ABSTRACT Teachers are at high-risk of poor sleep. Teachers face many individual factors (e.g. teacher self-efficacy) and organisational factors (e.g. stressful milestones of the school year) that may influence their sleep. This study explores the impact of individual and organisational factors on the sleep of Australian teachers. An online survey was completed by 775 (89% female, 52.6% 35–55 years old) Australian school teachers. Questions included demographics, school milestones, organisational support, and the Pittsburgh Sleep Quality Index (PSQI). Descriptive statistics determined the milestones of the school year where the poorest sleep was reported. A binomial logistic regression model was used to model the likelihood of experiencing poor sleep quality as a function of demographic variables. A majority of teachers (59%) reported less than 7 hours of sleep per night, and poor sleep quality (79%). During stressful times of the school year, such as report writing, 35% of teachers believed their sleep to be less than 5 hours. To improve sleep, these individual and organisational factors should be incorporated into sleep education provided to teachers and organisations.

Sleep is an essential element in every human being's life, as it impacts the quality of an individual's life. When a person's sleep is uncomfortable and irregular, this leads to the possibility of exposure to many mental and physical illnesses that affect activity. This study aimed to measure the quality of sleep and its relationship to stress and anxiety, and what practices and attitudes that people do regarding their sleep. This is a cross-sectional study was employed to assess sleep quality and its relationship with stress and anxiety, and attitudes toward sleep among adults in Riyadh, Saudi Arabia between December 2024 and April 2025. Data were collected via a self-administered survey consisting of 5 validated questionnaires (satisfaction with life scale, hospital anxiety and depression scale, perceived stress scale [PSS], dysfunctional beliefs and attitudes about sleep [DBAS], and Pittsburgh sleep quality index [PSQI]). Logistic regression was used to find the influencing factors for each outcome. The study involved 687 participants. Married individuals were less likely to experience anxiety compared to singles (OR = 0.21, 95% CI: 0.1-0.45, P < .001). Older age was significantly associated with higher odds of elevated DBAS and poor PSQI scores. Individuals aged 61 and above had increased odds of dysfunctional beliefs about sleep (OR = 6.00, 95% CI: 1.25-28.74, P = .025) and poor sleep quality (OR = 22.65, 95% CI: 4.37-117.39, P < .001) compared to those age 18 to 20. Individuals with higher levels of DBAS, PSS, and anxiety (hospital anxiety and depression scale) were significantly associated with poorer sleep quality, as measured by PSQI score. Specifically, higher level of DBAS increased the odds of poor sleep (OR = 1.01, 95% CI:1.00-1.02, P = .02). Similar with stress, 1 point increase in the PSS scale, increased the poor quality of sleeping (OR = 1.16, 95% CI: 1.08-1.24, P < .001), while the anxiety level increased the odds of poor quality of sleeping 14% (OR = 1.14, 95% CI: 1.08-1.19, P = .001). This study emphasizes a robust relationship between inadequate sleep quality and psychological factors. A variety of demographic factors, including age, gender, marital status, nationality, and employment, are identified as substantial contributors.

Abstract Bedtime procrastination (BP) has been identified as a significant factor contributing to poor sleep health. While previous studies have examined its behavioral and psychological correlates, its functional role in individuals with clinical insomnia remains unclear. This study aims to investigate the primary functions of BP in individuals with insomnia and examine its relationship with smartphone use. A total of 80 young adults (mean age 22.3 ± 2.4 years, 80% female) with clinical insomnia participated in the study. BP, insomnia severity, and emotion regulation were assessed using self-report questionnaires. Additionally, sleep patterns were monitored using a daily sleep diary and actigraphy. A structured interview using functional analysis was conducted to analyze the individual functions of BP, classifying responses into seven categories based on antecedents, behaviors, and consequences. Participants spent an average of 95.9 (SD = 38.3) minutes per day using their smartphones in the three hours before bedtime. Most participants used their phones every day during this window (78.6%). The most common functions of BP were emotion regulation (49.3%), reward (14.3%), and sleep induction (10.7%). In addition, adaptive emotion regulation strategies significantly moderated the relationship between BP and smartphone use in the three hours before bedtime (β = 0.34, 95%CI = [0.02 ~ 0.23]). Our findings suggest that for individuals with clinical insomnia, BP, which is largely driven by smartphone use, can serve as a tool for emotion regulation. Interventions targeting BP should incorporate strategies considering individual functions of BP, rather than merely restricting media use.

Rapid urbanization and significant lifestyle changes have become the risk drivers for the epidemiology of diseases. With urban transitions, substantial persistence of pollutants in the environment has been observed. Epidemiological studies indicate a strong relationship between air pollution and exacerbation of asthma and other allergic diseases due to particulate matter (PM). PMs in bioaerosols and aeroallergens induce the immune response, eliciting systemic inflammation. Continuous exposure to PM2.5 along with gases like nitrogen oxide aggravate oxidative stress and inflammatory responses. Other pollutants elevate blood glucose, inducing poor sleep patterns which in turn induce low-grade chronic inflammation. This in turn acts as a trigger for adipocyte dysfunction and reduced energy expenditure. Taken together, air pollution, allergy, and obesity constitute a jigsaw with missing pieces. Transient Receptor Protein (TRP) channels have important roles in allergic rhinitis, systemic inflammation, adipogenesis, and obesity development, underscoring a potential role as a common mechanistic link. The goal of this review is to summarize and comprehend the intricate network connecting these “modern-day hyperendemic diseases” and the plausible role played by TRP in shaping their epidemiology. Bioactive compounds in dietary spices also modulate TRP channels. Thus, spices position themselves as potential regulators at the interface of environmental sensing, inflammation, and metabolism, indicating spice-based interventions may represent an adjunct strategy to alleviate the pollution-induced allergy and obesity risk.

Nocturnal symptoms and sleep disturbances are increasingly recognized as clinically important in chronic obstructive pulmonary disease (COPD) but remain understudied in low- and middle-income countries. Objective: To assess sleep quality and determine its association with respiratory symptoms in Vietnamese outpatient COPD patients. We conducted a cross-sectional study on 289 consecutive patients (≥ 40 years old) with a confirmed COPD diagnosis who were being monitored at the COPD management unit of Bach Mai Hospital, Hanoi (February-May 2025). Demographic and clinical data (mMR dyspnea, CAT score, FEV₁ after bronchodilator use, comorbidities, number of hospitalizations in the previous year) were collected through structured interviews and medical record reviews. Sleep was assessed using the Pittsburgh Sleep Quality Index (PSQI). The mean PSQI total score was 9.2 ± 3.7; 85.8% of participants met the threshold for poor sleep. 65.4% of patients had mMRC ≥ 2, 88.9% had CAT ≥ 10, and 88.9% of patients had an acute hospitalization in the past year. High CAT score, mMRC, long disease duration, and FEV1 below 50% were risk factors for impaired sleep quality as measured by PSQI. Poor sleep quality is highly prevalent among Vietnamese outpatient COPD patients and is closely associated with respiratory symptom burden and disease severity. These findings underscore the importance of routine sleep quality assessment in COPD management and suggest that optimizing symptom control may contribute to improved sleep and overall patient outcomes.

Sleep is increasingly understood as a socially embedded phenomenon. This study examined how structural and functional aspects of social support, as well as loneliness, relate to sleep health in a German sample of middle-aged adults (N = 5388). Drawing on the socio-ecological model of sleep health, we assessed the contributions of social support dimensions while accounting for age, sex, and socioeconomic status, as well as psychological covariates. The results of the binary logistic regression showed that functional support (ESSI), friend network size (LSNS6), and loneliness (CES-D item 14) significantly (p < 0.001) predicted sleep health (PSQI), while family network size did not. The portion of explained variance was small (4%-5%). Results remained robust after adjusting for age, sex, and socioeconomic status, but no longer when including psychological covariates (GAD-7, SWLS, CES-D), in which case only the friend network size remained significant (p = 0.019). Women were significantly more affected by poor sleep health than men, and with higher socioeconomic status, fewer people reported suffering from poor sleep (all: p < 0.001). Additional subgroup analysis revealed higher age as a risk factor for worse sleep health in women only, while the friend network was only relevant in men. Our findings highlight the importance of distinguishing between structural and functional dimensions of social support in sleep health research and interventions, and suggest a potential sex-by-age interaction. Future research should promote equity by including diverse populations and longitudinally examine how social support, especially friend networks, affects sleep across genders, ages, and contexts.

Background Nighttime screen use is associated with poor sleep and may represent an emerging public health concern. Medical students are a vulnerable group for harmful health behaviour due to demanding studies, while also playing a key role in future health promotion. The study examined the prevalence of nighttime screen use among medical students in different countries and explored associations with sleep and smartphone addiction symptoms. Methods The Medical Student Health Survey, an international cross-sectional study, was conducted at four sites in Germany (GER), Austria (AU), Hungary (HU), and Japan (JA). Nighttime screen use was assessed via five self-developed items, sleep via Pittsburgh Sleep Quality Index (PSQI), and smartphone addiction symptoms via Smartphone Addiction Scale–Short Version (SAS-SV). Group differences were tested using chi-square, Welch’s t-test, Z-test, and Kruskal–Wallis test (Bonferroni-adjusted), and associations were evaluated using Kendall’s tau-b and a multivariable binary logistic regression model. Results Data from n = 1,262 medical students (mean age 21–24, 56%–73% female) were analysed. The median pre-bedtime screen use was 30 minutes, and the median end of use was between 5 and 10 minutes before bedtime. Approximately 96%–98% used a screen within 1 hour of bedtime; 69%–89% used a smartphone/tablet. SAS-SV scores ranged from M = 23.2 (HU) to M = 27.0 (JA), and the prevalence of smartphone addiction ranged from 12.6% (GER) to 27.9% (JA). PSQI scores ranged from M = 4.33 (JA) to M = 5.22 (HU), and prevalence of poor sleep quality ranged from 25% (JA) to 40.7% (HU). Prolonged pre-bedtime screen use was associated with increased sleep latency (τ = 0.106, p &amp;lt;.001), shorter distance between the end of screen use and bedtime, and increased SAS-SV scores (τ = −0.150, p &amp;lt;.001). Screen use after waking up at night was associated with poorer sleep quality (τ = 0.174, p &amp;lt;.001), poorer outcome in five of seven PSQI subscales (all τ &amp;gt; 0.1, p &amp;lt;.001), and increased SAS-SV scores (τ = 0.165, p &amp;lt;.001). Sleep disturbance by a device was associated with poorer sleep quality (τ = 0.170, p &amp;lt;.001), poorer outcome in three of seven PSQI subscales (all τ &amp;gt; 0.1, p &amp;lt;.001), and increased SAS-SV scores (τ = 0.135, p &amp;lt;.001). In the logistic regression, screen use after nocturnal awakenings and device-related sleep disturbance remained independently associated with poor sleep quality (all p ≤.04), whereas smartphone addiction symptoms showed no independent association. Conclusions The findings highlight the need for targeted interventions to promote health-conscious screen use among medical students.

Background: Primary dysmenorrhea is a common gynecological condition among young women and may significantly impair daily activities and academic performance. Sleep quality has been suggested as an important modifiable risk factor, as poor sleep may increase inflammatory mediators and prostaglandin production involved in menstrual pain. However, findings regarding the relationship between sleep quality and primary dysmenorrhea remain inconsistent across populations. Objective: To determine the association between sleep quality and the incidence of primary dysmenorrhea among female engineering students at Nusa Cendana University. Methods: This analytical observational study employed a cross-sectional design. A total of 60 female students who met the inclusion and exclusion criteria were recruited using purposive sampling. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while the severity of dysmenorrhea was evaluated using the WaLIDD score. Data were analyzed using univariate and bivariate analyses. The Mann–Whitney test was applied to determine the association between sleep quality and primary dysmenorrhea. Results: The majority of respondents (.7%) had poor sleep quality. Primary dysmenorrhea was reported by 86.7% of participants, with moderate severity being the most common (45%), followed by mild (33.3%) and severe dysmenorrhea (8.3%). Statistical analysis demonstrated a significant association between sleep quality and the incidence of primary dysmenorrhea (p &lt; 0.001). Conclusion: Poor sleep quality is significantly associated with the occurrence of primary dysmenorrhea among female engineering students at Nusa Cendana University. Improving sleep quality may represent a potential non-pharmacological approach to reducing menstrual pain in this population.

Recent studies revealed that more than half of French business leaders are at risk of burnout. They sacrifice sleep, physical activity and often work over 60 hours weekly. Poor sleep and lack of exercise contribute to major health issues in general population. To date, no study explored the variations in health status among business leaders across different types and sizes of companies. This study aims to assess health among French business leaders, focusing on sleep quality, physical activity, anxiety, and stress levels across their different organizational contexts. We hypothesized that hierarchical positions and level of responsibility was associated with severity of health issues. 361 business leaders (158 women/203 man) agreed to complete questionnaires including: Pittsburg Sleep Quality Index, Perceived Stress Scale, Global Anxiety Disorder and International Physical Activity Questionnaire. RStudio software was utilized for descriptive statistics and analyses. Results revealed that 67.3% of them have poor sleep, 47.8% are highly stressed, and 22.5% have very low levels of physical activity. Women exhibit worse mental health and top leaders of small enterprise experience more stress, practice less physical activity and have poorer sleep The findings underscore the need for targeted health promotion strategies for leaders that take into consideration sex and organizational context.

This study aimed to shed light on the potential relationships between blood volatile organic compounds (VOCs) and sleep health as well as mortality. We employed generalized linear (GL), restricted cubic spline (RCS), weighted quantile sum (WQS), quantile-based g-calculation (QGC), and Bayesian kernel machine regression (BKMR) models to assess the relationship between blood VOCs-including bromoform (NHANES code: LBXVBF), bromodichloromethane (LBXVBM), chloroform (LBXVCF), dibromochloromethane (LBXVCM), and methyl tert-butyl ether (LBXVME)-and sleep health indicators (trouble sleeping, sleep disorders, and insufficient (< 6h/day) or excessive (> 9h/day) sleep) in participants from the NHANES 2007-2012. The Cox proportional hazards regression model was also used for survival analysis. The baseline profile categorized by sex showed that women had a higher prevalence of trouble sleeping, whereas men were more prone to insufficient sleep. We did not observe significant linear-correlations between VOCs and both increased sleep duration and poor sleep patterns, as shown by the weighted linear/logistic regression models. The RCS regression model indicated significant non-linear relationships (P for non-linear < 0.05) between certain VOC and sleep health. Adjusted QGC analysis highlighted LBXVBF as a crucial factor related to poor sleep quality (weighted 0.733). The BKMR analysis showed a positive trend between VOC levels (55th to 75th percentiles) and poor sleep pattern. Furthermore, the adjusted COX-RCS analysis identified LBXVME (P for non-linear = 0.0359) as a risk factor for all-cause mortality. This study investigated the non-linear association between VOC exposure and sleep function, suggesting that VOC exposure may be linked to poor sleep patterns among U.S. adults.

Poor sleep is a concerning public health problem in the United States. Previous sleep interventions often face barriers such as high costs, limited accessibility, and low user engagement. Recent advancements in artificial intelligence (AI) technologies offer a novel approach to overcoming these limitations. In response, our team developed a prototype AI sleep chatbot powered by a large language model to deliver personalized, accessible sleep support. This study aimed to examine the feasibility, usability, acceptability, and preliminary efficacy of the AI chatbot for sleep promotion. We conducted a quasi-experimental, single-group study with adults in the United States aged 18 to 75 years who self-reported poor sleep. The chatbot was integrated into a commercially available messaging app. Participants were asked to engage with a virtual sleep therapist via texting over 2 weeks. The chatbot provided ongoing, individualized sleep guidance and adapted recommendations based on participants' prior conversations. Feasibility, usability, and acceptability were descriptively summarized. Sleep was assessed using questionnaires before and after the intervention. Of the 107 adults who enrolled in the study, 88 (82.2%) completed chatbot registration. Among these 88 participants, 65 (73.9%) initiated interactions, and 44 (50%) completed the 2-week intervention. The final analysis included 42 adults (mean age 36, SD 11 years; n=12, 28.6% male). On average, participants engaged with the chatbot for 58 (SD 42) minutes, with each chat session lasting approximately 9 (SD 6) minutes. Most reported favorable experiences with the chatbot. The average usability score was 85.2 (SD 10.7) out of 100, which was well above the benchmark of 68. The chatbot was rated as highly acceptable, with a satisfaction score of 27.3 (SD 4.1) out of 32. All participants perceived the chatbot as effective, with ratings ranging from "slightly effective" to "extremely effective." The preliminary evidence showed improved sleep outcomes after chatbot use: total sleep time increased by 1.4 hours (P<.001); sleep onset latency decreased by 30.9 minutes (P<.001); sleep efficiency increased by 7.8% (P=.007); and scores improved for perceived sleep quality (mean difference [MD] -5.4; P<.001), insomnia severity (MD -7.9; P<.001), daytime sleepiness (MD -4.7; P<.001), and sleep hygiene skills (MD -13.2; P<.001). No significant change was observed in sleep environment (MD -1.1; P=.16). Our AI chatbot demonstrated satisfactory feasibility, usability, and acceptability. Improvements were observed following chatbot use, although causality cannot be established. These findings highlight the potential of integrating state-of-the-art large language models into behavioral interventions for sleep promotion. Future research should include objective sleep measurements and conduct randomized controlled trials to validate the study findings. If confirmed, this AI chatbot could be scaled to support sleep health on a broader level.

The growing and pervasive use of social network sites (SNS) has raised concerns about their impact on adolescent mental health during this sensitive developmental phase. Existing longitudinal studies are constrained by methodological limitations and limited exploration of underlying mechanisms. We investigated the longitudinal associations between SNS use and depressive and anxiety symptoms in adolescents and whether sleep mediated these associations. We analysed longitudinal data from 2350 adolescents from 31 schools in London, participating in the Study of Cognition, Adolescents, and Mobile Phones (SCAMP). The exposure was self-reported duration of SNS use at baseline (aged 11-12 years). Outcomes were depressive and anxiety symptoms at follow-up, analysed as symptom severity and clinically significant symptoms (aged 13-15 years). The associations between SNS use and depressive and anxiety symptoms were assessed via multi-level ordinal logistic regression (symptom severity) and logistic regression (clinically significant symptoms). The mediation effects of insufficient sleep, sleep onset latency, and sleep disturbance were assessed by mediation analysis. Compared to 0-30 min per day, more than 3 h per day of SNS use at baseline was associated with higher severity levels of depressive and anxiety symptoms (adjusted odds ratio (OR) = 1.47, 95% CI 1.12, 1.93 and OR = 1.40, 95% CI 1.06, 1.83, respectively) and clinically significant depressive and anxiety symptoms at follow-up (OR = 1.70, 95% CI 1.19, 2.42 and OR = 1.60, 95% CI 1.11, 2.31, respectively). The associations between total and weekend SNS use and depressive symptom severity were stronger in girls than boys. Other associations were similar by gender. Insufficient sleep duration (particularly on weekdays) and sleep onset latency at baseline partly mediated the associations of SNS use and depressive and anxiety symptoms (proportion of mediation ranged between 11.1% and 33.1%). The mediation effects of sleep disturbance were less marked. In a large longitudinal cohort, we found that SNS use exceeding 3 h per day is associated with increased risks of depressive and anxiety symptoms in adolescents. Findings from mediation analysis suggest that addressing poor sleep hygiene in relation to SNS use might mitigate the negative impact of high SNS use. Our findings may inform the development of early secondary school curricula incorporating digital literacy and sleep hygiene education.

Fatigue is a burdensome effect of ovarian cancer that is associated with poor sleep and quality of life. Self-acupressure is recommended in clinical guidelines but has substantial barriers to implementation. Use of a mobile app may address these barriers. To investigate whether 6 weeks of true self-acupressure (TSA), learned via a mobile app, improves cancer fatigue, sleep, and quality of life in women with ovarian cancer compared with sham self-acupressure (SSA) and usual care (UC) and whether changes are sustained during an 18-week washout period. This phase 3 single-blind randomized clinical trial was conducted from October 2019 to December 2023. Data collection ended in November 2024. Participants included ovarian cancer survivors who were fatigued (based on a Brief Fatigue Inventory [BFI] score ≥4) and who were recruited from tumor registries and social media. Randomization (1:1:1) to 6 weeks of TSA or SSA, taught via mobile app, or UC. The primary outcome was the change in the BFI from baseline to week 6. Secondary analyses were the BFI score at week 24 and sleep disturbance (based on the Pittsburgh Sleep Quality Index) and quality of life (based on the Functional Assessment of Cancer Therapy-Ovarian) administered at baseline and at weeks 6, 12, and 24. Among the 360 participants who were screened, 171 women were randomized (mean [SD] age, 56 [12] years). Of the 160 participants who were allocated to the arms, 53 (33.1%) received TSA, 56 (35.0%) received SSA, and 51 (31.9%) received UC. Of these, the proportion achieving a clinically normal fatigue level at the end of treatment was 58.5% for the TSA arm, 51.1% for the SSA arm, and 17.6% for the UC arm. At 6 weeks, the BFI change scores were significantly better in the TSA arm but not in the SSA arm when they were compared with the UC-only arm (TSA vs UC: adjusted mean difference, -1.23 [95% CI, -2.17 to -0.29] and SSA vs UC: adjusted mean difference, -0.91 [95% CI, -1.83 to 0.02]). TSA and SSA change scores did not differ significantly from one another. The relative benefit of self-acupressure compared with UC on fatigue persisted at 24 weeks (TSA vs UC: mean difference, -1.38 [95% CI, -2.36 to -0.41] and SSA vs UC: mean difference, -0.97 [95% CI, -1.93 to -0.02]). Neither TSA nor SSA was significantly different than UC or each other for sleep quality. Only TSA significantly improved quality of life vs UC (odds ratio, 2.85 [95% CI, 1.20 to 6.80]). Neither true nor sham self-acupressure led to any adverse events. In this randomized clinical trial, TSA and SSA significantly reduced fatigue compared with UC, and these changes were both clinically meaningful and sustained. No impact was observed on sleep quality. Self-acupressure, taught via a mobile app, offered a safe and low-cost option for managing cancer fatigue. ClinicalTrials.gov Identifier: NCT03763838.

Survey of health-related quality of life and academic-life balance among doctor of pharmacy students.

Exploring the longitudinal relationships between poor sleep, sleep apnoea, and depression during pregnancy.

Development, validation, and four-year clinical application of a novel, rapid, sensitive, and high-throughput UPLCMS/MS method for the simultaneous determination of vitamins A and E in 50 μL of serum.

In the present era, environmental attitudes and awareness, along with digital behavior among the youth, play a crucial role in ensuring the sustainability of natural resources and societal well-being. This study examined the digital behavior, mental health, and environmental attitudes among the undergraduate students at Krishnagar Government College, West Bengal, India. A structured questionnaire-based survey was conducted using a ‘Google Form’ to collect the responses anonymously. A total of 398 responses were received from the surveyed students, which is approximately 16 to 18% of the enrolled college student population. Key findings suggest that excessive screen time was associated with poorer sleep, increased academic stress, and concentration difficulties. Students who seldom sought support were significantly more likely to report severe stress, while eco-conscious behaviors, such as using reusable drinking water bottles, were associated with more positive overall well‑being. Clustering of the responses revealed distinct student profiles based on usage and stress metrics, suggesting targeted interventions. This study provided a comprehensive statistical overview of college students’ well‑being in the digital age and offered evidence-based recommendations for college campus policies and support programs.

Previous meta-analyses have shown that loneliness is associated with poor health (r = -.35) and sleep challenges (r = .29). However, that work is confounded by the inclusion of unhealthy populations, with chronic conditions, such as HIV, Alzheimer's disease, diabetes, and severe mental illness. This pre-registered meta-analysis (PROSPERO CRD42019119135) examined whether loneliness is linked to poorer health in healthy populations. Meta-analysis. PsycINFO, ERIC, PubMed, and Web of Science were searched for articles before January 2024 to examine the association between loneliness and health outcomes (categorized as general health, physical health [functional disability], sleep function, sensory acuity, and health service use) for healthy samples. A total of 167 articles (303,643 participants; 334 effect sizes; 158 independent samples) from 36 countries were included in the meta-analysis. Loneliness is associated with worse health for healthy populations (r = -.22), with the largest effect (r = -.23) for general health. Loneliness was not related to health service use for healthy populations. Heterogeneity was substantial, with only 7.2% of the total variance attributable to sampling error, indicating that over 92% of the variability in effect sizes reflected true differences across studies. Higher loneliness was associated with poorer health for healthy people. There are limited studies using (1) objective measurements of health outcomes in relation to loneliness, and (2) studies exploring the association among young people, highlighting a need for more work in those areas.

Poor subjective sleep quality independently predicts greater disability and functional decline in older adults at-risk of dementia

Multidimensional qualities of sleep health following serious physical combat injuries: A Latent Class Analysis: The ADVANCE cohort study.