Assessment of Prolonged Physiological and Behavioral Changes Associated With COVID-19 Infection

Radin, Jennifer M; Quer, Giorgio; Ramos, Edward; Baca-Motes, Katie; Gadaleta, Matteo; Topol, Eric J.; Steinhubl, Steven R.

doi:10.1001/jamanetworkopen.2021.15959

Cited by 78 publications

(56 citation statements)

References 6 publications

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“…The study used an invasive method (muscle sympathetic nerve activity) in 16 subjects after COVID-19 infection, which contrasts with our study that showed similar results through HRV analysis, a simple, dependable, inexpensive, and noninvasive method [32]. Using wearable sensor data, Radin and colleagues [44] reported a prolonged physiological impact of SARS-CoV-2 infection, lasting approximately 2-3 months, on resting heart rate which may reflect ANS dysfunction.…”

Section: Discussioncontrasting

confidence: 74%

Role of Body Mass and Physical Activity in Autonomic Function Modulation on Post-COVID-19 Condition: An Observational Subanalysis of Fit-COVID Study

Freire

Lira

Morano

et al. 2022

IJERPH

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The harmful effects of coronavirus disease 2019 (COVID-19) can reach the autonomic nervous system (ANS) and endothelial function. Therefore, the detrimental multiorgan effects of COVID-19 could be induced by deregulations in ANS that may persist after the acute SARS-CoV-2 infection. Additionally, investigating the differences in ANS response in overweight/obese, and physically inactive participants who had COVID-19 compared to those who did not have the disease is necessary. The aim of the study was to analyze the autonomic function of young adults after mild-to-moderate infection with SARS-CoV-2 and to assess whether body mass index (BMI) and levels of physical activity modulates autonomic function in participants with and without COVID-19. Patients previously infected with SARS-CoV-2 and healthy controls were recruited for this cross-sectional observational study. A general anamnesis was taken, and BMI and physical activity levels were assessed. The ANS was evaluated through heart rate variability. A total of 57 subjects were evaluated. Sympathetic nervous system activity in the post-COVID-19 group was increased (stress index; p = 0.0273). They also presented lower values of parasympathetic activity (p < 0.05). Overweight/obese subjects in the post-COVID-19 group presented significantly lower parasympathetic activity and reduced global variability compared to non-obese in control group (p < 0.05). Physically inactive subjects in the post-COVID-19 group presented significantly higher sympathetic activity than active subjects in the control group. Parasympathetic activity was significantly increased in physically active subjects in the control group compared to the physically inactive post-COVID-19 group (p < 0.05). COVID-19 promotes changes in the ANS of young adults, and these changes are modulated by overweight/obesity and physical activity levels.

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Section: Discussioncontrasting

confidence: 74%

Role of Body Mass and Physical Activity in Autonomic Function Modulation on Post-COVID-19 Condition: An Observational Subanalysis of Fit-COVID Study

Freire

Lira

Morano

et al. 2022

IJERPH

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“…For example, studies do not consistently find a relationship between initial disease severity and subsequent functional impairment [ 25–27 ], and contrasting mechanisms for post–COVID-19 dyspnea ranging from pulmonary injury to impaired systemic oxygen extraction have been described [ 28–31 ]. Observed tachycardias have been attributed to autonomic dysfunction [ 32 , 33 ], but standard testing has shown limited benefit [ 34 ]. Other mechanisms have been proposed, such as endocrinopathies [ 35 , 36 ] or alterations in sensory processing and central sensitization [ 37 ], which is seen in both fibromyalgia and ME [ 38 , 39 ], but further research is needed.…”

Section: Discussionmentioning

confidence: 99%

Identification of Distinct Long COVID Clinical Phenotypes Through Cluster Analysis of Self-Reported Symptoms

Kenny

McCann

O’Brien

et al. 2022

Open Forum Infectious Diseases

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Background We aimed to describe the clinical presentation of individuals presenting with prolonged recovery from coronavirus disease 2019 (COVID-19), known as long COVID. Methods This was an analysis within a multicenter, prospective cohort study of individuals with a confirmed diagnosis of COVID-19 and persistent symptoms >4 weeks from onset of acute symptoms. We performed a multiple correspondence analysis (MCA) on the most common self-reported symptoms and hierarchical clustering on the results of the MCA to identify symptom clusters. Results Two hundred thirty-three individuals were included in the analysis; the median age of the cohort was 43 (interquartile range [IQR], 36–54) years, 74% were women, and 77.3% reported a mild initial illness. MCA and hierarchical clustering revealed 3 clusters. Cluster 1 had predominantly pain symptoms with a higher proportion of joint pain, myalgia, and headache; cluster 2 had a preponderance of cardiovascular symptoms with prominent chest pain, shortness of breath, and palpitations; and cluster 3 had significantly fewer symptoms than the other clusters (2 [IQR, 2–3] symptoms per individual in cluster 3 vs 6 [IQR, 5–7] and 4 [IQR, 3–5] in clusters 1 and 2, respectively; P < .001). Clusters 1 and 2 had greater functional impairment, demonstrated by significantly longer work absence, higher dyspnea scores, and lower scores in SF-36 domains of general health, physical functioning, and role limitation due to physical functioning and social functioning. Conclusions Clusters of symptoms are evident in long COVID patients that are associated with functional impairments and may point to distinct underlying pathophysiologic mechanisms of disease.

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“…While the use of machine learning in the detection and prognostication for COVID-19 based on chest radiographs and CT scans have been questioned in a systematic review that discussed how none of the current studies are of potential clinical use due to biases or methodological flaws 19 , the use of machine learning to enable a continuous and passive COVID-19 early detection is both very promising—for the potential to be scaled up effectively to a large fraction of the population—and repeatable—since we used a strictly separated test set for each of the cross-validation folds. Furthermore, the prediction algorithms developed as part of the DETECT system could be adapted to study the long term health problems due to COVID-19 20 – 25 , or the effects of COVID-19 vaccine on vital signs and individual behavior 26 – 28 . For future infectious pathogen epidemics and pandemics, the new machine learning algorithms developed from the DETECT data can be adapted and re-used for early detection of various types of infections, towards the development of a new system to monitor the spread of future viral illness and prevent future outbreaks or pandemics.…”

Section: Discussionmentioning

confidence: 99%

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

et al. 2021

Self Cite

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Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81–0.85], or AUC = 0.78 [0.75–0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76–0.79], or AUC of 0.70 [0.69–0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.

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Assessment of Prolonged Physiological and Behavioral Changes Associated With COVID-19 Infection

Cited by 78 publications

References 6 publications

Role of Body Mass and Physical Activity in Autonomic Function Modulation on Post-COVID-19 Condition: An Observational Subanalysis of Fit-COVID Study

Role of Body Mass and Physical Activity in Autonomic Function Modulation on Post-COVID-19 Condition: An Observational Subanalysis of Fit-COVID Study

Identification of Distinct Long COVID Clinical Phenotypes Through Cluster Analysis of Self-Reported Symptoms

Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms

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