title: Mobile health monitoring to characterize depression symptom trajectories in primary care.
abstract: BACKGROUND
  Classification of depression severity can guide treatment decisions. This study examined whether using repeated mobile health assessments to determine symptom trajectories is a potentially useful method for classifying depression severity.
  
  
  METHODS
  344 primary care patients with depression were identified and recruited as part of a program of mobile health symptom monitoring and self-management support. Depression symptoms were measured weekly via interactive voice response (IVR) calls using the Patient Health Questionnaire (PHQ-9). Trajectory analysis of weekly IVR PHQ-9 scores from baseline through week 6 was used to subgroup patients according to similar trajectories. Multivariable linear regression was used to determine whether the trajectories predicted 12-week PHQ-9 scores after adjusting for baseline and 6-week PHQ-9 scores.
  
  
  RESULTS
  The optimal trajectory analysis model included 5 non-intersecting trajectories. The subgroups of patients assigned to each trajectory had mean baseline PHQ-9s of 19.7, 14.5, 9.5, 5.0, and 2.0, and respective mean decreases in PHQ-9s over six weeks of .3, 2.0, 3.6, 2.3, and 1.9. In regression analyses, each trajectory significantly predicted 12-week PHQ-9 scores (using the modal trajectory as a reference) after adjusting for both baseline and 6-week PHQ-9 scores.
  
  
  CONCLUSIONS
  Treatment history was unknown, findings may not be generalizable to new episodes of treatment.
  
  
  CONCLUSIONS
  Depression symptom trajectories based on mobile health assessments are predictive of future depression outcomes, even after accounting for typical assessments at baseline and a single follow-up time point. Approaches to classify patients׳ disease status that involve multiple repeated assessments may provide more accurate and useful information for depression management compared to lower frequency monitoring.
authors.affiliation: Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: ppfeiffe@umich.edu.
authors.forename: Paul N
authors.initials: PN
authors.lastname: Pfeiffer
authors.affiliation: Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Kipling M
authors.initials: KM
authors.lastname: Bohnert
authors.affiliation: Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Kara
authors.initials: K
authors.lastname: Zivin
authors.affiliation: Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Matheos
authors.initials: M
authors.lastname: Yosef
authors.affiliation: Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: Marcia
authors.initials: M
authors.lastname: Valenstein
authors.affiliation: Department of Family Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: James E
authors.initials: JE
authors.lastname: Aikens
authors.affiliation: Center for Clinical Management Research, VA Ann Arbor Health System, Ann Arbor, MI, USA; Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
authors.forename: John D
authors.initials: JD
authors.lastname: Piette
mesh_terms: D000328:Adult; D003863:Depression / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011320:Primary Health Care; D011379:Prognosis; D012720:Severity of Illness Index; D017216:Telemedicine
keywords: Depression; Mobile health monitoring; Symptom trajectories