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Patient factors that influence clinicians' decision making in self-management support: a clinical vignette study

Background and aim Self-management support is an integral part of current chronic care guidelines. The success of self-management interventions varies between individual patients, suggesting a need for tailored self-management support. Understanding the role of patient factors in the current decision making of health professionals can support future tailoring of self-management interventions. The aim of this study is to identify the relative importance of patient factors in health professionals’ decision making regarding self-management support. Method A factorial survey was presented to primary care physicians and nurses. The survey consisted of clinical vignettes (case descriptions), in which 11 patient factors were systematically varied. Each care provider received a set of 12 vignettes. For each vignette, they decided whether they would give this patient self-management support and whether they expected this support to be successful. The associations between respondent decisions and patient factors were explored using ordered logit regression. Results The survey was completed by 60 general practitioners and 80 nurses. Self-management support was unlikely to be provided in a third of the vignettes. The most important patient factor in the decision to provide self-management support as well as in the expectation that self-management support would be successful was motivation, followed by patient-provider relationship and illness perception. Other factors, such as depression or anxiety, education level, self-efficacy and social support, had a small impact on decisions. Disease, disease severity, knowledge of disease, and age were relatively unimportant factors. Conclusion This is the first study to explore the relative importance of patient factors in decision making and the expectations regarding the provision of self-management support to chronic disease patients. By far, the most important factor considered was patient’s motivation; unmotivated patients were less likely to receive self-management support. Future tailored interventions should incorporate strategies to enhance motivation in unmotivated patients. Furthermore, care providers should be better equipped to promote motivational change in their patients.

Car use: a matter of dependency or choice? The case of commuting in Noord-Brabant.

Car use in the sprawled urban region of Noord‐Brabant is above the Dutch average. Does this reflect car dependency due to the lack of competitive alternative modes? Or are there other factors at play, such as differences in preferences? This article aims to determine the nature of car use in the region and explore to what extent this reflects car dependency. The data, comprising 3,244 respondents was derived from two online questionnaires among employees from the High‐Tech Campus (2018) and the TU/e‐campus (2019) in Eindhoven. Travel times to work by car, public transport, cycling, and walking were calculated based on the respondents’ residential location. Indicators for car dependency were developed using thresholds for maximum commuting times by bicycle and maximum travel time ratios between public transport and car. Based on these thresholds, approximately 40% of the respondents were categorised as car‐dependent. Of the non‐car‐dependent respondents, 31% use the car for commuting. A binomial logit model revealed that higher residential densities and closer proximity to a railway station reduce the odds of car commuting. Travel time ratios also have a significant influence on the expected directions. Mode choice preferences (e.g., comfort, flexibility, etc.) also have a significant, and strong, impact. These results highlight the importance of combining hard (e.g., improvements in infrastructure or public transport provi-sion) and soft (information and persuasion) measures to reduce car use and car dependency in commuting trips.

Predicting Electric Vehicle Charging Demand using Mixed Generalized Extreme Value Models with Panel Effects

In the past 5 years Electric Car use has grown rapidly, almost doubling each year. To provide adequate charging infrastructure it is necessary to model the demand. In this paper we model the distribution of charging demand in the city of Amsterdam using a Cross-Nested Logit Model with socio-demographic statistics of neighborhoods and charging history of vehicles. Models are obtained for three user-types: regular users, electric car-share participants and taxis. Regular users are later split into three subgroups based on their charging behaviour throughout the day: Visitors, Commuters and Residents

Towards a zero emission city logistics maturity model.

With the approach of the zero emission zone implementation in 30-40 cities mandated by the Dutch Klimaatakkord, comes the need to determine whether the SMEs located within these zones are aware of the coming changes and if they are, how far they have come in their preparation. This paper delves into the development of the zero emission city logistics maturity model tool which is used to indicate the progress of these small to medium enterprises in light of reaching fully zero emission city logistics operations. The paper starts off with a review of existing maturity models which forms the baseline for the zero emission city logistics maturity model in rubric form. A QuickScan analysis is developed in order to facilitate data collection by students who then approach businesses and use the QuickScan results to benchmark the businesses progress against other businesses. This paper then concludes with the preliminary results from the initial QuickScans performed by HBO level students.

Publieke inkoop en logistiek

Het project Publieke Inkoop en Logistiek van het Onderzoeksprogramma Urban Technology loopt 1 jaar. Een mooi moment voor een terug- en vooruitblik. Wat hebben we gedaan? Met welke resultaten? En hoe gaan we verder?

Correlates of tourist vacation behavior: a combination of CHAID and Loglinear Logit Analysis