title: Developing an algorithm capable of discriminating depressed mood in people with spinal cord injury.
abstract: METHODS
  Cross-section design.
  
  
  OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
authors.forename: A
authors.initials: A
authors.lastname: Craig
authors.affiliation: Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
authors.forename: D
authors.initials: D
authors.lastname: Rodrigues
authors.affiliation: 1] Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia [2] Key University Centre for Health Technologies, University of Technology, Sydney, New South Wales, Australia.
authors.forename: Y
authors.initials: Y
authors.lastname: Tran
authors.affiliation: Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
authors.forename: R
authors.initials: R
authors.lastname: Guest
authors.affiliation: 1] Discipline of Psychiatry, Sydney Medical School-Northern, St Leonards, The University of Sydney, St Leonards, New South Wales, Australia [2] Department of Mental Health, Blacktown-Mt Druitt Clinical School, School of Medicine, University of Western Sydney, Penrith, New South Wales, Australia.
authors.forename: R
authors.initials: R
authors.lastname: Bartrop
authors.affiliation: Rehabilitation Studies Unit, Sydney Medical School-Northern, The University of Sydney, Kolling Institute of Medical Research, St Leonards, New South Wales, Australia.
authors.forename: J
authors.initials: J
authors.lastname: Middleton
mesh_terms: D000328:Adult; D000465:Algorithms; D003430:Cross-Sectional Studies; D003863:Depression / Q000175:diagnosis* / Q000209:etiology*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011569:Psychiatric Status Rating Scales; D011584:Psychology; D013119:Spinal Cord Injuries / Q000150:complications*; D013223:Statistics as Topic