Human behavior is the focus of many studies in the social, health, and behavioral sciences. Yet, few studies use behavioral observation methods to collect objective measures of behavior as it occurs in daily life, out in the real worldpresumably the context of ultimate interest. Here we provide a review of recent studies focused on measuring human behavior using smartphones and their embedded mobile sensors. To draw attention to current advances in the field of smartphone sensing, we describe the daily behaviors captured using these methods, which include movement behaviors (physical activity, mobility patterns), social behaviors (faceto-face encounters, computer-mediated communications), and other daily activities (nonmediated and mediated activities). We conclude by pointing to promising areas of future research for studies using Smartphone Sensing Methods (SSMs) in the behavioral sciences.

Sociability as a disposition describes a tendency to affiliate with others (vs. be alone). Yet, we know relatively little about how much social behavior people engage in during a typical day. One challenge to documenting behavioral sociability tendencies is the broad number of channels over which socializing can occur, both in-person and through digital media. To provide an assessment of individual differences in everyday social behavior patterns, here we used smartphone-based mobile sensing methods (MSMs) in four studies (total N = 1078) to collect real-world data about the sensed social behaviors of young adults across four communication channels: conversations, phone calls, text messages, and messaging and social media application use. To examine individual differences, we first focused on establishing between-person variability in daily social behavior, examining stability of and relationships among daily sensed social behavior tendencies. To explore factors that may explain the observed individual differences in sensed social behavior, we then expanded our focus to include other time estimates (e.g., times of the day, days of the week) and personality traits. In doing so, we present the first large-scale descriptive portrait of behavioral sociability patterns, characterizing the degree of social behavior young adults typically engaged in and mapping behavioral to self-reported personality dispositions. Our discussion focuses on how the observed sociability patterns compare to previous research on young adults' social behavior. We conclude by pointing to areas for future research aimed at understanding sociability using mobile sensing and other naturalistic observation methods for the assessment of social behavior.

User interaction paerns with mobile apps and notications are generally complex due to the many factors involved. However a deep understanding of what inuences them can lead to more acceptable applications that are able to deliver information at the right time. In this paper, we present for the rst time an in-depth analysis of interaction behavior with notications in relation to the location and activity of users. We conducted an in-situ study for a period of two weeks to collect more than 36,000 notications, 17,000 instances of application usage, 77,000 location samples, and 487 days of daily activity entries from 26 students at a UK university. Our results show that users' aention towards new notications and willingness to accept them are strongly linked to the location they are in and in minor part to their current activity. We consider both users' receptivity and aentiveness, and we show that dierent response behaviors are associated to dierent locations. ese ndings are fundamental from a design perspective since they allow us to understand how certain types of places are linked to specic types of interaction behavior. is information can be used as a basis for the development of novel intelligent mobile applications and services. CCS Concepts: •Human-centered computing ! HCI design and evaluation methods; Empirical studies in ubiquitous and mobile computing;

People around the world own digital media devices that mediate and are in close proximity to their daily behaviours and situational contexts. These devices can be harnessed as sensing technologies to collect information from sensor and metadata logs that provide fine‐grained records of everyday personality expression. In this paper, we present a conceptual framework and empirical illustration for personality sensing research, which leverages sensing technologies for personality theory development and assessment. To further empirical knowledge about the degree to which personality‐relevant information is revealed via such data, we outline an agenda for three research domains that focus on the description, explanation, and prediction of personality. To illustrate the value of the personality sensing research agenda, we present findings from a large smartphone‐based sensing study (N = 633) characterizing individual differences in sensed behavioural patterns (physical activity, social behaviour, and smartphone use) and mapping sensed behaviours to the Big Five dimensions. For example, the findings show associations between behavioural tendencies and personality traits and daily behaviours and personality states. We conclude with a discussion of best practices and provide our outlook on how personality sensing will transform our understanding of personality and the way we conduct assessment in the years to come. © 2020 European Association of Personality Psychology

The assessment of psychological situations in everyday life presents a number of methodological challenges, largely stemming from the need to assess situations as they occur in their natural context. Digital media devices (e.g., smartphones, wearables, smart home appliances) that come equipped with a wide array of sensors address these challenges by making it possible to measure objective information about situations, many times, with great fidelity, over long periods of time, in a way that is both unobtrusive and ecologically valid. This chapter provides an overview of mobile sensing methods (MSMs) and describes how they can be used to capture objective information about situational cues (e.g., social interactions, objects, activities, locations, time). It then describes opportunities for psychological research using MSMs to provide insights into everyday situational cues, characteristics, and classes. It concludes by discussing some of the practical considerations and challenges associated with using MSMs to assess situations.