123
Pain, 45 (1991) 123-128 0 1991 Elsevier Science Publishers B.V. 0304-3959/91/$03.50 ADONIS 030439599100113N
PAIN 01782
Validation of hourly pain intensity profiles with chronic pain patients Robert N. Jamison a and Gregory K. Brown ’ a Pain Treatment Service, Department and ‘Department
of
Anesthesia,Brigham and Women’s Hospital, Boston, MA (U.S.A.), of Ps_whiatty, University of Pennsylvania, Philadelphia, PA (U.S.A. j
(Received 10 August 1990, revision received 23 October 1990, accepted 29 October 1990)
Summary
This study examines how empirically derived pain intensity profiles relate to psychological adjustment in chronic pain patients. Hourly pain intensity ratings over the course of a day of 189 chronic pain patients were categorized into 6 groups using a hierarchical regression analysis technique. The profiles reflected linear, curvilinear, multilinear or “no profile” effects. Judges’ ratings of pain intensity profiles were found to be less reliable than ratings based on empirical analysis. The majority of patients showed linear profiles while 26% of the patients showed no consistent relationship between hourly pain intensity and time of day. Patients who showed no consistent trend in their pain ratings reported having significantly higher ratings of emotional distress. They tended to have conflicts at home and showed more pain behavior than patients who had a distinct pain profile. Support is given for the potential clinical utility of pain rating profiles. Key words: Chronic pain; Pain intensity; Emotional distress
Increasing attention has been given in the assessment of chronic pain in humans. Appraisals of pain are important for determining the patient’s diagnosis [15], for evaluating the patient’s cognitive perspective [20] and for establishing a treatment plan 1121.Despite some inherent limitations, a number of different subjective methods of pain assessment are currently being used in chronic pain treatment programs. The most common include numeric pain intensity ratings. Pain intensity rating methods have evolved from designs originally developed by Budzynski et al. [4] and Melzack [16]. Typically, numeric pain intensity ratings consist of asking the patient to rate his/her pain on a numerical scale of O-10, with 0 equaling no pain and 10 equaling excruciating pain. Numeric ratings of pain have been found to be easy to administer and to be clinically
Correspondence to: Robert N. Jamison, Ph.D., Pain Treatment Service, Department of Anesthesiology, Brigham and Women’s Hospital, Boston, MA 02115, U.S.A.
useful. A number of studies investigating the utility of self-monitored pain intensity ratings have shown them to be both reliable and valid [7,13]. In many pain programs, patients are asked to rate their pain on an hourly basis for the duration of several waking days as part of an initial evaluation. The monitoring of pain intensity over a l-2 week period has a number of beneficial uses in the assessment and treatment of the chronic pain patient. First, compared with a single index of perceived pain intensity, more information can be obtained about how the pain intensity fluctuates under varying conditions, Second, pain intensity ratings can be used as a baseline in evaluating various treatment approaches. Third, the monitoring of pain intensity can serve as a compliance measure for participation in a treatment program. Finally, it has been argued that by monitoring pain intensity, the patient may experience a sense of control over his/her pain and “catastrophize” less after recognizing that pain intensities fluctuate over time [3,8]. There is some suggestion that the monitoring of behavior alone can be useful as an intervention strategy [9]_ Limitations of pain monitoring include (a) the inability to detect brief rises and falls in intensity, (b) the possible heightening of pain intensity because of the attention drawn to the
pain. and (c) the potential for imprecise measurement \“r exaggeration of the pain [l]. Despite these limitations. numerical rating scales have consistently demonstrated their validity as pain intensity measures [12]. Once pain ratings have been made. pain profiles can be derived by averaging the hourly pain ratings over the course of a day and graphically presenting these average values in the form of a histogram or bar chart. The printed visual display can be useful in offering specific behavioral intervention strategies based on the pattern of pain. Once initial pain profile baselines are obtained, these baselines can be used as an index for improvement. Support for the reliability of pain profiles comes from the results of the study by Kerns et al. [13] which showed that pain intensity ratings tended to remain stable over a 2 week period. To date, however. there has been no research examining the validity of pain rating profiles. Also, no attempt has been made to empirically classify pain rating profiles. The aim of this study is to examine how pain rating profiles which reflect patterns of pain intensity over the course of a day can be useful in classifying and evaluating a heterogeneous group of chronic pain patients. The goals of this study are: (1) to determine the incidence of certain pain rating profiles among chronic pain patients; (2) to determine whether differences exist hetween profile groups on demographic. physical and psychosocial variables; and (3) to examine differences between patients who show distinct pain profiles and those who do not show evidence of consistent pain profile patterns.
Methods
One hundred and ninety-five (195) chronic pain patients, who were referred by a physician for treatment of their pain, participated in this study. The patients were taken from a sample of consecutive referrals to a multidisciplinary hospital-based pain center. They ranged in age from 18 to 76 (mean = 42.1) and reported the following as a primary pain site: low back (33.3%), head (20.5%), lower extremity (15.8%), cervical back (14.4%). upper extremity (7.7%), abdominal (4.1%). and chest (4.1%). Sixty-six percent (66.2%) of the patients were female and 95.4% were Caucasian. They averaged 12.6 years of education (SD. = 3.1) and 69.4% were married. Forty-three percent (42.8%) reported being out of work at the time of the initial evaluation and 13.4% were receiving worker’s compensation. The pain duration ranged from 3 to 360 months (mean = 52.2; SD. = 56.0) and, at the time of the initial evaluation, the pain intensity ratings ranged from 3 to 10 on a O--10 scale (mean = 8.13; S.D. = 1.86).
All patients N~I-c mtervietied and cxamtned b\ ,I physician as part of an Initial evaluation. The exarni~;,ng physicians were anesthesiology residents. fcllo\\> ;~nd attrndings. Each patient \vas asked to complete a con1 prehensive pain qurxtionnaire which capered dcrnographic information, psychosocial data, pain descriptors. family support issues. activity levels and the ~L’I-ccived impact of pain on their daily lives. The patient> also were asked to complete an SC‘L.-90-K 151. \vhich 11 ;t checklist of items commonly used among medical ~O~LIlations to assess emotional distress. As part of the Initial assessment, each patient wa:, requested to monitor their pain intensity by maklnp hourly ratings of their pain for I week using daily index cards. Hourly ratings were made using a 0~10 scale \~ith word descriptors to measure pain intensity. The pain rating key is as follows: 0 --“no pain”: 1 2 = “mild. tolerable. low-level pain”: 3 4 = “ moderate. pain can be ignored at times”: S -6 = “ intense, distressing, hut able to continue activities”; 7 8 =‘I very intense, difficult to concentrate. interferes with activity”: Y IO -“excruciating, incapacitating, worst pain possible.” This scale is similar to the one developed by Melzack [16] and described by others [ 13.201. The patients were instructed to monitor their pain while they v,ere awake and to mark those times when they took ant medication. The patients were asked to discontinue USCof an! narcotic or tranquilizing medication while monitoring their pain intensity. Patients were excluded from this studv if they had fewer than 5 day:, of pain ratings 01 had ‘less than 40 h pain ratings over the course of their Inonitoring period. SIX patients were omitted from the analyses because of mi.\sing data. Thus, IX9 patients were used in the analyses.
The pain rating data were initially examined separately for each subject by the hour of the day using all available data for the total rating period. In order to determine the presence of a linear, quadratic or cubic effect over the course of the day, a hierarchical regression analysis was employed. Hourly pain ratings were examined on the first step, followed by squared and cubed ratings on the second and third steps. respectively. semipartial correlations ( .YT’ ) were The squared, evaluated in order to determine which particular profile (i.e., a linear, quadratic or cubic effect) accounted for most of the variance in the model. Thus, the relative accuracy of profile “fit” was determined by the change in ST? as the equations were entered. The sign of beta weights were used in classifying the direction of each significant profile. In those cases where the .sr’ change values were non-significant and the total K’ was Io\b (i.e.. none of the equations helped in identifying a pain
125 TABLE
I
CRITERIA FOR HIERARCHICAL Pn~file type
1. 2. 3. 4. 5. 6.
Positive linear Uegative linear Inverted U U curve Poly-curved Yo pattern
DETERMINING REGRESSION
PROFILE ANALYSES
Beta
sr2
+ -
sign. sign.
Beta
+ _
sr2
Beta
accounted for by the pain intensity and the pain behavior variables.
profiles
(total
R2)
R2
Results
sign.
sign. sign. sign. sign. sign.
Il.S.
Il.S.
Fig. 1 is a depiction of the results of the polynomial regression analyses of the pain card profiles. The majority of the patients showed linear profiles, while less than a quarter of the patients presented with curvilinear profiles. Eight percent (8%) of the patients showed significant total R2 values, while no single pattern emerged. Twenty-six percent (26%) of the patients showed non-significant total R2 values suggesting no trend between hourly pain intensity ratings and time of day. No notable differences were found between the profile pattern types and pain ratings, medication usage, pain duration, age, sex, or education. However, analyses showed significant differences between groups on pain site, expression of pain, and emotional distress factors. These results are presented in Table II. For those dependent variables in which an ANOVA was used, post hoc tests using Student-Newman-Keuls statistics were undertaken. Few significant differences were found between groups based on pain site. Upper extremity pain patients showed the highest ratio of “no slope” profiles. Those patients who presented with a “U” curve profile reported that others often were unaware when they were in pain (group 3 different from groups 4 and 6 at the P < 0.05 level). Also, patients with a negative linear profile and a “U” profile showed a lower verbal reaction to their pain (group 4 different from groups 2 and 3 at the P -c0.05level). Those patients who presented with a “flat” profile showed significant elevations on the SCL-90 suggesting the presence of considerable emotional distress, while patients with a negative linear profile presented with the least emotional distress (group
n.s.
n.s. ll.S.
sign. sign.
ll.S.
Il.?,.
ON
ST’
tl.S.
n.s. *
Il.S.
BASED
Hour3
Hour 2
Hour
TYPES
sig”. = significant at P < 0.05. n.,. = not significant.
rating pattern), the patients were classified as having no consistent pain pattern. Table I shows the criteria for determining profile types. To determine concurrent validity of the profile types, the printed histograms of the averaged hourly ratings for each patient were presented to 10 individuals who were blind to the study. They were asked to classify each of the pain profiles into 6 categories. Pictorial representations of profile types were presented on 3 x 5 cards and reflected the following pain profiles: (1) a positively sloped linear line: (2) a negatively sloped linear line; (3) a “U” curve; (4) an inverted “U” curve; and (5) a flat line. The sixth category was labeled “other” for those profiles which did not fit into any of the previous categories. The raters were encouraged to place the profiles into one of the first 5 categories and to avoid using the “other” category, if possible. Interrater correlations ranged from 0.84 to 0.31 with a mean of 0.56. Agreement between classifications based on the regression analyses and classification based on rater judgment was r = 0.64. When those profiles who were judged to be in the “other” category were omitted from the correlational analyses (N = 15), the validity coefficicnt improved to r = 0.67. In order to determine the degree of inter-rater agreement between the 10 raters, a parallel model goodness-of-fit analysis was run. A significant chi-square was obtained (x2 = 210.6, P < 0.0001) suggesting that a parallel model of maximum likelihood reliability was not found. Thus, despite modest agreement between raters, classification of pain profiles based solely on subjective inspection of the mean hourly pain ratings was found to be less reliable than classification using regression analyses. Following the classification of patients according to pain rating profile types as determined by the results of the regression analyses, differences between groups were examined on the validity variables using &i-squared tests or a l-way analysis of variance (ANOVA). Also, correlations were run between amount of variance
LINEAR
‘\8% In
43%
(n = 61)
‘/
/
4
CURVILINEAR (n I 40)
21%
14%
6
POLVSLOPE OR NO SLOPE 36% @=66)
I-w 6%
6
I
26%
Fig. 1. Classification of pain intensity rating profiles based on polynomial regression analyses (N = 189).
126 TABLE
II
FREQUENCIES
OF PAIN SITES. PAIN BEHAVIOR
AND
EMOTIONAL
DISTRESS
FACTORS
BETWEEN
PROFILE
TYPI-.S -_
Variable
N
Pattern 1
2
3
4
5
6
(%)
35 34
33.x 31.1
15.4 4.4
3.1 6.7
16.9 24.4
1.1 6.6
23.1 26.7
xz = 14.61 x1 y 5.31
Lower extremity Cervical Upper extremity Abdominal Chest
31 28 15 8 8
35.5 28.6 13.3 50.0 50.0
3.2 3.6 13.3 _ 12.5
10.7 10.7 13.3
10.7 10.7
14.3 14.3
i2.1 32. I
X.7
33.3
_
25.0 12.5
25.0 25.0
x1 x’ = x.‘== XL = x‘ =
173 173
3.3 4.1
3.2 2.9
2.7 2.9
3.7 4.8
3.6 3.5
3.7 3.7
176 174 173
64.9 60.6 63.5
53.3 49.0 50.6
59.5 62.7 63.8
64.0 59.4 60.5
71.1 61.9 66.2
68.7 65.9 67.0
Low back Head
Pain expression Verbal reaction SCL-90 fi+
+
Somatization Depression Global
’ 1 = never: 5 = always. + + T scores. * P
Pain, 45 (1991) 123-128 0 1991 Elsevier Science Publishers B.V. 0304-3959/91/$03.50 ADONIS 030439599100113N
PAIN 01782
Validation of hourly pain intensity profiles with chronic pain patients Robert N. Jamison a and Gregory K. Brown ’ a Pain Treatment Service, Department and ‘Department
of
Anesthesia,Brigham and Women’s Hospital, Boston, MA (U.S.A.), of Ps_whiatty, University of Pennsylvania, Philadelphia, PA (U.S.A. j
(Received 10 August 1990, revision received 23 October 1990, accepted 29 October 1990)
Summary
This study examines how empirically derived pain intensity profiles relate to psychological adjustment in chronic pain patients. Hourly pain intensity ratings over the course of a day of 189 chronic pain patients were categorized into 6 groups using a hierarchical regression analysis technique. The profiles reflected linear, curvilinear, multilinear or “no profile” effects. Judges’ ratings of pain intensity profiles were found to be less reliable than ratings based on empirical analysis. The majority of patients showed linear profiles while 26% of the patients showed no consistent relationship between hourly pain intensity and time of day. Patients who showed no consistent trend in their pain ratings reported having significantly higher ratings of emotional distress. They tended to have conflicts at home and showed more pain behavior than patients who had a distinct pain profile. Support is given for the potential clinical utility of pain rating profiles. Key words: Chronic pain; Pain intensity; Emotional distress
Increasing attention has been given in the assessment of chronic pain in humans. Appraisals of pain are important for determining the patient’s diagnosis [15], for evaluating the patient’s cognitive perspective [20] and for establishing a treatment plan 1121.Despite some inherent limitations, a number of different subjective methods of pain assessment are currently being used in chronic pain treatment programs. The most common include numeric pain intensity ratings. Pain intensity rating methods have evolved from designs originally developed by Budzynski et al. [4] and Melzack [16]. Typically, numeric pain intensity ratings consist of asking the patient to rate his/her pain on a numerical scale of O-10, with 0 equaling no pain and 10 equaling excruciating pain. Numeric ratings of pain have been found to be easy to administer and to be clinically
Correspondence to: Robert N. Jamison, Ph.D., Pain Treatment Service, Department of Anesthesiology, Brigham and Women’s Hospital, Boston, MA 02115, U.S.A.
useful. A number of studies investigating the utility of self-monitored pain intensity ratings have shown them to be both reliable and valid [7,13]. In many pain programs, patients are asked to rate their pain on an hourly basis for the duration of several waking days as part of an initial evaluation. The monitoring of pain intensity over a l-2 week period has a number of beneficial uses in the assessment and treatment of the chronic pain patient. First, compared with a single index of perceived pain intensity, more information can be obtained about how the pain intensity fluctuates under varying conditions, Second, pain intensity ratings can be used as a baseline in evaluating various treatment approaches. Third, the monitoring of pain intensity can serve as a compliance measure for participation in a treatment program. Finally, it has been argued that by monitoring pain intensity, the patient may experience a sense of control over his/her pain and “catastrophize” less after recognizing that pain intensities fluctuate over time [3,8]. There is some suggestion that the monitoring of behavior alone can be useful as an intervention strategy [9]_ Limitations of pain monitoring include (a) the inability to detect brief rises and falls in intensity, (b) the possible heightening of pain intensity because of the attention drawn to the
pain. and (c) the potential for imprecise measurement \“r exaggeration of the pain [l]. Despite these limitations. numerical rating scales have consistently demonstrated their validity as pain intensity measures [12]. Once pain ratings have been made. pain profiles can be derived by averaging the hourly pain ratings over the course of a day and graphically presenting these average values in the form of a histogram or bar chart. The printed visual display can be useful in offering specific behavioral intervention strategies based on the pattern of pain. Once initial pain profile baselines are obtained, these baselines can be used as an index for improvement. Support for the reliability of pain profiles comes from the results of the study by Kerns et al. [13] which showed that pain intensity ratings tended to remain stable over a 2 week period. To date, however. there has been no research examining the validity of pain rating profiles. Also, no attempt has been made to empirically classify pain rating profiles. The aim of this study is to examine how pain rating profiles which reflect patterns of pain intensity over the course of a day can be useful in classifying and evaluating a heterogeneous group of chronic pain patients. The goals of this study are: (1) to determine the incidence of certain pain rating profiles among chronic pain patients; (2) to determine whether differences exist hetween profile groups on demographic. physical and psychosocial variables; and (3) to examine differences between patients who show distinct pain profiles and those who do not show evidence of consistent pain profile patterns.
Methods
One hundred and ninety-five (195) chronic pain patients, who were referred by a physician for treatment of their pain, participated in this study. The patients were taken from a sample of consecutive referrals to a multidisciplinary hospital-based pain center. They ranged in age from 18 to 76 (mean = 42.1) and reported the following as a primary pain site: low back (33.3%), head (20.5%), lower extremity (15.8%), cervical back (14.4%). upper extremity (7.7%), abdominal (4.1%). and chest (4.1%). Sixty-six percent (66.2%) of the patients were female and 95.4% were Caucasian. They averaged 12.6 years of education (SD. = 3.1) and 69.4% were married. Forty-three percent (42.8%) reported being out of work at the time of the initial evaluation and 13.4% were receiving worker’s compensation. The pain duration ranged from 3 to 360 months (mean = 52.2; SD. = 56.0) and, at the time of the initial evaluation, the pain intensity ratings ranged from 3 to 10 on a O--10 scale (mean = 8.13; S.D. = 1.86).
All patients N~I-c mtervietied and cxamtned b\ ,I physician as part of an Initial evaluation. The exarni~;,ng physicians were anesthesiology residents. fcllo\\> ;~nd attrndings. Each patient \vas asked to complete a con1 prehensive pain qurxtionnaire which capered dcrnographic information, psychosocial data, pain descriptors. family support issues. activity levels and the ~L’I-ccived impact of pain on their daily lives. The patient> also were asked to complete an SC‘L.-90-K 151. \vhich 11 ;t checklist of items commonly used among medical ~O~LIlations to assess emotional distress. As part of the Initial assessment, each patient wa:, requested to monitor their pain intensity by maklnp hourly ratings of their pain for I week using daily index cards. Hourly ratings were made using a 0~10 scale \~ith word descriptors to measure pain intensity. The pain rating key is as follows: 0 --“no pain”: 1 2 = “mild. tolerable. low-level pain”: 3 4 = “ moderate. pain can be ignored at times”: S -6 = “ intense, distressing, hut able to continue activities”; 7 8 =‘I very intense, difficult to concentrate. interferes with activity”: Y IO -“excruciating, incapacitating, worst pain possible.” This scale is similar to the one developed by Melzack [16] and described by others [ 13.201. The patients were instructed to monitor their pain while they v,ere awake and to mark those times when they took ant medication. The patients were asked to discontinue USCof an! narcotic or tranquilizing medication while monitoring their pain intensity. Patients were excluded from this studv if they had fewer than 5 day:, of pain ratings 01 had ‘less than 40 h pain ratings over the course of their Inonitoring period. SIX patients were omitted from the analyses because of mi.\sing data. Thus, IX9 patients were used in the analyses.
The pain rating data were initially examined separately for each subject by the hour of the day using all available data for the total rating period. In order to determine the presence of a linear, quadratic or cubic effect over the course of the day, a hierarchical regression analysis was employed. Hourly pain ratings were examined on the first step, followed by squared and cubed ratings on the second and third steps. respectively. semipartial correlations ( .YT’ ) were The squared, evaluated in order to determine which particular profile (i.e., a linear, quadratic or cubic effect) accounted for most of the variance in the model. Thus, the relative accuracy of profile “fit” was determined by the change in ST? as the equations were entered. The sign of beta weights were used in classifying the direction of each significant profile. In those cases where the .sr’ change values were non-significant and the total K’ was Io\b (i.e.. none of the equations helped in identifying a pain
125 TABLE
I
CRITERIA FOR HIERARCHICAL Pn~file type
1. 2. 3. 4. 5. 6.
Positive linear Uegative linear Inverted U U curve Poly-curved Yo pattern
DETERMINING REGRESSION
PROFILE ANALYSES
Beta
sr2
+ -
sign. sign.
Beta
+ _
sr2
Beta
accounted for by the pain intensity and the pain behavior variables.
profiles
(total
R2)
R2
Results
sign.
sign. sign. sign. sign. sign.
Il.S.
Il.S.
Fig. 1 is a depiction of the results of the polynomial regression analyses of the pain card profiles. The majority of the patients showed linear profiles, while less than a quarter of the patients presented with curvilinear profiles. Eight percent (8%) of the patients showed significant total R2 values, while no single pattern emerged. Twenty-six percent (26%) of the patients showed non-significant total R2 values suggesting no trend between hourly pain intensity ratings and time of day. No notable differences were found between the profile pattern types and pain ratings, medication usage, pain duration, age, sex, or education. However, analyses showed significant differences between groups on pain site, expression of pain, and emotional distress factors. These results are presented in Table II. For those dependent variables in which an ANOVA was used, post hoc tests using Student-Newman-Keuls statistics were undertaken. Few significant differences were found between groups based on pain site. Upper extremity pain patients showed the highest ratio of “no slope” profiles. Those patients who presented with a “U” curve profile reported that others often were unaware when they were in pain (group 3 different from groups 4 and 6 at the P < 0.05 level). Also, patients with a negative linear profile and a “U” profile showed a lower verbal reaction to their pain (group 4 different from groups 2 and 3 at the P -c0.05level). Those patients who presented with a “flat” profile showed significant elevations on the SCL-90 suggesting the presence of considerable emotional distress, while patients with a negative linear profile presented with the least emotional distress (group
n.s.
n.s. ll.S.
sign. sign.
ll.S.
Il.?,.
ON
ST’
tl.S.
n.s. *
Il.S.
BASED
Hour3
Hour 2
Hour
TYPES
sig”. = significant at P < 0.05. n.,. = not significant.
rating pattern), the patients were classified as having no consistent pain pattern. Table I shows the criteria for determining profile types. To determine concurrent validity of the profile types, the printed histograms of the averaged hourly ratings for each patient were presented to 10 individuals who were blind to the study. They were asked to classify each of the pain profiles into 6 categories. Pictorial representations of profile types were presented on 3 x 5 cards and reflected the following pain profiles: (1) a positively sloped linear line: (2) a negatively sloped linear line; (3) a “U” curve; (4) an inverted “U” curve; and (5) a flat line. The sixth category was labeled “other” for those profiles which did not fit into any of the previous categories. The raters were encouraged to place the profiles into one of the first 5 categories and to avoid using the “other” category, if possible. Interrater correlations ranged from 0.84 to 0.31 with a mean of 0.56. Agreement between classifications based on the regression analyses and classification based on rater judgment was r = 0.64. When those profiles who were judged to be in the “other” category were omitted from the correlational analyses (N = 15), the validity coefficicnt improved to r = 0.67. In order to determine the degree of inter-rater agreement between the 10 raters, a parallel model goodness-of-fit analysis was run. A significant chi-square was obtained (x2 = 210.6, P < 0.0001) suggesting that a parallel model of maximum likelihood reliability was not found. Thus, despite modest agreement between raters, classification of pain profiles based solely on subjective inspection of the mean hourly pain ratings was found to be less reliable than classification using regression analyses. Following the classification of patients according to pain rating profile types as determined by the results of the regression analyses, differences between groups were examined on the validity variables using &i-squared tests or a l-way analysis of variance (ANOVA). Also, correlations were run between amount of variance
LINEAR
‘\8% In
43%
(n = 61)
‘/
/
4
CURVILINEAR (n I 40)
21%
14%
6
POLVSLOPE OR NO SLOPE 36% @=66)
I-w 6%
6
I
26%
Fig. 1. Classification of pain intensity rating profiles based on polynomial regression analyses (N = 189).
126 TABLE
II
FREQUENCIES
OF PAIN SITES. PAIN BEHAVIOR
AND
EMOTIONAL
DISTRESS
FACTORS
BETWEEN
PROFILE
TYPI-.S -_
Variable
N
Pattern 1
2
3
4
5
6
(%)
35 34
33.x 31.1
15.4 4.4
3.1 6.7
16.9 24.4
1.1 6.6
23.1 26.7
xz = 14.61 x1 y 5.31
Lower extremity Cervical Upper extremity Abdominal Chest
31 28 15 8 8
35.5 28.6 13.3 50.0 50.0
3.2 3.6 13.3 _ 12.5
10.7 10.7 13.3
10.7 10.7
14.3 14.3
i2.1 32. I
X.7
33.3
_
25.0 12.5
25.0 25.0
x1 x’ = x.‘== XL = x‘ =
173 173
3.3 4.1
3.2 2.9
2.7 2.9
3.7 4.8
3.6 3.5
3.7 3.7
176 174 173
64.9 60.6 63.5
53.3 49.0 50.6
59.5 62.7 63.8
64.0 59.4 60.5
71.1 61.9 66.2
68.7 65.9 67.0
Low back Head
Pain expression Verbal reaction SCL-90 fi+
+
Somatization Depression Global
’ 1 = never: 5 = always. + + T scores. * P
