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. 2025 Jun 11;38(5):781–792. doi: 10.1002/jts.23180

Examining the between‐ and within‐person associations among perceived sleep quality and mental health symptoms in emergency medical service clinicians

Enzo G Plaitano 1, Shengzi Zeng 2,3, Mariel Emrich 4, Emily E Patton 5, E Kate Webb 6, Maria L Pacella‐LaBarbara 7, Marley S Barduhn 8, Bryce Hruska 9,✉
PMCID: PMC12551621  PMID: 40501003

Abstract

Disturbed sleep is a known risk factor for heightened mental health symptoms, and this association may be particularly problematic among emergency medical service (EMS) clinicians. Yet, associations between daily sleep quality and daily mental health symptoms are understudied among this vulnerable group. We used ecological momentary assessments to examine between‐ and within‐person associations between perceived sleep quality and mental health symptoms in 79 EMS clinicians employed at a large agency in central New York. Participants completed eight daily assessments (558 total) on perceived sleep quality and symptoms of posttraumatic stress disorder (PTSD) and depression. Multilevel regression models examined between‐ and within‐person effects of sleep quality, controlling for covariates. Between‐person effects in each model accounted for 17.0% and 31.0% of the total variance, respectively; within‐person effects explained 1.0% per model. Poorer between‐person perceived sleep quality was associated with higher PTSD and depressive symptom severity; perceived sleep quality 1.0 standard deviation (SD) below the sample mean was related to 58.8% and 16.3% increases in PTSD and depressive symptoms, respectively. There was also a within‐person effect for depressive symptoms: On days when a participant's perceived sleep quality was 1.0 SD below their average, depressive symptom severity increased by 3.0%. Poorer subjective sleep may be an important risk factor for mental health symptoms at the between‐person level. EMS policies supporting healthy sleep may benefit clinicians who routinely experience poor sleep. Day‐to‐day subjective sleep quality may increase the risk for depressive symptoms. Interventions to improve sleep and manage depressive symptom fluctuations when sleep is poor might be helpful.

Emergency medical service (EMS) clinicians are first responders who deliver life‐saving care in the prehospital setting (Plat et al., 2011). This care often includes ambulance transport, the assessment and management of vital signs, and medication administration. Due to the nature of their occupation, EMS clinicians are repeatedly placed in unpredictable situations and exposed to frequent job‐related stressors. Witnessing death, serious injuries, and serious health events are routine experiences for EMS clinicians that can increase mental health burden. Compared to other first responders, including firefighters and police officers, EMS clinicians report elevated posttraumatic stress disorder (PTSD) and depressive symptoms (Huang et al., 2022). There is a pressing need to identify modifiable risk factors that can be targeted to ensure a healthy workforce.

Disrupted sleep is a hallmark of the EMS profession, primarily due to a demanding shiftwork schedule (Patterson et al., 2010, 2012). Shiftwork is defined as “any arrangement of work hours other than standard daylight hours” and often includes rotating day/night shifts, which are related to higher rates of fatigue and poor sleep quality (Patterson et al., 2015, 2012). In fact, approximately half of EMS clinicians in the United States report poor sleep quality and insufficient sleep while on duty. In addition to shiftwork, long shift lengths, inconsistent work schedules, and limited time off contribute to higher fatigue and poor sleep in EMS clinicians (Patterson et al., 2015, 2012, 2018).

Although PTSD and depression have been suggested as risk factors for sleep disturbances (Ettensohn et al., 2016; Wallace et al., 2017), emerging research indicates that disturbed sleep may also increase the risk for PTSD (Cox et al., 2017) and depression (Riemann, 2003). Poor perceived sleep quality has been associated with a 60.0% higher incidence of screening positive for PTSD among military veterans (DeViva et al., 2021), and meta‐analytic findings highlight that interventions with demonstrated improvements in sleep quality can lead to less severe symptoms of depression and stress across varied patient populations (Scott et al., 2021). Lastly, a recent meta‐analysis examining health care workers found that disturbed sleep was correlated with higher self‐reported depressive and stress symptom levels (Liu et al., 2022). Collectively, these findings suggest that worse sleep quality is related to both PTSD and depressive symptoms. This directionality is particularly relevant for EMS clinicians given the sleep disruption that characterizes the profession (Patterson et al., 2010, 2012).

Despite the high prevalence of sleep disturbances and mental health symptoms among EMS clinicians, the association between sleep quality and mental health symptom severity is understudied in this population. In a recent study, Nguyen et al. (2023) found that higher insomnia symptoms and wakefulness during sleep predicted higher PTSD and depressive symptoms 6 months after beginning emergency service work in a sample of Australian paramedics. Additionally, a survey of first responders in South Korea found associations between poorer sleep quality and symptoms of both PTSD and depression. Though these studies highlight the risks for EMS clinicians, none examined the associations between daily sleep quality and mental health symptoms.

The current study used ecological momentary assessment (EMA) to consider the concurrent daily associations between perceived sleep quality and both PTSD and depressive symptom severity among EMS clinicians. EMA is a data collection technique in which respondents complete short online surveys, often delivered directly to respondents’ smartphones, one or more times per day over a fixed period (Trull & Ebner‐Priemer, 2013). An important advantage of EMA is that it allows for the consideration of both between‐ and within‐person associations (Yang et al., 2019). Between‐person associations reflect stable characteristics that differentiate individuals from each other, whereas within‐person associations reflect the degree to which experiences or behaviors fluctuate within the same person over time (Stange et al., 2019; Yang et al., 2019). These different associations can offer complementary insights into how sleep is related to mental health: The presence of a between‐person association would shed light on who is at the highest risk (e.g., EMS clinicians who typically have poor sleep tend to have higher mental health symptoms), whereas, in contrast, the presence of a within‐person association would indicate when an individual is at the highest risk (e.g., on occasions when EMS clinicians experience worse sleep than usual, they also experience worse mental health symptoms).

Although various methods exist to measure sleep health, many studies examine subjective sleep quality, which has been correlated with longer sleep duration (via sleep diaries) and higher sleep efficiency (via actigraphy; A. E. Carney et al., 2022). Given the individualized and perceptual nature of sleep in the context of shiftwork, the current study used subjective sleep quality to capture personal evaluations of participants’ sleep experience. To our knowledge, the present study is the first to examine both between‐ and within‐person associations between sleep and mental health symptoms in this high‐risk EMS population.

We hypothesized that EMS clinicians with lower perceived sleep quality would experience more severe mental health symptoms (i.e., a between‐person association) and that when an EMS clinician experienced lower perceived sleep quality than normal, they would report increased mental health symptoms (i.e., a within‐person association). We tested these associations in a concurrent effects model, as opposed to cross‐lagged models, in which sleep quality and mental health symptoms were reported during the same assessment.

METHOD

Participants

Participants included 79 licensed EMS clinicians employed at the largest EMS agency in Central New York. Inclusion criteria consisted of being 18 years of age or older and having at least one EMS shift scheduled at the recruiting EMS agency within the next 8 days. We chose this sampling window because the typical schedule at the recruitment site consisted of four consecutive EMS shifts followed by 4 days off from work.

Procedure

Study procedures were approved by Syracuse University's Human Subjects Review Board (IRB approval number: 19–114). This study was a collaboration between Syracuse University and American Medical Response, Inc. (AMR), which is the largest private medical transportation service in the United States and the primary EMS provider in the Central New York region.

Individuals were recruited by email from June 2019 to August 2019. The email included a link to an initial online survey that collected eligibility information, informed consent, and contact details. Eligible and consented participants received an email containing a link to an EMA at 6:00 a.m. each day for 8 days following this initial survey. The EMAs were collected through REDCap (Harris et al., 2009). We instructed participants to complete the EMA upon receipt if they already worked at AMR that day or if they were not working at AMR that day. If they were scheduled to work at AMR later that day, we asked them to complete the EMA after their shift. Participants were compensated $2 (USD) for the screening, $4 for the first EMA, and an additional $1 for each subsequent EMA, with a $20 bonus awarded for completing all EMAs. The maximum compensation for this study was $82.

Measures

Perceived sleep quality

Perceived sleep quality was measured using a single item from the Consensus Sleep Diary (CSD; C. E. Carney et al., 2012), a valid and reliable measure used to track subjective sleep. This measure was developed by a core workgroup of insomnia experts and validated with patient focus groups and lexical analysis. The CSD has demonstrated strong correlations with sleep time, measured through actigraphy, in community samples (rs = .63–.75; Dietch & Taylor, 2021). Participants were asked to rate the perceived sleep quality of their most recent sleep episode (i.e., “How would you rate the quality of your sleep?”) on a scale of 0 (very poor) to 4 (very good). The CSD has been validated in prior studies of health care professionals, including physicians (Thimmapuram et al., 2021).

PTSD symptom severity

PTSD symptom severity was assessed using the short‐form PTSD Checklist for DSM‐5 (SF‐PCL‐5; Price et al., 2016). The SF‐PCL‐5 includes four items from the full 20‐item PCL‐5 (Weathers et al., 2013), with each item corresponding to one of the four PTSD symptom clusters defined in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM‐5; American Psychiatric Association, 2013). PCL‐5 items included in the SF‐PCL‐5 are Item 1 (intrusions cluster), Item 7 (negative alterations in cognitions and mood cluster), Item 9 (avoidance cluster), and Item 18 (alterations in arousal and reactivity cluster; Price et al., 2016). This four‐item scale has demonstrated comparable diagnostic utility to the 20‐item PCL‐5 in samples of adults exposed to traumatic events (r = .95) and combat veterans (r = .93), with good internal consistency (Cronbach's α = .83).

Participants were instructed to think about a stressful work‐related or personal event that occurred at least 1 month ago and indicate how much each symptom still bothered them on each day of the study, rating responses on a scale of 0 (not at all) to 4 (extremely). A total PTSD symptom severity score was created by summing all items (range: 0–16). In the current sample, internal consistency ranged from adequate, Cronbach's α = .74, to excellent, Cronbach's α = .94. Although stressors from infrequent major events, such as the death of a patient, can lead to a stress response, everyday stressors may also lead to stress responses (Smyth et al., 2018). Therefore, we did not limit inclusion to only events with exposure to death or threatened death (i.e., DSM‐5 Criterion A exposure).

Depressive symptom severity

Depressive symptom severity was measured using the three‐item Mental Health Inventory–Depression Scale (MHI‐d), which is derived from the validated and reliable Short‐Form Health Survey (Ware & Sherbourne, 1992). The MHI‐d has demonstrated good internal consistency (Cronbach's α = .77) and excellent detection of major depressive disorder (area under the curve [AUC] = .91) in community samples of adults (Cuijpers et al., 2009).

Participants were asked to indicate how much of the time that day they had experienced each symptom (e.g., “You felt downhearted and blue today”), rating responses on a scale of 1 (all the time) to 6 (none of the time). Items were summed to create a total depressive symptom severity score (range: 3–18). In the current sample, internal consistency ranged from moderate, Cronbach's α = .61, to very good, Cronbach's α = .88, across time.

Covariates

Study day, age, race, sex, night shift status, total number of night shifts (i.e., night shift total), weekend status, and total number of weekend shifts (i.e., weekend total) were included as covariates. Study day (0 = Day 1, 7 = Day 8) was included as a Level 1 variable to account for day‐to‐day changes in mental health symptoms. Age (in years), race (0 = White, 1 = non‐White), and sex (0 = male, 1 = female) were included as Level 2 variables. We included demographic characteristics as covariates given their associations with PTSD and depression (Lee, 2019; Olff et al., 2007; Spoont & McClendon, 2020). Night shift status (0 = shift not ending after midnight, 1 = shift ending after midnight) was assessed with two items that were completed when working a shift: One item asked about shift start time, and one item asked about shift end time. Weekend status was determined from the survey date automatically recorded by REDCap (0 = weekday, 1 = weekend). Night shift totals and weekend totals were computed by summing the number of night shifts and the number of weekend days for each participant, respectively. We included night shift status, night shift total, weekend status, and weekend total to account for their potential impact on perceived sleep quality.

Data analysis

Statistical analyses were performed using Stata (Version 17 BE). We first examined descriptive statistics, including the mean and standard deviations of the person‐means associated with PTSD symptom severity, depressive symptom severity, and sleep quality. These statistics provide a between‐person measure of the average and variability of these variables. We also examined the within‐person standard deviation (iSD), which is calculated by taking the mean of all within‐person standard deviations and, thus, offers an indication of within‐person variability. Finally, we examined the mean values of the focal variables on each day of the study, providing a measure of the average daily levels of PTSD symptom severity, depressive symptom severity, and sleep quality across the full assessment period.

Following this analysis, we performed preliminary analyses examining missing data and used Fisher's exact tests to compare respondents with complete data to those who were excluded due to missing data. For our main analyses, we used multilevel regression with restricted maximum likelihood and a first‐order autoregressive error term. EMA responses were at Level 1, and participants were at Level 2. We first examined an initial set of intercept‐only models to determine the intraclass correlation coefficients (ICCs) associated with each of the mental health outcomes. Next, we performed a set of primary models examining the concurrent associations between within‐ and between‐person sleep quality and our target outcomes. Because restricted maximum likelihood does not permit using ‐2 log‐likelihood (‐2LL), Akaike information criterion, and Bayesian information criterion values to compare nested models that differ in their fixed effects (Hoffman, 2015), we used an alternative set of statistics to evaluate the models. First, we calculated pseudo‐R 2 values reflecting the proportion of total variance in each outcome that was attributable to all the within‐ and between‐person fixed effects included in the model. Second, we performed a multivariate Wald test examining whether the within‐ and between‐person sleep quality effects were collectively statistically significant. Third, we evaluated the individual within‐ and between‐person sleep quality effects in each model. The coefficient representing the within‐person effect was created by subtracting each participant's personal average for sleep quality across the study period from each of their daily sleep quality scores. The coefficient representing the between‐person effect was created by subtracting the grand mean of sleep quality across all participants from each participant's personal sleep quality average. Both primary models included study day, age, race, sex, night shift status, night shift total, weekend status, and weekend total as covariates. To better understand any individual within‐ and between‐person associations observed, we calculated the predicted means for both mental health outcomes when the statistically significant within‐ and between‐person sleep quality variables were at their mean level and at 1.0 standard deviation above and below their mean levels.

RESULTS

Participant characteristics

Participant characteristics are included in Table 1. Participants were predominantly White (92.5%) adults between 19 and 59 years old, with an average age of 31 years (SD = 9.38). Slightly over half of the sample identified as male (50.6%). A majority of the sample obtained either a college (43.0%) or graduate degree (11.4%). Most participants were employed full time (82.3%), held a nonsupervisory role (94.9%), and were certified at the emergency medical technician (EMT; 60.8%) level. Almost half of the participants held a second EMS or firefighting job (48.1%), which is common among EMS clinicians (Rivard et al., 2020).

TABLE 1.

Participant characteristics

Variable M SD
Age (years) 30.72 9.38
n %
Male sex 40 50.6
Race
White 72 91.1
Black/African American 3 3.8
Asian 2 2.5
More than one 2 2.5
Educational attainment
Graduate degree 3 3.8
College degree 33 41.8
Some college 34 43.0
High school/GED 9 11.4
Full‐time employment 65 82.3
Nonsupervisor 75 94.9
Certification level
EMT‐basic 48 60.8
EMT‐paramedic 31 39.2
Second EMS/fire job 38 48.1
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Note: N = 79. EMT = emergency medical technician; GED = generalized education diploma; EMS = emergency medical service.

a

Range: 19–59 years.

Preliminary analyses

In total, we delivered 696 daily EMAs to 87 EMS clinicians. We included EMAs in the analytic sample if they were 80.0% or more complete, given that completion rates in EMA protocols typically range between 70.0% and 90.0% (Fisher & To, 2012). Overall, 575 (82.6%) EMAs met this criterion. Eight participants did not work a shift due to schedule changes during the data collection period and were excluded so that all participants in the analytical sample had at least one workday EMA that was 80.0% or more complete. This final analytic sample consisted of 558 (80.2%) daily EMAs and a total of 79 participants.

Participants in the analytic sample submitted an average of 7.08 complete assessments (Mdn = 8.00 assessments, SD = 1.63, range: 1–8). Participants excluded from the analytical sample did not differ significantly from those who were included with regard to age, sex, educational attainment, full‐time status, or EMS certification. However, participants in the analytical sample were more likely to be White (92.5% vs. 38.0%), p < .001 (Fisher's exact test). To help account for this differential attrition, we included race as a covariate in the primary models. Missing data were minimal among the 558 EMAs in the final analytic sample, with 550 EMAs missing no data and eight EMAs missing only one item each.

Descriptive statistics

On average, participants worked 1.70 night shifts (SD = 1.86, range: 0–6) and 2.02 weekend days (SD = 0.45, range: = 0–3). Of the 558 shifts reported, 23.5% (n = 131) were night shifts, and 27.2% (n = 152) occurred on weekend days. Participants rated their sleep quality as “fair” (M = 2.19), with a higher within‐person standard deviation (iSD = 0.72) compared to between‐person variation (SD = 0.67), suggesting considerable individual fluctuations in sleep quality over time. Additionally, on average, participants reported relatively low levels of PTSD symptom severity (M = 2.32, SD = 2.55, range: 0–14) and moderate levels of depressive symptom severity (M = 6.31, SD = 2.24, range: 3–16). Table 2 reports daily mean levels of PTSD symptom severity, depressive symptom severity, and perceived sleep quality for each of the 8 days of data collection. Mean scores for PTSD symptom severity, depressive symptom severity, and perceived sleep quality were 3.15, 6.71, and 2.03 on Day 1 and 1.66, 5.58, and 2.29 on Day 8, respectively.

TABLE 2.

Daily mean values for the variables of interest

Day
Variable 1 2 3 4 5 6 7 8
PTSD symptom severity 3.15 3.00 2.04 2.54 2.27 2.00 1.86 1.66
Depressive symptom severity 6.71 6.42 6.61 6.25 6.52 6.17 6.20 5.58
Perceived sleep quality 2.03 2.04 2.20 2.27 2.19 2.38 2.16 2.29
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Note: PTSD = posttraumatic stress disorder

Initial models

ICCs were 0.70, 95% CI [0.62, 0.77], for the model with PTSD symptom severity and

0.55, 95% CI [0.44, 0.65], for the model with depressive symptom severity. As such, roughly 70.0% of the variance in daily PTSD symptom severity and 55.0% of the variance in depressive symptom severity were due to differences between people, and 30.0% of the variance in PTSD symptom severity and 45.0% of the variance in depressive symptom severity were due to differences within participants each day.

Primary models

PTSD symptom severity

The pseudo‐R 2 values for the between‐ and within‐effects in the model were .17 and .01, respectively. Thus, the model's between‐person effects accounted for 17.0% of the total variance in PTSD symptom severity, and the within‐person effects explained 1.0%. Examining the between‐ and within‐person effects for sleep quality specifically, a multivariate Wald test demonstrated that their collective effects were statistically significant, χ2(2, N = 79) = 19.23, p < .001.

When we examined each variable individually, we found that the between‐person sleep quality effect was statistically significant, B = ‐1.67, p < .001, indicating that EMS clinicians with lower perceived sleep quality during their most recent sleep episode reported higher levels of PTSD symptom severity (see Table 3). Predicted means further indicated that EMS clinicians whose sleep quality was 1.0 standard deviation below the average of all EMS clinicians reported 58.8% higher PTSD symptom severity compared to those with average perceived sleep quality. In contrast, the within‐person associations were not statistically significant, B = ‐0.13, p = .140.

TABLE 3.

Multilevel regressions examining sleep quality as a predictor of mental health symptoms

PTSD symptom severity Depressive symptom severity
Variable Estimate SE 95% CI p Estimate SE 95% CI p
Fixed effects
Intercept 2.66 0.44 [1.81, 3.41] < .001 6.68 0.39 [5.81, 7.34] < .001
Sleep qualityBetween −1.67 0.40 [‐2.46, ‐0.88] < .001 −1.50 0.35 [‐2.18, ‐0.82] < .001
Sleep qualityWithin −0.13 0.08 [‐0.30, ‐0.04] .140 −0.25 0.10 [‐0.45, ‐0.05] .013
Day −0.19 0.04 [‐0.26, ‐0.12] < .001 −0.10 0.04 [−0.17, ‐0.03] .005
Age 0.02 0.03 [‐0.04, 0.08] .499 0.06 0.03 [0.01, 0.11] .023
Race −0.42 1.13 [‐2.64, 1.81] .713 −0.12 0.98 [‐2.04, 1.79] .899
Sex 0.98 0.60 [‐0.20, 2.16] .105 0.47 0.52 [‐0.55, 1.48] .368
Night shift status 0.38 0.19 [0.01, 0.76] .047 −0.07 0.22 [‐0.51, 0.37] .758
Night shift total −0.02 0.16 [‐0.33, 0.29] .898 0.12 0.14 [‐0.15, 0.40] .366
Weekend status −0.23 0.15 [‐0.53, 0.07] .133 −0.20 0.18 [‐0.54, 0.15] .258
Weekend total −0.21 0.54 [‐1.27, 0.85] .695 −0.77 0.47 [‐1.69, 0.17] .109
Random effects
Intercept 2.21 0.21 [1.83, 2.67] – 1.87 0.19 [1.53, 2.29] –
Residual 1.60 0.07 [1.46, 1.74] – 1.77 0.06 [1.65, 1.90] –
ρ 0.31 0.06 [0.18, 0.43] – 0.12 0.06 [0.01, 0.23] –
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Note: PTSD = posttraumatic stress disorder; CI = confidence interval.

We also found a statistically significant negative association between study day and PTSD symptom severity, B = ‐0.19, p < .001, indicating that symptoms decreased across data collection, as well as between night shift status and PTSD symptom severity, B = 0.38, p = .047, indicating that working a night shift was associated with higher levels of PTSD symptom severity.

Depressive symptom severity

The pseudo‐R 2 values for the between‐ and within‐effects in the model were .31 and .01, respectively. Thus, the model's between‐person effects explained 31.0% of the total variance in depressive symptom severity, and the within‐person effects accounted for 1.0%. When we examined the between‐ and within‐person effects of sleep quality specifically, a multivariate Wald test indicated their collective effects were statistically significant, χ2(2, N = 79) = 24.81, p < .001.

We also observed statistically significant between‐ and within‐person effects for perceived sleep quality and depressive symptom severity (see Table 3). The between‐person effect indicated that EMS clinicians with lower perceived sleep quality during their most recent sleep episode reported higher levels of depressive symptom severity, B = ‐1.50, p < .001. Predicted means suggest that EMS clinicians who reported perceived sleep quality 1.0 standard deviation below the sample average reported 16.3% higher depressive symptom severity compared to those with average perceived sleep quality.

Additionally, the significant within‐person effect indicated that on days when an EMS clinician experienced lower perceived sleep quality relative to their personal average during the study period, they also reported higher levels of depressive symptom severity on that day, B = ‐0.25, p < .013. Predicted means suggest that on any given day, when an EMS clinician reported sleep quality 1 standard deviation below their personal average, they reported depressive symptom severity 3.0% higher than their average.

Finally, a statistically significant negative association was observed between study day, age, and depressive symptom severity. Symptoms decreased across data collection, B = ‐0.10, p = .005, and were higher for EMS clinicians who were older, B = 0.06, p = .023.

DISCUSSION

EMS clinicians play a vital and irreplaceable role in public health and safety, but the demands of their occupation—characterized by frequent night shifts and long, irregular hours— position them at a high risk for sleep disturbances (Patterson et al., 2010, 2012). To our knowledge, the current study is the first to examine the between‐ and within‐person associations between perceived sleep quality and mental health symptom severity (i.e., PTSD and depressive symptoms) in this high‐risk population.

At the between‐person level, we found that EMS clinicians with lower perceived sleep quality reported higher PTSD and depressive symptom severity compared to those with higher perceived sleep quality. These results align with a growing literature suggesting that disturbed sleep increases the risk for negative mental health symptoms among high‐risk populations. A recent systematic review of six studies utilizing intensive data collection methods (e.g., EMAs) highlighted that worse subjective sleep quality was associated with higher next‐day PTSD symptoms, and, in turn, higher daytime PTSD symptoms were associated with worse subjective nighttime sleep quality across numerous other populations (Slavish et al., 2022). Such results support a bidirectional association between sleep quality and mental health and are worthy of future exploration in EMS clinicians specifically.

The link between poor sleep quality and mental health symptoms might be explained by the role of sleep in facilitating memory and emotional processes. Recent meta‐analytic findings from laboratory studies suggest that healthy sleep, compared to sleep loss, significantly protects against developing PTSD‐like intrusions after lab‐analogue trauma (Varma et al., 2024), potentially by facilitating the integration of traumatic memories into autobiographical memory schemes (Zeng et al., 2021), restoring cognitive control (Harrington & Cairney, 2021), and alleviating maladaptive affective responses (Van Someren, 2021). Additionally, a meta‐analysis examining the impact of sleep on emotion found that sleep loss significantly reduced positive mood, moderately increased negative mood, and reduced adaptive emotion regulation strategies (Tomaso et al., 2021). Taken together, evidence from both laboratory and observational studies underscores the critical neurophysiological role of sleep in better processing stressful experiences and self‐regulating emotions (Dolan et al., 2023).

Moreover, at the within‐person level, we also found that on days when a participant's perceived sleep quality was lower than what was typical for them, their depressive symptom severity was also elevated. Similarly, studies using sleep tracking devices have reported associations between higher day‐to‐day variability in sleep duration and efficiency and both mood disturbances and increased depressive symptoms across populations (Fang et al., 2021; Lorenz et al., 2020). Notably, our nonsignificant within‐person association between perceived sleep quality and PTSD symptoms is consistent with findings from studies of trauma‐exposed young adults, where a similar lack of significance was reported, though other indices, such as total sleep time, have been related to increased PTSD symptom levels (Schenker et al., 2023). This finding may also be attributed to the relatively low mean levels of PTSD symptom severity in our sample and/or reflect that much of the variability in PTSD symptoms was due to differences between individuals. Thus, our findings indicate that PTSD symptom severity had less day‐to‐day variability compared to depressive symptom severity. Together, these findings underscore the associations between daily fluctuations in sleep and depressive symptoms in high‐risk EMS clinicians and highlight the possible implications of enhancing individual sleep quality to help mitigate depressive symptom severity.

In addition to these primary findings, working a night shift was associated with more severe PTSD symptoms in EMS clinicians. Prior work suggests that night shift schedules are associated with higher health risk behaviors in EMS clinicians, including physical inactivity and unhealthy eating habits (Barth et al., 2022; Mansouri et al., 2022). Additionally, negative perceptions of night shifts are associated with higher PTSD symptom levels in hospital shift workers (Cousin Cabrolier et al., 2023). Emerging research in humans and animal models suggests that the disruption of circadian system–linked sleep, often present during these night shifts, may be a key risk factor for PTSD psychopathology (Agorastos & Olff, 2021). Such findings warrant examining the implications of night shifts among this vulnerable population.

Policy‐level interventions targeting sleep might be more readily accepted by EMS clinicians, as opposed to interventions that directly address mental health, given that mental illness stigma is a chief concern among the EMS workforce (Haugen et al., 2017). Since 2013, numerous professional EMS organizations have included priority goals of reducing fatigue and sleep disturbances in EMS clinicians (Bowman et al., 2013; Patterson & Robinson, 2019). To date, however, there has been little movement in achieving these goals. Currently, rotating 12‐hr shifts are the most common schedule (Barth et al., 2022; Weaver et al., 2015). Yet, studies suggest that intershift recovery is higher for shorter (12 hr or less) or longer (12 hr or more) shifts, as rotating 12‐hr schedules often require sudden changes in day/night shift patterns (Geiger‐Brown et al., 2012; Patterson et al., 2015). Similarly, in nurses, 12‐hr shifts and rotating day/night shifts have been associated with less sleep time and lower sleep quality (Benzo et al., 2022; Di Muzio et al., 2021).

Given associations between shift length or schedules and lower sleep quality in previous studies and our current between‐person findings suggesting associations between lower perceived sleep quality and higher PTSD and depressive symptom severity, EMS agencies may consider reducing the frequency of rotating 12‐hr day and night shifts to help decrease the sleep disturbance that accompanies rotating shifts and contributes to increased mental health burden in this already high‐risk population (Patterson et al., 2015). Additionally, a national study suggests that over 75.0% of EMS clinicians work 40 hr or more weekly, which often includes a second job (Rivard et al., 2020). Thus, it may also be important to consider how working overtime may increase the risk of sleep disturbances.

Although these system‐level EMS agency changes may help reduce between‐person associations between sleep quality and mental health burden through enacting policies that are especially relevant to EMS clinicians who more routinely experience poor sleep, web‐based digital interventions delivered to EMS clinicians at times when they are at risk for sleep disturbance may help reduce within‐person fatigue and promote better sleep quality (Patterson et al., 2015, 2023). These interventions could include brief 10–15‐min online education modules on sleep health, which have been shown to positively impact future sleep quality among EMS clinicians (Barger et al., 2018; Patterson et al., 2023). However, these studies have not examined whether administering digital interventions to reduce sleep disturbances can subsequently reduce mental health symptom severity in EMS clinicians who report these disturbances. Given that we identified a within‐person association between daily sleep quality and future depressive symptoms, future studies should examine whether the prompt delivery of brief mental health intervention resources on occasions when sleep is disturbed may also help offset depressive symptoms that might be experienced. Thus, these digital interventions could both monitor daily sleep quality and shiftwork schedules and deliver an infusion of resources for sleep and depression on occasions when an EMS clinician is likely to experience sleep disturbance. Though shiftwork may be unavoidable, this future intervention might help EMS clinicians both manage current depressive symptoms and improve sleep quality during subsequent episodes of sleep.

To our knowledge, this novel study was the first to demonstrate between‐ and within‐person associations among sleep quality and mental health symptoms in EMS clinicians. Yet, the results should be interpreted while considering several limitations. First, our study was not designed for cross‐lagged models that would allow us to examine whether the sleep‐to–mental health or mental health–to‐sleep association is strongest in this population. Future studies should consider an EMA design that supports cross‐lagged models. Second, this study only included self‐reported measures of sleep quality, PTSD symptoms, and depressive symptom severity at one time daily. Though the measures were brief to promote EMA completion, a single item was used to capture perceived sleep quality versus a sleep diary to collect total sleep time or sleep latency; similarly, the survey of PTSD symptoms did not include a formal assessment of a Criterion A traumatic event. Future longitudinal studies could explore the consequences of cumulative repeated stressors (e.g., stress “pile‐up” response) given the difficulties in isolating a single traumatic event in this particularly high‐risk population and consider additional factors that may mediate the relations between sleep and mental health symptoms among EMS clinicians (e.g., exaggerated attributions of sleep quality, a tendency to catastrophize). Passive sensing can incorporate different data, including physiology, location, audio, and smartphone application use, and can monitor and predict changes in depressive symptoms, sleep disorders, substance use, and stress responses (Cornet & Holden, 2018). Future studies among EMS clinicians could incorporate passive sensing data, including geospatial data to account for external contexts (e.g., environmental factors) and actigraphy data to supplement perceived sleep quality assessments. Third, this study was limited by a sampling window of 8 days. Given that long shifts, inconsistent schedules, and limited time off have been associated with worse sleep in EMS clinicians, a longer sampling window would offer more opportunities to capture these characteristics and determine how they impact mental health symptoms (Patterson et al., 2012, 2015, 2018). Fourth, we observed differential attrition such that White participants were more likely to be in the analytical sample than non‐White participants. Though we included race as a covariate to account for the differential attrition, such bias limits the generalizability of our findings to all EMS clinicians. Future oversampling of EMS clinicians who represent racial minority groups may address this gap. Fifth, we limited our focus to the main effects of within‐ and between‐person sleep quality on mental health symptoms. Although these analyses yielded foundational data among EMS clinicians, future studies should build on our work by considering potential moderating factors that may impact the reported associations. Finally, this study only included participants from one EMS agency in Central New York Although this agency is the largest private ambulance service in the United States, future studies should include a larger national sample size with EMS clinicians from multiple EMS agencies and states, given variations in interagency policies regarding sleep‐related issues.

This study provides important initial insight into the associations between perceived sleep quality and mental health symptoms in EMS clinicians. The results indicated between‐person associations between perceived sleep quality and PTSD and depressive symptom severity, as well as a within‐person association between perceived sleep quality and depressive symptom severity. Future research should consider how EMS policy‐level efforts might address how self‐reported subjective sleep quality impacts both PTSD and depressive symptoms and examine how digital health interventions that target daily subjective sleep quality might reduce daily depressive symptom severity in EMS clinicians.

AUTHOR NOTE

Bryce Hruska was supported by a competitively awarded Syracuse University CUSE grant (Innovative and Interdisciplinary Research Grant, Award Number: II‐3278‐2022). Enzo G. Plaitano was supported by a National Institute on Drug Abuse T32 training grant (5T32DA037202‐10), awarded to the Dartmouth Center for Technology and Behavioral Health, and a National Institute on Drug Abuse F31 Ruth L. Kirschstein National Research Service Award Individual Predoctoral Fellowship (1F31DA062393‐01). Emily E. Patton was supported by a National Institute on Alcohol and Alcoholism T32 training grant (T32AA031818) awarded to the National Center for Sexual Violence Prevention within the Mark Chaffin Centers for Healthy Development in the School of Public Health at Georgia State University.

The authors would like to thank Nicolas Corbishley for his assistance in ensuring the successful completion of the project.

OPEN PRACTICES STATEMENT

The study reported in this article was not formally preregistered. The deidentified data, along with a codebook that support the findings of this study, are available in openICPSR at https://doi.org/10.3886/E208225V3 (Reference Nohttps://doi.org/10.3886/E208225V3).

Plaitano, E. G. , Zeng, S. , Emrich, M. , Patton, E. E. , Webb, E. K. , Pacella‐LaBarbara, M. L. , Barduhn, M. S. , & Hruska, B. (2025). Examining the between‐ and within‐person associations among perceived sleep quality and mental health symptoms in emergency medical service clinicians. Journal of Traumatic Stress, 38, 781–792. 10.1002/jts.23180

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