Center for Digital Health

Context Matters

With COVID-19 introducing a new landscape of stressors for U.S. adults, we need to investigate what variables contribute to heightened risk for depression symptoms.

This month, an article released information on the prevalence of depression symptoms among U.S. adults before and after the onset of COVID-19. From a sample of 1,441 respondents during the COVID-19 pandemic and 5065 respondents from before the pandemic, depression symptom prevalence was “3-fold higher during the COVID-19 pandemic than before” (Ettman, 2020). Diving deeper into the data, the report revealed that being lower income, having less than $5000 in savings, and having exposure to more life stressors were associated with greater risk of depression symptoms during COVID-19 (Ettman, 2020). These results suggest that additional research is needed to investigate the contextual factors influencing depression symptomatology among U.S. adults during COVID-19. While social distancing guidelines may limit the availability of in-person research, digital interfaces give an opportunity to collect data both proactively and retrospectively.

Fortunately, researchers across the U.S. started to shift lenses to the digital landscape to understand human behavior before the pandemic arrived. Numerous studies exist investigating social media, web-based applications, and mobile devices for contextual data. This provides encouragement that behavior can be observed and recorded without physically being present. For example, a study conducted by Zhao and colleagues in 2019 collected Twitter data to further understand the stressors experienced by individuals of sexual and gender minority status. By extracting 35,053,757 tweets, including geolocation information, the study team was able to conduct a comprehensive analysis of the mental health signals (i.e. affect processes such as positive or negative emotions, anger, anxiety, and sadness) experienced by a sample of individuals traditionally understudied (Zhao, 2019). Similarly, researchers have used Twitter data to understand symptom classifications between groups of individuals. In 2014, researchers from John Hopkins aggregated Twitter data to identify and evaluate trends of PTSD incidence in and around U.S. military institutions (Coppersmith, 2014). Through their investigation, they discovered that PTSD symptoms were more present among individuals deployed into combat than counterparts-- a critical contextual variable for intervention development (Coppersmith, 2014).

With COVID-19 introducing a new landscape of stressors for U.S. adults, we need to investigate what variables contribute to heightened risk for depression symptoms. To start, we can investigate which technology-based methodologies have been effective in capturing individual-based data in the past, and how we can incorporate these tools into research today. In light of the recent findings on depression symptom prevalence across U.S. adults before and during COVID-19, digital interfaces may be a hopeful pathway to further understanding of what situational factors may be at play.

 

References:

  1. Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic. JAMA Netw Open. 2020;3(9):e2019686. doi:10.1001/jamanetworkopen.2020.19686
  2. Zhao Y, Guo Y, He X, et al. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal. 2020;26(2):765-786. doi:10.1177/1460458219839621
  3. Coppersmith, G., Dredze, M., & Harman, C. (2014, June). Quantifying mental health signals in Twitter. In Proceedings of the workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality (pp. 51-60)