Introduction

Surgery is one of the most demanding safety–critical professions. The operating theatre can be a stressful environment (Menon et al. 2016; Demirtas et al. 2004). There is ample evidence that when physicians are under stress, quality of care is indeed reduced (Wallace et al. 2009). Stress also affects the physicians themselves. Long-term exposure to stress has been associated with a number of ill-health outcomes such as burn-out (Unterbrink et al 2007), cardiovascular diseases (Peter and Siegrist 2000) and depression (Oskrochi et al. 2018). Detection of mental stress is therefore not only extremely important to detect, reduce and prevent the adverse effects of mental stress on quality of care, but also on the physicians themselves.

While accurate and reliable measurements of stress are important, measuring stress is challenging, as stress is perceived and coped with differently by individuals. Measures of stress vary from questionnaires to biochemical evaluations such as cortisol measurements to heart rate variability. There is an increasing interest into more objective measurements of mental stress, as these cannot easily be manipulated and provide an accurate representation of the stress level (Amirian et al. 2014).

One objective measurement which can be used for measuring mental stress in the surgical setting is heart rate variability (HRV) (Jarvalen-Pasasen et al. 2018; Thielmann and Böckelmann 2016). Heart rate variability is the variation in the interval between successive normal NN intervals, which has been shown to decrease as mental stress increases. Variations in heart rate (HRV) can be calculated in the time domain and in the frequency domain [as a power spectral density (PSD) analysis] as well as with non-linear analysis (Sassi et al. 2015; Sammito et al. 2015). In both time and frequency domain analyses, the time intervals between successive normal NN intervals are determined first. The NN intervals are recorded by measuring the difference between two R waves in the QRS complex. Time domain indices of HRV are more direct measures of variations in interbeat intervals (IBI) and include SDNN (standard deviation of IBI), SDANN (the standard deviation of the average IBI), RMSSD (the square root of the mean squared differences of successive IBIs), and NN50 (the number of interval differences of successive IBIs larger than 50 ms). While some specific time domain indices are thought to reflect parasympathetic control of cardiac output (with cardiac output rising in response to stress), other time domain indices cannot be assigned clearly (Schaffer et al. 2017). Time domain indices do not provide detailed information on sympathetic control; the main advantage of using time domain measures is that they are easy to calculate. Frequency domain measures perform more complex calculations on IBI (Fourier transforms), expressing variability in terms of a power density spectrum (energy in specific frequency bands). Frequency domain measures can be calculated for any frequency band, but the most common ones are LF (low frequency, 0.04–0.15 Hz) and HF (high frequency, 0.15–0.4 Hz), but also VLF (very low frequency, < 0.04 Hz) is sometimes used, as is the LF/HF ratio (ratio low frequency/high frequency). Specific frequency bands are thought to reflect sympathetic and/or parasympathetic control, and therefore give more detailed information on the effects of stress on the autonomic nervous system.

However, calculating time and frequency domain measures of HRV is not straightforward and a number of factors need to be considered before analysis. For example, artefact correction is essential. HRV analysis should always be performed on normal-to-normal beat interval data (i.e. all intervals between adjacent R waves in the QRS complexes resulting from sinus node depolarizations) (Lippman et al. 1994). Artefacts such as missed, extra or misaligned beats can significantly alter HRV parameters (Peltola et al. 2012), and analyses using sports watches without correcting the raw data, for example, deliver unreliable results (Sammito and Böckelmann 2016).

Non-linear dynamics methods indicate qualitative aspects of the series of NN intervals (Sammito et al. 2015). These methods can be used for both long-term and short-term measures and has the advantage of being less prone to artefacts.

When evaluating HR and HRV in the field of occupational medicine, several modifiable and non-modifiable factors should be taken in account, as they can affect HR and HRV, the most relevant being alcohol, breathing, fitness activities, sex, cardiovascular diseases, temperature, body weight, noise, age, psychiatric disorders, smoking, hazardous substances, shift work including night shift, metabolic disorders, stress/mental tension and circadian rhythm/time of the day (Sammito et al. 2015).

The aim of this review was to evaluate the current use of HRV measurements within the surgical setting: with what purpose are they used, how long is it measured; to assess which methods are being used for analysing HRV (time domain/frequency domain/non-linear dynamics); and to assess whether HRV was measured correctly (i.e. whether artefacts were corrected).

Methods

Search strategy and study eligibility

This review was conducted and reported according to the Preferred Reporting items for Systematic reviews and Meta-Analyses (PRISMA) statement. The databases Medline, Embase, and PsycINFO were searched up to June 19, 2018 for studies regarding heart rate variability as a measurement of mental stress in the surgical setting. The search strategy was created in collaboration with a clinical librarian (see Appendix 1). For the database searches, Medical Subject Heading terms and additional free entry terms for stress, heart rate variability and terms related to the surgical profession were used. Duplicates were removed. Title and abstract of all studies were screened by the authors. The reference lists of the included articles were screened for additional relevant publications.

Studies were selected for full text analysis based on a predetermined set of inclusion and exclusion criteria. Studies that were included described a surgical procedure affected by mental stress, which was measured by means of HRV. Both studies with surgeons as well as with surgical residents as the subject of the study were included. Articles based on physical stress, non-surgical professions, medical students, no HRV parameters and no surgical outcome were excluded from this analysis. The study inclusion process is summarized in a PRISMA flowchart (Fig. 1). Differences in inclusion were resolved by plenary discussion. Studies were screened for full text if dubiety for inclusion was present amongst the authors. A total of 17 studies met the inclusion criteria and were thus included. A summary of the selected studies is presented in Table 1.

Fig. 1
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PRISMA flowchart

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Table 1 Evidence table
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Data extraction and quality assessment

Data were extracted from the eligible articles by all investigators. Discrepancies were immediately resolved by plenary discussion. The following data were extracted from each article: number of participants; aim of study; type of stress measurements; HRV measurement devices; HRV parameters (time and frequency domain); artefact corrections; factors possibly interfering with HRV; length of HRV measurements; additional measurements used for assessment of mental stress and main findings. The methodological quality of the studies included was assessed using the Newcastle–Ottawa Scale, which assessed the selection of study groups, the comparability of study groups and the ascertainment of either the exposure or outcome.

Statistical analysis

As a result of the large heterogeneity of the included studies, it was not possible to perform a meta-analysis. Data were therefore summarized and displayed in descriptive statistics.

Results

A total of 518 articles derived from Pubmed, EMBASE and PsycINFO were identified. 78 duplicates were removed, and thus 440 articles were screened for eligibility. 412 articles were excluded based on title and abstract. 11 articles were excluded based on full-text analysis. These articles included medical students as participants (n = 2), no HRV measurement present (n = 4), no surgical stress measurement (n = 2) or other reasons (n = 3). A total of 17 studies were included in the systematic review.

All included studies describe a surgical setting in which the surgeon's mental stress is measured by means of HRV. 8 of the 17 included studies had less than nine participants included in their studies.

HRV parameters

53% of the studies (n = 9) evaluated HRV by both domain measures and 35% (n = 6) of studies evaluated HRV only by frequency domain measures, while 6% of studies (n = 1) evaluated HRV solely by time domain measures. Finally, 6% of studies (n = 1) used a different method of evaluating HRV, namely beat-to-beat HRV compared with baseline HRV.

In 88% of the studies (n = 15), frequency domain measures were used to evaluate HRV. In all of these studies, a low-frequency (LF) component of 0.04–0.15 Hz and a high-frequency (HF) component of 0.15–0.4 Hz were calculated/determined. In 82% of the studies (n = 14), the LF/HF ratio was calculated based on these components, the remaining 18% of the studies (n = 3) did not calculate the LF/HF ratio. In 29% of the studies (n = 5), an additional very-low frequency (VLF) component of < 0.04 Hz was calculated as well as the HF and LF components. Furthermore, 18% of the studies (n = 3) included the total power (TP), the sum of all frequency components, in their analysis. 6% of the studies (n = 1) evaluated HRV by means of HFnu, the high-frequency component in normalized units (HFnu = ((HF/TP-VLF)) × 100).

In 59% of the studies (n = 10), time domain measures were used to evaluate HRV. Multiple time domain measures can be evaluated. A variety of time measures can be found in a singular study, and thus overlap between time domain measures can be present.

18% of the studies (n = 3) evaluated the mean R–R interval, which is the mean time elapsed between successive heartbeats. In 41% of the studies (n = 7) SDNN, the standard deviation of normal to normal interval was calculated. In 35% of the studies (n = 6), RMSSD, the square root of the mean normal to normal interval, was calculated. 18% of studies calculated pNN50, which is the percentage of adjacent pairs of normal to normal intervals differing by more than 50 ms in the recordings. 12% of the studies (n = 2) included the HRV coefficient (C_HRV), which was calculated by the following formula: C_HRV = SDNN/NN × 100. Finally, 6% of the studies (n = 1) calculated the difference between the longest and shortest R–R interval.

Artefact correction

For accurate HRV measures, a correction of artefacts needs to be performed (Lippman et al. 1994). Artefacts such as missed, extra or misaligned beats can cause significant alterations into HRV parameters, and therefore any aberrant beat should be corrected prior to HRV analysis (Peltola et al. 2012). This systematic review therefore analysed whether the included studies included artefact correction. 24% (n = 4) of the studies performed artefact correction in their analysis. If artefact correction took place, recordings were visually inspected and manually corrected.

HRV measurement purpose

The included studies were classified into subgroups according to why the study used HRV measures of mental stress: (1) studies evaluating whether mental stress was present in certain situations (n = 9; for results, see Table 2), (2) studies evaluating the differences in mental stress between different operating techniques or operating room environments (n = 3; for results, see Table 3), (3) studies evaluating the changes in mental stress between performing surgery and assisting surgery (n = 3; for results, see Table 4), and (4) remaining studies not classifiable to the other subgroups (n = 3; for results, see Table 5). One study compared mental stress between different operating techniques as well as between performing and assisting surgery, so fits in both (2) and (3).

Table 2 Overview of aim, studies evaluating whether mental stress was present in certain situations
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Table 3 Overview of aim, studies evaluating the changes in mental stress between different operating techniques or operating room environments
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Table 4 Overview of aim and studies evaluating the changes in mental stress between performing surgery and assisting surgery
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Table 5 Overview of aim; remaining studies not classifiable to the other subgroups
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Duration of HRV measurements

The duration of HRV measurements differed between studies. This is a reflection of the fact that different studies evaluated different procedures and different participants and had different aims. In this systematic review, studies were divided into three groups: long duration (24 h and longer), short duration (less than 24 h ranging from 11 min to 16 h), and studies measuring throughout the whole procedure (which did not mention the exact duration of the HRV measurements). 24% of the studies (n = 4) were long duration, 53% (n = 9) were short duration and 24% (n = 4) were whole procedures.

Factors affecting HRV

Certain factors such as smoking, alcohol consumption, caffeine consumption, medication use and the presence of cardiovascular diseases or diabetes are known to affect HRV.

In 76% of the studies (n = 13) included at least one of these factors was mentioned in the method. 41% of the studies (n = 7) assessed smoking habits of the participants; of those, only non-smokers were included in four studies; in two studies, some participants smoked on a regular basis; and in one study participants were asked not to smoke for 24 h before the measurement. 18% of the studies (n = 3) assessed alcohol consumption among the participants: in two studies participants were asked not to consume alcohol 24 h before the procedure, and one study reported that all participants had a low to moderate general alcohol consumption.

18% of the studies (n = 3) assessed caffeine consumption: in one study, participants were asked not to consume caffeine 24 h before the procedure, in one study participants were asked not to consume caffeine on the day of the procedure, and in one study there were no constrictions regarding caffeine consumption.

65% of the studies (n = 11) assessed medication use amongst participants: nine studies reported no use of any medication, one study reported no use of beta-blockers, and one study reported looking into medication use, but no outcome was mentioned in the article.

53% of the studies (n = 9) assessed the presence of cardiovascular disease among participants: eight studies reported no presence of disease, and one study reported no family history of cardiac diseases amongst all participants. Finally, 18% of the studies (n = 3) assessed the presence of diabetes among participants: all three of these studies reported the absence of diabetes among all participants.

All information concerning the measurement of heart rate variability and factors affecting heart rate variability is summarized in Table 6.

Table 6 Overview of HRV parameters, artefact corrections and other information in the included studies
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Additional measurements used for the assessment of mental stress

In almost all included studies, HRV was not the only used measurement of mental stress. Only 18% of the studies (n = 3) used HRV as the only measurement of mental stress. 35% of the studies (n = 6) used heart rate (HR) in combination with HRV to measure mental stress. 12% (n = 2) used the STAI (State Trait Anxiety Inventory) in addition to HRV. The remaining 35% of the studies (n = 6) used a combination of different subjective and objective measures of stress and fatigue. Combinations included HR and STAI (n = 1), HR and VAS (visual analogue scale) (n = 2), NASA-TLX (NASA Task Load Index) and STAI (n = 1) and STAI in combination with observer ratings, HR, HR and salivary cortisol (n = 2).

Discussion

This systematic review evaluated the different methods used in the studies for the analysis of HRV (time domain/frequency domain/non-linear dynamics), to assess whether HRV is being measured correctly (i.e. whether artefacts were corrected) and to evaluate the current use of HRV measurements in the surgical setting (short-term vs. long-term measurements) to identify areas for improvement in future HRV research within the surgical setting.

This systematic review showed that HRV shows to be a good objective assessment method of stress induced in the surgical setting: it was able to pinpoint stressors during operations, determine which operating techniques induced most stress for surgeons, and indicate differences in stress levels between performing and assisting surgery. In addition, this review showed a lack of artefact correction: even though artefact correction is essential for reliable HRV calculations, only four studies (24%, n = 4) mentioned correcting for artefacts. The review also showed studies evaluating the long-term effects of mental stress and its recovery were lacking.

Almost all studies in this review used frequency domain measures, while half of the studies also included time domain measures. The fact that frequency domain measures are being used more often might be because of the fact that when analysing stationary short-term recordings, the task force recommends the use of frequency domain methods (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology 1996). The third method that can be used for calculating variations in heart rate are non-linear analyses, but these methods were not used in any of the included studies. In theory, this is a third method of cardiologists that can be used in HRV research; however because of the characterizing complex systems, successful application in the medical science fields is restricted For future research, the standards of measurement, physiological interpretation, and clinical use can be used to standardize the research into HRV as a measure of stress.

When evaluating the studies included, different goals can be identified for the use of HRV. These goals can be measuring stress during a specific operation, or assessing changes in stress levels between various surgical environments. HRV showed to be a good objective assessment method of stress induced in the workplace environment and was able to pinpoint stressors during operations. In addition, HRV was able to determine which operating techniques provided most stress for surgeons and to determine differences in stress levels between performing and assisting in the surgical procedure. Although different purposes for using HRV were found, the majority of studies had the same overall interest: measuring stress at a specific moment in time, namely during an operation. The included studies were mainly focused on the evaluation of short-term stress, instead of long-term stress and its recovery as the majority of the studies had a short duration of measurement.

Although short-duration measurements can inform us of the level of mental stress during the time frame or situation of interest, measurements of longer duration provide us with vital information on the recovery of stress. Long-term measurements (24 h or more), as opposed to short-term HRV monitoring, enable assessing stress and recovery patterns during normal working hours as well as during leisure time and during sleep (Jarvelin-Pasanen et al. 2018).

Only four of the included studies performed long-term measurements and investigated the long-term effects and recovery of mental stress. These studies found that working night shifts decreased the HRV of surgeons (Amirian et al. 2014) and that higher perceived stress in the operating room is associated with a decreased HRV at night (Rieger et al. 2014). This seems to indicate that stress increases during night shifts and that surgeons are still recovering from high stress of the operating room at night. To identify the long-term effects of stress and prevent its adverse effects on surgeons’ health, more research is needed with long-term HRV measurements, also to better understand if and how surgeons recover from mental stress during working hours.

As heart rate variability is a measure with complex underlying physiological mechanisms, it can be affected by many confounding factors, such as age, weight, physical activity, cardiac innervation, cigarette smoking, alcohol consumption, caffeine consumption, medication use, and core temperature (Jarvelin-Pasanen et al. 2018). The majority of the included studies reported on some of the above-mentioned factors and used these factors as exclusion criteria. Because of the interpersonal differences in HRV, it is recommended participants always serve as their own control.

Heart rate variability is an objective and reliable way of non-invasively monitoring stress in the clinical situation (Böhm et al. 2001; Prichard et al. 2012; Song et al. 2009). This review shows that HRV can be used successfully for different purposes to assess mental stress in the surgical setting, including the effect of operating techniques/environment on mental stress of surgeons and the change in mental stress between performing surgery and assisting. In addition, HRV shows to be a good objective assessment method of stress induced in the workplace environment, as it is able to pinpoint stressors during operations. This review also showed that the current studies are mainly focussed on the short-term measurement of mental stress. There is thus a lack of studies on the long-term effects of mental stress on surgeons, and its recovery.

To standardize HRV research, we further recommend that future research adheres to a single guideline, with using artefact correction to be the most pressing issue.