Feedback About a Person’s Social Context - Personal Networks and Daily Social Interactions

The social context of a person, meaning their social relationships and daily social interactions, is an important factor for understanding their mental health. However, personalised feedback approaches to psychotherapy do not consider this factor sufficiently yet. Therefore, we developed an interactive feedback prototype focusing specifically on a person’s social relationships as captured with personal social networks (PSN) and daily social interactions as captured with experience sampling methodology (ESM). We describe the development of the prototype as well as two evaluation studies: Semi-structured interviews with students (N = 23) and a focus group discussion with five psychotherapy patients. Participants from both studies considered the prototype useful. The students considered participation in our study, which included social context assessment via PSN and ESM as well as a feedback session, insightful. However, it remains unclear how much insight the feedback procedure generated for the students beyond the insights they already gained from the assessments. The focus group patients indicated that in a clinical context, (social context) feedback may be especially useful to generate insight for the clinician and facilitate collaboration between patient and clinician. Furthermore, it became clear that the current feedback prototype requires explanations by a researcher or trained clinician and cannot function as a stand-alone intervention. As such, we discuss our feedback prototype as a starting point for future research and clinical implementation

The Feedback Loop Between Smartphone Usage and Momentary Well-Being: Tackling Methodological Challenges by Combining Ecological Momentary Assessments with Passive Smartphone Data

Given the pervasive role of smartphones in modern life, research into their impact on well-being has flourished. This study addresses methodological shortcomings of previous research by using smartphone-log data and Ecological Momentary Assessments (EMA) to explore the bidirectional within-person relationship between smartphone usage and momentary well-being variables (i.e., affect valence, loneliness, positive affect, and negative affect). We further examine different categories of smartphone usage, namely Communication, Social Media, and Other app usage. We analyze three samples (N1 = 225, N2 = 17, N3 = 13; with T1 = 7874, T2 = 2566, and T3 = 1533 EMA reports) with multilevel models to test our preregistered hypotheses. Our results suggest that smartphone usage within an hour before EMA assessment, especially using Social Media apps, is associated with reduced affect valence and increased loneliness on a within-person level. Loneliness is associated with heightened smartphone usage than usual, particularly the use of Social Media apps, within the hour following EMA assessments. While our results suggest that the associations between smartphone usage and affect may be weak (range standardized β = .00–.04), the bidirectional association with loneliness is more pronounced (range standardized β = .04– .09). While temporary reductions in smartphone usage, particularly social media use, may alleviate feelings of loneliness, further research employing intervention designs is warranted to establish experimental evidence

Capturing the dynamics of the social environment through experience sampling methods, passive sensing, and egocentric networks: Scoping Review

BACKGROUND: Social interactions are important for well-being, and therefore, researchers are increasingly attempting to capture people's social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. The experience sampling method (ESM) is often used in psychology to study the dynamics within a person and the social environment. In addition, passive sensing is often used to capture social behavior via sensors from smartphones or other wearable devices. Furthermore, sociologists use egocentric networks to track how social relationships are changing. Each of these methods is likely to tap into different but important parts of people's social environment. Thus far, the development and implementation of these methods have occurred mostly separately from each other. OBJECTIVE: Our aim was to synthesize the literature on how these methods are currently used to capture the changing social environment in relation to well-being and assess how to best combine these methods to study well-being. METHODS: We conducted a scoping review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: We included 275 studies. In total, 3 important points follow from our review. First, each method captures a different but important part of the social environment at a different temporal resolution. Second, measures are rarely validated (>70% of ESM studies and 50% of passive sensing studies were not validated), which undermines the robustness of the conclusions drawn. Third, a combination of methods is currently lacking (only 15/275, 5.5% of the studies combined ESM and passive sensing, and no studies combined all 3 methods) but is essential in understanding well-being. CONCLUSIONS: We highlight that the practice of using poorly validated measures hampers progress in understanding the relationship between the changing social environment and well-being. We conclude that different methods should be combined more often to reduce the participants' burden and form a holistic perspective on the social environment

A shortened version of Raven’s Standard Progressive Matrices for children and adolescents

Numerous developmental studies assess general cognitive ability, not as the primary variable of interest, but rather as a background variable. The Raven’s Progressive Matrices is an easy to administer non-verbal test that is widely used to measure general cognitive ability. However, the relatively long administration time (up to 45 minutes) is still a drawback for developmental studies as it often leaves little time to assess the primary variable of interest. Therefore, we used a machine learning approach - regularized regression in combination with cross validation - to develop a short 15 item version. We did so for two age groups, namely 9 to 12 years and 13 to 16 years. The short versions predicted the scores on the standard full 60 items versions to a very high degree r = 0.89 (9-12 years) and r = 0.93 (13-16 years). We, therefore, recommend using the short version to measure general cognitive ability as a background variable in developmental studies

Behapp:Digital phenotyping platform & app (iOS, Android)

Behapp is a research instrument for use in (medical) scientific research contexts. Behapp facilitates the collection of personal smartphone-based data that is descriptive of a person's social behavior in terms of mobility and communication

sj-docx-1-amp-10.1177_25152459231202677 – Supplemental material for It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data

Supplemental material, sj-docx-1-amp-10.1177_25152459231202677 for It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data by Anna M. Langener, Gert Stulp, Nicholas C. Jacobson, Andrea Costanzo, Raj R. Jagesar, Martien J. Kas and Laura F. Bringmann in Advances in Methods and Practices in Psychological Science</p

A Template and Tutorial for Preregistering Studies Using Passive Smartphone Measures

Passive smartphone measures hold significant potential and are increasingly employed in psychological and biomedical research to capture an individual's behavior. However, utilizing passive smartphone measures presents methodological challenges during data collection and analysis. Researchers are faced with multiple decisions when working with such measures, which can result in different conclusions. Unfortunately, the transparency of these decision-making processes is often lacking. Although there have been some attempts to preregister digital phenotyping studies, a template for registering such studies is currently missing. This could be problematic due to the high level of complexity that requires a well-structured template. Here we propose a preregistration template that is easy to use and understandable for researchers