Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study

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.