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

实施蚁群优化算法来开发健康行为决定因素的短尺度

• 批准号: 431064501
• 负责人: Dr. Anne Moehring
• 金额: --
• 依托单位: Abteilung für Präventionsforschung und Sozialmedizin
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/431064501?language=en
• 关键词: Implementation; Ant; Colony; Optimization; Algorithm

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.

背景：在基于人群的行为预防和流行病学研究中使用电子和移动健康干预措施，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口中的适用性取决于评估的负担，因此限制了其在人口一级的效果。这凸显了对心理测量学上稳健的短量表需求的增加。短量表的构建对研究者提出了心理测量学的挑战，而传统的项目选择方法，如验证性因子分析（CFA）和项目反应理论（IRT）的应用不足以解决这些问题。自动元启发式优化算法可以作为一种省时的方法来考虑这些问题，并为心理计量学固体短量表选择项目集。目标：在这个项目的过程中，我想用蚁群优化（ant colony optimization， ACO）算法开发有效可靠的短量表来评估与健康相关行为的自我效能和决策平衡。因此，将解决以下问题：1)在多大程度上，蚁群算法是酒精和烟草消费以及体育活动领域的一种适当的项目选择方法？2)用蚁群算法优化的短尺度是否与用常规方法构建的短尺度相当甚至更可靠？3)尺度在不同时间点是否不变，蚁群算法是否可以选择测量不变项集？方法：数据将来自研究合作项目“健康风险行为早期干预”（EARLINT）的5个项目，其中n = 12.372名受试者来自酒精消费、烟草消费和体育活动领域。蚁群算法是一种自动的、省时的优化方法。将该算法优化的短尺度与CFA和IRT尺度开发的尺度进行比较。此外，纵向数据将通过使用多组CFA来建立不同时间点的测量不变性，从而检查量表在不同时间点之间是否具有可比性。预期益处：考虑到临床实践中用于调查的时间资源有限，同时频繁使用多行为测试，显然需要心理测量学上可靠的短量表。蚁群算法可以为这些评估制定可靠的短尺度。本项目旨在提供以下数据分析脚本：1)为进一步研究蚁群算法的实施提供数据分析脚本；2)将新构建的短尺度与目前应用的尺度进行比较；3)利用纵向数据建立短尺度在不同时间点的测量不变性。