Resilience After COVID-19: A Descriptive, Cross-Sectional Study

Abstract

The main purpose of this study was to examine the relationship between resilience and health-related quality of life in patients following COVID-19 disease among those with and without lingering symptoms. The study design is descriptive and cross-sectional. Participants (n = 97) were adults who had earlier contracted COVID-19 disease and were in post-infection status between July and October 2020. Participants completed the following instruments: Connor-Davidson Resilience Scale, Short-Form 12-item Health Survey, and Hospital Anxiety and Depression Scale. Approximately 35% post-COVID-19 patients had a low level of resilience. The impact on the health status and resilience of those who had reported symptoms after 6 months was also significant. Age and depression had a significant impact on level of resilience. This relationship can affect patient recovery and negatively impact the ability to cope with COVID-19 disease. It is necessary to implement specialized training for clinicians on the effects of long-term COVID-19 to improve patient care. Long COVID symptoms might manifest months after an acute COVID-19 illness; clinicians who can confirm patient reports of these symptoms may help patients recover and become more resilient.

Introduction

The COVID-19 pandemic disrupted the lives of citizens worldwide in a broad range of areas, including posing a threat to good health and safety. Early in the pandemic, physical health and safety were high priorities and which explains why social distancing and masks were needed. Shortly after the pandemic began, it became clear that mental health was of a concern due to its increasing likelihood from a multitude of factors that included isolation, fear of the virus, and disruption to everyday routines (Bourmistrova et al., 2022). Individuals who contracted SARS-CoV-2 early in the pandemic, when at the time there was no treatment or vaccine, not only suffered the isolation and loneliness from social restrictions, but also suffered from the uncertainty and fear from the immediate and long-term effects of the virus. The consistent application of strong coping skills and resilience were necessary to promote whole body health and wellness and ensure a high quality of life (Bourmistrova et al., 2022).

Background

Post-COVID Persistent Symptoms

SARS-CoV-2 was initially thought to be a virus with a well specified set of symptoms, with the major symptoms being respiratory in nature (Hu et al., 2021). Over time, a post-COVID-19 syndrome emerged, with individuals infected with SARS-CoV-2 reporting a variety of persistent symptoms, with nearly every organ system affected (Rajan et al., 2021). For approximately 25% of COVID-19 survivors, symptoms were reported ranging from weeks to years after the initial SARS-CoV-2 infection (Rajan et al., 2021).

Contextual Factors That May Influence Health Outcomes: Location and Timing

Whilst nearly all countries were affected by COVID-19 in 2020, some countries were affected earlier than others. It is noteworthy to say that the scientific community was learning about the virus daily, and the virus was continuously evolving. Therefore, even short intervals of time between waves of SARS-CoV-2 infection, across various countries, is important in terms of contextual information that should be considered when understanding the symptoms experienced, how the infected coped, and their levels of resilience. With this in mind, Spain has been one of the European countries that was most affected by the COVID-19 pandemic during the first semester of 2020, with a higher prevalence (10%) in the area of Madrid (Pollán et al., 2020).

Contextual Factors That May Influence Health Outcomes: Psychological

Resilience is the ability to maintain one’s orientation toward existential purposes despite enduring adversities and stressful events. It foresees an attitude of persistence before the obstacle and openness to change (Sisto et al., 2019). Resilience is a multidimensional characteristic that varies with context, time, age, gender, and cultural origin, as well as within an individual subjected to different life circumstances (Connor & Davidson, 2003). The quality of life is affected by the process of overcoming acute illnesses. Once the disease is resolved, physical and mental recovery after COVID-19 depends on many factors (demographic, social, and clinical). A study by PeConga et al., (2020) concluded that there is a risk of mental illness associated with COVID-19, particularly in the short term, especially in those most directly affected by the disease (PeConga et al., 2020). On a psychological level, the study by Özdin et al., (2020) described high scores on both anxiety and depression scale during the COVID-19 pandemic and reported that being female and suffering from a history of previous psychiatric disorders were risk factors for anxiety; moreover, living in urban areas is a risk factor for depression (Özdin & Bayrak Özdin, 2020).

Contextual Factors That May Influence Health Outcomes: Risk Because of Immunosuppression

Transplant patients have a different experience of illness and relationship with the healthcare environment compared to the general population; moreover, their level of resilience against COVID-19 is also different. The study by Ozca et al. (2021) carried out in kidney transplant patients concluded that kidney transplant patients seem to be resistant to psychological stress induced by social distancing and periods of confinement. Strict adherence to infection control measures is deliberately suggested in this infection-prone population (Ozcan et al., 2021). Liver transplantation is a long and traumatic process, which includes several stages, with quite different impacts on the patient’s general condition and their needs. A study by Jover-Aguilar et al. (2020) prior to the pandemic concluded that patients receiving a liver transplant (LT) show normal levels of resilience (Jover-Aguilar et al., 2020). Furthermore, a current study by Robles-Bello et al. (2020), on LT patients, found that self-efficacy, optimism, and higher educational level were protective factors for resilience (Robles-Bello et al., 2020). Early research by Colmenero identified that immunosuppressed patients such as LT patients have a greater risk factor for the development of serious symptoms from COVID-19 compared to the general population (ref). One can hypothesize that serious symptoms are associated with baseline state of immunosuppression; however, higher mortality in LT has not been observed (Colmenero et al., 2021).

Little is known about the symptom experience among those that contracted COVID-19 during this time. Even less is known about the populations that contracted COVID-19 at the time and were considered to be at high risk, such as immunosuppressed patients. Therefore, the purposes of this study were to examine (a) the relationship between resilience and health-related quality of life (HRQoL) in patients following COVID-19 disease among those with and without lingering symptoms and (b) examine the relationship between resilience and health-related quality of life in a subgroup of patients, LT patients, who are immunosuppressed.

Methods

Research Design

The study design was descriptive and cross-sectional. This study is part of a larger study Sero-Cov19_TH which aimed to analyze the incidence, evolution, and conditioning factors of SARS-CoV-2 humoral response within the first 12 months post-SARS-CoV-2 infection in LT recipients as compared to immunocompetent individuals (Caballero-Marcos et al., 2021). Our study has been developed under the central hypothesis that persistence of symptoms, the capacity for resilience, quality of life, and mental health improves over time (Aiyegbusi et al., 2021).

Participants and Setting

The study population were patients participating in the Sero-Cov19_TH study at Hospital General Universitario Gregorio Marañón location. The patient sample in the parent study was formed by LT patients from several hospitals nationwide and the non-LT sample was formed by our single center. In our study the sample was collected from our single center, at Hospital General Universitario Gregorio Marañón only, which has caused a large size difference between transplanted and non-transplanted patient samples. Our sample was formed by patients who participated in the parent study and who consented to participate in our study at the 6-month check-up.

Inclusion/Exclusion Criteria: The inclusion criteria were LT and non-LT patients who were able to complete questionnaires and who had COVID-19 disease during the first wave of the pandemic. Those patients with known psychiatric pathology, non-LT patients and LT patients who, due to any health condition, could not complete the questionnaires were excluded.

Study Procedure

Data Collection

The data were collected during July and October 2020, dates corresponding to 3 and 6 months after the SARS-CoV-2 infection of the participants. COVID-19 RNA testing of nasopharyngeal/oropharyngeal swab specimens was performed by real-time reverse transcriptase-polymerase chain reaction assay (Huang et al., 2020). The management of the participants was the same in both appointments. A nurse from the research team contacted the participants by telephone so that they could personally attend an appointment with two nurses from the same research team. At the first appointment, in July 2020, the patients completed a self-reported questionnaire on paper and pencil, of the sociodemographic and clinical variables, which included: age, sex, comorbidities prior to COVID-19, treatment received for COVID-19, and assessment of the disease (need for hospital admission, respiratory support, ICU admission, presence of signs and symptoms, and clinical events). After completing it, which took no more than 5 to 7 minutes, the participants handed in the completed questionnaire to the nurses in the hospital office.

At the second appointment, in October 2020, the participants again completed the self-reported questionnaire on paper and pencil on the sociodemographic and clinical variables in the same way as the previous appointment in July 2020. In addition, at this appointment, the responsible nurses provided the resilience, quality of life, and mental health questionnaires on paper, which the participants also completed individually. The completion time was between 7 and 10 minutes and they were completed on paper and pencil. The participants handed in the completed questionnaires to the nurse. Participants did not receive any type of incentive for participation in the study.

Instruments

Clinical variables instrument: All clinical information was extracted from reliable electronic medical data sources and recorded in a Red-Cap database. Demographic data, comorbidities, clinical features, laboratory parameters, and transplant-related information were documented. The documentation process was the patient’s own report in a personal interview with the research nurse regarding the signs and symptoms at 3 and 6 months after COVID-19. The research nurse handed to the patient a questionnaire with the most frequent signs and symptoms according to previous studies such as fever, cough, skin reaction, headache, asthenia, myalgia, ageusia, arthralgia, gastrointestinal symptoms, and hair loss (Guan et al., 2020). The nurse also asked and reported other signs and symptoms described as having had cardiovascular events, respiratory infections, and experienced other events (dizziness, memory loss). Severe COVID-19 was defined as admission to the intensive care unit, requirement of mechanical ventilation, or death, whichever occurred first, according to a previous study describing the clinical characteristics of COVID-19 in China (Guan et al., 2020). Resilience: The Connor-Davidson Resilience Scale (CD-RISC) was used, for which the normality values are 70 to 88 (Connor & Davidson, 2003). CD-RISC is a 25-items self-rated measure of resilience that has sound psychometric properties (Cronbach’s alpha .89 and reliability .87) (Connor & Davidson, 2003). This scale was validated previously in a Spanish population (García León et al., 2018), and has been previously used in LT patients (Fernandez et al., 2015). The CD-RISC global reliability, Cronbach’s alpha, for our sample was .931.

Quality of Life: For the quality of life dimension, two instruments were used, the European Quality of Life-5 Dimensions (EQ-5D) and the Short-Form 12-item Health Survey (version 2) (SF12v2), which together cover a broad and important domain of health for various purposes (Johnson & Pickard, 2000). The EQ-5D is a generic health-related quality of life measurement instrument that can be used both in relatively healthy individuals (general population) and in groups of patients with different pathologies (Badia et al., 1999). Although EQ-5D consists of multiple dimensions with simple items, the use of Cronbach’s alpha was .85 in a previous study for evaluating its reliability (Tran et al., 2012). The EQ-5D reliability, Cronbach’s alpha, for our sample was .791. Besides, to measure quality of life, the SF12v2 health survey was used, for which the normal value is 50 in the physical and mental score (Schmidt et al., 2012). The SF12v2 is more sensitive than the EQ-5D when it comes to detecting indirect effects on health because the former contains more socially oriented dimensions (for example, social functioning) that better reflect the characteristics of the care situation (Johnson & Pickard, 2000). SF12v2 Cronbach’s alpha was of .89 and .76 for the 12-item Physical Component Summary and the 12-item Mental Component Summary, respectively (Ware et al., 1996). The SF12v2 global reliability, Cronbach’s alpha, for our sample was .723.

Depression and Anxiety Symptoms: Hospital Anxiety and Depression Scale (HADS) is a 14-item self-report screening scale that consists of a 7-item anxiety subscale and a 7-item depression subscale and it was used (Herrero et al., 2003) to measure the level of anxiety and depression where cut-off points >8 indicates borderline case and >11 indicates case. The internal consistency, as assessed by Cronbach’s alpha, .84 for the depression subscale and .85 for the anxiety subscale (Herrero et al., 2003). The HADS-anxiety reliability, Cronbach’s alpha, for our sample was .901, and HADS-depression Cronbach’s alpha was .863.

Ethical Considerations

Ethical approval for this study and the written consent form was obtained by the Hospital General Universitario Gregorio Marañón Ethical Committee (code Sero-Cov19_TH on the August 24, 2020, 19/2020 act). Moreover, the study was conducted in accordance with the principles articulated in the Declaration of Helsinki (World Medical Association Declaration of Helsinki, 2013).

Data Analytic Plan

Data was analyzed using descriptive statistics. The student’s t-test and the chi-square test were used to compare the different variables SPSS software, version 26.0 statistical package (SPSS Inc., Chicago, IL, USA). A descriptive and Spearman correlation analysis was performed for nonparametric data. In the multivariate study, a logistic regression analysis was applied to the variables that had a significant relationship in the bivariate analysis. In all cases, only p values less than .05 were considered statistically significant.

The criteria used to establish the categorical limits in the EQ-5D (Badia et al., 1999), SF12v2 (Ware et al., 1996), HADS (Herrero et al., 2003), and CD-RISC (Connor & Davidson, 2003) variables are those described by the authors of the scales. Planned specifically for the research question, resilience was recoded into two categories, using the cut-off points proposed according to the CD-RISC authors (Connor & Davidson, 2003): non-resilient (low resilience) and resilient (normal and above) (Mabikwa, Greenwood, Baxter, & Fleming, 2017).

Validity, Reliability, and Rigor

This study is registered on ClinicalTrials.gov with the Identifier: NCT04410471. This study followed the STROBE criteria for quantitative studies (Cuschieri, 2019).

Results

Participant Characteristics

A total of n = 97 participated, from n = 118 participants approached, with a response rate of 82%. Of the sample, 74% (n = 72) were men with a mean age of 62.67 years old (SD: 15.10/IR: 20). The majority of participants were married (74%) and had university education (28%). Approximately 55% were retired and 40% were working at the time of contracting SARS-CoV-2. Approximately 71% identified as being religious minded. Of the sample, 18% were LT patients. Roughly, 39% had diabetes, 63% had high blood pressure, 17% had cardiovascular disease, 5% (n=5) had chronic obstructive pulmonary disease (COPD), and 6% (n=6) had asthma. Regarding the COVID-19 disease status: 81% required hospital admission because of COVID-19. Roughly, 10% had severe COVID-19 necessitating admission to the ICU and/or mechanical ventilation (Table 1).

Table 1.

Participants’ Characteristics.

Variables	n (%)
Sociodemographic and occupational variables
Sex
Male	72 (74.20%)
Female	25 (25.80%)
Marital status
Single	11 (11.30%)
Married	72 (74.20%)
Separated/divorced	4 (4.10%)
Widowed	9 (9.30%)
NA	1 (1.00%)
Academic level
Without regulated education	15 (15.50%)
Secondary education	21 (21.60%)
High school education	17 (17.50%)
Professional training education	16 (16.50%)
University education	27 (27.80%)
NA	1 (1.00%)
Employment status
Not working/unemployed	4 (4.10%)
Working	39 (40.20%)
Retired	53 (54.60%)
NA	1 (1.00%)
Religion
Not pursuing any religion	27 (27.80%)
Pursuing religion	69 (71.10%)
NA	1 (1.00%)
Health-related variables
Diabetes mellitus
No	59 (60.80%)
Yes	38 (39.20%)
High blood pressure
No	36 (37.10%)
Yes	31 (62.90%)
Cardiovascular disease
No	81 (83.50%)
Yes	16 (16.50%)
Chronic obstructive pulmonary disease
No	92 (94.80%)
Yes	5 (5.20%)
Asthma
No	6 (6.20%)
Yes	91 (93.80%)
COVID-19 infection related variables
Required hospital admission
No	18 (18.60%)
Yes	79 (81.40%)
Has suffered severe COVID-19
No	87 (89.70%)
Yes	10 (10.30%)
Has the patient needed ventilatory support?
No	90 (92.80%)
Yes	7 (7.20%)
Has the patient required non-invasive mechanical ventilation?
No	94 (96.90%)
Yes	3 (3.10%)
Has the patient required invasive mechanical ventilation?
No	90 (92.80%)
Yes	6 (6.20%)
Has the patient required admission to the intensive care unit?
No	88 (90.70%)
Yes	9 (9.30%)

Note. NA = no answer.

Three months after infection, 33% of the overall sample had signs and symptoms related to the infection, of which 9% (n = 3) had suffered from severe COVID. Up to 20% reported one symptom, 8% reported two symptoms, 4% reported three symptoms, and 1% reported up to four symptoms. The signs and symptoms referred to were fever (2%), cough (4%), skin reaction (10%), headache (2%), asthenia (7%), myalgia (9%), ageusia (3%), arthralgia (2%), gastrointestinal symptoms (2%), and hair loss (4%). In addition, 11% described having suffered other clinical incidents first 3 months after COVID-19: 6% had cardiovascular events, 1% had respiratory infections, and 4% experienced other events (dizziness, memory loss).

At 6 months, from the same sample, 56% described signs and symptoms, representing an increase of 22% compared to the third month, of which 11% had suffered from severe COVID-19. Approximately 28% had one symptom, 10% had two symptoms, 11% had three symptoms, 2% had four symptoms, and 3% experienced up to five symptoms. The signs and symptoms referred to were cough (12%), skin reaction (19%), headache (9%), asthenia/fatigue (20%), myalgia (23%), anosmia (3%), ageusia (3%), arthralgias (5%), gastrointestinal symptoms (8%), and hair loss (5%). In addition, 12% (n = 12) described having suffered other post-COVID-19 clinical incidences: 1% had cardiovascular events, 3% had respiratory infections, 1% had other infections (urinary infection), and 7% had other clinical events (anxiety/depression, paresthesia, memory loss, loss of balance, dizziness, and gout). No statistically significant relationships have been found between medical record, sociodemographic variables, and severity of infection with the report of signs and signs, neither at 3 nor 6 months.

The surveyed patients presented a low level of resilience at 35%. Our results did not reveal significant differences in the average resilience level (p = .322) between the population of LTs (n = 17) and the non-transplanted population (n = 80) after overcoming the COVID-19 infection, nor in the other dimensions of the CD-RISC.

The results of the bivariate analysis have shown that patients who have reported symptoms in the third month of infection have a significant impact on their health status: mobility, self-care, activities of daily living, pain/discomfort, physical state, and level of depression (Table 2). The impact on the state of health and resilience of those who have reported symptoms at the sixth month has also been significant (Table 2).

Table 2.

Variables Influenced by Persistence of Post-COVID-19 Symptoms. Bivariate Analysis.

Variable	Symptoms month 3	NO symptoms month 3	p	Symptoms month 6	NO symptoms month 6	p
Categorical data
EQ5D mobility
I have no problems in walking about	18 (56%)	51 (78%)	0.042*	34 (63%)	35 (81%)	0.116
I have some problems in walking about	13 (41%)	14 (22%)		19 (35%)	8 (19%)
I am confined to bed	1 (3%)	0		1 (2%)	0
EQ5D self-care
I have no problems with self-care	26 (81%)	64 (99%)	0.004*	50 (93%)	40 (93%)	0.352
I have some problems washing or dressing myself	5 (16%)	0		2 (4%)	3 (7%)
I am unable to wash or dress myself	1 (3%)	1 (1%)		2 (3%)	0
EQ5D usual activities
I have no problems with performing my usual activities	18 (56%)	57 (88%)	0.002*	36 (67%)	39 (91%)	0.015*
I have some problems with performing my usual activities	12 (38%)	7 (11%)		15 (28%)	4 (9%)
I am unable to perform my usual activities	2 (6%)	1 (1%)		3 (5%)	0
EQ5D pain/discomfort
I have no pain or discomfort	11 (34%)	46 (71%)	0.003*	21 (39%)	36 (84%)	0.001*
I have moderate pain or discomfort	17 (53%)	16 (25%)		26 (48%)	7 (16%)
I have extreme pain or discomfort	4 (13%)	3 (4%)		7 (13%)	0
EQ5D anxiety/depression
I am not anxious or depressed	20 (63%)	45 (69%)	0.194	28 (52%)	37 (86%)	0.001*
I am moderately anxious or depressed	8 (25%)	18 (28%)		20 (37%)	6 (14%)
I am extremely anxious or depressed	4 (12%)	2 (3%)		6 (11%)	0
CD-RISC resilience
Low resilience	11 (34%)	23 (35%)	0.233	23 (43%)	11 (26%)	0.044*
Normal resilience	17 (53%)	25 (39%)		24 (44%)	18 (42%)
High resilience	4 (13%)	17 (26%)		7 (13%)	14 (32%)
CD-RISC Personal competence, high standards, and tenacity
Low Personal competence	12 (38%)	26 (40%)	0.544	23 (43%)	15 (35%)	0.056
Normal personal competence	16 (50%)	26 (40%)		26 (48%)	16 (37%)
High personal competence	4 (12%)	13 (20%)		5 (9%)	12 (28%)
CD-RISC trust in one’s instincts, tolerance of negative affect, and strengthening effects of stress
Low Strengthening effects of stress	13 (41%)	22 (34%)	0.324	25 (46%)	10 (23%)	0.028*
Normal Strengthening effects of stress	12 (37%)	19 (29%)		17 (32%)	14 (33%)
High Strengthening effects of stress	7 (22%)	24 (37%)		12 (22%)	19 (44%)
CD-RISC positive acceptance of change and secure relationships
Low positive acceptance of change	12 (37%)	18 (28%)	0.310	20 (37%)	10 (23%)	0.185
Normal positive acceptance of change	15 (47%)	28 (43%)		24 (44%)	19 (44%)
High positive acceptance of change	5 (16%)	19 (29%)		10 (19%)	14 (33%)
CD-RISC control
Low control	13 (41%)	26 (40%)	0.142	24 (44%)	15 (35%)	0.039*
Normal control	14 (44%)	18 (28%)		21 (39%)	11 (26%)
High control	5 (15%)	21 (32%)		9 (17%)	17 (39%)
CD-RISC spiritual influences
Low Spirituality	13 (41%)	28 (43%)	0.262	24 (44%)	17 (40%)	0.829
Normal Spirituality	14 (44%)	19 (29%)		17 (32%)	16 (37%)
High Spirituality	5 (15%)	18 (28%)		13 (24%)	10 (23%)
Quantitative data
VAS health status	70.91 (SD 17.87)	76.18 (SD 14.64)	0.210	69.89 (SD 16.19)	80.16 (SD 13.62)	0.465
SF12v2 physical score	41.85 (SD 12.24)	49.70 (SD 7.48)	0.000*	43.83 (SD 10.95)	51.22 (SD 6.69)	0.000*
SF12v2 mental score	49.21 (SD 11.79)	50.10 (SD 11.44)	0.627	47.43 (SD 12.12)	52.79 (SD 10.03)	0.029*
Anxiety score	6.50 (SD 4.97)	5.12 (SD 4.28)	0.154	6.96 (SD 5.02)	3.84 (SD 3.13)	0.002*
Depression score	5.13 (SD 4.92)	3.20 (SD 3.30)	0.006*	4.87 (SD 4.33)	2.53 (SD 3.11)	0.051*

Note.EQ5D = European Quality of Life-5 Dimensions; CD-RISC25 = Connor-Davidson Resilience Scale 25-item; VAS = Visual Analog Scale; SF12v2: Short Form 12-item Health Survey (version 2).

^*Significant correlation at p < .05 level (bilateral).

Regarding the specific analysis of the population of LT patients (n = 17), the mean year after LT was 8.41 (SD: 7,459) with a median of 6 (IR: 29). A Spearman’s correlation of resilience was performed on the data of the LT population, revealing a significant correlation at the .05 level (bilateral). The mean score for resilience was 73.77 (SD 17.53), whereas, for quality of life: the physical score was 47.11 (SD 9.97; p = .761; ρ = .08); the mental score was 49.81 (SD 11.50; p = .10; ρ = .605), the HADS-anxiety score was p = .047; ρ = −.488 and HADS-depression was p = .005; ρ = −.649. Our results reveal that LT patients reported a decline in their physical quality of life 6 months after COVID-19 infection. The level of resilience was in the normal range, and it was positively correlated with mental health status.

Multivariate Analysis

In the multivariate analysis, the following variables had the most specific weight that affected the resilience level of the patients 6 months after COVID-19 infection (Table 3): age (OR = 0.956) and HADS depression (OR = 0.673).

Table 3.

Variables That Influence the Resilience of Patients 6 Months After COVID-19 Infection. Multivariate Analysis.

Variable	Regression coefficient (β)	Standard error	Odds ratio [95% CI]	p *
Age	−.045	0.022	0.956 [0.915, 0.998]	.042*
EQ5D mobility	.803	0.644	2.232 [0.632, 7.885]	.213
EQ5D anxiety/depression	.913	0.629	2.493 [0.727, 8.553]	.146
HADS depression	−.396	0.126	0.673 [0.526, 0.862]	.002*
SF12 mental score	.045	0.037	1.046 [0.974, 1.125]	.217
Liver transplantation	−.318	0.746	0.727 [0.168, 3.141]	.670
Cons	2.541	2.342	12.698	.278

Note.EQ5D = European Quality of Life-5 Dimensions; SF12v2 = Short Form 12-item Health Survey (version 2); HADS = Hospital Anxiety Depression Scale.

^*Statistically significant (p < .05).

Discussion

The resilience capacity of chronic patients is an aspect that must be considered in nursing interventions, in an effort to improve the capacities to overcome and cope with the consequences of the disease (Kim et al., 2019). In the general population, multi-organ symptoms after COVID-19 are being reported due to the increasing number of patients (The Lancet, 2020). Cases of long COVID-19 are having a great impact on health and social aspects (No Author, 2020). A generally accepted timeframe that differentiates the duration of acute and post-acute infection from long COVID-19 is 28 days (Mendelson et al., 2020). Recent findings have revealed a confusing disease with many, varied, and often remitting and relapsing symptoms and an uncertain prognosis (Ladds et al., 2020). Data from the U.K. National Institute of Statistics has estimated that 1 in 10 COVID-19 patients have symptoms beyond 12 weeks after infection (Office for National Statistics, n.d.). This corresponds to our results in which 54% of our patients describe signs and symptoms 6 months after infection. In our study, 81.40% of patients required hospital admission. In addition, our patients described multiple pathologies such as asthma, diabetes mellitus, and/or arterial hypertension, and the sequelae-recovery processes may have been affected. According to Saloner et al. (2020), the recurrent and prolonged symptoms that patients describe significantly affect their quality of life (Saloner et al., 2020). Our results are also related to the fact that the persistence of signs and symptoms at 6 months affects the quality of life of patients in our study. The study by Garrigues et al., (2020) carried out in patients who required hospitalization for COVID-19, shows that the health-related quality of life was quite satisfactory, related to having a professional activity before infection (Garrigues et al., 2020). This result contrasts with our study given that in our population 54.60% of the patients were retired and 40.20% were working. Moreover, we did not observe a significant relationship with quality of life, due to a probable selection bias, as they were non-comparable population samples.

Our results show that the mental health status and the level of anxiety/depression are associated with the persistence of symptoms at 6 months. Li et al. (2020) describe a hospital stay longer than 14 days as being a risk factor for suffering from anxiety and depression. In our study, 81% required hospital admission; however, only 10% (n = 10) suffered severe COVID-19. Furthermore, according to Özdin and Bayrak Özdin (2020), the groups who are most psychologically affected by the COVID-19 pandemic are women, people with previous psychiatric illnesses, people living in urban areas, and those with an accompanying chronic illness (Özdin & Bayrak Özdin, 2020). According to our findings, gender is not a factor that is significantly related to the level of resilience and our entire population comes from an urban area, Madrid; therefore, it is not possible to analyze this variable. Furthermore, in the studied sample of LT patients the vast majority were men. Tomasoni et al. (2021) concluded that a considerable proportion of COVID-19 patients still experienced psychological distress and symptoms after hospital discharge. González-Sanguino et al. (2020), in a study of a Spanish population at the beginning of the pandemic, described that 18.70% of the population may have a possible diagnosis of depression and 21.60% have a potential diagnosis of anxiety. A recent study by Gramaglia et al. (2022), showed that female sex and depressive symptoms at 4-month follow-up were associated with depressive symptoms after 12 months (Gramaglia et al., 2022). These results partially relate with those of our study, since gender is not a significant factor in our results, and age is, but they support the need for intervention in the prevention and treatment of depression to improve resilience after COVID-19. These studies were carried out during the first wave under home lockdown, with a high number of deaths, absence of vaccination and overall feelings of uncertainty, confusion, and social distress. These findings support our results, where patients who reported symptoms 6 months after infection also describe poorer physical and mental quality of life and a related effect on their capacity of resilience. Despite having found similar results to those of the study, it should be noted that a large number of studies on COVID-19 have been published worldwide and with very different samples. This disparity may be considered as a limitation to support the validity of our conclusions.

The level of resilience in our results, was significantly related to the persistence of symptoms 6 months after infection; however, neither spirituality nor the pursuing of a religion have been significantly related variables. This result contrasted with the study by Roberto et al. (2020) in the United States, who highlighted the relationship between resilience and spirituality as important factors among women for coping with the pandemic. Resilience, quality of life, and mental health status were significantly related to the presentation of symptoms related to COVID-19 6 months after infection. On behalf of health institutions, health professionals should adapt their interventions to improve resilience and promote the speedy recovery of patients. Ongoing care and multidisciplinary rehabilitation should be provided (Halpin et al., 2021), through the use of educational interventions (Kaim et al., 2020) together with promoting physical activity to improve resilience and depressive symptoms (Carriedo et al., 2020).

Our results have not shown a difference in the level of resilience among the LT and non-transplant population. Moreover the sample size of the transplant cohort was limited. In our study, patients with a history of LT described a decline in their quality of life after SARS-CoV-2 infection. Mental health, anxiety, and depression affect quality of life after LT (Miller et al., 2013). Åberg (2020) has described that LT improves the quality of life of patients despite suffering symptoms as a result of immunosuppression, and that the quality of life remains similar to that of the general population according to Yang et al. (2014). Also, LT has been associated with an improvement in post-transplant anxiety and depression (Benzing et al., 2015). According to our results, the level of resilience is in the normal range and is positively correlated with the state of mental health and the level of anxiety/depression. Jover-Aguilar et al. (2020) described that LT patients over 10 years old show adequate levels of resilience, which is associated with good general health, vitality, mental health, and active social activity. In the current pandemic situation, our findings suggest that the experience of being transplanted could facilitate the process of adaptation to a new infection.

Strengths and Limitations

This study describes the impact on resilience in two different patient samples who have suffered from COVID-19 infection. The design can be easily reproduced, and the information is available through fundamental guidance and policy procedures. The patients surveyed were diagnosed and constant monitoring of their illness was performed by hospital health personnel since the infection can affect the experience of the disease. The response rate obtained in this study is 82% in a sample of n = 119 patients. This study was designed as a cross-sectional study; therefore, we do not have a value of resilience and quality of life prior to the COVID-19 infection of the surveyed patients. Hospital admission for COVID-19 was not an inclusion criterion, but given the special situation of uncertainty, the percentage of hospital admission for COVID-19 was high. The sample had other chronic diseases; although the aim was for patients to report new or sustained signs and symptoms since SARS-CoV-2 infection, having other chronic diseases may be exacerbations of another co-occurring condition or chronic disease. The signs and symptoms questionnaire used was prepared based on signs and symptoms described in previous studies on COVID-19. We kept an open question to the patient to be able to report other signs and symptoms apart from those described in the questionnaire.

This study was carried out in mid-2020 when national lockdown had recently ended, and society was tired of the pandemic and the consequences of social restrictions. In addition, the rising incidence, the lack of treatment, and the absence of a vaccine suggested a foreboding outlook for the future. All these aspects could have affected our results in terms of the mental health of our participants.

Future Implications and Recommendations

The results of the current study support previous studies describing the impact of COVID-19 infection on the resilience and mental health of patients. Recently the WHO has published that prolonged COVID affects individuals’ mental health and can have important consequences for them, their families, and society (Rajan et al., 2021). The current study has obvious disadvantages as well as advantages, as it reports statistically significant data that must be considered within intervention programs in a clinical setting.

On behalf of health institutions, mental health professionals must adapt their interventions to improve resilience and promote the speedy recovery of patients. This study captured the importance of these aspects and suggested that the experience of being transplanted could facilitate the process of adapting to a new infection. We consider that it is necessary to evaluate the resilience of patients, as this will support the implementation of individualized care strategies.

Nursing Implications

These results can be of great importance to health authorities. In a recent publication by the WHO (Rajan et al., 2021), nurses should be considered health professionals of reference for the provision of multidisciplinary and multi-specialized approaches for evaluation and management. Also, the development of context-appropriate guidelines for health professionals, especially in primary care, may allow case management to be tailored to disease manifestations and the involvement of different organ systems, while bearing in mind the service (and research) needs it generates. Thus, it is necessary to implement patient registries and other well-functioning surveillance systems; create patient cohorts; as well as monitoring those affected as a means of supporting research that is critical to understanding and treating long COVID (Rajan et al., 2021).

Conclusions

The level of resilience was affected by the persistence of signs and symptoms associated with COVID-19 six months after infection. Age and depression impact the resilience level significantly. This relationship can affect patient recovery and negatively impact the ability to cope with the disease. Trust in one's instincts, tolerance of negative affect, and strengthening effects of stress and control are significantly related to having signs and symptoms of COVID-19. The health status, activities of daily living, pain/discomfort, and anxiety/depression, worsened in those patients who suffered from signs and symptoms six months after infection. It is necessary to implement specialized training for clinicians on the effects of long-term COVID-19 to improve patient care. Long COVID symptoms might manifest months after an acute COVID-19 illness; clinicians who can confirm patient reports of these symptoms may help patients recover and become more resilient.

Author Biographies

Víctor Fernández-Alonso, PhD, MSc, RN, is a research nurse at the Hepatology and Liver Transplantation Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Sara Rodríguez-Fernández, RN, is a research nurse at the Microbiology Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Laura Secadas-Rincón, MSc, RN, is a research nurse at the Hepatology and Liver Transplantation Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

Manuela Pérez-Gómez, RN, is a nurse practitioner at the Hepatology and Liver Transplantation Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain.

María Nieves Moro-Tejedor, PhD, RN, is a nurse practitioner at the Nursing Research Support Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain, and lecturer at the Red Cross University College of Nursing, Spanish Red Cross, Autonomous University of Madrid, Madrid, Spain.

Magdalena Salcedo, PhD, MD, is Head of the Hepatology and Liver Transplantation Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain.