Identifying Chinese Medicine patterns of Tension-type Headache (TTH) and its implication on understanding TTH subgroups

doi:10.21203/rs.2.19621/v1

PDF

Research article

Identifying Chinese Medicine patterns of Tension-type Headache (TTH) and its implication on understanding TTH subgroups

https://doi.org/10.21203/rs.2.19621/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted 31 Dec, 2019

You are reading this latest preprint version

Background Acupuncture is commonly used to relieve tension-type of headache (TTH), however, there is a lack of consistent approach of devising acupuncture interventions for TTH due to limited evidence for symptom patterns according to Chinese medicine. This study aimed to identity common Chinese medicine symptom patterns of TTH.

Methods We applied a validated Chinese Medicine Headache Questionnaire to a group of headache sufferers. The questionnaire consisted of information about headache, aggravating and relieving factors and accompanying symptoms. The Migraine Disability Assessment Test (MIDAS) was used to assess disability and the Perceived Stress Scale (PSS) for the level of stress. Information about comorbidities was collected. The modified International Headache Society TTH diagnostic criteria (ICHD-II) were used to screen the participants. Principal component analysis was used for factor extraction and Two-Step cluster analyses for clustering. One-way analysis of variance (ANOVA) was used to compare cluster groups in disability and stress.

Results In total 170 participants, including 114 females and 56 males, met the selection criteria. The commonest headache features were continuous pain (64%) and fixed location (74%). Headache was aggravated by overwork (74%), stress (74%), and mental strain (70%) and relieved by sleeping (78%). The commonest accompanied symptoms were fatigue (71%) and neck stiffness (70%). Four clusters were identified with 46, 34, 46 and 44 participants in Clusters 1-4, respectively. Assessed by experts, the four clusters could be assigned to three different patterns, including Ascendant hyperactivity of Liver-Yang (Cluster 1), Dual Qi and Blood deficiency (Cluster 2), Liver depression forming Fire (Cluster 3), and an Un-labelled group (Cluster 4). The four clusters differed in their key signs and symptoms. Additionally, over 75% participants in clusters 1 and 2 were episodic TTH, over one third in Cluster 3 having chronic TTH, and the majority in Cluster 4 were in-frequent TTH. The three patterns identified also differed in levels of disability and some elements of coping as measured with PSS.

Conclusion The three symptom patterns identified are common clinical presentations of TTH. The new information will contribute to further understanding of the subtypes of TTH and guide the development of targeted interventions, including acupuncture, for clinical practice and research.

Neurology

Integrative & Complementary Medicine

Tension-type Headache

Pattern Identification

Chinese Medicine Syndrome Differentiation

Cluster analysis

Principal Component Analysis

Signs and Symptoms

Symptomatology

Tension-type Headache (TTH) is the second most prevalent chronic disorders in the world[1]. It is a significant cause of distress and disruption to life [2], resulting in marked reductions in quality of life and engagement in social and family activities [3]. However, the treatment strategies for TTH remain unspecific as the mechanisms underlying this prevalent disorder are unknown [2–4]. Chinese medicine (CM) has a long history of treating headaches. Acupuncture, a key treatment modality of CM, is recommended as a prophylactic treatment for chronic TTH [5]. Since TTH is not a standardised disorder in CM, recognition and treatment of such a disorder in clinical practice have to be based on the CM classification of general headache. In CM, each health condition is sub-divided into a few common patterns or syndromes based on signs and symptoms. Those patterns are important as they guide the selection of the supposed optimal acupuncture protocol.

Identifying CM patterns involves a complicated process of synthesising and analysing clinical symptoms and signs of the patient’s condition to determine the location, cause and nature of the condition [6]. Diagnosis of TTH largely relies on textbook information or expert opinion, and is not based on research evidence. Consequently, variations in the diagnosis of TTH among practitioners is common [7]. Such variations would hinder advancement in research and clinical practice. Nevertheless, studies have shown that it is possible to standardise and validate patterns using objective methods and evidence-based approaches [8–14]. Cluster analysis, a multivariate statistical method is commonly used to identify homogeneous subgroups and has been recognised as a suitable technique for identifying CM patterns of diseases [15–17].

The aims of this study were to: 1) explore CM patterns of TTH based on data collected using a validated Chinese Medicine Headache Questionnaire (CMHQ); and, (2) to explore if identified CM patterns differed on information collected in modern TTH headache research, including headache features, severity of headache-related disability assessed with Migraine Disability Assessment Test (MIDAS) and number of comorbidities, psychological profiles, such as anxiety, depression and self-perceived level of stress.

Study design

A bilingual cross-sectional survey was conducted from February 2011 to June 2012. To obtain a broad sample of the headache population, a paper-based survey and an online survey were delivered in parallel. The online survey, which was anonymous and general population-based, was performed via the SurveyMonkey® platform (www.surveymonkey.com). The paper-based survey involved a collaborative multi-site investigation. The questionnaires were administrated at three sites: Melbourne, Beijing and Chengdu. The survey conducted in Melbourne was nested in a clinical trial, which carried out at RMIT University Bundoora campus, Australia. The two Chinese sites were the Acupuncture and Moxibustion Department, Beijing Hospital of Traditional Chinese Medicine (TCM) Affiliated to Capital Medical University, and Affiliated Hospital of Chengdu University of TCM.

Ethics

The survey protocol was reviewed and assessed by the relevant ethics departments. In Melbourne site, the online approach and the paper-based investigation obtained approval from the College Human Ethics Advisory Network (CHEAN) of the College of Science Engineering and Health, RMIT University. The other two collaboration sites of Beijing and Chengdu were granted permission by the Department of Science Research, of the Beijing TCM Hospital, and for the Chengdu site, by the Department of Science and Technology, Chengdu University of TCM respectively.

Recruiting and inclusion criteria

Potential headache sufferers aged from 18 to 65 years old were eligible to participate if they: were able to read English or Chinese; met the ICHD-II criteria of TTH or probable TTH [18]; and, had one day or more of TTH attacks per month for at least one year. The modified International Headache Society TTH diagnostic criteria (ICHD-II) were used to screen the participants. Exclusion criteria were: TTH onset after 50 years old; had more than 4 migraine attacks without aura (ICHD-II 1.1 migraine) per month; had any migraine attack with aura (ICHD-II 1.2) per month; had been hospitalised because of the head or neck injury; or, had migraine attacks which were not able to be distinguished from TTH.

Measurements

Demographic characteristics of the participants collected from this survey included gender, age, ethnicity, marital status and education. Each of the listed instruments included in the survey was available in two language versions, i.e., the English version of CMHQ (Appendix 1) was used at the Melbourne site whereas the Mandarin version (Appendix 2) was used at the Chinese sites. As to the online survey, both language versions were available.

Chinese Medicine Headache Questionnaire (CMHQ). The CMHQ is used to assist CM pattern identification for headache disorders and found to be reliable and valid in capturing essential clinical indicators for making a CM diagnosis [19]. It is a symptom-based data collection tool consisting of a total 193 items which are grouped into three broad categories of pain description, aggravating and relieving factors, and accompanying symptoms. Responses to each item presented were on a 5-point Likert scale rating from 0 to 4 indicating never, seldom, sometimes, often and almost always.

Migraine Disability Assessment (MIDAS) Questionnaire. The MIDAS was initially designed for the migraine population to evaluate the severity of migraine. Studies have shown it is also valid and reliable in evaluating disability associated with TTH [20–25].

Perceived Stress Scale (PSS). The PSS is a widely used instrument in measuring nonspecific psychological stress. Its 10-item version is among the most widely used tool to measure global perceived stress in relation to the health-related outcomes. Those questions were designed to tap how unpredictable, uncontrollable and overloaded respondents find their lives [26, 27].

Comorbidity checklist. A comorbidity checklist was used to assess both somatic and mental comorbidity of TTH. Development of the checklist was based on the Cumulative Illness Rating Scale (CIRS) [28] and the World Mental Health Composite International Diagnostic Interview (WMH-CIDI) [29]. The items in this checklist were reformatted in a coherent manner and the comorbidity checklist comprised two parts involving 18 checkboxes to tick, i.e., somatic comorbidity assessment evaluated by 15 CIRS items and the mental comorbidity assessment by 3 categories summarised from the WMH-CIDI.

Statistical analysis

SPSS 18.0 was used for data analysis. A p value < .05 was considered to be statistically significant. First, descriptive statistics were conducted to summarise demographic characteristics and questionnaires answers. Chi-Squared tests were used to examine the difference in categorical outcomes, such as TTH associated disability level (MIDAS level), comorbidity checklist information, gender, age range, marital status, education level and ethnicity. Second, factor analysis and cluster analysis were conjunctively applied to obtain effective clusters and identify meaningful CM patterns for TTH. Specifically, the principal component analysis (PCA) was used for factor extraction in condensing respondents’ responses to diagnostic information obtained from CMHQ items,whereas the Two-Step cluster algorithm then used for grouping these identified factors into clusters for further evaluation [30, 31]. ANOVA was used to assess the cluster difference in MIDAS levels and in PSS. Third, Chi-Squared tests and ANOVA were employed to compare the characteristics of the resulting clusters, which enables further examining the group differences among the CM pattern types, in MIDAS levels and in PSS levels of the participants. Multiple comparisons were performed to compare group means via post hoc tests with Bonferroni correction when significant differences were observed in means across groups. For missing data handling, both case deletion and imputation methods were applied. Cases having more than 30% missing values within the total 193 items in CMHQ were deleted from the dataset, whereas cases having less than 30% missing values were remedied via the expectation–maximisation algorithm [32].

Multivariate methods for pattern identification

Evaluation and interpretation of data for pattern identification had four sequential steps (Fig. 1). The first step was to reduce the items of CMHQ into a smaller datasets using factor analysis; the second step was to assess the factors extracted and to label those factors in a clinical meaningful manner; the third step was to group (clinical meaningful) factors into clusters using cluster analysis; the final step was the identification of TTH patterns, that is to label the clusters into clinically meaningful CM patterns. Sixteen teaching and research staff across universities and hospitals with their professional backgrounds covering Chinese medicine, acupuncture, modern medicine, and statistics, etc., were invited to provide their experts’ opinions in the 2nd and 3rd steps to ensure that the labels assigned to factors and clusters were of clinical relevance and significance. Only the labels that reached 70% agreement among 16 evaluators were retained.

From February 2011 to June 2012, a total of 565 respondents took part in the survey, and 497 completed it. After applying the selection criteria and excluding those ineligible and those with more than 30% of missing data, 170 participants were finally included for data analysis. Figure 2 illustrates the participant selection process. Among them, 70.6% were female and 29.4% were male (M:F=1:2.4). The average age of the participants was 38 years (SD=12). Defined by headache days per month, a majority (63%) of the included participants were Episodic Tension-Type Headache (ETTH) sufferers, whereas 23% and 14% were of chronic TTH (CTTH) and infrequent subtypes, respectively. Sociodemographic characteristics including information such as ethnicity, marital status and education are shown in Table 1.

According to the CMHQ, the key features of the headaches were pain with a fixed location (74%), of continuous (66.7%) and intermittent (52.7%) nature, with tight (35.3%), heavy (34.1%) and pulsating (34.1%) sensations, and affecting the neck (61.3%) and eyes (57.2%). The majority of participants had frequent headache either being worse at the end of the day (31.2%) or at no particular time (35.9%). As to the aggravating and relieving factors, overwork (74.1%), stress (73.6%), mental strain (70%), being tired (68.1%), lack of sleep (68.1%), anger or irritability (65.8%), anxiety (excessive worry) (65.5%) nervousness (56.3%), and muscular strain (muscle tightness) (53.1%), were identified as the commonest aggravating factors of headache (≥50%), whereas sleeping (77.7%), medication (62.7%), lying-down (62.4%), pressing on the pain area (62.1%) and massage (50.9%) were the commonest relieving factors of the headaches (≥ 50%). Apart from headaches, neck (60%), shoulder (45.3%) and lower back (35.3%) were the most common painful areas. Of the female-related items, bright red colored menstrual blood (50.5%), dark colored menstrual blood (62.4%), headache before period (51.6%) and abdominal pain during periods (52.7%) were the most commonly referred items. Overall, the most common TTH accompanying symptoms were fatigue (71.3%), neck stiffness (70%) and neck pain (60%).

TTH pattern identification

The exploratory analytic methods of factor analysis and cluster analysis were conjointly used given the relatively large number of CMHQ items. Firstly, PCA was applied and resulted in 41 clinical meaningful factors, including 12 from CMHQ part 1, 13 from part 2, and 16 from part 3, were labelled and retained for TTH pattern identification (Table 2). Secondly, using the Two-Step cluster analysis, four distinct cluster groups were identified.. Lastly, based on the clinical characteristics of each cluster and expert opinions, the four clusters were labelled as Ascendant hyperactivity of Liver yang (Cluster 1), Dual Qi and Blood deficiency (Cluster 2), Liver depression forming fire (Cluster 3) and an Un-labelled group (Cluster 4) (Table 3). The first three are common patterns of headache presented in Chinese medicine clinical practice.

Cluster comparisons

Table 4 summaries the characteristics of subjects according to the four clusters. The four clusters differed in aspects of demographic characteristics, stress levels, pain intensity (indicated by MIDAS item B), and disability levels (indicated by MIDAS), and TTH subtypes.

Disability level was classified based on the MIDAS scores. The disability level ranged from level 1 to level 4. The mean MIDAS score of the current sample was 22.64 lost days, at a severe disability level (≥21 lost days due to headache over the last three months). One-way ANOVA results indicated no cluster differences in the overall MIDAS scores. However, there were significant cluster group differences in MIDAS items 4 (days reduced in household work) and MIDAS B (degree of headache intensity). Post hoc t tests with Bonferroni correction found clusters 2 and 4 were statistically different, with Cluster 2 having more non-productive days at home (8.2 days) due to headache and more severe headache (6.3) than cluster 4 (mean: 3 days, mean intensity: 4.7) (Appendix 3). Indicated by the Chi Square test results, there was a statistically significant cluster difference in the disability level (p=.017). This was largely due to about 50% the participants in Clusters 2 and 3 having a higher level of disability (levels 3 and 4), whereas 50% of Cluster 4 had the lowest level of disability (level 1).

The PSS scores were calculated by summing each item, an average score of 16.72 was found. A mean score of PSS-10 around 13 is considered to be the average of 2387 healthy respondents in the United States [16, 27], with the normative data ranging from 12.1 to 14.7. Comparing with the norm, the existing sample had a relatively higher perceived stress than the general population. The PSS does not provide a cut-point to quantify the level of stress. The score ranges from 0-40 were however interpreted arbitrarily by another study, i.e., a low perceived stress level of 0–13, moderate perceived stress of 14–26, and a high perceived stress of 27–40 [33]. The average scores for factors of “Perceived Distress” and “Perceived Coping” were 9.39 and 6.35 respectively. Based on a higher PSS score corresponding to a higher level of perceived stress, a higher score in factor of “Perceived Distress” indicates a higher degree of general distress. It is also necessary to aware that a lower score in “Perceived Coping” factor reflects better coping ability since the four positively stated items (4,5,7,8) in this factor are reversed scored (e.g., 0 = 4, 1 = 3, 2 = 2, 3 = 1 and, 4 = 0) and then summing across all items when calculating the overall score of PSS-10. Like the PSS overall score, there are no cut-offs for the two factors either. One-way ANOVA results indicated there was a statistically significant cluster difference observed on PSS items of 3, 5, 8 (Appendix 4) and “Perceived Coping” factor. Detected by the post hoc t tests with Bonferroni correction, significant differences between Cluster 1 and Clusters 3 and 4 and between Cluster 2 and Clusters 3 and 4 were found (Appendix 5). Participants in Clusters 1 and 2 seemed to cope with stress better than the other two clusters.

Comorbidities of TTH participants were calculated by counting the total number of somatic comorbidities and mental comorbidities separately. All participants had a low number of comorbidities. Of the somatic comorbidity, the body systems of upper gastrointestinal and ophthalmological and otorhinolaryngology had a higher response (9.4% and 8.8% respectively) than others. In regard to the mental comorbidity, anxiety disorders and mood disorders were reported by 12 participants each (7.1%). There were no significant differences in somatic comorbidities among the identified four TTH clusters. Although there was no statistically significant difference in mental comorbidity among clusters, cluster 4 participants suffered no mental comorbidity at all.

Profile of the clusters

Cluster 1 (n=46) had a moderate level of pain, moderate level of disability, and moderate distribution in both physical and mental comorbidity. Participants in this pattern may have more emotional changes than others, such as reporting feeling nervous and “stressed” (Item 3). However, compared with others, they mostly often felt that things were going their way (Item 5) and tended to perform best in coping ability (PSS “Perceived Coping” factor) when compared with.

Cluster 2 (n=34) not only had the overall highest pain intensity and severest disability among all four patterns, it also had the largest number of participants having a physical comorbidity. Nevertheless, participants in this pattern mostly often felt on top of things (Item 8).

Cluster 3 (n=46) had a moderate headache intensity and severe disability, which was similar to Cluster 2. However, participants in this type of TTH pattern seemed to be impacted greatly by their headache. They could hardly feel on top of things (item 8) different to those in Cluster 2, nor could they feel that things were going their way (Item 5) when compared with Cluster 1.

Cluster 4 (n=44) was un-labelled as there were insufficient characteristics of the symptoms and signs for CM diagnosis. However, it may be the least affected cluster not only because it had the lowest level (mild) of pain among the four clusters, but also had no mental comorbidity. Although participants in this cluster had the least emotional problems as they rarely felt nervous and “stressed” (item 3), they however had a poor coping ability as indicated by “Perceived Coping” factor.

In summary, four clusters were identified, among them, participants in Cluster 2 experienced the most severe headache and had the highest disability level. In contrast, Cluster 4 presented mild headache intensity, moderate disability and was free from mental comorbidity. Based on the ANOVA results on MIDAS, PSS and comorbidity checklist, the characteristics of the identified four clusters of TTH participants are summarised in Table 5. The four clusters were not only distinguishable in CM patterns, they also differed in aspects of subtypes of TTH, stress level, pain intensity (indicated by MIDAS item B), and disability level (indicated by MIDAS).

The present study identified distinct CM patterns of TTH through a cluster analysis of 170 TTH participants in a bilingual cross-sectional survey. The results of this study suggest that TTH can be divided into four clusters based on symptoms and signs that are significant to the diagnostic process in Chinese medicine. The four clusters were not only distinguishable in CM patterns, but also differed in aspects of subtypes of TTH (ETTH, frequent ETTH, and CTTH), stress level, pain intensity, and disability level. These findings expand the existing understanding of TTH symptomatology in Western medicine and TTH patterns in Chinese medicine, which may help advance our understanding of the symptoms associated with TTH and subgroups of TTH.

TTH has been shown to be associated with a number of symptoms. The common TTH characteristics and associated symptoms identified in the present study are consistent with the findings of other studies [34–37]. The main similarities are the precipitating factors such as physical activity, stress/tension, when tired, lack of sleep, specific foods/drinks, alcohol, and skipping meals, and some accompanying symptoms such as fatigue, insomnia, and irritability. Emotion-related factors may have impacted on the presence of TTH. The present study found that stress and/or tension (73.6%) was the leading precipitating factors, and the finding is consistent with others (49.4% [34], 74.5% [38], 63% in men and 77% in women [39], 52.5% [37]). Only a small percentage of anxiety disorders and mood disorders were detected (7.1% respectively). This is probably due to more than three-quarters of the respondents were ETTH sufferers, as it has been shown that psychiatric comorbidities are more common in CTTH patients [40, 41] whereas those having less frequent TTH tend to having a lesser degree of psychiatric comorbidity [42]. These results expand the common understanding of TTH symptomatology in terms of its pain description, trigger factors, and accompanying symptoms, as it provides better understanding of symptomology of TTH. With this knowledge, it is possible that more targeted treatments could be developed.

In summary, a considerable similarity of reported features and associated symptoms on TTH were observed between the present study and studies investigating factors and symptoms associated with TTH from western medical aspects. Inevitably, due to the differences in sampling, methods of studies, and the time points when each study took place, there are some discrepancies in the results reported by the above-mentioned studies and the present studies. For example, specific foods/drinks, as an aggravating factor, varied from 2–35%, skipping meals from 24.8–52.9%, smoking from 8.6 to 38%. In addition, the present study observed that bilateral headache was the most common location of TTH (71%) and followed by pain experience in the forehead (52.7%). Although the study by Li et al. [35] also listed bilateral and forehead headache being the commonest TTH, their findings were different with bilateral headache being 79.9% and forehead 34.1%.

In CM, TTH is not a recognised disorder. Literature in CM diagnosis of TTH is limited, yet there is no golden CM standard to classify TTH, since none established TTH eligibility criteria in CM diagnosis. Due to this, differentiation diagnostic criteria of pattern identification in treatment varied, which may also indicate a need to establish the CM differentiation diagnosis of TTH in helping identify the significance of clinical outcome led by the variation from practitioners [43]. The CMHQ contains a series of accompanying symptoms that may be similar to other studies, a major difference between our investigation and other studies were that most CMHQ items detecting signs and symptoms were originally set for diagnosis purposes for distinguishing CM patterns. The present study captured a series of accompanying symptoms that are seldom mentioned by others. For instance, sore eyes (43.5%), thirst (40.6%), bloating (30.0%), joint stiffness (40.6%), muscle twitching (31.8%), increased forgetfulness (50.0%), sighing often (41.1%), and, inability to concentrate (47.1%) were common symptoms experienced by TTH suffers in this sample.

The aim of cluster analysis is to differentiate a group of individuals into subgroups with homogeneous attributes that are diverse from other subgroups [44]. For example, using the hierarchical clustering, one study identified subgroups of individuals with headache who self-medicate which could be helpful to tailor intervention strategies for prevention of medication-overuse headache[45]. Another recent study on diabetic peripheral neuropathy (DPN) using cluster and factor analysis identified distinct groups of patients with respect to its clinical impacts on symptom patterns and comorbidities. Such comprehensive approaches could endorse the subgrouping to individualise the evaluation of patients with DPN. In CM syndrome research, exploratory analysis methods were valuable in understanding and verifying the CM patterns in a series of diseases and conditions defined by modern medicine. Those studies covers a range of topics such as Chronic obstructive pulmonary disease (COPD) [46–48], metabolic syndrome [49], chronic fatigue syndrome [50, 51], diabetic nephropathy [52], acquired immune deficiency syndrome (AIDS) [53, 54], lung cancer [55], preoperative colorectal cancer [56], stroke [57, 58], diabetes mellitus [59], diabetic retinopathy [60], excessive menstruation [61], functional constipation [62], uterus myoma [59], acute myocardial infraction [63], acute pancreatitis [64], posthepatitic cirrhosis [15], chronic low back pain [65], fibromyalgia [66–69], and coronary heart disease [70–72]. Generally, those studies identified explainable CM patterns and interpreted those modern diseases in a reasonable way. In the present study, the use of other measurements enhanced the understandings to the identified patterns in aspects of headache features, severity of headache-related disability, comorbidities, and psychological profiles, which reflect the multidimensional perspectives of TTH. The patterns identified were not only different in symptom manifestations, but also in disability and self-perceived stress and coping.

Very few studies have examined the differences between ETTH and CTTH beyond headache days. In modern medicine, identification of subtypes of TTH under ICHD-II is mainly distinguished by the frequency of headache attacks on the basis of epidemiological studies [73]. Within ETTH, its infrequent subtype occur at lower frequency (< 1 day a month) than the frequent subtype (≥ 1 day a month). The present study indicated that the infrequent ETTH reported much lower headache intensity (mild, mean of 2.78) than other two (moderate, mean of 5.85 and 5.81 respectively) and showed the lowest level of disability. This is in line with the description of ICHD-II that such infrequent subtype has very little impact on the individual whereas the chronic subtype in the present study is associated with a high level of disability[74]. Since ETTH and CTTH also differ in the level of disability and some symptomatology.it is possible to sub-categorise TTH from a multi-dimensional perspective, but not just limited to the frequency of headache.

In the present study, the four CM patterns differed from the current TTH subtypes. The three patterns not only differ in headache frequency, but also in headache intensity and disability. Over three-quarters of participants in Clusters 1 and 2 had frequent ETTH and about one-fifth had CTTH, whereas one-third in Cluster 3 had CTTH, and half had frequent ETTH. All these three clusters had very few participants with infrequent ETTH, whereas one-third of Cluster 4 was having infrequent ETTH (< 1 day). Those results indicate that the CM pattern identification goes beyond headache frequency as it focuses on symptoms and signs that TTH sufferers experience in addition to their headache frequency.

Currently, there is a significant gap in understanding sub-types of TTH. On one hand, the IHS diagnostic criteria for TTH are designed to distinguish TTH from other types of headaches to some degree, and to classify TTH into three subtypes based upon attack frequency only. Non-headache symptoms associated with TTH are, however, not explained or accounted for. On the other hand, despite several epidemiological studies observing a series of aggravating and relieving factors and accompanying symptoms of TTH, clinical practice to date has not given adequate attention to TTH symptoms. The current study fills those gaps by further understanding non-headache symptoms in TTH and using knowledge of pattern identification and advanced statistical methods to identify three clinically-meaningful subgroups of TTH. The presence of these subgroups of TTH sufferers indicates that there is a need to go beyond frequency and relieving factors of TTH. Addressing headache as well as accompanying non-headache symptoms may lead to more efficient treatment strategies.

This study has important strengths. To the best of authors’ knowledge, the present investigation is the first study using exploratory statistical method to research TTH-related symptoms as well as identifying CM patterns of TTH. Our study presented an original statistical methodology that allowed the identification of clinical CM patterns. The method applied, that is using objective exploratory analytic approaches to the symptom-based clinical variables of TTH participants, provides an alternative to current modern medicine approaches in understanding the symptoms associated with TTH and subgroups of TTH. The survey was both hospital-based and general population-based. As a result, it should be applicable to the majority of TTH population.

In summary, the findings expand the existing understanding of TTH symptomatology and TTH patterns. They provide essential information for future research on subgroups of TTH. Nevertheless, several limitations of the current study should be considered. Firstly, the present results could be limited due to its sample size, as some other possible patterns may be observed with a larger sample size. Secondly, relying on exploratory analysis has its drawbacks, since statistically-determined clusters can be affected by many factors. Although we conducted exploratory analysis, we relied on experts’ opinions when interpreting the generated factors and labelling the grouped symptoms and signs. However, expert opinions may be subjective. The present study minimised this potential limitation by a combined approach of exploratory analysis and expert opinions. Both internal and external experts were consulted during the processes of evaluation, determination and labelling of clusters. Finally, this study is a cross-sectional study, which only analysed the symptom distribution collected at a specific duration over the last 3 months. The presence and the severity of symptoms observed may change over time. Future studies may use longitudinal cohort approaches to evaluate the stability of the identified CM patterns over time, and to assess the effect of interventions.

This study provides new and critical information for determining the symptom patterns of TTH. Through cluster analysis of information relevant to Chinse medicine. The identified patterns not only differed in symptoms and signs, but also in level of disability and stress. The subgroup classification will guide targeted intervention design, including acupuncture, for clinical practice and research.

Vos, T., et al., Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 2015. 386: p. 743-800.
Devlin, I., Headache in general practice, in Headache, Orofacial Pain and Bruxism P. Selvaratnam, K. Niere, and M. Zuluaga, Editors. 2009, Churchill Livingstone: Edinburgh. p. 3-11.
Mueller, L., Tension-type, the forgotten headache. How to recognize this common but undertreated condition. Postgrad Med, 2002. 111(4): p. 25-6, 31-2, 37-8 passim.
Jensen, R., Diagnosis, epidemiology, and impact of tension-type headache. Curr Pain Headache Rep, 2003. 7(6): p. 455-9.
Carville, S., et al., Diagnosis and management of headaches in young people and adults: summary of NICE guidance. BMJ, 2012. 345: p. e5765.
Maciocia, G., The foundations of Chinese medicine: a comprehensive text for acupuncturists and herbalists. 1989: Elsevier Churchill Livingstone.
Chen, R.Q., et al., An evidence-based validation of traditional Chinese medicine syndromes. Complementary Therapies in Medicine, 2010. 18(5): p. 199-205.
Portney, L.G. and M.P. Watkins, eds. Foundations of Clinical Research: Applications to Practice. 3rd ed. 2008, Prentice Hall.
Chen, R.Q., C.M. Wong, and T.H. Lam, Construction of a traditional Chinese medicine syndrome-specific outcome measure: the Kidney Deficiency Syndrome Questionnaire (KDSQ). BMC Complement Altern Med, 2012. 12: p. 73.
Lin, J.-D., et al., BCQs: A Body Constitution Questionnaire to assess Stasis in traditional Chinese medicine. European Journal of Integrative Medicine, 2012. 4(4): p. e379-e391.
Lu, C., et al., Pattern Differentiation in Traditional Chinese Medicine Can Help Define Specific Indications for Biomedical Therapy in the Treatment of Rheumatoid Arthritis. The Journal of Alternative and Complementary Medicine, 2009: p. 110306074839071.
Xiong, G., et al., Factor analysis on symptoms and signs of chronic low-back pain based on Traditional Chinese Medicine theory. J Altern Complement Med, 2011. 17(1): p. 51-5.
Park, Y.-J., et al., Development of a Valid and Reliable Phlegm Pattern Questionnaire. Journal of alternative and complementary medicine (New York, N.Y.), 2011. 17: p. 851-8.
Kang, B.-K., et al., Reliability and validity of the Korean Standard Pattern Identification for Stroke (K-SPI-Stroke) questionnaire. BMC complementary and alternative medicine, 2012. 12: p. 55.
Biondi, D.M., Cervicogenic headache: a review of diagnostic and treatment strategies. J Am Osteopath Assoc, 2005. 105(4 Suppl 2): p. 16S-22S.
Cohen, S., T. Kamarck, and R. Mermelstein, A global measure of perceived stress. Journal of Health and Social Behavior, 1983. 24: p. 385-396.
Bendtsen, L., et al., Guidelines for Controlled Trials of Drugs in Tension-Type Headache: Second Edition. Cephalalgia, 2010. 30(1): p. 1-16.
Olesen, J., The international classification of headache disorders. 2nd edition (ICHD-II). Revue Neurologique, 2005. 161(6-7): p. 689-691.
Hao, X., Development of Chinese Medicine Headache Questionnaire for Tension-type Headache. 2015, RMIT University: Melbourne. p. 474.
Stewart, W.F., et al., Development and testing of the Migraine Disability Assessment (MIDAS) Questionnaire to assess headache-related disability. Neurology, 2001. 56(6 Suppl 1): p. S20-8.
Georgoudis, G., A. Parasxou, and P. Chara, Functional Assessment in Greek Tension-TypeHeadache Sufferers: Validity, Reliability,Responsiveness and Psychometrics of the MigraineDisability Assessment Questionnaire (MIDAS). British Journal of Medicine & Medical Research, 2015. 56(4): p. 312-314.
Grazzi, L., et al., Pharmacological behavioural treatment for children and adolescents with tension-type headache: preliminary data. Neurological Sciences, 2004. 25(3): p. s270-s271.
Karlı, N., et al., Impact of sex hormonal changes on tension-type headache and migraine: a cross-sectional population-based survey in 2,600 women. Journal of Headache & Pain, 2012. 13(7): p. 557-565.
Zandifar, A., et al., Reliability and validity of the migraine disability assessment scale among migraine and tension type headache in Iranian patients. Biomed Res Int, 2014. 2014(1): p. 978064.
Kılıç, S., et al., P211 The evaluation of tension-type headache, migraine and others type of headache in the adolescent by Midas measurement. European Journal of Paediatric Neurology, 2009. 13: p. S85-S86.
Cohen, S., T. Kamarck, and R. Mermelstein, A global measure of perceived stress. J Health Soc Behav, 1983. 24(4): p. 385-96.
Cohen, S. and G. Williamson, Perceived stress in a probability sample of the United States, in The social psychology of health: Claremont Symposium on applied social psychology, S. Spacapan and S. Oskamp, Editors. 1988, Sage: Newbury Park, CA.
Linn, B.S., M.W. Linn, and L. Gurel, Cumulative illness rating scale. J Am Geriatr Soc, 1968. 16(5): p. 622-6.
World Mental Health Composite International Diagnostic Interview. 1990, WHO.
SPSS, The SPSS TwoStep cluster component. A scalable component to segment your customers more effectively. 2001, SPSS Inc.: Chicago.
Shih, M.-Y., J.-W. Jheng, and L.-F. Lai, A Two-Step Method for Clustering Mixed Categroical and Numeric Data. Tamkang Journal of Science and Engineering, 2010. 13(1): p. 11-19.
Hair, J.F., et al., Multivariate Data Analysis 7th ed. 2009, Upper Saddle River, NJ: Prentice Hall.
Bhat, R., et al., Analysis of the perceived stress score (PSS) and blood pressure (BP) during examinations in medical students. Journal of Clinical and Diagnostic Research, 2012. 5: p. 1331-1335.
Zivadinov, R., et al., Migraine and tension-type headache in Croatia: a population-based survey of precipitating factors. Cephalalgia, 2003. 23(5): p. 336-43.
Li, X., et al., Clinical characteristics of tension-type headache in the neurological clinic of a university hospital in China. Neurol Sci, 2012. 33(2): p. 283-7.
Hao, X.A., et al., Factors associated with conflicting findings on acupuncture for tension-type headache: qualitative and quantitative analyses. J Altern Complement Med, 2013. 19(4): p. 285-97.
Zhang, G.G., et al., Variability in the traditional Chinese medicine (TCM) diagnoses and herbal prescriptions provided by three TCM practitioners for 40 patients with rheumatoid arthritis. J Altern Complement Med, 2005. 11(3): p. 415-21.
Sun-Edelstein, C. and A. Mauskop, Complementary and alternative approaches to the treatment of tension-type headache. Curr Pain Headache Rep, 2008. 12(6): p. 447-50.
Rasmussen, B.K., Migraine and tension-type headache in a general population: precipitating factors, female hormones, sleep pattern and relation to lifestyle. Pain, 1993. 53(1): p. 65-72.
Mitsikostas, D.D. and A.M. Thomas, Comorbidity of headache and depressive disorders. Cephalalgia, 1999. 19(4): p. 211-7.
Puca, F., et al., Psychiatric comorbidity and psychosocial stress in patients with tension-type headache from headache centers in Italy. The Italian Collaborative Group for the Study of Psychopathological Factors in Primary Headaches. Cephalalgia, 1999. 19(3): p. 159-64.
Siberstein, S.D., R.B. Lipton, and D.W. Dodick, eds. Wolff's headache and other head pain. Eighth Edition ed. 2008, Oxford University Press: New York.
Coeytaux, R.R., et al., Variability in the diagnosis and point selection for persons with frequent headache by traditional Chinese medicine acupuncturists. J Altern Complement Med, 2006. 12(9): p. 863-72.
Ali Masood, M. and M. N. A. Khan, Clustering Techniques in Bioinformatics. International Journal of Modern Education and Computer Science, 2015. 7.
Mehuys, E., et al., The Heterogeneity Of Headache Patients Who Self-Medicate: A Cluster Analysis Approach. Pain, 2016. 157(7): p. 1464.
Won, J.C., et al., Clinical Phenotype of Diabetic Peripheral Neuropathy and Relation to Symptom Patterns: Cluster and Factor Analysis in Patients with Type 2 Diabetes in Korea. Journal of Diabetes Research, 2017. 2017: p. 1-9.
Rennard, S., et al., Identification of Five Chronic Obstructive Pulmonary Disease Subgroups with Different Prognoses in the ECLIPSE Cohort Using Cluster Analysis. Annals of the American Thoracic Society, 2015. 12.
Burgel, P.R., J.L. Paillasseur, and N. Roche, Identification of clinical phenotypes using cluster analyses in COPD patients with multiple comorbidities. Biomed Res Int, 2015. 2014(3): p. 420134.
Kim, S., et al., A cluster analysis of chronic obstructive pulmonary disease in dusty areas cohort identified three subgroups. Bmc Pulmonary Medicine, 2017. 17(1): p. 209.
陈世波, 2型糖尿病合并代谢综合征中医证候研究. 2003, 中国中医科学院: 北京.
Huang, X.-b., et al., Cluster analysis on traditional Chinese medicine syndromes of chronic fatigue syndrome. China Journal of Traditional Chinese Medicine and Pharmacy, 2006. 21(10): p. 592-594.
胡海乐, 慢性疲劳综合征中医证型分布规律研究. 2012, 广州中医药大学: 广州.
牟新, 糖尿病肾病肾功能不全期中医证候学研究. 2006, 北京中医药大学: 北京.
关静, 艾滋病常见中医证候的研究. 2007, 北京中医药大学: 北京.
Jull, G. and K. Niere, Cervical headache: a review, in Grieve’s modern manual therapy – the vertebral column, G. Boyling and G. Jull, Editors. 2004, Churchill.
Hung, P.-H., J.-L. Fuh, and S.-J. Wang, Validity, Reliability and Application of the Taiwan Version of the Migraine Disability Assessment Questionnaire. Journal of the Formosan Medical Association, 2006. 105(7): p. 563-568.
宋晓锋, 大肠癌术前中医证候分布特点研究. 2009, 广州中医药大学: 广州.
Jull, G., et al., Further clinical clarification of the muscle dysfunction in cervical headache. Cephalalgia, 1999. 19(3): p. 179-85.
Zito, G., G. Jull, and I. Story, Clinical tests of musculoskeletal dysfunction in the diagnosis of cervicogenic headache. Man Ther, 2006. 11(2): p. 118-29.
Lin, J.S., et al., BCQ-: A Body Constitution Questionnaire to assess Yin-Xu. Part II: evaluation of reliability and validity. Forsch Komplementmed, 2012. 19(6): p. 285-92.
Watson, D.H. and P.H. Trott, Cervical headache: an investigation of natural head posture and upper cervical flexor muscle performance. Cephalalgia, 1993. 13(4): p. 272-84; discussion 232.
Zito, G., Clinical features of cervicogenic and temporomandibular-related headache, in Headache, Orofacial Pain and Bruxism, P. Selvaratnam, K. Niere, and M. Zuluaga, Editors. 2009, Churchill Livingstone.
李可, 功能性便秘的中医证候分布研究. 2009, 北京中医药大学.
高铸烨, et al., 急性心肌梗死中医四诊信息的聚类分析. 中华中医药学刊, 2007. 25(9): p. 1855-1857.
Wang, Z.-m., G. Wang, and J.-c. Ji, Applied research of principal component analysis and cluster analysis in the early diagnosis of pancreatitis. Chinese Journal of New Clinical Medicine, 2013. 3(4): p. 326-329.
Rabey, M., et al., Somatosensory nociceptive characteristics differentiate subgroups in people with chronic low back pain: a cluster analysis. Pain, 2015. 156(10): p. 1874.
Auvinet, B., et al., The interest of gait markers in the identification of subgroups among fibromyalgia patients. BMC musculoskeletal disorders, 2011. 12: p. 258.
Auvinet, B. and D. Chaleil, Identification of subgroups among fibromyalgia patients. Reumatismo, 2012. 64: p. 250-60.
Vincent, A., et al., OMERACT-based fibromyalgia symptom subgroups: An exploratory cluster analysis. Arthritis research & therapy, 2014. 16: p. 463.
田松, 冠心病中医辨证规律的临床流行病学研究. 2004, 湖南中医学院: 长沙.
Hao, Y.-n., A brief introduciton to Chinese Medicine for medical practitioners. 2006, Beijing: People's Medical Publishing House.
Suijlekom, H.A.v. and F. Antonaci, Cervicogenic Headache, in Handbook of Headache, P. Martelletti and T.J. Steiner, Editors. 2011, Springer. p. 471-482.
Jensen, R. and P. Torelli, Chapter 31 - Treatment of tension-type headache, in Handbook of Clinical Neurology, F.B. Michael J. Aminoff and F.S. Dick, Editors. 2010, Elsevier. p. 377-386.
The International Classification of Headache Disorders: 2nd edition. Cephalalgia, 2004. 24 Suppl 1: p. 9-160.

Acknowledgments

The authors acknowledge Prof. Albrecht Molsberger, Dr. Fayyaz Ahmed, Prof. Robert Helme, Prof. Stephen Gibson, Prof. Jiang Zhu, Prof. Lixing Lao, Prof. Paul Martin, Prof. John Reece, Prof. Jiying Zhou and Dr. Jerusa Alecrim for their support during the CMHQ development and survey investigation.

Abbreviations

ANOVA: One way analysis of variance

CHEAN: College Human Ethics Advisory Network

CIRS: Cumulative Illness Rating Scale

CM: Chinese medicine

CMHQ: Chinese Medicine Headache Questionnaire

CTTH: Chronic Tension-type headache

ICHD-II: Headache Society TTH diagnostic criteria (2nd edition)

IHS: International Headache Society

MIDAS: Migraine Disability Assessment Test

PCA: Principal component analysis

PSS: Perceive Stress Scale

SD: Standard Deviation

TCM: Traditional Chinese medicine

TTH: Tension-type Headache

ETTH: Episodic Tension-Type Headache

WMH-CIDI: World Mental Health Composite International Diagnostic Interview

Funding

Combined Electroacupuncture and Cognitive Behavioural Therapy for Tension-type Headache: A Randomised Controlled Trial (NHMRC grant, 555414, 2008-2012)

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. All requests to access personal data will be handled in accordance with the procedures by the Ethics Committee.

Authors’ contributions

Xinyu Hao: first author; study conception and design, data analysis, manuscript drafting

Fanrong Liang & Linpeng Wang: data collection, data interpretation

Ken Greenwood: methodology, CMHQ validation

Charlie Xue: study conception and funding acquisition, methodology, data interpretation, critical review of the MS

Ying Li & Zhen Zheng: co-corresponding authors; study conception, data analysis, manuscript revision

Ethics approval and consent to participate

The study was approved by the College Human Ethics Advisory Network (CHEAN) of Science Engineering and Health, RMIT University (protocol approval number: BSEHAPP 10-11, May 23, 2011) and subsequently ratified by collaboration sites. Participation to the study was on a voluntary basis: all participants were provided information explaining the purpose of the study and they were informed consent before inclusion. All data were anonymous.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Author details

College of Acupuncture-Moxibustion and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
Department of Acupuncture, Beijing Traditional Chinese Medical Hospital, Capital Medical University, Beijing, China
School of Social Science, University of Sunshine Coast, Queensland, Australia.
School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia.
Graduate School, Chengdu University of Traditional Chinese Medicine, Chengdu, China

Disclosure

No conflict of financial interest to declare.

Additional file

(see: Supplementation files)

Table 1. Sociodemographic characteristics of the included subjects

	Frequency (n)	Percent (%)
Gender (n=170)
Women	120	70.6
Men	50	29.4
Age range (n=166)
20-29	55	33.1
30-39	44	26.5
40-49	31	18.7
50-59	26	15.7
60+	10	6.0
Marital status (n=165)
Single	64	38.8
Married	77	46.7
Partnered	9	5.5
Divorced	13	7.9
Separated	2	1.2
Ethnicity distribution (n=164)
Chinese Asian	79	48.2
Australian Peoples	42	25.6
British	13	7.9
New Zealand Peoples	3	1.8
Southern European	3	1.8
Irish	3	1.8
Western European	3	1.8
South Eastern European	1	.6
Northern European	1	.6
Eastern European	1	.6
Maritime South-East Asian	1	.6
Arab	1	.6
Central Asian	1	.6
Mainland South-East Asian	1	.6
Southern Asian	1	.6
Other north-east Asian	1	.6
Other Asian	1	.6
Had more than one ethnicity	8	4.9
Level of education (n=164)
Postgraduate degree level	40	24.4
Graduate diploma and graduate certificate level	8	4.9
Bachelor degree level	72	43.9
Advanced diploma and diploma level	18	11.0
TAFE level	10	6.1
Secondary education	14	8.5
Primary education	2	1.2

Table 2. Extraction of factors

CMHQ PART1 Pain description	CMHQ PART2 Aggravating & relieving factors	CMHQ PART3 Accompanying symptoms
FAC1.1F1CentralHead	FAC 2.1F1Mental	FAC 3.1F1LiverQi&Fire
FAC 1.1F2WholeHead	FAC 2.1F2Food	FAC 3.1F2Eye
FAC 1.1F3LateralHead	FAC 2.1F3WeatherChange	FAC 3.1F3BoneJointWind
FAC 1.3F1RhythmHeadache	FAC 2.1F4NoFood&Drink	FAC 3.1F4PoorDigestion
FAC 1.3F2ExplosiveNotDull	FAC 2.1F5MentalStrain	FAC 3.1F5LiverSpleenFire
FAC 1.3F3SharpHeadache	FAC 2.1F6MuscularStrain	FAC 3.1F6YinDeficiency
FAC 1.3F4TightHeadache	FAC 2.1F7Oil&Spicy	FAC 3.1F7LiverAttackStomach
FAC 1.3F7DistendingHeadache	FAC 2.1F8WindDamp	FAC 3.1F8ENT
FAC 1.3F8EmptyHeadache	FAC 2.1F9PhysicalStrain	FAC 3.1F9LightSound
FAC 1.5F1LateOfDay	FAC 2.1F10Alcohol-DragCigar	FAC 3.1F10TemperatureSensitivity
FAC 1.5F2BothEnd	FAC 2.2F1Rest	FAC 3.1F11Constipation
FAC 1.5F3AllDay	FAC 2.2F2PhysicalStimulation	FAC 3.1F12BloodDeficiency
	FAC 2.2F3EatingRelated	FAC 3.1F13YangDeficiency
		FAC 3.1F14SpleenDeficienyOfBowel
		FAC 3.1F16Tinnitus
		FAC 3.1F17Insomnia
Included: n=12	Included: n=13	Included: n=16
* In this table, “FAC” is the abbreviation of “factor”, whereas the numbers 1.X after it indicate their section number. For instance, FAC1.6F1 denotes the extracted first factor of Section 1.6, which summarised items of Forehead, Back of the head, and Top of the head.

Table 3. Summary of cluster characteristics based according to the CMHQ data

Cluster 1 (n=46)

Cluster 2 (n=34)

Cluster 3

(n=46)

Cluster 4 (n=44)

Location

Quality

• Forehead; Back of the head; Top of the head

• PAIN QUALITY: Throbbing; Pulsating; Pounding; Tight; A “band-like” sensation

• Forehead, Back of the head, Top of the head; Both side of the head

• PAIN QUALITY: Throbbing; Pulsating; Pounding

• Worse in the morning and at night; All day

Whole head;

No particular location

Explosive;

NOT Dull;

Sharp; Piercing

Aggravating

Relieving factors

AGGRAVATING BY:

Dehydration

Hunger / Being hungry

Chocolate

Muscular strain(muscle tightness)

Poor posture in sitting, standing or sleeping

Teeth grinding

AGGRAVATING BY:

Change of weather;

Change in temperature;

Hot weather; Cold weather; Dehydration; Hunger; Chocolate

RELIEVING BY:

Exercise; Massage

Pressing the pain area; Warmth

Coldness; Medication; Eating

AGGRAVATING BY:

Stress; Nervousness

Irritability

Excessive worry; Depression

Tension or conflict related

AGGRAVATING BY:

Windy days

Damp weather / Humid weather

Rainy days

Accompanying symptoms

Sensitivity to light (or to bright lights)

Sensitivity to sound

“Pins and needles” or numbness in the hands and feet; Faintness; Dizziness; Watery bowel motion; Loose bowel motion

Dry mouth; Thirst; Bitter taste in the mouth

Belching; Bloating;

Indigestion;

Fear of being hot

Table 4. Cluster comparisons of demographic data, TTH subtypes, MIDAS, PSS, and CIRS items

		TTH clusters				Total (n)	p-value† Chi-Square	p-value† ANOVA
		C1	C2	C3	C4
Age		44^v.34	33^v.4	37^v.1	29^v.12	143	N/A	*0.000⁰⁵**
Gender (n)	F	35	27	30	28	120	.307	N/A
Gender (n)	M	11	7	16	16	50	.307	N/A
Age range	20-29	6	8	17	24	55	*.001⁰¹²⁵**	N/A
	20-29	10	12	10	12	44
	20-29	10	8	6	7	31
	20-29	14	4	8	0	26
	20-29	5	2	2	1	10
Marriage status	Single	13	10	16	25	64	.047	N/A
	Married	22	15	25	14	77
	Partnered	3	4	0	2	9
	Divorced	6	4	1	2	13
	Separated	0	1	0	1	2
Education level	Postgraduate	13	8	8	11	40	.968	N/A
	Graduate	3	2	1	2	8
	Bachelor	17	14	21	20	72
	Diploma	4	5	3	6	19
	TAFE	4	1	3	2	10
	Secondary Edu	4	4	4	2	14
	Primary Edu	0	0	1	1	2
Ethnicity	Oceania	19	18	4	4	45	*.000⁰¹²⁵**	N/A
	European	14	7	2	2	25
	Arab	0	0	1	0	1
	Asian	7	5	35	38	85
	had > 1 ethnicity	5	3	0	0	8
TTH subtypes n (%)	Infrequent ETTH	1(2%)	1(3%)	7(15%)	15(34%)	24(14%)	*.000⁰¹²⁵**	N/A
	Frequent ETTH	36(78%)	26(76%)	23(50%)	22(50%)	107(63%)
	CTTH	9(20%)	7(21%)	16(35%)	7(16%)	39(23%)
MIDAS item	Q1	44(1.07)	33(2.30)	43(2.74)	44(3.86)	164(7.771)	N/A	.408
	Q2	44(5.34)	33(9.67)	43(9.81)	44(5.50)	164(7.43)	N/A	.157
	Q3	44(3.95)	33(6.82)	43(4.00)	44(3.23)	164(4.35)	N/A	.209
	Q4	44(6.05)	33(8.15)	43(4.35)	44(2.93)	164(5.19)	N/A	*.038⁰⁵**
	Q5	44(2.18)	33(4.24))	432.65	443.86	164(3.17)	N/A	.606
	MIDAS A	44(20.45)	33(26.73)	43(20.53)	44(15.82)	164(20.49)	N/A	.259
	MIDAS B	44(5.45)	33(6.30) ^v.4	43(5.42)	44(4.68) ^v.2	164(5.41)	N/A	*.015⁰⁵**
	MIDAS SUM (mean score)	44(18.59)	33(31.18)	43(23.56)	44(19.39)	164(22.64)	N/A	.310
MIDAS disability level n (%)	Level 1	12(27%)	3(1%)	14(33%)	22(50%)	51(31%)	*.017⁰⁵**	N/A
	Level 2	5(11%)	7(21%)	3(7%)	4(9%)	19(12%)
	Level 3	14(32%)	8(24%)	8(19%)	7(16%)	37(23%)
	Level 4	13(30%)	15(45%)	18(42%)	11(25%)	57(35%)
PSS score (score by item)	Sum	16.68	16.19	18.79	15.11	16.72	N/A	.092
	Perceived Distress	9.85	10.06	10.22	7.52	9.39	N/A	.066
	Perceived Coping	5.04^v.3,4	5.18^v.3,4	7.35*^v.1,2	7.59*^v.1,2	6.35	N/A	*.000⁰¹⁷**
Comorbidity Checklist (number of items)	Somatic comorbidity	46	34	46	44	42.9%	.588	N/A
Comorbidity Checklist (number of items)	Mental comorbidity	8	9	5	0	12.9%	.060	N/A
Note 1: Australia is a county of immigration. In section of ethnicity, the category of “had more than 1 ethnicity” indicated a group of participants in this country share more than one ethnicity. For example, an Australian person may have his/her mother of Irish ethnicity and father of Greek. In such case, these participants may tick two options and in data analysis, he/she was classified as participant had more than one ethnicity. Note 2: Both Chi-Square and ANOVA were applied to access cluster differences for comparison. Chi-Square tests examine categorical outcomes, whereas ANOVA assess the means of each cluster. p values correspond to comparisons between the clusters using Chi-square test or ANOVA, as appropriate ⁰⁵. The mean difference is significant at the 0.05 level. ⁰¹²⁵. The mean difference is significant at the 0.0125(0.05/4) level. *⁰¹⁷. The mean difference is significant at the 0.017(0.05/3) level. “^v.”. denotes the clusters differed with post-hoc Bonferroni correction, whereas the “^x(figure)” after “^v.” indicates specific cluster or clusters.

[Due to technical limitations, table 5 could not be displayed here. Please see the supplementary files section to access table 5.]

PDF

Version 1

posted 31 Dec, 2019

You are reading this latest preprint version

Identifying Chinese Medicine patterns of Tension-type Headache (TTH) and its implication on understanding TTH subgroups

Status:

Version 1

Abstract

Figures

Background

Methods

Study design

Ethics

Recruiting and inclusion criteria

Measurements

Statistical analysis

Multivariate methods for pattern identification

Results

Discussion

Conclusions

References

Declarations

Tables

Table 1. Sociodemographic characteristics of the included subjects

Table 2. Extraction of factors

Table 3. Summary of cluster characteristics based according to the CMHQ data

Table 4. Cluster comparisons of demographic data, TTH subtypes, MIDAS, PSS, and CIRS items

Supplementary Files

Status:

Version 1