In spite of the effective roll-out of COVID immunizations and treatments across the UK, the virus continues to pose critical risks, including acute illness, death, long-term complications, and the rise of modern variations.
Long COVID, also known as the post-COVID condition, affects a significant number of people, with side effects lingering well beyond the initial infection. Whereas much of what we know about long COVID comes from subjective patient reports, research into its prevalence, clinical highlights, and risk factors is still in its early stages.
Efforts to define and categorize long COVID have driven to different terms and classifications. The UK’s National Institute for Health and Care Excellence (NICE) categorizes COVID based on symptoms lasting.
In short, long COVID emerged as a complex condition, primarily characterized by persistent symptoms after a COVID-19 diagnosis.
The rise of digital health technologies, like wearable devices, offers a new way to study long COVID by collecting objective, passive data alongside self-reported symptoms.
A longitudinal, self-enrolled, community, case–control study published on The Lancet Digital Health aimed to quantify the prevalence and severity of long COVID symptoms over 12 weeks and identify risk factors for developing the condition.
Methods of study
Researchers launched a longitudinal case-control study to probe long COVID, recruiting participants across the UK between August 2020 and May 2021 through a smartphone app, media outlets, and promotions within the Fitbit app.
The study concentrated on adults who reported a COVID-19 diagnosis verified by a positive antigen or PCR test before February 2022. Participants subscribed electronically and provided data from wearable devices and questionnaires.
As understanding of COVID-19 evolved, researchers made protocol adjustments, including an expanded sociodemographic questionnaire. Participants contributed data from sources like geolocation and wearable devices, offering insights into heart rate, sleep, and physical activity.
The study analyzed two main types of data: passive (from wearable devices) and active (self-reported via questionnaires). Active data included responses to the PHQ-8 depression scale, GAD-7 anxiety scale, and a COVID-19 symptoms questionnaire.
The researchers compared 1,200 COVID-19-positive participants with 3,600 age- and sex-matched controls without a COVID-19 diagnosis. They examined metrics such as resting heart rate, heart rate variability, sleep patterns, and activity levels across three phases: acute COVID-19 (0–4 weeks), ongoing COVID-19 (4–8 weeks), and post-COVID-19 (12–16 weeks).
Logistic regression models helped identify differences between the groups, accounting for variables like age, sex, BMI, ethnicity, and smoking status. To explore long COVID risk factors, the study defined potential long COVID cases based on changes in resting heart rate 12 weeks after diagnosis and the persistence of symptoms over 12 weeks.
The analysis considered historical activity and sleep data, as well as demographic and health factors. The researchers used logistic regression to classify participants into short and long COVID groups based on symptom duration and data patterns.
This study, approved by King’s College London’s Ethics Panel and reported according to STROBE guidelines, offers valuable insights into the physiological and psychological impact of long COVID, emphasizing the role of wearable technology in covering long-term health outcomes.
Findings
Among the 17,667 participants, 1200 COVID-19-positive cases and 3600 controls were analyzed. The study found that resting heart rate significantly increased during the acute, ongoing, and post-COVID-19 phases. Higher physical activity levels before infection reduced the risk of long COVID. However, depressive symptoms persisted after COVID-19 and increased the likelihood of developing long COVID.
Mobile health technologies and wearable devices offer valuable tools for tracking long COVID recovery and understanding its risk factors. They also provide extensive historical data that can aid in managing the condition. The study highlights the long-term impact of COVID-19 on mental health.
Funding: The study was funded by the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, Medical Research Council, UK Research and Innovation, and King’s College London.
