Self-report adherence measures in chronic illness: retest reliability and predictive validity
- PMID: 18953223
- DOI: 10.1097/MLR.0b013e31817924e4
Self-report adherence measures in chronic illness: retest reliability and predictive validity
Abstract
Background: Patient self-report is a practical method for measuring adherence, but little is known about its optimal use.
Objectives: To examine the retest reliability and predictive validity of 3 different types of self-report adherence measures among patients with common chronic illnesses.
Research design: Correlation and regression analyses of data from an ongoing randomized controlled trial.
Subjects: Patients (N = 415) aged >or=40 years recruited from a primary care network with arthritis, asthma, chronic lung disease, congestive heart failure, depression, and/or diabetes mellitus, plus impairment in >or=1 basic activity and/or a score of >or=4 on the 10-item Center for Epidemiologic Studies Depression Scale.
Measures: : Self-report adherence (administered variously at baseline, 2, 4, and 6 weeks, and 6 months): number of pills taken/number of pills prescribed (PT/PP), using 1-7 days recall, and global reports of medication adherence and overall adherence tendencies. Six-month functional outcomes: Health Assessment Questionnaire (HAQ) and Short Form-36 (SF-36).
Results: Correlation coefficients among contemporaneously administered 1-7 days PT/PP measures were >or=0.78. Correlations among PT/PP measures and global adherence measures, and among PT/PP measures at 2 and 4 weeks, ranged from 0.11 to 0.54. PT/PP measures using >or=3-4 days recall significantly predicted adjusted 6-month HAQ but not SF-36 score.
Conclusions: Self-report PT/PP and general medication adherence measures tap different behavioral constructs. Self-reported PT/PP at a given point in time is not necessarily representative of medication adherence over time. Among chronically ill patients, 3-4 days recall of PT/PP yield adherence estimates, which are practically as reliable and valid as longer intervals and which predict functional outcomes.
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