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.

Numerous developmental studies assess general cognitive ability, not as the primary variable of interest, but rather as a background variable. 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 min) 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 approachregularized regression in combination with cross-validationto 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-item 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. Statement of contribution What is already known on this subject?Raven's Standard Progressive Matrices is widely used to measure cognitive ability as background variable in developmental studies.A drawback is its long administration time (up to 45 min), and it would therefore be helpful to develop a shortened version.Although short versions of the RSPM exist, no short version is suitable for children and adolescents. What does this study add?We used a machine learning approach to develop shortened 15-item versions for two age groups (9-12 and 13-16 years).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

BACKGROUND Social interactions are important for well-being, and therefore researchers increasingly attempt to capture people’s social environment. Many different disciplines have developed tools to measure the social environment, which can be highly variable over time. Psychologists often use experience sampling methods to study dynamics within a person and the social environment. Additionally, various disciplines use digital phenotyping to longitudinally capture social behavior in a passive manner via sensors from smartphones or other wearable devices. Furthermore, sociologists use repeated egocentric networks to track how social relationships are changing. Each of those methods are likely to tap into different but important parts of people’s social environments. A development and implementation of these various methods has occurred thus far largely separately from each other. OBJECTIVE Our aim was to synthesize the literature on how these methods are currently used to capture changing social environments in relation to well-being and to assess how to combine those methods best to study well-being. METHODS We conducted a narrative systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). RESULTS We included 275 studies. Three important points follow from our review. First, each method captures a different but important part of the social environment and at a different resolution. Second, measures are rarely validated, which undermines the robustness of conclusions drawn. Third, a combination of methods is currently lacking but is essential in understanding well-being. CONCLUSIONS We discuss how different methods can be productively combined to form a holistic perspective on the social environment. Lastly, we highlight the practice of using poorly validated measures which will hamper progress in understanding the relationship between the changing social environment and well-being.