• A Craig, D Rodrigues, Y Tran, R Guest, R Bartrop, and J Middleton.
• Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
• Spinal Cord. 2014 May 1; 52 (5): 413-6.

Study DesignCross-section design.ObjectivesThe 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.SettingSCI rehabilitation unit, rehabilitation outpatient clinic and Australian community.MethodsParticipants 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.ResultsAt 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.ConclusionClinical 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.

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