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A pilot study to determine whether combinations of objectively measured activity parameters can be used to differentiate between mixed states, mania, and bipolar depression.

Jan ScottArne E VaalerOle Bernt FasmerGunnar MorkenKaroline Krane-Gartiser
Published in: International journal of bipolar disorders (2017)
The findings should be treated with caution as this was a small-scale pilot study and we did not control for prescribed treatments, medication adherence, etc. However, the insights gained should encourage more widespread adoption of statistical approaches to the classification of cases alongside the application of more sophisticated modelling of activity patterns. The difficulty of accurately classifying cases of bipolar depression requires further research, as it is unclear whether the lower prediction rate reflects weaknesses in a model based only on actigraphy data, or if it reflects clinical reality i.e. the possibility that there may be more than one subtype of bipolar depression.
Keyphrases
  • bipolar disorder
  • depressive symptoms
  • sleep quality
  • machine learning
  • deep learning
  • big data
  • artificial intelligence