title: Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study.
abstract: BACKGROUND
  Ecological momentary assessment (EMA) is a useful method to tap the dynamics of psychological and behavioral phenomena in real-world contexts. However, the response burden of (self-report) EMA limits its clinical utility.
  
  
  OBJECTIVE
  The aim was to explore mobile phone-based unobtrusive EMA, in which mobile phone usage logs are considered as proxy measures of clinically relevant user states and contexts.
  
  
  METHODS
  This was an uncontrolled explorative pilot study. Our study consisted of 6 weeks of EMA/unobtrusive EMA data collection in a Dutch student population (N=33), followed by a regression modeling analysis. Participants self-monitored their mood on their mobile phone (EMA) with a one-dimensional mood measure (1 to 10) and a two-dimensional circumplex measure (arousal/valence, -2 to 2). Meanwhile, with participants' consent, a mobile phone app unobtrusively collected (meta) data from six smartphone sensor logs (unobtrusive EMA: calls/short message service (SMS) text messages, screen time, application usage, accelerometer, and phone camera events). Through forward stepwise regression (FSR), we built personalized regression models from the unobtrusive EMA variables to predict day-to-day variation in EMA mood ratings. The predictive performance of these models (ie, cross-validated mean squared error and percentage of correct predictions) was compared to naive benchmark regression models (the mean model and a lag-2 history model).
  
  
  RESULTS
  A total of 27 participants (81%) provided a mean 35.5 days (SD 3.8) of valid EMA/unobtrusive EMA data. The FSR models accurately predicted 55% to 76% of EMA mood scores. However, the predictive performance of these models was significantly inferior to that of naive benchmark models.
  
  
  CONCLUSIONS
  Mobile phone-based unobtrusive EMA is a technically feasible and potentially powerful EMA variant. The method is young and positive findings may not replicate. At present, we do not recommend the application of FSR-based mood prediction in real-world clinical settings. Further psychometric studies and more advanced data mining techniques are needed to unlock unobtrusive EMA's true potential.
authors.affiliation: Faculty of Behavioural and Movement Sciences, Section Clinical Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands. j.a.g.j.asselbergs@vu.nl.
authors.forename: Joost
authors.identifier: http://orcid.org/0000-0003-0526-1499
authors.initials: J
authors.lastname: Asselbergs
authors.forename: Jeroen
authors.identifier: http://orcid.org/0000-0002-4510-997X
authors.initials: J
authors.lastname: Ruwaard
authors.forename: Michal
authors.identifier: http://orcid.org/0000-0001-6888-5608
authors.initials: M
authors.lastname: Ejdys
authors.forename: Niels
authors.identifier: http://orcid.org/0000-0002-7831-462X
authors.initials: N
authors.lastname: Schrader
authors.forename: Marit
authors.identifier: http://orcid.org/0000-0001-5430-9810
authors.initials: M
authors.lastname: Sijbrandij
authors.forename: Heleen
authors.identifier: http://orcid.org/0000-0002-8144-8901
authors.initials: H
authors.lastname: Riper
mesh_terms: D061725:Accelerometry; D000293:Adolescent; D000328:Adult; D000339:Affect; D040421:Cell Phone; D003625:Data Collection; D003863:Depression / Q000175:diagnosis* / Q000523:psychology; D005260:Female; D006801:Humans; D008297:Male; D063731:Mobile Applications; D009426:Netherlands; D010865:Pilot Projects; D011594:Psychometrics; D057566:Self Report; D000068997:Smartphone; D013334:Students / Q000523:psychology*; D060145:Text Messaging; D055815:Young Adult
keywords: affect; data mining; ecological momentary assessment; experience sampling; mobile phone sensing