Relationships among quality of life, severity, and control measures in asthma: an evaluation using factor analysis

J Allergy Clin Immunol. 2005 May;115(5):1049-55. doi: 10.1016/j.jaci.2005.02.008.


Background: Validated psychometric tools measuring quality of life, asthma control, and asthma severity have been developed, but their relationships with each other and with other important patient-centered outcomes have not been rigorously assessed.

Objective: To use factor analysis to evaluate the relationships of these validated tools with each other and with other patient-centered outcomes.

Methods: Surveys were completed by a random sample of 2854 Health Maintenance Organization members age 18 to 56 years with persistent asthma. Surveys included demographic information; validated tools measuring generic (Short Form-12; SF-12) and asthma-specific (Juniper Mini Asthma Quality of Life Questionnaire; AQLQ) quality of life, asthma control (Asthma Therapy Assessment Questionnaire), and asthma symptom severity (Asthma Outcomes Monitoring System); self-described severity, control, and course over time; and history of acute exacerbations.

Results: Principal component analysis suggested a 5-factor model that accounted for approximately 59% of the variability. The most prominent rotated factor reflected asthma symptom frequency (19.4% of variability), was measured by the symptom subscale of the AQLQ, and was the only factor significantly related to the Asthma Therapy Assessment Questionnaire, Asthma Outcomes Monitoring System, or the self-reported assessments of severity, control, or course. Other factors included symptom bother (12.1% of variability), reflected by the environment and emotion AQLQ subscales; activity limitation (13.9% of variability), reflected by the activity AQLQ subscale and the SF-12 physical component scale; mental health (8.3% of variability), reflected by the SF-12 mental component scale; and acute exacerbations (5.0% of variability), not measured by any of the validated scales.

Conclusion: Distinct components of patient-reported asthma health status can be identified by factor analysis. Distinct constructs of severity versus control cannot be identified by the use of these tools alone.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Asthma / prevention & control*
  • California
  • Emotions
  • Environmental Exposure
  • Factor Analysis, Statistical
  • Female
  • Health Status Indicators
  • Humans
  • Male
  • Middle Aged
  • Quality of Life*
  • Risk Factors
  • Sickness Impact Profile
  • Surveys and Questionnaires