Cross-sectional and longitudinal AUD symptom networks: They tell different stories

被引:7
|
作者
Conlin, William E. [1 ,3 ]
Hoffman, Michaela [2 ]
Steinley, Douglas [1 ]
Sher, Kenneth J. [1 ]
机构
[1] Univ Missouri, Dept Psychol Sci, Columbia, MO USA
[2] Med Univ South Carolina, Dept Psychiat & Behav Sci, Charleston, SC USA
[3] Dept Psychol Sci, Psychol Bldg,200 South Seventh St, Columbia, MO 65211 USA
关键词
Alcohol use disorder; Addiction; Symptom networks; Temporal networks; Cross-lagged panel network analysis; CENTRALITY; ALCOHOL; STRESS; FORBES; MARKON; WRIGHT;
D O I
10.1016/j.addbeh.2022.107333
中图分类号
B849 [应用心理学];
学科分类号
040203 ;
摘要
Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.
引用
收藏
页数:7
相关论文
共 50 条
  • [21] THE CROSS-SECTIONAL SHAPES OF LONGITUDINAL HYDRAULIC FRACTURES
    SEGALMAN, D
    JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME, 1984, 106 (04): : 554 - 561
  • [22] A COMPARISON OF EFFICIENCIES OF LONGITUDINAL, MIXED LONGITUDINAL, AND CROSS-SECTIONAL DESIGNS
    BERGER, MPF
    JOURNAL OF EDUCATIONAL STATISTICS, 1986, 11 (03): : 171 - 181
  • [23] CROSS-SECTIONAL AND LONGITUDINAL-STUDIES IN THE ELDERLY
    EXTONSMITH, AN
    PROCEEDINGS OF THE NUTRITION SOCIETY, 1984, 43 (03) : 289 - 294
  • [24] From cross-sectional slicing to longitudinal slicing
    Fukunaga T.
    Journal of Applied Biomechanics, 2021, 37 (03) : 173 - 175
  • [25] Bias in cross-sectional analyses of longitudinal mediation
    Maxwell, Scott E.
    Cole, David A.
    PSYCHOLOGICAL METHODS, 2007, 12 (01) : 23 - 44
  • [26] Symptom clusters and networks analysis in acute-phase stroke patients: a cross-sectional study
    Siyu Zhou
    Yuan Zhang
    Huijuan He
    Xiangrong Wang
    Mengying Li
    Na Zhang
    Jiali Song
    Scientific Reports, 15 (1)
  • [27] Telematics combined actuarial neural networks for cross-sectional and longitudinal claim count data
    Duval, Francis
    Boucher, Jean-Philippe
    Pigeon, Mathieu
    ASTIN BULLETIN-THE JOURNAL OF THE INTERNATIONAL ACTUARIAL ASSOCIATION, 2024, 54 (02) : 239 - 262
  • [28] Bariatric surgery patients in AUD treatment in Norway-an exploratory cross-sectional study
    Bramness, Jorgen G.
    Lien, Lars
    Moe, Jenny S.
    Toft, Helge
    Pandey, Susmita
    Lid, Torgeir G.
    Strommen, Magnus
    Andersen, John R.
    Bolstad, Ingeborg
    ALCOHOL AND ALCOHOLISM, 2024, 59 (02):
  • [29] Menopausal Symptom Differences in Arizona: A Cross-Sectional Survey of Women From Different Socioeconomic Backgrounds
    De Mello, Alanna N.
    Buras, Matthew R.
    Kling, Juliana M.
    JOURNAL OF WOMENS HEALTH, 2019, 28 (06) : 6 - 6
  • [30] Impact of different white matter hyperintensities patterns on cognition: A cross-sectional and longitudinal study
    Wang, Junjun
    Zhou, Ying
    He, Yaode
    Li, Qingqing
    Zhang, Wenhua
    Luo, Zhongyu
    Xue, Rui
    Lou, Min
    NEUROIMAGE-CLINICAL, 2022, 34