Coping with depression: A dynamic networks approach to the study of social network constellation, cohesion and conflict

Humanity is now witnessing one of the highest rates of displacement since the beginning of its history, with an unprecedented 79.5 million people around the world being forced to leave their homes;among whom are 26 million refugees. Since 2011, the protracted Syrian war has threatened the stability and well-being of all persons affected by the war. In these complex emergencies, regular access to resources, pathways to building social ties, and utilization of existing service networks (such as education, healthcare, and protection) are disrupted. Method. Nine Syrian refugees and Lebanese host currently living in Lebanon screened positive for clinical depression and receiving interpersonal psychotherapy (IPT) by Lebanese providers were recruited for the study. A novel social network assessment tool using a dynamic network framework was designed and preliminarily tested to explore social support and conflict in the sample during IPT. Changes in social support and conflict resolution were assessed pre-and post-IPT in the depressed selection. Results. To our knowledge, this is the first-of-its-kind study to adopt a dynamic, multiplex, open-system approach to identifying, classifying, and exploring temporal changes in the social network roles in both refugees and host population(s) with specific goal orientation. This is also the first to study these in the context of individuals with a mental health problem receiving IPT for depression treatment. Outcomes indicate promise of the use of the dynamic network theory's survey approach (aka network goal analysis) among depressed participants and provides important insights about pathways through which persons activate social support and resolve conflict in a humanitarian emergency setting. Discussion. Amidst war, economic downturn, COVID-19 pandemic, and recent bomb blasts, communities have been fragmented and their social ties, severed. Increasing rates of common mental disorders have worsened peoples' capabilities for survival. This novel dynamic network approach to the study of social support and conflict resolution brings into focus pathways and social roles among depressed individuals crucial for social support, with implications for policy makers and mental health practitioners. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Daily dynamics and mechanisms of anxious symptomatology in the general population: A network study during the COVID-19 pandemic.

To understand the interplay between anxiety symptoms and their maintaining psychological processes in the population, an analysis of longitudinal within-person relationships is required. A sample of 1706 individuals completed daily measures during a 40-day period with strict mitigation protocols. Data of 1368 individuals who completed at least 30 assessments were analyzed with the multilevel vector autoregressive (mlVAR) model. This model estimates a temporal, a contemporaneous, and a between-person network. Uncontrollability of worry, generalized worry, fear of being infected, fear of significant others being infected, and threat monitoring had the highest outstrength within the temporal network, indicating that daily fluctuations in these components were the most predictive of next-day fluctuations in other components. Of specific connections, both fear of self and fear of close others being infected predicted generalized worry and threat monitoring. In turn, generalized worry and threat monitoring engaged in several positive feedback loops with other anxiety symptoms and processes. Also, intolerance of uncertainty was predictive of other components. The findings align with the mechanisms both in the metacognitive therapy (MCT) model and in the intolerance of uncertainty model of generalized anxiety disorder (GAD).

Coping with depression: A dynamic networks approach to the study of social network constellation, cohesion and conflict

Humanity is now witnessing one of the highest rates of displacement since the beginning of its history, with an unprecedented 79.5 million people around the world being forced to leave their homes;among whom are 26 million refugees. Since 2011, the protracted Syrian war has threatened the stability and well-being of all persons affected by the war. In these complex emergencies, regular access to resources, pathways to building social ties, and utilization of existing service networks (such as education, healthcare, and protection) are disrupted. Method. Nine Syrian refugees and Lebanese host currently living in Lebanon screened positive for clinical depression and receiving interpersonal psychotherapy (IPT) by Lebanese providers were recruited for the study. A novel social network assessment tool using a dynamic network framework was designed and preliminarily tested to explore social support and conflict in the sample during IPT. Changes in social support and conflict resolution were assessed pre-and post-IPT in the depressed selection. Results. To our knowledge, this is the first-of-its-kind study to adopt a dynamic, multiplex, open-system approach to identifying, classifying, and exploring temporal changes in the social network roles in both refugees and host population(s) with specific goal orientation. This is also the first to study these in the context of individuals with a mental health problem receiving IPT for depression treatment. Outcomes indicate promise of the use of the dynamic network theory's survey approach (aka network goal analysis) among depressed participants and provides important insights about pathways through which persons activate social support and resolve conflict in a humanitarian emergency setting. Discussion. Amidst war, economic downturn, COVID-19 pandemic, and recent bomb blasts, communities have been fragmented and their social ties, severed. Increasing rates of common mental disorders have worsened peoples' capabilities for survival. This novel dynamic network approach to the study of social support and conflict resolution brings into focus pathways and social roles among depressed individuals crucial for social support, with implications for policy makers and mental health practitioners. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Dynamics of Loneliness Among Older Adults During the COVID-19 Pandemic: Pilot Study of Ecological Momentary Assessment With Network Analysis.

OBJECTIVE: The COVID-19 pandemic has had potentially severe psychological implications for older adults, including those in retirement communities, due to restricted social interactions, but the day-to-day experience of loneliness has received limited study. We sought to investigate sequential association, if any, between loneliness, activity, and affect. METHODS: We used ecological momentary assessment (EMA) with dynamic network analysis to investigate the affective and behavioral concomitants of loneliness in 22 residents of an independent living sector of a continuing care retirement community (mean age 80.2; range 68-93 years). RESULTS: Participants completed mean 83.9% of EMA surveys (SD = 16.1%). EMA ratings of loneliness were moderately correlated with UCLA loneliness scale scores. Network models showed that loneliness was contemporaneously associated with negative affect (worried, anxious, restless, irritable). Negative (but not happy or positive) mood tended to be followed by loneliness and then by exercise or outdoor physical activity. Negative affect had significant and high inertia (stability). CONCLUSIONS: The data suggest that EMA is feasible and acceptable to older adults. EMA-assessed loneliness was moderately associated with scale-assessed loneliness. Network models in these independent living older adults indicated strong links between negative affect and loneliness, but feelings of loneliness were followed by outdoor activity, suggesting adaptive behavior among relatively healthy adults.

Pruning digital contact networks for meso-scale epidemic surveillance using foursquare data

With the recent advances in human sensing, the push to integrate human mobility tracking with epidemic modeling highlights the lack of groundwork at the mesoscale (e.g., city-level) for both contact tracing and transmission dynamics. Although GPS data has been used to study city-level outbreaks in the past, existing approaches fail to capture the path of infection at the individual level. Consequently, in this paper, we extend epidemics prediction from estimating the size of an outbreak at the population level to estimating the individuals who may likely get infected within a finite period of time. To this end, we propose a network science based method to first build and then prune the dynamic contact networks for recurring interactions;these networks can serve as the backbone topology for mechanistic epidemics modeling. We test our method using Foursquare's Points of Interest (POI) smart phone geolocation data from over 1.3 million devices to better approximate the COVID-19 infection curves for two major (yet very different) US cities, (i.e., Austin and New York City), while maintaining the granularity of individual transmissions and reducing model uncertainty. Our method provides a foundation for building a disease prediction framework at the mesoscale that can help both policy makers and individuals better understand their estimated state of health and help the pandemic mitigation efforts. © 2021 ACM.