Characteristics of female and male visitors to practitioners of acupuncture in the HUNT3 Study

Am J Chin Med. 2013;41(5):995-1010. doi: 10.1142/S0192415X13500675.

Abstract

Characteristics of female and male visitors to practitioners of acupuncture were investigated in a large cross-sectional adult population in Central Norway. A total population health survey, HUNT3, conducted in 2008 with 50,827 respondents provided the data. Demographic variables, lifestyle, health, and use of conventional medicine were analyzed using multivariable logistic regression models. The one year prevalence of visiting a practitioner of acupuncture was 5.7% for females and 2.2% for males. Visitors of both genders were five times more likely to have had somatic complaints in the preceding year and were 2-3 times more likely to report poor global health than female or male non-visitors. Also, visitors of both genders were more likely to do hard physical activities every week, and they were less likely to live alone or be daily smokers. Further, female visitors were characterized by having higher education and were more likely to have a paid job than other females. Corresponding differences were not seen among males. Age showed limited associations with being a visitor, and for females only. Valid for both genders, our findings draw a picture of visitors to acupuncture treatment as persons who actively contribute to promoting their health through lifestyle choices of physical activity and non-smoking while simultaneously having worse global health and higher burdens of somatic complaints than other adults. In contrast to males, it is suggested that females may be more dependent on personal income, as indicated by higher education and being in a paid job, in choosing acupuncture treatment in addition to conventional medicine.

MeSH terms

  • Acupuncture Therapy / statistics & numerical data*
  • Ambulatory Care / statistics & numerical data*
  • Cross-Sectional Studies
  • Female
  • Health Personnel / statistics & numerical data*
  • Health Surveys / statistics & numerical data*
  • Humans
  • Income
  • Life Style
  • Logistic Models
  • Male
  • Motor Activity
  • Multivariate Analysis
  • Norway / epidemiology
  • Prevalence
  • Sex Factors
  • Smoking
  • Time Factors