Implementation of the Ant Colony Optimization Algorithm for the development of short-scales for determinants of health behavior

背景技术背景：在基于人口的行为预防和流行病学研究中使用电子和移动卫生干预措施，促进了多行为测试的使用。这些方法作为短期干预措施在人群中的适用性取决于评估的负担，因此限制了其在人群水平上的影响。这突出了对心理测量稳健的短尺度的需求增加。短量表的构建对研究者提出了心理测量学的挑战，传统的项目选择方法，如验证性因素分析（CFA）和使用项目反应理论（IRT）进行测量，不能充分解决这些问题。自动元启发式优化算法，而不是作为时间效率的方法，考虑到这些问题，并选择项目集的psychometrically坚实的short-scales.GOALS：在这个项目的过程中，我想使用蚁群优化（ACO）算法开发有效和可靠的短期尺度的自我效能和决策平衡的评估方面的健康相关的行为。因此，以下问题将得到解决：1）在多大程度上是ACO算法的一个适当的方法，在酒精和烟草消费领域的项目选择，以及身体活动？2)用ACO算法优化的短尺度与用传统方法构建的短尺度相比，甚至更可靠吗？3)尺度在不同的时间点上是不变的吗？ACO算法可以用来选择度量不变的项集吗？实验方法：数据将来自研究合作“健康风险行为的早期干预”（EARLINT）的5个项目，其中n = 12.372例受试者来自饮酒、吸烟和体育活动领域。ACO算法将被用作自动和时间有效的优化方法。将通过该算法优化的短尺度与通过CFA和IRT缩放开发的尺度进行比较。此外，纵向数据将被用来建立测量不变性在不同的时间点，通过使用多组CFA，从而检查是否在不同的时间点的规模是可比的。预期的好处：考虑到有限的时间资源在临床实践中的调查和并发频繁使用的多行为测试，很明显，心理测量学上可靠的短尺度是必需的。ACO算法可用于为这些评估开发可靠的短期量表。该项目旨在提供以下内容：1）用于进一步研究的ACO算法的实施的数据分析脚本，2）新构建的短尺度与当前应用的尺度的比较，3）使用纵向数据来建立跨不同时间点的短尺度的测量不变性。

BACKGROUND: The usage of e- and m-health interventions in population-based behavioral prevention and epidemiological research facilitates the use of multi-behavioral tests. The applicability of these approaches in a population as short intervention depends on the burden of the assessment and therefore limits its effect at the population level. This highlights the increased demand for psychometrically robust short scales. The construction of short-scales presents psychometric challenges to researchers and conventional methods of item selection, such as confirmatory factor analysis (CFA) and the use of item response theory (IRT) for scaling, can not address these problems adequately. Automatic metaheuristic optimization algorithms could be used instead as time efficient methods to take these problems into consideration and select itemsets for psychometrically solid short-scales.GOALS: In the course of this project, I want to use the ant colony optimization (ACO) algorithm to develop valid and reliable short-scales for the assessment of self-efficacy and decisional balance in regard to health-related behaviors. Therefore, the following issues will be addressed: 1) To what extent is the ACO algorithm an adequate method of item selection in the domains of alcohol and tobacco consumption, as well as physical activity? 2) Are short-scales that were optimized with the ACO algorithm comparable to or even more reliable than short-scales constructed with conventional methods? 3) Are the scales invariant across different points of time and can the ACO algorithm be used to select measurement invariant itemsets?METHOD: Data will be used from 5 projects of the research collaboration “Early interventions in health risk behaviors” (EARLINT) with up to n = 12.372 subjects from the domains of alcohol consumption, tobacco consumption and physical activity. The ACO algorithm will be used as an automatic and time efficient optimization method. The short-scales which are optimized by this algorithm will be compared to scales that were developed by CFA and IRT scaling. Additionally, longitudinal data will be used to establish measurement invariance across different points of time by using multiple group CFA, thus examining whether the scales are comparable across different points of time.EXPECTED BENEFIT: Considering the limited time resources in clinical practice for surveys and the concurrent frequent use of multi-behavioral tests, it is clear that psychometrically reliable short-scales are required. The ACO algorithm can be used to develop reliable short-scales for these assessments. The project aims to provide the following: 1) data analysis scripts for the implementation of the ACO algorithm for further research, 2) comparison of the newly constructed short-scales with currently applied scales, 3) use of longitudinal data for the establishment of measurement invariance in the short-scales across different points of time.