Article

The development of reliable
screen technology for predicting those at risk of depression in the long-term
remains a challenge. The objective of this research was to determine factors that
classify correctly adults with spinal cord injury (SCI) with depressed mood and
to develop a diagnostic algorithm that could be applied for prediction of
depressed mood in the long-term. SETTING: SCI rehabilitation unit, rehabilitation
outpatient clinic and Australian community. METHODS: Participants included 107
adults with SCI. The assessment regimen included demographic and injury
variables, negative mood states, pain intensity, health-related quality of life
and self-efficacy. Participants were divided into those with 'normal' mood versus
those with elevated depressed mood. Discriminant function analysis (DFA) was then
used to isolate factors that in combination, best classify the presence or
absence of depressed mood. RESULTS: At the time of assessment, 24 participants
(22.4%) had elevated depressed mood. DFA identified six factors that
discriminated between those with depressed mood (P<0.01) and those with normal
mood, explaining 61% of the variance. Factors consisted of pain intensity, mental
health, emotional and social functioning, self-efficacy and fatigue. DFA
correctly classified 91.7% (n=22 of 24) of those with depressed mood and 95.2%
(n=79 of 83) of those without. Demographic, injury and physical health function
variables were not found to discriminate depressed mood. CONCLUSION: Clinical
implications of applying a diagnostic algorithm for detecting depression in
adults with SCI are discussed. Prospective research is needed to test the
predictive efficacy of the algorithm.

Langue : ANGLAIS