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Challenges in Using mHealth Data From Smartphones and Wearable Devices to Predict Depression Symptom Severity: Retrospective Analysis.

Shaoxiong SunAmos A FolarinYuezhou ZhangNicholas CumminsRafael Garcia-DiasCallum StewartYatharth RanjanZulqarnain RashidPauline CondePetroula LaiouHeet SankesaraFaith MatchamDaniel LeightleyKatie M WhiteCarolin OetzmannAlina IvanFemke LamersSara SiddiSara K SimblettRaluca Ileana NicaAki RintalaDavid C MohrInez Myin-GermeysTil WykesJosep-Maria HaroBrenda W J H PenninxSrinivasan VairavanVaibhav A NarayanPeter AnnasMatthew HotopfRichard James Butler Dobsonnull null
Published in: Journal of medical Internet research (2023)
This work contributes to our understanding of how these mobile health-derived features are associated with depression symptom severity to inform future work in stratified analyses.
Keyphrases
  • depressive symptoms
  • sleep quality
  • electronic health record
  • patient reported
  • heart rate
  • current status
  • big data
  • machine learning
  • physical activity
  • blood pressure
  • deep learning