ObjectivesThe My Positive Health (MPH) dialogue tool is increasingly adopted by healthcare professionals in the Netherlands as well as abroad to support people in their health. Given this trend, the need arises to measure effects of interventions on the Positive Health dimensions. However, the dialogue tool was not developed for this purpose. Therefore, this study aims to work towards a suitable measurement scale using the MPH dialogue tool as starting point.DesignA cross-sectional study design.Participants and settingsA total of 708 respondents, who were all members of the municipal health service panel in the eastern part of the Netherlands, completed the MPH dialogue tool.MethodsThe factor structure of the MPH dialogue tool was explored through exploratory factor analysis using maximum likelihood extraction. Next, the fit of the extracted factor structure was tested through confirmatory factor analysis. Reliability and discriminant validity of both a new model and the MPH scales were assessed through Cronbach’s alpha tests.ResultsSimilar to the MPH dialogue tool, the extracted 17-item model has a six-factor structure but named differently, comprising the factors physical fitness, mental functions, future perspectives, contentment, social relations and health management. The reliability tests suggest good to very good reliability of the aimed measurement tool and MPH model (Cronbach’s alpha values ranging from, respectively, 0.820 to 0.920 and 0.882 to 0.933). The measurement model shows acceptable discriminant validity, whereas the MPH model suggests overlap between domains.ConclusionThe results suggest that the current MPH dialogue tool seems reliable as a dialogue, but it is not suitable as a measurement scale. We therefore propose a 17-item model with improved, acceptable psychometric properties which can serve as a basis for further development of a measurement scale.

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Self-rated health (SRH), individuals’ overall perception of their health, is a key predictor of health events. To target disease prevention efforts, it is important to understand how SRH develops over time. The goal of this short communication is to find prototypic SRH trajectories by applying dynamic time warping, a time series comparison technique initially developed for speech recognition. Revealing prototypic SRH trajectories can help direct disease prevention efforts towards trajectories that are more likely to result in adverse health events. Based on data from a Dutch representative sample of 2,154 individuals, our dynamic time warp analysis suggests that Dutch individuals do not typically show a steady growth or decline in SRH. Instead, we identified four relatively stable SRH trajectories that differed in average SRH. One of these trajectories is a path of consistent low SRH.