lower and upper body tests of muscle performance (e.g., strength and strength endurance). A multiple linear regression analysis further confirmed that BF% was the strongest predictor of OPT completion time and a large difference in BF% between the best and poorest performers on the OPT circuit (best performers, BF%: 17.0 ± 2.6; poorest performers, BF%: 27.6 ± 3.1) was reported.⁴⁶ However, several other studies (see Tables 3 and 4) did not find a significant association between BF% and performance of individual firefighting OPTs though a significant positive relationship was found for circuits of firefighting OPTs,^53,62 A plausible explanation is that as the duration of firefighting OPTs increases it becomes more important for firefighters to possess a lower BF%. Since greater BF% is associated with lower levels of cardiorespiratory fitness,⁶⁹ it is likely that firefighters with high BF% also have lower relative (e.g., mL/kg/min) VO2max, similar to athletes.⁷⁰ While the performance of short-duration firefighting tasks (less than 60 s) may not suffer due to this physiological profile, performance decrements are likely to become more pronounced with longer durations of physical activity. This is because metabolically inactive fat tissue adds mass that does not contribute to muscular contractions needed for firefighting tasks^15,16 and effectively adds to the external load carriage requirements. Supporting this explanation, findings from a 1982 study by Davis et al.⁵⁰ reported that a lower BF% was associated with a greater resistance to fatigue in firefighters.

On the fireground, work efficiency is a primary operational goal for firefighters due to the limited cylinder air supply in their tanks, which restricts firefighters’ work duration. Consequently, many departments conduct annual air consumption drills and training. Studies in this review that assessed the relationship between BF% with work efficiency were conducted by Langford et al.,¹⁹ Jagim et al.,²⁸ and Norris et al.²⁶ A common finding in all three studies was that greater BF% was associated with lower work efficiency measures. Thus, when interpreting the findings of the current meta-analysis, most studies focused on the association between body composition and time to complete discrete firefighting tasks or circuits of OPTs. More specifically, time to complete firefighting OPTs and work efficiency should not be used interchangeably, as research has demonstrated variability in firefighters’ air consumption when work rate is standardized.⁷¹ As all three studies on work efficiency^19,26,28 were published within the last three years, this emerging area of inquiry offers opportunities for future research to further the understanding of the optimal physiological profile for enhancing firefighter work efficiency.

Regarding BF%, several other interesting findings were reported. First, Ras et al. (2024)²⁷ found the strongest association with firefighting OPTs, but this correlation was not statistically significant when controlling for age and sex. This highlights the fact that body composition is influenced by both age and sex. As individuals age, there is a general decline in FFM and a concurrent increase in BF%.⁷² Additionally, there are well-noted sex differences in body composition.⁷³ Females have a greater BF% due to sex-specific fat and have less FFM than males.⁷³ These sex differences in FFM translate to lower strength and power output capabilities in females. For these reasons, firefighters who are either older and/or female may find performing firefighting tasks more challenging. This can also explain why Ras et al. (2023)⁵⁴ did not find a significant influence of BF% on the performance of firefighting tasks once age and sex were controlled for, which other included studies did not control for in their analyses. Another interesting finding was by Mota et al.,⁵⁷ who found that greater BF% was associated with lower functional balance scores. Considering that slips, trips, and falls are the leading cause of firefighting musculoskeletal injury,² further research to understand the potential influence of body composition on this mechanism of musculoskeletal injury in firefighter populations is recommended.

The lack of association between BMI and the ability to perform firefighting OPTs (Figure 2(c)) is likely due to BMI's inability to differentiate between proportions of FFM and FM that comprise total body mass. For example, a high BMI can result from either obesity or a high level of FFM.²⁴ Therefore, BMI fails to accurately reflect body composition, leading to the findings reported in the present review. As discussed in the preceding paragraphs, there is evidence that FFM and BF% influence the performance of firefighting OPTs. Therefore, it is not recommended to use BMI as a metric to assess a firefighter's capability to perform their occupational duties effectively.

Only two studies examined the influence of FM on the ability to perform firefighting OPTs,^26,62 which is a shortcoming in the existing literature. However, considering other scientific findings, greater amounts of FM would hinder the performance of firefighting OPTs that require muscle force production and high levels of aerobic capacity.¹⁶ For example, greater levels of intramuscular fat can result in impaired muscle shortening⁷⁴ and motor unit activation,⁷⁵ which would reduce muscle force generation. Indeed, FM has been found to be negatively associated with muscle strength and power,⁷⁶ which are critical performance characteristics for firefighters. In terms of aerobic capacity, FM itself does not appear to negatively impact absolute (e.g., L/min) VO2max.⁷⁷ However, greater BF% has been found to have a strong negative association with relative (e.g., mL/kg/min) VO2max.⁶⁹ Considering that FM is additional mass that is metabolically inactive, the findings by Norris et al.²⁶ that greater FM is associated with lower work efficiency are intuitive. However, there was limited evidence regarding the influence of FM on OPTs and therefore further research on the influence of FM on firefighter occupational performance does not appear to be warranted. Another limitation of the current literature is the inability to determine whether sex differences influence the relationship between body composition metrics and OPT performance. Many studies included only male firefighters,^{19,26,45–47,58,64} with a single study exclusively examining females,⁵¹ while all other studies were comprised of a majority (>90%) of male firefighters. Given the well-documented differences in body composition between sexes⁷³ and the importance of supporting females entering the fire service, future research should investigate these distinctions to provide a more comprehensive understanding and foster inclusivity in firefighter fitness and performance standards.

Practical implications

The findings of this review have important implications for practitioners working with firefighters to enhance occupational performance. Considering the association between poor body composition and the physical demands of firefighting,^15,16 it is plausible that firefighters with unfavorable body composition profiles (e.g., obesity) tend to perform occupationally tasks at lower work rates and work efficiency. Based on the focus of the present review, a primary recommendation is that increasing FFM should be the focus of strength and conditioning programs for firefighters, given the pooled correlations of the included studies for occupational circuits and individual firefighting tasks (Figure 2(a)). Resistance training exercises must be incorporated into firefighting training to increase or maintain FFM.¹⁵ For firefighters, consideration must be given to their unique occupational demands (e.g., occupational tasks) and constraints (e.g., exercising on-duty, access to resistance training equipment) to effectively design exercise programs.¹⁵ Specific exercise program design recommendations, including resistance training specifically for firefighters, are provided by Abel et al.¹⁵

Furthermore, while BF% was found to have a lesser influence on the ability to perform OPTs than FFM, there are clear health concerns for firefighters with high levels of BF%.⁸ For adults who may have high BF%, evidence supports that the most effective approach to improving BF% would be through nutrition and exercise.⁷⁸ A systematic review and meta-analysis by Olateju and colleagues⁷⁸ concluded that the most effective plan for addressing obesity is with an exercise program consisting of aerobic and resistance exercise, combined with a hypocaloric diet. However, to implement body composition management interventions with firefighters, practitioners must consider the specific challenges they experience in consuming a healthy diet; such challenges include the eating culture at the fire station, sleep disturbances due to night calls, support from leadership, sedentary work, and generational influences.

Limitations

The review had several limitations that merit consideration. First, the heterogeneity in study designs and task simulations may affect the comparability of results across studies. The variability in the type, number, order, and duration of tasks included in the occupational circuits (Supplmentary Table 1) could significantly influence the outcome measures. For example, the durations of the occupational circuits of OPTs studied were short and might not accurately reflect the more extended and physically demanding real-life firefighting scenarios. However, because firefighters respond to a wide range of emergencies with no standard scenario, the pooled correlations with 95% confidence intervals can be interpreted as representing the possible range of associations between body composition metrics and performance across diverse emergency situations firefighters may encounter. Secondly, the overall sample from included studies mainly consisted of male career firefighters, with female and volunteer firefighters being underrepresented. According to recent Federal Emergency Management Agency (FEMA) statistics, volunteer firefighters comprise the majority (69.9%) of firefighters in the United States.⁷⁹ Lastly, many of the studies reviewed consisted of small samples of firefighters participating in the research on a volunteer basis, which may not accurately represent the entire department, as fitter individuals are more likely to volunteer for such studies.⁸⁰ This phenomenon, known as the ‘Healthy Worker Effect’ (HWE), indicates that healthier individuals are more inclined to participate in health-related research.⁸⁰ The HWE is potentially problematic because firefighters with lower levels of physical fitness, who are less likely to volunteer for studies, are critical to understanding, as they may struggle more with the demands of firefighting tasks.¹⁶ Several studies reported mean BF% which would indicate above average (e.g., very good) body composition, such as Misner et al.⁴⁴ who reported mean BF% for male and females of 9.5 ± 4.6% and 20.7 ± 5.1%, respectively.

Conclusions

In summary, the current body of literature indicates that greater levels of FFM and lower BF% positively influence the performance of firefighting occupational tasks. Specifically, increased FFM was related to a faster work rate on OPTs. In addition, increased BF% was related to a slower work rate during OPTs. Therefore, health and wellness programs within fire departments should implement assessments and exercise training aimed at achieving optimal levels of both FFM and BF%. By focusing on these body composition metrics, fire departments may enhance occupational performance and address other concerns within the fire service such as obesity-related health issues.

Supplemental Material

sj-docx-1-wor-10.1177_10519815251346151 - Supplemental material for Relationships between body composition and firefighter occupational performance: A systematic review and meta-analysis

Supplemental material, sj-docx-1-wor-10.1177_10519815251346151 for Relationships between body composition and firefighter occupational performance: A systematic review and meta-analysis by Joel R Martin, Mark G Abel, Kayleigh Newman, Marcie Fyock-Martin and Nicholas C Clark in WORK

Footnotes

Acknowledgements

The authors have no acknowledgments.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

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