The effects of body posture and temperament on heart rate variability in dairy cows

The effects of body posture and temperament on heart rate variability in dairy cows

Physiology & Behavior 139 (2015) 437–441 Contents lists available at ScienceDirect Physiology & Behavior journal homepage: www.elsevier.com/locate/p...

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Physiology & Behavior 139 (2015) 437–441

Contents lists available at ScienceDirect

Physiology & Behavior journal homepage: www.elsevier.com/locate/phb

The effects of body posture and temperament on heart rate variability in dairy cows Lilli Frondelius a,⁎, Kirsi Järvenranta a, Taija Koponen b, Jaakko Mononen a,b a b

MTT Agrifood Research Finland, Animal Production Research, Halolantie 31 A, 71750 Maaninka, Finland University of Eastern Finland, Department of Biology, PL 1627, 70211 Kuopio, Finland

H I G H L I G H T S • The posture (lying, standing) of the cow affected heart rate variability. • The emotional reactivity of the cow was related to heart rate variability. • Linear and non-linear methods measuring heart rate variability complement each other.

a r t i c l e

i n f o

Article history: Received 21 August 2014 Received in revised form 24 November 2014 Accepted 2 December 2014 Available online 3 December 2014 Keywords: Cow Heart rate variability Temperament Handling Posture

⁎ Corresponding author. E-mail address: lilli.frondelius@mtt.fi (L. Frondelius).

http://dx.doi.org/10.1016/j.physbeh.2014.12.002 0031-9384/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t Reactivity of cattle affects many aspects of animal production (e.g. reduced milk and meat production). Animals have individual differences in temperament and emotional reactivity, and these differences can affect how animals react to stressful and fear-eliciting events. Heart rate variability (HRV) is a good indicator of stress and balance of the autonomous nervous system, and low parasympathetic activity is connected with higher emotional reactivity. The study had two specific aims: (1) to compare HRV in dairy cows for standing and lying postures (no earlier results available), and (2) to assess whether dairy cows' emotional reactivity is connected to their HRV values. Eighteen dairy cows were subjected twice to a handling test (HT): morning (HT1) and afternoon (HT2), to evaluate emotional reactivity (avoidance score, AS). HRV was measured during HT (standing). HRV baseline values, both standing and lying down, were measured one week before HTs. HRV was analyzed with time and frequency domain analyses and with the Recurrence Quantification Analysis (RQA). Heart rate (HR), low-frequency/high-frequency band ratio (LH/HF), % determinism (%DET) and longest diagonal line segment in the recurrence plot (Lmax) were higher (p b 0.05) while the cows were standing than when lying down, whereas the root mean square of successive R–R intervals (RMSSD) (p b 0.05) and power of the high-frequency band (HF) (p b 0.1) were higher while the animals were lying down. HR, the standard deviation of all interbeat intervals (SDNN), RMSSD, HF, power of the low-frequency band (LF), % recurrence (%REC), %DET, Shannon entropy (p b 0.05), and HF (p b 0.1) were higher during the handling test compared to standing baseline values. AS (i.e. tendency to avoid handling) correlated positively with SDNN (r = 0.48, p b 0.05), RMSSD (r = 0.54, p b 0.05), HF, RMSSD (r = 0.46, p b 0.1) and LF (r = 0.57, p b 0.05), and negatively with %DET (r = − 0.53, p b 0.05), entropy (r = −0.60, p b 0.05) and Lmax (r = −0.55, p b 0.05) in the baseline HRV measurements. AS correlated positively with SDNN (r = 0.43, p b 0.1) and HF (r = 0.53, p b 0.05) during HT. Some HRV parameters (HR, LF, %REC, %DET) indicated that the handling test may have caused stress to the experimental cows, although some HRV results (SDNN, RMSSD, HF, entropy) were controversial. The correlations between HRV variables and AS suggest that the emotional reactivity of the cow can be assessed from the baseline values of the HRV. It is debatable, however, whether the handling test used in the present study was a good method of causing mild stress in dairy cattle, since it may have even induced a positive emotional state. The posture of the cow affected HRV values as expected (based on results from other species), so that while standing a shift towards more sympathetic dominance was evident. Our results support the idea that linear (time and frequency domain) and non-linear (RQA) methods measuring HRV complement each other, but further research is needed for better understanding of the connection between temperament and HRV. © 2014 Elsevier Inc. All rights reserved.

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1. Introduction The heart rate (HR) of a healthy individual is irregular [1]. This irregularity, i.e. heart rate variability (HRV), is due to beat-to-beat variation [2]. The autonomic nervous system (ANS) controls the heart's sinoatrial node (SA), which is the pace-making unit of the heart [3]. Parasympathetic activity (often referred as vagal tone) dominates during rest and sympathetic activity dominates during activity. Thus HRV can be used as an indicator of the sympathovagal balance of the ANS [2]. Changes in HRV can be detected already in the anticipatory state when behavioral evidence of stress is not yet visible [4]. HRV is known to be a valid indicator of stress [5] and it has been used in many studies with production animals to assess stress [4,6]. In calves the use of HRV as an indicator of sympathovagal balance has been validated with autonomic blockade tests [7]. HRV has also proved to be a useful tool, for instance in assessing pain in animals [e.g. 8–10]. Research with other species of animals [4,11] and humans [2,12] show that physical activity and body posture (standing vs. lying down) also affects heart rate variability. Temperament or reactivity is described as ‘consistent differences in behavior between individuals’ [13] and it is evident that animals have individual differences in temperament [14–17]. These temperament traits are consistent over time (e.g. agitated behavior: [18]) and different situations (e.g. reactivity to various stimuli: [15,19]). Reactivity of cattle affects many aspects of animal production. Highly reactive animals can be a risk for people handling those animals [18]. High reactivity and fear of humans can also be linked to reduced production (milk: [20, 21]; meat: [22]). Bourguet et al. (2010) [23] discovered that more reactive cows, as indicated by a separation test and human exposure test during rearing, had higher stress responses (e.g. heart rate, behavior, post mortem muscle temperature) during the slaughter procedure. They concluded that emotional reactivity can predict the stress level of animals during aversive events. Low parasympathetic activity (and high sympathetic activity) is connected with higher emotional reactivity [24], and it is possible that HRV and temperament are associated [25]. In quail, tonic immobility responses to restraint are genetically linked with changes in sympathovagal balance, and it is possible to affect behavioral responses to stress using genetic selection [25]. In cattle, HRV variables differ between various breeds and it has been speculated that these differences may arise either from metabolism or temperament of the animals [26]. Generally, individuals with low vagal tone are possibly more vulnerable to stress [4]. Kovács et al. (2014) [27] concluded that HR and short-term HRV are effective tools for assessing stress in human–cattle relationships. In previous studies with cattle, HRV measurements have been done either when animals are lying down or standing. However, none of the publications present comparison of HRV between lying and standing postures [e.g. 9,26,28,29]. In this study we compared HRV while animals were standing and lying down. Another aim of the study was to assess whether the emotional reactivity of dairy cows, as measured by a handling test, is connected to its HRV values. In both tests we wanted to use an extensive set of HRV variables as suggested in previous studies with cattle [26,29]. 2. Materials and methods 2.1. Housing and management of experimental animals The study was conducted at MTT Agrifood Research Finland (Maaninka, Finland) from December 2010 to January 2011. The experimental animals were 14 Holstein-Friesian and 4 Ayrshire cows, both primiparous and multiparous, all in their late-lactation period. Outside the experiments the cows were loose-housed in a curtain-wall barn with rubber-matted cubicles and automatically scraped passageways. They were fed with 6–8 kg of concentrate (barley-rapeseed meal mixture 80:20 in kg, dry matter approximately 86% and 92% respectively) per

day and grass silage (energy content 10.8 MJ ME/kgDM) ad libitum. The cows were routinely milked twice daily at 6.00–8.00 and 16.00–18.00. 2.2. HRV measurements The experiment included baseline HRV measurements and a handling test (HT) with HRV measurement (Table 1). Polar Equine RS800CX Science (Polar Electro Oy, Finland) was used to measure the heart rate variability (R–R interval). It contained a measuring belt with two electrodes, a transmitting unit and a receiving data logger. This system is designed especially for scientific use and primarily for horses. Baseline values of HRV were measured one week prior to the handling test (Table 1). Cows were divided into three groups of six cows and measurements for these groups were conducted on three successive days, one group per day. After the morning milking one group was moved to the experimental cubicles and tethered. A Polar measuring belt was attached around the cow as described in Hopster and Blokhuis (1994) [30] and secured with a girth. To improve conductivity the electrodes were wetted with water. HRV was measured for 2 h, both in the morning and in the afternoon. Cows were tethered during the whole period between morning and afternoon measurements. Measuring belts were removed after the morning measurement and attached again before the afternoon measurement. The data for the first 30 min were ignored, this being considered the time required for the cattle to habituate to the measuring belts. Simultaneously, the behavior of the cows was video recorded (Axis Q1755-E, Axis communications AB, Sweden) from up behind. The video-recordings were used for selecting both standing and lying bouts for the HRV analyses. The Handling Test (HT, see below) was carried out one week after the baseline HRV measurement (Table 1). A Polar measuring belt was attached half an hour before HT (habituation) and HRV were measured during HT. The test was conducted twice, once in the morning (HT1) and once in the afternoon (HT2). The cows were tethered during the whole time between the HT1 and HT2 measurements. 2.3. The handling test In the HT the cows were tethered with the same procedure as during the HRV baseline measurements. The cows were standing during the HT. The experimenter (female), previously unknown to the experimental cows, handled a cow from both sides. Handling included touching and pinching of the cow and it started from the head and moved towards the rear of the cow. Every area of the body (head/ears, side/ back/front legs, rear/hind legs/tail) was handled for 1 min on both sides and the total test time was 6 min. The behavior of the cows was video recorded as in the HRV baseline measurements and also with a hand-held camera behind the cow. The behavior of the cows during the six-minute handling period was scored from the video material by the handler before analyses of the HRV data. An eight-point ordinal scale was used to evaluate the reactions of the cows (Table 2). The reactions were first scored separately during the handling of each of the six body areas (head/ears, side/back/front legs and rear/hind legs/tail from the both sides). Finally, the scores from the morning and the afternoon were totalled to create an avoidance score (AS) with a theoretical Table 1 The schedule of the experiment. The first test day of each test week was Monday. HRV = heart rate variability measurements, HT = Handling test. Test week

Daya

Group 1 (n = 6)

1

1 2 3 1 2 3

Baseline HRV

2

Group 2 (n = 6)

Group 3 (n = 6)

Baseline HRV Baseline HRV HT & HRV HT &HRV HT & HRV

L. Frondelius et al. / Physiology & Behavior 139 (2015) 437–441 Table 2 Avoidance scoring used in the handling test. The total avoidance score (AS) was calculated as a sum (ranging from 12 to 96) based on the twelve parts of the handling test (see text for the details). Score

Behavioral response

1 2 3 4 5 6 7 8

Cow takes non-aggressive contact to the handler No reaction or contact to the handler Cow pays attention, but does not react Cow freezes Cow steps aside slightly Cow steps aside Cow steps aside considerably Extremely strong avoidance reaction and/or the cow is aggressive

minimum of 12 and a theoretical maximum of 96. The higher the AS of a cow, the more it showed avoidance behavior.

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balance [2]. Fast Fourier Transformation (FFT) was used to calculate the frequency domain parameters and limits for the HF band were set to 0.2–0.58 Hz and for the LF band to 0.04–0.2 Hz, in accordance with previous studies with cattle [4]. RQA is also mathematically challenging. It has been applied successfully in recent cattle studies to reveal nonlinear processes of HRV [26, 29]. RQA is ideally suited for physiological systems because of its independence of data set size, non-stationarity of data, and fewer assumptions regarding statistical distributions of the data [34]. Parameters used were recurrence (%REC), determinism (%DET), (Shannon) entropy and longest sequence of successive recurrence points (Lmax) (Table 4). The %REC and %DET are parameters of regularity of HRV in multidimensional space [4]. Entropy works as a rough measure of the information content of the trajectories [35] and it corresponds to the richness of deterministic structuring of the series [4]. Lmax is inversely related to the largest positive Lyapunov exponent [35]. A small Lmax indicates a large amount of chaos [4].

2.4. HRV analyses 2.5. Statistics Kubios HRV 2.0 software was used to preprocess the data and calculate selected HRV parameters (Table 2). This software is especially designed to analyze HRV data [31]. R–R intervals that had a deviation of more than 5% or three consecutive errors were ignored. The detrending method was ‘smoothness priors’ with λ = 1000. Based on the video recordings, a total of six 5-minute undisturbed standing and lying bouts (3 from the morning and 3 from the afternoon) were selected for further analyses from the HRV data collected during the baseline measurements. From the HRV data collected during the handling test (animals were standing) one 5-minute bout was selected both from the morning and the afternoon. The 5-minute recording period meets the recommendations of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology [2]. HRV analyses included time-domain and frequency domain parameters as well as nonlinear analysis (Recurrence Quantification Analysis or RQA; Table 3). This kind of multivariate approach is particularly recommended for evaluating stress loads that are quantitatively and qualitatively different [29]. Time domain analysis (Table 2) describes temporal variations in the heart rate [2]. HR gives the pulse as beats per minute, whereas the standard deviation of normal-to-normal intervals (SDNN) is calculated on the basis of all interbeat intervals and is a measure of the overall variation in HR [32]. By contrast, the root mean square of successive interbeat interval differences (RMSSD) describes short-term variability in HR, and is often connected to vagal activity [2,4]. Frequency domain analysis (Table 2) is mathematically more complicated, and provides information on how variation is distributed as a function of frequency [2]. The high frequency (HF) band is connected to respiratory sinus arrythmia and thus to vagal activity [2], whereas the low frequency (LF) band is often associated with both sympathetic and vagal activities [33]. LF/HF ratio is considered to indicate sympathovagal

For the baseline measurements the sample size was 18, but because good quality HRV data were not obtained from all experimental cows in the HTs, the sample size decreased in HT1 to 14 and in HT2 to 15. Mean values of all the 5-minute lying or standing (n = 18) bouts and mean values of HT1 and HT2 (n = 17) were utilized in the statistical analyses. If a cow had data only from one HT (one case in HT1 and two cases in HT2), those values were used instead of a mean. All the statistical analyses were made with SAS software for Windows version 9.2 through SAS Enterprise Guide version 4.3 (SAS Institute Inc., Cary, NC, USA). From the baseline measurements differences in HRV between standing and lying bouts were compared using the mixed procedure (paired t-test). Differences in HRV between the baseline measurements (during the standing bouts) and handling test were compared with the same statistical test. The appropriateness of the model was studied from residuals and the assumptions of the model were considered to be met by data if the distribution of the residuals was approximately normal. In preliminary analyses the effects of covariates (age, weight, milk production) were also evaluated, but none of the covariates had a significant effect. The results are expressed as mean ± standard deviation (SD). Correlations between HRV parameters and AS were tested with Spearman's rank correlation coefficient (rs). The significance level in all analyses was set to p b 0.05, but also tendencies (0.05 ≤ p b 0.1) are expressed.

3. Results HR, LH/HF, %DET and Lmax were higher while the cows were standing than when they were lying down, whereas RMSSD and HF were higher while the animals were lying down (Table 4). Posture had no

Table 3 The classification and definitions of the HRV parameters and the theoretical effect of stress on these parameters. Type of analyses

HRV parameter

Definition

Effect of stressa

Time domain

HR SDNN RMSSD HF LF LF/HF Recurrence (%REC) Determinism (%DET) Entropy Lmax

Mean heart rate, (bpm) Standard deviation of all interbeat intervals, (ms) Root mean square of successive interbeat interval differences, (ms) Power of the high-frequency band (0.2–0.58 Hz) Power of the low-frequency band (0.04–0.2 Hz) Power of the low-frequency component divided by power of the high-frequency band Percentage of recurrent points in the recurrence plot Percentage of recurrent points that appear in sequence, forming diagonal lines in the recurrence plot Shannon entropy of the deterministic line segment length distributed in a histogram Longest sequence of successive recurrence points

↑ ↓ ↓ ↓ ↑ ↑ ↑ ↑ ↓ ↑

Frequency domain

Recurrence quantification analyses

a

[2,4].

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Table 4 Comparison of heart rate variability variables (mean ± SD) between standing and lying dairy cows (n = 18), and between baseline and the handling test measurements (n = 17). Comparison between lying cows and the handling test was not conducted. Standing/baseline HR SDNN RMSSD HF LF LF/HF %REC %DET Entropy Lmax

75.9 ± 0.929 13.8 ± 1.162 8.14 ± 0.916 17.2 ± 4.577 106 ± 22.13 8.80 ± 1.128 45.5 ± 0.657 99.6 ± 0.055 3.64 ± 0.032 330 ± 15.80

Lying

Handling test a

84.1 ± 2.293a 23.5 ± 1.787a 12.4 ± 1.056a 54.3 ± 11.01a 341 ± 48.21a 11.6 ± 2.113b 49.1 ± 1.260a 99.7 ± 0.059a 3.83 ± 0.046a 338 ± 24.79

73.5 ± 1.190 14.4 ± 1.369 11.8 ± 1.495a 40.2 ± 12.73b 124 ± 33.19 5.42 ± 0.143a 44.5 ± 1.662 99.0 ± 0.207a 3.62 ± 0.074 277 ± 22.17a

Differences (paired t-test) between standing and lying, and baseline and handling test. a p b 0.05. b p b 0.1.

effects on SDNN, LF, % REC or Entropy. All the HRV parameters were higher during HT than baseline, except Lmax (Table 4). AS correlated positively with SDNN, RMSSD, HF and LF, and negatively with %DET, Entropy and Lmax in the baseline HRV measurements (Table 5). There were only two positive correlations between AS and the HRV parameters during the HT. 4. Discussion Our study is the first to show that body posture has an effect on HRV in cattle. Dairy cows' RMSSD were lower and LH/HF, %DET and Lmax were higher while standing compared to lying. These changes indicate a shift towards a more sympathetic regulation of the ANS, as expected [2,12]. It should be borne in mind, however, that we did not take into consideration the vigilance (which may affect on HRV) of the cows when they were lying down. Sleeping of cows cannot be judged reliably based on behavioral observations only [36], and sleep is known to affect HRV in other species, e.g. in humans [37] and rats [38]. An increase in HR and LF during the handling test compared to baseline may indicate a shift towards more dominant sympathetic activity and higher stress in experimental cows during the handling test. The elevated mean heart rate observed here is in agreement with results from earlier handling studies [beef cattle: 39; horses: 6]. An increase in LF has been found also in stressed calves in the study of Mohr et al. (2002) [29]. However, other results within the time and frequency domains were not so easily interpreted. SDNN and RMSSD increased during the handling test, as did HF. Values for HF and RMSSD correlate well with each other and these parameters are connected to the vagal tone [40]; the increase of these values is in conflict with the general standpoint [4]. According to evidence in the literature SDNN also decreases during stress [4]. Increased %REC and %DET during HT demonstrates loss of complexity and a shift towards a more deterministic control and higher stress levels. Similar results are found in calves [29] and cows [26] during stress. In addition entropy increased, although higher values of entropy

are connected with richness of deterministic structuring and thus higher HRV [4]. However, increased entropy in stressed calves was also reported by Mohr et al. (2002) [29]. Controversial results compared to the general standpoint, similar to ours, have been obtained in several other studies, and these reflect the diversity of factors that may affect the sympatho-vagal balance. Schubert et al. (2009) [41] assessed short term stress reactivity in humans with a speech task. They discovered that SDNN, HF and LF increased while LF/HF ratio remained constant and they speculated that the breathing pattern during speech could be the explanation for these results. Sgoifo et al. (1997) [42] observed that restraint increased RMSSD in rats, and Inagaki et al. (2004) [43] found that during stressexpecting state HF remained constant, whereas LF and LF/HF ratio increased. The two branches of ANS work in a coordinated way [44] and this study and all the examples above are good examples of how a balance between parasympathetic and sympathetic activity can produce different HRV values depending on the situation and the stressor. One possible explanation for the results in the present study is that HT was not a sufficiently negative stressor and AS may have been inadequate to differentiate negative and positive reactions of the cow. Previous (positive) handling affects the reaction (behavior, HR) of the cows in later human–animal interactions [45] and our experimental animals were already quite used to handling situations. This means that the handling test may not have caused stress in the cows. The handling may even have induced positive emotions in the experimental cows. McCraty et al. (1995) [46] obtained similar results, increased HF and LF while LH/HF remained unchanged, in humans who self-induced positive emotions. In sheep, during the presumably positive emotional situation, RMSSD also increased compared to the negative situation [47]. Behavioral reactivity in HT correlated with seven baseline HRV parameters, but only two of these correlations were as expected: cows with higher AS (i.e. higher emotionality) had higher LF and lower entropy than cows with lower AS. Increased value of LF generally indicates higher stress levels and low entropy values indicate loss of complexity in control of HRV [4]. However, higher baseline SDNN, RMSSD and lower baseline %DET, and Lmax are connected to vagal tone and complexity of HRV [4,29]. Thus, in our study, correlations between AS and these HRV parameters are controversial because higher emotionality should be associated with to lower HRV [24]. During the handling test only HF correlated positively with AS. Although some of the correlations are unexpected, our results suggest that more reactive cows can be distinguished from less reactive cows from their baseline HRV values, but during the handling these differences between the cows with different temperaments were less clear. In conclusion: 1. The posture of the cow affected HRV values as expected, so that while standing a shift towards more sympathetic dominance was evident. 2. The correlations between HRV variables and AS suggest that the reactivity of the cow can be assessed from the baseline values of the HRV. 3. Some HRV parameters indicated that the handling test may have caused stress to the experimental cows, whereas other parameters were not affected as expected. 4. Our results support the idea that linear (time and frequency domain) and non-linear (RQA) methods measuring HRV complement each

Table 5 Spearman's correlation between heart rate variability (baseline and during the handling test) and avoidance score (AS) during the handling test. SDNN

RMSSD

HF

LF

LF/HF

%REC

%DET

Entropy

Lmax

Baseline (n = 18) AS 0.11

HR

0.48a

0.54a

0.46b

0.57a

−0.05

−0.16

−0.53a

−0.60a

−0.55a

Handling test (n = 17) AS 0.26

0.43b

0.41

0.53a

0.39

−0.23

−0.41

−0.24

−0.31

a b

p b 0.05. p b 0.1.

0.09

L. Frondelius et al. / Physiology & Behavior 139 (2015) 437–441

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