title: The development of the ProMAS: a Probabilistic Medication Adherence Scale.
abstract: Current self-report medication adherence measures often provide heavily skewed results with limited variance, suggesting that most participants are highly adherent. This contrasts with findings from objective adherence measures. We argue that one of the main limitations of these self-report measures is the limited range covered by the behaviors assessed. That is, the items do not match the adherence behaviors that people perform, resulting in a ceiling effect. In this paper, we present a new self-reported medication adherence scale based on the Rasch model approach (the ProMAS), which covers a wide range of adherence behaviors. The ProMAS was tested with 370 elderly receiving medication for chronic conditions. The results indicated that the ProMAS provided adherence scores with sufficient fit to the Rasch model. Furthermore, the ProMAS covered a wider range of adherence behaviors compared to the widely used Medication Adherence Report Scale (MARS) instrument, resulting in more variance and less skewness in adherence scores. We conclude that the ProMAS is more capable of discriminating between people with different adherence rates than the MARS.
authors.affiliation: Human Technology Interaction, Eindhoven University of Technology, Eindhoven, the Netherlands ; Behavior, Cognition and Perception, Philips Research, Eindhoven, the Netherlands.
authors.forename: Mieke
authors.initials: M
authors.lastname: Kleppe
authors.affiliation: Behavior, Cognition and Perception, Philips Research, Eindhoven, the Netherlands.
authors.forename: Joyca
authors.initials: J
authors.lastname: Lacroix
authors.affiliation: Human Technology Interaction, Eindhoven University of Technology, Eindhoven, the Netherlands.
authors.forename: Jaap
authors.initials: J
authors.lastname: Ham
authors.affiliation: Human Technology Interaction, Eindhoven University of Technology, Eindhoven, the Netherlands.
authors.forename: Cees
authors.initials: C
authors.lastname: Midden
keywords: methodology; probabilistic models; questionnaire design