Abstract
Summary
Long-COVID fatigue substantially correlates with depression and poorer sleep. Decreased peak exercise heart rate, which may reflect mild autonomic dysfunction, and long-COVID memory impairment, are independent risk factors for long-COVID fatigue.
Background
Fatigue is the most prevalent and debilitating long-COVID (coronavirus disease) symptom; however, risk factors and pathophysiology of this condition remain unknown. We assessed risk factors for long-COVID fatigue and explored its possible pathophysiology.
Methods
This was a nested case-control study in a COVID recovery clinic. Individuals with (cases) and without (controls) significant fatigue were included. We performed a multidimensional assessment evaluating various parameters, including pulmonary function tests and cardiopulmonary exercise testing, and implemented multivariable logistic regression to assess risk factors for significant long-COVID fatigue.
Results
A total of 141 individuals were included. The mean age was 47 (SD: 13) years; 115 (82%) were recovering from mild coronavirus disease 2019 (COVID-19). Mean time for evaluation was 8 months following COVID-19. Sixty-six (47%) individuals were classified with significant long-COVID fatigue. They had a significantly higher number of children, lower proportion of hypothyroidism, higher proportion of sore throat during acute illness, higher proportions of long-COVID symptoms, and of physical limitation in daily activities. Individuals with long-COVID fatigue also had poorer sleep quality and higher degree of depression. They had significantly lower heart rate [153.52 (22.64) vs 163.52 (18.53); P = .038] and oxygen consumption per kilogram [27.69 (7.52) vs 30.71 (7.52); P = .036] at peak exercise. The 2 independent risk factors for fatigue identified in multivariable analysis were peak exercise heart rate (OR: .79 per 10 beats/minute; 95% CI: .65–.96; P = .019) and long-COVID memory impairment (OR: 3.76; 95% CI: 1.57–9.01; P = .003).
Conclusions
Long-COVID fatigue may be related to autonomic dysfunction, impaired cognition, and decreased mood. This may suggest a limbic-vagal pathophysiology.
Clinical Trials Registration
clinicaltrials.gov; NCT04851561.
Keywords: post-COVID, post-viral fatigue
Long coronavirus disease (COVID), a late sequela manifesting as ongoing symptoms persisting at least 4 weeks following the onset of acute coronavirus disease 2019 (COVID-19), has been widely reported [1]. It is estimated to affect approximately 10% of the infected individuals [2]. Long-COVID fatigue was reported among 63% and 28% of COVID-19–infected individuals at 6 and 12 months, respectively [3, 4]. The high prevalence reported at 1-year follow-up was similar for individuals with severe and nonsevere acute COVID-19 [4].
The pathophysiology behind long COVID is unknown. The presumed mechanisms include direct and indirect damage to the central nervous system (CNS) secondary to either viral invasion or inflammation, persistent inflammatory response, negative psychosocial aspects associated with the pandemic, direct damage to muscle fibers or neuromuscular junction, and possibly autonomic dysfunction as well as direct or indirect cardiac toxicity [5, 6].
While identifying exclusive risk factors for long-COVID fatigue will assist in detecting populations at risk, unveiling the mechanisms behind this phenomenon is imperative in the search for therapeutic approaches. Nested case-control studies were recommended in order to identify explanatory mechanisms for the major long-COVID manifestations [7]. Accordingly, we aimed to assess risk factors for long-COVID fatigue and to implement a multidimensional assessment in order to cast light on its possible pathophysiology.
METHODS
Study Design and Population
We conducted a nested case-control study in a cohort of individuals who attended our COVID recovery clinic at Rabin Medical Center, Beilinson Hospital. Adults (age ≥18 years) who recovered from COVID-19 were invited for a comprehensive medical evaluation (Supplementary Appendix 1). During a clinic visit, all individuals were evaluated by an internist using a preplanned questionnaire and were asked to grade 14 symptoms as 0 (not present) to 3 (severe) scale. In addition, all individuals underwent pulmonary function testing and were evaluated by a pulmonologist.
Using a computerized algorithm, we randomly sampled individuals who visited our clinic and invited them to participate in the current study (Supplementary Appendix 2). In order to meet our inclusion criteria, an individual had to be at least 2 months following a polymerase chain reaction (PCR)–proven diagnosis of COVID-19.
Individuals with debilitating cardiovascular, neurological, muscular, or other conditions were excluded from participation (for detailed exclusion criteria, see Supplementary Appendix 3).
Classification of Cases and Controls
In order to define individuals with clinically significant long-COVID fatigue, we created a classification tool. The tool is based on the Institute of Medicine criteria and case definition for myalgic encephalitis/chronic fatigue syndrome, adopted by the Centers for Disease Control and Prevention (CDC) [8]. We modified the portion on fatigue to suit the time frame and relations with COVID-19.
Cases (ie, having significant long-COVID fatigue) were defined as having a substantial impairment in the ability to engage in pre-illness levels of occupational, educational, social, or personal activities that persisted for more than 6 weeks and that was accompanied by fatigue, which is often profound, of new onset (appeared after the diagnosis of COVID-19), not the result of ongoing excessive exertion, and not substantially alleviated by rest. These symptoms must have been present for at least half of the daytime, to at least a moderately severe degree. Those who did not meet all of these criteria were defined as controls.
Since the risk factors for long COVID remain largely unknown, we decided to perform an unmatched rather than a matched case-control study in order to facilitate maximal identification of possible risk factors.
During the first study visit (see below), each participant independently filled out a designated form in which she/he was requested to answer on the aforementioned criteria (Supplementary Appendix 4). The research team was blinded to participants’ answers and consequent case definition at the time of recruitment and during the study visit.
Hypothetical Mechanisms for Fatigue
In order to assess the explanatory pathophysiology for long-COVID fatigue we assumed 4 hypothetical mechanisms: neurocognitive, psychosocial, neuromuscular, and cardiopulmonary. We have also considered several other potential contributing factors for fatigue, among these were thyroid dysfunction, iron or vitamin B-12 deficiency, and cytomegalovirus (CMV) or Epstein-Barr virus (EBV) infection. We planned our multidimensional assessment based on the aforementioned mechanisms, while examining all possible manifestations for each mechanism.
Evaluation Protocol of Cases and Controls
All participating individuals (cases and controls) were assessed following the study protocol. The detailed assessment procedure, including questionnaires, physical examination, blood tests, cognitive evaluation, and exercise physiology evaluation including a cardiopulmonary exercise test (CPET), is detailed in Supplementary Appendix 5.
The variables deriving from the questionnaires were assessed as potential predictors of fatigue or used to characterize it but were not used for classification of cases and controls.
Data Collection
We collected demographic data (including socioeconomic status following the classification of the state Central Bureau of Statistics [9]), habits, physical activity before COVID-19 and afterward, comorbidities, and pharmacotherapy. These data were extracted from the questionnaires completed during the first study visit. Missing data were managed by re-approaching the patients to complete absent details.
Acute COVID-19 history (disease severity according to the World Health Organization criteria [10], symptoms of the acute phase, need for hospitalization, complications, pharmacotherapy directed at COVID-19) and long-COVID symptoms as well as pulmonary function tests on COVID recovery clinic visit were extracted from hospital’s electronic medical charts.
Statistical Analysis
The dependent variables were compared between individuals with long-COVID fatigue (cases) and those without (controls) using Student’s t test or the Mann-Whitney U test for continuous variables and the chi-square test for categorical variables. Correlations between the independent variables were assessed using the Spearman’s rank correlation coefficient.
Multivariable analysis was implemented using logistic regression models. Independent variables were selected to be included in the multivariable model based on the bivariate analysis and clinical logic (P < .05). Collinearity was assessed using variance inflation factor (VIF) and bivariate correlations. Variables with suspected collinearity (VIF >3.0 or correlation coefficient >0.4) were assessed in separate models. We used the Akaike information criterion (AIC) and identified the optimal multivariable model as the model for which the AIC was minimal. Odds ratios (ORs) and 95% confidence intervals (CIs) were obtained from the logistic regression models.
Sensitivity analyses were performed to assess the classification of cases and controls. We implemented the same long-COVID fatigue classification tool (see above), while using 2 alternative classification definitions: (1) a more restrictive definition, by which an individual was classified as a case if the symptoms were not substantially alleviated by rest, in addition to all other requirements (see Supplementary Appendix 4), and (2) a more liberal definition, by which an individual was classified as a case even if the symptoms did not persist beyond 6 weeks and/or were present less than half of the time, and/or at a mild degree.
For all analyses, P < .05 was considered statistically significant.
Data analysis was undertaken using IBM SPSS version 27 (IBM Corporation, Armonk, NY, USA).
Compliance With Research Ethics and Guidance
The Research Ethics Committee at Rabin Medical Center approved the study protocol (RMC-0834-20). All participants signed an informed consent prior to participation. All methods were performed in accordance with the relevant guidelines and regulations. The study was registered at clinicaltrials.gov (NCT04851561).
Role of the Funding Source
The funding was served for covering the expenses of CPET and blood tests. The funders had no role in the study’s design, conduct, and reporting.
RESULTS
A total of 144 individuals were recruited between 2 March and 30 June 2021. Following recruitment, 3 individuals appeared to have an exclusion criterion and were therefore excluded (Figure 1). Accordingly, 141 individuals were included for analysis. The mean age of the study population was 47 (SD: 13) years and 83 (59%) were women. The first study visit occurred at an average of 212 (SD: 74) days following COVID-19 diagnosis. The CPET was conducted at an average of 28 (SD: 19) days following the first study visit. By applying our long-COVID fatigue classification tool, 66 individuals (46.8%) were classified as having long-COVID fatigue.
Figure 1.
Participant flow diagram. Abbreviations: COVID, coronavirus disease; COVID-19, coronavirus disease 2019.
Sociodemographic and Clinical Characteristics
Those with long-COVID fatigue had more children [2.80 (SD: 1.81) vs 2.09 (SD: 1.67); P = .023] and a lower proportion of hypothyroidism [3 (4.5%) vs 11 (14.7%); P = .045]. No other sociodemographic or clinical background characteristics differed between the groups (Table 1). Acute sore throat was the only acute illness variable that differed between those with long-COVID fatigue and those without, the proportion among the former was 3.8 times higher [17 (26.6%) vs 5 (7.0%); P = 0.002] (Table 2).
Table 1.
Demographics and Clinical Characteristic of the Study Population
| Nonsignificant Fatigue (n = 75; 53.2%) | Long-COVID Fatigue (n = 66; 46.8%) | P a | |
|---|---|---|---|
| Women, n (%) | 43 (57.3) | 40 (60.6) | .694 |
| Age at enrollment, mean (SD), years | 45.21 (14.45) | 48.85 (11.51) | .211 |
| Born in Israel, n (%) | 61 (81.3) | 57 (86.4) | .420 |
| Marital status, n (%) | .053φ | ||
| Single | 20 (26.7) | 8 (12.1) | |
| Married | 48 (64.0) | 44 (66.7) | |
| Divorced | 6 (8.0) | 10 (15.2) | |
| Widow | 1 (1.3) | 4 (6.1) | |
| Relationship | 53 (70.7) | 54 (81.8) | .122 |
| Number of children, mean (SD) | 2.09 (1.67) | 2.80 (1.81) | .023 |
| Age of the oldest child, mean (SD), years | 23.59 (12.38) | 24.00 (12.53) | .889 |
| Age of the youngest child, mean (SD), years | 16.45 (10.81) | 16.37 (11.20) | .930 |
| Age of youngest child <5 years, n (%) | 11 (20.0) | 9 (15.8) | .561 |
| Living alone, n (%) | 8 (10.7) | 5 (7.6) | .527 |
| Type of residence, n (%) | .959 | ||
| Apartment | 41 (54.7) | 36 (54.5) | |
| Two-family dwelling | 9 (12.0) | 7 (10.6) | |
| Private home | 25 (33.0) | 23 (34.8) | |
| Unemployed, n (%) | 6 (8.0) | 8 (12.1) | .414 |
| Healthcare workers, n (%) | 10 (13.3) | 5 (7.6) | .269 |
| Socioeconomic status (deciles of city of residence according to the CBS), mean (SD) | 6.95 (1.72) | 6.73 (1.67) | .443 |
| Smoking status, n (%) | .219 | ||
| Never smoked | 53 (70.7) | 39 (59.1) | |
| Past smoker | 16 (21.3) | 16 (24.2) | |
| Current smoker | 6 (8.0) | 11 (16.7) | |
| Cigarette pack-years, mean (SD) | 12.29 (12.63) | 14.09 (19.55) | .936 |
| Use of cannabis, n (%) | 3 (4.1) | 5 (7.7) | .473φ |
| Use of alcohol, n (%) | 35 (46.7) | 32 (48.5) | .867 |
| Alcohol servings per week, mean (SD) | 1.40 (1.35) | 1.10 (1.45) | .247 |
| Body mass index, mean (SD), kg/m2 | 27.46 (5.15) | 27.54 (4.96) | .935 |
| Body fat percentage, mean (SD) | 30.97 (7.78) | 33.34 (8.13) | .120† |
| Recreational physical activity | |||
| Prior to COVID-19, mean (SD), minutes/week | 144.80 (218.91) | 140.15 (114.36) | .379 |
| Following COVID-19, mean (SD), minutes/week | 49.18 (84.69) | 49.28 (121.13) | .457 |
| The ratio (%) of physical activity pre-and post-COVID-19,b mean (SD) | 36.06 (45.59) | 33.87 (76.49) | .169 |
| Decline in physical activity, n (%) | 40 (53.3) | 43 (65.2) | .155 |
| Background illnesses, n (%) | |||
| Diabetes mellitus | 6 (8.0) | 6 (9.1) | .817 |
| Hypertension | 10 (13.3) | 9 (13.6) | .958 |
| Ischemic heart disease | 2 (2.7) | 2 (3.0) | 1.000φ |
| Hypothyroidism | 11 (14.7) | 3 (4.5) | .045 |
| Veno-thromboembolism | 0 | 1 (1.5) | .468φ |
| Chronic kidney disease | 0 | 1 (1.5) | .468φ |
| Asthma | 4 (5.3) | 5 (7.6) | .734φ |
| Dyslipidemia | 14 (18.7) | 13 (19.7) | .877 |
| Charlson comorbidity score, mean (SD) | 0.13 (0.38) | 0.17 (0.41) | .589 |
| Regular use of medications | |||
| Any medication, N (%) | 40 (53.3) | 37 (56.1) | .746 |
| Types of regular medications, n (%) | |||
| Aspirin | 7 (9.3) | 4 (6.1) | .470 |
| Beta-blockers | 3 (4.0) | 5 (7.6) | .474φ |
| Statins | 16 (21.3) | 14 (21.2) | .986 |
| ACEi | 5 (6.7) | 5 (7.6) | 1.000φ |
| Angiotensin receptor blockers | 4 (5.3) | 3 (4.5) | 1.000φ |
| Calcium channel blockers | 5 (6.7) | 6 (9.1) | .592 |
| Thiazide | 1 (1.3) | 3 (4.5) | .340φ |
| Proton pump inhibitors | 6 (8.0) | 3 (4.5) | .502φ |
| Metformin | 5 (6.7) | 5 (7.6) | 1.000φ |
| GLP-1 agonists | 3 (4.0) | 2 (3.0) | 1.000φ |
| Levothyroxine | 10 (13.3) | 2 (3.0) | .029 |
| SSRIs | 5 (6.7) | 5 (7.6) | 1.000φ |
| ICS/LABA inhaler | 3 (4.0) | 2 (3.0) | 1.000φ |
| Tamsulosin | 2 (2.7) | 1 (1.5) | 1.000φ |
| Vitamin B-12 | 15 (20.0) | 20 (31.3) | .128 |
| Vitamin D | 17 (22.7) | 21 (32.8) | .181 |
| Vitamin C | 5 (6.7) | 6 (9.4) | .555 |
Abbreviations: ACEi, angiotensin-converting enzyme inhibitors; CBS, Israel Central Bureau of Statistics; COVID, coronavirus disease; COVID-19, coronavirus disease 2019; GLP-1, glucagon-like peptide 1; ICS/LABA, inhaled corticosteroids plus long-acting beta agonist; SSRI, selective serotonin reuptake inhibitor.
Calculated using Mann-Whitney U test or Student’s t test (†) for continuous variables and chi-square test or Fisher’s exact test (φ) for categorical variables.
Calculated for those with physical activity >0 minutes per week prior to COVID-19.
Table 2.
Characteristics of the Acute Illness (COVID-19)
| Nonsignificant Fatigue (n = 75; 53.2%) | Long-COVID Fatigue (n = 66; 46.8%) | P a | |
|---|---|---|---|
| COVID-19 severity, n (%) | .492φ | ||
| Mild | 64 (85.3) | 51 (77.3) | |
| Moderate | 7 (9.3) | 9 (13.6) | |
| Severe | 4 (5.3) | 6 (9.1) | |
| Admission to hospital, n (%) | 8 (10.7) | 6 (9.1) | .755 |
| Length of in-hospital stay, mean (SD),b days | 9.25 (6.16) | 8.83 (6.74) | .950 |
| Symptomatic acute infection, n (%) | 73 (97.3) | 65 (98.5) | 1.000φ |
| Duration (in days) of acute symptoms, mean (SD)c | 10.81 (6.16) | 11.35 (4.79) | .262 |
| Fever ≥38°C, N (%) | 44 (60.3) | 38 (58.5) | .829 |
| COVID-19 symptoms,d n (%) | |||
| Sore throat | 5 (7.0) | 17 (26.6) | .002 |
| Nasal congestion | 14 (19.2) | 19 (29.7) | .151 |
| Fatigue | 54 (74.0) | 47 (73.4) | .943 |
| Headache | 40 (54.8) | 38 (59.4) | .589 |
| Anosmia/dysgeusia | 32 (44.4) | 32 (50.0) | .517 |
| Cough | 34 (46.6) | 33 (52.4) | .499 |
| Dyspnea | 27 (38.0) | 28 (44.4) | .451 |
| Chest pain | 18 (25.4) | 25 (39.1) | .088 |
| Gastrointestinal symptoms | 16 (22.5) | 16 (25.0) | .737 |
| Myalgia | 39 (53.4) | 41 (66.1) | .134 |
| Infiltrates on chest radiogram, n (%) | 9 (47.4) | 8 (40.0) | .643 |
Abbreviations: COVID, coronavirus disease; COVID-19, coronavirus disease 2019.
Calculated using Mann-Whitney U test for continuous variables and chi-square test or Fisher’s exact test (φ) for categorical variables.
Calculated only for those who were hospitalized.
Calculated only for those who had symptoms.
Individuals who reported symptom intensity of moderate to severe were counted as positive.
On their first visit at the recovery clinic, 60 (90.9%) of those with long-COVID fatigue reported at least 1 significant symptom apart from fatigue, compared with 57 (76.0%) of those without fatigue (P = 0.019). Those with fatigue tended to have a higher prevalence of the other long-COVID symptoms (Table 3). The heart rate and saturation at rest as well as pulmonary function tests during the clinic visit were similar between the 2 groups.
Table 3.
Characteristics of Long COVID at Time of Recovery Clinic Visit
| Nonsignificant Fatigue (n = 75; 53.2%) | Long-COVID Fatigue (n = 66; 46.8%) | P a | |
|---|---|---|---|
| Time interval (in days) from COVID-19 diagnosis to clinic visit, mean (SD) | 113.68 (52.84) | 126.49 (81.57) | .812 |
| Long-COVID symptoms,b n (%) | |||
| At least 1 long-COVID symptom | 57 (76.0) | 60 (90.9) | .019 |
| Headache | 4 (5.5) | 9 (14.3) | .082 |
| Anosmia/dysgeusia | 12 (16.0) | 18 (27.3) | .103 |
| Cough | 3 (4.0) | 12 (18.2) | .006 |
| Dyspnea | 24 (32.9) | 27 (40.9) | .326 |
| Chest pain | 13 (17.3) | 22 (33.3) | .028 |
| Palpitations | 5 (6.8) | 6 (9.1) | .608 |
| Paresthesia | 6 (8.2) | 14 (21.2) | .029 |
| Concentration impairment | 7 (9.6) | 30 (45.5) | <.001 |
| Memory impairment | 13 (17.8) | 31 (47.0) | <.001 |
| Irritability | 6 (8.0) | 18 (27.7) | .002 |
| Emotional distress | 8 (10.7) | 23 (34.8) | .001 |
| Myalgia | 20 (26.7) | 31 (47.7) | .010 |
| Arthralgia | 2 (2.7) | 10 (15.6) | .007 |
| Weakness | 9 (12.0) | 12 (18.5) | .286 |
| Rash | 0 | 1 (1.5) | .285φ |
| Hair loss | 7 (9.3) | 11 (17.7) | .147 |
| Insomnia | 11 (14.7) | 29 (43.9) | <.001 |
| Vital signs, mean (SD) | |||
| Heart rate, beats/minute | 76.61 (11.21) | 78.02 (12.93) | .470 |
| Saturation, % | 98.69 (1.20) | 98.75 (1.87) | .293 |
| Infiltrates on chest radiogram, n (%) | 8 (12.5) | 3 (6.0) | .342φ |
| Pulmonary function tests, mean (SD) | |||
| FEV1, % of expected | 95.20 (14.82) | 95.27 (14.11) | .979† |
| FVC, % of expected | 97.02 (14.63) | 96.64 (13.24) | .884† |
| FEV1/FVC | 0.83 (0.07) | 0.83 (0.06) | .643 |
| TLC, % of expected | 96.50 (18.91) | 96.36 (12.45) | .441 |
| DLCO, % of expected | 88.30 (15.76) | 86.16 (14.46) | .526 |
| Blood parameters, mean (SD) | |||
| White blood cells, K/μL | 6.61 (1.73) | 6.82 (2.07) | .611 |
| Hemoglobin, g/dL | 13.66 (1.18) | 13.66 (1.30) | .839 |
| Thyroid-stimulating hormone, mIU/L | 2.02 (1.24) | 2.17 (1.27) | .374 |
| Fasting serum glucose, mg/dL | 94.65 (15.38) | 97.44 (16.99) | .336 |
| Creatinine, mg/dL | 0.78 (0.14) | 0.77 (0.16) | .538 |
| Creatine phosphokinase, U/L | 150.50 (237.59) | 114.23 (77.92) | .954 |
| C-reactive protein, mg/dL | 0.39 (0.52) | 0.29 (0.28) | .898 |
| Hemoglobin A1c, % | 5.51 (0.63) | 5.69 (0.56) | .587 |
Abbreviations: COVID, coronavirus disease; COVID-19, coronavirus disease 2019; DLCO, diffusing capacity for carbon monoxide; FEV1, forced expiratory volume; FVC, forced vital capacity; TLC, total lung capacity.
Calculated using Mann-Whitney U test or Student’s t test (†) for continuous variables and chi-square test or Fisher’s exact test (φ) for categorical variables.
Individuals who reported symptom intensity of moderate to severe were counted as positive.
None of the participating individuals had a major finding on physical examination. Results of the comprehensive blood tests performed were similar between the 2 groups, including thyroid function tests and vitamin B-12 levels (Supplementary Tables 1–5). There was no evidence for an acute or recent CMV or EBV infection in our study population (Supplementary Table 6).
Neurocognitive, Sleep, and Mood
Individuals with significant long-COVID fatigue reported substantially higher proportions of physical limitations and different manifestations of fatigue (Table 4). The sleep assessment revealed that those with long-COVID reported difficulties in all aspects of sleep: they had poorer sleep quality [global Pittsburgh Sleep Quality Index score of 11.30 (SD: 4.14) vs 6.32 (SD: 3.03); P < .001]; higher scores of sleepiness [total Epworth Sleepiness Scale score of 12.11 (SD: 5.25) vs 8.39 (SD: 4.46); P < .001]; and higher insomnia scores [total Insomnia Severity Index score of 17.82 (SD: 5.93) vs 8.65 (SD: 5.66); P < .001]. The population of individuals with long-COVID fatigue also had approximately 3 times higher scores in the Patient Health Questionnaire-9 assessing depression (while excluding the components on fatigue and sleep disorders): 9.71 (SD: 4.53) versus 3.32 (SD: 3.46) (P < .001).
Table 4.
Assessment at First Study Visit
| Nonsignificant Fatigue (n = 75; 53.2%) | Long-COVID Fatigue (n = 66; 46.8%) | P a | |
|---|---|---|---|
| Time interval (in days) from COVID-19 diagnosis to first study visit, mean (SD) | 207.55 (67.11) | 218.03 (80.63) | .585 |
| Assessment of functional capacity (SF-36 component on physical limitation),b n (%) | |||
| Vigorous activities | 46 (63.0) | 56 (87.5) | .001 |
| Moderate activities | 10 (13.3) | 37 (56.1) | <.001 |
| Lifting or carrying groceries | 10 (13.3) | 38 (58.5) | <.001 |
| Climbing several flights of stairs | 36 (48.0) | 54 (83.1) | <.001 |
| Climbing 1 flight of stairs | 12 (16.0) | 22 (33.8) | .014 |
| Bending, kneeling, or stooping | 10 (13.3) | 26 (39.4) | <.001 |
| Walking more than 1 kilometer | 20 (27.0) | 42 (64.6) | <.001 |
| Walking several blocks | 19 (25.7) | 39 (60.0) | <.001 |
| Walking 1 block | 3 (4.1) | 20 (30.8) | <.001 |
| Bathing or dressing yourself | 1 (1.3) | 8 (12.1) | .013φ |
| Subjective report on fatigue, n (%) | <.001 | ||
| Yes, even before COVID-19 | 19 (25.3) | 3 (4.5) | |
| Yes, after I was diagnosed with COVID-19 | 38 (50.7) | 63 (95.5) | |
| Not at all | 18 (24.0) | 0 | |
| Characteristics of fatigue,c n (%) | |||
| Morning waking up | 25 (43.9) | 43 (65.2) | .018 |
| Need for siesta | 32 (57.1) | 49 (75.4) | .033 |
| Feeling fatigued throughout the entire day | 21 (36.8) | 60 (90.9) | <.001 |
| Feeling fatigued during the evening | 44 (77.2) | 62 (93.9) | .007 |
| Need for longer sleeping hours | 28 (50.9) | 54 (81.8) | <.001 |
| Need for more coffee servings per day | 8 (14.0) | 19 (28.8) | .049 |
| Fatigue burdens me with daily home tasks | 14 (25.0) | 50 (75.8) | <.001 |
| Fatigue burdens me with employment tasks | 13 (22.8) | 49 (74.2) | <.001 |
| Fatigue burdens me with sport activities | 29 (55.8) | 53 (80.3) | .004 |
| Subjective impression of cognitive decline, n (%) | 34 (45.9) | 58 (87.9) | <.001 |
| Sleep assessment, mean (SD) | |||
| Global PSQI score | 6.32 (3.03) | 11.30 (4.14) | <.001 |
| Total ESS score | 8.39 (4.46) | 12.11 (5.25) | <.001 |
| Total ISI score | 8.65 (5.66) | 17.82 (5.93) | <.001 |
| Depression assessment, mean (SD) | |||
| Total PHQ-9 scored | 3.32 (3.46) | 9.71 (4.53) | <.001 |
Abbreviations: COVID, coronavirus disease; COVID-19, coronavirus disease 2019; ESS, Epworth Sleepiness Scale; ISI, Insomnia Severity Index; PHQ-9, Patient Health Questionnaire; PSQI, Pittsburgh Sleep Quality Index; SF-36, 36-item Short Form Survey.
Calculated using Mann-Whitney U test for continuous variables and chi-square test or Fisher’s exact test (φ) for categorical variables.
For each category, individuals who responded a moderate or severe limitation were counted as positive.
Excluding those who responded that they do not experience fatigue. For each category, individuals who responded on moderate or severe limitation were counted.
For the current calculation, the questions on fatigue or sleep were omitted.
Individuals with long-COVID fatigue also had a higher proportion of subjective report of cognitive impairment [58 (87.9%) vs 34 (45.9%); P < .001] (Table 4). However, when adjusted for age, the 2 groups performed similarly in the cognitive fatigue task, and those with long-COVID fatigue did not exhibit signs of cognitive fatigue (Supplementary Table 7).
Cardiopulmonary Exercise Testing
The performance on CPET is presented in Table 5. Although being able to complete the test in terms of work rate, heart rate, and respiratory exchange ratio requirements, individuals with long-COVID fatigue had significantly lower heart rate [153.52 (SD: 22.64) vs 163.52 (SD: 18.53) beats/minute; P = .038] and oxygen consumption per kilogram [27.69 (SD: 7.52) vs 30.71 (SD: 7.52); P = .036] at peak exercise. These differences were not noted at rest; however, they were evident at the anaerobic threshold (Supplementary Table 8). All other CPET components were similar between the 2 groups.
Table 5.
Cardiopulmonary Exercise Test: Exercise Peak Parameters
| Nonsignificant Fatigue (n = 63; 55.8%) | Long-COVID Fatigue (n = 50; 44.2%) | P a | |
|---|---|---|---|
| Time interval (in days) from COVID-19 diagnosis to CPET, mean (SD) | 238.92 (69.35) | 242.66 (87.48) | .892 |
| Maximum work rate, mean (SD), Watts | |||
| Expected | 200.97 (59.10) | 187.70 (70.04) | .113 |
| Observed | 192.67 (73.00) | 172.70 (76.83) | .096 |
| Percent of expected | 98.48 (32.31) | 95.92 (34.16) | .615 |
| Oxygen consumption, mean (SD), L/minute | |||
| Expected | 2.31 (0.59) | 2.18 (0.70) | .113 |
| Observed | 2.29 (0.60) | 2.06 (0.66) | .042 |
| Percent of expected | 100.17 (17.17) | 96.37 (18.31) | .207 |
| Oxygen consumption, mean (SD), % of normal | 99.60 (17.40) | 96.07 (18.33) | .327 |
| Oxygen consumption per weight, mean (SD), V′O2/kg | 30.71 (7.52) | 27.69 (7.52) | .036† |
| Respiratory exchange ratio, mean (SD) | 1.08 (0.06) | 1.07 (0.09) | .250† |
| Heart rate, mean (SD), beats/minute | |||
| Expected | 154.87 (14.18) | 149.44 (11.21) | .060 |
| Observed | 163.52 (18.53) | 153.52 (22.64) | .038 |
| Percent of expected | 105.95 (11.14) | 102.69 (12.93) | .239 |
| Oxygen pulse, mean (SD), mL | 14.25 (3.90) | 13.17 (3.35) | |
| Expected | 14.97 (3.63) | 14.50 (4.07) | .343 |
| Observed | 14.25 (3.90) | 13.17 (3.35) | .206 |
| Percent of expected | 95.62 (18.50) | 93.69 (13.68) | .960 |
| Systolic blood pressure, mean (SD), mmHg | 175.48 (16.87) | 173.98 (19.82) | .880 |
| Diastolic blood pressure, mean (SD), mmHg | 74.29 (5.60) | 75.51 (5.97) | .331 |
| Minute ventilation, mean (SD), L/minute | |||
| Expected | 100.91 (19.83) | 93.68 (25.09) | .047 |
| Observed | 83.54 (22.89) | 75.34 (28.88) | .065 |
| Percent of expected | 83.16 (16.78) | 80.50 (22.37) | 0.486† |
| Tidal volume, mean (SD), L | 2.01 (0.61) | 1.84 (0.58) | .122 |
| Breathing frequency (times per minute), mean (SD) | |||
| Expected | 32.78 (4.39) | 31.56 (3.92) | .116 |
| Observed | 43.24 (8.81) | 42.02 (8.47) | .493† |
| Percent of expected | 133.09 (30.60) | 131.53 (28.53) | .752 |
| Minute ventilation/CO2 output, mean (SD) | 31.32 (3.67) | 31.28 (3.62) | .961† |
Abbreviations: COVID, coronavirus disease; COVID-19, coronavirus disease 2019; CPET, cardiopulmonary exercise test.
Calculated using Mann-Whitney U test or Student’s t test (†).
Multivariable Analysis
Since long-COVID fatigue highly correlated with sleep disturbances and with the degree of depression (ρ > 0.5, P < .001 for all), we did not introduce these variables into the multivariable model.
Two independent risk factors were identified for significant long-COVID fatigue: peak exercise heart rate (OR: .79 per 10 beats/minute; 95% CI: .65–.96; P = .019) and long-COVID memory impairment (OR: 3.76; 95% CI: 1.57–9.01; P = .003).
Sensitivity Analyses
A total of 43 and 76 individuals were classified as having significant long-COVID fatigue using the restrictive and liberal definitions, respectively (Supplementary Tables 9–18). Multivariable analysis revealed the same independent risk factors using both definitions: using the restrictive definition—peak exercise heart rate (OR: .60 per 10 beats/minute; 95% CI: .47–.78; P < .001) and long-COVID memory impairment (OR: 3.03; 95% CI: 1.17–7.85; P = .023); using the liberal definition—peak exercise heart rate (OR: .81 per 10 beats/minute; 95% CI: .67–.99; P = .037) and long-COVID memory impairment (OR: 4.16; 95% CI: 1.65–10.49; P = .003).
DISCUSSION
In this multidimensional assessment of long-COVID fatigue we compared recovered individuals with and without significant fatigue, at an average of approximately 8 months following acute illness. Most individuals were recovering from mild (82%) or nonsevere (93%) COVID-19. Those with significant long-COVID fatigue had decreased peak exercise heart rate and reported a higher rate of cognitive symptoms, predominantly memory impairment.
Those with long-COVID fatigue achieved the CPET requirements in terms of work rate and respiratory exchange ratio and met the expected values for all test parameters. Nonetheless, when compared with those without fatigue, individuals with long-COVID fatigue had lower peak oxygen consumption per weight and significantly decreased peak heart rate, at an average of 10 beats per minute lower than those without fatigue. No differences in heart rate were noted while at rest. These findings suggest that individuals with significant long-COVID fatigue experience a slightly impaired chronotropic response.
It was previously suggested that autonomic dysfunction may play a role in the pathophysiology of long-COVID, particularly in symptoms resulting from the cardiovascular system [5]. This assumption was derived from several reports on orthostatic hypotension and postural tachycardia syndrome occurring following the acute disease [11, 12]. Long-COVID may also involve other components of the autonomic nervous system, such as the sudomotor, gastrointestinal, and pupillomotor functions [13]. Suggested mechanisms for long-COVID, including direct viral invasion, endothelial dysfunction and inflammation, microthrombosis, and capillary congestion, may affect the central and peripheral nervous system by either direct viral activity or by vascular compromise and demyelination [5, 13].
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) shares similarities with long-COVID fatigue not only in terms of symptoms such as persistent exhaustion and pain but also with respect to its association with autonomic dysfunction [14, 15]. A meta-analysis comparing CPET results of individuals with ME/CFS with healthy controls demonstrated that the former exhibit impaired chronotropic response [16]. The impairment in chronotropic response in our study was more subtle than that demonstrated in ME/CFS, with small deviations within the range of normal, and results in a discrepancy between the severe symptoms reported by those with long-COVID fatigue and the absence of overt findings on routine clinical tests.
The close association between long-COVID fatigue and cognitive symptoms implies a common pathophysiology. Individuals with fibromyalgia, a syndrome that shares similarities with long-COVID, exhibit low heart rate variability (HRV) [17]. It was assumed that their symptoms are related to a low vagal tone, responsible for the low HRV and consequent affective disorders through the vagal ventral branch [17]. Reduced vagal tone was also associated with cognitive impairment among individuals with ME/CFS [18]. It is therefore possible that long-COVID fatigue is mediated through CNS involvement of the limbic system, responsible for cognitive and emotional symptoms, as well as for low vagal tone. This can be somewhat supported by a recent study that reported nonspecific magnetic resonance imaging (MRI) alternations in the thalamus [19].
The strong positive correlation between fatigue and sleep disturbances is not surprising considering the significant interrelations between fatigue, the autonomic nervous system, and sleep [20]. Individuals with ME/CFS have poorer sleep quality and tend to experience insomnia [21, 22]. Sleep disorders and poor sleep quality have been suggested as risk factors for fibromyalgia and other chronic pain syndromes [23]. Accordingly, it is possible that individuals with poorer sleep quality are at increased risk for long-COVID fatigue. Depression, which was present to some extent in most individuals with long-COVID fatigue, is almost always associated with poorer sleep quality [24]. The interaction of the autonomic nervous system with several stressors and immune imbalances has been suggested as the pathophysiology leading to depression [25]. Sleep disturbances and mood disorders might interact with long-COVID’s autonomic dysfunction to aggravate fatigue.
Our study has several limitations. Residual confounding is a concern, particularly since we did not measure anxiety, a commonly reported long-COVID symptom [3]. However, since we assessed depression and considering the strong correlation reported for both conditions, this limitation is unlikely to change the results. A few other components were also missing, mainly the assessment of HRV at rest and brain functional MRI. Additionally, since we assessed manifold parameters, one may argue that adjustment for multiple comparisons is required. Some experts advocate against multiple comparisons [26], particularly in observational studies and when an in-depth assessment includes multiple variables that share similar thematic fields. However, in the absence of correction for multiple comparisons, our findings should be considered preliminary and further validation in another population is warranted.
Another potential limitation is the nonuniform interval between the acute illness (COVID-19) and study recruitment. However, no differences in the time interval between diagnosis and recruitment were noted among those with long-COVID fatigue and those without. Accordingly, it is implausible to significantly affect the results.
All participating individuals were sampled from the cohort of individuals who attended the COVID recovery clinic. The study is lacking a control group of recovered individuals who do not have long-COVID. Future studies should aim to include a non–COVID-infected control group in order to control for possible sequelae of lockdowns and stress associated with the pandemic [27].
Our study implies that long-COVID fatigue results from mild autonomic dysfunction. Future rehabilitation programs and other interventions should take into account subtle changes in the chronotropic competence of symptomatic recoverees.
Further studies are warranted to establish these novel preliminary findings and to elucidate the role of the limbic system and vagus nerve in the pathophysiology of long-COVID fatigue.
Supplementary Material
Contributor Information
Ili Margalit, Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; COVID Recovery Clinic, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel.
Dana Yelin, COVID Recovery Clinic, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel.
Moshe Sagi, Department of Internal Medicine F, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Maya Merav Rahat, Department of Internal Medicine E, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Liron Sheena, Department of Internal Medicine F, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Nadav Mizrahi, Department of Internal Medicine B, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Yael Gordin, The Hebrew University-Hadassah Medical School, Hebrew University, Jerusalem, Israel.
Hadar Agmon, Department of Internal Medicine B, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Nitzan Karny Epstein, Department of Internal Medicine B, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Alaa Atamna, Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; COVID Recovery Clinic, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel.
Ori Tishler, COVID Recovery Clinic, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; Department of Internal Medicine F, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Vered Daitch, Department of Internal Medicine E, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Tanya Babich, Research Authority, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Donna Abecasis, Functional MRI Center, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Yoni Yarom, Medix Sport Medicine Center, Tel Aviv, Israel.
Shirit Kazum, Department of Cardiology, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Dorit Shitenberg, Pulmonary Institute, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Erik Baltaxe, Pulmonary Institute, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Odelia Elkana, Behavioral Sciences, Academic College of Tel Aviv-Yafo, Tel Aviv-Jaffa, Israel.
Irit Shapira-Lichter, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel; Functional MRI Center, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; Sagol School of Neuroscience, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel.
Leonard Leibovici, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel; Research Authority, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Dafna Yahav, Infectious Diseases Unit, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel; COVID Recovery Clinic, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel.
Supplementary Data
Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.
Notes
Financial support. This work was supported in part by Prof. Amnon Shashua’s Research Fund, Tel Aviv University - Faculty of Medicine Research Funds, and by the Israel Insurance Association Research Fund. The funding organizations had no role in the planning, writing or analyzing of the data in this study.
Potential conflicts of interest. The authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest.
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