Psychological Factors as Predictors of Sports Injuries in Weight Trainers: A 9-Month Prospective Cohort Study

Abstract

Background:

Weight training contributes significantly to physical fitness but carries a high injury risk. Key contributors include high training frequency, insufficient recovery, and training through fatigue—often influenced by psychological factors such as exercise addiction, obsessive passion, athletic identity, and muscle dysmorphia.

Purpose/Hypothesis:

The purpose was to identify demographic, training, and psychological predictors of sports injuries—specifically exercise addiction, passion for exercise, athletic identity, and muscle dysmorphia—to guide injury prevention strategies among weight trainers. It was hypothesized that psychological factors such as obsessive passion, exercise addiction, athletic identity, and muscle dysmorphia are significant predictors of injury risk in weight trainers.

Study Design:

Cohort study; Level of evidence, 2.

Methods:

A total of 381 weight trainers (mean age 31.9 ± 8.6 years; 62.2% women) completed an online baseline survey assessing demographics, training patterns, injury history, coaching status, and psychological constructs, including obsessive passion, exercise addiction, athletic identity, and muscle dysmorphia. Sports injuries, defined as any musculoskeletal complaint from weight training or competition, were tracked biweekly from November 2023 to September 2024, and incidence was calculated per 1000 training hours. Logistic regression with backward selection was used to identify significant predictors.

Results:

Over 9 months, 22.05% of participants reported 157 injuries, with an incidence rate of 1.86 injuries per 1000 training hours. Men had a slightly higher injury rate (23.61%) than women (21.9%). The most affected regions were the lower back (24.2%), shoulders/neck (20.38%), and knees (16.6%). A significant regression model (χ²(3, N = 381) = 27.4, P = .001) revealed that previous injuries increased the risk of future injury by 3.74 times. Obsessive passion and exercise addiction increased injury risk by 73% and 12%, respectively, while training with a partner reduced injury risk by 70%.

Conclusion:

Our study showed that psychological factors, particularly obsessive passion and exercise addiction, significantly elevate injury risk in weight trainers. Training with a partner offers protective benefits. Injury prevention programs should incorporate psychological screening, promote rest and recovery, and encourage supportive training environments.

Weight training is essential for physical fitness, improving muscle hypertrophy, strength, and endurance, but it also carries a significant risk of sports injuries. Injury rates vary across different strength-training modalities, ranging from 0.2 to 18.9 per 1000 hours for CrossFit, ² 2.4 to 3.3 for weightlifters, ¹ 1.0 to 4.4 for powerlifters, ¹ 0.24 to 1 for bodybuilders, and 4.5 to 6.1 for strongmen. ²⁰ These injuries lead to not only physical harm but also increased medical costs, absenteeism, and long-term health issues such as osteoarthritis, which can hinder continued exercise. ¹⁰ Given these factors, understanding the causes and risk factors for these injuries is crucial for implementing effective prevention strategies in weight training.

Sports injuries have traditionally been viewed through a physical lens, focusing on biomechanical and clinical factors. ²³ However, modern frameworks, such as the biopsychosocial model, recognize that the risk and cause of injuries result from the complex interaction of an athlete's internal biological and psychological traits, combined with external environmental and sociocultural influences, which shape behavior and affect injury susceptibility through choices, exposures, and hazards in training. ⁴⁰ This highlights the need to identify psychological factors that contribute to injury risk, enabling more effective prevention strategies. Recent frameworks emphasize training load as a key factor in injury risk,^5,25 with high-frequency training, insufficient recovery, and pushing through fatigue^14,33 potentially associated with compulsive behaviors that disregard warning signs and safety limits. However, psychological factors like exercise addiction, ¹⁶ obsessive passion,^9,24 athletic identity,^21,22,37 and muscle dysmorphia^26,31 remain underexplored. These factors can drive athletes to increase training load, neglect recovery, and suppress fatigue or pain,^17,41 thus heightening their injury risk and warrant further investigation.

Research in sport and exercise psychology has increasingly recognized the importance of psychological factors in injury risk. Exercise addiction, often termed the “dark side of sports,” is characterized by a compulsive drive for excessive physical activity, neglecting recovery, ignoring warning signs, and prioritizing training over personal responsibilities, all of which could elevate injury risk.^12,34 This addiction may serve as a maladaptive coping strategy, with athletes using overtraining to manage stress, making them more susceptible to serious injuries.^12,34 Passion for exercise is defined as a strong psychological and emotional commitment to engage in forms of exercise that have personal meaning and are integrated into one's identity. Passion can be classified as either harmonious or obsessive (OP), with harmonious promoting balance, resilience, and stress management while reducing injury risk. ⁹ However, OP can increase injury risk by promoting maladaptive behaviors such as overtraining, pain denial, emotional suppression, and rumination, which cause athletes to ignore signals of fatigue, discomfort, or early signs of injury, ultimately leading them to push their bodies beyond safe limits and increasing the risk of injuries. ¹⁵ OP also leads to high-intensity and frequent workouts, causing exercise dependence with symptoms such as tolerance and withdrawal, ⁴ which contribute to overtraining and inadequate recovery, ultimately increasing the risk of injury.^9,24 Additionally, athletic identity, how strongly an individual identifies as an athlete, ⁶ may pressure individuals to train through pain to maintain their image,^30,39 often influenced by the “playing hurt” mentality prevalent in certain sports. ³⁷ However, research presents mixed findings, indicating that the relationship between athletic identity and injury risk is complex and varies across sports.^19,21,22 For instance, lower scores in athletic identity are associated with an increased injury risk in youth ice hockey, ²² while higher scores may either heighten risk in some athletes ²¹ or reduce specific risks, such as shoulder injuries in tennis players. ¹⁹ Finally, muscle dysmorphia, characterized by dissatisfaction with muscularity, leads some athletes, particularly bodybuilders, to adopt unhealthy exercise and diet practices, such as excessive training and extreme dieting, which can increase the risk of injury.^3,31,36 Perfectionism, low self-esteem, and social anxiety, commonly associated with muscle dysmorphia,^8,31 can heighten the risk of sports injuries, with these psychological factors exacerbated by societal pressures to attain an ideal physique. ³¹ While these factors appear to collectively elevate vulnerability to sport injury, there is a lack of research on their specific effect on weight trainers, indicating a significant information gap that requires further exploration.

Research suggests that factors like exercise addiction, ¹⁶ passion for exercise (especially obsessive passion),^9,24 athletic identity,^21,22,37 and muscle dysmorphia ³¹ contribute to an increased risk of sports injuries among athletes. While some studies have examined these factors in specific sports, such as obsessive passion in recreational running ²⁴ and rugby, ⁹ or athletic identity in ice hockey players ²² and other team sports, ²¹ their role in weight training remains underexplored. The unique aspects of weight training, such as high-intensity movements and strong social dynamics, make it challenging to apply findings from other sports. In weight training environments, strong social dynamics—such as the pressure to push physical limits driven by competition, peer comparisons, or group expectations—can amplify psychological factors like obsessive passion, exercise addiction, athletic identity, and muscle dysmorphia. These factors can lead to behaviors that increase injury risk, such as overtraining or neglecting proper recovery. Furthermore, retrospective data in some studies ⁹ may introduce bias. Longitudinal studies are needed to better understand how these psychological factors evolve and affect injury risk in weight trainers. Therefore, using a longitudinal design, the aim of this study is to identify demographic, training, and psychological risk factors specific to weight training, ultimately guiding the development of strategies to reduce injury risk and promote safer training practices. It is hypothesized that psychological factors such as obsessive passion, exercise addiction, athletic identity, and muscle dysmorphia are significant predictors of injury risk in weight trainers.

Methods

Study Design

This prospective cohort study investigated injury risk factors in weight trainers by examining psychological, behavioral, and demographic variables through baseline online questionnaires and tracking injuries and training activities biweekly over 9 months.

Participants

Using the Peduzzi et al ²⁸ formula for logistic regression sample size, $N=\frac{10k}{p}$ , we calculated that a study with 10 independent variables and a 27% positive case proportion ²⁰ requires 371 participants. Accounting for a 20% dropout rate, the adjusted sample size is 445 participants, $\frac{371}{1-0.20}=445$ . Although the initial calculation was based on up to 10 variables, the final model included only 4 predictors, selected through a prespecified strategy using Akaike information criterion–based stepwise regression, clinical relevance, and multicollinearity assessment. This resulted in a more parsimonious, robust, and interpretable model with improved predictive validity.

This prospective cohort study recruited 461 weight trainers from university clubs, bodybuilding clubs, local gyms, and fitness centers in Shahrood, Tehran, and Behshahr, Iran, through in-person visits and online advertisements between September and November 2023. Eligible participants were between 18 and 60 years old, had been engaged in at least 180 minutes (3 times) of weight training per week for the 3 months previous, and met health criteria set by the Physical Activity Readiness Questionnaire. Exclusion criteria included participation in other relevant studies, clinical conditions that affect physical performance, joint surgeries, or ongoing rehabilitation programs.

Participants were informed about the study's processes, risks, and benefits and provided written informed consent. The study was approved by the Ethics Committee of Shahrood University of Technology (approval number IR.SHAHROODUT.REC. 1402.028) and adhered to the Declaration of Helsinki guidelines.

Baseline Measurements

An online questionnaire, created using Google Forms based on previous research,^20,33 was distributed to weight trainers through WhatsApp, Telegram, Instagram, and email. These contact details were provided to the researchers during the participant recruitment phase, which took place between September and November 2023. The survey, divided into 3 sections—demographics, weight training profile, and injury history—collected data on participants’ sex, age, height, weight, training experience, frequency, and duration. It also addressed past weight training injuries, specifying location and nature, and included a question to confirm that participants were injury-free at the start of the study, excluding those with preexisting conditions from the analysis.

An online questionnaire, which also assessed participants’ passion for exercise, athletic identity, exercise addiction, and muscle dysmorphia, was distributed via a separate link.

Passion for exercise was assessed using the 12-item Passion Scale, which is divided into 2 subscales: harmonious passion and obsessive passion for exercise. ³⁸ Participants rated statements (eg, “I interpret threats as positive opportunities” or “I have a tough time controlling my need to do exercise”) on a 7-point Likert scale. Each subscale score was calculated as the average of 6 items, with the total score being an average of both subscales. Research showed good internal reliability for these subscales, with Cronbach's α values of 0.88 for obsessive passion and 0.78 for harmonious passion. ³⁸

Athletic identity was measured using a 10-item version of the Athletic Identity Measurement Scale. Participants rated statements such as “I consider myself an athlete” on a 7-point Likert scale, with total scores ranging from 10 to 70. ⁶ Higher scores indicated a stronger identification with the athlete role. The scale exhibited good internal consistency with a Cronbach's α of 0.76. ⁶

Exercise addiction was evaluated using the 6-item Exercise Addiction Scale, based on Griffith's revised behavioral addiction scale. ³⁵ Participants rated items such as “Exercise is the most important thing in my life” on a 5-point Likert scale. The total scores ranged from 6 to 30, with higher scores suggesting a greater risk of exercise addiction. The internal consistency was strong, with a Cronbach's α of 0.84. ³⁵

Muscle dysmorphia was assessed using the Muscle Appearance Satisfaction Scale, a 19-item multidimensional self-report measure. ³ It includes 5 subscales: Bodybuilding Dependence (5 items), Muscle Checking (4 items), Substance Use (4 items), Injury (3 items), and Muscle Satisfaction (3 items). Participants rated items on a 5-point Likert scale, with higher scores indicating less satisfaction with muscle appearance. The Muscle Appearance Satisfaction Scale demonstrated good internal consistency, with Cronbach's α values ranging from 0.73 to 0.89. ³

Follow-up Survey

An 11-question online survey, distributed biweekly over 9 months from November 2023 to September 2024, gathered data on weight training duration, frequency, and injuries, taking 1 to 5 minutes to complete. The first 2 questions assessed training exposure, while the remaining focused on injury details, including location, type, and time-loss injuries. Four questions from the Oslo Sports Trauma Research Center Overuse Injury Questionnaire ⁷ were also included. Participants reporting no injuries were directed to the end, while those with injuries answered additional questions for further clarification. Sport injury was defined as “any musculoskeletal complaint experienced by an athlete resulting from weight training or competition, regardless of whether it requires medical treatment or leads to time lost from training activities.” ¹¹ Time-loss injury was defined as “an injury that prevents an athlete from fully participating in subsequent weight training sessions.” Injuries were classified anatomically into 17 regions based on the consensus classification system by Fuller et al, ¹¹ including head/face, neck/cervical spine, shoulder/clavicula, upper arm, elbow, forearm, wrist, hand/fingers, chest/upper back, abdomen, lower back/pelvis, hip/groin, thigh, knee, lower leg/Achilles tendon, ankle, and foot/toes. Participants who reported injuries were assessed by an orthopaedic specialist (fellowship trained in sports medicine, 10 years of experience) or physical therapist (MSc in physiotherapy, 5 years of experience working with injured athletes), with phone consultations used when in-person evaluations were not feasible. If a participant did not complete the follow-up questionnaire within 3 days, a reminder was sent, and a follow-up call was made after 8 days to ensure completion. Any incomplete or unclear information was clarified through direct contact with participants.

Statistical Analysis

Descriptive statistics summarized participants’ characteristics. Exposure time in hours was calculated for each group over 9 months (November 2023–September 2024) and used to determine the incidence rate per 1000 training hours with 95% confidence intervals. The incidence rate formula was as follows: ( $\mathrm{Injury}\mathrm{Rate}=(\frac{\mathrm{Number}\mathrm{of}\mathrm{Injuries}}{\mathrm{Number}\mathrm{of}\mathrm{Exposure}\mathrm{Hours}})\times 1000$ ). Univariate logistic regression assessed relationships between independent variables and sports injury as the dependent variable. Variables with a P value <.20 were included in a multivariable logistic regression model using forward selection. Variance inflation factor analysis ensured no multicollinearity among independent variables in the final model, with a maximum variance inflation factor of 1.3 (values >3 suggest multicollinearity; Appendix Table A1). ²⁷ Odds ratios (ORs) and 95% confidence intervals were calculated for each risk factor in univariate and multivariate analyses. Statistical analyses were conducted using SPSS IBM version 26, with significance set at .05.

Results

Of the 461 initial participants, 80 withdrew due to personal, health, or time constraints, leaving 381 weight trainers who completed 84,618 training hours. A total of 381 weight trainers (62.2% female, mean age 31.9 ± 8.6 years) completed the study after 80 participants withdrew from an initial cohort of 461, contributing to a combined training volume of 84,618 hours. Missing data in our study were minimal (<5%), assessed as missing completely at random and handled via case-wise deletion to ensure robust analysis. The injury incidence rate was 1.86 injuries per 1000 hours, with 22.05% of participants sustaining injuries. Men had a slightly higher injury rate (23.61%) than women (21.9%). The most common injuries affected the lower back (24.2%), shoulders and neck (20.38%), and knees (16.6%). Most injuries (57.96%) were minor with no training time lost, while 19% caused 1 to 3 days off, 11% led to 4 to 7 days off, 5.7% resulted in 8 to 28 days off, and 6.3% caused over a month of missed training (Appendix Figure A1).

Over 9 months, most injured weight training athletes faced limitations, with only 12.4% continuing training without issues; others trained with some difficulties (26.4%), partially participated (19.46%), or stopped training entirely (42.0%). Regarding training modifications, 45.14% reported no changes in training intensity, while 26.55% made minor adjustments, 18.58% implemented moderate modifications, and 9.73% underwent major changes. In terms of performance effect, 44.25% of athletes reported no decline, whereas 7.97% experienced a significant negative effect. Postinjury pain levels also varied, with 18.6% reporting no pain, 40.7% experiencing mild pain, 30.97% moderate pain, and 9.73% severe pain (Appendix Figures A2 -A6).

Univariate Logistic Regression

The study assessed various factors influencing injury risk in weight trainers. Demographically, variables such as age, height, weight, and body mass index were not significant predictors of injury (P > .05). However, training-related factors played a key role, with increased training frequency and duration significantly raising injury risk by 35% and 78% per additional session and hour per week, respectively (χ² (1) = 9.26, P = .002; χ² (1) = 4.01, P = .0450). Training with a partner reduced injury risk by 34% (χ² (1) = 4.88, P = .027). However, other training-related factors, such as sport experience, participation in training courses, engagement in other sports, warm-up and cool-down routines, and coach presence, did not significantly affect injury risk (P > .05). Injury history was a strong predictor, with weight trainers having a previous injury being 2.92 times more likely to sustain a new injury (χ² (1) = 17.80, P < .001) (Table 1).

Table 1.

Participants’ Characteristics, Training Profiles, and Coaching Status by Injury Status ^a

Variable	Total (N = 381)	Uninjured (n = 297)	Injured (n = 84)	B	SE	Wald	OR (95% CI)	P Value
Sex
Male (R)	144 (37.8)	110 (76.4)	34 (23.6)
Female	237 (62.2)	187 (78.9)	50 (21.1)	−0.145	0.25	0.33	0.86 (0.52-1.41)	.57
Age, mean ± SD, y	31.9 ± 8.6	32.2 ± 8.7	30.9 ± 8.6	−0.017	0.015	1.42	0.98 (0.95-1.01)	.23
Height, mean ± SD, cm	168.8 ± 9.5	168.7 ± 9.5	169.0 ± 9.4	0.003	0.013	0.046	1 (0.97–1.02)	.83
Weight, mean ± SD, kg	76.3 ± 19.4	77.0 ± 20.2	74.0 ± 15.9	−0.009	0.007	1.60	0.99 (0.97-1)	.21
BMI, mean ± SD, b kg/m2	26.5 ± 4.9	26.8 ± 5.1	25.8 ± 4.3	−0.04	0.027	2.60	0.95 (0.90-1)	.11
Training experience, mean ± SD, y	3 ± 8.5	3 ± 8.5	3 ± 6.5	0.007	0.019	0.123	1.007 (0.97-1.04)	.72
Training frequency (sessions/week), b median ± IQR	4 ± 2	4 ± 2	5 ± 2	0.30	0.10	9.18	1.35 (1.11-1.65)	.002 c
Session duration (minutes/session), median ± IQR	1.5 ± 1	1.5 ± 1	1.5 ± 1	0.18	0.24	0.584	1.2 (0.75-1.91)	.44
Weekly duration (hours/week), b median ± IQR	6 ± 5	5 ± 5	6 ± 5	0.076	0.04	4.08	1.07 (1-1.16)	.04 c
Participation in other sports
No (R)	235 (62)	185 (78.7)	50 (21.3)
Yes	144 (38)	110 (76.4)	34 (23.6)	−0.13	0.25	0.28	0.87 (0.53-1.44)	.59
Warm-up
Never (R) b	25 (16.6)	18 (72)	7 (28)
Sometimes	34 (22.5)	30 (88.2)	4 (11.8)	−1.07	0.69	2.38	0.34 (0.08-1.33)	.12
Always	92 (60.9)	72 (78.3)	20 (21.7)	−0.33	0.51	0.43	0.71 (0.26-1.94)	.51
Cool-down
Never (R)	46 (30.5)	36 (78.3)	10 (21.7)
Sometimes	43 (28.5)	36 (83.7)	7 (16.3)	−0.35	0.54	0.43	0.7 (0.24-2.04)	.51
Always	62 (41.1)	48 (77.4)	14 (22.6)	0.05	0.47	0.011	1.05 (0.41-2.63)	.92
Coach supervision
No (R)	39 (27.7)	31 (79.5)	8 (20.5)
Yes	102 (72.3)	81 (79.4)	21 (20.6)	0.005	0.47	0.001	1 (0.40-2.50)	.99
Partner support
No (R) b	94 (66.2)	70 (74.5)	24 (25.5)
Yes	48 (33.8)	43 (89.6)	5 (10.4)	−1.08	0.53	4.19	0.34 (0.12-0.95)	.04 c
Training course enrollment
No (R)	228 (60.2)	180 (78.9)	48 (21.1)
Yes	151 (39.8)	115 (76.2)	36 (23.8)	0.16	0.25	0.41	1.17 (0.71-1.91)	.52
Injury history
No (R) b	248 (65.6)	210 (84.7)	38 (15.3)
Yes	130 (34.4)	85 (65.4)	45 (34.6)	1.07	0.25	17.7	2.92 (1.77-4.82)	.001 c

^aValues are presented as number (%) unless otherwise indicated. BMI, body mass index; OR, odds ratio; R, reference category.

^bVariables entered into the multivariable logistic analysis.

^cVariables that significantly predict sport injury.

Psychological factors, such as obsessive passion, exercise addiction, athletic identity, and muscle dysmorphia, significantly predicted injury risk among weight trainers. Obsessive passion increased the injury risk by 56% with each unit increase (χ² (1) = 22.35, P < .001), while exercise addiction raised the risk by 13% (χ² (1) = 16.41, P < .001). Athletic identity was associated with a 3% rise in injury risk per unit increase (χ² (1) = 8.58, P = .003), and muscle dysmorphia was linked to a 3% increase in injury risk per unit score increase (χ² (1) = 6.89, P = .009). In contrast, harmonious passion did not significantly predict injury occurrence (P = .318) (Table 2).

Table 2.

Comparison of Passion for Exercise, Athletic Identity, Exercise Addiction, and Muscle Dysmorphia Between Injured and Uninjured Weight Trainers ^a

Variable	Total (N = 381)	Uninjured (n = 297)	Injured (n = 84)	B	SE	Wald	OR (95% CI)	P Value
Passion for exercise (1-7), mean ± SD
Harmonious passion	5.6 ± 1.0	5.5 ± 1.0	5.7 ± 1.0	0.12	0.123	0.98	1.129 (0.88-1.43)	.32
Obsessive passion b	3.3 ± 1.3	3.1 ± 1.3	3.9 ± 1.4	0.44	0.098	20.75	1.56 (1.28-1.89)	.001 c
Athletic identity (10-70), b mean ± SD	46.3 ± 12.6	45.3 ± 12.1	49.8 ± 13.4	0.03	0.01	8.31	1.03 (1-1.05)	.004 c
Exercise addiction (6-30), b mean ± SD	18.4 ± 4.4	17.9 ± 4.2	20.1 ± 4.8	0.12	0.032	14.76	1.13 (1.06-1.20)	.001 c
Muscle dysmorphia (17-85), b mean ± SD	45.7 ± 10.6	44.9 ± 10.4	48.4 ± 11.1	0.03	0.012	6.86	1.03 (1-1.05)	.009 c
Bodybuilding dependence b	10.8 ± 3.6	10.5 ± 3.5	11.9 ± 3.7	0.11	0.035	10.02	1.11 (1.04-1.19)	.002 c
Muscle checking	9.3 ± 3.8	9.3 ± 3.7	9.6 ± 4.0	0.022	0.033	0.476	1.023 (0.96-1.09)	.49
Substance use b	6.1 ± 2.3	6.0 ± 2.2	6.4 ± 2.6	0.082	0.053	2.37	1.085 (0.97-1.20)	.12
Injury b	9.3 ± 3.4	9.1 ± 3.4	10.2 ± 3.5	0.097	0.038	6.62	1.10 (1.02-1.18)	.01 c
Muscle satisfaction	10.2 ± 2.7	10.1 ± 2.5	10.3 ± 3.1	0.029	0.047	0.38	1.029 (0.93-1.12)	.54

^aValues are presented as number (%) unless otherwise indicated. BMI, body mass index; OR, odds ratio.

^bVariables entered into the multivariable logistic analysis.

^cVariables that significantly predict sport injury.

Multivariate Logistic Regression

In the univariate analysis, several variables with a P value <.20 were included in the multivariable logistic regression model using forward selection. The multivariate logistic regression analysis identified that injury history, training with a partner, obsessive passion, and exercise addiction significantly predicted injury risk among weight trainers, explaining 18% to 28% of the variance risk (χ²(3, N = 381) = 27.4, P = .001). Athletes with a previous injury were 3.74 times more likely to sustain a new injury (P = .006). Training with a partner reduced injury risk by 70% (P = .04), suggesting protective benefits from partner support. Additionally, each unit increase in obsessive passion raised the injury risk by 73% (P = .02), while exercise addiction led to a 12% increase in risk (P = .02) (Table 3).

Table 3.

Results of Multivariate Logistic Regression for Predicting Sports Injuries in Weight Trainers ^a

Variable	B	SE	Wald	df	OR (95% CI)	P Value
Partner support (no) (R)	−1.19	0.58	4.19	1	0.30 (0.10-0.95)	.04
Injury history	1.32	0.48	7.48	1	3.74 (1.45-9.61)	.006
Obsessive passion	0.55	0.23	5.78	1	1.73 (1.11-2.71)	.02
Exercise addiction	0.12	0.05	5.96	1	1.12 (1.02-1.23)	.02

^aOR, odds ratio; R, reference category.

Discussion

This prospective cohort study aimed to identify key factors linked to sports injuries in weight trainers. The primary findings of our study revealed an injury incidence rate of 1.86 per 1000 training hours, with 22.05% of participants sustaining at least 1 injury over a 9-month period. A history of injury increased the risk of a new injury by 3.74-fold, while training with a partner reduced the risk by 70%. Additionally, obsessive passion (OR, 1.73) and exercise addiction (OR, 1.12) were significant predictors of injury in the multivariate analysis.

The present study reported an injury rate of 1.85 injuries per 1000 hours of training among weight trainers, which is higher than the 0.42 injuries reported in bodybuilders ¹⁸ but slightly lower than the 2.6 injuries found in weight trainers and powerlifters. ²⁹ A review study found lower injury rates in bodybuilding (0.24 to 1 injury per 1000 hours) and higher rates in strongman competitions (4.5 to 6.1 injuries) and Highland games (7.5 injuries). ²⁰ Injury rates are influenced by the nature of the sport, training intensity, supervision, and physical conditioning. Strength sports involving intense movements, like powerlifting and strongman, have higher injury rates, while bodybuilding, with its controlled movements, results in fewer injuries. Our study, which reflects both high-intensity and controlled resistance training, likely captures the combined effects of these training modalities. These findings underscore the importance of balancing training intensity with appropriate supervision to minimize injury risk, particularly for weight trainers engaging in both training styles.

In our study, the injury prevalence rate among weight trainers was 22.05%, lower than the rates found in similar research.^18,32 Hsia ¹⁸ reported that 74.5% of bodybuilders and fitness athletes experienced at least 1 injury in the past year, while Siewe et al ³² found that 43.3% of powerlifters sustained injuries during routine training. The higher injury risk in professional athletes is likely due to intense training loads with heavy weights. Differences in injury rates across studies may result from variation in study design, population characteristics, and follow-up periods. Our 9-month prospective design reduced bias in injury reporting compared with the retrospective studies by incorporating structured follow-ups and detailed training logs to track workload progression.^18,32

The most commonly injured sites among weight trainers in this study were the lower back (24.2%), shoulders and neck (20.38%), and knees (16.6%), which aligns with findings in previous studies on weight trainers and powerlifters.^20,32 Similarly, Hsia ¹⁸ reported a comparable injury distribution: lower back (34%), shoulder (33.4%), and knee (21.6%). While the general injury patterns are consistent across studies, differences in percentages may stem from sport type and training intensity variations.

This study found that demographic variables such as sex, age, height, weight, and body mass index were not significant predictors of injury risk in weight training. This is consistent with findings by Keogh and Winwood, ²⁰ Raske and Norlin, ²⁹ and Willick et al ⁴² but contradicts studies such as Hsia ¹⁸ and Siewe et al, ³² which found associations between injury risk and demographic factors such as age and sex.

Our findings indicate that although higher training frequency and duration were linked to increased injury risk in bivariate analyses, only training with a partner remained a significant protective factor in the multivariate analysis. Supporting this, Hsia ¹⁸ and Grier et al ¹⁴ found a link between increased training frequency and higher injury rates in bodybuilders. Keogh and Winwood ²⁰ also identified contributors such as fatigue, poor recovery, technical errors, excessive loading, inadequate preparation (eg, warm-ups), lack of supervision, risky exercises, and insufficient rest. In contrast, Siewe et al ³² found no link between intrinsic/extrinsic factors and injury risk in powerlifters. Training with a partner may reduce injury risk in weight training by offering assistance during heavy lifts (acting as a spotter), providing feedback on technique, encouraging motivation and focus, and preventing overexertion. While not a guaranteed safeguard, training with a partner significantly improves training safety and may reduce the likelihood of injuries from technical errors or fatigue.

This study revealed that individuals with a history of injuries were 3.74 times more likely to experience new injuries. This finding aligns with Ángel Rodríguez et al, ² who reported a 3-fold increase in reinjury risk among CrossFit athletes. However, some studies^1,20,32 found no such association. These discrepancies may result from differing injury definitions, populations, and methodological designs. For instance, Siewe et al ³² focused on powerlifters and defined injury based on pain that impairs performance, whereas Aasa et al ¹ and Keogh and Winwood ²⁰ conducted systematic reviews that included both retrospective and prospective studies on injuries among weightlifters and powerlifters. Previous injuries may elevate the reinjury risk due to factors like scar tissue, physical deficits, altered movement patterns, and psychological barriers (eg, fear and reduced confidence). A holistic approach, incorporating physical, functional, and psychological components, is essential for minimizing reinjury risk. Effective collaboration between health care professionals, coaches, and trainers plays a crucial role in ensuring safe return-to-play decisions.

This study found that higher levels of obsessive passion and exercise addiction significantly predicted increased risk of sports injuries among weight trainers. Specifically, each unit increase in obsessive passion was associated with a 73% increase in injury risk. These findings are consistent with previous studies, such as Naderi et al, ²⁴ who reported a 91% increase in injury risk for recreational runners, and Deroche et al, ⁹ who identified obsessive passion as a predictor of perceived injury susceptibility in competitive runners. Obsessive passion in sports is a maladaptive psychological trait that significantly elevates the risk of sports-related injuries. Athletes exhibiting obsessive passion often adhere to rigid and compulsive training routines, driven by internal pressures while ignoring key bodily warning signs like fatigue, pain, or previous injuries. This inflexibility leads to delayed recovery, increased physical strain, and a higher risk of overuse injuries. Psychologically, such individuals may adopt maladaptive coping strategies—such as denial, emotional suppression, and continuing to train despite discomfort—which further compromise physical well-being. ¹³ Elevated stress levels may also impair cognitive functions like attention and decision-making, resulting in poor training choices. Thus, obsessive passion should not be dismissed as mere dedication but recognized as a critical psychological risk factor. Integrating psychological screening and education into injury prevention strategies, especially for unsupervised or self-directed athletes, is essential for promoting long-term health, safety, and performance.

Exercise addiction also emerged as a significant predictor, with each unit increase associated with a 12% increase in injury risk. This aligns with Hausenblas and Downs, ¹⁶ who found that individuals at risk for exercise addiction often train despite adverse conditions. This association is driven by both biological and psychological mechanisms. Biologically, individuals with exercise addiction often continue intense training despite pain, fatigue, or injury, leading to chronic overload, microtrauma, and impaired recovery, which elevate the risk of overuse injuries. Psychologically, the compulsive need to exercise is maintained by withdrawal symptoms—such as anxiety, irritability, and restlessness—when unable to train, often resulting in premature return to activity and worsening of injuries. Exercise addiction also shares key features with other addictive disorders, including tolerance (needing more exercise for the same effect), mood disturbances, and poor impulse control. ¹³ These maladaptive patterns compromise physical health and overall well-being. ¹³ Integrating psychological assessments into injury prevention programs, particularly for athletes engaging in unsupervised training, may therefore be crucial for reducing injury risk and improving long-term outcomes. This holistic perspective underscores the importance of addressing both mind and body in athlete care.

Unexpectedly, athletic identity and muscle dysmorphia were not significant predictors of injury risk in the multivariate analysis. While some studies have reported associations between strong athletic identity and injury risk—particularly in team sports like ice hockey^21,22—such effects were not observed here. ¹⁹ Strong athletic identity has been shown to encourage training under unsafe conditions (eg, insufficient recovery, excessive load), yet in this study, its influence may have been mitigated by overlapping variance with obsessive passion. Similarly, although muscle dysmorphia is characterized by compulsive exercise behaviors in pursuit of an idealized physique,^3,31 its unique contribution to injury risk may have been diminished in the presence of stronger predictors. It is possible that future research employing mediation or moderation models could better elucidate the complex interplay among these psychological variables.

Practical Implications

To support injury prevention, practitioners should incorporate brief psychological screening tools—such as the Athletic Identity Measurement Scale, Exercise Addiction Scale, and the Passion Scale—into routine assessments to identify at-risk athletes early and guide tailored interventions. Partner-based training, which our findings show reduces injury risk by 70%, should be encouraged for its safety and motivational benefits. Educational efforts are also needed to promote awareness of overtraining risks and the importance of psychological well-being. Overall, a holistic, biopsychosocial approach integrating physical, psychological, and social strategies can enhance athlete safety, performance, and long-term engagement.

Limitations

This study has several limitations. First, injury data were self-reported, which may introduce recall and reporting biases, potentially affecting the accuracy of injury incidence and the observed associations with psychological factors. Although clear definitions and regular follow-up were used to minimize bias, future studies should incorporate objective verification (eg, clinical assessments or medical records) to enhance validity. Second, convenience sampling from sports clubs and online platforms may limit generalizability and introduce selection bias, as the sample may not represent the broader population of weight trainers. More diverse and representative sampling strategies are recommended in future research. Third, psychological variables—such as exercise passion, athletic identity, exercise addiction, and muscle dysmorphia—were assessed only at baseline, limiting the ability to capture their dynamic changes over time. Repeated longitudinal assessments are needed to understand their evolving influence on injury risk. Additionally, important factors such as training intensity, exercise technique, and individual biomechanical differences were not comprehensively assessed, and the 9-month follow-up period may be insufficient to capture long-term injury and recovery patterns. Future research should consider extended follow-up durations and more detailed monitoring of training-related variables. Lastly, there was a discrepancy between the number of predictors assumed in the initial sample size calculation and the number included in the final model. This, however, reflects a conservative planning strategy to ensure sufficient statistical power. The reduction in predictors followed a prespecified modeling approach based on Akaike information criterion–driven stepwise selection, clinical relevance, and multicollinearity assessment, ultimately yielding a more parsimonious, valid, and reliable model.

Despite its limitations, this study has notable strengths. The diverse sample enhances the understanding of injury risks across different weight training demographics, while detailed training reports allow for a thorough analysis of injury patterns. By examining psychological factors such as passion, athletic identity, exercise addiction, and muscle dysmorphia, the study provides valuable insights into their role in injury occurrence. The prospective design and high follow-up rates strengthen data reliability, offering a solid foundation for future research on injury prevention in weight training.

Conclusion

Our study showed that psychological factors, particularly obsessive passion and exercise addiction, significantly elevate injury risk in weight trainers. Training with a partner offers protective benefits. Injury prevention programs should incorporate psychological screening, promote rest and recovery, and encourage supportive training environments.

Appendix

Appendix Table A1.

Multicollinearity Diagnostics: Tolerance and Variance Inflation Factor for Predictor Variables

Variable	Tolerance	Variance Inflation Factor
Sex	0.63	1.58
Age	0.70	1.42
Height	0.51	2.15
Weight	0.57	2.02
Training experience	0.53	2.11
Training frequency	0.84	1.18
Session duration	0.85	1.17
Participation in other sports	0.83	1.21
Warm-up	0.74	1.35
Cool-down	0.80	1.26
Coach supervision	0.78	1.28
Partner support	0.84	1.19
Training course enrollment	0.66	1.51
Injury history	0.71	1.40
Passion for exercise	0.78	1.31
Athletic identity	0.87	1.15
Exercise addiction	0.57	2.03
Muscle dysmorphia	0.47	2.30

Appendix Figure A1.

Injury percentages based on injury severity in weight trainers over 9 months.

Appendix Figure A2.

Distribution of injuries by affected body region among weight trainers over a 9-month period.

Appendix Figure A3.

Postinjury participation levels among weight trainers over a 9-month period.

Appendix Figure A4.

The extent of training modifications made by weight trainers after injury over a 9-month period.

Appendix Figure A5.

The effects of injury on athletic performance among weight trainers over a 9-month period.

Appendix Figure A6.

The self-reported pain levels experienced by weight trainers after injury over a 9-month period.