Study: Presentation of long COVID and associated risk factors in a mobile health study. Image Credit: Ralf Liebhold/Shutterstock

Smartphone-based assessment of prolonged COVID symptoms and risk factors

In a recent study published in medRxiv* Prepress server, researchers quantified features and risk factors for post-acute coronavirus disease 2019 (COVID-19) or long-term COVID syndrome (LCOVID) based on the integration of (active) and passive survey data. [mobile health (mHealth) wearable device] data.

Stady: Long-term COVID presentation and associated risk factors in a mobile health study. Image Credit: Ralph Liebhold / Shutterstock


LCOVID refers to persistent symptoms of COVID-19 after the acute phase of the disease and has been reported to affect many individuals globally. LCOVID symptoms, prevalence, risk factors, and clinical features were largely documented during the initial pandemic wave based on personal data, and further investigation is needed to better understand LCOVID. Smartphone app-based data provides objective health assessments, and wearables allow long follow-ups.

about studying

In this study, researchers conducted a mobile health analysis to assess LCOVID prevalence and symptoms and identify risk factors that include active data and passive data.

Confirmed or confirmed COVID-19 by antibody testing (prior to February 1, 2022) was recorded by a cohort science study application between August 2020 and May 2021 and formed the case group. For comparison, the 3,600 non-COVID-19 individuals who made up the control group were included. Participants were asked to fill out survey questionnaires to obtain data on COVID-19 onset symptoms, vaccination status, mental health and mood related to acute (<4 weeks) and persistent (from four to 12 weeks) COVID-19 period. weeks), and post-COVID (12 weeks).

Portable health metrics included heart rate (HR), physical activity (PA) or number of steps, sleep, moods, and symptoms. An individual was determined to have LCOVID based on (i) physiologically persistent from COVID-19 diagnosis onwards lasting 12 weeks, and (ii) self-reported persistent COVID-19 symptoms for 12 weeks. In addition, risk factors for developing LCOVID were assessed, and the Pubmed database was searched between study start and July 1, 2022 for LCOVID studies involving the use of wearable technologies or portable health.

For the diagnosis of LCOVD, resting heart rate variability (RHRV) over 12 weeks after infection with COVID-19 and historical PA values ​​and sleep durations obtained passively 1-2 years prior to severe acute respiratory syndrome coronavirus 2 (SARS-CoV) -2) Infection was assessed. Participants with persistent symptoms after being diagnosed with COVID-19 were also considered to be diagnosed with LCOVID. If symptoms were documented ≥1 once weekly for 12 weeks, the individual was included in the LCOVID symptom-based group (L).Sim). Logistic regression modeling was used for the analysis.


A total of 1,743 individuals were considered for the final analysis, of whom 44% (n = 759) completed expanded sociodemographic questionnaires. Among the negative measures, RHR values ​​were significantly elevated among cases as compared to controls during all three study periods. The differences in HR during acute COVID-19 (0.6 bpm) were lower than in the continuous phase (1.1 bpm) and comparable to the post-SARS-CoV-2 infection period (0.5 bpm) over 12 a week.

The overall trend in HR changes showed a peak in the first week of COVID-19 diagnosis, with a subsequent decrease in the following week, followed by a second long-term rise thereafter, indicating two sub-stages of acute COVID-19 in less than four weeks. Step count was significantly but negatively affected during severe COVID-19 and has not changed significantly after that. Increased sleep duration but significantly decreased sleep efficiency was observed between cases and controls across the three study time points.

All self-documented mental health measures were significantly and negatively affected throughout all study periods, with a decrease in mean differences between the two groups over time. Anxiety and depression were significantly and persistently affected at the group level after the diagnosis of COVID-19. Greater historical PA levels and average periods of heavy PA 1–2 years before COVID-19 was negatively diagnosed with LCOVID risk.

Female gender was positively but not significantly associated with LCOVID. Among the self-reported symptoms, most had the greatest counts and disease severity around the time of COVID-19 diagnosis. Fatigue was the most persistent symptom (>140 days) among moderate to severe cases, while breathing difficulties and cough persisted for longer periods among mild cases. More than 160 people (12%) reported persistent COVID-19 symptoms and were classified as L.Sim wolSim The group consists of elderly people.

RHR values ​​were consistently greater in L.Sim cohort. In addition, the increases and decreases in the HR pattern observed during acute COVID-19 were more prominent among L.Sim group of participants. of interest, LSim The group of individuals had a higher step count with differences in the rate of reduction in steps close to the date of diagnosis of COVID-19, while stepping back was found to be more similar. Differences in sleep durations between Lsymp and the short symptom-based COVID group (S.Sim) showed peaks close to the date of diagnosis of COVID-19.

Age was the most prominent risk factor for LCOVID, with the LCOVID risk being 6.4 times higher and 6.5 times higher among individuals over 60 years of age and 50 to 60 years of age, respectively, compared to those aged 10 to 30 years. Of the 2,144 records examined, eight studies investigated LCOVID using digital technology, of which six used self-reported questionnaire data to characterize symptoms of LCOVID, including users of the ZOE COVID app. Three studies evaluated wearable sensor-based data to assess changes in HR, sleep, and PA after a diagnosis of COVID-19, two of which showed a consistent pattern of RHR elevations and reductions, lasting more than 4 months in a few cases.

Overall, the study results show that devices based on wearable and mobile health sensors can identify the presence of LCOVID and monitor recovery.

*Important note

medRxiv publishes preliminary scientific reports that are not subject to peer review, and therefore should not be considered conclusive, guide clinical practice/health-related behavior, or be treated as established information.

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