Background

No matter whether it is an obligation or vocation, work remains a central topic for every individual. In this context, we are facing a working society in a state of ongoing change. The world of work is becoming more diverse, more digital, and more global. It provides new opportunities, but also risks. Currently, four generations are working together in many branches [1]. From baby boomers to Generation X and Y to Generation Z, which could not be more different. They have different claims on work and leisure time. This also results in different ideas of loyalty and flexibility [1]. As a result, stress in the workplace can be perceived differently and making occupational health assessments necessary on an ongoing basis and requiring constant reassessment.

An established subjective assessment instrument for mental stress is the effort reward imbalance model (ERI) according to Siegrist [2]. The ERI questionnaire reveals satisfactory psychometric properties and can be recommended for further research in the era of economic globalization [3]. The model is used to determine the relationship between the work performance/overcommitment (effort) and the experienced reward [2]. The baseline assumption of the model is that an imbalance between the lack of occupational rewards and the expenditures can lead to adverse stress reactions. If the reward perceived after work performance becomes insufficient, a specific form of social crisis may occur—the so-called gratification crises [2]. Here, individually and socially expected relationships are disappointed. The concept of ERI is exposed to enormous subjective individual variations in a defined work environment and is evaluated very differently between individuals [2]. In this regard, ERI values below 1.0 indicate a balance between effort and reward; values above 1.0 indicate an imbalance of effort and reward [2, 3]. Various studies have shown, for example, an increased risk of cardiovascular disease [2, 4] and the increased occurrence of psychological symptoms such as depression [5, 6] in association with a high ERI ratio.

The overcommitment (OC) subscale of the ERI describes the tendency to overspend oneself without regard to one’s resources [2]. So it is an intrinsic, person-related factor. Overcommitment is also associated with health risks. It is associated with vital exhaustion [7] or burnout [8]. Furthermore, it can lead to musculo-skeletal disorders [9], inflammation [10], or impaired immunocompetence [10].

Heart rate variability (HRV) analysis is a possible method for objective monitoring of workload, e.g., in the context of an occupational health examination [11]. Guidelines define HRV as variations over time between consecutive heartbeats. They also see HRV as a very sensitive indicator of dysregulation of the autonomic nervous system (ANS) [12, 13]. It is a non-invasive measurement to evaluate the stress of the cardiovascular system [14]. The vagus nerve, which stimulates the atria of the heart and modulates the self-sustaining sinus rhythm of the sinus or Keith flack node, is an essential part of HRV tone. The interaction between sympathetic and parasympathetic nervous systems can be estimated as different demands with the analysis of HRV [13]. Parasympathetic activity dominates in rest and recovery phases of the body, whereas sympathetic activity dominates in chronic state of stress [13]. HRV analysis differs time, frequency, and nonlinear domains. An overview of HRV metric is given by [14, 15], or the current guidelines [12, 13]. The ANS is involved in stress regulation, so (work-related) chronic stress has been associated with reduced HRV and reduced parasympathetic modulation [16]. For example, HRV markers of vagal function are the root mean square of successive differences (RMSSD), percentage of successive NN intervals that differ by more than 50 ms (pNN50), high frequency power (HF), and standard deviation of point plot to the transverse diameter (SD1) [13]. But other parameters (e.g. low frequency power (LF), LF/HF ratio (LF/HF)) are without clear assignment and can be influenced by the sympathetic and parasympathetic nervous system [13]. Analyzing HRV, it should be noted that there is an age dependency of HRV [17], and it is also necessary to know which recording time is necessary (e.g., 24-h, short-term (5 min), and ultrashort-time (<5 min)) for according parameters and which parameters are relevant for the question to be determined [18].

The aim of this project was to systematically evaluate the literature on heart rate variability as an objective indicator for mental stress in individuals with different levels of ERI and/or OC. We hypothesized that a high ERI ratio or high OC is associated with an increased reduction in vagal tone.

Methods

This systematic literature review examined heart rate variability in context of effort reward imbalance and/or overcommitment in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement for reporting systematic reviews [19]. The electronic databases PubMed, Ovid, Cochrane Library, Scopus and Web of Science, PsyInfo, Psyndex, and Livio were used. The deadline was February 01, 2021. Search terms were defined as “overcommitment” OR “effort reward imbalance” AND “heart rate variability” OR “HRV” OR “cardiac autonomic control” OR “autonomic function” OR “parasympathetic activity” OR “parasympathetic nervous system” OR “cardiac vagal tone” OR “autonomic cardiac modulation” OR “vagus nerve” OR “vagal tone” OR “vagal activity” OR “coefficient of variation” OR “autonomic nervous system OR “sympathetic” OR “parasympathetic” OR “sympathetic nerve activity” OR “neural control” OR “activation of the sympathetic nervous system”. Only articles from 2005 to 2021 were included. Inclusion criteria were studies with different levels of ERI and/or OC, more than 10 participants (in each group), measurement of HRV 24 h, recording of heart rate through Holter ECG or chest belt, full-text in English or German language, and human subjects. Papers with case-control studies, intervention studies, cross-sectional studies, or longitudinal studies were included.

Exclusion criteria were HRV assessment with pulse rate automatic or photoplethysmography, diagnosis of mental or neurological diseases, endocrine diseases (diabetes, thyroid gland disease), cardiac diseases, hypertension, other heart rhythm-related diseases, and intake of drugs influencing HRV. Review articles, guidelines, single-case studies, theses, dissertations, and scientific conference abstracts were also excluded. The national guideline on HRV does not suggest the method of pulse rate or photoplethysmography of measurement [14], so that was an exclusion criteria.

Next to the literature research, a hand search was performed by checking the reference lists of the included studies (no result). One study was included in the databases after the literature search (due to a subsequent publication). An overview of the procedure is shown in Fig. 1. The complete study protocol is available at Prospero https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=234228.

Fig. 1
figure 1

Flow chart in the context of the systematic literature search

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The included articles were transferred to the reference manager Citavi 6 (Swiss Academic Software, Wädenswil, Switzerland) without duplicates. Two authors (B.T. and J.H.) independently screened titles and abstracts according to the inclusion and exclusion criteria. The full-text of each relevant article was obtained, which was independently screened by two authors (B.T. and J.H.). If no full-text was available, the authors were contacted. Disagreements were resolved through discussion with a third reviewer (I.B.).

The methodological quality of the research question relevant studies was evaluated using the Standard for Reporting Diagnostic Accuracy Studies (STARD) guidelines [20, 21], which follows the recommendations of [22] and [18]. All studies were also evaluated independently by two authors (B.T. and J. H.) using a modified STARD for HRV by [23]. It included 25 items (maximum of points). We have slightly modified two assessment tools [24], but the maximum score did not change. The items are shown in Table 1. Disagreement was solved by (I.B.) and discussion.

Table 1 Evaluation points (P) of STARDHRV followed by Dobbs et al. [23] and modified by Grässler et al. [24]
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From the included studies, the changes in all HRV parameters used were collected. Due to the limited data available, a descriptive discussion of the results was conducted without further statistical analysis. Increases were marked with an upward arrow, decreases with a downward arrow, and no change with an arrow pointing to the left and right. Significant changes were marked with an asterisk. Table 2 explains the parameters used in the review and the affiliation to the ANS.

Table 2 Overview of the HRV parameters evaluated in the review and their importance
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Results

The initial search resulted in 649 records and included one study, which was published after literature research [25]. After removing duplicates and exclusions based on title and abstract, only five full-texts were assessed for eligibility. Four studies used ERI [26,27,28,29], and one study used OC [25]. The professional groups were different (four studies): nurses [26, 27], employees of different sectors/branches [28, 29], and kindergarten teachers [25]. Two studies studied the same subjects, but reported different HRV parameters in the two publications, so they were both listed [26, 27]. All studies came from Europe (Germany and Italy). An overview of the included studies is shown in Table 3. The literature search revealed five studies with HRV analysis using ERI and/or OC, but ECG recordings were too short (3 min, 45 min, 2 h, 18 h) or too long (36 h), so they were excluded from the review [30,31,32,33,34]. Only one study examined risk factors related to cardiovascular disease, but only with the glycemic status [28]. All studies examined daytime and nighttime separately. Subject populations varied widely, and ranged from 53 [26, 27] to 9937 [28]. All study protocols were different. Two studies used classification with the ERI ratio [26, 27] and one a cutoff of OC [25], and each compared the groups. One study divided into age groups, compared them with RMSSD as the only parameter, and included ERI as a coefficient [29]. One study averaged ERI and RMSSD and ran various model calculations. Glycemic status and the inflammation parameter CRP were also included [28]. One study examined only women [25], two with more than two-thirds [26, 27], and two with less than 20% [16, 29]. Where possible, no gender differences were found in the studies.

Table 3 Results of the systematic research
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Outcome heart rate variability

One study used a chest belt [28], and the other four used classic Holter ECGs.

The time periods for HRV analysis varied widely among the studies. Borchini et al. analyzed 2 h of the 24 h recordings, each at the 5 different phases (working day working, non-working, night and resting day with day and night phase) [27]. The duration of HRV derivation in each phase was not standardized. Table 3 presents the outcome of all HRV measures.

Four studies used RMSSD as a marker of vagal function [25, 26, 28, 29]. RMSSD decreased with higher ERI or OC outcomes. It was significant for 24 h and night phase [25], overworking day, but not sleep [29], and also negative associated with ERI [28]. No significance was found in one publication [26]. The parasympathetic-associated parameter pNN50 decreased in kindergarten teachers with high overcommitment in 24 h and night phase [25]. For the SDNN (parasympathetic and sympathetic nervous system), SDANN and SDNN Index (both parameters without clear assignment to parasympathetic or sympathetic nervous system) at working day [26] and for SDNN in night phase [25] are decreased in subjects with higher ERI or OC. The frequency domain parameter HF showed the same tendency [25, 27]. The two studies that used LF and LF/HF showed opposite trends. LF and LF/HF increased at higher ERI [25], but also decreased [27]. The trend of HRV parameters looks adaptive to the stress situation related to higher ERI or OC.

One study found age-dependent effects for LF and HF at night. This study also examined work experience, which had no effect on HRV [25]. The study with age-related research found a lower RMSSD in higher ERI, which was most pronounced in employees aged 35–44 years [29].

Quality assessment

The study quality of HRV methodology was evaluated with STARDHRV [23] and modified according to [24]. The scores for all studies were 15 [28], 16 [25, 26], 17 [29], and 19.5 [27].

Full marks were achieved in all studies for points 1, 2, 9, 14, and 29. Zero points were found in the case of elevation points 5, 6, and 13 in all studies. The other points showed a heterogeneous allocation from 0 to 1. This evaluation is attached as Supplement 1.

Monitoring during the work could lead to movement artifacts, which limits the assessment. Three studies reported exclusion criteria about diseases and medication [25,26,27] and two did not report [28, 29]. Three studies performed a manual inspection of NN intervals [25,26,27]; other publications did not do so [28, 29]. Only one of the studies reported the percentages of adjusted material [29].

Summary of the results

The observed studies showed an adjustment of HRV by reduction of parasympathetic mediated HRV parameters thus at higher subjective stress (higher ERI or OC). The study quality of the HRV methodology was moderate. The average score for all studies was 16.7/25 points.

Discussion

The purpose of the review is to systematically evaluate the literature on heart rate variability as an objective indicator for mental stress in individuals with different levels of ERI and/or OC.

All studies used HRV during work and examined day and night phases. The selected HRV parameters are able to provide information about the measured strain (effort reward imbalance and/or OC). It should be noted that there are different study protocols and different recording times, so these values are only comparable to a limited degree.

Comparisons and statements about cardiovascular risk factors cannot be made. No gender differences were found on the basis of the studies either.

Deficiencies were found in the methodological quality and in the quality of the study reports. The numbers of subjects are very small (except for one study), so a generalization is not possible.

A trend can be seen so that the predominantly parasympathetic mediated parameters (e.g., RMSSD, pNN50, HF) decreased as an adaptation to workload (high ERI or OC) with a decrease. HRV parameters with both parasympathetic and sympathetic influences also decreased (e.g., SDNN, SDANN) or increased (e.g., LF, LF/HF). This is concerning, especially if HRV cannot be adequately adjusted by nighttime sleep, which hypothesizes a lack of recovery. Nonlinear parameters were not used. Minor age-related effects and not effects of work experience of HRV parameters could be found; both should not be overinterpreted.

Conclusions

This systematic review shows that there is a high need and a great potential for occupational health studies among different professional groups with mental stress. HRV is a valid objective method for visualizing stress, i.e., for measuring strain [13]. We recommend the use of 24-h ECGs to evaluate the “night” recovery phase. For the assessment of mental stress, the parasympathetic dominant HRV parameters were shown to be effective markers for this. Other parameters (e.g., without clear assignment or nonlinear parameters) should be used as a complement.