Important features of bench press performance in non-disabled and Para athletes: A scoping review

Rob Buhmann; Mark Sayers; Julia O’Brien; David Borg

doi:10.1371/journal.pone.0310127

. 2024 Nov 11;19(11):e0310127. doi: 10.1371/journal.pone.0310127

Important features of bench press performance in non-disabled and Para athletes: A scoping review

Rob Buhmann ^1,^*, Mark Sayers ¹, Julia O’Brien ², David Borg ^3,⁴

Editor: Jeremy P Loenneke⁵

PMCID: PMC11554178 PMID: 39527599

Abstract

Purpose

Understanding important features for performance in non-disabled bench press and Paralympic powerlifting may inform talent identification and transfer models. The aim of this scoping review was to describe features associated with bench press performance.

Methods

We conducted a systematic search of three electronic databases (PubMed, SportDiscus and EMBASE) to identify studies involving non-disabled and Para athlete populations that investigated features related to bench press one-repetition maximum (1RM), across six domains (anthropometric, body composition, demographic, technical, disability and neuromuscular). Search terms included “resistance training”, “para powerlifting’ and “one repetition max”. No date restrictions were include in searches. Studies using adult participants, with at least six-months of bench press experience, who were able to bench press their body mass were included.

Results

Thirty-two studies met our inclusion criteria. Twenty-four studies involved non-disabled athletes (total n = 2,686; 21.9% female) and eight involved Para athletes (total n = 2,364; 39.4% female). Anthropometric (17 studies) and body composition (12 studies) features were most studied; half of the 32 studies investigated features from a single domain. Of anthropometric variables, upper arm circumference shared the strongest association with bench press 1RM in non-disabled (r = 0.87) and para-athletes (r = 0.65). Upper limb fat free mass (r = 0.91) and body mass index (r = 0.46) were the body composition variables sharing the strongest association with bench press 1RM in non-disabled and para-athletes. Few studies considering the uncertainty of their results. Practices of open and transparent science, such as pre-registration and data sharing, were absent.

Conclusion

The development of bench press talent identification and sport transfer models will require future studies to investigate both non-training and training modifiable features, across multiple domains. Large longitudinal studies using information from athlete monitoring databases and multivariable model approaches are needed to understand the interacting features associated with bench press performance, and for the development of talent identification models.

Introduction

The bench press is one of three lifts in non-disabled powerlifting and the sole lift in Paralympic powerlifting. Bench press is also a popular exercise in individual and team sport strength training programs [1, 2]. To perform the exercise, athletes lay supine on a flat bench and lower a barbell to bottom of their sternum, momentarily hold the bar motionless, before raising the barbell to full elbow extension.

The World Powerlifting Championships are held annually but powerlifting is not an Olympic event. Paralympic Powerlifting on the other hand has been a full medal event since 1984 for men and 2000 for women. During competition, athletes aim to bench press as much weight as possible, while adhering to competition regulations [3, 4].

Key determinants of bench press performance have been investigated in non-disabled athletes [5–8], and more recently, in Para athlete populations [9]. For example, in both non-disabled and Para athlete populations, studies have investigated the association between anthropometric [6–8, 10], body composition [10–12], and technical [5, 13–15] features, and bench press one-repetition maximum (1RM). Understanding the factors that underpin maximum bench press performance could be useful for identifying talented young athletes for selection in state or national powerlifting training programs, similar to the approach used in Olympic weightlifting [16]. Many sports use talent identification models encompassing a range of anthropometric, physical performance and cognitive variables [17] but no uniform approach is currently available for in powerlifting. Information on important features of bench press performance could also be used to identify Para athletes who are good candidates for sport transfer into Paralympic powerlifting. This may be particularly relevant to older Para athletes in power-based sports, as the age-related decline in muscular power is faster than the decline in muscular strength [18]. Para athletes often report difficulty transitioning into retirement [19] and prolonging their career by transferring into a sport with an older age of peak performance may be beneficial for para athlete well-being.

Despite the popularity of the bench press exercise, and its use in non-disabled powerlifting and Paralympic powerlifting competitions, no previous study has synthesized literature studying features of bench press performance. The aim of this scoping review was to summarise features related to bench press 1RM performance in non-disabled and Para athletes. We chose a scoping review approach as our intention was to summarise a broad range of studies that have investigated features related to bench press strength, and to provide recommendations for future studies.

Method

The scoping review was conducted according to the methods outlined by Arksey and O’Malley [20]. Reporting was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) extension for Scoping Reviews [21]. Ethical approval for this study is not required as it is a systematic review using publicly available data. A protocol for the review was preregistered on the Open Science Framework and is available at https://doi.org/10.17605/OSF.IO/Z52XY.

Search strategy and selection criteria

The search strategy combined indexed terms with keywords for bench press performance in non-disabled and Para athlete populations. No date restrictions were placed on the search strategy. The search strategy included (but was not limited to) the keywords “resistance training”, “para powerlifting”, “bench press”, “one repetition max” and “strength” with combinations of Boolean operators. Specific search strings used to search databases are provided in S1 File. Searches were completed in the PubMed, SportDiscus and EMBASE databases. The search was conducted on September 8, 2023.

To be included studies needed to be peer-reviewed research articles that reported findings on demographic, anthropometric, body composition, biomechanical, or neuromuscular features related to bench press 1RM. Observational and experimental study designs were included. Studies were included if participants had at least six-months experience in performing the bench press exercise, and if participants were able to bench press a weight equivalent to, or greater than, their body mass. Only studies written in English were included.

We excluded studies that: 1) focused on bench throw or isometric bench press performance, which are distinctly different to the traditional bench press lift; 2) investigated the influence of supplements or ergogenic aids on bench press performance; 3) included non-disabled and Para athletes, but did not separately report the results for each population were excluded; and 4) included strength-based predictor variables, for example, 1RM pull up strength. Conference proceedings, study protocols, letters to the editor, commentaries, and systematic or scoping reviews were excluded.

Title and abstract screening were completed using Rayyan [22]. Two authors (RLB, DNB) determined the eligibility of each record for full-text review, with disagreements resolved by discussion. Full text reviews were completed by authors RLB and DNB. At the full-text review stage we hand-searched the reference lists of studies to identify further studies for inclusion.

Charting the data

Data collection involved the extraction of general study information (i.e., author names, year of publication) in addition to details about the study objective, design and sample, the data analysis methods, and the key study findings. We recorded whether each study was pre-registered, whether a dataset was publicly shared, and whether study reporting was guided by a checklist, for example, the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement.

We extracted the study sample size, the number of female participants, the sample age, body mass, height, and absolute and relative bench press 1RM. We also extracted whether or not supportive equipment (e.g. bench shirt or wrist straps) was used during 1RM attempts. Where studies did not state that supportive equipment was used, we assumed it was not. Additionally, we assumed that supportive equipment was used in studies analysing competition data. When correlations were reported, we extracted the type of correlation used (e.g., Pearson, Spearman), the correlation coefficient value and the associated p-value and confidence interval, and level of confidence. We also noted whether studies visualised the relationship between the feature of interest and bench press 1RM using a scatter plot. For studies using regression analysis, we noted the response distribution used (e.g., Gaussian), and extracted the coefficient of determination (R²) value, or an adjusted R² value. From studies using between group comparisons, we extracted information about the groups being compared, the mean difference in 1RM between groups and the associated confidence interval, and the level of confidence.

Data were independently extracted (RLB, DNB, JLO) and entered onto a data charting form (Excel spreadsheet). Data extraction checks were undertaken by a second, independent author to the original extraction.

Collating, summarising and reporting the results

Narrative synthesis was used to describe important features (variables) associated with 1RM bench press performance. Features of interest were extracted from included studies and were grouped into six domains, according to whether the feature was an anthropometric, body composition, demographic, technical, neuromuscular or disability variable. Once organized into the six domains, we collated the number of studies that: 1) examined the correlation between specific variables and bench press 1RM; 2) used regression models to investigate the association between a set of variables and bench press 1RM; and 3) compared bench press 1RM between groups. Results from the narrative synthesis were used to develop a set of recommendations to guide future research.

Data summaries were generated in R [23] using packages from the tidyverse [24], and are presented as count (percentage) or median (1^st and 3^rd quartile), unless otherwise stated. We calculated the proportion of female participants included in studies. Confidence intervals on proportions were calculated using the Clopper-Pearson method for the binomial distribution [25] via the R package binom [26].

Most studies used correlation analysis to investigate whether there was an association between feature of interest x and bench press 1RM. Ranges of correlation coefficients (point estimates) are presented in tabular form, according to each feature studied. We calculated 95% confidence intervals (CIs) on correlation coefficients using the Fisher z method [27] via the R package psychometric [28]. While no formal analyses were undertaken to aggregate correlation effect sizes, associations were considered positive, negative, or unclear, based on 95% CIs. Features were deemed to have a positive or negative association, depending on the direction of the effect, with bench press 1RM when the 95% CI of both the smallest and largest correlation coefficient excluded the null value of zero. Associations were deemed unclear when the 95% CI of the smallest or largest correlation coefficient included zero. In interpreting the results, we primarily focused on features that had been investigated by at least two studies.

The PRISMA flowchart was produced using the PRISMA2020 Shiny application [29]. All other figures were generated using the R package ggplot2 [30]. The data and R code are located in the ‘para_power_review’ repository available at 10.5281/zenodo.10532751.

Results

Search strategy to identify relevant studies

Our search strategy returned 3,999 records from three databases (Fig 1). We removed 1,488 duplicate records before title and abstract screening. Title and abstract screening identified 113 articles for full-text review. The three main reasons for exclusion after full-text review were 1) inclusion of features that were not an anthropometric, body composition, technical, neuromuscular or disability related variable; 2) inclusion of participants with less than 6 months of resistance training experience, 3) not using 1RM as the outcome measure (Fig 1). Data extraction was completed for 32 studies.

We included male but excluded female athletes from three studies [7, 15, 31], as the female athlete groups were not able to bench press their body mass, and therefore, did not meet our inclusion criteria. Similarly, only the “high strength” group from one study [32] was included, as the “low strength” group were not able to bench press their body mass. Results were taken from the cross-validation set in the study by Hetzler and Colleagues [11].

Study population

Non-disabled athlete studies

The 24 studies on non-disabled athletes involved 2,686 individuals (Fig 2A). Of the 2,686 athletes, 21.9% were female (Fig 2B). All 24 studies involved males. Six studies involved females but female groups from only three studies met the inclusion criteria and were therefore included in the review (Table 1). The median number of participants per study was 30 (1^st and 3^rd quartile = 20 to 68). The two most common studied populations were college athletes or university students, and powerlifters (Table 1). Ten studies (41.7%) involved college athletes or university students with a background in resistance training, and seven studies (29.2%) recruited sub-elite, national or international level powerlifters.

Table 1. Summary of non-disabled (n = 24) and para (n = 8) athlete studies.

First author	Year	Population	Sample size, n	Women, n (%)	Age, years	Body mass, kg	Height, m	1RM, kg	1RM to body mass ratio
Non-disabled athlete studies
Cross-sectional studies
McLaughlin [50]	1984	National and international powerlifters	45	0 (0)	NR	94.2	NR	255.9	2.72
Wagner [46]	1992	College students	24	0 (0)	21.5	85.3	1.78	118.9	1.39
Brechue [12]	2002	National powerlifters	20	0 (0)	NR	92.5	1.69	168.9	1.83
Hetzler [11]	2010	Division 1 college football players	118	0 (0)	19.7	103.1	1.84	138.3	1.35
Caruso [8]	2012	College students and athletes	36	0 (0)	NR	86.8	NR	104.4	1.20
Winwood [45]	2012	Semi-professional rugby players	23	0 (0)	22.0	102.6	1.85	132.2	1.29
Ye [47]	2013	National powerlifters	20	0 (0)	30.0	93.2	1.69	179.0	1.92
Akagi [39]	2014	College athletes	18	0 (0)	20.6	69.6	1.72	91.9	1.32
Kerksick [32]	2014	Recreationally trained	66	0 (0)	24.5	91.9	1.79	124.2	1.35
Schumacher [43]	2016	Division 2 college footballers	34	0 (0)	20.4	99.1	1.82	129.2	1.30
Loturco [49]	2017	Rugby and combat sport athletes	36	0 (0)	24.2	86.8	1.81	109.7	1.28
Ferland [7]	2020	Sub-elite powerlifters	9	0 (0)^*	27.5	93.5	1.76	146.9	1.57
Reya [5]	2021	Competitive powerlifters	13	0 (0)	26.0	93.8	1.78	155.0	1.65
Ferrari [6]	2022	National powerlifters	74	23 (31)	M: 25.9 W: 28.9	M: 84.0 W: 63.0	M: 1.74 W: 1.62	130.4	1.60
Massini [41]	2022	Well trained	30	0 (0)	23.7	78.7	1.79	82.5	1.05
Zaras [44]	2023	Physical education and sports science students	21	0 (0)	22.6	76.6	1.79	95.4	1.25
Experimental studies
Nacleiro [31]	2017	Resistance trained athletes	242	0 (0)^*	22.4	73.3	1.75	84.5	1.15
Garcia-Ramos [48]	2018	College students	30	0 (0)	21.2	72.3	1.78	78.1	1.08
Perez-Castilla [42]	2020	Sports science students	20	0 (0)	22.5	77.9	1.78	81.0	1.04
Garcia-Ramos [15]	2021	College students	12	0 (0)^*	19.9	78.1	1.80	94.2	1.21
Lee [51]	2023	Recreationally trained lifters	20	0 (0)	22.0	84.9	1.77	109.0	1.28
Prospective cohort studies
Mann [40]	2012	Division 1 college athletes	289	0 (0)	19.6	103.5	1.86	145.7	1.43
Retrospective cohort studies
Latella [37]^#	2018	International competitive powerlifters	1368	518 (38)	23–78^†	R^‡	NR	R	R
Solberg [38]^#	2019	International powerlifters	118	48 (41)	M: 34.1 W: 34.5	R^‡	NR	R	NR
Para athlete studies
Cross-sectional
Loturco [52]	2019	National Paralympic powerlifters	17	NR	NR	NR	NA	131.8	1.92
Hamid [10]	2019	National and state Paralympic powerlifters	52	9 (17)	M: 23.0^§ W: 28.0^§	M: 66.6^§ W: 66.1^§	NA	M: 91.3 W: NR	M: 1.38 W: NR
Iturricastillo [13]	2019	National wheelchair basketballers	9	0 (0)	34.0	NR	NA	81.7	NR
Teles [33]	2021	Competitive Paralympic powerlifters	19	0 (0)	28.1	77.59	NA	114.4	1.47
Romarate [14]	2021	National wheelchair basketballers	10	0 (0)	27.9	60.3	NA	73.5	1.22
Retrospective cohort							NA
Lopes-Silva [35]#	2022	Paralympic powerlifters (world record holders)	40	20 (50)	M: 23.7 W: 21.9	M: 58.9 W: 66.7	NA	M: 155.1 W: 111.0	M: 2.53 W: 1.66
Severin [36]#	2023	International Paralympic powerlifters	328	157 (48)	R	R^‡	NA	R	NR
Lopes-Silva [34]#	2023	Paralympic powerlifters	1889	745 (39)	M: 32.9 W: 33.6	M: 75.1 W: 62.6	NA	M: 167.2 W: 90.1	M: 2.23 W: 1.44

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Note. Age, body mass, height, 1RM and 1RM to body mass ratio values are the sample or group mean, unless otherwise indicated. Height was considered not applicable for studies involving Para athlete populations.

M = Men, NA = Not applicable, NR = Not reported, R = Longitudinally reported, W = Women, 1RM = One repetition maximum.

# Study used longitudinal competition data

* Female group from the study excluded because the group mean 1RM to body mass ratio was less than 1.

† Range.

‡ Competition weight categories.

§ Group median.

For non-disabled athlete male groups, the median (study sample average) age, body mass, and height was 22.5 years (1^st and 3^rd quartile = 21.1 to 24.9; Fig 2C), 86.8 kg (1^st and 3^rd quartile = 78.3 to 93.7), and 1.78 m (1^st and 3^rd quartile = 1.76 to 1.80). The median 1RM and 1RM to body mass ratio was 124.2 kg (1^st and 3^rd quartile = 94.2 to 145.7; Fig 2D) and 1.30 (1^st and 3^rd quartile = 1.21 to 1.57; Fig 2E).

Only one non-disabled athlete study involving females [6] reported complete age, body mass and height information for the female sample (Table 1). However, 1RM data were not reported separately for males and females.

Para athlete studies

The eight studies involving Para athlete populations included a total of 2,364 individuals (Fig 2A). Of these, 39.4% were female (Fig 2B). All eight studies involved males; four studies involved females, and it was unclear whether one study involved females (Table 1). The median number of participants per study was 30 (1^st and 3^rd quartile = 15 to 121). International and national Paralympic powerlifters were the most common populations studied, with two studies (25%) involving national level wheelchair basketball players (Table 1).

Four [10, 13, 33, 34] of eight studies (50%) involving Para athletes reported participant impairment type, three [33–35] of the eight studies (37.5%) reported origin of injury (i.e., congenital or acquired), and five [13, 14, 34–36] of the eight studies (62.5%) reported sport classification information. The three studies [10, 13, 33] that did not use competition data and reported impairment type recruited a total of 80 participants. In this group of 80 Para athletes, the most common impairment types were spinal cord injury (n = 30/80; 37.5%) and amputation (n = 21/80; 26.3%).

For male Para athlete groups, the median (study sample average) age and body mass 28.1 years (1^st and 3^rd quartile = 27.9 to 32.9; Fig 2C) and 67.7 kg (1^st and 3^rd quartile = 60.0 to 75.7). The median 1RM and 1RM to body mass ratio was 102.8 kg (1^st and 3^rd quartile = 84.1 to 144.9; Fig 2D) and 1.47 (1^st and 3^rd quartile = 1.38 to 2.23; Fig 2E).

Three studies reported age and body mass for female Para athlete groups but only two studies reported absolute and relative 1RM (Table 1). The average sample age of female groups ranged from 21.9 to 33.6 years. The average sample body mass of female groups ranged from 62.6 to 66.7 kg. Absolute and relative 1RM for two female groups are reported in Table 1.

Study design

Non-disabled athlete studies

In non-disabled athlete populations, cross-sectional study designs were most common (16/24; 66.7%), followed by experimental studies (5/24; 20.1%), retrospective cohort studies (2/24; 8.3%), and prospective cohort studies (1/24; 4.2%; Table 1). Two studies [37, 38] used published competition results.

Para athlete studies

Cross-sectional study designs (5/8; 62.5%) were most common in Para athlete populations, followed by retrospective cohort studies (3/8; 12.5%; Table 1). Three studies [34–36] involving Para athletes used published competition results.

Features related to bench press one repetition maximum performance

Fig 3A provides a graphical summary of the number of studies that have explored anthropometric, body composition, demographic, technical, neuromuscular and disability features. Fig 3B shows the number of studies that investigated specific feature combinations. Supportive equipment was used during 1RM assessments in 11 studies, including four studies involving Para-athletes and seven studies involving non-disabled athletes.

Non-disabled athlete studies

Anthropometric features were investigated by 16 (66.7%) studies involving non-disabled athletes [6–8, 11, 12, 32, 37, 39–46]. Thirteen studies used correlation analysis to investigate 36 unique anthropometric features (Table 2). Of the features studied more than once, body mass, triceps muscle thickness, flexed upper arm circumference, arm cross-sectional area, forearm circumference, and calf and thigh circumference were positively associated with 1RM bench press (Table 2). The association between height, triceps fascicle length, triceps pennation angle, arm length, chest circumference, hip circumference, and bench press 1RM was unclear (Table 2). Using a between group design, two studies [37, 38] investigated the association between relative or absolute bench press strength and competition weight classes. Latella et al. [37] found that relative strength was generally higher in the lightest weight classes compared to the heaviest weight classes, with effect sizes ranging from a Cohens d of 0.73 (95% CI = 0.39 to 1.07) to 1.11 (95% CI = 0.71 to 1.50). Solberg et al. [38] found that absolute bench press strength increased in a hierarchical manner with weight categories, in elite male and female powerlifters. Three studies included anthropometric features in a regression model [8, 32, 42]. Included features were arm length [8, 42], biacromial width [8], and height [32].

Table 2. Summary of correlations between anthropometric variables and 1RM bench press for non-disabled athletes.

Variable	Studies, n	Corr’s, n	Correlation coefficient (95% CI)	Association
Whole body
Body mass [6–8, 11, 32, 40, 45]	7	7	0.16 (0.05, 0.27) to 0.80 (0.29, 0.96)	Positive
Height [6, 7, 11, 32, 40, 45]	6	6	–0.43 (–0.85, 0.33) to 0.53 (0.44, 0.61)	Unclear
Upper limb
Upper arm circumference flexed [5, 6, 11, 43, 45]	5	6	0.58 (0.04, 0.86) to 0.90 (0.85, 0.94)	Positive
Arm cross sectional area [11, 41, 43]	3	3	0.68 (0.44, 0.83) to 0.98 (0.97, 0.99)	Positive
Arm length [6, 8, 11]	3	3	–0.07 (–0.29, 0.16) to 0.50 (0.35, 0.62)	Unclear
Triceps muscle thickness [12, 44]	2	2	0.53 (0.13, 0.78) to 0.81 (0.57, 0.92)	Positive
Forearm circumference [5, 6]	2	2	0.26 (0.03, 0.46) to 0.70 (0.24, 0.90)	Positive
Triceps fascicle length [5, 44]	2	2	0.16 (–0.29, 0.55) to 0.27 (–0.33, 0.71)	Unclear
Triceps pennation angle [5, 44]	2	2	–0.46 (–0.81, 0.12) to 0.41 (–0.03, 0.72)	Unclear
Upper arm circumference relaxed [6]	1	1	0.87 (0.80, 0.92)	Positive
Forearm muscle thickness [12]	1	1	0.82 (0.59, 0.93)	Positive
Wrist circumference [6]	1	1	0.78 (0.67, 0.86)	Positive
Biceps muscle thickness [12]	1	1	0.77 (0.50, 0.90)	Positive
Forearm to upperarm length ratio [5]	1	1	0.60 (0.07, 0.87)	Positive
Triceps cross-sectional area [5]	1	1	0.58 (0.04, 0.86)	Positive
Arm length to height ratio [5]	1	1	–0.31 (–0.74, 0.29)	Unclear
Arm span [5]	1	1	–0.02 (–0.56, 0.54)	Unclear
Trunk
Chest circumference [5, 6, 43, 45]	4	4	0.15 (–0.44, 0.65) to 0.67 (0.52, 0.78)	Unclear
Hip circumference [6, 45]	2	2	0.22 (–0.21, 0.58) to 0.35 (0.13, 0.54)	Unclear
Pectoralis major cross-sectional area [39]	1	1	0.86 (0.66, 0.95)	Positive
Subscapula muscle thickness [12]	1	1	0.85 (0.65, 0.94)	Positive
Neck circumference [6]	1	1	0.79 (0.69, 0.86)	Positive
Waist circumference [6]	1	1	0.79 (0.69, 0.86)	Positive
Chest muscle thickness [12]	1	1	0.77 (0.50, 0.90)	Positive
Biacromial width [8]	1	1	0.34 (0.01, 0.60)	Positive
Abdomen muscle thickness [12]	1	1	0.35 (–0.11, 0.69)	Unclear
Chest circumference to height ratio [5]	1	1	0.32 (–0.28, 0.74)	Unclear
Chest depth [5]	1	1	0.15 (–0.44, 0.65)	Unclear
Iliac to acromial width ratio [5]	1	1	–0.20 (–0.68, 0.39)	Unclear
Lower limb
Calf circumference [6, 45]	2	3	0.43 (0.22, 0.60) to 0.67 (0.36, 0.85)	Positive
Thigh circumference [6, 45]	2	3	0.25 (0.02, 0.45) to 0.51 (0.32, 0.66)	Positive
Tibialis anterior muscle thickness [12]	1	1	0.82 (0.59, 0.93)	Positive
Calf muscle thickness [12]	1	1	0.78 (0.52, 0.91)	Positive
Hamstrings muscle thickness [12]	1	1	0.69 (0.36, 0.87)	Positive
Quadriceps muscle thickness [12]	1	1	0.67 (0.32, 0.86)	Positive
Ankle circumference [6]	1	1	0.54 (0.36, 0.68)	Positive

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Note. Features are arranged by number of studies, number of correlations, association, and correlation coefficient.

CI = Confidence interval.

Body composition features were investigated by 11 (45.8%) studies involving non-disabled athlete populations [6, 7, 11, 12, 32, 40, 41, 43, 45, 47]. All 11 studies used correlation analysis to investigate a total of 48 unique features (Table 3). Only whole-body features had been investigated by more than one study, with lean body mass, body mass index and skeletal muscle mass positively associated with bench press 1RM (Table 3). The association between fat mass and 1RM and between body fat percentage and 1RM was unclear (Table 3). No studies investigated body composition features in a non-disabled athlete population using a between group design. Three studies included body composition features in a regression model [8, 32, 41]. Included features were: body mass [8], fat free mass [32, 41], fat mass [32], appendicular fat free mass index [41], upper limb fat free mass [41], and arm cross sectional area [41].

Table 3. Summary of correlations between body composition variables and 1RM bench press for non-disabled athletes.

Variable	Studies, n	Corr’s, n	Correlation coefficient (95% CI)	Association
Whole body
Lean body mass [5–7, 11, 12, 32, 41, 43, 45]	9	11	0.49 (0.18, 0.71) to 0.88 (0.72, 0.95)	Positive
Body fat percentage [6, 7, 11, 32, 43, 45]	6	6	–0.57 (–0.71, –0.39) to 0.62 (–0.08, 0.91)	Unclear
Fat mass [5, 7, 32]	3	4	–0.12 (–0.63, 0.46) to 0.62 (–0.08, 0.91)	Unclear
Lean body mass to height ratio [6, 12]	2	2	0.87 (0.70, 0.95) to 0.88 (0.82, 0.92)	Positive
Skeletal muscle mass [45, 47]	2	2	0.59 (0.23, 0.81) to 0.88 (0.72, 0.95)	Positive
Body mass index [7, 40]	2	2	0.58 (0.50, 0.65) to 0.79 (0.26, 0.95)	Positive
Skeletal muscle mass to height ratio [47]	1	1	0.87 (0.70, 0.95)	Positive
Bone mineral content total [7]	1	1	0.87 (0.65, 0.96)	Positive
Percentage lean body tissue [7]	1	1	0.86 (0.46, 0.97)	Positive
Appendicular lean mass [41]	1	1	0.83 (0.67, 0.92)	Positive
Muscle thickness sum of 13 sites [12]	1	1	0.81 (0.57, 0.92)	Positive
Bone mass [5]	1	1	0.65 (0.15, 0.88)	Positive
Bone mineral density total [7]	1	1	0.59 (0.11, 0.85)	Positive
Sum of skinfolds [43]	1	1	0.46 (0.14, 0.69)	Positive
Upper limb
Upper limb fat free mass [41]	1	1	0.91 (0.82, 0.96)	Positive
Muscle thickness sum of upper body sites [12]	1	1	0.85 (0.65, 0.94)	Positive
Bone mineral content arms [7]	1	1	0.83 (0.55, 0.94)	Positive
Arms total mass [7]	1	1	0.77 (0.22, 0.95)	Positive
Lean mass arms [7]	1	1	0.72 (0.11, 0.94)	Positive
Upper limb fat mass [7]	1	1	0.61 (–0.09, 0.91)	Unclear
Upper limb boy fat percentage [7]	1	1	0.53 (–0.21, 0.88)	Unclear
Bone mineral density arms [7]	1	1	0.43 (–0.11, 0.77)	Unclear
Triceps skinfold [43]	1	1	0.31 (–0.03, 0.59)	Unclear
Trunk
Bone mineral density ribs [7]	1	1	0.91 (0.75, 0.97)	Positive
Bone mineral content ribs [7]	1	1	0.85 (0.60, 0.95)	Positive
Bone mineral content trunk [7]	1	1	0.85 (0.60, 0.95)	Positive
Trunk total mass [7]	1	1	0.83 (0.37, 0.96)	Positive
Lean mass trunk [7]	1	1	0.82 (0.34, 0.96)	Positive
Bone mineral content pelvis [7]	1	1	0.81 (0.51, 0.93)	Positive
Bone mineral content spine [7]	1	1	0.79 (0.47, 0.93)	Positive
Bone mineral density trunk [7]	1	1	0.79 (0.47, 0.93)	Positive
Bone mineral density spine [7]	1	1	0.62 (0.16, 0.86)	Positive
Bone mineral density pelvis [7]	1	1	0.53 (0.02, 0.82)	Positive
Subscapula skinfold [43]	1	1	0.47 (0.16, 0.70)	Positive
Abdominal skinfold [43]	1	1	0.43 (0.11, 0.67)	Positive
Fat mass trunk [7]	1	1	0.65 (–0.02, 0.92)	Unclear
Trunk body fat percentage [7]	1	1	0.49 (–0.26, 0.87)	Unclear
Bone mineral content head [7]	1	1	0.38 (–0.16, 0.75)	Unclear
Bone mineral density head [7]	1	1	0.06 (–0.47, 0.56)	Unclear
Lower limb
Muscle thickness sum of lower body sites [12]	1	1	0.79 (0.53, 0.91)	Positive
Bone mineral content legs [7]	1	1	0.79 (0.47, 0.93)	Positive
Legs total mass [7]	1	1	0.72 (0.11, 0.94)	Positive
Bone mineral density legs [7]	1	1	0.57 (0.08, 0.84)	Positive
Lower limb fat mass [7]	1	1	0.50 (–0.25, 0.87)	Unclear
Lean mass legs [7]	1	1	0.47 (–0.28, 0.86)	Unclear
Lower limb body fat percentage [7]	1	1	0.38 (–0.38, 0.83)	Unclear

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Note. Features are arranged by number of studies, number of correlations, association, and correlation coefficient.

CI = Confidence interval.

Eight (33.3%) studies involving non-disabled athlete populations investigated demographic features, being age [6, 7, 11, 32, 38, 40] or sex [31, 37]. Five studies used correlation analysis to investigate the association between age and bench press 1RM (Table 4). The association between age and bench press 1RM was unclear (Table 4). Two studies investigated differences in demographic variables using a between group design [37, 38]. Latella et al. [37] found that relative 1RM bench press strength was on average higher in males compared to females (Cohen’s d = 1.84, 95% CI = 1.71 to 1.97). Relative bench press 1RM was also higher in the open age competition (23 to 39 years) compared to master’s categories (≥40 years), with the standardised difference between the Open age group and Masters groups ranging from medium (d = 0.58, 95% CI = 0.35 to 0.81) to large (d = 1.33, 95% CI = 0.94 to 1.71). Solberg et al. [38] found that bench press performance continues to improve until mid-to-late 30’s ties, in elite male and female powerlifters. One study included sex in a regression model [31]. Regression analysis results are summarised below.

Table 4. Summary of correlations between demographic, technical and neuromuscular variables and 1RM bench press for non-disabled athletes.

Variable	Studies, n	Corr’s, n	Correlation coefficient (95% CI)	Association
Demographic
Age [6, 7, 11, 32, 40]	5	5	–0.18 (–0.39, 0.05) to 0.68 (0.03, 0.93)	Unclear
Technical
Load-velocity relationship [48]	1	2	0.99 (0.98, 1) to 1 (1, 1)	Positive
Shoulder flexion angle at sticking point [5]	1	1	0.49 (–0.08, 0.82)	Unclear
Shoulder abduction angle at sticking point [5]	1	1	0.47 (–0.11, 0.81)	Unclear
Horizontal shoulder adduction angle sticking point ⁵	1	1	0.25 (–0.35, 0.70)	Unclear
Horizontal bar displacement [5]	1	1	0.24 (–0.36, 0.70)	Unclear
Lumbar arch height [5]	1	1	0.2 (–0.39, 0.68)	Unclear
Foot ground reaction force during bench press [5]	1	1	0.12 (–0.46, 0.63)	Unclear
Elbow flexion angle at sticking point [5]	1	1	–0.4 (–0.78, 0.19)	Unclear
Vertical bar displacement [5]	1	1	–0.31 (–0.74, 0.29)	Unclear
Neuromuscular
Shoulder press rate of force development [44]	1	7	0.49 (0.07, 0.76) to 0.72 (0.42, 0.88)	Positive
Deltoid EMG during bench press [5]	1	1	0.22 (–0.38, 0.69)	Unclear
Triceps voluntary activation [5]	1	1	0.18 (–0.41, 0.67)	Unclear
Latissimus dorsi EMG during bench press [5]	1	1	–0.26 (–0.71, 0.34)	Unclear
Triceps EMG during bench press [5]	1	1	–0.17 (–0.66, 0.42)	Unclear
Pectoralis major EMG during bench press [5]	1	1	–0.09 (–0.61, 0.49)	Unclear

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Note. Features are arranged by number of studies, number of correlations, association, and correlation coefficient.

CI = Confidence interval, EMG = Electromyography

Technical features were investigated by eight (33.3%) studies involving non-disabled athlete populations [5, 15, 31, 42, 48–51]. Two studies used correlation analysis to investigate a total of nine unique technical features, with no individual feature investigated by more than a single study (Table 4). Two studies examined differences in technical features using a between group design, with both studies reporting that optimum bench press 1RM occurs at about 200% of the biacromial distance [46, 51]. Four studies included technical features in a regression model [15, 31, 42, 49], namely, mean barbell velocity [15, 31, 42] and propulsive barbell velocity [49].

Neuromuscular features were investigated by two (8.3%) studies involving non-disabled athlete populations [5, 44]. Both studies used correlation analysis to study a total of six unique features (Table 4). No feature was investigated by more than one study, and only shoulder press rate of force development was found to be positively associated with bench press 1RM (Table 4). No studies included neuromuscular features in a regression model.

Seven non-disabled athlete studies reported regression models [8, 15, 31, 32, 41, 42, 49]. Several models included barbell velocity (peak or average) during a submaximal contraction as a univariate predictor [15, 42, 49]. These models explained 95% to 96% of the variance in bench press 1RM. Four studies fitted regression models with more than one predictor variable [8, 31, 32, 41]. In resistance trained male athletes, barbell velocity and sex explained 84% of the variance in bench press 1RM [31]. In recreationally trained males, fat mass, fat free mass and height explained 82% of the variance in bench press 1RM [32]. In well trained males, appendicular fat free mass index, whole body fat free mass, upper limb fat free mass and arm cross sectional area explained 82% of the variance in bench press 1RM [41]. In male college students and athletes, body mass, arm length and biacromial width explained 64% of the variance in bench press 1RM [8].

Para athlete studies

Disability features were investigated by three (37.5%) studies involving Para athletes [33–35]. These three studies used a between group design to investigate the effect of origin of impairment on bench press 1RM. Two studies [33, 34] reported that athletes with an acquired impairment had on average a higher bench press 1RM than those with congenital impairments (Cohen’s d = 0.96 [33]; η_p² = 0.01 [34]). In contrast, another study [35] found no difference in absolute or relative bench press 1RM between acquired and congenital impairments (d = –0.39 to 0.64), in males and females. Two separate studies found that athletes with acquired impairments held more Paralympic records (70% vs. 30%) [35] and won more medals (males = 61% vs. 39%; females = 64% vs. 36%) [34] compared to athletes with congenital impairments. Lopes-Silva et al. [34] found that Para athletes with a limb deficiency had a higher number of medals than those with other types of deficiencies, with other deficiencies including leg length differences, impairments in range of movement and muscle power, hypertonia, ataxia, athetosis and short stature.

Anthropometric features were investigated by only one study involving Para athletes [10]. Using correlation analysis, the authors found that of the nine unique anthropometric features studied, body mass, relaxed and flexed arm circumference, and hip circumference were positively associated with bench press 1RM (Table 5). The same study also investigated four unique body composition features, with body mass index and lean body mass positively associated with bench press 1RM (Table 5).

Table 5. Summary of correlations between disability, anthropometric, body composition, demographic, and technical variables and 1RM bench press for Para athletes.

Variable	Studies, n	Corr’s, n	Correlation coefficient (95% CI)	Association
Anthropometric
Upper arm circumference flexed [10]	1	2	0.65 (0.46, 0.78) to 0.69 (0.51, 0.81)	Positive
Upper arm circumference relaxed [10]	1	2	0.54 (0.31, 0.71) to 0.66 (0.47, 0.79)	Positive
Arm length [10]	1	2	0.04 (–0.24, 0.31) to 0.07 (–0.21, 0.34)	Unclear
Forearm length [10]	1	2	0.03 (–0.24, 0.30) to 0.12 (–0.16, 0.38)	Unclear
Body mass [10]	1	1	0.41 (0.15, 0.61)	Positive
Hip circumference [10]	1	1	0.31 (0.04, 0.54)	Positive
Biacromial width [10]	1	1	0.01 (–0.26, 0.28)	Unclear
Waist circumference [10]	1	1	0.01 (–0.26, 0.28)	Unclear
Height [10]	1	1	–0.05 (–0.32, 0.23)	Unclear
Body composition
Body mass index [10]	1	1	0.46 (0.21, 0.65)	Positive
Lean body mass [10]	1	1	0.39 (0.13, 0.60)	Positive
Fat mass [10]	1	1	0.24 (–0.04, 0.48)	Unclear
Body fat percentage [10]	1	1	0.14 (–0.14, 0.40)	Unclear
Demographic
Age [10]	1	1	0.35 (0.09, 0.57)	Positive
Technical
Barbell mean propulsive velocity [13, 14]	2	4	–0.94 (–0.99, –0.76) to –0.98 (–0.99, –0.91)	Negative

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Note. Features are arranged by number of studies, number of correlations, association, and correlation coefficient.

CI = Confidence interval.

The demographic feature of age was investigated by two Para athlete studies [10, 36]. Using correlation analysis, Hamid et al. [10] found there was a positive association between age and bench press 1RM (Table 5). Using regression analysis, Severin et al. [36] examined the peak age of competition performance for male and female Paralympic powerlifters, considering the effect of weight category. Paralympic powerlifters in heavier weight categories were older than those in lower weight categories. Male Paralympic powerlifters achieved their heaviest competition lift at 36 years of age. Females achieved their heaviest lift at 41 years of age. Using a between group design, Lopes-Silva et al. [34] investigated the effect of sex on bench press performance in Para athletes, reporting that bench press 1RM was on average higher in males compared with females.

Technical features were investigated by three (37.5%) studies involving Para athlete populations [13, 14, 52]. Using correlation analysis, two studies [13, 14] found that barbell mean propulsive velocity was negatively associated with bench press 1RM (Table 5). Using regression analysis, Loturco et al. [52] found that peak or mean propulsive barbell velocity during submaximal contractions explained 87% or more of the variation in bench press 1RM in males (R² = 0.88 to 0.91), females (R² = 0.87 to 0.90), and a short stature group (R² = 0.78 to 0.83). In a small group of nine national wheelchair basketballers, propulsive barbell velocity during submaximal contractions explained 94% of the variation in bench press 1RM [13].

No studies involving Para athlete populations investigated the association between neuromuscular features and bench press 1RM.

Data analysis

Non-disabled athlete studies

Twelve studies (50%) used correlation analyses only, four (16.7%) used group difference analyses, another four studies (16.7%) used linear regression, three studies (12.5%) used both correlation and linear regression, and one study [50] used descriptive analyses only (Table 6).

Table 6. Summary of data analysis methods.

Author	Year	Study design	Sample size, n	Correlation analysis	Correlation uncertainty	Correlation p-value	Scatter plot	Regression analysis	R-squared	Between group analysis	Group difference effect size	Group difference uncertainty
Non-disabled athlete studies
McLaughlin [50]	1984	Cross-sectional	45	F	NA	NA	NA	F	NA	F	NA	NA
Wagner [46]	1992	Cross-sectional	24	F	NA	NA	NA	F	NA	T	F	F
Brechue [12]	2002	Cross-sectional	20	Pearson	F	Partial	Partial	F	NA	F	NA	NA
Hetzler [11]	2010	Cross-sectional	118	Pearson	F	Partial	F	F	NA	F	NA	NA
Caruso [8]	2012	Cross-sectional	36	Pearson	F	T	F	T	T	F	NA	NA
Winwood [45]	2012	Cross-sectional	23	Pearson	F	F	F	F	NA	F	NA	NA
Ye [47]	2013	Cross-sectional	20	Pearson	F	T	Partial	F	NA	F	NA	NA
Akagi [39]	2014	Cross-sectional	18	Pearson	F	T	T	F	NA	F	NA	NA
Kerksick [32]	2014	Cross-sectional	66	Pearson	F	Partial	F	T	T	F	NA	NA
Schumacher [43]	2016	Cross-sectional	34	Pearson	F	Partial	F	F	NA	F	NA	NA
Loturco [49]	2017	Cross-sectional	36	F	NA	NA	T	T	T	F	NA	NA
Ferland [7]	2020	Cross-sectional	9	Pearson	F	Partial	F	F	NA	F	NA	NA
Reya [5]	2021	Cross-sectional	13	Pearson, Spearman	T	Partial	F	F	NA	F	NA	NA
Ferrari [6]	2022	Cross-sectional	74	Pearson	F	F	F	F	NA	F	NA	NA
Massini [41]	2022	Cross-sectional	30	Pearson	F	T	Partial	T	T	F	NA	NA
Zaras [44]	2023	Cross-sectional	21	Pearson	F	Partial	F	F	NA	F	NA	NA
Nacleiro [31]	2017	Experimental	242	F	NA	NA	F	T	T	F	NA	NA
Garcia-Ramos [48]	2018	Experimental	30	Pearson	F	F	F	F	NA	F	NA	NA
Perez-Castilla [42]	2020	Experimental	20	F	NA	NA	Partial	T	T	F	NA	NA
Garcia-Ramos [15]	2021	Experimental	12	F	NA	NA	T	T	T	F	NA	NA
Lee [51]	2023	Experimental	20	F	NA	NA	NA	F	NA	T	F	F
Mann [40]	2012	Prospective cohort	289	Pearson	F	F	F	F	NA	F	NA	NA
Latella [37]	2018	Retrospective cohort	1368	F	NA	NA	NA	F	NA	T	Cohen’s d	T
Solberg [38]	2019	Retrospective cohort	118	F	NA	NA	NA	F	NA	T	Mean difference	T
Para athlete studies
Loturco [52]	2019	Cross-sectional	17	F	NA	NA	T	T	T	F	NA	NA
Hamid [10]	2019	Cross-sectional	52	Pearson	F	T	F	F	NA	F	NA	NA
Iturricastillo [13]	2019	Cross-sectional	9	Pearson	F	T	T	T	T	F	NA	NA
Romarate [14]	2021	Cross-sectional	10	Pearson	T	T	T	F	NA	F	NA	NA
Teles [33]	2021	Cross-sectional	19	F	NA	NA	NA	F	NA	T	Cohen’s d	F
Lopes-Silva [35]	2022	Retrospective cohort	40	F	NA	NA	NA	F	NA	T	F	F
Severin [36]	2023	Retrospective cohort	328	F	NA	NA	T	T	T	F	NA	NA
Lopes-Silva [34]	2023	Retrospective cohort	1889	F	NA	NA	NA	F	NA	T	F	F

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Note. F = False, NA = Not applicable, Partial = Reported only for some associations, T = True

Fifteen studies used correlation analyses. Only one of these 15 studies reported uncertainty on correlations (i.e., 95% Cis), the confidence intervals around reported correlation coefficients are shown in Fig 4. Four of the 15 studies reported p-values for correlations, seven reported p-values for some correlations, and another four studies did not report p-values (Table 1). Of the 19 studies that used correlation analysis or linear regression, 12 studies did not show a scatter plot, four studies displayed scatter plots for some variables, and three studies displayed scatter plots for all variables (Table 1). All studies that used linear regression reported an R² or adjusted R² value (Table 6).

Fig 4 — Closed circles indicate the point estimate, while error bars indicate the 95% confidence interval. Black intervals indicate instances when the 95% confidence interval included the null value of zero. The plot illustrates the influence of small study sample sizes on the uncertainty of correlation coefficients. Because study sample sizes were relatively small—median n of 30 for both non-disabled and Para athlete studies—there were instances where the point estimate was large enough to be of interest, but the 95% confidence interval included the null. For example, the confidence interval of 42.1% (16/38) of correlations that would commonly be interpreted as ‘moderate’ (i.e., r = –0.3 to –0.5 or r = 0.3 to 0.5) included the null.

Of the four studies that used group difference analyses, two did not report an effect size for the group difference, with the other two studies reporting a mean or standardised mean difference (Table 6). Both studies that reported a group difference effect size reported it with a measure of uncertainty (Table 6).

No studies involving non-disabled athlete populations were pre-registered or publicly shared their data.

Para athlete studies

Three studies (37.5%) used group difference analyses, two studies (25%) used linear regression, two studies (25%) used correlation analyses only, and one (12.5%) study used both correlation and linear regression (Table 6). Of the three studies that used correlation analyses, only one study reported uncertainty on correlations (i.e., 90% Cis). The confidence intervals around reported correlation coefficients are shown in Fig 4. All three studies using correlation analyses reported p-values for correlations (Table 6). Five studies used correlation or linear regression, with four of these displaying scatter plots for all studied variables (Table 6). All three studies that used linear regression reported an R² or adjusted R² value. Of the three studies that used group difference analyses, only one study reported an effect size for the group difference (i.e., Cohen’s d) but did not report it with a measure of uncertainty (Table 6). No studies involving Para athletes were pre-registered or publicly shared their data.

Discussion

There are seven key insights from our review of studies investigating features related to 1RM bench press performance. First, there was large heterogeneity in the specific variables studied. Second, few studies investigated features across multiple domains, for example, including anthropometric, body composition and technical features. Third, female participants were generally underrepresented. Fourth, information on participant impairment type and severity were often not reported by studies involving Para athletes. Fifth, there was an over reliance on bivariate correlation analysis, with relatively few studies considering a multivariable modelling approach. Sixth, despite using relatively small sample sizes, few studies considered the uncertainty of their results. Seventh, no studies considered open science or transparent research practices in the form of pre-registration, explicitly stating that they followed reporting guidelines, or sharing their data. Based on these findings, we make seven recommendations to guide future research, which are described in Table 7.

Table 7. Recommendations for studies investigating features related to 1RM bench press performance.

Recommendation	Description
1	Consider training and non-training modifiable features across several domains of anthropometric, body composition, technical, and in Para athlete populations, disability impairment and type.
2	Include female athletes in studies, and report sample characteristics and study results separately for males and females.
3	When studying Para athletes, report information on impairment type and severity, origin of impairment, and where applicable, sport classification.
4	Combine features using a multivariable model approach, considering the potential influence of confounding, and modifying effects.
5	Preregister study aims, hypotheses and methods on a public platform, such as, on the Open Science Framework.
6	Use reporting checklists (https://www.equator-network.org/) to guide the communication of study methods and results. Emphasis should be placed on the uncertainty of results (e.g., a 95% confidence interval) when communicating study findings.
7	Share enough data to reproduce the key study findings, or a synthetic dataset, on a public platform

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Studies involving Para athletes did not consistently report information on impairment type, origin of impairment, or if applicable, sport class. The organisation of Para powerlifting does not encourage differentiation between impairments in research—i.e., provided that Para athletes have one of the eight eligible impairments then they can compete in the appropriate sex and weight category. However, there is evidence that origin of impairment may influence bench press strength [34, 35], with higher strength values reported in athletes with acquired impairments compared to those with congenital impairments [33, 34]. It is possible that congenital impairments result in physiological changes that limit the potential for strength development, or that athletes with acquired impairments have high levels of strength development prior to their impairment, and these benefits persist post impairment. Irrespective, impairment type and origin of impairment are likely important variables that should be included in any Para sport transfer models, and therefore, need to be reported by studies. We recommend that future studies provide complete information on impairment type, origin of impairment, and sport class.

At the most recent Paralympic games in Tokyo, females accounted for 49.4% of 178 Para powerlifters [4]. Similarly, of the 196 entrants in the Tokyo Olympic weightlifting event, 49% were female [53]. Despite balanced participant rates at the elite level, we found that females were underrepresented across the 32 studies in the review (Table 1). The relative absence of female participants is disappointing but not surprising [54, 55]. We recommend that future studies include female participants, to better reflect the progress in female sport participation. It is reasonable to suggest that the specific features related to bench press strength may differ between the sexes [56]. The inclusion of females in future research will be essential to ensure that talent identification and sport transfer models are designed to cater for the unique and emerging cohorts of females in sport. We recommend that future works consider the effect of sex in any statistical modelling, and that study results be reported according to biological sex [57]. As per current consensus, when the effect of female sex hormones is not of interest, we recommend that studies report information on the menstrual status of participants, and a record of cycle or pill day taken, which can be collected via calendar tracking [57].

A total of 111 unique features from six domains (Fig 3) were investigated across the 32 studies in the review. Despite the wide range of unique features studied, only half of the studies in the review investigated features from different domains (Fig 3B). Features from multiple domains are likely important for bench press performance, and these are likely to be different for male and female athletes, and between non-disabled and Para athlete populations [56]. Talent identification and sport transfer models should consider features across multiple domains, and their interactions.

Features within the anthropometric and body composition domains demonstrated the strongest associations with bench press 1RM. In non-disabled athlete populations, body mass, arm circumference and cross-sectional area, triceps muscle thickness, and forearm, thigh, and calf circumference (Table 2), lean body mass, lean body mass to height ratio, body mass index and skeletal muscle mass (Table 3) were positively associated with bench press 1RM. In Para athlete populations, body mass, arm circumference and hip circumference were positively associated with bench press performance (Table 5), and there was evidence that origin of impairment was an important consideration [34, 35]. While features outside of the anthropometric and body composition domain are likely important, there was insufficient evidence to determine their association with bench press performance. The demographic feature of age was important in Para athlete populations (Table 5) but its association with bench press 1RM was unclear in non-disabled populations (Table 4). It is likely that age is a proxy of training history, and therefore, may not be a feature worth including in talent identification or sport transfer models.

Technical and neuromuscular features were least studied compared to other domains (Fig 3A). There was evidence that movement velocity at submaximal loads shared a strong association with bench press 1RM [15, 48, 58]. While load-velocity relationships may allow for the estimation of an individual’s 1RM, the slope of the relationship appears unaffected by strength [59] and is unlikely to be useful in identifying talented lifters. Given that bench press and Para powerlifting are strength sports, the ability to produce high levels of voluntary force is important [5]. Neuromuscular variables provide insight into voluntary force production, and deficits in these variables may be associated with poorer performance in strength-based sports [60]. For instance, in Para powerlifting, athletes with neurological impairments, such as cerebral palsy or spinal cord injury, may have poor force producing capability compared to athletes with other types of impairments [61, 62]. Consequently, neuromuscular variables may be useful for developing talent transfer models, where the goal is to determine if Para athletes participating in other sports are good candidates for Para powerlifting. Many commonly studied features, such as arm circumference and arm cross-sectional area, are modifiable with training. From a talent identification perspective these features may offer limited information, as modifiable factors could be improved in a range of athletes and alone may not identify talented young athletes [63]. Modifiable factors may also be more useful in the context of Para powerlifting transfer, where promising athletes are identified within sports requiring similar qualities [64]. We recommend that future works consider investigating a combination of modifiable and non-modifiable features, across several domains.

There was a primary reliance on bivariate correlation analysis (56% of studies) to investigate the association between features of interest and bench press 1RM. While correlation analysis is a useful exploratory data analysis tool, the development of talent identification and sport transfer models will require more advanced analytical methods. Multivariable modelling [65], confounding and effect modification [66], and non-linear modelling approaches, including tree-based methods, all require consideration. Future works should also consider using directed acyclic graphs to illustrate the assumptions about the causal structure of the relationships between the features being modelled [67]. We encourage researchers to engage a statistician or someone with statistical expertise [68].

We found that study sample sizes were generally small—the median sample size of both non-disabled and Para athlete studies was 30 individuals. Despite this, few studies considered the uncertainty of their results (Table 6). Fig 4 illustrates the potential problem with not considering the uncertainty of study results, with respect to correlation coefficients. There were instances where the point estimate was large enough to be of interest, but the 95% CI included the null value of zero (Fig 4). For example, there were 38 correlations that would commonly be interpreted as ‘moderate’, i.e., r = –0.3 to –0.5 or r = 0.3 to 0.5 [69]; however, for 42.1% of these correlations, the confidence interval included the null value of zero, and therefore, no or only a ‘trivial’ association between the features of interest and bench press 1RM could not be ruled out. Failure to consider uncertainty of correlations may encourage performance staff to adopt weak evidence in their practice. For example, several trainable anthropometric factors have moderate correlation coefficients but their confidence intervals cross zero (see Fig 4). A focus on these factors may waste time that could be better spent focusing on other trainable factors underpinning performance. We recommend that future studies report results with a measure of uncertainty and considered the uncertainty when communicating the study results.

There was an absence of open science and transparent research practices across the 32 studies in the review. No studies were preregistered. Preregistration involves authors registering their aims, hypotheses, and methods on a publicly available platform, for example, the Open Science Framework [70]. Importantly, registration occurs before data collection. Preregistration helps guard against questionable research practices, including p-hacking, the post-hoc manipulation of hypotheses, and selectively discarding non-significant results [71]. No study shared their data, which is consistent with practices in the field [72]. Aside from data sharing improving the confidence in study results, it can help future studies overcome issues with small study sample sizes. Future works could use shared data in their analytical process, in the form of Bayesian informative prior distributions, which has the potential to improve the certainty of study results [73]. Finally, we found that no studies in the review indicated that they followed a reporting guideline. Following reporting guidelines improves research transparency. Guidelines for a range of study designs can be found on the Enhancing the Quality and Transparency of health Research (EQUATOR) network (https://www.equator-network.org/). We recommend that future studies preregister their aims, hypotheses, and methods, that authors share their data, and that reporting checklists are used to guide the reporting of study methods and results.

Limitations

Several limitations of the review require acknowledgment. First, consistent with scoping review practices, we did not consider the quality of the 32 studies in the review. Second, we did not formally aggregate correlation coefficients using meta-analytical modelling. Our intention was to summarise the features investigated, rather than comment specifically on the strength of associations between certain features and bench press 1RM. Nonetheless, several features that were investigated by multiple studies, such as chest circumference (Table 2), fat mass (Table 3) and age (Table 4), were deemed to have an unclear association with bench press 1RM. It is possible that formal aggregation of these studies by conducting a meta-analysis would show a positive association with bench press 1RM. Third, our review underestimates the number of females included in studies of non-disabled athletes, as female groups from three studies [7, 15, 31] were excluded after applying our inclusion criteria. Including the female groups from these three studies would have changed the proportion of females studied from 21.9% to 34.9%; however, it would not have changed our substantive conclusion that female athletes were generally underrepresented across the 32 studies in the review.

Future directions

This review has highlighted several directions for future research. Firstly, longitudinal studies are needed to better understand which factors cause improvements in bench press performance. Prospective studies following athletes (non-disabled and Para) who progress from sub-elite to elite competition or remain in sub-elite competition would provide stronger evidence for the factors important for bench press performance than is currently available. Models of Para powerlifting performance based on an athlete monitoring system, where the bench press is performed regularly as part of a standardized test battery, would also be useful for understanding which factors are important for performance. While analysis of competition results provides some useful information, to progress our understanding of the development of bench press performance in Para athletes, empirical studies are needed. Longitudinal studies and models based on athlete monitoring systems would help identify important non-modifiable performance factors and benefit talent identification.

There is a need to better understand the neuromuscular factors associated with bench press performance. These variables were one of the most uncommonly studied and may be particularly relevant for Para athletes. Many impairments (e.g. spinal cord injury, cerebral palsy) impact motor unit recruitment and firing, which are important determinants of voluntary force production [61]. Other factors impacted by these impairments, such as patterns of inter-muscle coordination may also be important for performance in Para athletes, but our understanding of these factors is limited based on current work.

Conclusion

Our review of studies investigating features related to bench press performance provides several insights. There was large heterogeneity in the specific variables studied, and studies to date have generally focused on features from one domain (e.g., anthropometric), rather than investigating features across multiple domains. We found preliminary evidence that anthropometric and body composition features were positively associated with bench press performance, in both non-disabled and Para athlete populations. Technical and neuromuscular features were relatively understudied, limiting any conclusions that can be made about their impact on performance at present. We highlight the need for future studies to investigate features from multiple domains, using multivariable modelling approaches that consider how features may interact. We also highlight the need for practices of open science and transparent research. Large longitudinal studies that use information from athlete monitoring databases are likely needed to better understand the specific features associated with bench press performance, and for the development of talent identification and sport transfer models.

Supporting information

S1 File. Systematic search strategy.

The systematic search strategy used to search scientific databases.

(DOCX)

pone.0310127.s001.docx^{(12.8KB, docx)}

S1 Checklist. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

(DOCX)

pone.0310127.s002.docx^{(83.9KB, docx)}

Data Availability

All data files and code are available from https://zenodo.org/records/10532754.

Funding Statement

This project was supported by the Australian Sports Commission and Australian Government through a 2022 Australian Institute of Sport Research Grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PONE-D-24-20584Important Features of Bench Press Performance in Non-Disabled and Para Athletes: A Scoping ReviewPLOS ONE

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Reviewer #1: Manuscript Review: PONE-D-24-20584

Manuscript Summary:

The study analyzes factors influencing bench press performance in non-disabled athletes and Para athletes, aiming to provide insights for talent identification and transfer models in weightlifting. The hypothesis posits significant differences in bench press performance determinants between these two groups, influenced by variables such as age, sex, body mass, height, and muscular strength.

Main Findings and Highlights:

- Key determinants of bench press performance in non-disabled athletes include age, sex, body mass, height, and muscular strength.

- For Para athletes, adaptations due to disability, such as balance, stability, and modified technique, are crucial.

- The study discusses the implications for personalized talent identification and training programs in weightlifting for different athlete groups.

Points to Consider:

1) Grammatical or Agreement Errors:

Consistency in Verb Tenses: Review the entire manuscript to ensure consistency in the use of verb tenses.

Subject-Verb Agreement: Ensure proper agreement between subjects and verbs. For instance, on page 40, paragraph 1, line 1, the sentence could be reviewed for consistency.

2) Contrasting Content:

Clarity: Some sections might present contradictory or confusing information. A thorough review and clarification of these points are recommended.

3) Inconsistent Results:

Justification of Discrepancies: Some results appear inconsistent with the initial hypotheses or existing literature. These discrepancies should be clearly reviewed and justified within the manuscript.

4) Argumentative and Scientific Rhetoric Improvements:

Strengthen Arguments: Improve the connection between results and conclusions to ensure a solid and consistent argument.

Technical Language: Use more precise and technical language when describing methods and results to avoid ambiguities.

5) Reader Engagement:

Practical Examples: Incorporate practical examples or case studies to illustrate the applicability of the results in sports practice, making the manuscript more attractive to readers interested in bench press performance.

Hypothesis Confirmation:

Based on the main conclusions, the initial hypothesis does not appear objectively (I suggest it appears, it makes everything clearer!), however, implicitly the implied hypothesis seems partially confirmed. Significant differences were found in bench press performance determinants between non-disabled athletes and Para athletes, particularly regarding age, sex, body mass, height, and muscular strength. However, the study identified areas where results did not fully align with the hypothesis, such as the lack of comprehensive studies across multiple domains and the underrepresentation of female participants.

Conclusion Review:

The conclusion partially addresses the study's objectives. It provides valuable insights into bench press performance characteristics but could more explicitly link results to the study's initial objectives. Emphasizing how the findings contribute to understanding performance determinants and informing talent identification and transfer models in weightlifting would enhance the conclusion.

Recommendations:

Grammar and Agreement: Conduct a thorough grammatical review, focusing on verb tense consistency and subject-verb agreement.

Clarify Content: Address and resolve any contradictory information to ensure clarity.

Justify Inconsistencies: Clearly explain any discrepancies between results and initial hypotheses.

Strengthen Arguments: Enhance the connection between results and conclusions with precise, technical language.

Engage Readers: Use practical examples or case studies to illustrate the real-world applicability of the findings.

Conclusion Enhancement: Directly link the results to the study's objectives, providing a robust synthesis of findings.

By addressing these points, the manuscript will improve in clarity, cohesion, and impact, making it more informative and engaging for readers interested in bench press performance in non-disabled and Para athletes.

Reviewer #2: This scoping review explored variables related to bench press performance (1RM) in both able-bodied and para-athlete populations. Of the 32 studies included, the authors noted body composition and anthropometric variables had the strongest association with bench press performance. The study is well-written and fills an important gap in the literature. I have minor specific comments below.

1) Abstract– If character limits allow, could the authors identify all six of the domains into which features were grouped?

2) Introduction – the introduction is concise, references relevant literature, and provides the rationale for the current study.

3) Methods, exclusion criteria – It seems that excluding those who cannot bench press their body weight (especially for women) artificially limits the ranges of bench press performance for analysis. To me, this could limit valuable information on the predictors of performance if those with lower performance are not included. Could the authors include an analysis without this exclusion criterion (being able to bench press body mass) to see if the analysis changes?

4) Methods, selection criteria – was supportive equipment (e.g. bench shirt or wrist wraps) taken into account during 1RM assessment in the studies included? Please clarify.

5) Table 2 – waist circumference is listed twice (under upper limb and trunk categories) with slightly different correlation coefficients reported from the same study (reference #6). Please clarify.

6) Figure 4 is not brought up in the manuscript until the discussion. In my opinion, a description of this figure should first be mentioned in the results section, with commentary on the implications of this in the discussion.

7) The discussion proposes sound recommendations for future research based on the previous studies reviewed.

Reviewer #3: As per notes attached.

General comments:

Title

Are presented satisfactorily.

Abstract

It is written in a structured way, however, the methodology is written in a very summarized way which ends up making the findings and conclusions of the article.

It would be very important to have more details on how the study was carried out, such as query words, length of time it was consulted, that is, articles published within a certain period of time.

Furthermore, more numerical data should be presented in order to provide an initial overview of the manuscript.

Please confirm that the keywords are listed as descriptors in health sciences.

Introduction

Although the introduction is of a very good length, and initially with a contextualization of Powerlifting, whether conventional or palympic, and more specifically the bench press. However, the introduction cannot move from the general to the specific, that is, the association between performance and anthropometric characteristics associated with health. And on the other hand, the problem is not clear, not allowing us to understand the objectives, much less justify them. It would also be important to present research hypotheses to be answered.

Methods

It should present more clearly the design of the study.

The keywords researcher and the period of time researched are not included in the manuscript. As it turns out, the database and the language, therefore, the methodology is not complete, it would be good to complement it.

Results

Are presented satisfactorily.

Discussion

It should reaffirm the objectives and start discussing the results in the chronological order that appear in the item results.

Please present the limitations of the study.

Conclusion

Are presented satisfactorily.

References

Are presented satisfactorily, review their formatting. Of the 71 references, 37 are current and 34 have been published for more than five years. In this sense, it would be good to check current studies and, if necessary, include them in the manuscript.

Overview

The manuscript presented addresses a relevant research topic.

It would be advisable to do a general review.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Felipe J. Aidar

**********

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Submitted filename: PONE.docx

pone.0310127.s003.docx^{(13.8KB, docx)}

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Submitted filename: PONE-D-24-20584v1.doc

pone.0310127.s004.doc^{(46.5KB, doc)}

PLoS One. 2024 Nov 11;19(11):e0310127. doi: 10.1371/journal.pone.0310127.r002

Author response to Decision Letter 0

12 Aug 2024

We have attached a word document containing the responses to reviewers with this submission.

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Submitted filename: ResponsesToReviewers_R1.docx

pone.0310127.s005.docx^{(122.6KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0310127.r003

Decision Letter 1

Jeremy P Loenneke

26 Aug 2024

Important Features of Bench Press Performance in Non-Disabled and Para Athletes: A Scoping Review

PONE-D-24-20584R1

Dear Dr. Buhmann,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Jeremy P Loenneke

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: N/A

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #2: (No Response)

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #2: No

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PLoS One. doi: 10.1371/journal.pone.0310127.r004

Acceptance letter

Jeremy P Loenneke

5 Sep 2024

PONE-D-24-20584R1

PLOS ONE

Dear Dr. Buhmann,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 File. Systematic search strategy.

The systematic search strategy used to search scientific databases.

(DOCX)

pone.0310127.s001.docx^{(12.8KB, docx)}

S1 Checklist. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.

(DOCX)

pone.0310127.s002.docx^{(83.9KB, docx)}

Attachment

Submitted filename: PONE.docx

pone.0310127.s003.docx^{(13.8KB, docx)}

Attachment

Submitted filename: PONE-D-24-20584v1.doc

pone.0310127.s004.doc^{(46.5KB, doc)}

Attachment

Submitted filename: ResponsesToReviewers_R1.docx

pone.0310127.s005.docx^{(122.6KB, docx)}

Data Availability Statement

All data files and code are available from https://zenodo.org/records/10532754.

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PERMALINK

Important features of bench press performance in non-disabled and Para athletes: A scoping review

Rob Buhmann

Mark Sayers

Julia O’Brien

David Borg

Roles

Abstract

Purpose

Methods

Results

Conclusion

Introduction

Method

Search strategy and selection criteria

Charting the data

Collating, summarising and reporting the results

Results

Search strategy to identify relevant studies

Fig 1. The PRISMA flow diagram detailing the databases searched and records retrieved, the number of records screened, the full texts retrieved, and the number of studies included in the scoping review.

Study population

Non-disabled athlete studies

Fig 2.

Table 1. Summary of non-disabled (n = 24) and para (n = 8) athlete studies.

Para athlete studies

Study design

Non-disabled athlete studies

Para athlete studies

Features related to bench press one repetition maximum performance

Fig 3.

Non-disabled athlete studies

Table 2. Summary of correlations between anthropometric variables and 1RM bench press for non-disabled athletes.

Table 3. Summary of correlations between body composition variables and 1RM bench press for non-disabled athletes.

Table 4. Summary of correlations between demographic, technical and neuromuscular variables and 1RM bench press for non-disabled athletes.

Para athlete studies

Table 5. Summary of correlations between disability, anthropometric, body composition, demographic, and technical variables and 1RM bench press for Para athletes.

Data analysis

Non-disabled athlete studies

Table 6. Summary of data analysis methods.

Fig 4. Caterpillar plot of correlations (n = 183) between features of interest and one-repetition maximum bench press performance.

Para athlete studies

Discussion

Table 7. Recommendations for studies investigating features related to 1RM bench press performance.

Limitations

Future directions

Conclusion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Jeremy P Loenneke

Roles

Author response to Decision Letter 0

Decision Letter 1

Jeremy P Loenneke

Roles

Acceptance letter

Jeremy P Loenneke

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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