Skip to main content

The relationship between hypertension and health-related quality of life: adjusted by chronic pain, chronic diseases, and life habits in the general middle-aged population in Japan

  • The Erratum to this article has been published in Environmental Health and Preventive Medicine 2016 21:525

Abstract

Objectives

The aim of this study was to examine the relationship between hypertension and health-related quality of life (HRQoL) adjusted by chronic pain, other chronic diseases, and life habits in the general middle-aged population in Japan.

Methods

This study is a population-based cross-sectional study. In this study, 1117 participants aged 40–65 years and living in Shika Town completed a self-administered questionnaire including Short Form-36 (SF-36). The scores of SF-36 among hypertensives were compared with those of normotensives. The independent association of hypertension with each SF-36 subscale was analyzed using a multiple linear regression model adjusted by age, BMI, chronic pain, chronic diseases, sleep, exercise, and occupational status. We analyzed two groups; Group 1 which contained 846 participants completed the questionnaire without coronary heart disease and cerebral vascular disease, Group 2 which contained 686 participants without coronary heart disease, cerebral vascular disease, or diseases accompanied by chronic pain (gastroduodenal ulcer, fracture, osteoarthritis, osteoporosis, rheumatoid arthritis, and disc herniation).

Results

In Group 2, hypertensive women had a lower general health perception than normotensive women [unstandardized coefficients; B = −8.84, 95 % confidence interval (95 % CI) = −13.3 to −4.34, standardized coefficients; β = −0.200, p < 0.001], whereas hypertensive men had higher social functioning than normotensive men (B = 5.66, 95 % CI = 1.30–10.0, β = 0.149, p < 0.05) after adjusting by chronic pain and life habits.

Conclusions

These results may be due to the sex difference in the light of the perception for health.

Introduction

Hypertension is one of the most common chronic diseases and is a major risk factor for multiple conditions. For example, the development of coronary heart disease (CHD) and cerebral vascular disease (CVD) is a major problem worldwide because around 54 % of strokes and 47 % of CHD cases are attributable to high blood pressure [1]. Hypertension, especially in mild to moderate stages, is usually considered as an asymptomatic condition. Despite the association between hypertension and health-related quality of life (HRQoL) having been focused on for the last several decades and numerous studies having addressed the impact of hypertension, this association is a still controversial issue. The influences of high blood pressure and the awareness of having hypertension require further investigation [2]. Previous studies showed a lower quality of life among subjects with hypertension [3]. By contrast, it has been pointed out that impaired HRQoL in hypertensives may be secondary to the awareness of hypertension and comorbidities not due to hypertension per se [49]. The inconsistent results on the association between hypertension and HRQoL might be due to differences in several factors, such as participants, study design, measures of HRQoL, and other confounders, but it seems unlikely that there is a simple explanation for it.

Chronic pain is also a common and highly prevalent factor that lowers HRQoL [1012], however, this is the first study to adjust for chronic pain while studying the relationship between hypertension and HRQoL in the general population.

Many previous studies demonstrated the relationships of CHD [13, 14], CVD [15, 16], asthma or chronic obstructive pulmonary disease [17, 18], several cancers [19, 20], collagen disease [21], and allergic diseases [22, 23] with HRQoL. In addition, there is strong evidence showing that the onset of hypertension is related to bad habits, namely, physical inactivity, bad dietary habits, smoking, and obesity [24, 25]. Bad habits such as in terms of sleep [26, 27], physical activity [28, 29], smoking [30], alcohol consumption [31], and obesity [3234] also cause lower HRQoL.

To clarify the relationship between hypertension and HRQoL, it is important to analyze subjects without any complications that lower HRQoL apparently and are mostly symptomatic, for example, CVD, CHD, and diseases accompanied by chronic pain. Therefore, we performed a complete survey on the general population in a region in Japan using multidimensional analysis adjusted by chronic pain, other chronic diseases [3, 35, 36] that lower HRQoL, and life habits after excluding subjects with some complications.

Materials and methods

Study design and participants

The study’s design was cross-sectional. It was performed in Shika Town, Ishikawa Prefecture, a rural part of Japan, in October to December 2012. The total population of Shika Town is about 23,000 people, and the rate of the population aged over 65 years is 34.5 % (2011), which is higher than that for the whole of Japan (23.3 %, 2011). On September 1, 2005, Togi Town was merged into this town. Therefore, we selected two model districts, Horimatsu District [n = 2293 (2011)] and Higashimasuho District [n = 1411 (2011)]. These districts almost correspond to the neighborhood association districts of the former Shika Town and the former Togi Town, respectively. Neighborhood associations are voluntary groups whose membership is drawn from a small, geographically delimited, and exclusive residential area (a neighborhood) and whose activities are multiple (for example, waste management, and cultural, sport, and funeral activities) and are centered on that same area [37]. Moreover, the population demographics of these two model districts are similar to that of the whole of Shika Town. A questionnaire was given to all inhabitants aged 40–65 years (n = 1291) who lived in these two model districts, by trained interviewers.

Assessment instruments and definitions

We used a self-administered questionnaire that interviewers who were trained in the use of standardized procedures handed to the participants when visiting them; they explained this study and how to complete the questionnaire. A few days later, they visited them again to collect the completed forms, which were put in a sealed envelope.

Demographics

We collected data on general demographic characteristics, including age, sex, family members, occupational status, type of health insurance, housing type, education, height, and weight. Body mass index (BMI) was calculated from the height and weight and categorized as follows: underweight as BMI <18.5 kg/m2, normal weight as BMI 18.5–24.9 kg/m2, and overweight or obese as BMI ≥25 kg/m2.

Medical history

We asked the participants to circle all diseases that they had suffered from until the present time by using the disease checklist, including no disease history, hypertension, diabetes mellitus, cerebral infarction, cerebral hemorrhage, angina pectoris, cardiac infarction, cancer, gastroduodenal ulcer, pollenosis, bronchial asthma, atopic dermatitis, depression, insomnia, osteoporosis, rheumatoid arthritis, and disc herniation. In addition, we asked about the conditions of any treatment that they had received [physician-diagnosed and taking medication 1, physician-diagnosed and recovering 2, physician-diagnosed or pointed out in a medical check and observation (including diet and exercise therapy) 3, physician-diagnosed and cessation of treatment 4].

Chronic pain, personal lifestyle, and behavior

Chronic pain was defined as pain that was experienced continuously for more than 3 months, or was experienced more than twice a week and had occurred within the last 1 month. We asked, “Do you have pain that has lasted more than 3 months?”, and if participants answered yes, we determined that they had chronic pain. In addition, we asked about the site where they experienced chronic pain (head, neck, shoulder, elbow, hand, back, lower back, hip joint, knee, foot, chest, and abdomen). With regard to exercise, we asked two questions: “How many times do you exercise a week?” and “How many times do you walk for more than 30 min a day per week?” Participants chose 1 option from 5 (every day, 5–6 times a week, 3–4 times a week, 1–2 times a week, and never). The participants also answered questions about their sleep (sleeping soundly, difficulty falling asleep, nocturnal awakening, and sleeping poorly) in the present month and their smoking history.

Short Form-36

MOS Short Form Health Survey (SF-36) is one of the generic instruments for investigating HRQoL; it is a self-administered questionnaire used all over the world [38]. It was translated, adapted, and validated for use in Japan [39, 40]. The Japanese version of SF-36v2 is now available [41]. The SF-36 was divided into eight domains, measuring physical functioning (PF), role limitation due to physical problems (RP), bodily pain (BP), general health perceptions (GH), vitality (VT), social functioning (SF), role limitation due to emotional problems (RE), and mental health (MH). Each domain was scored on a scale of 0–100, where 0 represented the worst overall health status and 100 the best.

Statistical analysis

We used t test and one-way analysis of variance (ANOVA) for continuous variables, and Chi square analysis for categorical variables to analyze the difference between groups of independent variables. Furthermore, we used one-way analysis of covariance (ANCOVA), defining age as a covariate. Multiple linear regression analysis was used to estimate the independent impacts of each variable on the eight SF-36 dimensions stratified by sex. We used independent variables {age, BMI ≥ 25 kg/m2 (no 1, yes 2), hypertension (no 1, yes 2), diabetes mellitus (no 1, yes 2), allergic diseases (no 1, yes 2), cancer (no 1, yes 2), insomnia and depression (no 1, yes 2), chronic pain (no 1, yes 2), occupational status (employed 1, unemployed 2), sleep condition [good (including sleeping soundly) 1, not good (others) 2], and frequency of exercise in a week (more than 1–2 times a week 1, never 2)}. All hypothesis tests were two-sided and p < 0.05 was considered significant. All analyses were performed using SPSS ver. 19.0.

Ethics statement

This study was approved by the ethics committee of Kanazawa University (examination number 1373). All participants provided written informed consent for the study.

Results

Participant characteristics

A flow chart of the participants in this study is shown in Fig. 1. A total of 1117 participants (response rate: 86.5 %) returned the questionnaire and agreed to participate, and 879 participants completed the questionnaire about diseases, BMI, SF-36, and chronic pain. We defined 846 participants as “Group 1” after excluding 33 participants with CVD or CHD. Then, we defined 686 participants as “Group 2” after excluding 160 participants with diseases accompanied by chronic pain (gastroduodenal ulcer, fracture, osteoarthritis, osteoporosis, rheumatoid arthritis, and disc herniation).

Fig. 1
figure1

Flow chart of participants of Shika study in 2012

There were no significant differences in the general characteristics and prevalence of disease between Group 1 and Group 2 (Table 1). In Group 2, mean age was 54.2 years old and 54.2 % were women. In addition, 25.2 % of the men and 20.4 % of the women reported hypertension.

Table 1 General characteristics of participants in Shika study, 2012

Relationship between participant characteristics, diseases, and HRQoL adjusted by age

Table 2 displays the relationships between participant characteristics and the scores of SF-36 in Group 2. Men and women aged 60–65 had higher VT and MH than those aged 40–49. After adjusting by age, men who were overweight or obese had significantly lower PF than those with normal BMI. Although men who were underweight were very few in number (n = 6), they had significantly lower RP, GH, VT, SF, RE, and MH than those who had a normal BMI or were overweight. Participants with chronic pain and those who slept poorly had significantly lower scores for almost all of SF-36. Men who had a habit of exercising more than 1–2 times a week scored higher on PF, VT, SF, and MH than those who did not exercise. Finally, the unemployed had significantly lower RP, SF, and RE in men, and lower PF, RP, and GH in women.

Table 2 The relationships between characteristics of participants and the scores of SF-36 among participants stratified by sex

Participants with no disease showed significantly higher HRQoL, whereas those with a disease showed lower HRQoL in both Group 1 and Group 2 (Table 3). Hypertensive women in Group 1 showed lower GH and VT than normotensive women after adjusting by age. In addition, hypertensive women showed lower PF and BP in Group 2. An exception to this relationship was recognized in hypertensive men in Group 2. In Group 1, there was no difference in the scores of SF-36 subscales between hypertensive men and normotensive men after adjusting by age. In Group 2, however, hypertensive men were associated with higher SF.

Table 3 The relationships between diseases and the scores of SF-36 among participants stratified by group and sex

Relationship between hypertension and HRQoL by multiple regression analysis

We performed multiple regression analysis in Group 2 adjusting by age, BMI, chronic diseases, chronic pain, occupational status, sleep condition, and exercise (Tables 4, 5, 6, 7). The SF of hypertensive men was significantly higher than that of normotensive men, whereas the GH of hypertensive women was significantly lower than that of normotensive women. Overweight was associated with lower PF, BP, SF, and RE only among men. There were strong relationships between chronic pain and lower HRQoL among men, while there were strong relationships between sleep condition and lower HRQoL among women. Poor sleep was associated with lower scores of all SF-36 subscales in women, whereas it was related to lower BP, GH, VT, and MH in men. In women with chronic pain, lower BP, GH, and VT were prevalent. On the other hand, men with chronic pain were associated with lower values for almost all SF-36 subscales (PF, RP, BP, GH, VT, and MH). Lower BP was more strongly related to chronic pain (B = −0.148, 95 % CI = −25.0 to −14.2, β = −0.385 in men, B = −22.6, 95 % CI = −27.2 to −18.1, β = −0.469 in women) than the other SF-36 subscales.

Table 4 Results of multiple linear regression analysis of the SF-36 scores: physical functioning (PF), role physical (RP), bodily pain (BP), and general health (GH), among men of Group 2
Table 5 Results of multiple linear regression analysis of the SF-36 scores: vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH) among men of Group 2
Table 6 Results of multiple linear regression analysis of the SF-36 scores: physical functioning (PF), role physical (RP), bodily pain (BP), and general health (GH), among women of Group 2
Table 7 Results of multiple linear regression analysis of the SF-36 scores: vitality (VT), social functioning (SF), role emotional (RE), and mental health (MH) among women of Group 2

Discussion

Hypertension was found to be associated with lower general health perceptions in women, whereas it was associated with higher social functioning in men. This study seems to be the first to analyze the relationship between hypertension and HRQoL adjusted by chronic pain. Chronic pain was previously shown to be associated with worse HRQoL [1012, 42]. Since about 30 % of the participants in this study had chronic pain, we could not ignore its influence on the relationship between hypertension and HRQoL, although hypertensives did not have a significantly higher prevalence of chronic pain than normotensives (data not shown). This study clarified the more precise relationship between hypertension and HRQoL.

Many previous studies have shown that hypertension is relevant not only to a worse general health perceptions but also to worse scores of other subscales of SF-36 [5, 43]. Inconsistency between studies might have resulted from differences in the study populations and the factors used to adjust their relationships. Our results coincide with the finding that women generally report worse health and HRQoL than men do [43]. In addition, hypertensive women perceive a lower health status than hypertensive men do [44, 45]. We assume that, for middle-aged women, hypertension is one of the first lifestyle-related diseases that they experience, which is subsequently suffered for a long time. These assumptions may account for our results showing that hypertensive women had lower general health perceptions than normotensive women.

This study indicated that hypertensive men showed better social functioning than normotensive men. The reason why hypertensive men showed better social functioning than normotensive men was unclear. In contrast to physical and mental health concepts that tend to “end at the skin” [46], the social functioning scale extends measurement beyond the individual to capture both the quantity and the quality of social activities with others [41]. Perhaps, personality is related to better social functioning. Some studies have shown the association between hypertension and Type A personality because such a personality is considered to be involved in aggressive and competitive interpersonal interactions and dominant behavior [47, 48]. Hozawa et al. reported that sociable and cheerful subjects have higher home systolic blood pressure [49]. This may explain our results showing that hypertensive men had better social functioning than normotensive men.

However, we had only subjective data about medical history: participants who indicated, “I have high blood pressure and am under medication” accounted for 83.7 % of all hypertensive participants in this study. If their hypertension control and adherence to hypertensive medication were good, the result that hypertension did not prevent their involvement in social activities was considered to be valid. As we did not ask about the extent of hypertension control and adherence to medication, we could not draw inferences on these relationships.

In this study, an overweight or obese status was related to worse physical functioning, bodily pain, social functioning, and role limitation due to emotional problems in men, but not in women. The results showed that physical health (physical functioning, bodily pain) and mental health (social functioning, role emotional) were affected. Overweight or obese subjects were correlated with impaired physical HRQoL but not with impaired mental HRQoL [32, 33, 50, 51]. However, mental HRQoL was most impaired at the extremes of BMI and this impairment was comparable to the impairment due to certain chronic conditions [52]. In this study, we decided to use BMI of 25 kg/m2 as a threshold for two categories, although the WHO definition of obesity is BMI ≥30 kg/m2 in Western countries. Since the extent of the influence of obesity on health differs among ethnic groups, the WHO has proposed alternative cut-offs for Asians (a BMI cut-off of 23 as overweight and 25 as obese), and the Japan Society for the Study of Obesity set BMI ≥25 as obesity in Japan. If the number of severely obese participants were to increase, the result may become similar to those in previous studies on obesity and HRQoL.

Chronic pain and poor sleep were associated with worse physical and mental HRQoL. In particular, chronic pain seemed to have a strong influence on HRQoL in men, whereas sleep condition was associated with lower HRQoL in women.

Other studies indicated that poor sleepers complained of worse QoL than good sleepers [26, 27, 53]. These results are consistent with this study. With regard to sex differences in sleep, women had better sleep quality than men, whereas women had more sleep-related complaints than men, and menstrual cycles, pregnancy, and menopause were considered to alter the sleep architecture [54]. Since this study was based on subjective data and women were found to be more sensitive to poor sleep, our results suggest stronger associations between HRQoL and sleep condition among women than among men.

In recent years, sex differences in pain have been a topic attracting increased interest. Women were found to be at increased risk of chronic pain and they seemed to experience more severe clinical pain. Multiple biopsychosocial mechanisms contribute to these sex differences in pain, including sex hormones, endogenous opioid function, genetic factors, pain coping and catastrophizing, as well as gender roles [55]. On the basis of these considerations, we suppose that our results reflect that men reported their pain less than women and the influence of pain on their HRQoL seemed to be greater than for women when men reported their pain.

A main strength of our study is that we obtained a high response rate in this survey from a community-based sample in the Japanese population. In addition, we conducted the study in the entirety of the survey areas, depending on address. Therefore, we could minimize selection bias, along with CVD, CHD, and diseases accompanied by chronic pain being excluded, followed by sex stratification. This seems to be the first study to analyze the relationship between hypertension and HRQoL adjusted by chronic pain as an important confounder.

However, our study had some limitations. Since it had a cross-sectional design, we cannot directly infer causal relationships. Second, our data were based only on self-reports, not objective data like medical records and blood tests. Third, we summarized the presence of chronic pain as a yes/no variable, and did not consider the intensity of the chronic pain or its location. To demonstrate causal relationships, we have to perform longitudinal research with data based on objective diagnoses.

Conclusion

After adjusting by chronic pain and life habits, hypertension itself was associated with lower HRQoL, especially general health perceptions, in women, whereas hypertension was associated with higher social functioning in men. These results may be due to the sex difference in the light of the perception for health.

References

  1. 1.

    Arima H, Barzi F, Chalmers J. Mortality patterns in hypertension. J Hypertens. 2011;29 Suppl:S3–7.

    Article  Google Scholar 

  2. 2.

    Trevisol DJ, Moreira LB, Kerkhoff A, Fuchs SC, Fuchs FD. Health-related quality of life and hypertension: a systematic review and meta-analysis of observational studies. J Hypertens. 2011;29:179–88.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Alonso J, Ferrer M, Gandek B, Ware JE Jr, Aaronson NK, Mosconi P, et al. Health-related quality of life associated with chronic conditions in eight countries: results from the International Quality of Life Assessment (IQOLA) Project. Qual Life Res. 2004;13:283–98.

    Article  PubMed  Google Scholar 

  4. 4.

    Vathesatogkit P, Sritara P, Kimman M, Hengprasith B, E-Shyong T, Wee HL, et al. Associations of lifestyle factors, disease history and awareness with health-related quality of life in a Thai population. PLoS One. 2012;7:e49921.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Wang R, Zhao Y, He X, Ma X, Yan X, Sun Y, et al. Impact of hypertension on health-related quality of life in a population-based study in Shanghai, China. Public Health. 2009;123:534–9.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Tapp RJ, Dunstan DW, Phillips P, Tonkin A, Zimmet PZ, Shaw JE. Association between impaired glucose metabolism and quality of life: results from the Australian diabetes obesity and lifestyle study. Diabetes Res Clin Pract. 2006;74:154–61.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Korhonen PE, Kivela SL, Kautiainen H, Jarvenpaa S, Kantola I. Health-related quality of life and awareness of hypertension. J Hypertens. 2011;29:2070–4.

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Aydemir O, Ozdemir C, Koroglu E. The impact of co-morbid conditions on the SF-36: a primary-care-based study among hypertensives. Arch Med Res. 2005;36:136–41.

    Article  PubMed  Google Scholar 

  9. 9.

    Mena Martin FJ, Martin Escudero JC, Simal Blanco F, Carretero Ares JL, Arzua Mouronte D, Herreros Fernandez V. Health-related quality of life of subjects with known and unknown hypertension: results from the population-based Hortega study. J Hypertens. 2003;21:1283–9.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Abu Bakar N, Tanprawate S, Lambru G, Torkamani M, Jahanshahi M, Matharu M. Quality of life in primary headache disorders: A review. Cephalalgia. 2015. pii:0333102415580099. [Epub ahead of print].

  11. 11.

    Langley PC. The prevalence, correlates and treatment of pain in the European Union. Curr Med Res Opin. 2011;27:463–80.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    McDermott AM, Toelle TR, Rowbotham DJ, Schaefer CP, Dukes EM. The burden of neuropathic pain: results from a cross-sectional survey. Eur J Pain. 2006;10:127–35.

    Article  PubMed  Google Scholar 

  13. 13.

    Norris CM, Ghali WA, Galbraith PD, Graham MM, Jensen LA, Knudtson ML. Women with coronary artery disease report worse health-related quality of life outcomes compared to men. Health Qual Life Outcomes. 2004;2:21.

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Oldridge NB, Stump TE, Nothwehr FK, Clark DO. Prevalence and outcomes of comorbid metabolic and cardiovascular conditions in middle- and older-age adults. J Clin Epidemiol. 2001;54:928–34.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Xie J, Wu EQ, Zheng ZJ, Croft JB, Greenlund KJ, Mensah GA, et al. Impact of stroke on health-related quality of life in the noninstitutionalized population in the United States. Stroke. 2006;37:2567–72.

    Article  PubMed  Google Scholar 

  16. 16.

    van Wijk I, Gorter JW, Lindeman E, Kappelle LJ, van Gijn J, Koudstaal PJ, et al. Mental status and health-related quality of life in an elderly population 15 years after limited cerebral ischaemia. J Neurol. 2007;254:1018–25.

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Sprenkle MD, Niewoehner DE, Nelson DB, Nichol KL. The Veterans Short Form 36 questionnaire is predictive of mortality and health-care utilization in a population of veterans with a self-reported diagnosis of asthma or COPD. Chest. 2004;126:81–9.

    Article  PubMed  Google Scholar 

  18. 18.

    Domingo Salvany A, Lamarca R, Ferrer M, Garcia Aymerich J, Alonso J, Felez M, et al. Health-related quality of life and mortality in male patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med. 2002;166:680–5.

    Article  PubMed  Google Scholar 

  19. 19.

    Jones GL, Ledger W, Bonnett TJ, Radley S, Parkinson N, Kennedy SH. The impact of treatment for gynecological cancer on health-related quality of life (HRQoL): a systematic review. Am J Obstet Gynecol. 2006;194:26–42.

    Article  PubMed  Google Scholar 

  20. 20.

    Saad ED, Adamowicz K, Katz A, Jassem J. Assessment of quality of life in advanced non-small-cell lung cancer: an overview of recent randomized trials. Cancer Treat Rev. 2012;38:807–14.

    Article  PubMed  Google Scholar 

  21. 21.

    Hurst NP, Kind P, Ruta D, Hunter M, Stubbings A. Measuring health-related quality of life in rheumatoid arthritis: validity, responsiveness and reliability of EuroQol (EQ-5D). Br J Rheumatol. 1997;36:551–9.

    CAS  Article  PubMed  Google Scholar 

  22. 22.

    Tripathi A, Patterson R. Impact of allergic rhinitis treatment on quality of life. Pharmacoeconomics. 2001;19:891–9.

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Maksimovic N, Jankovic S, Marinkovic J, Sekulovic LK, Zivkovic Z, Spiric VT. Health-related quality of life in patients with atopic dermatitis. J Dermatol. 2012;39:42–7.

    Article  PubMed  Google Scholar 

  24. 24.

    Yates T, Haffner SM, Schulte PJ, Thomas L, Huffman KM, Bales CW, et al. Association between change in daily ambulatory activity and cardiovascular events in people with impaired glucose tolerance (NAVIGATOR trial): a cohort analysis. Lancet. 2014;383(9922):1059–66.

    Article  PubMed  Google Scholar 

  25. 25.

    Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–29.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Leger D, Morin CM, Uchiyama M, Hakimi Z, Cure S, Walsh JK. Chronic insomnia, quality-of-life, and utility scores: comparison with good sleepers in a cross-sectional international survey. Sleep Med. 2012;13:43–51.

    Article  PubMed  Google Scholar 

  27. 27.

    Sasai T, Inoue Y, Komada Y, Nomura T, Matsuura M, Matsushima E. Effects of insomnia and sleep medication on health-related quality of life. Sleep Med. 2010;11:452–7.

    Article  PubMed  Google Scholar 

  28. 28.

    Heesch KC, van Uffelen JG, van Gellecum YR, Brown WJ. Dose-response relationships between physical activity, walking and health-related quality of life in mid-age and older women. J Epidemiol Community Health. 2012;66:670–7.

    Article  PubMed  Google Scholar 

  29. 29.

    Blacklock RE, Rhodes RE, Brown SG. Relationship between regular walking, physical activity, and health-related quality of life. J Phys Act Health. 2007;4:138–52.

    PubMed  Google Scholar 

  30. 30.

    Funahashi K, Takahashi I, Danjo K, Matsuzaka M, Umeda T, Nakaji S. Smoking habits and health-related quality of life in a rural Japanese population. Qual Life Res. 2011;20:199–204.

    Article  PubMed  Google Scholar 

  31. 31.

    Saito I, Okamura T, Fukuhara S, Tanaka T, Suzukamo Y, Okayama A, et al. A cross-sectional study of alcohol drinking and health-related quality of life among male workers in Japan. J Occup Health. 2005;47:496–503.

    Article  PubMed  Google Scholar 

  32. 32.

    Takahashi Y, Sakai M, Tokuda Y, Takahashi O, Ohde S, Nakayama T, et al. The relation between self-reported body weight and health-related quality of life: a cross-sectional study in Japan. J Public Health (Oxf). 2011;33:518–26.

    Article  Google Scholar 

  33. 33.

    Korhonen PE, Seppala T, Jarvenpaa S, Kautiainen H. Body mass index and health-related quality of life in apparently healthy individuals. Qual Life Res. 2014;23(1):67–74.

    Article  PubMed  Google Scholar 

  34. 34.

    Larsson U, Karlsson J, Sullivan M. Impact of overweight and obesity on health-related quality of life—a Swedish population study. Int J Obes Relat Metab Disord. 2002;26:417–24.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Saito I, Inami F, Ikebe T, Moriwaki C, Tsubakimoto A, Yonemasu K, et al. Impact of diabetes on health-related quality of life in a population study in Japan. Diabetes Res Clin Pract. 2006;73:51–7.

    Article  PubMed  Google Scholar 

  36. 36.

    Vaatainen S, Keinanen Kiukaanniemi S, Saramies J, Uusitalo H, Tuomilehto J, Martikainen J. Quality of life along the diabetes continuum: a cross-sectional view of health-related quality of life and general health status in middle-aged and older Finns. Qual Life Res. 2014;23(7):1935–44.

    Article  PubMed  Google Scholar 

  37. 37.

    Pekkanen R. Japan’s Dual Civil Society—members without advocates. Stanford, Stanford University Press. 2006.

  38. 38.

    Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30:473–83.

    Article  PubMed  Google Scholar 

  39. 39.

    Fukuhara S, Bito S, Green J, Hsiao A, Kurokawa K. Translation, adaptation, and validation of the SF-36 Health Survey for use in Japan. J Clin Epidemiol. 1998;51:1037–44.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Fukuhara S, Ware JE Jr, Kosinski M, Wada S, Gandek B. Psychometric and clinical tests of validity of the Japanese SF-36 Health Survey. J Clin Epidemiol. 1998;51:1045–53.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Fukuhara S, Suzukamo Y. Manual of SF-36v2 Japanese version. Kyoto: Institute for Health Outcomes and Process Evaluation Research; 2004.

    Google Scholar 

  42. 42.

    Nakamura M, Nishiwaki Y, Ushida T, Toyama Y. Prevalence and characteristics of chronic musculoskeletal pain in Japan. J Orthop Sci. 2011;16:424–32.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Bardage C, Isacson DG. Hypertension and health-related quality of life. An epidemiological study in Sweden. J Clin Epidemiol. 2001;54:172–81.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Krousel Wood MA, Re RN. Health status assessment in a hypertension section of an internal medicine clinic. Am J Med Sci. 1994;308:211–7.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Kaplan SH. Patient reports of health status as predictors of physiologic health measures in chronic disease. J Chronic Dis. 1987;40 Suppl:27S–40S.

    Article  Google Scholar 

  46. 46.

    Ware JE. The assessment of health status. In: Aiken LH, Mechanic D, editors. Applications of social sciences to clinical medicine and health policy. New Brunswick: Rutgers University; 1986. p. 204–28.

    Google Scholar 

  47. 47.

    Davies M. Blood pressure and personality. J Psychosom Res. 1970;14:89–104.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Harrell JP. Psychological factors and hypertension: a status report. Psychol Bull. 1980;87:482–501.

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Hozawa A, Ohkubo T, Tsuji I, Kikuya M, Matsubara M, Suzuki T, et al. Relationship between personality and self-measured blood pressure value at home: the Ohasama study. Clin Exp Hypertens. 2002;24:115–23.

    Article  PubMed  Google Scholar 

  50. 50.

    Wee HL, Wu Y, Thumboo J, Lee J, Tai ES. Association of body mass index with Short-Form 36 physical and mental component summary scores in a multiethnic Asian population. Int J Obes (Lond). 2010;34:1034–43.

    Article  Google Scholar 

  51. 51.

    Huang IC, Frangakis C, Wu AW. The relationship of excess body weight and health-related quality of life: evidence from a population study in Taiwan. Int J Obes (Lond). 2006;30:1250–9.

    Article  Google Scholar 

  52. 52.

    Jia H, Lubetkin EI. The impact of obesity on health-related quality-of-life in the general adult US population. J Public Health (Oxf). 2005;27:156–64.

    Article  Google Scholar 

  53. 53.

    Leger D, Scheuermaier K, Philip P, Paillard M, Guilleminault C. SF-36: evaluation of quality of life in severe and mild insomniacs compared with good sleepers. Psychosom Med. 2001;63:49–55.

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Krishnan V, Collop NA. Gender differences in sleep disorders. Curr Opin Pulm Med. 2006;12:383–9.

    Article  PubMed  Google Scholar 

  55. 55.

    Bartley EJ, Fillingim RB. Sex differences in pain: a brief review of clinical and experimental findings. Br J Anaesth. 2013;111:52–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This study was supported by a Health Labour Sciences Research Grant for “Study on elucidation of the actual situation of intractable pain and development of countermeasures” (Principal Researcher: Takahiro Ushida, 2011–2012) and the fund for Priority Research Systems from Kanazawa University (No. 22,608) 2011–2012. We would like to thank the staff of the Health and Welfare Center of Shika Town; Norio Fujisawa, the chief of this section; a Health and Labour Sciences Research Grant; and the staff of the Department of Environmental and Preventive Medicine, Kanazawa University Graduate School of Medical Sciences.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nakamura Hiroyuki.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kitaoka, M., Mitoma, J., Asakura, H. et al. The relationship between hypertension and health-related quality of life: adjusted by chronic pain, chronic diseases, and life habits in the general middle-aged population in Japan. Environ Health Prev Med 21, 193–214 (2016). https://doi.org/10.1007/s12199-016-0514-6

Download citation

Keywords

  • Health-related quality of life
  • Hypertension
  • Chronic pain
  • Sex difference