Pain Medicine 2015; 16: 2261–2270 Wiley Periodicals, Inc.
Brief Research Report The Association of (Effective and Ineffective) Analgesic Intake, Pain Interference and Heart Rate Variability in a Cross-Sectional Occupational Sample
*Department of Psychology, the Ohio State University, Columbus, Ohio, USA; †Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Heidelberg, Germany Reprint requests to: Dr. Julian Koenig, Department of Psychology, Emotions and Quantitative Psychophysiology Lab, the Ohio State University, 175 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA. Tel: +1 (614) 688-5793; FAX: +1 (614) 6888261; E-mail: [email protected]. Conflict of Interest: Joachim E. Fischer was the major shareholder of HealthVision Ltd. until December 2012, which organized the data collection. Since January 1, 2013, he now serves as a scientific consultant to Health Vision Ltd. All other authors declare no conflict of interest. Funding Sources: This work was supported by internal funds of the Mannheim Institute of Public Health. Julian F. Thayer was supported by a Humboldt Senior Research Award. Abstract Objective. Persistent pain is associated with dysfunction of the autonomic nervous system, in particular a loss of vagal inhibitory control, that can be indexed by decreased vagally mediated heart rate variability (vmHRV). Effective treatment (e.g., analgesic self-medication) may lead to a restoration of vmHRV. The objective of this article was to further explore the relationship of pain and vagal control
and to investigate the effect of analgesic selfmedication on the association of vmHRV and pain. Methods. We used a large cross-sectional data set on pain ratings and analgesic intake from the Mannheim Industrial Cohort Study for secondary analysis. The root mean square of successive differences, a measure of vmHRV corresponding to the parasympathetic regulation of the heart, was derived from 24-hour electrocardiogram recordings. Results. The frequency of analgesic intake and interference of pain are significantly associated. Individuals that report greater pain interference with their normal work routine (including both work outside the home and housework) and frequent analgesic intake have significantly lower vmHRV. Subjects with ineffective analgesic intake (reporting great pain interference and high frequent analgesic intake) had the lowest vmHRV. Individuals effectively taking analgesics (reporting no or low pain interference and high frequent analgesic intake) showed greater vmHRV compared to those ineffectively taking. Analysis revealed significant differences and linear trends on vmHRV between all groups. Conclusion. In line with previous research, vmHRV is inversely associated with pain interference. Analgesic intake mediates the association of vmHRV and pain. Effective analgesic self-medication may lead to a restoration in vmHRV. These results further support the vagus nerve as an objective indication of pain severity and treatment efficacy in patients with persistent pain. Key Words. Analgesic Intake; Heart Rate Variability; Vagus Nerve; Inhibitory Pathways Introduction The persistent experience of pain can lead to dysfunction of the autonomic nervous system (ANS). The beat-
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Julian Koenig, Dr. sc. hum.,* Marc N Jarczok, Dr. sc. hum.,† Joachim E Fischer, MD, MSc,† and Julian F Thayer, PhD*
Koenig et al.
Table 1
Sample characteristics
more, lower HRV is associated with extended pain related sick leave in employees [7]. Thus, HRV is of interest as a potential biomarker for specific pain related diseases [8].
Sample Characteristics 4742 (844/3898) 41.07 (10.99) 25.87 (4.02) 2487 (52.4) 1695 (35.7) 458 (9.7) 71 (1.5) 31 (0.7) 2635 (55.6) 1467 (30.9) 406 (8.6) 195 (4.1) 39 (0.8) 47.33 (9.00) 47.99 (9.71) 52.22 (7.11) 39 (0.8) 553 (11.7) 2306 (48.6) 1570 (33.1) 274 (5.8) 42.75 (22.28) 29.49 (13.29) 36.12 (16.82) 440 (9.3) 173 (3.6) 43 (0.9)
to-beat variation in the heart rate (HR), heart rate variability (HRV), is mainly driven by the ANS and allows teasing out the relative contributions of sympathetic and parasympathetic activity underlying autonomic control of the heart. Reduced HRV compared with healthy controls is reported in patients with complex regional pain syndrome [1], fibromyalgia [2], chronic neck pain [3], irritable bowel syndrome [4], and headache [5,6]. Further-
Table 2
Recently, we provided evidence that pain is inversely correlated to decreased vagal control indexed by vagally mediated HRV (vmHRV) in healthy subjects [9] and a large cross-sectional occupational cohort [10]. A decrease in vagal activity mirrors a disruption in one of the major descending inhibitory pathways involved in the endogenous modulation of the processing of nociceptive information. Suppression of vagally mediated descending inhibitory pathways results in greater somatic and visceral input via the spinothalamic track, which in turn may provide a mechanism underlying increased pain sensitivity in those with chronic pain. We, therefore, propose that impaired vagal control contributes to the central sensitization in chronic pain leading to an increase in the excitability of neurons in the central nervous system [11] and a shift to emotionrelated circuitry activity in the brain [12]. In addition to serving as a potential pathway involved in the chronification of pain, vmHRV may further provide an additional outcome for the relief of pain due to therapeutic interventions, resulting in a restoration of HRV measures [13–15]. Recent experimental research has shown that ventral periaqueductal gray stimulation increases vmHRV and decreases pain in individuals with chronic pain [16], suggesting that analgesia with deep brain stimulation in chronic pain is associated with increased vagal parasympathetic activity. Pharmaceutical analgesic self-medication is common and broadly accepted in individuals that experience pain. Recently, we found an association of ibuprofen use and HRV in a small sample of subjects [17]. This article aims to further explore the association of pain interference, analgesic self-medication, and HRV in a large cross-sectional sample of 4,742 individuals. Methods We used a large cross-sectional data set from the Mannheim Industrial Cohort Study for secondary analysis. The data was collected as part of a voluntary health risk assessment that was offered to all employees
Contingency table analysis on the relation of analgesic intake and pain interference Analgesic Intake, n (%)
Pain Interference, n
Never
Seldom
Sometimes/month
Sometimes/week
Daily
Total
Not at all A little bit Moderately Quite a bit Extremely Total
1656 (66.6) 621 (25.0) 141 (5.7) 57 (2.3) 12 (0.5) 2487 (52.4)
854 (50.4) 616 (36.3) 160 (9.4) 56 (3.3) 9 (0.5) 1695 (35.7)
116 (25.4) 204 (44.5) 79 (17.2) 50 (10.9) 9 (2.0) 458 (9.7)
5 (7.0) 23 (32.4) 19 (26.8) 19 (26.8) 5 (7.0) 71 (1.5)
4 (12.9) 3 (9.7) 7 (22.6) 13 (41.9) 4 (12.9) 31 (0.7)
2635 (55.6) 1467 (30.9) 406 (8.6) 195 (4.1) 39 (0.8)
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N (w/m) Age, mean years (SD) BMI, mean kg/m2 (SD) Analgesic intake, n (%) Never Seldom Sometimes per month Sometimes per week Daily Pain interference, n (%) Not at all A little bit Moderately Quite a bit Extremely SF-12 scores, mean t-score (SD) Global health Mental health Physical health Self-rated health, n (%) Poor Fair Good Very good Excellent Night RMSSD, ms (SD) Day RMSSD, ms (SD) 24-hour RMSSD, ms (SD) Other medication, n (%) Blood pressure Blood lipid Blood glucose
Analgesic Intake and Vagal Control
during working hours. An agent independent from the employer conducted the health risk assessments and data collection (HealthVision Ltd., Berlingen, Switzerland). A total of 13 study sites (Companies from the secondary and tertiary sectors) from all over Germany with a total of 14,469 participants collected between 2010 and 2012 were available. Participants were invited to take part in the “Work Health Check” and were offered a detailed individual report containing their health status as assessed by medical examination and self reports. This sample encompassed the entire workforce between 17 and 65 years. The Ethical Committee of the Mannheim Medical Faculty, Heidelberg University, approved secondary analysis of this data. All participants gave written informed consent prior to examination. Details on the measurements and population are published elsewhere [18,19]. After completing an online questionnaire, participants were able to schedule a medical examination including a 24-hour recording of HR. Demographic variables (age and sex) were obtained from the online questionnaire. The questionnaire had to be completed prior to being able to schedule the medical examination. All participants were enrolled and examined between 10 AM and 5 PM on a typical workday (Monday–Friday) during work hours. On arrival, a medical examination was performed. Subjects’ body measures (weight and height) were taken and BMI was obtained according to common calculation (kg/m2), as classified according to the WHO standard [20]. Three scores (general health, physical health, and mental health score) were derived from the SF-12 questionnaire
[21] and used as a self-reported measure of health related quality of life, with greater T scores indicating greater health related quality of life. Self-rated health (SRH) was assessed using the first item of the SF-12 (“In general, would you say your health is. . .”), with response categories (“excellent,” “very good,” “good,” “fair,” “poor”). Item number 8 of the SF-12 questionnaire (“During the past 4 weeks how much did pain interfere with your normal work [including both work outside the home and housework]?”) was used as pain-related measure for further analysis. Response categories were: 1) “not at all,” 2) “a little bit,” 3) “moderately,” 4) “quite a bit,” or 5) “extremely.” Analgesic intake was assed by a single item, asking subjects “Do you take analgesics occasionally or regularly?” Participants had to indicate their answer as 1) “never,” 2) “seldom,” 3) “sometimes per month,” 4) “sometimes per week,” or 5) “daily.” Furthermore, we aimed to further control for medication intake other than analgesics. We were able to retrieve data on the intake of medication against high blood pressure, high blood lipid, and high blood glucose. These variables were scored dichotomously (taking/not taking) and some of the included subjects had missing data (blood pressure: n 5 30; blood glucose: n 5 39; blood lipid: n 5 38). HRV HR was recorded as beat-to-beat intervals (IBI) using a t6 Suunto Memory Belt (SuuntoVantaa, Finland), sampling at a rate of 1,000 Hz. The IBI represents the time 2263
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Figure 1 Group difference on RMSSD based on analgesic intake and pain interference; illustrated are Bonferroni-posthoc comparisons; *: indicates a significant difference P < 0.05; **: indicates a significant difference P < 0.01 ***: indicates a significant difference P < 0.001; ns: not significant.
49 (3.4) 10 (0.7)
69 (2.6) 14 (0.5)
10 (2.5)
33 (8.4)
55 (13.8)
43.57 (5.83)
44.57 (10.81)
406 (323/83) 44.93 (10.16) 26.67 (4.24) 36.84 (20.07) 25.79 (11.08) 31.32 (14.63) 40.02 (8.51)
Moderately
7 (1.6)
26 (5.8)
54 (11.9)
48.38 (8.32)
45.09 (10.37)
458 (307/151) 41.15 (10.46) 26.02 (4.23) 39.08 (19.18) 27.12 (11.14) 33.10 (14.11) 42.95 (9.48)
* Includes missing data; P values are derived from ANOVA or chi-square test for categorical variables.
156 (10.7)
50.26 (4.66)
56.17 (3.55) 181 (6.9)
46.95 (9.81)
49.53 (8.73)
22 (0.9)
1467 (1179/288) 41.99 (10.66) 26.20 (4.11) 40.95 (21.24) 28.61 (13.16) 34.78 (16.33) 45.36 (7.87)
6 (0.4)
80 (3.2)
2635 (2194/441) 39.45 (11.03) 25.43 (3.84) 45.27 (23.02) 30.97 (13.61) 38.12 (17.27) 50.7 (7.28)
53 (3.2)
228 (9.2)
n, sex (m/w) Age, mean years (SD) BMI, mean kg/m2 (SD) Night RMSSD, mean ms (SD) Day RMSSD, mean ms (SD) 24-hour RMSSD, mean ms (SD) SF-12: General health, mean T-score (SD) SF-12: Mental health, mean T-score (SD) SF-12: Physical health, mean T-score (SD) Other Medication: Blood pressure,* n (%) Other medication: blood lipid,* n (%) Other medication: blood glucose,* n (%)
138 (8.2)
53.49 (6.24)
A Little Bit
52.09 (6.60)
48.92 (9.30)
Not at All
47.68(9.73)
2487 (2193/294) 41.28 (11.28) 25.75 (3.91) 43.87 (22.83) 30.02 (13.63) 36.94 (17.32) 48.78 (8.4)
n, sex (m/w) Age, mean years (SD) BMI, mean kg/m2 (SD) Night RMSSD, mean ms (SD) Day RMSSD, mean ms (SD) 24-hour RMSSD, mean ms (SD) SF-12: General health, mean T-score (SD) SF-12: Mental health, mean T-score (SD) SF-12: Physical health, mean T-score (SD) Other medication: blood pressure,* n (%) Other medication: blood lipid,* n (%) Other medication: blood glucose,* n (%)
Sometimes/month
7 (3.7)
18 (9.6)
44 (23.3)
35.78 (6.74)
42.60 (12.80)
195 (166/29) 47.32 (9.58) 27.66 (4.45) 34.88 (18.61) 24.23 (10.85) 29.55 (13.99) 33.79 (8.71)
Quite a Bit
4 (5.9)
7 (10.1)
9 (13.0)
42.00 (10.56)
42.33 (12.31)
71 (52/19) 44.16 (10.21) 26.96 (4.39) 38.44 (21.18) 26.96 (12.33) 32.70 (15.83) 35.79 (9.57)
Sometimes/week
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Groups by Pain Interference
1695 (1320/375) 40.49 (10.66) 25.92 (4.08) 42.49 (22.19) 29.59 (13.28) 36.04 (16.69) 47.11 (8.70)
Never
Seldom
Differences between groups based on analgesic intake and pain interference
Groups by Analgesic Intake
Table 3
Brief Research Report The Association of (Effective and Ineffective) Analgesic Intake, Pain Interference and Heart Rate Variability in a Cross-Sectional Occupational Sample
*Department of Psychology, the Ohio State University, Columbus, Ohio, USA; †Mannheim Institute of Public Health, Social and Preventive Medicine, Mannheim Medical Faculty, University of Heidelberg, Heidelberg, Germany Reprint requests to: Dr. Julian Koenig, Department of Psychology, Emotions and Quantitative Psychophysiology Lab, the Ohio State University, 175 Psychology Building, 1835 Neil Avenue, Columbus, OH 43210, USA. Tel: +1 (614) 688-5793; FAX: +1 (614) 6888261; E-mail: [email protected]. Conflict of Interest: Joachim E. Fischer was the major shareholder of HealthVision Ltd. until December 2012, which organized the data collection. Since January 1, 2013, he now serves as a scientific consultant to Health Vision Ltd. All other authors declare no conflict of interest. Funding Sources: This work was supported by internal funds of the Mannheim Institute of Public Health. Julian F. Thayer was supported by a Humboldt Senior Research Award. Abstract Objective. Persistent pain is associated with dysfunction of the autonomic nervous system, in particular a loss of vagal inhibitory control, that can be indexed by decreased vagally mediated heart rate variability (vmHRV). Effective treatment (e.g., analgesic self-medication) may lead to a restoration of vmHRV. The objective of this article was to further explore the relationship of pain and vagal control
and to investigate the effect of analgesic selfmedication on the association of vmHRV and pain. Methods. We used a large cross-sectional data set on pain ratings and analgesic intake from the Mannheim Industrial Cohort Study for secondary analysis. The root mean square of successive differences, a measure of vmHRV corresponding to the parasympathetic regulation of the heart, was derived from 24-hour electrocardiogram recordings. Results. The frequency of analgesic intake and interference of pain are significantly associated. Individuals that report greater pain interference with their normal work routine (including both work outside the home and housework) and frequent analgesic intake have significantly lower vmHRV. Subjects with ineffective analgesic intake (reporting great pain interference and high frequent analgesic intake) had the lowest vmHRV. Individuals effectively taking analgesics (reporting no or low pain interference and high frequent analgesic intake) showed greater vmHRV compared to those ineffectively taking. Analysis revealed significant differences and linear trends on vmHRV between all groups. Conclusion. In line with previous research, vmHRV is inversely associated with pain interference. Analgesic intake mediates the association of vmHRV and pain. Effective analgesic self-medication may lead to a restoration in vmHRV. These results further support the vagus nerve as an objective indication of pain severity and treatment efficacy in patients with persistent pain. Key Words. Analgesic Intake; Heart Rate Variability; Vagus Nerve; Inhibitory Pathways Introduction The persistent experience of pain can lead to dysfunction of the autonomic nervous system (ANS). The beat-
2261
Downloaded from http://painmedicine.oxfordjournals.org/ by guest on June 9, 2016
Julian Koenig, Dr. sc. hum.,* Marc N Jarczok, Dr. sc. hum.,† Joachim E Fischer, MD, MSc,† and Julian F Thayer, PhD*
Koenig et al.
Table 1
Sample characteristics
more, lower HRV is associated with extended pain related sick leave in employees [7]. Thus, HRV is of interest as a potential biomarker for specific pain related diseases [8].
Sample Characteristics 4742 (844/3898) 41.07 (10.99) 25.87 (4.02) 2487 (52.4) 1695 (35.7) 458 (9.7) 71 (1.5) 31 (0.7) 2635 (55.6) 1467 (30.9) 406 (8.6) 195 (4.1) 39 (0.8) 47.33 (9.00) 47.99 (9.71) 52.22 (7.11) 39 (0.8) 553 (11.7) 2306 (48.6) 1570 (33.1) 274 (5.8) 42.75 (22.28) 29.49 (13.29) 36.12 (16.82) 440 (9.3) 173 (3.6) 43 (0.9)
to-beat variation in the heart rate (HR), heart rate variability (HRV), is mainly driven by the ANS and allows teasing out the relative contributions of sympathetic and parasympathetic activity underlying autonomic control of the heart. Reduced HRV compared with healthy controls is reported in patients with complex regional pain syndrome [1], fibromyalgia [2], chronic neck pain [3], irritable bowel syndrome [4], and headache [5,6]. Further-
Table 2
Recently, we provided evidence that pain is inversely correlated to decreased vagal control indexed by vagally mediated HRV (vmHRV) in healthy subjects [9] and a large cross-sectional occupational cohort [10]. A decrease in vagal activity mirrors a disruption in one of the major descending inhibitory pathways involved in the endogenous modulation of the processing of nociceptive information. Suppression of vagally mediated descending inhibitory pathways results in greater somatic and visceral input via the spinothalamic track, which in turn may provide a mechanism underlying increased pain sensitivity in those with chronic pain. We, therefore, propose that impaired vagal control contributes to the central sensitization in chronic pain leading to an increase in the excitability of neurons in the central nervous system [11] and a shift to emotionrelated circuitry activity in the brain [12]. In addition to serving as a potential pathway involved in the chronification of pain, vmHRV may further provide an additional outcome for the relief of pain due to therapeutic interventions, resulting in a restoration of HRV measures [13–15]. Recent experimental research has shown that ventral periaqueductal gray stimulation increases vmHRV and decreases pain in individuals with chronic pain [16], suggesting that analgesia with deep brain stimulation in chronic pain is associated with increased vagal parasympathetic activity. Pharmaceutical analgesic self-medication is common and broadly accepted in individuals that experience pain. Recently, we found an association of ibuprofen use and HRV in a small sample of subjects [17]. This article aims to further explore the association of pain interference, analgesic self-medication, and HRV in a large cross-sectional sample of 4,742 individuals. Methods We used a large cross-sectional data set from the Mannheim Industrial Cohort Study for secondary analysis. The data was collected as part of a voluntary health risk assessment that was offered to all employees
Contingency table analysis on the relation of analgesic intake and pain interference Analgesic Intake, n (%)
Pain Interference, n
Never
Seldom
Sometimes/month
Sometimes/week
Daily
Total
Not at all A little bit Moderately Quite a bit Extremely Total
1656 (66.6) 621 (25.0) 141 (5.7) 57 (2.3) 12 (0.5) 2487 (52.4)
854 (50.4) 616 (36.3) 160 (9.4) 56 (3.3) 9 (0.5) 1695 (35.7)
116 (25.4) 204 (44.5) 79 (17.2) 50 (10.9) 9 (2.0) 458 (9.7)
5 (7.0) 23 (32.4) 19 (26.8) 19 (26.8) 5 (7.0) 71 (1.5)
4 (12.9) 3 (9.7) 7 (22.6) 13 (41.9) 4 (12.9) 31 (0.7)
2635 (55.6) 1467 (30.9) 406 (8.6) 195 (4.1) 39 (0.8)
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N (w/m) Age, mean years (SD) BMI, mean kg/m2 (SD) Analgesic intake, n (%) Never Seldom Sometimes per month Sometimes per week Daily Pain interference, n (%) Not at all A little bit Moderately Quite a bit Extremely SF-12 scores, mean t-score (SD) Global health Mental health Physical health Self-rated health, n (%) Poor Fair Good Very good Excellent Night RMSSD, ms (SD) Day RMSSD, ms (SD) 24-hour RMSSD, ms (SD) Other medication, n (%) Blood pressure Blood lipid Blood glucose
Analgesic Intake and Vagal Control
during working hours. An agent independent from the employer conducted the health risk assessments and data collection (HealthVision Ltd., Berlingen, Switzerland). A total of 13 study sites (Companies from the secondary and tertiary sectors) from all over Germany with a total of 14,469 participants collected between 2010 and 2012 were available. Participants were invited to take part in the “Work Health Check” and were offered a detailed individual report containing their health status as assessed by medical examination and self reports. This sample encompassed the entire workforce between 17 and 65 years. The Ethical Committee of the Mannheim Medical Faculty, Heidelberg University, approved secondary analysis of this data. All participants gave written informed consent prior to examination. Details on the measurements and population are published elsewhere [18,19]. After completing an online questionnaire, participants were able to schedule a medical examination including a 24-hour recording of HR. Demographic variables (age and sex) were obtained from the online questionnaire. The questionnaire had to be completed prior to being able to schedule the medical examination. All participants were enrolled and examined between 10 AM and 5 PM on a typical workday (Monday–Friday) during work hours. On arrival, a medical examination was performed. Subjects’ body measures (weight and height) were taken and BMI was obtained according to common calculation (kg/m2), as classified according to the WHO standard [20]. Three scores (general health, physical health, and mental health score) were derived from the SF-12 questionnaire
[21] and used as a self-reported measure of health related quality of life, with greater T scores indicating greater health related quality of life. Self-rated health (SRH) was assessed using the first item of the SF-12 (“In general, would you say your health is. . .”), with response categories (“excellent,” “very good,” “good,” “fair,” “poor”). Item number 8 of the SF-12 questionnaire (“During the past 4 weeks how much did pain interfere with your normal work [including both work outside the home and housework]?”) was used as pain-related measure for further analysis. Response categories were: 1) “not at all,” 2) “a little bit,” 3) “moderately,” 4) “quite a bit,” or 5) “extremely.” Analgesic intake was assed by a single item, asking subjects “Do you take analgesics occasionally or regularly?” Participants had to indicate their answer as 1) “never,” 2) “seldom,” 3) “sometimes per month,” 4) “sometimes per week,” or 5) “daily.” Furthermore, we aimed to further control for medication intake other than analgesics. We were able to retrieve data on the intake of medication against high blood pressure, high blood lipid, and high blood glucose. These variables were scored dichotomously (taking/not taking) and some of the included subjects had missing data (blood pressure: n 5 30; blood glucose: n 5 39; blood lipid: n 5 38). HRV HR was recorded as beat-to-beat intervals (IBI) using a t6 Suunto Memory Belt (SuuntoVantaa, Finland), sampling at a rate of 1,000 Hz. The IBI represents the time 2263
Downloaded from http://painmedicine.oxfordjournals.org/ by guest on June 9, 2016
Figure 1 Group difference on RMSSD based on analgesic intake and pain interference; illustrated are Bonferroni-posthoc comparisons; *: indicates a significant difference P < 0.05; **: indicates a significant difference P < 0.01 ***: indicates a significant difference P < 0.001; ns: not significant.
49 (3.4) 10 (0.7)
69 (2.6) 14 (0.5)
10 (2.5)
33 (8.4)
55 (13.8)
43.57 (5.83)
44.57 (10.81)
406 (323/83) 44.93 (10.16) 26.67 (4.24) 36.84 (20.07) 25.79 (11.08) 31.32 (14.63) 40.02 (8.51)
Moderately
7 (1.6)
26 (5.8)
54 (11.9)
48.38 (8.32)
45.09 (10.37)
458 (307/151) 41.15 (10.46) 26.02 (4.23) 39.08 (19.18) 27.12 (11.14) 33.10 (14.11) 42.95 (9.48)
* Includes missing data; P values are derived from ANOVA or chi-square test for categorical variables.
156 (10.7)
50.26 (4.66)
56.17 (3.55) 181 (6.9)
46.95 (9.81)
49.53 (8.73)
22 (0.9)
1467 (1179/288) 41.99 (10.66) 26.20 (4.11) 40.95 (21.24) 28.61 (13.16) 34.78 (16.33) 45.36 (7.87)
6 (0.4)
80 (3.2)
2635 (2194/441) 39.45 (11.03) 25.43 (3.84) 45.27 (23.02) 30.97 (13.61) 38.12 (17.27) 50.7 (7.28)
53 (3.2)
228 (9.2)
n, sex (m/w) Age, mean years (SD) BMI, mean kg/m2 (SD) Night RMSSD, mean ms (SD) Day RMSSD, mean ms (SD) 24-hour RMSSD, mean ms (SD) SF-12: General health, mean T-score (SD) SF-12: Mental health, mean T-score (SD) SF-12: Physical health, mean T-score (SD) Other Medication: Blood pressure,* n (%) Other medication: blood lipid,* n (%) Other medication: blood glucose,* n (%)
138 (8.2)
53.49 (6.24)
A Little Bit
52.09 (6.60)
48.92 (9.30)
Not at All
47.68(9.73)
2487 (2193/294) 41.28 (11.28) 25.75 (3.91) 43.87 (22.83) 30.02 (13.63) 36.94 (17.32) 48.78 (8.4)
n, sex (m/w) Age, mean years (SD) BMI, mean kg/m2 (SD) Night RMSSD, mean ms (SD) Day RMSSD, mean ms (SD) 24-hour RMSSD, mean ms (SD) SF-12: General health, mean T-score (SD) SF-12: Mental health, mean T-score (SD) SF-12: Physical health, mean T-score (SD) Other medication: blood pressure,* n (%) Other medication: blood lipid,* n (%) Other medication: blood glucose,* n (%)
Sometimes/month
7 (3.7)
18 (9.6)
44 (23.3)
35.78 (6.74)
42.60 (12.80)
195 (166/29) 47.32 (9.58) 27.66 (4.45) 34.88 (18.61) 24.23 (10.85) 29.55 (13.99) 33.79 (8.71)
Quite a Bit
4 (5.9)
7 (10.1)
9 (13.0)
42.00 (10.56)
42.33 (12.31)
71 (52/19) 44.16 (10.21) 26.96 (4.39) 38.44 (21.18) 26.96 (12.33) 32.70 (15.83) 35.79 (9.57)
Sometimes/week
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2264
Groups by Pain Interference
1695 (1320/375) 40.49 (10.66) 25.92 (4.08) 42.49 (22.19) 29.59 (13.28) 36.04 (16.69) 47.11 (8.70)
Never
Seldom
Differences between groups based on analgesic intake and pain interference
Groups by Analgesic Intake
Table 3
