How to use machine learning and fuzzy cognitive maps to test hypothetical scenarios in health behavior change interventions: a case study on fruit intake.

BACKGROUND: Intervention planners use logic models to design evidence-based health behavior interventions. Logic models that capture the complexity of health behavior necessitate additional computational techniques to inform decisions with respect to the design of interventions. OBJECTIVE: Using empirical data from a real intervention, the present paper demonstrates how machine learning can be used together with fuzzy cognitive maps to assist in designing health behavior change interventions. METHODS: A modified Real Coded Genetic algorithm was applied on longitudinal data from a real intervention study. The dataset contained information about 15 determinants of fruit intake among 257 adults in the Netherlands. Fuzzy cognitive maps were used to analyze the effect of two hypothetical intervention scenarios designed by domain experts. RESULTS: Simulations showed that the specified hypothetical interventions would have small impact on fruit intake. The results are consistent with the empirical evidence used in this paper. CONCLUSIONS: Machine learning together with fuzzy cognitive maps can assist in building health behavior interventions with complex logic models. The testing of hypothetical scenarios may help interventionists finetune the intervention components thus increasing their potential effectiveness.

FCMpy: a python module for constructing and analyzing fuzzy cognitive maps.

FCMpy is an open-source Python module for building and analyzing Fuzzy Cognitive Maps (FCMs). The module provides tools for end-to-end projects involving FCMs. It is able to derive fuzzy causal weights from qualitative data or simulating the system behavior. Additionally, it includes machine learning algorithms (e.g., Nonlinear Hebbian Learning, Active Hebbian Learning, Genetic Algorithms, and Deterministic Learning) to adjust the FCM causal weight matrix and to solve classification problems. Finally, users can easily implement scenario analysis by simulating hypothetical interventions (i.e., analyzing what-if scenarios). FCMpy is the first open-source module that contains all the functionalities necessary for FCM oriented projects. This work aims to enable researchers from different areas, such as psychology, cognitive science, or engineering, to easily and efficiently develop and test their FCM models without the need for extensive programming knowledge.

Attitudes Underlying Reliance on Complementary and Alternative Medicine.

Objective: We aimed to map attitudes underlying complementary and alternative medicine (CAM) use, especially those involved in "dysfunctional CAM reliance," that is, forgoing biomedical treatment in a life-threatening situation in favor of alternative treatment. Analyses of modifiable determinants of CAM use were conducted at a sufficiently specific level to inform intervention development. Methods: We collected usable data on CAM-related attitudinal beliefs from 151 participants in Budapest with varying degrees of CAM use, which we analyzed using confidence interval-based estimation of relevance plots. Results: Although there were beliefs that the entire sample shared, there was a marked difference between the biomedical and CAM groups. These differences were beliefs concerning trust in various medical systems, the level of importance assigned to emotions in falling ill, and vitalism or Eastern concepts. Regarding CAM users in general, the most successful intervention targets are beliefs in vitalism on the one hand, and distrust in biomedicine on the other. In addressing dysfunctional CAM use specifically, the most significant beliefs pertain to "natural" cures and reliance on biomedical testing. Conclusions: Albeit much research has been carried out on the motivations behind CAM use, rarely do studies treat CAM users separately in order to scrutinize patterns of nonconventional medicine use and underlying cognition. This is the first study to begin pinpointing specific attitudes involved in dysfunctional CAM use to inform future intervention development. Such interventions would be essential for the prevention of incidents and mortality.

Network approach in health behavior research: how can we explore new questions?

Background: The network approach has recently been introduced to clinical psychology and provides a powerful framework for analyzing variables in a system. Since then, its applications have rapidly spread to various fields of social sciences. Unlike in the case of clinical psychology, the peculiarities of the phenomena under study in social sciences have not received sufficient attention. In this paper, along with practical illustrations, we discuss what a system of psychological variables represents and what the interrelationships between the variables mean in the context of health behavior research. Additionally, we explore the structural analysis of the system which has not been the focus of the recent applications of network analysis in health psychology. Discussion: In this paper, we illustrate two approaches of incorporating observable behavioral variables in a system and strategies for investigating structural components of the system. We illustrate these two approaches with an analysis of cross-sectional data on adolescents' beliefs and behavior with respect to registering their choice regarding organ donation in the Netherlands. Furthermore, with this paper, we wish to facilitate a larger discussion on conceptualizing networks of psychological variables, which will guide the analysis and the interpretation of node level interactions as well as network level structures.