title: Using the NANA toolkit at home to predict older adults' future depression.
abstract: Depression is currently underdiagnosed among older adults. As part of the Novel Assessment of Nutrition and Aging (NANA) validation study, 40 older adults self-reported their mood using a touchscreen computer over three, one-week periods. Here, we demonstrate the potential of these data to predict future depression status.
  
  
  
  We analysed data from the NANA validation study using a machine learning approach. We applied the least absolute shrinkage and selection operator with a logistic model to averages of six measures of mood, with depression status according to the Geriatric Depression Scale 10 weeks later as the outcome variable. We tested multiple values of the selection parameter in order to produce a model with low deviance. We used a cross-validation framework to avoid overspecialisation, and receiver operating characteristic (ROC) curve analysis to determine the quality of the fitted model.
  
  
  
  The model we report contained coefficients for two variables: sadness and tiredness, as well as a constant. The cross-validated area under the ROC curve for this model was 0.88 (CI: 0.69-0.97).
  
  
  
  While results are based on a small sample, the methodology for the selection of variables appears suitable for the problem at hand, suggesting promise for a wider study and ultimate deployment with older adults at increased risk of depression.
  
  
  
  We have identified self-reported scales of sadness and tiredness as sensitive measures which have the potential to predict future depression status in older adults, partially addressing the problem of underdiagnosis.
authors.affiliation: University of Sheffield, UK. Electronic address: Jacob.andrews@sheffield.ac.uk.
authors.forename: J A
authors.initials: JA
authors.lastname: Andrews
authors.affiliation: University of Sheffield, UK.
authors.forename: R F
authors.initials: RF
authors.lastname: Harrison
authors.affiliation: University of Manchester, UK.
authors.forename: L J E
authors.initials: LJE
authors.lastname: Brown
authors.affiliation: University of Glasgow, UK.
authors.forename: L M
authors.initials: LM
authors.lastname: MacLean
authors.affiliation: University of Reading, UK.
authors.forename: F
authors.initials: F
authors.lastname: Hwang
authors.affiliation: Generic Robotics Ltd, Reading, UK.
authors.forename: T
authors.initials: T
authors.lastname: Smith
authors.affiliation: University of Sheffield, UK.
authors.forename: E A
authors.initials: EA
authors.lastname: Williams
authors.affiliation: University College Dublin, Ireland.
authors.forename: C
authors.initials: C
authors.lastname: Timon
authors.affiliation: Designability, Bath, UK.
authors.forename: T
authors.initials: T
authors.lastname: Adlam
authors.affiliation: Oxford Technologies Ltd, UK.
authors.forename: H
authors.initials: H
authors.lastname: Khadra
authors.affiliation: University of Sheffield, UK; Ontario Shores Centre for Mental Health Sciences, Canada.
authors.forename: A J
authors.initials: AJ
authors.lastname: Astell
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003866:Depressive Disorder / Q000175:diagnosis*; D005260:Female; D015577:Geriatric Assessment / Q000379:methods*; D006801:Humans; D053609:Lethargy / Q000175:diagnosis; D016015:Logistic Models; D008297:Male; D011237:Predictive Value of Tests; D011569:Psychiatric Status Rating Scales / Q000592:standards; D011594:Psychometrics / Q000295:instrumentation*; D012372:ROC Curve; D012189:Retrospective Studies
keywords: Depression; Lasso; Machine learning; Older adults; Technology; Touchscreen