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Growth Mixture Modeling of Depression Symptoms Following Traumatic Brain Injury

Overview of attention for article published in Frontiers in Psychology, August 2017
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Title
Growth Mixture Modeling of Depression Symptoms Following Traumatic Brain Injury
Published in
Frontiers in Psychology, August 2017
DOI 10.3389/fpsyg.2017.01320
Pubmed ID
Authors

Rapson Gomez, Clive Skilbeck, Matt Thomas, Mark Slatyer

Abstract

Growth Mixture Modeling (GMM) was used to investigate the longitudinal trajectory of groups (classes) of depression symptoms, and how these groups were predicted by the covariates of age, sex, severity, and length of hospitalization following Traumatic Brain Injury (TBI) in a group of 1074 individuals (696 males, and 378 females) from the Royal Hobart Hospital, who sustained a TBI. The study began in late December 2003 and recruitment continued until early 2007. Ages ranged from 14 to 90 years, with a mean of 35.96 years (SD = 16.61). The study also examined the associations between the groups and causes of TBI. Symptoms of depression were assessed using the Hospital Anxiety and Depression Scale within 3 weeks of injury, and at 1, 3, 6, 12, and 24 months post-injury. The results revealed three groups: low, high, and delayed depression. In the low group depression scores remained below the clinical cut-off at all assessment points during the 24-months post-TBI, and in the high group, depression scores were above the clinical cut-off at all assessment points. The delayed group showed an increase in depression symptoms to 12 months after injury, followed by a return to initial assessment level during the following 12 months. Covariates were found to be differentially associated with the three groups. For example, relative to the low group, the high depression group was associated with more severe TBI, being female, and a shorter period of hospitalization. The delayed group also had a shorter period of hospitalization, were younger, and sustained less severe TBI. Our findings show considerable fluctuation of depression over time, and that a non-clinical level of depression at any one point in time does not necessarily mean that the person will continue to have non-clinical levels in the future. As we used GMM, we were able to show new findings and also bring clarity to contradictory past findings on depression and TBI. Consequently, we recommend the use of this approach in future studies in this area.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 27%
Researcher 11 18%
Student > Master 8 13%
Student > Doctoral Student 4 7%
Student > Postgraduate 3 5%
Other 7 12%
Unknown 11 18%
Readers by discipline Count As %
Psychology 20 33%
Medicine and Dentistry 8 13%
Neuroscience 3 5%
Social Sciences 3 5%
Nursing and Health Professions 2 3%
Other 6 10%
Unknown 18 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 September 2017.
All research outputs
#17,913,495
of 22,999,744 outputs
Outputs from Frontiers in Psychology
#20,728
of 30,225 outputs
Outputs of similar age
#227,704
of 317,366 outputs
Outputs of similar age from Frontiers in Psychology
#464
of 583 outputs
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