Herbal Medicines for Long COVID: A Phase 2 Pilot Clinical Study

Background: Infections of Coronavirus Disease-2019 (COVID-19) can cause long-term effects known as long COVID. This pilot study aimed to evaluate the feasibility of a clinical study as well as the efficacy and safety of traditional East Asian herbal medicines in alleviating fatigue and cognitive dysfunction “brain fog” in patients with long COVID. Methods: This prospective pilot study investigated the use of three types of herbal medicines, Bojungikki-tang (BIT), Kyungok-go (KOG), and Cheonwangbosim-dan (CBD), for a 12-week period as potential treatments for fatigue and cognitive dysfunction in patients with long COVID. Forty-five patients with long COVID were recruited, and one of three drugs was given based on the patient’s symptoms and pattern identification. The effect of herbal medications on fatigue and cognitive function outcomes was assessed over a 36-week period, with patient adherence closely monitored. Results: After 12 weeks of herbal drug administration, fatigue symptoms improved significantly across all groups, with treatment success rates of 80%, 53.33%, and 46.67% in the BIT, KOG, and CBD groups, respectively. However, “brain fog” symptoms showed less improvement, with treatment success rates of 40%, 46.67%, and 13.33% in the BIT, KOG, and CBD groups, respectively. All adverse events reported were mild and unrelated to the medication. The study design was found to be feasible with high medication adherence. Conclusions: This study demonstrated the feasibility of conducting a clinical trial with three herbal medicines to treat long COVID symptoms like fatigue and “brain fog.”

Keywords:

Subject: Public Health and Healthcare - Other

1. Introduction

Although a significant number of patients have reported persistent symptoms following acute coronavirus disease 2019 (COVID-19), a global consensus on terminologies and definitions has yet to be established. The definition of long COVID, also known as chronic or postacute sequelae of SARS-CoV-2 infection (PASC), has, is not well defined, and different countries and institutions use different terms or definitions. According to the World Health Organization, post-COVID-19 conditions are defined as symptoms that usually occur within three months of the onset of COVID-19 symptoms, persist for at least two months, and are not explained by other alternative diagnoses [1]. In South Korea, chronic COVID-19 syndrome (long COVID) is defined as the persistence of one or more symptoms or signs that cannot be explained by another disease after 12 weeks of COVID-19 diagnosis [2]. These symptoms are diverse and can affect various systems, commonly including fatigue, shortness of breath, cough, chest pain, joint pain, muscle aches, “brain fog” (cognitive difficulties), headache, heart palpitations, sleep disturbances, depression, anxiety, and loss of taste or smell. A meta-analysis of 41 studies found that the global combined prevalence of PASC was 43%, with fatigue, memory problems, and dyspnea being the most common symptoms [3]. Similarly, other studies have found that fatigue and cognitive impairment are the most common and frequently reported PASC symptoms [4,5]. In addition, unlike other common symptoms of PASC, such as dyspnea and depression, there are currently no established and effective treatments for these conditions [6]. It is important to note that the prevalence of long COVID can vary based on factors such as the definition of long COVID used in the study, the population being studied, and the follow-up duration.

Several risk factors appear to be associated with PASC, including female gender, ethnic minority status, socioeconomic deprivation, smoking, obesity, and comorbidities [7]. Data from the United States, 153,543 adults, including 19,985 with long COVID, revealed that 14% of adults aged 18–84 (35.11 million) and 15.5% of adults at the working age of 18–64 (30.65 million) had long COVID by November 2022 [8]. In addition, approximately 27.7 million adults and 24.2 million adults at the working age may experience adverse socioeconomic and mental health outcomes due to long COVID. Furthermore, the estimated annual earnings loss among working-age adults was $175 billion. These findings highlight the significant public health and economic impact of long COVID, underscoring the need for further investigation and intervention [8].

Despite the significant impacts of long COVID, there is currently a lack of treatment methods supported by concrete evidence. There are several approaches available to manage symptoms and improve overall well-being in individuals with long COVID. For fatigue in patients with long COVID-19, treatment options include medications, alternative medicine, cognitive behavioral therapy, and exercise therapy. However, studies evaluating the effectiveness of these interventions are limited and lack high-quality evidence [9]. To date, the treatment strategies for various post-COVID-19 syndromes are inadequate. However, traditional medicine has been identified as a potential solution due to its ability to relieve symptoms, improve organic injuries, and affect immune function [10]. According to a previous study, a significant number of patients with post-COVID-19 syndrome who were identified with particular syndromes, such as Lung and Spleen Qi Deficiency and Qi and Yin Deficiency, in traditional East Asian medicine showed improvement in symptoms after treatment [11]. Furthermore, randomized controlled trials have suggested that traditional medicine can improve fatigue in post-COVID-19 patients [12]. However, these pieces of evidence appear to be insufficient for complementary and alternative medicine (CAM). There is a lack of published studies regarding the effectiveness and safety of CAM interventions for long COVID [13], highlighting the need for more clinical studies in this area.

According to reports, Korean medicine (KM) doctors consider Qi Deficiency as a major cause of chronic fatigue syndrome and prioritize prescribing Bojungikki-tang (BIT; Bu-Zhong-Yi-Qi-Tang in Chinese or Hochu-ekki-to in Japanese) as a treatment [14]. In Japan, Bojungikgi-tang is also frequently used to treat fatigue associated with long COVID [15]. During the pandemic period, the most commonly prescribed herbal medicine in South Korea for fatigue, weakness, and dry cough was Kyungok-go (KOG) [16]. Furthermore, Cheonwangbosim-dan (CBD) has the potential to improve or treat physical and mental illnesses, such as cognitive dysfunction, neurosis, insomnia, and cardiac malfunction-induced disease [17]. Therefore, these herbal medicines are considered potentially useful in alleviating long COVID symptoms. It is necessary to establish clinical evidence for these herbal medicines, not only to develop traditional medical treatment strategies for long COVID but also to provide them to the growing number of patients.

This was a phase 2 preliminary clinical trial that aimed to evaluate the safety and efficacy of frequently used herbal medicines in patients experiencing fatigue or “brain fog” associated with long COVID. The research objectives were to assess the feasibility of the intervention and gather evidence for future large-scale studies. Our data demonstrate a new possibility of herbal medicines against long COVID symptoms.

2. Materials and methods

2.1. Trial design

This was a prospective pilot study conducted at Kyung Hee University Korean Medicine Hospital in Seoul, Republic of Korea, with the aim of testing the feasibility and acceptability of using selected herbal medicines in patients experiencing fatigue or “brain fog” after COVID-19. This study was approved by the Institutional Review Board of Kyung Hee University KM Hospital (KOMCIRB 2020-12-002-001). The protocol of this study was published as a protocol paper [18] and registered at cris.nih.go.kr (KCT0006252) prior to the start of the clinical trial.

2.2. Participants

Patients with fatigue or cognitive symptoms were recruited for this study. Each patient was prescribed three KM herbal medications based on syndrome differentiation and symptom type. Since this clinical trial did not include a control group, randomization, allocation concealment, and blinding were not implemented. Patients had monthly visits for a 12-week medication period, and their symptoms and blood biochemistry were assessed up to 36 weeks after participation. The enrollment, intervention, and assessment schedules are summarized in Table 1.

This study included individuals over the age of 19 who had recovered from COVID-19, reported persistent fatigue or cognitive dysfunction for more than four weeks after their diagnosis but not before, achieved a total score of more than 76 points on the Checklist Individual Strength (CIS), had no overall cognitive function issues, and could provide written informed consent. However, individuals with conditions that could potentially cause fatigue or “brain fog” or interfere with drug intake or absorption as well as those with other allergic, hepatic, or nephrotic diseases, were excluded from the study. All participants provided written informed consent prior to participating in this study. All of the entry criteria are listed in Table S1.

2.3. Intervention

The KM syndrome differentiation was conducted by a Korean Medicine doctor specializing in internal medicine specialist, with over 10 years of clinical experience. Patients were classified into either the fatigue or cognitive dysfunction group based on the primary symptoms observed after COVID-19. Subsequently, they were assigned to the appropriate herbal medicine intervention group according to their specific sub-syndrome differentiation pattern. The criteria for syndrome differentiation can be found in Table 2. Based on syndrome differentiation, three different herbal medications were prescribed to each patient for 12 weeks. Among patients with fatigue, the pattern/syndrome of Lung-Spleen Qi Deficiency group received BIT (Kracie Bojungikgitang Extract Fine Granule, Kolmar Pharma, Korea) as one sachet (3.75 g) twice a day before or in between meals. In addition, the syndrome of Dual Deficiency of Qi and Yin group received KOG, KBPharm, Korea) as one sachet (20 g) twice a day in the morning and evening before or in between meals. Furthermore, the cognitive dysfunction group received CBD (Soonsimhwan, Hanpoong Pharm, Korea) as one sachet (one dose) once a day in between meals. All medications and their respective dosages were approved by the Ministry of Food and Drug Safety (MFDS). All medication ingredients are detailed in Table 3.

2.4. Outcome measures

The primary endpoint of this study was to determine the treatment success rate after 12 weeks of herbal medication for fatigue and cognitive dysfunction in patients who had recovered from COVID-19. The fatigue or cognitive dysfunction was assessed before and 13 weeks after the administration of herbal medicines using a Visual Analog Scale (VAS) score ranging from 0 to 100. A change in VAS score between before and after treatment greater than 15 points was defined as treatment success. The secondary endpoints included evaluating medication adherence and utilizing CIS [19], Chalder Fatigue Scale and Subscale [20], EQ-5D-5L for health-related quality of life [21], Pittsburgh Sleep Quality Index-K [22], Korean–Montreal Cognitive Assessment scale for cognitive function [23], Cognitive Failure Questionnaire for cognitive function [24], Beck’s Depression Inventory [25], Digit Span Test in the Korean–Wechsler Adult Intelligence Scale for assessing changes in cognitive dysfunction [26], and Korean–Boston Naming Test-15 for measuring language proficiency in cognitive impairments [27]. During the 12-week treatment period, patients had monthly visits. Following that, they had follow-up visits every 12 weeks over a total period of 24 weeks for outcome assessment.

To evaluate the study’s feasibility, the patient recruitment rate was calculated as the ratio of the number of recruited patients during the study period to the planned number of study patients (45 and 15 patients per intervention group, respectively). Dropout reasons were documented, and the dropout rate was calculated and compared for all study patients as well as for each intervention group.

The safety of the drugs was evaluated by monitoring the incidence of adverse events (AEs). AEs referred to all harmful and unintended symptoms of signs, including abnormal laboratory test results in patients received herbal medicine, without necessarily having a causal relationship. At Weeks 0 and 13, laboratory investigations, including blood chemistry, were performed, and an electrocardiogram was assessed. Laboratory tests determined the levels of red blood cells, hemoglobin, hematocrit, platelets, white blood cells, fasting blood sugar, BUN, creatinine, AST, ALT, and blood electrolytes (Na, K, Cl). AEs were assessed at all in-person visits occurring at 1, 5, 9, 13, 25, and 37 weeks. Detailed research methodology can be found in the previously published protocol paper [18].

2.5. Statistical analysis

To assess the feasibility of the study design, the subject recruitment rate, dropout rate, and dropout reasons were analyzed. Since this trial was conducted as a pilot study, the sample size was not calculated beforehand. The dropout rate was calculated for all participants and for each herbal medication group, and the dropout reasons were also assessed. For continuous variables used to assess the potential clinical effectiveness of the herbal medications, descriptive statistics such as standard deviation, median, minimum, and maximum values were presented for each time point. Statistical analysis was conducted on an intention-to-treat basis, and the last observation carried forward analysis was employed to handle missing data while evaluating effectiveness. An analysis of covariance (ANCOVA) was used to compare the different herbal medication groups (p < 0.05). In cases where the basic assumptions of ANCOVA, such as normality and equal variance of data, were violated, the Kruskal–Wallis test was used. The statistical analysis was conducted using the R software (R 4.1.2, The R Foundation, www.r-project.org, accessed on August 9, 2022). A full analysis set was used to analyze variables for effectiveness evaluation, while data for safety evaluation were analyzed using the safety set. All statistical tests, unless otherwise specified, were two-sided with a significance level of 5%.

3. Results

3.1. Participants

A total of 49 potential participants were screened, with 45 included in the study. A flow chart of patients enrolled in this study is depicted in Figure 1 and Figure 2.

3.2. Baseline characteristics

In this study, fatigue and “brain fog” symptoms were assessed in patients with long COVID. Herbal drugs for fatigue symptoms (BIT and KOG) and “brain fog” (KOG) were prescribed to patients based on their symptoms and pattern identification diagnosis. Patients in the CBD group were older (average age: 51.33 years) than those in the fatigue herbal drug groups (BIT and KOG). The average duration of symptoms was 89.47 days in the BIT group, 114.8 days in the KOG group, and 119 days in the CBD group. The body mass index, educational level, and occupational status were comparable among the groups. When the National Early Warning Score (NEWS) was considered, the KOG and CBD groups showed a more severe status during the acute COVID-19, with fatigue symptoms being more severe in the BIT and KOG groups and “brain fog” symptoms being more severe in the CBD group (Table 4).

3.3. Feasibility assessment

Between August 2021 and July 2022, the targeted recruitment of 45 participants was successfully completed, achieving a 100% recruitment and enrollment rate. Among the included participants, 14 (31%) dropped out due to various reasons. A significant number of these dropouts were attributed to unexpected diseases, with six individuals experiencing a reinfection of COVID-19. However, no withdrawals were linked to AEs from the administered drug. In the BIT group, seven participants dropped out due to unexpected diseases (n = 3), pregnancy (n = 1), and withdrawal of consent (n = 3). In the KOG group, two dropped out due to unexpected diseases. In the CBD group, five dropped out due to unexpected diseases (n = 2) and withdrawal (n = 3) (Table 4 and Figure 1). Medication adherence among participants who completed 12 weeks of drug administration was high (92.27%, 96.26%, and 85.66% in the BIT, KOG, and CBD groups, respectively, Table 5). Considering these factors as well as participant recruitment, intervention administration, and long-term follow-up, the study design can be considered feasible.

3.4. Assessments of fatigue and “brain fog” symptoms

After 12 weeks of herbal drug administration, fatigue symptoms improved significantly in each group, with treatment success rates of 80%, 53.33%, and 46.67% in the BIT, KOG, and CBD groups, respectively. However, treatment success rates for “brain fog” symptoms were comparatively low (40%, 46.67%, and 13.33% in the BIT, KOG, and CBD groups, respectively). In addition, CIS showed significant improvement in each group at 13 weeks (mean difference: −29.53, 95% confidence interval [CI]: −40.96 to −18.2 in the BIT group, −31.47, 95% CI: −43.47 to −19.46 in the KOG group, and −23.4, 95% CI: −34.79 to −12.01 in the CBD group), 25 weeks, and 37 weeks, with no significant difference between groups after treatments. Similarly, the VAS score for fatigue showed significant improvement at 13 weeks (mean difference: −32.53, 95% CI: −46.07 to −19.00 in the BIT group, −30.13, 95% CI: −47.11 to −13.16 in the KOG group, and −16.73, 95% CI: −27.48 to −5.98 in the CBD group), 25 weeks, and 37 weeks in each group, with no significant difference between groups after treatments. In terms of the total, physical health, and mental health scores of ChFQ, significant improvement in each group was observed at 13 weeks (mean difference: −23.67, 95% CI: −35.09 to −12.24 in the BIT group, −21.27, 95% CI: −32.91 to −9.62 in the KOG group, and −10.93, 95% CI: −19.93 to −1.93 in the CBD group for the total score; −17.27, 95% CI: −25.58 to −9.15 in the BIT group, −14.2, 95% CI: −21.41 to −6.99 in the KOG group, and −6.27, 95% CI: −11.99 to −0.54 in the CBD group for the physical health score; −6.4, 95% CI: −10.98 to −1.82 in the BIT group, −7.07, 95% CI: −11.98 to −2.16 in the KOG group, and −4.67, 95% CI: −8.3 to −1.03 in the CBD group for the mental health score), 25 weeks, and 37 weeks, with no significant difference between groups (Table 5).

In terms of “brain fog” symptoms, no significant improvements were observed in each group. The VAS score for “brain fog” did not show significant improvement at 13 weeks (mean difference: −6.27, 95% CI: −26.71 to 14.17 in the BIT group, −5.4, 95% CI: −27.98, 17.18 in the KOG group, and 1.4, 95% CI: −10.8 to 13.6 in the CBD group), 25 weeks, or 37 weeks, with no significant difference between groups. However, the Korean–Montreal Cognitive Assessment score showed significant improvement in the KOG group at 13 weeks (mean difference: 1.80, 95% CI: 0.7 to 2.83), 25 weeks (mean difference: 1.87, 95% CI: 1.03 to 2.70), and 37 weeks (mean difference: 1.47, 95% CI: 0.53 to 2.40). In addition, the Cognitive Failure Questionnaire score showed significant improvement in the BIT and KOG groups at 37 weeks (mean difference: −4.40, 95% CI: −7.57 to −1.23 in the BIT group, −3.93, 95% CI: −11.57 to 3.70 in the KOG group, and −11.67, 95% CI: −21.07 to −2.26 in the CBD group), with no significant difference between groups. Furthermore, there was no significant improvement in the BIT and CBD groups at each follow-up period. Scores for the DF-forward test and DF-backward test did not show significant improvements at 13 weeks (mean difference: 0.27, 95% CI: −0.27 to 0.8 in the BIT group, 0, 95% CI: −0.47 to 0.47 in the KOG group, and −0.07, 95% CI: −0.46 to 0.42 in the CBD group for the DF-forward score and 1.4, 95% CI: 0.4 to 2.4 in the BIT group, 0.4, 95% CI: −0.26 to 1.06 in the KOG group, and 0.8, 95% CI: −0.21 to 1.81 in the CBD group for the DF-backward score), 25 weeks, or 37 weeks. Furthermore, the Korean–Boston Naming Test–15 score did not show significant improvement at 13 weeks (mean difference: 0.13, 95% CI: −0.06 to 0.33 in the BIT group, 0.13, 95% CI: −0.06 to 0.33 in the KOG group, and 0.33, 95% CI: −0.21 to 0.87 in the CBD group), 25 weeks, or 37 weeks (Table 5).

3.5. Other outcome and safety assessments

The EQ-5D did not show a significant difference between groups at 13 and 37 weeks, but there was a significant difference at 25 weeks (mean difference: 0.06, 95% CI: 0.02 to 0.11 in the BIT group, −0.01, 95% CI: −0.05 to 0.03 in the KOG group, and 0.03, 95% CI: −0.06 to 0.11 in the CBD group). In addition, the Pittsburgh Sleep Quality Index-K score showed significant improvement in the KOG group at 13 weeks (mean difference: −2.71, 95% CI: −3.76 to −1.67), 25 weeks (−2.08, 95% CI: −3.28 to −0.89), and 37 weeks (−2.82, 95% CI: −3.94 to −1.70), with no significant improvements in the BIT and KOG groups. Furthermore, the Beck’s Depression Inventory score showed significant improvement in the BIT and KOG groups at 13 weeks (mean difference: −3.80, 95% CI: −6.86 to −0.74 in the BIT group, −4.73, 95% CI: −6.87 to −2.59 in the KOG group, and −5.53, 95% CI: −11.21 to 0.14 in the CBD group), 25 weeks, and 37 weeks (Table 5).

Among the 45 participants, 10 (22.22%) reported AEs, with no significant differences in the incidence rates of AEs among groups (p = 0.2805). All AEs were mild, and none were found to be causally related to the medication. Furthermore, there were no significant differences among groups in terms of the proportion of normal laboratory test results after treatment termination (Table 6).

4. Discussion

Through this pilot study, we found that the research design and intervention for this study were feasible. Although a considerable number of participants dropped out during the follow-up period due to the COVID-19 pandemic, there were no AEs directly related to the intervention, and adherence to the medication was high. Fatigue symptoms improved significantly in all groups, regardless of the type of prescription administered. However, “brain fog” symptoms did not improve significantly, even in the CBD group where this symptom was predominant.

In this study, three interventions were selected as treatments for fatigue and “brain fog” symptoms that may occur after COVID-19. BIT had been widely used as a prescription to treat various symptoms of impaired gastric function caused by dietary irregularities or overexertion. It has been approved by the Korean regulatory agency (Ministry of Food and Drug Safety, MFDS) for weakness, fatigue, lethargy, post-disease debility, poor appetite, night sweating, and vulnerability to fatigue due to a lack of vitality and sluggish gastrointestinal movement. It is composed of Ginseng Radix, Atractylodis Rhizoma Alba, Astragali Radix, Angelicae Gigantis Radix, Zizyphi Fructus, Bupleuri Radix, Citri Unshius Pericarpium, Glycyrrhizae Radix et Rhizoma, Cimicifugae Rhizoma, and Zingiberis Rhizoma Recens. It has been to have many clinical effects, and the therapeutic mechanism may be related to immunological, neuropsychiatric, anti-inflammatory, and musculoskeletal regulation [28]. It has been suggested to be effective in alleviating chronic fatigue-related general symptoms [29]. In addition, it can improve the quality of life, fatigue, and immunological status of elderly patients with weakness [30]. A previous case report reported the use of BIT for fatigue symptoms after COVID-19 [31]. KOG has been widely used to treat chronic diseases and is approved for conditions such as illness-related weakness, post-disease debility, weak constitution, physical fatigue, lethargy, and menopausal disorders, serving as a tonic and strengthener in Korea. It is composed of Rehmanniae Radix, Ginseng Radix, Wolfiporia Cocos, and Honey. Its therapeutic mechanism may be related to immune system activation [32], fatigue reduction [33,34], and cognitive function enhancement [35] as well as its anti-inflammatory effects [36,37,38]. CBD has been used for the treatment of anxiety, palpitation, and cognitive dysfunction. It has been approved in Korea for a variety of conditions, including insomnia, anxiety, restlessness, thirst, palpitations, shortness of breath, nervous debility, “brain fog,” and feverishness. Its therapeutic effect may be attributed to its antioxidant, anticancer, anti-inflammatory, and growth-promoting properties without significant toxicity or AEs [39]. In animal models, it has been found to inhibit learning and memory impairment, suggesting a potential effect on cognitive function [40]. CBD has also been shown to reduce inflammatory responses [41] and improve sleep quality in patients with primary insomnia [42]. Furthermore, it exhibits neuroprotective [43], vasorelaxant, hypotensive [44], and anti-Alzheimer’s disease [45,46] effects. It is commonly prescribed for alleviating the psychological and behavioral symptoms of dementia related to Alzheimer’s disease [47].

This study has some limitations. First, it was a pilot study with no control group. Therefore, concrete evidence regarding the efficacy and safety of these three herbal medications for long COVID could not be established. However, the feasibility of the study design was confirmed, and future large-scale clinical trials are required. Second, the outcomes assessed may be inappropriate for assessing fatigue and “brain fog” in patients with long COVID. The definition of long COVID is currently ambiguous, and there is no clear description of the underlying pathologic mechanism of its symptoms. While the diagnosis and treatment of long COVID-related fatigue may be approached by considering post-viral syndrome, approaching “brain fog” is more challenging due to its distinct presentation from cognitive impairments observed in dementia. One possible reason for the lack of significant improvement in cognitive function before and after drug treatment in patients with “brain fog” in this study is that the condition they are experiencing differs from cognitive decline in Alzheimer’s dementia. As a result, existing assessment tools designed for patients with dementia may not be able to adequately evaluate their cognitive function, which is a significant consideration in this context. Third, approximately 30% of study participants dropped out, demonstrating a high dropout rate. This may be attributed to the study’s long-term participation requirements, which included a 12-week intervention period followed by a six-month follow-up. Furthermore, the ongoing COVID-19 pandemic has led to a significant number of patient re-infections. When conducting clinical trials for the management of patients with long COVID, it is important to consider the potential for a high dropout rate in the study design.

5. Conclusions

The feasibility of this study, which included patients with long COVID experiencing fatigue and “brain fog” symptoms and given three different herbal medicines (BIT, KOG, and CBD) for 12 weeks based on their symptom patterns, has been demonstrated. Given the impact of long COVID and the scarcity of studies evaluating the effects of interventions on fatigue and “brain fog,” this study is deemed valuable. Future clinical studies with an appropriate control group and a sufficient sample size are needed to establish the evidence for herbal medicine in the management of long COVID-19.

Supplementary Materials

Supplementary material associated with this article can be found, in the online version, at the website of this paper posted on Preprints.org.

Author Contributions

Tae-Hun Kim: conceptualization, methodology, investigation, data curation, project administration, supervision, writing-original draft preparation, writing-review and editing; Jiwon Yoon: conceptualization, formal analysis, visualization, validation, writing-original draft preparation, writing-review and editing; Sanghyun Kim: conceptualization, visualization, validation, writing-review and editing; Byoung-Kab Kang: formal analysis, visualization, validation; Jung Won Kang: validation, writing-review and editing; Sunoh Kwon: conceptualization, methodology, resources, project administration, supervision, funding acquisition, writing-original draft preparation, writing-review and editing; All authors agree to be accountable for all aspects of work ensuring integrity and accuracy.

Funding

This study was supported by the Korea Institute of Oriental Medicine (KSN1823223).

Data Availability Statement

Data are included in this article and/or supp. materials.

Acknowledgements

The authors would like to express their gratitude to all nurses and participants of Kyung Hee University Korean Medicine Hospital who helped in conducting the present study.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the result.

Abbreviations

AEs, Adverse events; BDI, Beck’s Depression Inventory; BIT, Bojungikki-tang; CAM, Complementary and alternative medicine; CFQ, Cognitive Failure Questionnaire; CBD, Cheonwangbosim; ChFS, Chalder Fatigue Scale; CIS, Checklist Individual Strength; COVID, Coronavirus disease; COVID-19, coronavirus infectious disease 2019; K-BNT-15, Korean-Boston Naming Test-15; KM, Korean Medicine;MFDS, The Ministry of Food and Drug Safety; KOG, Kyungok-go, PSQI-K, the Korean version of Pittsburgh Sleep Quality Index; SARS-CoV-2, severe acute respiratory syndrome coronavirus-2; VAS, Visual Analog Scale; WAIS, Digit Span Test in Korean-Wechsler Adult Intelligence Scale

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Figure 1. Flow chart for the study subjects.

Figure 2. Timeline for the study subjects.

Table 1. Schedule summary.

Visit	0	V1	V2	V3	V4	V5	V6	UV *
Week	−7	1	5	9	13	25	37	-
Visit Window (±Days)	-	2	±7	±7	±7	±7	±7	-
Informed consent for the study	●
Demographic information	●
Participation in other clinical trials	●
Medical history	●
Confirmation of diagnosis of COVID-19 infection	●
Medication history	●
Vital signs	●	●	●	●	●	●	●	●
Electrocardiogram	●				●			●
Laboratory test	●				●			●
Blood collection for immune response and metabolite analysis	●				●	●	●	●
Pregnancy **	●
Evaluation of fatigue or “brain fog”	●
CIS	●		●	●	●	●	●	●
VAS (0–100) score for fatigue or “brain fog”	●		●	●	●	●	●	●
Inclusion/exclusion criteria	●
KM syndrome differentiation		●
Prescription of herbal medicine ***		●	●	●
Drug adherence			●	●	●			●
Check for combination therapy			●	●	●	●	●	●
ChFQ		●	●	●	●	●	●	●
EQ-5D-5L		●	●	●	●	●	●	●
PSQI-K		●	●	●	●	●	●	●
K-MoCA		●	●	●	●	●	●	●
CFQ		●					●	●
BDI		●	●	●	●	●	●	●
Digit span test in K-WAIS (DF, DB, and DF-DB)		●			●	●	●
K-BNT-15		●			●	●	●
Adverse events check		●	●	●	●	●	●	●

●, check on visit * UV, unscheduled visit (), ** only fertile women, *** Evaluation for the continuation of herbal medicine administration at 5th and 9th weeks.

Table 2. KM syndrome differentiation for allocation to treatment group.

	Fatigue		Cognitive Dysfunction
Code	BIT	KOG	CBD
Syndrome differentiation classification	Lung-Spleen Qi Deficiency	Dual Deficiency of Qi and Yin	Heart Yin Deficiency Heat
Symptom	Fatigue, appetite loss, cold sweat, shortness of breath, chest tightness, anxiety and others	Fatigue, dry cough and others	Forgetfulness, fever, insomnia, heart palpitation, stomatitis, tongue needles and others
Tongue diagnosis	Pale tongue, thin white fur	Dry mouth, dry tongue	Red tongue and low tongue coated
Pulse diagnosis	Vacuous, large, weak pulse/surging, large pulse	Fine pulse/vacuous, weak pulse	Fine, rapid pulse
Urine/feces	Difficult stool to pass/sloppy stool	Dry stool	Inhibited stool/sloppy stool

Table 3. Composition of Bojungikgi-tang, Kyungok-go and Cheonwangbosim-dan.

Fomula / Product name	Composition		Manufacturer / License Code	Group / KM syndrome differentiation
Bojungikgi-tang (BIT, CV1) Kracie Bojungikgi-tang Extract Fine Granule	Ginseng Radix	4.0g	Kyungbang Pharmaceutical Co.,Ltd 201507212	Fatigue lung-spleen qi deficiency
	Panax ginseng C. A. Meyer	4.0g
	Atractylodis Rhizoma Alba	4.0g
	Atractylodes japonica Koidzumi	4.0g
	Astragali Radix	4.0g
	Astragalus membranaceus Bunge	4.0g
	Angelicae Gigantis Radix	3.0g
	Angelica gigas Nakai	3.0g
	Zizyphi Fructus	2.0g
	Zizyphus jujuba Miller var. inermis Rehder	2.0g
	Bupleuri Radix	2.0g
	Bupleurum falcatum Linné	2.0g
	Citri Unshius Pericarpium	2.0g
	Citrus unshiu Markovich	2.0g
	Glycyrrhizae Radix et Rhizoma	1.5g
	Glycyrrhiza uralensis Fischer	1.5g
	Cimicifugae Rhizoma	1.0g
	Cimicifuga heracleifolia Komarov	1.0g
	Zingiberis Rhizoma Recens	0.5g
	Zingiber officinale Roscoe	0.5g
Kyungok-go (KOG, CV2) Kyungbang Kyungokgo	Rehmanniae Radix	16.0g	Kyungbang Pharmaceutical Co.,Ltd 201708619	Fatigue dual deficiency of qi and yin
	Rehmannia glutinosa Liboschitz ex Steudel	16.0g
	Ginseng Radix	2.5g
	Panax ginseng C. A. Meyer	2.5g
	Poria Sclerotium	5.0g
	Poria cocos Wolf	5.0g
	Mel Honey	16.6g
Cheonwang-bosim-dan (CBD, CV3) Soonsimhwan	Rehmanniae Radix	500.0mg	Hanpoong Pharmaceutical Co.,Ltd 200100075	“brain fog” heart yin deficiency
	Rehmannia glutinosa Liboschitz ex Steudel	500.0mg
	Ginseng Radix	62.5mg
	Panax ginseng C. A. Meyer	62.5mg
	Scrophulariae radix	62.5mg
	Scrophularia buergeriana Miquel	62.5mg
	Salviae Miltiorrhizae Radix	62.5mg
	Salvia miltiorrhiza Bunge	62.5mg
	Polygalae Radix	62.5mg
	Polygala tenuifolia Willdenow	62.5mg
	Platycodonis Radix	62.5mg
	Platycodon grandiflorum A. De Candolle	62.5mg
	Poria Sclerotium	62.5mg
	Poria cocos Wolf	62.5mg
	Schisandrae Fructus	125.0mg
	Schisandra chinensis (Turcz.) Baillon	125.0mg
	Angelicae Gigantis Radix	125.0mg
	Angelica gigas Nakai	125.0mg
	Asparagi Radix	125.0mg
	Asparagus cochinchinensis Merrill	125.0mg
	Liriopis seu Ophiopogonis Tuber	125.0mg
	Liriope platyphylla Wang et Tang	125.0mg
	Thujae Orientalis Semen	125.0mg
	Thuja orientalis Linné	125.0mg
	Zizyphi Semen	125.0mg
	Zizyphus jujuba Miller var. spinosa Hu ex H. F. Chou	125.0mg
	Coptidis Rhizoma	250.0mg
	Coptis japonica Makino	250.0mg

*The dosage of each composition in the medicine is listed based on the raw material equivalent corresponding to a daily dose disclosed by the KFDA. Detailed information is provided at the address below.

Table 4. Baseline characteristics.

		BIT (n = 15)	KOG (n = 15)	CBD (n = 15)	P-value
Age (mean, SD)		43.80 (14.31)	45.33 (12.57)	51.33 (15.71)	0.3656*
Sex (M/F)		9/6	8/7	6/9	0.6548**
Body mass index (mean, SD)		24.21 (3.56)	23.17 (3.67)	23.41 (3.07)	0.8233*
Final level of education
	Less than elementary school	0	0	0	0.1742**
	Elementary school	0	0	0
	Middle school	0	0	0
	High school	2 (13.33 %)	5 (33.33 %)	7 (46.67 %)
	University or college	13 (86.67 %)	10 (66.67 %)	8 (53.33 %)
Occupation
	Physical labor	0	1(6.67 %)	6(40.00 %)	0.6692**
	Non-physical labor	10 (66.67 %)	11(73.33 %)	8(53.33 %)
	Others (unemployed, etc)	5 (33.33 %)	3(20.00 %)	1(6.67 %)
Time duration since initial infection of COVID-19 (days, SD)		8.87 (3.46)	10.53 (4.61)	10.20 (8.08)	0.3310*
NEWS* (SD)		1.00 (2.54)	2.47 (4.26)	2.07 (4.03)	0.3540*
Symptom durations
	Fatigue (days, SD)	89.47 (33.25)	114.80 (92.11)
	“Brain fog” (days, SD)			119.13 (97.95)
Baseline CIS score (mean, SD)		100.60 (10.66)	96.60 (15.36)	105.4 (17.93)	0.3331*
Baseline VAS for fatigue symptom (mean, SD)		76.87 (14.58)	73.27 (9.05)	70.33 (20.34)	0.4900*
Baseline VAS for “brain fog” (mean, SD)		49.27 (24.86)	40.60 (26.08)	62.00 (24.65)	0.1039*

BIT: Bojungikgi- tang group; KOG: Kyungok-go group; CBD: Cheonwangbosim-dan group; NEWS: The national early warning score; CIS: Checklist individual strength; VAS: visual analogue scale; *Kruskal–Wallis test; ** Chi-squared test; SD: standard deviation.

Table 5. Primary and secondary outcomes.

			BIT (n = 15)	KOG (n = 15)	CBD (n = 15)	P-value***
Outcomes for feasibility assessment
Treatment success rate of VAS for fatigue (%)*			12 (80)	8 (53.33)	7 (46.67)	0.1431
Treatment success rate of VAS for “brain fog” (%)*			6(40)	7 (46.67)	2 (13.33)	0.1225
			(n= 13)	(n= 14)	(n= 14)
Medication adherence (%)**			92.27(16.28)	96.26 (15.75)	85.66 (23.61)
Outcomes for fatigue symptoms
CIS (mean difference from baseline with 95% CI)**
	At 5 weeks		-18.4, 95% CI[-26.89, -9.91]	-23, 95% CI[-33.03, -12.97]	-8.93, 95% CI[-16.27, -1.6]	0.0181
	At 9 weeks		-27.2, 95% CI[-36.15, -18.25]	-25.93, 95% CI[-35.46, -16.41]	-15.53, 95% CI[-24.8, -6.27]	0.0394
	At 13 weeks		-29.53, 95% CI[-40.96, -18.1]	-31.47, 95% CI[-43.47, -19.46]	-23.4, 95% CI[-34.79, -12.01]	0.3391
	At 25 weeks		-30.2, 95% CI[-40.68, -19.72]	-25.33, 95% CI[-35.78, -14.89]	-26.87, 95% CI[-41, -12.73]	0.7643
	At 37 weeks		-29.6, 95% CI[-39.11, -20.09]	-29.2, 95% CI[-39.72, -18.68]	-26.07, 95% CI[-40.95, -11.18]	0.6119
VAS for fatigue (mean difference from baseline with 95% CI)**
	At 5 weeks		-28.87, 95% CI[-40.27, -17.47]	-22.27, 95% CI[-36.95, -7.59]	-6.47, 95% CI[-13.12, 0.18]	0.0275
	At 9 weeks		-30.07, 95% CI[-42.12, -18.01]	-27.67, 95% CI[-43.02, -12.32]	-14, 95% CI[-25.25, -2.75]	0.2353
	At 13 weeks		-32.53, 95% CI[-46.07, -19]	-30.13, 95% CI[-47.11, -13.16]	-16.73, 95% CI[-27.48, -5.98]	0.2509
	At 25 weeks		-28.6, 95% CI[-40.93, -16.27]	-28.73, 95% CI[-43.44, -14.03]	-15.2, 95% CI[-24.56, -5.84]	0.2537
	At 37 weeks		-32.27, 95% CI[-46.45, -18.08]	-20.07, 95% CI[-36.12, -4.01]	-15.67, 95% CI[-26.24, -5.1]	0.2784
ChFQ (mean difference from baseline with 95% CI)**
	At 5 weeks	Total score	-12.73, 95% CI[-21.48, -3.99]	-11.33, 95% CI[-18.93, -3.73]	-5.27, 95% CI[-9.66, -0.87]	0.1198
		Physical health score	-9.13, 95% CI[-14.11, -4.15]	-7.2, 95% CI[-12.11, -2.29]	-1.53, 95% CI[-4.78, 1.71]	0.0461
		Mental health score	-3.6, 95% CI[-8.53, 1.33]	-4.13, 95% CI[-7.42, -0.85]	-3.73, 95% CI[-5.44, -2.03]	0.3220
	At 9 weeks	Total score	-20.07, 95% CI[-29, -11.13]	-15.2, 95% CI[-25.83, -4.57]	-5.6, 95% CI[-12.08, 0.88]	0.0199
		Physical health score	-13.8, 95% CI[-19.62, -7.98]	-9.87, 95% CI[-16.27, -3.46]	-2.27, 95% CI[-6.42, 1.89]	0.0139
		Mental health score	-6.27, 95% CI[-10.36, -2.17]	-5.33, 95% CI[-9.86, -0.8]	-3.33, 95% CI[-6.34, -0.32]	0.0174
	At 13 weeks	Total score	-23.67, 95% CI[-35.09, -12.24]	-21.27, 95% CI[-32.91, -9.62]	-10.93, 95% CI[-19.93, -1.93]	0.0991
		Physical health score	-17.27, 95% CI[-25.38, -9.15]	-14.2, 95% CI[-21.41, -6.99]	-6.27, 95% CI[-11.99, -0.54]	0.0808
		Mental health score	-6.4, 95% CI[-10.98, -1.82]	-7.07, 95% CI[-11.98, -2.16]	-4.67, 95% CI[-8.3, -1.03]	0.0469
	At 25 weeks	Total score	-22.4, 95% CI[-30.36, -14.44]	-17, 95% CI[-28.89, -5.11]	-14.6, 95% CI[-25, -4.2]	0.4634
		Physical health score	-16.53, 95% CI[-22.48, -10.59]	-11.93, 95% CI[-19.19, -4.67]	-8.73, 95% CI[-15.56, -1.91]	0.2541
		Mental health score	-5.87, 95% CI[-9.23, -2.5]	-5.07, 95% CI[-9.97, -0.16]	-5.87, 95% CI[-10.13, -1.6]	0.5669
	At 37 weeks	Total score	-25.53, 95% CI[-33.1, -17.97]	-18.27, 95% CI[-30.04, -6.49]	-14.93, 95% CI[-25.55, -4.32]	0.2411
		Physical health score	-19, 95% CI[-24.43, -13.57]	-12.6, 95% CI[-19.66, -5.54]	-8.27, 95% CI[-15.26, -1.28]	0.0717
		Mental health score	-6.53, 95% CI[-10.45, -2.62]	-5.67, 95% CI[-10.59, -0.74]	-6.67, 95% CI[-10.75, -2.58]	0.5402
Outcomes for “brain fog” symptoms
VAS for “brain fog” (mean difference from baseline with 95% CI)**
	At 5 weeks		-11.13, 95% CI[-32.3, 10.04]	-4.4, 95% CI[-22.91, 14.11]	-0.4, 95% CI[-9.84, 9.04]	0.0477
	At 9 weeks		-7.73, 95% CI[-27.2, 11.73]	-9.33, 95% CI[-29.15, 10.48]	-3.87, 95% CI[-17.04, 9.3]	0.0358
	At 13 weeks		-6.27, 95% CI[-26.71, 14.17]	-5.4, 95% CI[-27.98, 17.18]	1.4, 95% CI[-10.8, 13.6]	0.0472
	At 25 weeks		-6.73, 95% CI[-26.23, 12.77]	-7.27, 95% CI[-25.25, 10.71]	-5.87, 95% CI[-14.48, 2.75]	0.2454
	At 37 weeks		-12.73, 95% CI[-32.8, 7.33]	-7.13, 95% CI[-24.32, 10.06]	-7.13(-15.81, 1.54]	0.2193
K-MoCA (mean difference from baseline with 95% CI)**
	At 5 weeks		-0.53, 95% CI[-2.34, 1.28]	0.20, 95% CI[-0.79, 1.19]	0.87, 95% CI[-0.77, 2.50]	0.5767
	At 9 weeks		0.60, 95% CI[-1.10, 2.30]	1.33, 95% CI[0.43, 2.24]	1.27, 95% CI[-1.40, 3.93]	0.5382
	At 13 weeks		1.00, 95% CI[-0.51, 2.51]	1.80, 95% CI[0.77, 2.83]	1.13, 95% CI[-1.96, 4.22]	0.3176
	At 25 weeks		1.00, 95% CI[-0.29, 2.29]	1.87, 95% CI[1.03, 2.70]	0.80, 95% CI[-2.09, 3.69]	0.1525
	At 37 weeks		1.07, 95% CI[-0.30, 2.43]	1.47, 95% CI[0.53, 2.40]	1.00, 95% CI[-1.89, 3.89]	0.4059
CFQ (mean difference from baseline with 95% CI)**
	At 37 weeks		-4.40, 95% CI[-7.57, -1.23]	-3.93, 95% CI[-11.57, 3.70]	-11.67, 95% CI[-21.07, -2.26]	0.9368
DF-forward (mean difference from baseline with 95% CI)**
	At 13 weeks		0.27, 95% CI[-0.27, 0.8]	0, 95% CI[-0.47, 0.47]	-0.07, 95% CI[-0.46, 0.32]	0.4532
	At 25 weeks		0.07, 95% CI[-0.42, 0.56]	0.47, 95% CI[0.11, 0.82]	-0.07, 95% CI[-0.86, 0.73]	0.3573
	At 37 weeks		0.33, 95% CI[-0.01, 0.68]	0.47, 95% CI[0.11, 0.82]	-0.2, 95% CI[-0.96, 0.56]	0.1230
DF-backward (mean difference from baseline with 95% CI)**
	At 13 weeks		1.4, 95% CI[0.4, 2.4]	0.4, 95% CI[-0.26, 1.06]	0.8, 95% CI[-0.21, 1.81]	0.2095
	At 25 weeks		0.87, 95% CI[-0.04, 1.78]	0.47, 95% CI[-0.31, 1.25]	0.8, 95% CI[-0.23, 1.83]	0.8091
	At 37 weeks		1.13, 95% CI[0.13, 2.13]	1.07, 95% CI[0.36, 1.78]	0.8, 95% CI[-0.21, 1.81]	0.4459
K-BNT-15 (mean difference from baseline with 95% CI)**
	At 13 weeks		0.13, 95% CI[-0.06, 0.33]	0.13, 95% CI[-0.06, 0.33]	0.33, 95% CI[-0.21, 0.87]	0.5933
	At 25 weeks		0.13, 95% CI[-0.06, 0.33]	0.2, 95% CI[-0.11, 0.51]	0.07, 95% CI[-1.14, 1.28]	0.6532
	At 37 weeks		0.2, 95% CI[-0.11, 0.51]	0.27, 95% CI[-0.06, 0.6]	0.13, 95% CI[-1.07, 1.33]	0.6417
Other outcomes
EQ-5D-5L (mean difference from baseline with 95% CI)**
	At 5 weeks		0.01, 95% CI[-0.03, 0.04]	-0.03, 95% CI[-0.07, 0.02]	-0.04, 95% CI[-0.09, 0.01]	0.1040
	At 9 weeks		0.03, 95% CI[0, 0.06]	0.01, 95% CI[-0.05, 0.07]	-0.01, 95% CI[-0.05, 0.04]	0.2034
	At 13 weeks		0.06, 95% CI[0.02, 0.11]	0.02, 95% CI[-0.04, 0.07]	-0.01, 95% CI[-0.11, 0.08]	0.1417
	At 25 weeks		0.06, 95% CI[0.02, 0.11]	-0.01, 95% CI[-0.05, 0.03]	0.03, 95% CI[-0.06, 0.11]	0.3256
	At 37 weeks		0.07, 95% CI[0.03, 0.11]	0, 95% CI[-0.04, 0.05]	0.03, 95% CI[-0.06, 0.12]	0.4582
PSQI-K (mean difference from baseline with 95% CI)**
	At 5 weeks		0.14, 95% CI[-1.16, 1.44]	-2.14, 95% CI[-3.10, -1.19]	0.07, 95% CI[-1.14, 1.28]	0.0364
	At 9 weeks		-0.85, 95% CI[-1.92, 0.22]	-3.07, 95% CI[-4.10, -2.05]	-0.64, 95% CI[-2.19, 0.90]	0.0557
	At 13 weeks		-1.15, 95% CI[-2.96, 0.66]	-2.71, 95% CI[-3.76, -1.67]	-1.50, 95% CI[-3.35, 0.35]	0.6978
	At 25 weeks		-1.25, 95% CI[-3.40, 0.90]	-2.08, 95% CI[-3.28, -0.89]	-1.55, 95% CI[-4.72, 1.63]	0.9725
	At 37 weeks		-1.64, 95% CI[-3.72, 0.45]	-2.82, 95% CI[-3.94, -1.70]	-2.33, 95% CI[-4.51, -0.16]	0.9540
BDI (mean difference from baseline with 95% CI)**
	At 5 weeks		-3.40, 95% CI[-5.50, -1.30]	-2.20, 95% CI[-3.77, -0.63]	-1.27, 95% CI[-4.87, 2.34]	0.1882
	At 9 weeks		-3.27, 95% CI[-5.76, -0.77]	-3.40, 95% CI[-5.14, -1.66]	-4.13, 95% CI[-8.05, -0.21]	0.8568
	At 13 weeks		-3.80, 95% CI[-6.86, -0.74]	-4.73, 95% CI[-6.87, -2.59]	-5.53, 95% CI[-11.21, 0.14]	0.8669
	At 25 weeks		-4.60, 95% CI[-7.82, -1.38]	-3.80, 95% CI[-6.20, -1.40]	-6.53, 95% CI[-13.15, 0.08]	0.9885
	At 37 weeks		-4.47, 95% CI[-7.38, -1.55]	-4.00, 95% CI[-6.62, -1.38]	-6.33, 95% CI[-11.20, -1.47]	0.9212

BIT: Bojungikgi- tang group; KOG: Kyungok-go group; CBD: Cheonwangbosim-dan group; *Chi-squared test; **ANCOVA test (or Kruskal-Wallis test);*** p-value for group comparison; CI: confidence intervals.

Table 6. Safety outcomes.

		BIT (n = 15)	KOG (n = 15)	CBD (n = 15)	P-value**
Number participants with adverse events (%)*		5(33.33)	1(6.67)	4(26.67)	0.2805
Type of AEs (n)*
	Back pain	1	0	0
	Hypothyroidism	1	0	0
	Common cold	0	1	0
	High blood glucocorticoids level	0	0	1
	Hypertension	0	0	1
	Arthritis	0	0	1
	Animal hair allergy	1	0	0
	Insomnia	1	0	0
	Rhinitis	1	0	0
	Mucous cyst	0	0	1
Severity of AEs (n)*
	Mild	5	1	4
	Moderate	0	0	0
	Severe	0	0	0
Causality (n)*
	Drug-related AEs	0	0	0
	Non-related AEs	5	1	4
Number of participants with normal laboratory test results at 13 weeks (n, %)*		BIT (n=13)***	KOG (n=14)***	CBD (n=14)***
	BUN	10(76.92)	9(64.29)	12(85.71)	0.4423
	Creatinine	11(84.62)	14(100)	14(100)	0.0951
	AST	12(92.31)	14(100)	14(100)	0.3171
	ALT	12(92.31)	13(92.86)	13(92.86)	1.0000
	ECG	12(92.31)	14(100)	14(100)	0.3171

BIT: Bojungikgi- tang group; KOG: Kyungok-go group; CBD: Cheonwangbosim-dan group; *Chi-squared test; **p-value for group comparison; ***Those who completed laboratory tests after treatment termination (13 weeks).

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