Autonomic regulation, physical activity and perceived stress in subjects with musculoskeletal pain: 24-hour ambulatory monitoring

International Journal of Psychophysiology 86 (2012) 276–282

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Autonomic regulation, physical activity and perceived stress in subjects with musculoskeletal pain: 24-hour ambulatory monitoring David M. Hallman a, b,⁎, Eugene Lyskov a a b

Centre for Musculoskeletal Research, University of Gävle, SE-801 76 Gävle, Sweden Dept. of Public Health and Caring Sciences, Uppsala University, Uppsala Sweden

a r t i c l e

i n f o

Article history: Received 22 February 2012 Received in revised form 20 August 2012 Accepted 25 September 2012 Available online 14 October 2012 Keywords: Daily physical activity Ambulatory monitoring Heart rate variability Trapezius myalgia

a b s t r a c t The aim of the study was to investigate autonomic nervous system regulation, physical activity (PA) and perceived stress and energy during daily activities in subjects with chronic muscle pain in the neck–shoulders (trapezius myalgia) (n = 23) and symptom-free controls (n = 22). Subjects underwent 24-hour objective ambulatory monitoring of heart rate variability (HRV) and PA, and reported their perceived stress and energy in a diary. Standard HRV measures were extracted in time and frequency domains. The volume and pattern of different types of activities were quantified in terms of intensity and duration of walking, and time spent sitting, standing and lying during the 24-hour measurement. Results showed shortened inter-beat-intervals (higher heart rate) and reduced HRV in the pain group, most pronounced during sleep (p b 0.05). For overall PA, the pain group showed increased lying time, compared to controls (p b 0.05). A different activity pattern was found in the pain group, with reduced leisure time PA and increased PA during morning hours, in comparison with controls (p b 0.05). Both groups demonstrated low levels of perceived stress, whereas reduced energy was observed in the pain group (p b 0.05). In conclusion, monitoring of 24-hour HRV demonstrated diminished HRV among persons with chronic neck–shoulder pain. This reflected aberration in autonomic regulation, suggesting reduced parasympathetic activation and increased sympathetic tone as an element in maintenance of chronic muscle pain. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Perceived stress is a known risk factor for muscle pain (Bongers et al., 2002; Larsson et al., 2007) due to activation of physiological stress systems (Lundberg, 2002). The autonomic nervous system (ANS) is a key stress system intrinsically involved in nociception (Benarroch, 2006). Several studies indicate the involvement of the ANS in the development and maintenance of chronic muscle pain (Martinez-Lavin, 2007; Vierck, 2006). Generally, objective indications of ANS aberration are seen at both local and systemic levels. Chronic trapezius myalgia is associated with changes in muscle physiology, such as morphological disturbances (Larsson et al., 2001; Andersen et al., 2008) and insufficient metabolism (Sjøgaard et al., 2010), possibly related to sustained muscle activation and disturbed muscle circulation (Visser and van Dieën, 2006). Impaired blood flow in painful muscles is frequently found Abbreviations: ANS, Autonomic nervous system; BMI, Body mass index; EE, Energy expenditure; HRV, Heart rate variability; FM, Fibromyalgia; HF, High frequency; IBI, Inter-beat intervals; LF, Low frequency; PA, Physical activity; PNN50, Proportion of the number of differences between successive inter-beat intervals > 50 ms; SDNN, Standard deviation of normal-to-normal inter-beat intervals; VLF, Very low frequency. ⁎ Corresponding author at: Centre for Musculoskeletal Research, University of Gävle, SE-801 76 Gävle, Sweden. Tel.: +46 26648500, +46 736266413. E-mail address: [email protected] (D.M. Hallman). 0167-8760/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ijpsycho.2012.09.017

among subjects with trapezius myalgia, which partly seems to reflect excessive sympathetic activation (Maekawa et al., 2002; Passatore and Roatta, 2003). Results from previous studies have demonstrated impaired trapezius blood flow in patients during static contractions (Larsson et al., 1999; Hallman et al., 2011), pain stimulation with cold water (Acero et al., 1999), needle stimulation in the trapezius muscles (acupuncture) (Sandberg et al., 2005), and during computer work (Cagnie et al., 2012). Laboratory studies which used heart rate variability (HRV) to assess autonomic regulation in widespread muscle pain, e.g. fibromyalgia (FM) indicate basal hyperactivity of the sympathetic nervous system, which may lead to hypo-reactivity to physical and mental challenges (Martinez-Lavin and Hermosillo, 2000; Okifuji and Turk, 2002). Based on heart rate and blood pressure recordings during controlled rest and in response to stress, similar findings have been revealed among subjects with localised neck–shoulder symptoms (Gockel et al., 1995). However, other authors considered these deviations too small and inconsistent to prove the role of this mechanism in neck–shoulder pain (Sjörs et al., 2009). Assessment of autonomic function in persons with muscle pain is usually based on controlled laboratory examination. Often, these experiments use a relatively short duration of the stressors, which may not be relevant for activities that occur in daily life, resulting in poor ecological validity and difficulties in interpretation of the results.

D.M. Hallman, E. Lyskov / International Journal of Psychophysiology 86 (2012) 276–282

One valuable instrument, which complements laboratory diagnostics, is ambulatory monitoring of cardiovascular characteristics — especially when considering changes in HRV in relation to physical activity (PA) and perceived stress, as these factors could have a direct influence on autonomic regulation. Moderate and vigorous PA have been associated with increased resting HRV (Rennie et al., 2003; Melanson, 2000) indicating an improved autonomic regulation in favour of parasympathetic predominance in more active individuals. Inversely, higher perceived stress at work is associated with lower parasympathetic tone (Vrijkotte et al., 2000; Chandola et al., 2008). Only a limited number of studies have assessed ambulatory HRV in patients with chronic muscle pain (for example: Doğru et al., 2009; Haley et al., 2004; Martínez-Lavín et al., 1998), mainly focusing on widespread pain conditions such as fibromyalgia which is associated with a wide range of neurasthenic symptoms and sleep disorders. However, these studies did not objectively assess PA or included subjects with localised neck–shoulder pain. Recent studies, focusing on objective assessment of PA (Verbunt et al., 2009), indicate that persons with chronic muscle pain are less physically active than are nonsymptomatic subjects (van Weering et al., 2007) in accordance to the fear-avoidance model (Vlaeyen and Linton, 2008). Although some studies have shown decreased daily activity in patients, others have revealed different patterning throughout the day, typically with patients showing lower PA levels in the evening (van Weering et al., 2009). Thus, another possible explanation, more feasible to neck–shoulder pain, suggests that reduced PA is related to fatigue rather than fear of movement (Spenkelink et al., 2002). However, it is not known whether these patterns of PA can be seen in persons with chronic neck–shoulder pain, and how it affects ANS. Therefore, characterising daily PA requires objective assessment methods that provide detailed and precise information about volume and patterns of different activity types. The aim of the present study was to investigate differences in autonomic regulation, PA and perceived stress and energy between subjects with chronic neck–shoulder pain and healthy controls, by means of 24-hour ambulatory monitoring of HRV, PA and self-ratings. It was hypothesised that the pain group would show reduced HRV, altered pattern of physical activity and higher perceived stress compared to the control group. 2. Materials and methods 2.1. Subjects Twenty-three subjects with chronic neck–shoulder pain and 22 non-symptomatic controls took part in the present study. They were voluntarily recruited through advertising in local newspapers, and were interviewed in order to be evaluated in relation to specific criteria for selection to the case or control group, respectively. The pain and control groups were balanced with respect to age, gender and body mass index (BMI) (Table 1). The subjects mainly consisted of office workers and teachers, which were equally distributed across groups. Eligible subjects who reported chronic pain or muscle discomfort localised to the neck–shoulder region were physically examined by a physiotherapist. This examination included questions regarding localisation and sensation of pain and ratings of pain intensity, assessment of tender points and range of cervical axial rotation. Physical fitness (VO2max) was assessed using a sub-maximal cycle ergometer test, results presented elsewhere (Hallman et al., 2011). All subjects were classified as having chronic trapezius myalgia as they reported pain, stiffness and tender points in the trapezius muscles (Larsson et al., 2007). Inclusion criteria for the pain group were age between 20 and 50 years, perception of chronic pain, tightness of muscles and tender points during the physical examination. Chronicity of pain was defined

277

Table 1 Subject characteristics with means and standard deviations (SD) for the pain (n = 23) and control (n = 22) groups, respectively. Characteristics

Age BMI Male (n) Female (n) Neck pain current (0–10) Neck pain 6 months (0–10) Pain duration (years) Time fell asleep (h: min) Time woke up (h: min) Sleep duration (h: min)

Pain

Control

t-Test

Mean (SD)

Mean (SD)

p

40.5 (7.1) 25.2 (4.2) 2 21 2.7 (1.1) 3.6 (1.5) 9.5 (7.9) 23:12 (0:56) 5:59 (0.49) 6:47 (0:57)

41.0 (6.9) 24.5 (3.6) 2 20 0.04 (0.1) 0.1 (0.3) – 23:07 (0:37) 5:49 (0:43) 6:42 (1:00)

0.79 0.53

b0.001 b0.001 0.70 0.45 0.78

as persistent pain for the past six consecutive weeks and for at least 6 months. The pain was to be primarily localised to the neck and shoulders, in the absence of traumatic origin. Controls had to report themselves as healthy and non-symptomatic, and they had to be between 20 and 50 years of age to participate. Exclusion criteria were as follows: traumatic damage of the musculoskeletal system, diagnosis of rheumatism, diabetes, chronic neurological and endocrinology syndromes, as well as hypertension, coronary artery diseases and substance abuse. Persons who regularly used medication known to affect ANS function or pain perception (e.g. antidepressant, benzodiazepine, anti-inflammatory and beta-blocker drugs) were excluded. Because physical activity may change due to the absence from work, subjects who reported significant amounts of sick leave (>3 days) during the past three months were also excluded. The participants gave their written informed consent and received written information. The study was approved by the ethics committee at Uppsala University and was carried out according to the Helsinki declaration. 2.2. Ambulatory measurements 2.2.1. Heart rate variability A bipolar electrocardiogram was continuously recorded with the IDEEA system using a three-lead configuration. Data were collected at a 256 Hz sampling rate from pre-gelled Ag/AgC1 electrodes placed on the distal end of the sternum and bilaterally over the sixth rib. Consecutive inter-beat intervals (IBI) were calculated off line and additionally imported to Spike2 version 6.10 (Cambridge electronic design, Cambridge, UK). The IBIs were plotted as a function of time for visual inspection and semi-automatically editing to remove artefacts and ectopic beats. Data from eight subjects (controls: n = 4; Pain: n = 4) were excluded due to insufficient quality of the ECG recording. From a total of thirty-seven subjects, IBI time series were analysed in time and frequency domains according to Task Force standards (Task Force of the European Society of cardiology and the North American Society of Pacing and Electrophysiology, 1996). For time domain-HRV, we calculated SDNN (i.e., the standard deviation of normal-to-normal intervals) and pNN50 (i.e., the proportion of number of differences between successive intervals >50 ms). For frequency domain-HRV, Fast Fourier Transform filtering was applied on detrended data to calculate the spectral power density (ms 2) in the very low frequency range (VLF: b 0.04 Hz), low frequency range (LF: 0.04–0.15 Hz) and high frequency range (HF: 0.15–0.4 Hz). The natural logarithm was applied for VLF, LF and HF to obtain normal distribution. The relation between LF and HF was expressed in normalised units (LFnorm: LF/(LF+HF)×100). HRV variables were derived from twelve 30-minute windows placed over six pre-selected periods, i.e. during the evening (18.00– 19.00; 20.00–21.00), sleep (1-hour segment with low and stable HR between 01.00 and 03.00), morning (the first hour with activity in the morning: between 04.00 and 08.00) and day (10.00–11.00; 13.00– 14.00). Repeated measures ANOVA on all 30-minute segments of IBI

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and HRV revealed significant time trends consistent with the selected periods. Thus, these windows were averaged for each period, respectively. 2.2.2. Physical activity Objective ambulatory monitoring of PA during work and leisure time was assessed using the Intelligent Device for Energy Expenditure and Activity (IDEEA, MiniSun, Fresno, USA). IDEEA identifies multiple human physical activities based on limb movements, postures, transitions, and gaits. PA is quantified by type, duration, intensity, and expended energy (Zhang et al., 2003). The data collection device uses 5 sensors (i.e., inclinometers) attached with hypoallergenic adhesive tape on the trunk, thighs and feet. Calibration was performed with the subject in a seated position, in line with Zhang et al. (2003). Data extraction of physical activity was performed using the available software (Act view version 3.1, MiniSun, Fresno, USA). Duration of PAs was quantified as the percentage of time spent walking, sitting, standing and lying down, as well as walking distance (kilometres). Intensity of PA was quantified as the speed of walking (m/min) and energy expenditure (EE) during locomotion. Overall PA was analysed over a 24-hour period, while shorter periods were used to provide more detailed information on the physical activity pattern, i.e. evening (18.00–22.00), morning (first hour after awakening), mid-morning (09.00–12.00) and day (12.00–16.00). 2.2.3. Diary A pen and paper diary was used for subjects to take written notes about their self-reported stress and energy levels, physical activities, pauses and meals and physical and mental workload, as well as the time of waking up and falling asleep. The diary was easy to use and did not disturb the subjects in their daily activities. Self-ratings and activity notes were to be made at specific times, i.e. late afternoon (about 18.00); before going to bed (about 22.00); morning (30 min after getting up); lunch (about 12.00); directly after work (about 16.00). For each of these periods, the timing of specific activities and/or pauses was recalled and written down in the diary in order to obtain qualitative data on the character of work and leisure activities. 2.2.4. Pain The Borg CR-10 scale (Borg, 1998) was used for measuring pain intensity. This scale ranges from 0 “no pain” to 10 “extremely high pain” and allows subjects to rate numbers between the numerical anchors. The intensity of pain was assessed for the primary localisation of pain (i.e. the neck and/or shoulders), both momentarily and retrospectively (i.e., currently and during the past six months). 2.2.5. Stress and energy The Stress-Energy-Questionnaire (Kjellberg and Wadman, 2002) was used for measuring perceived stress and energy during various daily activities. This validated instrument contains two scales that are assumed to measure different aspects of mood at work. The stress scale includes six items, which range from positively evaluated low activation adjectives (i.e., “rested”, “relaxed” and “calm”) to negatively evaluated high activation adjectives (i.e., “tense”, “stressed” and “pressured”). The energy scale includes six items, which tap the dimension ranging from negatively evaluated low activation adjectives (i.e., “dull”, “inefficient” and “passive”) to positively evaluated high activation adjectives (i.e., “active”, “energetic” and “focused”). In the present study, the subjects were asked about their perceived stress and energy during the past 10 min, which allowed for reliable recall. The checklist uses a 6-point response scale (0–5), ranging from “not at all” to “extremely”. The stress and energy dimensions are calculated by averaging the six items for each scale, after reversing items standing for low stress and energy, respectively. Thus, low-to-high values are indicative of low-to-high perceived stress and energy levels.

2.3. Procedure The subjects underwent ambulatory monitoring of their PAs, HRV and stress-energy for 24 h during work and leisure activities. To ensure the least possible intrusiveness, subjects came to the laboratory at the end of a working day and then returned the next day after work. In this way, the total measurement period covered both leisure time and working hours. Anthropometric measurements and pain ratings were assessed in the laboratory prior to the ambulatory measurement. Prior to placement of activity sensors and ECG electrodes, the examiner gave the subjects' information on the equipment and how to use the diary. The subjects were instructed to avoid extensive exercise during the ambulatory recording; because the sensors were not water resistant, they were asked not to take a shower or bath. 2.4. Statistical analyses In order to analyse HRV, PA and perceived stress/energy responses, repeated measures ANOVA were applied, with group (pain–control) as a between-subjects variable and time as a within-subjects variable. Polynomial contrasts were used to investigate the interaction effects (group× time). BMI was included as a covariate in the ANOVA for energy expenditure due to their close association. To investigate the influence of PA and perceived stress on HRV, additional ANCOVA were performed for HRV indices using stress level and walking time as covariates. For overall (24-hour) PA, group differences in characteristics of intensity and duration of different activity types were analysed with t-tests. Spearman correlation coefficients were applied to explore the possible relationships, in the pain group, between PA and self-rated pain, stress and energy. In order to reduce the number of correlations, walking time was chosen for quantifying PA for the entire recording as well as for individual time segments. The level of significance was set at p ≤ 0.05 with p b 0.1 as the level for considering a statistical trend. All statistical analyses were performed using SPSS software (version 18.0). 3. Results 3.1. Subject characteristics Table 1 shows that there was no difference between the pain and control groups regarding age, gender, BMI and characteristics of sleep. Pain intensity showed relatively low inter-individual variability, whereas larger variability was seen for the chronicity of pain. 3.2. Heart rate variability Table 2 shows the results from HRV indices in the frequency domain. Significant time effects were seen for all HRV indices (p b 0.01). A clear peak during sleep with a reduction during the morning and day was seen in IBI, pNN50 and HF. Inversely, LFnorm was reduced from evening to sleep and increased during the morning and day. For SDNN, LF and VLF, variability was higher during the day compared with sleep (p b 0.05). A significant group effect was seen for IBI (i.e., a higher heart rate in the pain group) (F (1,35)=7.3; p=0.01). The HRV indices reflecting parasympathetic activity were lower in the pain group, significantly for pNN50 (F (1,35)=5.0; p=0.032) and HF (F (1,35)=8.4; p=0.006). Group effects were also found in LF (F (1,35)=7.9; p=0.008) and VLF (F (1,35)=5.8; p=0.022), as well as SDNN (F (1,35)=6.4; p=0.016), in terms of lower HRV in the pain group, compared with controls (Table 2, Figs. 1–2). The group-by-time interaction effect revealed a group difference in the quadratic trend for pNN50 (F (1,35) = 6.2; p = 0.018) in reduced HRV during sleep in the pain group (Fig. 1). A tendency towards interaction was seen for HF (F (1,35) = 3.5; p = 0.07) and LF (F (1,35) =

D.M. Hallman, E. Lyskov / International Journal of Psychophysiology 86 (2012) 276–282

SDNN

Table 2 Heart rate variability with means (SD) during the evening, sleep, morning and day for pain (n = 19) and control (n = 18) groups.

IBI (ms) Pain Control HF (log ms2) Pain Control LF (log ms2) Pain Control LFnorm (%) Pain Control VLF (log ms2) Pain Control

Evening

Sleep

Morning

Day

Mean (SD)

Mean (SD)

Mean (SD)

Mean (SD)

724 (94) 761 (101)

890 (122)⁎ 981 (98)

648 (69)⁎ 713 (75)

699 (84)⁎ 774 (83)

5.23 (0.81) 5.51 (0.79)

5.61 (0.85)⁎ 6.44 (0.75)

4.67 (0.63)⁎ 5.32 (0.80)

5.15 (0.79)⁎ 5.75 (0.68)

6.48 (0.61) 6.72 (0.59)

6.43 (0.68)⁎ 7.05 (0.58)

6.48 (0.55)⁎ 6.90 (0.66)

6.68 (0.50)⁎ 7.05 (0.44)

75 (9) 74 (7)

67 (13) 62 (14)

85 (5)† 81 (9)

7.26 (0.55) 7.43 (0.56)

6.99 (0.84) 7.37 (0.59)

7.58 (0.58) 7.90 (0.66)

80 (7) 76 (9) 7.33 (0.46)⁎ 7.92 (0.56)

Note: Prior to statistical analyses, the natural logarithm was applied on VLF (ms2), LF (ms2) and HF (ms2) for normal distribution. ⁎ Significant t-test (p b 0.05). † Non-significant tendency (p b 0.1).

3.5; p = 0.069). Interactions revealed a group difference in the linear trend for SDNN (F (1,35) = 3.0; p = 0.033) and VLF (F (1,35) = 4.1; p = 0.051), in the form of a reduction in HRV during the day in the pain group. No group or interaction effects were seen for LFnorm. Including walking time, stress ratings as covariates in the ANCOVA only marginally changed the results. Walking time came out as a significant covariate in the model, affecting the time pattern for IBI, p50NN, HF and LFnorm (p b 0.05). No such effects were observed for perceived stress (Table 3). 3.3. Physical activity In both groups, occupational PA mainly consisted of seated (49%) and standing (36%), and less time spent walking (7.6%). PA was increased during leisure compared to work. Table 4 shows 24-hour intensity and duration of different types of PA. The activity level was not significantly different between groups, apart from a non-significant trend for walking distance (pb 0.1), which was reduced in the pain group compared to controls. For duration of 24-hour PA, lying time was increased in the pain group (pb 0.05). Figs. 3–5 show PA patterning distributed over the 24-hour recording. Different activity patterns were found between groups. The pain group was more active during the morning, whereas the control group was more active during the evening. Walking time

90 80 70

Pain

60

Control

50 40 Evening

Sleep

Morning

Day

Fig. 2. Standard deviation of normal-to-normal RR intervals (SDNN) during evening, sleep, morning and day for pain and control groups. Error bars indicate standard error.

showed an interaction effect (group × time) with a group difference in the cubic trend (F (1,42) = 4.1; p = 0.049). A tendency towards interaction was seen for walking distance (F (1,42)=3.7; p=0.06) and EE during locomotion (F (1,42)=3.02; p=0.09). For lying, a significant interaction (group×time) was found (F (1,42)=3; p=0.034), with the pain group showing increased lying time during the evening (mean 34%±7), compared with controls (mean 29%±7). EE during motion showed a main effect of group when controlling for BMI (F (1,42)=6.3; p=0.016) in reduced EE in the pain group. No other group effect was significant. 3.4. Self-ratings of stress and energy Stress levels were similar between groups (pain mean 1.98 ± 0.64; control mean 1.79± 0.52) without any group difference over time. Energy levels revealed a group effect (F (1,42)= 5.1; p = 0.019) in the form of lower energy levels in the pain group (mean 3.12 ± 0.72) during the afternoon, in comparison with controls (mean 3.87 ± 0.54). The interaction (group× time) was non-significant. 3.5. Relations between physical activity and self-ratings Current pain intensity assessed prior to the ambulatory measurement correlated negatively with total walking time (r= −.525; p = 0.012) and distance (r= −.425; p = 0.049). These correlations were not significant for retrospectively recalled pain. Walking time during the evening correlated inversely with stress ratings before sleep (r = −.56; p = 0.037). Walking time tended to correlate positively with stress in the morning (r = .384; p = 0.07). Walking time during the afternoon correlated inversely with energy ratings during the evening (r = − .555; p = 0.006). 4. Discussion

pNN50 (%)

The main results demonstrated reduced HRV and elevated heart rate in subjects with chronic neck–shoulder pain. The subjects with chronic neck–shoulder pain spent more time lying down and seemed

30 25

Percent (%)

100

Milliseconds (ms)

Measure

279

20 15

Pain

10

Control

Table 3 Heart rate variability indices, ANCOVA with group and interaction effects, and p-values controlled for walking time and mean stress level. ANCOVA

5

IBI

SDNN

P50NN

HF

LF

LFnorm

VLF

p

p

p

p

p

p

p

0.007 0.29

0.006 0.023

0.020 0.038

0.005 0.31

0.002 0.14

0.37 0.31

0.005 0.081

0 Evening

Sleep

Morning

Day

Fig. 1. Proportion of the number of differences between successive RR intervals>50 ms (p50NN) during evening, sleep, morning and day for pain and control groups. Error bars indicate standard error.

Group Group × time

Note: For interaction effects (group × time) walking time came out as a significant covariate for IBI, p50NN, HF, and LFnorm (p b 0.05). No effects were observed for perceived stress.

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Walking-distance

Table 4 24-Hour physical activity, with means (SD) for the pain and control groups, respectively. Group

Walking Speed (m/min) Distance (km) Energy expenditure (kcal/min) Duration (%) Walk Lie Sit Stand

t-test

Pain (n = 23)

Control (n = 21)

Mean (SD)

Mean (SD)

69 (9) 4.1 (2.0) 4.3 (0.8)

73 (9) 5.6 (3.1) 4.6 (0.8)

p

0.13 0.08 0.14

2.0

Kilometres

Variable

2.5

1.5 Neck pain

1.0

Control 0.5 0.0

4.6 33.8 35.5 20.0

(2.2) (7.1) (14.1) (9.6)

5.6 (2.9) 29.3 (7.0) 36.4 (9.4) 21.0 (8.0)

n.s. 0.044⁎ n.s. n.s.

Note: Duration of time spent in different activities is expressed as percentage of total time (24-hour). n.s. = non-significant. ⁎ Significant group difference (p b 0.05).

Evening

Morning

Mid morning

Day

Fig. 4. Mean walking distance (kilometres) distributed over the day for pain and control groups. Error bars indicate standard error.

HRV is a noninvasive marker reflecting the activity of the sympathetic and parasympathetic (vagal) components of the ANS on the sinus node of the heart (Berntson et al., 1997; Task Force of the European Society of cardiology and the North American Society of Pacing and Electrophysiology, 1996). HF oscillations are predominantly modulated by parasympathetic activity through respiration, i.e. respiratory sinus arrhythmia. pNN50 is a time-domain surrogate for HF power, whereas SDNN is a global index of HRV reflecting both the long- and short-term components in the variability. Results from the HRV indices demonstrated diminished parasympathetic activation in the neck–shoulder pain group. This is in agreement with findings from laboratory investigations on localised muscle pain. During rest, increased sympathetic activation and reduced parasympathetic modulation of the heart were found in subjects with neck–shoulder pain (Gockel et al., 1995), whiplash-associated disorders (Kalezic et al., 2010) and low-back pain (Gockel et al., 2008; Kalezic et al., 2007). Overall, the present results from 24-hour HRV reflected a mild autonomic imbalance in the pain group. It also confirms recent findings from our laboratory (Hallman et al., 2011) suggesting that HRV in controlled rest may be an

adequate measure for detecting ANS aberration in persons with chronic neck–shoulder pain. Activation of parasympathetic HRV indices during sleep (Bilan et al., 2005; Bonnemeier et al., 2003) was not observed in patients suffering from widespread muscle pain (fibromyalgia) (Doğru et al., 2009; Martínez-Lavín et al., 1998). Reduced HF power of HRV suggested reduced nocturnal vagal activity in Gulf War veterans (Haley et al., 2004) who reported pain and fatigue as prominent symptoms. The present results demonstrated decreased pNN50 during sleep in the pain group, compared with controls. This may indicate that the subjects with chronic neck–shoulder pain had an altered circadian rhythm, primarily due to attenuated nocturnal parasympathetic activation. There are extensive interactions between the nociceptive and autonomic systems (Benarroch, 2006). Over-activity of the sympathetic nervous system is considered an important factor in the development and maintenance of chronic muscle pain (Passatore and Roatta, 2006; Vierck, 2006; Visser and van Dieën, 2006). In line with this view, signs of increased sympathetic activity were seen in shortened IBIs (i.e., a higher heart rate) concomitant with diminished HRV in the pain group. In contrast, we could not demonstrate any differences in normalised LF power, which is under the influence of both sympathetic and parasympathetic nervous systems (Berntson et al., 1997; Montano et al., 2009). Thus, reduced IBIs observed in the pain group may have resulted from either sympathetic activation or parasympathetic withdrawal, or both (Goldberger, 1999). Also, the VLF power was reduced in the pain group, with a less expressed increase during the day, in comparison with controls. The VLF has been suggested to reflect, apart from autonomic activity, thermoregulation and vasomotor activity (Akselrod et al., 1981; Kleiger et al., 2005).

Walking-time

EE-locomotion

to have a different PA pattern than controls, being less active during leisure and more active in the morning. PA was inversely related to pain intensity.

4.1. Autonomic regulation in neck–shoulder pain

4.8

12

4.5

10 8 6

Neck pain

4

Control

kcal/minute

Percentage of time

14

4.2 3.9

Neck pain

3.6

Control

3.3

2

3

0 Evening

Morning

Mid morning

Day

Fig. 3. Mean walking time (%) distributed over the day for pain and control groups. Error bars indicate standard error.

Evening

Morning

Mid morning

Day

Fig. 5. Mean energy expenditure (kcal/min) during locomotion distributed over the day for pain and control groups. Means are adjusted for BMI. Error bars indicate standard error.

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4.2. Physical activity In the present study, it was important to use advanced methods for objective assessment of physical activity, especially in light of the large discrepancies between self-reports and objective methods (van Weering et al., 2011). The present results showed that neck–shoulder pain subjects had a different PA pattern, in comparison with controls, with less PA during the evening and increased PA during the morning, in accordance with recent studies (Ryan et al., 2009; van Weering et al., 2009). A possible explanation could be that these individuals need all their capacity to perform activities during the day, consequently leading to reduced leisure-time activity (Spenkelink et al., 2002). Accordingly, the pain group showed reduced energy ratings during the afternoon, compared to controls. Fatigue is a common symptom among persons with pain in the neck–shoulder region (Fishbain et al., 2004), possibly due to a combination of several factors, e.g. inactivity, poor sleep (Nicassio et al., 2002; Wilson et al., 1998) and autonomic dysregulation (Clauw and Williams, 2002; Martinez-Lavin, 2007). Although we did not assess sleep disorders in the current study, no group difference in sleep duration was observed. Moreover, inactivity is related to increased basal sympathetic activity and reduced parasympathetic tone (Mueller, 2010; Joyner and Green, 2009; Carter et al., 2003), which reflects diminished adaptive resources of the cardiovascular system. The present study demonstrated partly support for the hypothesis of reduced PA in chronic neck–shoulder pain. Reduced EE during locomotion and increased lying time were found in the pain group. However, there were no significant group differences in the characteristics of walking. Recent studies have shown inconsistent results concerning reduced PA in muscle pain (van den Berg-Emons et al., 2007; van Weering et al., 2007). In the present study, the inverse correlations between current pain intensity and walking time, and the lack of such a relationship for recalled pain over 6 months, may suggest reduced activity levels in the more affected individuals, and perhaps that a reduction in daily PA level may occur when individuals are experiencing more severe episodes of muscle pain. This is the first study to date with objective assessment of 24-hour HRV and physical activity among persons with chronic neck–shoulder pain. HRV as a marker of autonomic regulation is intrinsically related to physical activity, both in terms of direct effects (Grossman et al., 2004) and long term effects (Melanson, 2000; Rennie et al., 2003). In line with this, we found significant associations between walking time and the time pattern of HRV, indicating a larger circadian variation of the ANS with increased walking time. In the present study, however, differences between groups in HRV were not likely caused by differences in daily PA. First, PA tended to be lower in the pain group, which could not directly lead to depressed HRV. Second, differences in HRV persisted during sleep, when subjects were inactive. Also, when statistically controlling for the effect of walking time and perceived stress, the results showed that IBIs and HRV remained significantly lower in the pain group. However, as data on aerobic fitness was not available for all subjects (Hallman et al., 2011), we could not entirely rule out the effects of physical fitness. Furthermore, as perceived stress levels were similar in both groups, the possibility of mental stress affecting HRV could be ruled out. Thus, the group differences in 24-hour HRV are in accordance with explanatory models which propose that pain induced changes in ANS can contribute to intensification of local muscle pain at both central and peripheral levels (Passatore and Roatta, 2006). Treatment of chronic neck–shoulder pain may benefit from strategies focusing on changes in ANS regulation. 4.3. Limitations A limitation of the study is the cross sectional design which makes the causal relationships unclear. Thus, reduced HRV could be a result of chronic pain or vice versa. Another limitation is the relatively low

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pain intensity reported in the pain group. Furthermore, in this study, a 24-hour period was used for characterising ANS regulation and PA, which seemed to be an appropriate time for monitoring, considering the clinical subjects. However, because there is a relatively large dayto-day variability in PA (Spenkelink et al., 2002), additional time/days may be required to confirm relatively small differences between groups. Another potential limitation is that pain intensity was not measured during the ambulatory assessment. Because current pain intensity was related to a reduced PA in the pain group, assessment of pain over time may have provided important information on PA and autonomic regulation in relation to muscle pain. The lack of an instrument for assessment of sleep disorders is another potential weakness, and it should be included in further studies on autonomic regulation in chronic neck– shoulder pain. Furthermore, as HRV could not be analysed in 4 patients and 4 of the controls, the statistical power was reduced. Larger groups are necessary if the main focus is to investigate associations between symptoms, ANS function and physical activity. It is also possible that assessment of breathing rate may have facilitated interpretation of HRV, as breathing rate below 0.15 Hz affects HRV in the low frequency range (Task Force of the European Society of cardiology and the North American Society of Pacing and Electrophysiology, 1996). Finally, momentary assessment of perceived stress and energy by paper diaries was not as precise as with electronic log devices. 4.4. Conclusion In conclusion, the observed changes in ambulatory HRV reflected aberration in autonomic regulation among persons with chronic neck–shoulder pain, and may suggest diminished parasympathetic activation and elevated sympathetic tone as an important element in maintenance of chronic muscle pain. Based on objectively assessed physical activity, subjects with chronic muscle pain were more inactive and had a different activity pattern than healthy controls. Treatment of chronic neck–shoulder pain may benefit from strategies focusing on changes in ANS regulation. Conflict of interest The authors declare that there is no conflict of interest. Acknowledgement Göran Sandström is acknowledged for engineering and data processing, and Margaretha Marklund for assistance in extracting physical activity data. Prof. Bengt Arnetz is acknowledged for valuable comments on the manuscript. References Acero, C.O., Kuboki, T., Maekawa, K., Yamashita, A., Clark, G.T., 1999. Haemodynamic responses in chronically painful, human trapezius muscle to cold pressor stimulation. Archives of Oral Biology 44, 805–812. Akselrod, S., Gordon, D., Ubel, F., Shannon, D., Berger, A., Cohen, R., 1981. Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control. Science 213, 220–222. Andersen, L.L., Suetta, C., Andersen, J.L., Kjær, M., Sjøgaard, G., 2008. Increased proportion of megafibers in chronically painful muscles. Pain 139 (3), 588–593. Benarroch, E., 2006. Pain-autonomic interactions. Neurological Sciences 27, s130–s133. Berntson, G., Bigger, J.T., Eckberg, D.L., Grossman, P., Kaufmann, P.G., Malik, M., Nagaraja, H., 1997. Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34, 623–648. Bilan, A., Witczak, A., Palusinski, R., Myslinski, W., Hanzlik, J., 2005. Circadian rhythm of spectral indices of heart rate variability in healthy subjects. Journal of Electrocardiology 38, 239–243. Bongers, P.M., Kremer, A.M., Laak, J.T., 2002. Are psychosocial factors, risk factors for symptoms and signs of the shoulder, elbow, or hand/wrist?: a review of the epidemiological literature. American Journal of Industrial Medicine 41, 315–342. Bonnemeier, H., Wiegand, U.K.H., Brandes, A., Kluge, N., Katus, H.A., Richardt, G., Potratz, J., 2003. Circadian profile of cardiac autonomic nervous modulation in healthy subjects. Journal of Cardiovascular Electrophysiology 14, 791–799. Borg, G., 1998. Borg's Perceived Exertion and Pain Scales. Human Kinetics, Champaign, IL.

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D.M. Hallman, E. Lyskov / International Journal of Psychophysiology 86 (2012) 276–282

Cagnie, B., Dhooge, F., Van Akeleyen, J., Cools, A., Cambier, D., Danneels, L., 2012. Changes in microcirculation of the trapezius muscle during a prolonged computer task. European Journal of Applied Physiology 112 (9), 3305–3312. Carter, J.B., Banister, E.W., Blaber, A.P., 2003. Effect of endurance exercise on autonomic control of heart rate. Sports Medicine 33 (1), 33–46. Chandola, T., Britton, A., Brunner, E., Hemingway, H., Malik, M., Kumari, M., Badrick, E., Kivimaki, M., Marmot, M., 2008. Work stress and coronary heart disease: what are the mechanisms? European Heart Journal 29 (5), 640–648. Clauw, D.J., Williams, D.A., 2002. Relationship between stress and pain in work-related upper extremity disorders: the hidden role of chronic multisymptom illnesses. American Journal of Industrial Medicine 41, 370–382. Doğru, M., Aydin, G., Tosun, A., Keleş, I., Güneri, M., Arslan, A., Ebinç, H., Orkun, S., 2009. Correlations between autonomic dysfunction and circadian changes and arrhythmia prevalence in women with fibromyalgia syndrome. Anadolu Kardiyoloji Dergisi 9, 110–117. Fishbain, D.A., Cutler, R.B., Cole, B., Lewis, J., Smets, E., Rosomoff, H.L., Rosomoff, R.S., 2004. Are patients with chronic low back pain or chronic neck pain fatigued? Pain Medicine 5, 187–195. Gockel, M., Lindholm, H., Alaranta, H., Viljanen, A., Lindquist, A., Lindholm, T., 1995. Cardiovascular functional disorder and stress among patients having neck–shoulder symptoms. Annals of the Rheumatic Diseases 54, 494–497. Gockel, M., Lindholm, H., Niemistö, L., Hurri, H., 2008. Perceived disability but not pain is connected with autonomic nervous function among patients with chronic low back pain. Journal of Rehabilitation Medicine 40, 355–358. Goldberger, J.J., 1999. Sympathovagal balance: how should we measure it? American Journal of Physiology — Heart and Circulatory Physiology 276 (4), H1273–H1280. Grossman, P., Wilhelm, F.H., Spoerle, M., 2004. Respiratory sinus arrhythmia, cardiac vagal control, and daily activity. American Journal of Physiology — Heart and Circulatory Physiology 287, H728–H734. Haley, R.W., Vongpatanasin, W., Wolfe, G.I., Bryan, W.W., Armitage, R., Hoffmann, R.F., Petty, F., Callahan, T.S., Charuvastra, E., Shell, W.E., Marshall, W.W., Victor, R.G., 2004. Blunted circadian variation in autonomic regulation of sinus node function in veterans with Gulf War syndrome. American Journal of Medicine 117, 469–478. Hallman, D.M., Lindberg, L.-G., Arnetz, B.B., Lyskov, E., 2011. Effects of static contraction and cold stimulation on cardiovascular autonomic indices, trapezius blood flow and muscle activity in chronic neck–shoulder pain. European Journal of Applied Physiology 111 (8), 1725–1735. Joyner, M.J., Green, D.J., 2009. Exercise protects the cardiovascular system: effects beyond traditional risk factors. The Journal of Physiology 587 (23), 5551–5558. Kalezic, N., Åsell, M., Kerschbaumer, H., Lyskov, E., 2007. Physiological reactivity to functional tests in patients with chronic low back pain. Journal of Musculoskeletal Pain 15, 29–40. Kalezic, N., Noborisaka, Y., Nakata, M., Crenshaw, A.G., Karlsson, S., Lyskov, E., Eriksson, P.-O., 2010. Cardiovascular and muscle activity during chewing in whiplashassociated disorders (WAD). Archives of Oral Biology 55, 447–453. Kjellberg, A., Wadman, C., 2002. Subjektiv stress och dess samband med psykosociala arbetsförhållanden och besvär. En prövning av Stress-Energimodellen (Subjective stress and its relation to psychosocial work conditions and health complaints. A test of the Stress-Energy model). Arbete och hälsa 2002; 12. National Institute for Working Life, Stockholm. Kleiger, R.E., Stein, P.K., Bigger, J.T., 2005. Heart rate variability: measurement and clinical utility. Annals of Noninvasive Electrocardiology 10, 88–101. Larsson, R., Öberg, P.A., Larsson, S.E., 1999. Changes of trapezius muscle blood flow and electromyography in chronic neck pain due to trapezius myalgia. Pain 79, 45–50. Larsson, B.L., Björk, J.B., Elert, J.E., Lindman, R.L., Gerdle, B.G., 2001. Fibre type proportion and fibre size in trapezius muscle biopsies from cleaners with and without myalgia and its correlation with ragged red fibres, cytochrome- c-oxidase-negative fibres, biomechanical output, perception of fatigue, and surface electromyography during repetitive forward flexions. European Journal of Applied Physiology 84 (6), 492–502. Larsson, B., Søgaard, K., Rosendal, L., 2007. Work related neck–shoulder pain: a review on magnitude, risk factors, biochemical characteristics, clinical picture and preventive interventions. Best Practice & Research Clinical Rheumatology 21, 447–463. Lundberg, U., 2002. Psychophysiology of work: stress, gender, endocrine response, and work-related upper extremity disorders. American Journal of Industrial Medicine 41, 383–392. Maekawa, K., Clark, G.T., Kuboki, T., 2002. Intramuscular hypoperfusion, adrenergic receptors, and chronic muscle pain. The Journal of Pain 3, 251–260. Martinez-Lavin, M., 2007. Biology and therapy of fibromyalgia. Stress, the stress response system, and fibromyalgia. Arthritis Research & Therapy 9, 216. Martinez-Lavin, M., Hermosillo, A.G., 2000. Autonomic nervous system dysfunction may explain the multisystem features of fibromyalgia. Seminars in Arthritis and Rheumatism 29, 197–199. Martínez-Lavín, M., Hermosillo, A.G., Rosas, M., Soto, M.E., 1998. Circadian studies of autonomic nervous balance in patients with fibromyalgia: a heart rate variability analysis. Arthritis and Rheumatism 41, 1966–1971. Melanson, E.L., 2000. Resting heart rate variability in men varying in habitual physical activity. Medicine and Science in Sports and Exercise 32, 1894–1901.

Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K.R., Iellamo, F., 2009. Heart rate variability explored in the frequency domain: a tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews 33, 71–80. Mueller, P.J., 2010. Physical (in)activity-dependent alterations at the rostral ventrolateral medulla: influence on sympathetic nervous system regulation. American Journal of Physiology — Regulatory, Integrative and Comparative Physiology 298 (6), R1468–R1474. Nicassio, P.M., Moxham, E.G., Schuman, C.E., Gevirtz, R.N., 2002. The contribution of pain, reported sleep quality, and depressive symptoms to fatigue in fibromyalgia. Pain 100, 271–279. Okifuji, A., Turk, D.C., 2002. Stress and psychophysiological dysregulation in patients with fibromyalgia syndrome. Applied Psychophysiology and Biofeedback 27, 129–141. Passatore, M., Roatta, S., 2003. Sympathetic nervous system: interaction with muscle function and involvement in motor control. In: Johansson, H., Windhorst, U., Djupsjöbacka, M., Passatore, M. (Eds.), Chronic Work-related Myalgia Neuromuscular Mechanisms behind Work-related Chronic Muscle Pain Syndromes. Gävle University Press, Gävle, pp. 243–263. Passatore, M., Roatta, S., 2006. Influence of sympathetic nervous system on sensorimotor function: whiplash associated disorders (WAD) as a model. European Journal of Applied Physiology 98, 423–449. Rennie, K.L., Hemingway, H., Kumari, M., Brunner, E., Malik, M., Marmot, M., 2003. Effects of moderate and vigorous physical activity on heart rate variability in a British study of civil servants. American Journal of Epidemiology 158, 135–143. Ryan, C.G., Grant, P.M., Dall, P.M., Gray, H., Newton, M., Granat, M.H., 2009. Individuals with chronic low back pain have a lower level, and an altered pattern, of physical activity compared with matched controls: an observational study. The Australian Journal of Physiotherapy 55, 53–58. Sandberg, M., Larsson, B., Lindberg, L.-G., Gerdle, B., 2005. Different patterns of blood flow response in the trapezius muscle following needle stimulation (acupuncture) between healthy subjects and patients with fibromyalgia and work-related trapezius myalgia. European Journal of Pain 9, 497–510. Sjøgaard, G., Rosendal, L., Kristiansen, J., Blangsted, A., Skotte, J., Larsson, B., et al., 2010. Muscle oxygenation and glycolysis in females with trapezius myalgia during stress and repetitive work using microdialysis and NIRS. European Journal of Applied Physiology 108 (4), 657–669. Sjörs, A., Larsson, B., Dahlman, J., Falkmer, T., Gerdle, B., 2009. Physiological responses to low-force work and psychosocial stress in women with chronic trapezius myalgia. BMC Musculoskeletal Disorders 10, 63. Spenkelink, C.D., Hutten, M.M., Hermens, H.J., Greitemann, B.O., 2002. Assessment of activities of daily living with an ambulatory monitoring system: a comparative study in patients with chronic low back pain and nonsymptomatic controls. Clinical Rehabilitation 16, 16–26. Task Force of the European Society of cardiology and the North American Society of Pacing and Electrophysiology, 1996. Heart rate variability: standards of measurement, physiological interpretation, and clinical use. European Heart Journal 93, 1043–1065. van den Berg-Emons, R.J., Schasfoort, F.C., de Vos, L.A., Bussmann, J.B., Stam, H.J., 2007. Impact of chronic pain on everyday physical activity. European Journal of Pain 11, 587–593. van Weering, M., Vollenbroek-Hutten, M.M.R., Kotte, E.M., Hermens, H.J., 2007. Daily physical activities of patients with chronic pain or fatigue versus asymptomatic controls. A systematic review. Clinical Rehabilitation 21, 1007–1023. van Weering, M.G.H., Vollenbroek-Hutten, M.M.R., Tönis, T.M., Hermens, H.J., 2009. Daily physical activities in chronic lower back pain patients assessed with accelerometry. European Journal of Pain 13, 649–654. van Weering, M., Vollenbroek-Hutten, M., Hermens, H., 2011. The relationship between objectively and subjectively measured activity levels in people with chronic low back pain. Clinical Rehabilitation 25 (3), 256–263. Verbunt, J.A., Huijnen, I.P.J., Köke, A., 2009. Assessment of physical activity in daily life in patients with musculoskeletal pain. European Journal of Pain 13, 231–242. Vierck, J.C.J., 2006. Mechanisms underlying development of spatially distributed chronic pain (fibromyalgia). Pain 124, 242–263. Visser, B., van Dieën, J.H., 2006. Pathophysiology of upper extremity muscle disorders. Journal of Electromyography and Kinesiology 16, 1–16. Vlaeyen, J.W., Linton, S.J., 2008. Fear-avoidance and its consequences in chronic musculoskeletal pain: a state of the art. Pain 85 (3), 317–332. Vrijkotte, T.G.M., van Doornen, L.J.P., de Geus, E.J.C., 2000. Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. Hypertension 35 (4), 880–886. Wilson, K.G., Watson, S.T., Currie, S.R., 1998. Daily diary and ambulatory activity monitoring of sleep in patients with insomnia associated with chronic musculoskeletal pain. Pain 75, 75–84. Zhang, K., Werner, P., Sun, M., Pi-Sunyer, F.X., Boozer, C.N., 2003. Measurement of human daily physical activity. Obesity Research 11, 33–40.