Psychological well-being in Europe after the outbreak of war in Ukraine.

The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual's personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.

Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing.

Social interactions are crucial to affective well-being. Still, people vary interindividually and intraindividually in their social needs. Social need regulation theories state that mismatches between momentary social desire and actual social contact result in lowered affect, yet empirical knowledge about this dynamic regulation is limited. In a gender- and age-heterogenous sample, German-speaking participants (N = 306, 51% women, Mage = 39.41, range 18-80 years) answered up to 20 momentary questionnaires about social interactions and affect while mobile sensing tracked their conversations, calls, and app usage over 2 days. Combining preregistered and exploratory analyses, we investigated how momentary affect relates to social dynamics, focusing on two states of mismatch between social desire and social contact: social deprivation (i.e., being alone but desiring contact) and social oversatiation (i.e., being in contact but desiring to be alone). We used specification curve analyses to scrutinize the operationalization of these constructs. Social oversatiation was associated with decreased positive affect and increased negative affect. Social deprivation, however, was unrelated to affect. Exploratory multilevel models showed that a higher desire to be alone was consistently associated with decreased affective well-being, whereas a higher desire for social contact was related to increased affective well-being. Mobile sensing data revealed differential association patterns between affect and face-to-face versus digital communication. We discuss implications for social need regulation, related studies on voluntary solitude, and advantages of combining experience sampling and mobile sensing assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Never miss a beep: Using mobile sensing to investigate (non-)compliance in experience sampling studies.

Given the increasing number of studies in various disciplines using experience sampling methods, it is important to examine compliance biases because related patterns of missing data could affect the validity of research findings. In the present study, a sample of 592 participants and more than 25,000 observations were used to examine whether participants responded to each specific questionnaire within an experience sampling framework. More than 400 variables from the three categories of person, behavior, and context, collected multi-methodologically via traditional surveys, experience sampling, and mobile sensing, served as predictors. When comparing different linear (logistic and elastic net regression) and non-linear (random forest) machine learning models, we found indication for compliance bias: response behavior was successfully predicted. Follow-up analyses revealed that study-related past behavior, such as previous average experience sampling questionnaire response rate, was most informative for predicting compliance, followed by physical context variables, such as being at home or at work. Based on our findings, we discuss implications for the design of experience sampling studies in applied research and future directions in methodological research addressing experience sampling methodology and missing data.

Snapshots of daily life: Situations investigated through the lens of smartphone sensing.

Daily life unfolds in a sequence of situational contexts, which are pivotal for explaining people's thoughts, feelings, and behaviors. While situational data were previously difficult to collect, the ubiquity of smartphones now opens up new opportunities for assessing situations in situ, that is, while they occur. Seizing this opportunity, the present study demonstrates how smartphones can help establish associations between the psychological perception and physical reality of situations. We employed an intensive longitudinal sampling design and investigated 9,790 situational snapshots experienced by 455 participants for 14 consecutive days. These snapshots combined self-reported situation characteristics from experience samplings with their corresponding objective cues obtained via smartphone sensing. More precisely, we extracted a total of 1,356 granular cues from different sensing modalities to account for the complexity of real-world situations. We applied linear and nonlinear machine learning algorithms to examine how well these cues predicted the perceived characteristics in terms of the Situational Eight Duty, Intellect, Adversity, Mating, pOsitivity, Negativity, Deception, Sociality (DIAMONDS), finding significant out-of-sample predictions for the five dimensions reflecting the situations' Duty, Intellect, Mating, pOsitivity, and Sociality. In a series of follow-up analyses, we further explored the data patterns captured by our models, revealing, for example, that those cues related to time and location were particularly informative of the respective situation characteristics. We conclude by interpreting the mapping between cues and characteristics in real-world situations and discussing how smartphone-based situational snapshots may push the boundaries of psychological research on situations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting personality from patterns of behavior collected with smartphones.

Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.