Background : Ecological Momentary Assessment (EMA), comprising repeated self-assessments in daily life, have shown promise as an intervention strategy for depression. Whether the content of such assessments influences affect has hardly received attention. The current study consists of two EMA intervention (EMI) modules, enabling us to compare the impact of EMI content on the course of momentary affect during the intervention. Methods : The intervention, implemented as add-on to regular depression treatment, consists of intensive self-monitoring (5x/day, 28 days) and weekly personalized feedback. Patients with depressive complaints (N = 110; M age = 32.9, SD = 12.2; 44.5% male) were randomly assigned to one of two treatment modules focusing on activities and positive affect (“Do”) or on thoughts and negative affect (“Think”). Results : Linear mixed models showed no significant ( p > .18) differences between the two modules on both positive and negative affect over time. Across modules positive affect showed an initial decreasing trend, leveling off towards the end of the intervention period. Negative affect did not change significantly over time ( p > .06). Limitations : Both modules assessed positive and negative affect, enabling a direct comparison but potentially decreasing the impact of their differential focus. Conclusions : In our sample, the focus of the EMI was not associated with differential effects on momentary affect. This implies that a focus on thoughts and negative affect compared to positive affect and activities may not lead to added adverse effects on mood, which is an often-voiced concern when using EMA in both research and clinical practice.

The past decade has seen rapid growth in research linking stable psychological characteristics (i.e., traits) to digital records of online behavior in Online Social Networks (OSNs) like Facebook and Twitter, which has implications for basic and applied behavioral sciences. Findings indicate that a broad range of psychological characteristics can be predicted from various behavioral residue online, including language used in posts on Facebook (Park et al., 2015) and Twitter (Reece et al., 2017), and which pages a person ‘likes’ on Facebook (e.g., Kosinski, Stillwell, & Graepel, 2013). The present study examined the extent to which the accounts a user follows on Twitter can be used to predict individual differences in self-reported anxiety, depression, post-traumatic stress, and anger. Followed accounts on Twitter offer distinct theoretical and practical advantages for researchers; they are potentially less subject to overt impression management and may better capture passive users. Using an approach designed to minimize overfitting and provide unbiased estimates of predictive accuracy, our results indicate that each of the four constructs can be predicted with modest accuracy (out-of-sample R ’s of approximately .2). Exploratory analyses revealed that anger, but not the other constructs, was distinctly reflected in followed accounts, and there was some indication of bias in predictions for women (vs. men) but not for racial/ethnic minorities (vs. majorities). We discuss our results in light of theories linking psychological traits to behavior online, applications seeking to infer psychological characteristics from records of online behavior, and ethical issues such as algorithmic bias and users’ privacy.