During the COVID-19 pandemic, healthcare workers have been exposed to substantial amounts of stress, with a negative impact on their mental and physical health. The 4YouandMe Stress and Recovery in Frontline Healthcare Covid-19 Workers Study aimed to understand how different facets of acute or sustained stress could be quantified using remote digital health technologies.
In this webinar, R&D Scientist at Cambridge Cognition, Dr Alex Anwyl-Irvine was joined by Dr Sarah Goodday and Dr Stephen Friend from 4YouandMe. The panellists discussed a sub-analysis of the Stress and Recovery in Frontline Healthcare COVID-19 Workers study focusing specifically on the impact of the pandemic on mood, sleep and cognition.
First, Dr Goodday discussed the rationale behind the Stress and Recovery in Frontline Healthcare Covid-19 Workers Study. Targeting a frontline healthcare worker population during the first stages of the pandemic offered a chance to follow an uniquely stressed population. Stress is challenging to measure accurately in the real world, partially due to gaps in how we traditionally measure stress from self-reported measures during infrequent periods. Further challenges are in disentangling subjective perceptions of stress from objective measures of stress and other associated factors such as mood, cognition and sleep. Better longitudinal measures of real-world, objectively measured stress and characterising temporal aspects of wellbeing, stress, sleep and activity over time may help to broaden knowledge of stress and what makes it risky for some individuals.
Participants in the study were all US frontline healthcare COVID-19 workers and were mostly nurses. They were asked to wear an Oura smart ring while off-shift and to complete regular assessments on multiple lifestyle and psychological factors using a smartphone app. Follow up lasted between four to six months.
The study was designed in a participant-centric way with feedback loops created through a series of regular phone check-ins and Zoom calls. Feedback was incorporated into the study in real-time. Using this study design methodology, the stress and recovery project had a much higher retention rate than other digital health studies – more than 80% were retained among the original enrolled study sample.
Analysing the data
Next, Dr Anwyl-Irvine discussed a sub-analysis that focused on a subset of participants who regularly completed questionnaires on mood assessments, including PROMIS, PHQ-9 and GAD. They had also completed cognitive assessments using the Cognition Kit N-back, which measures attention and working memory. By conducting an analysis using data from several time points, we can capture information that would otherwise have been missed.
Participants were grouped through unsupervised machine learning. Patterns were also identified across timeseries variables and a general linear model was constructed to compare these timeseries between groups. Two clear groups were identified, one of people who had increased mental health difficulties and the other with decreased mental health difficulties. The group with high mental health difficulties scored above a mild threshold for either anxiety or depression and were two times more likely to self-report a diagnosis of a mental health disorder.
Next, temporal patterns were characterised by Multivariate Vector Autoregressive Models using a custom Python library. The data showed that the group with the lowest scores for mental health displayed a tighter coupling between stress, low mood, and less physical activity. A novel result was that the group with more mental health difficulties showed increased N-back performance with less sleep, indicating a possible role of hypervigilance.
Through this analysis, it was shown that there are discrete groups based on baseline health, which were primarily weighted on mental health. Common temporal patterns were found connecting Mood, Stress, and Activity, N-back and Stress and also Sleep and Stress. The patterns vary by mental health grouping, with differences in the Cognition-Sleep and Stress-Activity relationships.
How to conduct a successful real-world study
Dr Friend ended the webinar by discussing the opportunities that come with conducting real-world studies involving several temporal measurements. These include understanding how a disease trajectory unfolds over time and rebuilding taxonomies of disease. It also helps to avoid aggregating disease trajectories and instead building a picture for an individual’s journey with their condition. For example, no two people with Parkinson’s disease are the same, and an individual’s symptoms of the condition change over time. Digital measures that are taken over several timepoints can also help to untangle causes and consequences, for example the complex relationships between mood, stress, sleep and cognition.
Dr Friend concluded by emphasising the elements that are needed for a successful study using digital health and wearables. These include creating a community and building trust, providing tailored and regular support and ensuring data privacy.