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A smartphone app to monitor mood symptoms in bipolar disorder: Development and usability study

  • Kelly Ann Ryan
  • , Pallavi Babu
  • , Rebecca Easter
  • , Erika Saunders
  • , Andy Jinseok Lee
  • , Predrag Klasnja
  • , Lilia Verchinina
  • , Valerie Micol
  • , Brent Doil
  • , Melvin G. McInnis
  • , Amy M. Kilbourne

Research output: Contribution to journalArticlepeer-review

Abstract

Background: There is considerable scientific interest in finding new and innovative ways to capture rapid fluctuations in functioning within individuals with bipolar disorder (BD), a severe, recurrent mental disorder associated with frequent shifts in symptoms and functioning. The use of smartphones can provide valid and real-world tools for use in measurement-based care and could be used to inform more personalized treatment options for this group, which can improve standard of care. Objective: We examined the feasibility and usability of a smartphone to capture daily fluctuations in mood within BD and to relate daily self-rated mood to smartphone use behaviors indicative of psychomotor activity or symptoms of the illness. Methods: Participants were 26 individuals with BD and 12 healthy control individuals who were recruited from the Prechter Longitudinal Study of BD. All were given a smartphone with a custom-built app and prompted twice a day to complete questions of mood for 28 days. The app automatically and unobtrusively collected phone usage data. A poststudy satisfaction survey was also completed. Results: Our sample showed a very high adherence rate to the daily momentary assessments (91% of the 58 prompts completed). Multivariate mixed effect models showed that an increase in rapid thoughts over time was associated with a decrease in outgoing text messages (β=-.02; P=.04), and an increase in impulsivity self-ratings was related to a decrease in total call duration (β=-.29; P=.02). Participants generally reported positive experiences using the smartphone and completing daily prompts. Conclusions: Use of mobile technology shows promise as a way to collect important clinical information that can be used to inform treatment decision making and monitor outcomes in a manner that is not overly burdensome to the patient or providers, highlighting its potential use in measurement-based care.

Original languageEnglish (US)
Article numbere19476
JournalJMIR Mental Health
Volume7
Issue number9
DOIs
StatePublished - Sep 2020

All Science Journal Classification (ASJC) codes

  • Psychiatry and Mental health

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