Mixed Methods for Research on Support Networks of People Experiencing Chronic Illness and Social Marginalization

Substantial research has focused on how social networks help individuals navigate the illness experience. Sociologists have begun to theorize beyond the binary of strong and weak social network ties (e.g., compartmental, elastic, and disposable ties), citing the social, economic, and health conditions that shape their formation. However, limited research has employed mixed social network methods, which we argue is especially critical for examining the "non-traditional" social support networks of marginalized individuals. We employ quantitative social network methods (i.e., the egocentric network approach) in addition to in-depth interviews and observations, with a novel tool for capturing network data about social groups, to surface these kinds of supportive relationships. Using the case of "nameless ties"-non-kin, non-provider ties who were unidentifiable by given name or were grouped by context or activity rather than individually distinguished-we show how mixed social network methods can illuminate supporters who are commonly overlooked when only using traditional social network analysis. We conclude with a proposal for mixed methods and group alter approaches to successfully observe liminal support ties that is ideal for research about individuals experiencing chronic disability, poverty, housing insecurity, and other forms of social marginalization.

A Text Messaging Intervention for Coping With Social Distancing During COVID-19 (StayWell at Home): Protocol for a Randomized Controlled Trial

BACKGROUND

Social distancing is a crucial intervention to slow down person-to-person transmission of COVID-19. However, social distancing has negative consequences, including increases in depression and anxiety. Digital interventions, such as text messaging, can provide accessible support on a population-wide scale. We developed text messages in English and Spanish to help individuals manage their depressive mood and anxiety during the COVID-19 pandemic.

OBJECTIVE

In a two-arm randomized controlled trial, we aim to examine the effect of our 60-day text messaging intervention. Additionally, we aim to assess whether the use of machine learning to adapt the messaging frequency and content improves the effectiveness of the intervention. Finally, we will examine the differences in daily mood ratings between the message categories and time windows.

METHODS

The messages were designed within two different categories: behavioral activation and coping skills. Participants will be randomized into (1) a random messaging arm, where message category and timing will be chosen with equal probabilities, and (2) a reinforcement learning arm, with a learned decision mechanism for choosing the messages. Participants in both arms will receive one message per day within three different time windows and will be asked to provide their mood rating 3 hours later. We will compare self-reported daily mood ratings; self-reported depression, using the 8-item Patient Health Questionnaire; and self-reported anxiety, using the 7-item Generalized Anxiety Disorder scale at baseline and at intervention completion.

RESULTS

The Committee for the Protection of Human Subjects at the University of California Berkeley approved this study in April 2020 (No. 2020-04-13162). Data collection began in April 2020 and will run to April 2021. As of August 24, 2020, we have enrolled 229 participants. We plan to submit manuscripts describing the main results of the trial and results from the microrandomized trial for publication in peer-reviewed journals and for presentations at national and international scientific meetings.

CONCLUSIONS

Results will contribute to our knowledge of effective psychological tools to alleviate the negative effects of social distancing and the benefit of using machine learning to personalize digital mental health interventions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04473599; https://clinicaltrials.gov/ct2/show/NCT04473599.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/23592.