Functioning Level as a Buffer: Longitudinal Associations Between Heart Rate Variability and Post-Traumatic Stress Disorder Risk Over 2 Years

Jae-Min Kim; Hee-Ju Kang; Ju-Wan Kim; Hyunseok Jang; Jung-Chul Kim; Ju-Yeon Lee; Sung-Wan Kim; Il-Seon Shin

doi:10.30773/pi.2025.0084

Psychiatry Investig > Volume 22(10); 2025 > Article

Kim, Kang, Kim, Jang, Kim, Lee, Kim, and Shin: Functioning Level as a Buffer: Longitudinal Associations Between Heart Rate Variability and Post-Traumatic Stress Disorder Risk Over 2 Years

Original Article

Psychiatry Investigation 2025;22(10):1131-1138.

Published online: September 16, 2025

DOI: https://doi.org/10.30773/pi.2025.0084

Functioning Level as a Buffer: Longitudinal Associations Between Heart Rate Variability and Post-Traumatic Stress Disorder Risk Over 2 Years

Jae-Min Kim^1,^*

, Hee-Ju Kang^1,^*

, Ju-Wan Kim¹

, Hyunseok Jang²

, Jung-Chul Kim²

, Ju-Yeon Lee¹

, Sung-Wan Kim¹

, Il-Seon Shin¹

¹Department of Psychiatry, Chonnam National University Medical School, Gwangju, Republic of Korea

²Division of Trauma, Department of Surgery, Chonnam National University Medical School and Hospital, Gwangju, Republic of Korea

Correspondence: Jae-Min Kim, MD, PhD Department of Psychiatry, Chonnam National University Medical School, 160 Baekseo-ro, Dong-gu, Gwangju 61669, Republic of Korea Tel: +82-62-220-6146, Fax: +82-62-225-2351, E-mail: jmkim@chonnam.ac.kr

^* These authors contributed equally to this work.

Received March 4, 2025 Revised June 8, 2025 Accepted July 12, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

This study examined the modifying effects of functional levels on the associations of the low frequency (LF) and high frequency (HF) components of heart rate variability (HRV) with post-traumatic stress disorder (PTSD) development.

Methods

Participants with physical injuries were recruited from a trauma center and followed for 2 years. Baseline assessments included LF, HF, and functional levels measured by the Social and Occupational Functioning Scale. Socio-demographic and clinical covariates were collected. PTSD diagnoses were made at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5. Logistic regression analyses were performed to assess associations. Among 538 participants, 58 (10.8%) developed PTSD during the study period.

Results

A significant modifying effect was found: lower LF/HF were significantly associated with PTSD in patients with lower functioning levels, but not in those with higher functioning levels, with significant interaction terms.

Conclusion

The study observed functioning level-dependent associations between LF/HF and PTSD development, highlighting the buffering effects of higher functioning levels. These findings underscore the importance of considering functional status in PTSD risk assessments and the potential benefits of interventions aimed at enhancing social and occupational functioning to mitigate PTSD risk.

Keywords: Post-traumatic stress disorder, Social functioning, Heart rate variability, Buffer, Longitudinal study

INTRODUCTION

Post-traumatic stress disorder (PTSD) is a debilitating psychiatric condition characterized by a range of psychological and physiological symptoms. These symptoms can severely impact an individual’s daily functioning and quality of life. The autonomic nervous system, which regulates physiological responses to stress, plays a crucial role in the development and maintenance of PTSD symptoms. Heart rate variability (HRV) is a widely recognized physiological marker used to assess ANS function, reflecting the balance between sympathetic and parasympathetic nervous system activity [1]. Reduced HRV is generally indicative of impaired cardiovascular adaptability, which can increase an individual’s vulnerability to PTSD [2].

HRV is typically analyzed through its spectral components, particularly the low frequency (LF) and high frequency (HF) bands. The LF band (0.04-0.15 Hz) is associated with both sympathetic and parasympathetic activity, while the HF band (0.15-0.40 Hz) is primarily linked to parasympathetic activity and is often influenced by respiratory patterns [3]. These spectral components provide valuable insights into the autonomic regulation of heart rate and have been extensively studied in relation to PTSD.

A substantial body of research, including several meta-analyses, has found significant associations between low HRV (both LF and HF components) and the risk of developing PTSD [2,4-7]. These findings suggest that individuals with reduced HRV may have a diminished capacity to cope with stress, thereby increasing their susceptibility to PTSD. However, the literature also includes studies that have reported inconsistent or nonsignificant associations between HRV and PTSD [8-11]. These discrepancies highlight the complexity of the relationship and underscore the need for further investigation to clarify the factors that may influence this association.

Given these mixed findings, it is important to explore potential moderating factors that could explain the variability in the HRV-PTSD relationship. One such factor is the level of social and occupational functioning, which can significantly influence an individual’s overall resilience and ability to manage stress. The Social and Occupational Functioning Scale (SOFAS) provides a comprehensive measure of an individual’s functional status, considering both social and occupational aspects of life [12]. High functioning levels, as measured by SOFAS, might buffer the adverse effects of low HRV on PTSD risk, while low functioning levels could exacerbate these effects. To date, no studies have investigated the moderating role of social and occupational functioning on the HRV-PTSD relationship.

This study aims to address this gap by investigating the moderating role of functioning levels, as measured by SOFAS, on the relationship between HRV (LF and HF components) and PTSD risk over a two-year period.

MATERIALS AND METHODS

Study overview and participants

This analysis is part of the Biomarker-based Diagnostic Algorithm for Post-Traumatic Syndrome (BioPTS) study, which aims to enhance PTSD diagnostic and predictive models. Detailed protocols are available in a prior publication [13]. The study prospectively enrolled patients admitted to the Trauma Center at Chonnam National University Hospital (CNUH), South Korea, from June 2015 to January 2021. Participants were included if they were: 1) 18 years or older at the time of injury; 2) hospitalized for more than 24 hours following a moderate to severe physical injury (Injury Severity Score, ISS ≥9) [14]; and 3) ability to understand the study protocol. Exclusion criteria included: 1) moderate or severe brain injury (Glasgow Coma Scale, GCS <10) [15]; 2) injuries from suicide attempts; 3) severe physical conditions preventing comprehensive psychiatric evaluation; 4) history of psychiatric disorders (psychotic disorders, bipolar disorder, or substance use disorders excluding depression and anxiety); 5) significant cognitive impairments due to organic or neurocognitive disorders; and 6) pre-existing convulsive disorders or anticonvulsant use. Baseline psychiatric assessments, including evaluations for previous psychiatric disorders and HRV measures, were conducted within one month of hospitalization after patients reached a stable condition beyond the acute post-injury state. The average duration from injury to baseline assessment was 8.8 (5.3) days. Follow-up evaluations were conducted via telephone at 3, 6, 12, and 24 months post-injury using the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5), based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [16]. The CNUH Institutional Review Board approved the study (CNUH 2015-148), and informed consent was obtained from all participants.

HRV data collection and analysis

LF and HF data were obtained using the SA-6000 HRV analyzer (Medicore Co.). Participants rested for 5 minutes before testing, removed any metal accessories, kept their eyes open, and lay comfortably. To minimize biases from movement or posture changes, participants remained still, breathing naturally without speaking. Electrode sensors were placed on both wrists and the left ankle, and a 3-minute recording was taken. A trained experimenter supervised to ensure protocol adherence. LF and HF (ms²) parameters were derived using the Medicore HRV Analysis System. Due to the lack of established reference values, LF and HF data were primarily dichotomized using median values; however, additional analyses were also conducted using these variables as continuous measures.

Functioning level

The SOFAS was used to evaluate participants’ functioning levels, focusing on social and occupational domains as per DSM-IV-TR guidelines [12]. SOFAS scores range from 1 to 100, with higher scores indicating better functioning. The scale assesses the individual’s ability to engage in social and occupational activities, which are critical post-trauma. Scores were recorded at baseline and dichotomized into higher functioning (>60) and lower functioning (≤60) groups, with 60 being the cutoff for moderate symptoms and functional difficulties; however, additional analyses were also conducted using SOFAS scores as a continuous measure.

Other baseline characteristics

To thoroughly assess factors potentially influencing PTSD development and HRV outcomes, various baseline characteristics were documented.

Socio-demographic characteristics

Collected data included age, sex, education duration, marital status (married or not), living status (alone or not), and employment status (employed or not).

Pre-trauma characteristics

Documented histories of psychiatric disorders—including depressive disorders, panic disorder, agoraphobia, social phobia, and generalized anxiety disorder—were assessed by patient self-reports, collateral reports from family members or caregivers when available, and review of medical records. Lifetime traumatic events were assessed using the Life Events Checklist [17], and childhood abuse experiences were evaluated with the Nemesis Childhood Trauma Interview [18], categorizing any abuse (emotional, physical, or sexual) as present. Physical disorders were screened using a comprehensive questionnaire. Smoking status (current smoker or not) and alcohol use (Alcohol Use Disorders Identification Test [AUDIT] score) [19] were also documented, along with body mass index.

Trauma related characteristics

The type of traumatic injury was categorized using the Life Events Checklist [17] to distinguish between unintentional and intentional injuries. Injury severity was assessed with ISS and GCS scores.

Peri-trauma characteristics

Symptoms and functional status during the peri-trauma period were evaluated. PTSD symptom severity was measured using CAPS-5, while anxiety and depression were assessed with the Hamilton Anxiety Rating Scale [20] and the Hamilton Depression Rating Scale (HAMD) [21], respectively. Baseline vital signs, including blood pressure (BP) and heart rate, were recorded.

Follow-up diagnoses of PTSD

The CAPS-5, a reliable and valid tool for PTSD assessment, was used for follow-up evaluations. Participants met DSM-5 criteria across symptom clusters, including symptom duration and functional significance. PTSD diagnosis was confirmed at 3, 6, 12, and 24 months post-trauma.

Statistical analysis

Participants who completed at least one follow-up were included in the analysis. Baseline characteristics were compared using t-tests or χ² tests. Correlations between LF, HF, and the SOFAS were estimated using Spearman’s rho coefficients. Logistic regression analyzed the individual associations of LF, HF, and functioning levels with PTSD development, adjusting for significant covariates. Additionally, based on previous mixed findings regarding HRV-PTSD associations and as a planned component of this study, the modifying effects of functional levels on the LF/HF-PTSD relationship were assessed using multinomial logistic regression with interaction terms. All tests were two-sided with a significance level of p<0.05, conducted using SPSS, version 21.0.

RESULTS

Recruitment and baseline data

The recruitment process and PTSD prevalence are illustrated in Figure 1. Out of 1,142 patients initially assessed at baseline, 580 (50.8%) underwent HRV evaluation. Supplementary Table 1 provides a comparison of baseline characteristics between those who completed the HRV evaluation and those who did not. It was found that higher ISS were significantly associated with non-completion of HRV assessment, whereas other variables showed no significant differences. Of the patients who completed the HRV assessment, 42 (7.4%) did not continue beyond the 3-month evaluation, resulting in a final cohort of 538 patients (92.6%) for analysis. There were no significant differences in baseline characteristics between those who completed the study and those who did not (all p>0.05). Within this cohort, 58 patients (10.8%) were diagnosed with PTSD over the 24-month period. Baseline characteristic comparisons between patients with and without PTSD are detailed in Supplementary Table 2. Factors significantly associated with a PTSD diagnosis included female sex, higher education levels, previous psychiatric disorders, prior traumatic events, and elevated anxiety and depressive symptoms. Lower HF (≤38 ms²) was significantly related to older age, higher anxiety and depressive symptoms, and increased heart rate (Supplementary Table 3). Additionally, lower functioning levels were significantly associated with older age, female sex, lower education levels, a higher number of physical disorders, current nonsmoking status, lower AUDIT scores, lower GCS scores, higher anxiety and depressive symptoms, higher systolic BP, and increased heart rate (Table 1). Based on these analyses and collinearity considerations, ten covariates were selected for further analysis: age, sex, education, previous psychiatric disorders, previous traumatic events, number of physical disorders, current smoking status, AUDIT scores, HAMD scores, and heart rate.

Individual associations

The correlations between LF and HF levels and SOFAS scores are presented in Supplementary Table 4. Each of these variables was found to be significantly correlated with one another. The individual associations of LF, HF, and SOFAS with PTSD development are shown in Figure 2. Lower HF levels were significantly associated with the development of PTSD after adjusting for the identified covariates. However, LF levels and SOFAS scores did not show significant associations with PTSD development in the adjusted model.

Interactive modifying associations

The interactive modifying effects of functioning levels on the relationship between LF/HF levels and PTSD development are illustrated in Figure 3. There were significant modifying effects observed: both lower LF and HF levels were significantly associated with the development of PTSD in patients with lower functioning levels, but not in those with higher functioning levels. These results were supported by significant interaction terms, highlighting the moderating role of social and occupational functioning in the relationship between HRV components and PTSD risk. These interaction effects remained significant when both LF/HF levels and SOFAS scores were analyzed as continuous variables (LF×SOFAS: Wald=6.129, p=0.009; HF×SOFAS: Wald=5.823, p=0.012).

DISCUSSION

The principal findings of this 2-year longitudinal study indicate that lower LF and HF components of HRV are significant predictors of PTSD development, particularly in patients with lower functioning levels. These associations were not statistically significant in those with higher functioning levels early post-injury, with significant interaction terms underscoring the importance of functional status.

Previous research on LF/HF and PTSD has primarily utilized cross-sectional designs, showing mixed results [4-11]. Direct comparisons with our prospective study are challenging, but our findings suggest HRV components are critical PTSD risk markers, especially among those with compromised social and occupational functioning. It is important to emphasize that examining moderating effects of functional status on the HRV-PTSD relationship was an integral, predefined objective of this study. This approach was motivated by previous research demonstrating mixed or inconsistent findings regarding the direct associations between HRV components and PTSD risk, highlighting the need to investigate potential moderating factors.

The buffering or protective effects observed in individuals with higher functioning levels can be explained through several plausible mechanisms. First, higher functioning individuals are more likely to have robust social support networks, which can alleviate stress and provide essential support, helping maintain physiological stability and reducing PTSD risk [22]. Second, these individuals often employ adaptive coping strategies like problem-solving and positive reframing, which buffer against the adverse effects of low HRV on PTSD [23]. Third, higher resilience, typically seen in those with better social and occupational functioning, enhances autonomic regulation, leading to better HRV and lower PTSD risk [24].

Conversely, individuals with lower functioning levels may lack strong social support networks, leading to feelings of isolation and helplessness, exacerbating stress responses and increasing PTSD vulnerability [25]. They are also more likely to use maladaptive coping strategies such as avoidance and substance use, which interfere with autonomic regulation, leading to lower HRV and higher PTSD risk [26]. Reduced resilience in these individuals further impairs stress management, increasing susceptibility to PTSD [27].

Regarding the roles of the LF and HF components of HRV, although these frequency bands reflect somewhat opposing autonomic processes—LF indicating a mix of sympathetic and parasympathetic modulation, and HF primarily representing parasympathetic modulation—each provides complementary insights into autonomic nervous system regulation [3]. Lower values in both LF and HF reflect reduced autonomic flexibility and adaptability to stress, which is relevant to PTSD vulnerability [4-7]. In this context, social and occupational functioning may act as critical moderators, influencing whether the autonomic dysregulation indicated by reduced LF and HF translates into increased PTSD risk. Higher functional levels appear to mitigate this autonomic vulnerability through enhanced coping and resilience, while lower functional levels appear to exacerbate it [28]. Future studies should further explore the distinct yet complementary roles of these HRV components in stress-related psychopathology.

These mechanisms collectively underscore the importance of considering social and occupational functioning when evaluating PTSD risk factors and developing interventions. Enhancing social support, promoting effective coping strategies, and building resilience should be integral to PTSD prevention and treatment programs.

A notable limitation of this study is its focus on individuals with physical injuries, which may limit the generalizability to those experiencing other trauma types [29]. Recruitment from a single trauma center aids consistency but may not represent broader populations. The HRV evaluation completion rate was 51%, with non-completion associated with higher ISS, indicating that more severely injured patients were less likely to complete HRV evaluations, potentially underestimating the HRV-PTSD association. HRV was measured only at baseline; future studies should incorporate repeated assessments to capture changes over time. Another potential limitation is that HRV may reflect underlying cardiovascular fitness, which itself may be associated with measures of social functioning. Thus, HRV and functioning level might not be entirely independent variables [30]. Future research should explicitly measure and control for cardiovascular fitness to clarify these relationships. Moreover, although anxiety and depressive symptoms at baseline were included as covariates, our analysis did not specifically determine whether participants had active psychiatric disorders at the exact time of injury. Given that pre-existing psychiatric conditions significantly increase PTSD risk and influence HRV [31], this represents a notable limitation. Future studies should explicitly assess and control for active psychopathology present at the time of traumatic events to further clarify these relationships. Further, while our covariate selection was theory-driven and clinically relevant, we recognize that the ratio of PTSD cases (n=58) to the number of covariates (10 variables) approaches commonly recommended thresholds, potentially affecting statistical power. Thus, larger studies or analyses employing methods to reduce or prioritize covariates would be beneficial to confirm and further clarify our findings. Additionally, follow-up via telephone interviews, though validated, may lack the depth and accuracy of in-person assessments, potentially affecting data reliability [32]. Furthermore, this study did not differentiate between acute and delayed-onset PTSD. Different risk factors may predict these PTSD subtypes, and our structured follow-up intervals did not specifically capture the precise timing of PTSD onset. Thus, it remains unclear whether HRV and functional levels differentially predict acute versus delayed-onset PTSD. Finally, while we controlled for numerous relevant covariates, our analytic approach does not fully distinguish direct from indirect effects of these variables. Additional analyses, such as mediation or structural equation modeling, would introduce complexity and potential ambiguity beyond the predefined scope and design of this study. Therefore, future research explicitly designed for these analytical approaches is needed to further clarify these complex relationships.

A principal strength of our study is its 2-year longitudinal design with a large cohort, allowing for robust analysis over time. Consecutive recruitment from the entire population of recently injured patients reduces selection bias, ensuring a representative sample. Regular follow-up evaluations minimize biases from inconsistent timing. Adherence to a rigorous research protocol ensured uniform evaluations and data collection, enhancing consistency and reliability. Collecting a wide array of potential baseline covariates allowed for comprehensive analysis. Reasonable long-term follow-up rates and analyses showing no evidence of selective attrition further support the credibility of our findings.

In conclusion, our study provides important insights into the role of HRV and functional status in predicting PTSD development. Lower LF and HF HRV components are significant predictors of PTSD only in patients with lower functioning levels, highlighting the need to consider functional status in PTSD risk assessments. For public health, community-based programs and interventions aimed at improving social and occupational functioning in trauma-exposed populations could prevent PTSD. These programs should focus on enhancing social support networks and resilience. For clinical implications, Clinicians should incorporate assessments of both HRV and functional status when evaluating PTSD risk in trauma patients. Tailoring interventions to improve social and occupational functioning could benefit patients with low HRV. For future research, investigating the efficacy of interventions aimed at improving social and occupational functioning on HRV and PTSD outcomes would be valuable. Exploring other potential moderating factors, such as genetic predispositions or environmental influences, could provide a more comprehensive understanding of PTSD risk. Moreover, future studies should explicitly differentiate between acute and delayed-onset PTSD by closely monitoring symptom onset and trajectories, thereby clarifying the predictive value of HRV and functional status for these PTSD subtypes. Research should also consider diverse populations and settings to enhance generalizability.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0084.

Supplementary Table 1.

Patient data at baseline by completion of the HRV evaluation in 1,142 patients with physical injuries

pi-2025-0084-Supplementary-Table-1.pdf

Supplementary Table 2.

Baseline characteristics by PTSD diagnosis over 2 years in 538 patients with physical injuries

pi-2025-0084-Supplementary-Table-2.pdf

Supplementary Table 3.

Baseline characteristics by lower (≤38 ms²) vs. higher (>38 ms²) HF component of heart rate variability in 538 patients with physical injuries

pi-2025-0084-Supplementary-Table-3.pdf

Supplementary Table 4.

Correlations between LF and HF components of heart rate variability and scores on SOFAS in 538 patients with physical injuries

pi-2025-0084-Supplementary-Table-4.pdf

Notes

Availability of Data and Material

The data that support the findings of study are available from the corresponding author (J-M Kim) upon reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Jae-Min Kim. Data curation: Jae-Min Kim, Hee-Ju Kang. Formal analysis: Jae-Min Kim, Hee-Ju Kang. Funding acquisition: Jae-Min Kim. Investigation: Jae-Min Kim, Hee-Ju Kang, Ju-Wan Kim, Hyunseok Jang, Jung-Chul Kim, Sung-Wan Kim, Il-Seon Shin. Methodology: Jae-Min Kim, Hee-Ju Kang, Ju-Wan Kim, Sung-Wan Kim, Il-Seon Shin. Project administration: Jae-Min Kim. Resources: Jae-Min Kim. Software: Jae-Min Kim. Supervision: Ju-Wan Kim, Hyunseok Jang, Jung-Chul Kim, Ju-Yeon Lee, Sung-Wan Kim, Il-Seon Shin. Validation: Ju-Yeon Lee, Sung-Wan Kim, Il-Seon Shin. Visualization: Jae-Min Kim. Writing—original draft: Jae-Min Kim, Hee-Ju Kang. Writing—review & editing: all authors.

Funding Statement

The study was funded by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. RS-2024-00440371) and CNUH-GIST research Collaboration grant (BCRI25063) funded by the Chonnam National University Hospital Biomedical Research Institute.

Acknowledgments

None

Figure 1.

Patient flow and prevalence of PTSD. HRV, heart rate variability; PTSD, post-traumatic stress disorder.

Figure 2.

Individual associations of LF and HF bands of heart rate variability, and functioning level with PTSD over 2 years in 538 patients with physical injuries. Odds ratios (95% confidence intervals) were calculated using binary logistic regression. Associations were analyzed between higher (>57 ms²) vs. lower (≤57 ms²) LF, higher (>38 ms²) vs. lower (≤38 ms²) HF, and higher (>60) vs. lower (≤60) functioning levels assessed by the Social and Occupational Functioning Assessment Scale at baseline for the development of PTSD over 2 years. Adjustments were made for age, sex, education, previous psychiatric disorders, previous traumatic events, number of physical disorders, current smoking status, scores on the Alcohol Use Disorders Identification Test, the Hamilton Depression Rating Scale, and heart rate. *p<0.05. LF, low frequency; HF, high frequency; PTSD, post-traumatic stress disorder.

Figure 3.

Interactive modifying associations of LF and HF bands of heart rate variability, and functioning level with PTSD over 2 years in 538 patients with physical injuries. aInteractive modifying associations of LF, HF, and functioning level assessed by the Social and Occupational Functioning Assessment Scale on PTSD were estimated using multinomial logistic regression; bodds ratios (95% confidence intervals) were calculated using binary logistic regression for higher (>57 ms²) vs. lower (≤57 ms²) LF and for higher (>38 ms²) vs. lower (≤38 ms²) HF at baseline on PTSD, adjusted for age, sex, education, previous psychiatric disorders, previous traumatic events, number of physical disorders, current smoking, scores on Alcohol Use Disorders Identification Test, Hamilton Depression Rating Scale, and heart rate. *p<0.05; **p<0.01. LF, low frequency; HF, high frequency; PTSD, post-traumatic stress disorder.

Table 1.

Baseline characteristics by lower (≤60) vs. higher (>60) functioning status assessed by Social and Occupational Functioning Assessment Scale in 538 patients with physical injuries

	Lower functioning (N=253)	Higher functioning (N=285)	Statistical coefficients	p^*
Socio-demographic characteristics
Age, years	59.7±16.3	54.6±17.2	t=+3.490	0.001
Sex, female	97 (38.3)	72 (25.3)	χ²=10.637	0.001
Education, years	10.2±4.4	11.1±4.0	t=-2.234	0.026
Marital status, unmarried	77 (30.4)	101 (35.4)	χ²=1.516	0.218
Living alone	39 (15.4)	40 (14.0)	χ²=0.204	0.652
Unemployed status	46 (18.2)	42 (14.7)	χ²=1.163	0.281
Pre-trauma characteristics
Previous psychiatric disorders	25 (9.9)	16 (5.6)	χ²=3.467	0.063
Previous traumatic events	14 (5.5)	15 (5.3)	χ²=0.019	0.890
Any childhood abuse	12 (4.7)	25 (8.8)	χ²=3.397	0.065
Physical disorders, numbers	2.3±2.7	1.7±1.9	t=+3.376	0.001
Current smoker	55 (21.7)	85 (29.8)	χ²=4.551	0.033
AUDIT, scores	8.8±9.6	11.4±10.2	t=-3.041	0.002
Body mass index, kg/m²	23.6±3.4	23.6±3.5	t=-0.096	0.924
Trauma related characteristics
Injury type, intentional	21 (8.3)	30 (10.5)	χ²=0.774	0.379
Injury Severity Score, scores	14.3±5.6	13.8±4.9	t=+1.116	0.265
Glasgow Coma Scale, scores	14.8±0.7	14.9±0.5	t=-2.018	0.044
Got surgery for the injury	133 (52.6)	135 (47.4)	χ²=1.450	0.229
Peri-trauma assessment scales and measurements
HAMA	6.5±5.4	3.1±3.1	t=+8.548	<0.001
HAMD	8.3±6.0	4.6±4.1	t=+8.112	<0.001
Systolic blood pressure, mm Hg	120.9±14.3	117.6±13.9	t=+2.688	0.007
Diastolic blood pressure, mm Hg	72.6±8.9	71.2±9.2	t=+1.697	0.090
Heart rate per minute	80.3±11.3	77.2±10.6	t=+3.207	0.001

Data are presented as mean±standard deviation or number (%).

^* t-tests or χ² tests, as appropriate between patients with and without 24-months follow-up evaluation.

AUDIT, Alcohol Use Disorders Identification Test; HAMA, Hamilton Anxiety Rating Scale; HAMD, Hamilton Depression Rating Scale.

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