This research explores the interplay between social media usage, offline and online interactions, perceived social support, and depression levels among university students. Analyzing data from 430...Show moreThis research explores the interplay between social media usage, offline and online interactions, perceived social support, and depression levels among university students. Analyzing data from 430 participants, this cross-sectional study leverages the Patient Health Questionnaire-9 (PHQ-9) to assess depression symptoms and employs both multivariate and univariate linear regression analyses, as well as MANOVA and ANOVA tests, to understand the connections between online/offline social interactions, perceived social support, and depression. Key findings indicate a significant relationship between the level of perceived social support (β = -0.49, p < .001) and frequency of online interactions (β = 0.43, p < .05) with depression levels. Notably, individuals reporting higher levels of perceived social support tend to exhibit lower depression levels, while those with frequent online interactions often show higher depression levels. The study also identifies marked gender differences in social media use and depression, with females showing greater susceptibility. These results underscore a nuanced relationship between active/passive social media engagement, gender, and mental health. The research emphasizes the need for mental health strategies that consider individual differences, particularly focusing on the quality of social support and patterns of online engagement, to effectively address mental health concerns in young adults.Show less
Research master thesis | Psychology (research) (MSc)
open access
Individuals with a lower socioeconomic status (SES) are at an increased risk for developing depressive symptoms. However, it has not been investigated whether this link is homogenous, or whether...Show moreIndividuals with a lower socioeconomic status (SES) are at an increased risk for developing depressive symptoms. However, it has not been investigated whether this link is homogenous, or whether specific depressive symptoms relate to SES differentially. In this thesis, I explored (1) which individual symptoms of depression are related to subjective social status (as a proxy for SES); (2) how specific indicators of SES are related to specific symptoms of depression; and (3) how the addition of stressors impacts the relations between SES indicators and depressive symptoms. I used data previously gathered from 448 students aged ≥18 at institutions of higher education in the Netherlands, as a part of the WARN-D study. Variables were assessed through a questionnaire that was administered online. I estimated three regularised partial correlation networks to explore shared variances among nine symptoms of depression, five indicators of SES, and seven stressors. The network analyses revealed that (1) subjective social status (as a proxy for SES) was negatively associated with guilt/worthlessness, depressed mood, anhedonia, trouble concentrating, and feeling tired, meaning that participants with higher scores on subjective social status had, on average, lower scores on these symptoms; (2) educational level (as one of multiple indicators of SES) was negatively associated with appetite disturbances, and the ability to get by financially was negatively associated with depressed mood, guilt/worthlessness, and appetite disturbances; (3) these associations diminished considerably or disappeared altogether when controlling for the stressor variables. Overall, all SES-depression associations were small in magnitude. The results suggest that patterns of depressive symptoms might differ between high-SES and low-SES individuals. Future research should explore the mechanisms behind these differences to guide prevention and intervention. My findings are consistent with previous research showing that symptom composite scores obscure important differences between individuals.Show less
Research master thesis | Psychology (research) (MSc)
open access
Ecological Momentary Assessment (EMA) is a data collection method that utilizes phone apps to gather data in daily life. EMA has many advantages, such as ecological validity. However, data...Show moreEcological Momentary Assessment (EMA) is a data collection method that utilizes phone apps to gather data in daily life. EMA has many advantages, such as ecological validity. However, data collection protocols are often intense, with multiple measurements per day, which can interrupt participants’ everyday activities and place a burden on them. This can reduce compliance. One way to tackle this is to provide participants with personalized data reports as an intrinsic reward. However, current frameworks to generate such reports are focused on single individuals in treatment, and not suitable for large-scale studies. Here we introduce a software to fill this gap, FRED (Feedback Reports on EMA Data), and showcase FRED by generating reports for 428 participants who took part in the WARN-D study. Participants were followed for 85 consecutive days, and received four daily and one weekly survey, resulting in up to 352 observations. We provided feedback to participants in the form of downloadable HTML-files, which were generated using the R programing environment. Reports included descriptive statistics, timeseries visualizations, and network analyses on selected variables. Furthermore, we assessed participants’ perceptions of the created reports (n=54), who judged reports mostly as understandable, insightful, and that reports resonated well with them. Given that FRED is flexible and can be adjusted to the needs of a particular research project, it provides a good basis to generate large numbers of personalized data reports.Show less
