Assessment of subjective sleep quality, recovery, and work-related behavior among emergency medical service personnel: a cross-sectional study

Introduction

Background

The work of emergency medical services (EMSs) plays an important role in preclinical health care worldwide (1). The quality of first aid and the time to first aid on site are considered crucial factors for patient survival (2). EMS personnel (EMSPs) therefore play a key role in saving lives and reducing out-of-hospital mortality, which requires a high level of responsibility and sensitivity in decision-making (3). Every year, more than 7.3 million emergency interventions are carried out by EMSs in Germany, and the number is increasing over time (4). EMSs are therefore an important and systemically relevant part of our society, and EMSPs health must be protected and strengthened to maintain optimum functionality.

Numerous stress factors in this profession are known and described in the literature (5,6). Shift work poses a particular challenge (7), being associated with increasing sleep problems (8). The effects of these stresses on the well-being, recovery processes and health of EMSPs have been partially investigated, but further research is needed (9,10).

In their effort-recovery model, Meijam and Mulder described the relationships among work demands, resources, task management strategies and personality traits (11). Together, these factors lead to physical, psychological and behavioral stress reactions (12). These reactions are characterized by greater impairment of health and well-being if there is no recovery during and after work. The balance between stress and recovery is therefore the basis for avoiding health impairments (13). Workloads can be balanced by sufficient recovery time during and after work, which prevents excessive accumulation of fatigue and stress and promotes work performance and motivation.

The theoretical model by Geurts and Sonnentag, “model of work, recovery and health”, describes the relationships between recovery and stress. The authors emphasize the importance of recovery (14). The model refers to the findings of other models known in occupational medicine—the “effort-reward imbalance model” by Siegrist 1996 (15) and the “job-demand control model” by Karasek and Theorell 1990 (16)—which predict a connection between workloads and the consequences of physical exhaustion and strain (e.g., cardiovascular stress reactions). The authors extend the previous explanations by dividing the consequences into acute and chronic. The transition is primarily modulated by recovery: insufficient recovery causes acute, short-term, physiological stress reactions to develop into chronic, physical impairments. A distinction can be made between internal recovery, e.g., during short breaks during working hours, and external recovery, e.g., after work and on vacation.

Based on the effort-recovery model, van Veldhoven and Sluiter investigated the influence of decision latitude and recreational opportunities on psychosocial work demands (17). Low recovery opportunities are associated with health problems. Job demands lead to short-term load effects via the regular work procedure under the influence of work potential and job control. “Job control” refers to various aspects of the scope for decision-making and control, such as break times, vacation and working time flexibility, whereas “work potential” comprises characteristics of the employee. In the further course of the process, load effects are reversible during recovery or can develop into long-term load consequences if they accumulate.

The importance of sleep has gained significant attention in research in recent years (18-21). A connection between sufficient sleep and physical and mental health has been described in the literature (22). Performance is also significantly influenced by sleep quality and quantity. A chronic lack of sleep (sleep deprivation) can lead to serious health problems, ranging from mental impairment to chronic illnesses (23). Years of sleep duration of less than 4–5 hours or sleeping more than 9 hours per night are associated with a significantly increased risk of coronary heart disease and diabetes mellitus (24).

Rationale and knowledge gap

Despite the growing recognition of sleep disturbances in EMSPs, there are still several knowledge gaps. Firstly, the interaction between occupational stress, individual behavioural patterns and recovery processes is not yet fully understood. Secondly, most existing research either relies solely on clinical populations or uses small convenience samples, which limits the generalisability of the findings. Thirdly, there is limited data on the prevalence of health-risky versus health-promoting behavioural patterns among EMSPs, and the potential impact of these patterns on sleep and well-being remains unclear.

Understanding the interplay between behavioural patterns, sleep and stress recovery in EMSPs is important for public and occupational health. Poor sleep has been linked to reduced cognitive performance, emotional dysregulation and an increased risk of cardiovascular disease and mental health disorders (25-30). In the context of emergency services, poor sleep and inadequate recovery can compromise the health of personnel and the safety and quality of patient care (7). Therefore, identifying specific behavioural risk profiles and their association with sleep and recovery could inform targeted preventive strategies, including interventions aimed at improving sleep hygiene, resilience, and workplace conditions.

There are a number of studies on sleep quality, fatigue and therapeutic approaches to sleep disorders in EMSPs, which were presented in a scoping review (31). However, these methods do not consider personal resources and work-related behavior, how EMSPs address stressful situations or how they cope with occupational demands. Allison et al. reported that 64% of all EMSPs had poor sleep quality (31). During the coronavirus disease 2019 (COVID-19) pandemic, an increase in this prevalence was observed, with stress, pain and sugary drinks also leading to poor sleep (32).

The frequent coincidence of severe fatigue and poor sleep was shown by Patterson et al. (33). Fatigue describes a state of physical, mental and emotional exhaustion independent of exertion with a lack of energy reserves, which manifests itself through extraordinary tiredness and a lack of rest despite adequate rest periods. However, fatigue can also mean “exhaustion” (34). Fatigue in EMSPs led to a 1.5-fold increase in the error rate, a 2-fold increase in the injury rate and a 3-fold increase in the willingness to engage in risky behavior compared with that of recovered employees, with 55% of subjects reporting fatigue at work (35). One study also revealed an association between fatigue and workplace injuries and reported a prevalence of 36.9% for low daytime sleepiness and 39.2% for high daytime sleepiness among EMSPs (36). Recent studies have consistently shown that EMSPs experience higher levels of fatigue and significantly poorer sleep quality than shift workers in other professions, such as nursing and the industrial sector (25,26). Furthermore, EMSPs experience higher rates of insomnia, depression, anxiety and post-traumatic stress disorder (PTSD) symptoms (25,26).

Schumann et al. investigated the recovery stress status of EMSPs during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic and reported a significant increase in stress and a significant decrease in recovery (37,38). This change was also found in control center dispatchers (39). Sleep deprivation has also been linked to impaired clearance of metabolic waste from the brain. A deficit in the removal of β-amyloid via the glymphatic system during sleep may increase the risk of neurodegenerative diseases, such as dementia (27,28). Furthermore, chronic sleep disruption has been associated with gastrointestinal disturbances and an increased likelihood of developing diabetes mellitus or cardiovascular disease (29,30). Circadian misalignment caused by shift work is one of the primary contributors to these outcomes (40,41). The circadian rhythm governs physiological processes in roughly 24-hour cycles, including the regulation of the sleep-wake rhythm. Studies have demonstrated increased oral temperature, elevated resting heart rate and higher urinary free norepinephrine levels during night shifts, alongside significant alterations in cortisol and adrenaline secretion patterns (41). Only a few studies have examined the stress, recovery processes and sleep quality of emergency service personnel in complex contexts (6,9,10,37-39). For example, the influence of potentially health-promoting or health-hazardous work-related behavior and experience patterns on these factors has rarely been researched.

Objective

The aim of this work was to record the quality of subjective sleep, recovery and stress in EMPSs and to contribute to the development of future preventive measures, taking into account individual work-related behavior to maintain the health of emergency services. An understanding of the influences and causalities can serve as a basis for later therapeutic approaches and behavioral interventions. It was hypothetically expected that this sample would have poorer sleep quality, insufficient recovery and higher stress levels. We present this article in accordance with the STROBE reporting checklist (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-114/rc).

Methods

Study design

The dataset was collected from September 01, 2017, to December 31, 2020, with the aim of gaining insights into improving the health of emergency services personnel. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The positive vote by the ethics committee of the Otto von Guericke University Magdeburg is available (ethics application registration number 61/13).

The study participants were surveyed online via questions on sociodemographic, health and work-related data and standardized questionnaires:

• The Pittsburgh Sleep Quality Index (PSQI) (42);
• Regensburg Insomnia Scale (RIS) (43);
• Recovery Stress Questionnaire [Germ. Erholungs-Belastungs-Fragebogen (EBF)] (44);
• Work-related behavior and experience patterns [Germ. arbeitsbezogenes Verhaltens- und Verlebensmuster (AVEM)] (45).

Test subjects

As part of a cross-sectional online survey, 508 datasets were collected from rescue service employees from all over Germany for the AVEM, EBF and PSQI questionnaires. A total of 367 of them also answered the RIS. Participation automatically constituted consent.

An announcement via the professional association “Deutscher Berufsverband Rettungsdienst e.V.” made it possible to reach EMSPs nationwide, which increased representativeness. This also minimized the selection bias with respect to the organizations reached. Flyers with a link to the online questionnaires were distributed via another partner in the rescue service. Contact was made with the participants in collaboration with a company by approaching them when they purchased personal protective equipment. By addressing the whole of Germany, a good regional mix was achieved and potential selection bias was reduced.

Only EMSPs (aid organizations/professional fire departments) that worked in active patient transport and EMSs, full-time in their profession, and had at least 3 years of professional experience were considered. Exclusion criteria were less than three years of professional experience or incomplete questionnaires.

Participation in the study was voluntary and anonymous. Consent was assumed due to the online completion of the questionnaires. The response rate could not be determined due to the online distribution via various channels.

Collection of sociodemographic, health and occupational data

First, sociodemographic and job-related data were collected. These are particularly important for identifying possible influencing factors and for comparing the differences in the results with the different structural and organizational framework conditions. In terms of sociodemographics, sex, age, height and weight [for the subsequent calculation of body mass index (BMI)] were initially surveyed. The occupation-specific data included, for example, organization, schooling, training, occupation in the EMS, predominantly staffed rescue vehicle, working hours, current shift model, federal state and operational area (urban, rural). Very sparsely populated and sparsely populated residential areas were included in the evaluation of the area of operation as “rural”, whereas densely populated and very densely populated residential areas together formed the characteristic “urban”.

AVEM

The AVEM questionnaire from Schaarschmidt and Fischer (45) is used to record work-related behaviors, attitudes and habits. A short version with 44 items was used. The respondents’ answers were then transferred to the psychodiagnostic Vienna Test System (Schuhfried, Mödling, Austria).

AVEM determines the personal resources available to an individual for coping with professional challenges by recording relatively stable personality traits. The questions are rated by the respondents on a five-point scale from “strongly agree” to “strongly disagree”.

Eleven dimensions are recorded: “subjective importance of work”, “work-related ambition”, “striving for perfection”, “willingness to work until exhausted”, “striving for perfection”, “distancing ability”, “tendency to resign in the face of failure”, “proactive problem-solving”, “inner calm and balance”, “experience of success at work”, “satisfaction of life”, and “experience of social support”, which are assigned to the three content areas of work engagement with work, resilience, and work-related emotions. No overall score is calculated for the respective area. The stanine values achieved in the different dimensions form an individual profile that is assigned to one of the four reference profiles (patterns) on the basis of a cluster analysis (45). There is a pattern membership to patterns A, B, G or S.

Pattern G represents healthy behavior that is characterized by a high level of work-related ambition combined with good distancing ability. In addition, there is strong proactive problem solving, a strong inner calm and balance with a low tendency to resign in the face of failure. Overall, this pattern is dominated by positive emotions such as a sense of professional success, experience of social support and life satisfaction.

Pattern S is characterized by a sparing attitude toward work; it is also characterized by low work-related ambition, a low willingness to work until exhausted and a high distancing ability. In addition, there is a high level of resilience and reduced subjective importance of work, with a low tendency to resign. This pattern is also characterized by a positive attitude toward life.

Patterns A and B are considered risk patterns that reflect psychological vulnerability and impairment. Risk pattern A is characterized by a high willingness to exert oneself and a low distancing ability, i.e., high efforts do not lead to a corresponding sense of achievement. In addition, there are low values for inner calm and balance in combination with a high tendency toward resignation; this leads to negative emotions such as low life satisfaction and can result in a gratification crisis.

The characteristics of risk pattern B are characterized by a high tendency to resign in the face of failure, low work-related ambition and a low level of inner calm and balance. The decisive difference from pattern S is the low ability to distance oneself from pattern B. This combination leads to low life satisfaction and a low experience of success at work, which can increasingly accompany burnout syndrome.

A pattern match means a match with a reference profile with a defined probability (45).

PSQI

The PSQI is a self-report questionnaire for assessing sleep quality and sleep disorders in the last four weeks (42). The questionnaire consists of 24 questions related to sleep habits over the last four weeks. First, qualitative characteristics such as sleeping times are recorded. Questions are then asked about the use of sleeping pills, triggers for poor sleep and other sleep characteristics, which are mainly rated on a four-point scale from “not at all during the last four weeks” to “three or more times per week”. The version used also explicitly asked about noise as a trigger for poor sleep. Five of the questions are to be assessed by a partner or roommate and are not included in the assessment. They merely serve as clinical information. The remaining 19 questions form the 7 components of subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disruption, sleep drug consumption and daytime sleepiness. Each of these seven components is rated according to an evaluation scheme with a score from 0 to 3. A PSQI total score is calculated from this score, which ranges between 0 and 21 points. Higher values indicate disturbed sleep. A total score of 0 to 5 is classified as healthy sleep, and a score above this indicates poor sleep. The instrument’s reliability and validity can be considered good (46).

EBF

The recovery stress questionnaire is a method for recording the current level of recovery and stress (44). The short form of the EBF-24/A (S2) questionnaire comprises 24 items. The basis for determining the state of recovery and stress is the recording of stress and stress sequences as a measure of stress and recovery for estimating resources; this is done by retrospectively assessing the frequency of recovery activities and mental and physical stress in the last 3 days and nights. Each question is rated on a seven-point scale from “never” to “all the time”. Following the survey, raw scores are formed for the twelve subscales, which can be converted into the main stress and recovery scales. The subscales of the main “strain” scale include “general stress-despondency”, “emotional stress”, “social tensions”, “unresolved conflicts-lack of success”, “overtiredness-time pressure”, “lack of energy-lack of concentration”, and “physical ailments”. The main scale “recovery” is made up of “success-capability”, “recovery in the social field”, “physical recovery”, “general recovery-wellbeing”, and “restorative sleep”. Owing to the high fluctuations, the raw scores cannot be interpreted as absolute values and can be evaluated only in comparison with other reference values (Kallus 1995). High absolute values indicate high stress/strain (reference range of 1.43–1.96) or good recovery activities (reference range of 2.11–3.45) (44).

This instrument has good reliability with high internal consistency and good variability with a high degree of sample independence (44).

RIS

The RIS is an instrument for recording the severity of psychological symptoms and sleep parameters of primary insomnia (43). The scale consists of 10 quantitative and qualitative items that measure the occurrence of different symptoms within the last 4 weeks. The frequency can be rated on a scale from 0 to 4. At the beginning of the questionnaire, the time taken to fall asleep and the average duration of sleep are recorded. Behavioral characteristics such as sleep interruption and depth of sleep are then assessed on a scale ranging from never [0] to always [4]. In addition, cognitive and emotional aspects, such as anxiety about sleep and rumination, are assessed. The evaluation is carried out by adding up the scale values. A score between 0 and 40 points is possible. A score of 0–12 points is considered normal. Scores of 13–24 points indicate clinically concerning sleep behavior, and scores of 25–40 points indicate pronounced insomnia.

In terms of validity, the RIS shows good discriminatory power in relation to healthy groups and a high level of agreement with the PSQI. In addition, good internal consistency can be seen with regard to reliability (43).

Using both instruments (PSQI, RIS) in parallel allowed the authors to assess general sleep disturbance as well as specific insomnia-related characteristics, increasing the validity and depth of the findings. This dual approach provides a more nuanced understanding of sleep behaviour in the context of occupational stress among paramedics.

Statistics

SPSS 28.0 software (Microsoft, IBM, Armonk, NY, USA) was used. The first step was to determine the descriptive characteristic values (mean, standard deviation, median, minimum and maximum values). The significance level of the error probability was set at P<0.05 (two-sided). For interpreting the level of significance, P<0.05 (*), P<0.01 (**) and P<0.001 (***) were defined.

The dataset was then checked for a normal distribution via Kolmogorov-Smirnov tests. In addition, Pearson’s Chi-squared test (χ²) was used to check the independence or correlation of the subgroups. Spearman’s correlation analysis was used to illustrate the relationships between the selected variables. The following values were used for interpretation: |r| >0.1 for a low correlation, |r| >0.3 for a moderate correlation, |r| >0.5 for a high correlation (47). With a Spearman’s correlation of r =0.3, a statistical power of 0.9 and a significance level of α =0.05, a sample size of n=112 would be required for a significant result (48). The general linear model (GLM) was used to analyze the influence of different covariates on the PSQI components. Partial η² <0.06 corresponds to a small effect, partial η² from 0.06 to 0.14 to a medium effect and partial η² >0.14 to a large effect (47).

Results

Sociodemographic and health-related data

Considering the exclusion criteria, 508 complete datasets were collected as part of the study. The average age of the respondents was 32.8±9.16 years (min 20–max 62) (Figure 1). A total of 45.5% of the respondents were between 25 and 34 years old, 18.3% were younger than 25 years, and 36.3% were 35 years and older, with only 2.4% being older than 55 years. In total, 430 (84.6%) of the respondents were male, and 78 (15.4%) were female.

Figure 1 Age distribution within the overall sample.

An average BMI of 27.9±5.79 kg/m² was determined as part of the assessment of the subjects’ state of health.

Occupation-specific data

Overall, the average number of years in the profession in the total sample was 11.4±8.08 years. EMSPs from different organizations were included in this study. A total of 229 (45.1%) of the test participants belonged to the municipal EMS, 206 (40.6%) worked for aid organizations, 34 (6.7%) worked for private EMS companies, 20 (5.7%) worked for professional fire departments, 5 (1%) worked for the German Armed Forces, and 5 (1%) worked for other companies

The total sample comprised 273 (53.7%) emergency paramedics (in German Notfallsanitäter), 141 (27.8%) paramedics (in German Rettungsassistenten) and 94 (18.5%) emergency medical technicians (EMTs, German Rettungssanitäter).

With respect to the means of rescue vehicles, 462 (90.1%) stated that they were deployed mainly on an ambulance [German Rettungswagen (RTW)], 16 (3.1%) worked mainly on patient transport [German Krankentransportwagen (KTW)], 14 (2.8%) on an emergency ambulance [German Notarzteinsatzfahrzeug (NEF)] and the remaining 16 respondents (3.2%) in intensive care transport [German Intensivtransportwagen (ITW)], emergency ambulance [German Notarztwagen (NAW)], rescue helicopter [German Rettungshubschrauber (RTH)] or other.

The predominant shift model for 371 (73.0%) respondents was a 12-hour shift, for 77 (15.2%) a 24-hour shift, for 37 (7.3%) an 8-hour shift and for 23 (4.5%) another shift model. A total of 184 (36.2%) respondents stated that they had already been working in the predominant shift model for 4–8 years, 148 (29.1%) had been working in the same model for between 1 and 3 years, 91 (17.9%) had been working for between 9 and 15 years, and 33 (6.5%) had been working for between 16 and 20 years. Thirty-five (6.9%) of the test subjects had worked in the current shift model for more than 20 years, and 17 (3.3%) had worked in the same model for less than 1 year.

The distribution between rural and urban areas was almost balanced, with 284 (55.9%) jobs in rural areas and 224 (44.1%) in urban areas.

The average number of regular weekly working hours was 44.3±7.00 hours per week, with actual working hours of 43.6±12.25 hours per week. The average on-call time was 9.4±8.85 hours per week. The average deployment time per week was 29.0±12.76 hours, with 6.2±2.20 deployments per 12 working hours.

Work-related behavior and experience

In this study, 378 of the 508 test subjects could be assigned to a pure (with a probability of over 95%), accentuated (<95% and >80%) or tentative pattern affiliation.

The average score, both the mean and the median of the AVEM dimensions, was within the normal range of between 4 and 6 points (Table 1).

With the exception of the dimensions “satisfaction of life” and “experience of social support”, the minimum values [1] and maximum values [9] were achieved by some respondents. The lowest values (median of 4 stanine points) were found in the dimensions “proactive problem solving”, “satisfaction of life” and “experience of social support”. The highest scores (median 6 points) were achieved in the categories “work-relation ambition”, “distancing ability”, and “inner calm and balance”. The median score of the other dimensions was 5 points. According to the definition, the AVEM patterns showed a characteristic expression of all 11 AVEM dimensions. In the sample of test subjects, this resulted in a distribution of 65 (17.2%) in pattern A, 66 (17.5%) in pattern B, 159 (42.1%) in pattern G and 88 (23.3%) in pattern S. For the remaining test subjects, there was either a combination of the AVEM patterns or no allocation.

Sleep behavior (PSQI)

The sleep quality of the test subjects, which was determined using the PSQI questionnaire, is shown in Table 2.

The minimum and maximum values were between 0 and 3 points for all 7 components. The PSQI total score ranged from 0–18 points, with a possible range of 0–21 points. The median score was 1 point for the components “subjective sleep quality”, “sleep latency”, “sleep duration”, “sleep disruptions” and “daytime sleepiness” and 0 points for the components “sleep efficiency” and “sleep drug consumption”. Low points in “sleep drug consumption” indicate rather infrequent use of sleep drugs, whereas high points in “sleep latency” indicate long periods of sleep. The median total score was 6 points, which indicates that the largest proportion of respondents could be classified as “poor sleepers” (>5 points). From the PSQI total score of the 496 datasets, it was possible to classify the test subjects into poor (>5 points) and good (0–5 points) sleepers. This resulted in 211 (42.5%) good sleepers and 285 (57.5%) poor sleepers.

EBF and the RIS

Statements on sleep quality and recovery status were collected using the EBF and the RIS (Table 3). Higher raw scores indicate greater stress and strain (B) or better recovery activities (E) (49).

The raw scores varied between 0 and 6 points for all of the subscales, except for “lack of energy-lack of concentration” and “physical ailments”. The main scale “recovery” and all its subscales presented higher medians than did the subscales of the main scale “strain”. The highest median score was 4 for the subscale (E) “general recovery-wellbeing”. At 2.29, the median of the main scale “strain” was above the reference range (reference range 1.43–1.96), and the median of the main scale “recovery” was still within the reference range at 3.3 (reference range 2.11–3.45).

The RIS total ranged from 0 to 34 points with a theoretically maximum possible range of 0 to 40 points. The median score of the total sample was 9 points. The results showed that this sample exhibited normal sleep behavior (0–12 points).

Correlation analyses

The results of the correlation analysis revealed the relationships between the AVEM dimensions and the PSQI components or the EBF subscales (Figure 2). The detailed values can be found in Figure 2. At first glance, some dimensions correlated significantly with almost all of the PSQI and EBF components, whereas others showed almost no significant correlations. The EBF subscale “stress” tended to have strongly significant negative correlations with the AVEM dimensions, whereas the EBF subscale “Recovery” tended to have strongly significant positive correlations.

Figure 2 Spearman correlation analysis of the AVEM dimensions with the PSQI scales and EBF subscales and main scales. *, P<0.05; **, P<0.01; ***, P<0.001. |r| >0.1 for a low correlation. |r| >0.3 for a medium correlation. |r| >0.5 for a high correlation. Positive correlations are marked in green, negative correlations in red. The intensity of the color stands for the strength of the significance. AVEM, work-related behavior and experience patterns; DA, distancing ability; EBF, recovery-stress questionnaire (germ.: Erholungs-Belastungs-Fragebogen); EN, lack of energy; ES, experience of social support; ESE, emotional stress; EW, experience of success at work; GR, general recovery; GS, general stress-despondency; GS, total score; IB, inner calm and balance; LS, satisfaction of life; OTP, overtiredness; PA, physical ailments; PP, proactive problem solving; PR, physical recovery; PSQI, Pittsburgh Sleep Quality Index; R, recovery:; RIS, restorative sleep; RS, restorative sleep; RSF, recovery in the social field; RT, tendency to resignation in the face of failure; S, strain:; SC, success-capability; SDA, sleep duration; SE, sleep efficiency; SK, sleep drum consumption; SL, sleep latency; SP, striving for perfection; SSQ, subjective sleep quality; SST, sleep disruptions; ST, social tension; SW, subjective importance of work; TS, daytime sleepiness; UC, unresolved conflicts; WA, work-related ambition; WE, willingness to work until exhausted.

“Work-related ambition” had a strongly significant but weak correlation with the “RIS sum” and all of the subscales of “recovery”, except “restorative sleep”.

The AVEM dimensions “Distancing ability” and “Satisfaction of life” were highly significantly negatively correlated with the EBF main and subscale “strain” scores and all PSQI components, as well as the RIS, except for “distancing ability” and “sleeping drug consumption”. There was also a strong correlation between the “satisfaction of life” dimension and the main “recovery” scale and the “general recovery-well-being” subscale.

The correlations of “tendency to resign in the face of failure” were almost exactly the opposite. There was a highly significant positive correlation with the RIS total score, the EBF main score and the subscale “strain” score and all PSQI components except “sleep efficiency”. There was a negative, highly significant correlation with the main and subscale “Recovery”. With the exception of the subscales “success-capability” and “recovery in the social field”, the correlation was medium.

“Proactive problem solving”, “inner calm and balance”, “experience of success at work” and “experience of social support” had similar correlation patterns with the PSQI components, the EBF scales and the RISS. Moderate or high significance of a negative, low correlation was found for the PSQI components “subjective sleep quality”, “sleep latency”, “sleep disruptions”, “daytime sleepiness” and “PSQI total score”. “Experience of social support” also correlated highly significantly and negatively with “sleep duration”. The other PSQI components showed little or no correlation with the abovementioned dimensions.

Results of the generalised linear model

A GLM was used to examine how the following covariates influenced the individual PSQI components: AVEM pattern, gender, age, area of work, training and organisation (see Figure 3).

Figure 3 PSQI characteristics taking into account AVEM pattern, sex, age, place of work, qualification, and organization from the variance analysis with assessment of effect size (η²). Partial η² <0.06 corresponds to a small effect, partial η² from 0.06 to 0.14 to a medium effect, and partial η² >0.14 to a large effect. The intensity of the color stands for the strength of the effect. AVEM, work-related behavior and experience patterns; GS, total score; PSQI, Pittsburgh Sleep Quality Index; SDA, sleep duration; SE, sleep efficiency; SK, sleep drug consumption; SL, sleep latency; SSQ, subjective sleep quality; SST, sleep disruptions; TS, daytime sleepiness.

The corrected model revealed highly significant differences between the AVEM groups with regard to the PSQI components and total score (P≤0.001), except for the “sleep efficiency” component (P=0.13). The AVEM pattern had the strongest effect on the “daytime sleepiness” component (η² =0.194) and the “PSQI total score” component (η² =0.182), followed by the “subjective sleep quality” component (η² =0.119), the “sleep latency” component (η² =0.079) and the “sleep disturbances” component (η² =0.073).

The remaining covariates (gender, age, field of work, training and organisation) did not demonstrate large or medium effects in the GLM analysis.

Discussion

Key findings

The aim of this study was to record stress, recovery and sleep quality in EMSPs and to investigate the influence of the dimensions of work-related behavior on important health components.

The average level of the dimensions of work-related behavior was within the normal range. In the sample examined, the AVEM patterns were distributed in favor of the health-promoting patterns: 42.1% in pattern G and 23.3% in pattern S. Nevertheless, 17.2% of the respondents had risk pattern A and 17.5% risk pattern B. More than half of the respondents classified themselves as poor sleepers on the PSQI. In contrast, the majority of respondents in the overall sample had unremarkable results on the RIS, which indicates that their sleep behavior was not suggestive of insomnia. Overall, the respondents in the EBF showed increased stress but still good, adequate recovery.

Most dimensions of work-related behavior correlate with recovery, stress and sleep behavior. Only three work-related behaviors, striving for perfection, the subjective importance of work and work-related ambition, correlated hardly or not at all with the characteristics of the general recovery processes. The analysis of the correlations between the AVEM dimensions and the EBF “recovery” scales revealed that a high “tendency to resign in the face of failure”, low “distancing ability”, low “satisfaction of life” and low “experience of social support”, which combine patterns A and B, were highly correlated with the main and subscale “recovery” of the EBF. “Proactive problem solving” and “experience of success at work” correlated highly significantly with the main scale “recovery”, similar to the PSQI components and the EBF scales of “strain”. Both dimensions also showed a highly significant, medium correlation with all “recovery” subscales. No significant effect sizes were observed for the covariates (gender, age, place of work, qualification, and organization) in the GLM analysis.

Strengths and limitations

This manuscript is notable for its relevance, methodological rigor and ability to link sleep and behavioral patterns, resulting in sound recommendations for health promotion in this challenging occupational field. The study of sleep quality and sleep disturbances in emergency service personnel addresses an important and often neglected topic. Sleep disorders and their impact on the health of emergency workers are crucial, as this occupational group works under particular physical and psychological stress, which has important implications for public health and the well-being of emergency workers. The use of established and validated questionnaires such as the PSQI, the RIS, the EBF and the AVEM ensures high measurement accuracy and comparability of the results with those of previous studies. With a sample of over 500 EMSPs and recruitment via various channels, such as organizations and professional associations from all over Germany, this study provides a solid basis for representative statements.

Despite these impressive results, the limitations of this study must be viewed critically. As the data were collected online, no response rate could be determined, and selection bias is possible, as more health-conscious participants (e.g., participants in studies or prevention programs) could be overrepresented (“healthy worker effect”). In addition, the feeling of social desirability could have influenced the answers, as only self-assessment instruments were used. In previous studies, a low correlation between objective measures and self-rated quality was observed (50). Owing to the large influence of self-assessment factors, the results must be considered in the context of the mental stress of the EMSP. Subjective sleep quality is also an important factor for well-being and health.

A further limitation could be the insufficient inclusion of cofactors in the results. Overall, sleep is influenced by many different factors that cannot be fully captured in their entirety (51). Sleep and living environments, general lifestyle habits, eating and drinking habits, constitutions and social environments, for example, can have considerable influences on individual sleep components, such as sleep duration, efficiency and time to fall asleep, as well as overall quality (51). These aspects should not be neglected when evaluating the relationship between AVEM results and sleep. Cofactors were not linked to the PSQI, so the influence of, e.g., age on sleep was not taken into account in the results. In addition to the factors and cofactors recorded, there are other possible influences on sleep, such as PTSD, illnesses, acute conflicts and events in private life or bullying, which were not considered here. As the questionnaires were completed before 2018, COVID-19 had no influence on the results. In the current situation, the pandemic must be taken into account as a relevant influence due to the changing stress situation. As a result of the COVID-19 pandemic, an increased prevalence of sleep disorders is possible in the future (52). Furthermore, focusing on subjective sleep metrics can lead to the overlooking of objective measures such as polysomnography, which could help to clarify discrepancies between perceived and physiological sleep quality.

The analysis of the sociodemographic data revealed 85.4% male test subjects and 14.6% female test subjects. The “Statistical Yearbook on the Healthcare Professional Situation” 2019 revealed a proportion of 74.3% men and 25.7% women in the German EMS in 2017 (53). The sample therefore showed a slight underrepresentation of women. In 2019, approximately 73.4% of staff were male, and 26.6% were female (54); this shows a slight increase in the proportion of women with a general increase in the number of employees in the health care sector.

The age of the sample presented here was between 20 and 62 years, resulting in an average age of 32.8 years. A survey by the Federal Statistical Office in 2020 revealed that the age distribution of German emergency services was 35.8% under 30 years, 27.2% 30–39 years, 19.8% 40–49 years, 13.6% 50–59 years and 3.7% over 60 years (54).

Comparison with similar studies and explanations of findings

On average, the dimensions of work-related behavior were medium and therefore within the normal range. In the sample, there was a distribution of approximately 35% of respondents with risk patterns A or B. Burnout, which is associated with pattern B, is usually associated with persistently high levels of stress at work and develops over time (55). A study of police officers by Müer revealed that shift work, unpredictable shift endings, weekend shifts, incompatibility of work and family, certain experiences, contact with citizens, a harsh tone at work, and family problems contributed significantly to the development of burnout (55). These influences can also be found in emergency services.

In the present study, approximately 65% of the subjects presented health-promoting patterns G and S. Compared with the AVEM patterns described by Voltmer et al., 60% of the doctors were assigned to patterns G or S (56). Further studies revealed that 39–43% of doctors in private practice were assigned to risk patterns (57), 47% among psychotherapy trainees (58), 41% among nursing staff in the hospital sector (59), 69% among medical students (60) and 27.9% among general practitioners (61). Some of these studies used the long form of the AVEM with 66 items. However, this is irrelevant, as the intercorrelations of the corresponding scales of the standard and short forms are between 0.95 and 0.97 (62). Although the severity of the AVEM risk patterns is lower than that of other occupational groups, it is concerning that there is a high number of employees with health-threatening AVEM risk patterns in emergency services. The health competence learned during training could support the development of health-oriented patterns.

An earlier study revealed that EMSPs in rural areas presented significantly poorer sleep quality than did those working in urban areas (63). A possible link between the common AVEM dimensions, poor sleep quality and working conditions in rural areas could be possible. Owing to the longer travel distances, rural rescue missions take more time. Furthermore, there has also been an increase in emergency operations in rural areas in Germany (64). Overall, a further increase in the urban-rural divide is predicted (65). The distribution between rural and urban areas was almost balanced, with 55.9% of jobs in rural areas and 44.1% in urban areas.

There are a number of studies that looked at the influence of different character traits and behavioral characteristics on various aspects of perceived fatigue, workload and stress, although the field of emergency services is rarely the focus (6,66).

Völker et al. [2023] investigated the influence of emotional reactivity and empathy on chronic stress and reported that lower empathy, lower emotional reactivity and greater suppression of feelings lead to a lower perception of stress. Compared with students, paramedics are more likely to use strategies to reduce stress and prevent emotional exhaustion (66). The AVEM construct also evaluates this behavior in the dimension “distancing ability”. Thielmann et al. [2022] investigated the relationship between the AVEM dimensions and impairments in the physical, psychological and social domains. They reported that samples A and B presented greater impairments. “Distancing ability”, “proactive problem solving”, “inner calm and balance” and “experience of success at work” were negatively correlated with physical ailments, whereas “inner calm and balance”, “experience of success at work”, “satisfaction of life” and “experience of social support” were negatively associated with psychological and social impairments (6).

Insufficient healthy sleep can be caused by insufficient sleep duration, irregular sleep times or poor sleep quality, among other factors (67). In addition to insomnia, excessive sleepiness, reduced productivity and cognitive impairment, symptoms such as muscle tension, palpitations or headaches can also occur (8). Inadequate sleep quality can have a significant effect on health and well-being and is associated with an increased risk of physical and mental illness (67,68). It is also associated with an increased risk of car and workplace accidents, arterial disease, cardiac arrhythmias, obesity, diabetes and hypertension, as well as bipolar disorder, anxiety, obsessive-compulsive disorder (OCD) and schizophrenia (69). Studies have shown that short sleep durations affect the nervous system, endothelial function, metabolic regulation and inflammatory and coagulation system processes, which can lead to a variety of autonomic disorders (8).

The results of the PSQI were correlated with the AVEM dimensions, which indicates an influence of work-related behavioral patterns on sleep quality. To date, no studies have investigated this correlation in emergency services. The participants were divided into “good” and “poor sleepers” based on the PSQI total score, with 57.5% of the sample being considered “poor sleepers”. This proportion is in the range of the values described in the literature, which sometimes reach 80% (70). A correlation between recorded PSQI scores and the actual experience of fatigue at work was demonstrated in one study by determining a correlation between symptoms of severe fatigue and higher PSQI scores (33). Given the impact of sleep on mental health and well-being, this is an alarming finding. The consequences of inadequate sleep quality can include poor judgment, increased mortality and morbidity, reduced performance, increased risk of accidents and injuries, reduced functioning and quality of life, negative impact on family well-being, increased health care utilization and motor vehicle accidents (71).

According to Patterson et al., medical errors occurred 50% more frequently in people with poor sleep quality. In addition, fatigue increased the risk of medical errors by 2.3 times and the acceptance of safety risks by 4.9 times (25).

Khan et al. [2020] reported a significant correlation between poor sleep quality, insomnia, and daytime sleepiness and an increased prevalence of depression and anxiety. Sleep disturbances due to shift work, in particular, led to an increased occurrence of these symptoms (70). Shift work is an important risk factor for significant sleep deprivation and increased daytime sleepiness in health care professionals, as it impairs sleep and daytime functioning (72). The unpredictability of events and emergencies during shifts, rather than the length of the shift, plays a role in the development of fatigue (73). Owing to their irregular schedules, shift workers often miss appointments, which can lead to feelings of frustration, isolation and depression in the long term (74). A study by Machi et al. revealed that doctors in the emergency department suffer from poor sleep quality and deterioration of short-term memory depending on their shift, which also affects the quality of their work with patients (75). Impaired driving ability due to insomnia can put staff, patients and other road users at risk when traveling by ambulance (76).

The EMSP achieved a median score of 3.3 points on the main “recovery” scale, which is in the upper reference range. Schumann et al. reported similarly lower recoveries of 3.03 and 2.98 points for control center dispatchers and EMSPs, respectively, during the pandemic (37,39). In another study, Schumann reported that EMSP in aid organizations had greater recovery (3.13) than those in professional fire departments did (2.65) (77). In all of these studies, “restorative sleep” scored the highest, and “success-capability” scored the lowest.

High resignation tendencies were significantly associated with poorer sleep, as confirmed by Zhang et al. This finding shows that greater resignation leads to poorer sleep quality (78).

“Distancing ability”, “satisfaction with life” and “experience of social support” were negatively correlated with the PSQI score. Low scores in these areas lead to poorer sleep quality. These findings confirm previous studies showing that greater life satisfaction is associated with better sleep (79-81). A positive influence of social support on sleep quality can also be found in the literature (82,83). If “distancing ability” is considered a resilience factor, an association with improved sleep quality can also be found here (84,85).

To date, no studies have investigated the severity of psychological symptoms and sleep parameters of primary insomnia using the RIS as a function of work-related behavior patterns, so it is not possible to compare the results with those of other studies.

The AVEM dimensions “subjective importance of work”, “work-related ambition” and “striving for perfection” show no or only slight correlations with the EBF scale “strain”. In contrast, “willingness to work until exhausted” correlated strongly with the main and subscales of strain, which was also confirmed by Thielmann et al. (6). A high “willingness to work until exhausted” can lead to overwork, which is associated with high stress in the literature. This increases the risk of cardiovascular disease (86).

Implications and actions needed

This study makes it possible to make statements on sleep quality, recovery and stress in the EMSs in order to gain insights for the development of preventive measures.

The dynamic interaction of stress and recovery processes influences health and illness (87). Impaired recovery processes can be predictors of future health impairments. It is therefore important to record and improve recovery in order to optimize health. Schulz et al. reported that 14% of “academic and allied health professionals and health care assistants” suffer from a conspicuous inability to recover, which places them in the middle compared with other occupational groups (88). There are no comparable studies on recovery in the EMSs. Recreational activities and leisure opportunities are considered important health indicators, as they contribute significantly to stress compensation (6,44). Recovery processes are subject to many influences in everyday private life, such as childcare, relationships with a partner and hobbies (37). Nevertheless, it seems sensible to establish company measures for recovery capacity. Alternating between different types of stress with individual performance requirements, the existence of room for maneuvering and a variety of work requirements have positive effects (87). Social support is considered a relevant factor influencing mental health, job satisfaction, self-esteem and the perception of stress (89) and should be promoted as part of company measures. The literature shows that high work stress is associated with lower life satisfaction, whereas social support increases life satisfaction (90). Social support has a positive influence on coping with trauma, resilience, and mental and physical health (91). A positive correlation with recovery has also been described in the literature (92).

Owing to their highly significant correlations, the AVEM dimensions could be used as an approach for the prevention and treatment of insomnia. In particular, “life satisfaction”, “distancing ability” and “resignation tendency”, as well as “experience of success at work” and “offensive problem solving”, appear to play important roles. As with sleep, distancing ability is associated with stress only as a resilience factor, but it also has a positive effect here (93). Offensive problem solving has many positive influences, e.g., on life satisfaction, decision-making quality and subjective well-being, which also influences recovery (94).

A direct study on the relationships between sleep and the dimensions of “life satisfaction”, “ability to distance oneself” and “tendency to resign” has not yet been conducted in this form, but correlations can be inferred. Cognitive emotional regulation (CER) is a strategy for the treatment and prevention of insomnia (95). Adaptive CER strategies include planning ahead, positive thought focusing and re-evaluating events. Maladaptive strategies such as blaming oneself or others, rumination and catastrophizing, on the other hand, can have negative effects (95). Distancing ability is defined as the ability to recover psychologically after work, which can be achieved, for example, by setting boundaries and withdrawing and distancing oneself from work-related problems (45); this is similar to the description of adaptive CER, which prevents insomnia. Thus, a positive influence of the distancing ability dimension on insomnia can be assumed. Resignation tendency is also defined in the professional context as a “tendency to resign oneself to failure and give up easily”, which is possibly reinforced by the more difficult adaptation and reassessment of problems described in the case of poor sleep (96).

Wolfradt [2006] reported that increased sleep problems and a negative work climate are associated with low life satisfaction and high performance stress. Subjects with fewer sleep problems and a negative work climate have greater life satisfaction, which emphasizes the connection between sleep problems and life satisfaction (97).

Roeser et al. [2013] reported that people with changing working hours had poorer scores for health, sleep and life satisfaction, even though they worked more frequently in the evening, at night or on weekends. However, changing working hours does not necessarily have negative effects if they are chosen in a self-determined manner and in accordance with one’s own circadian rhythm (98).

Despite the comparatively low prevalence of risk patterns in emergency services, the quality of sleep is poor. Surveys show that EMSP, especially younger workers, have little knowledge about sleep hygiene and only adhere to it to a limited extent, which reveals a need for improvement (99). The sleep hygiene practices used include sleep scheduling, adjusting environmental factors in the bedroom (e.g., light, noise), napping during the day and consuming substances such as caffeine, nicotine, etc. (7,99). Cognitive behavioral interventions and psychoeducation could be used as part of workplace health promotion programs (100). Furthermore, sleep diaries, cognitive behavioral therapy, sleep hygiene rules, stimulus control, sleep-restrictive measures to increase sleep pressure, strategies against fatigue, re-evaluations of the sleep window and relaxation techniques are recommended (101).

Educating EMSPs about sleep hygiene and the consequences of sleep deprivation can have a preventative effect. In Germany, this could be integrated as part of occupational health care, such as AMR 3.3 (“holistic occupational health care”), or in mandatory training courses. This holistic advice from the company doctor is an important part of occupational health and safety in emergency services; this could also be combined with the risk assessment of mental stress, among other things, which is required by law in Germany under the Occupational Health and Safety Act (5,102). In addition, multimedia information campaigns and health education programs should be established for pupils, students and trainees (71), which can be linked very well with training to become an emergency paramedic in the emergency services setting. The “Joint German Occupational Health and Safety Strategy” (GDA) emphasizes the importance of sufficient breaks, recovery periods and predictable working hours (103). This work confirms the influence of rest on sleep quality. In addition, scientific findings should be used to improve occupational health and safety and optimize shift schedules, e.g., by having a maximum of three consecutive night shifts and at least one evening off per week (103). Sleep intervention programs could be another option. A study among firefighters revealed a significant increase in sleep efficiency and a reduction in the rate of falling asleep, the number of awakenings, insomnia and the severity of nightmares after they participated in an intervention program (104). The impact of intervention programs on sleep quality in risk patterns A and B should be investigated. Possible measures include relaxation training (e.g., autogenic training), activities such as sport and exercise, promoting enjoyment, stress management strategies and creating a positive working environment and maintaining social contacts (45). In particular, moderate exercise has been shown in several studies to be effective in improving sleep quality and is an important preventive approach (105,106).

There should also be a preventative focus on optimizing stress and recovery. Health-promoting measures should be implemented in the workplace, and awareness of these measures should be increased (107). Future studies should evaluate the effectiveness of preventive interventions. Employees should be motivated, and managers should be trained. Technical tools to reduce stress and measures to prepare for stressful situations are essential. Psychologically based concepts for coping with stress and programs such as stress management after stressful events should be offered. Conscious leisure activities, time management, relaxation techniques and supervision are also important approaches for reducing stress (107).

In general, individual stress management can be influenced in the long term, e.g., through stress training programs (108). Schaarschmidt and Fischer presented some general behavioral therapy suggestions for patterns A and B (62). The development of health-promoting habits such as sports, relaxation exercises and hobbies is recommended to increase enjoyment of life and satisfaction. Cognitive restructuring, realistic goal setting and individual stress management are also helpful. This also includes saying no. Team mentality and social support, time management and leisure activities promote a positive working environment and help prevent overload. A healthy work-life balance as well as communication training and coaching against resignation, are crucial. These strategies target the weak points of risk patterns A and B and can help improve sleep quality (62).

As measures to prevent fatigue, various tools in the workplace, such as a manual resuscitation aid and an SMS platform for cell phones with tips on interventions in real time, have been investigated and successfully used (109,110). Other proactive and reactive strategies, such as fatigue monitoring, light exposure, shorter shifts (<24 h), access to caffeine, naps, education and risk management training, are also recommended in the literature (7,111,112). In particular, naps have been shown to reduce subjective fatigue and the impairment of physiological functions during the night shift (7).

Conclusions

The findings from this study emphasize the need to develop targeted preventive measures that consider individual work-related behavior and experience and promote sleep quality and the ability to recover.

Future research should incorporate objective measurements and further investigate the influence of the sleep environment, lifestyle habits and psychological stress to develop more comprehensive prevention strategies. The most important gaps in knowledge include the long-term health effects of AVEM risk patterns, the effectiveness of specific interventions (e.g., adjusting shift patterns or mindfulness training) and the role of organisational factors (e.g., leadership style or workplace policies) in regulating stress and recovery. These measures can sustainably improve the health of EMSP and reduce the risk of long-term physical and mental illness, ultimately leading to greater job satisfaction and performance.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-114/rc

Data Sharing Statement: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-114/dss

Peer Review File: Available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-114/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jphe.amegroups.com/article/view/10.21037/jphe-24-114/coif). B.T. serves as an unpaid editorial board member of Journal of Public Health and Emergency from July 2025 to June 2027. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The positive vote by the ethics committee of the Otto von Guericke University Magdeburg is available (ethics application registration number 61/13). The survey was conducted online. Participation automatically constituted consent.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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