Do selected lifestyle parameters affect the severity and symptoms of COVID-19 among elderly patients? The retrospective evaluation of individuals from the STOP-COVID registry of the PoLoCOV study

Abstract

Background

Older individuals tend to include less physical activity in their routine and are more prone to chronic diseases and severe medical complications, making them the most burdened group that is losing years of life due to pandemic-related premature mortality.

This research aimed to assess the lifestyle factors that affect the COVID-19 course among patients ≥ 65 years old.

Methods

The study included 568 convalescents (64.1% women and 35.9% men) with persistent clinical symptoms after isolation. The mean age was 70.41 ± 4.64 years (minimum: 65 years; maximum: 89 years). The patients completed the questionnaire during their in-person visit to the medical center. The survey included questions regarding their health status when suffering from COVID-19, basic sociodemographic data, and medical history concerning chronic conditions and lifestyle.

Results

Physical inactivity (p < 0.001) and feeling nervous (p = 0.026) increased the risk of having a severe disease course. Coronary artery disease raised both the risk of a severe disease course (p = 0.002) and the number of present symptoms up to 4 weeks (p = 0.039). Sleep disturbances increased the number of symptoms during infection (p = 0.001). The occurrence of any symptoms was also associated with the female sex (p = 0.004). The severity of the course was associated with longer persistent symptoms (p < 0.001) and a greater number of symptoms (p = 0.004); those with a more severe course were also at a greater risk of persistent symptoms for up to 4 weeks (p = 0.006). Senior citizens in the third pandemic wave suffered with more severe disease (p = 0.004), while illness during the fourth (p = 0.001) and fifth (p < 0.001) waves was associated with a lower risk of persistent symptoms for up to 4 weeks. The disease duration was significantly shorter among vaccinated patients (p = 0.042).

Conclusions

Elderly COVID-19 patients should re-think their lifestyle habits to consider a physical activity level that is adjusted to their abilities, in order to decrease the risk of a severe disease course and to further limit both the number and duration of symptoms.

The research was carried out in accordance with the Declaration of Helsinki, and approval from the Bioethics Committee of Lodz Regional Medical Chamber to conduct the study was obtained (approval number 0115/2021). The PoLoCOV-Study ClinicalTrials.gov identifier is NCT05018052.

1. Introduction

In 2019, in the city of Wuhan in the Chinese province of Hubei, the first cases of a new, highly infectious variant of the coronavirus—SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2), causing the disease called COVID-19 (ang. Coronavirus Disease 2019), were reported [1]. This disease may be asymptomatic, cause mild cold-like symptoms, and also lead to severe pneumonia with acute respiratory distress syndrome [2]. On March 11, 2020, cases of the disease had already been reported in approximately 114 countries, including Europe [3]. In 2021, SARS-CoV-2 infection was confirmed in nearly 400 million people worldwide, of which approximately 6 million died. Infection with the pathogen in Poland was detected in approximately 5 million people, and the death rate was 2.2% [4].

On January 30, 2020, the World Health Organization (WHO) declared the coronavirus (COVID-19) epidemic as a global health threat [5]. Due to the steadily increasing number of confirmed cases of COVID-19 and to avoid overloading the healthcare system, it was necessary to reduce person-to-person contact by prohibiting movement and closing many recreational facilities. This has resulted in a shift to a more sedentary lifestyle, significantly worsening the health of citizens, especially the elderly, as they are less active than younger people and more prone to chronic disease [6].

Many risk factors for severe COVID-19 have been defined [7], one of the most important being the age of patients [8] (patients 65 years or older are particularly at risk of severe disease [9], [10]). In addition to age, other risk factors for the severe course of COVID-19 infection are comorbidities such as hypertension, chronic obstructive pulmonary disease, and diabetes [11], [12], [13], [14]. It was observed that the persistence of the pandemic, the emergence of new virus mutations and the vaccination program (introduced in Poland at the end of 2020) led to significant changes in the clinical picture of the disease [15]. It has been found that the body's ability to respond to a viral infection is extremely important, which depends on various factors, including variants of the virus, implemented vaccinations, general health of the patient, individual predispositions or behavior related to lifestyle. Recognized beneficial factors for a healthy lifestyle include eating a healthy diet [16], maintaining a healthy body weight, avoiding stress [17], getting enough sleep [18], and exercising regularly [19].

The positive effect of a healthy lifestyle is related to the improvement of the processes of the immune system, as well as slowing down the processes related to the aging of the body. Regular exercise increases the number and activity of macrophages, thus reducing the risk of community-acquired infectious diseases and related mortality [20]. In the body, the activity of lymphocytes that destroy cells replicating viruses increases, and the pool of granulocytes in blood and tissues increases. The release of muscle-derived anti-inflammatory cytokines is also observed, coupled with the inhibition of pro-inflammatory cytokines [21]. In addition, the so-called immune memory, in the event of another attack of the same antigen, enables a faster and more effective reaction of the body [21].

A healthy lifestyle is aimed at ensuring optimal health and minimizing the risk of developing diseases known as social, i.e., cardiovascular diseases (hypertension, atherosclerosis, and ischemic heart disease), type 2 diabetes, bronchial asthma or obesity. In addition, it can significantly increase the body's resistance in people of all ages, contributing to a milder course of viral infection, including SARS-CoV-2 [22]. The benefits of a healthy lifestyle are visible not only in limiting the development of diseases and improving the physical fitness of patients, but also in the mental sphere —improving the mood (reducing anxiety and depression) and general well-being of patients (reducing mental stress and improving sleep quality). This is very important due to the negative impact of social distancing caused by the COVID-19 pandemic on the mental and physical health of people around the world [6].

Accordingly, this study aimed to assess whether (and which) factors of a healthy lifestyle influence the course of COVID-19 in the elderly. This study used data from the STOP-COVID registry, which was developed to assess the course of COVID-19 infection and its early and late cardiovascular complications in hospitalized and non-hospitalized patients. In addition, the registry focuses on the assessment of the incidence of COVID-19 debts taking into account complications and predictive factors.

2. Materials and methods

2.1. Patients and eligibility criteria

This retrospective study investigated the COVID-19 symptoms that were present during the isolation period and up to 4 weeks after the disease course among ≥ 65 years old patients. The mean age was 70.41 ± 4.64 years (min.: 65; max.: 89). All participants were natives of Poland. The medical assessment was based on a questionnaire that was prepared for the needs of the STOP-COVID registry of the PoLoCOV-Study (ClinicalTrials.gov identifier: NCT05018052). This research was carried out in accordance with the Declaration of Helsinki, and approval from the Bioethics Committee of Lodz Regional Medical Chamber to conduct the study was obtained (approval number 0115/2021). All subjects from individual groups were informed about the purpose of the study and gave their written consent to participate.

The decisive criteria for including patients in the study were:

• a)SARS-CoV-2 infection (asymptomatic; mild, moderate, and severe course; and hospitalization) confirmed by a Real-Time PCR or antigen test (in accordance with guidelines of the Ministry of Health of Poland in a specific timeframe);
• b)Age in the range of 65–89 years;
• c)Consent of the respondent to participate in the study.

The exclusion criteria were:

• a)No consent to participate in the study;
• b)Age< 65 years.

The patients completed the questionnaire during their in-person visit to the medical center. The survey included questions regarding their health status when suffering from COVID-19; basic sociodemographic data, such as age, gender, body mass and height (used to calculate body mass index—BMI); and medical history concerning chronic conditions. Additionally, the collected information included the course of COVID-19, including the date of onset of symptoms, and the duration or quantity of symptoms.

The isolation period of patients was taken into account, enabling to assign them to a specific wave of the pandemic, in accordance with the infection pattern in Poland (collectively based on [15], [23], [24]):

• 1.First (I) wave—from March 1, 2020 to August 30, 2020 (due to the small number of patients in the first wave, they were not included in the current analysis);
• 2.Second (II) wave—from September 1, 2020 to January 30, 2021;
• 3.Third (III) wave—from February 1, 2021 to August 30, 2021;
• 4.Fourth (IV) wave—from September 1, 2021 to December 31, 2021;
• 5.Fifth (V) wave—from January 1, 2022 to the end of the observation period (May 2022).

The distribution of five COVID-19 waves in the Polish population is shown in Fig. 1.

Fig. 1.

The prevalence of five waves of COVID-19 in Polish population [25].

The next part of the survey comprised single- and multiple-choice questions regarding the most common clinical symptoms of COVID-19, such as fever, subfebrile states, chills, cough, shortness of breath, rhinitis, smell/taste/hearing disorders, headaches, vomits, diarrhea, myalgia, arthralgia, and chest pain. Patients reported symptoms that were present during isolation due to SARS-CoV-2 infection or up to 4 weeks after COVID-19. The duration of the symptoms was determined from the first day when it occurred till the last day of symptoms. The sum of the symptoms was defined as the total number of symptoms that occurred during the course of the disease. Vaccination status was also taken into account in this study. During the program, the collection of data on vaccination status was started, which was introduced in Poland in December 2021. Those who completed the pre-isolation treatment regimen (i.e., two doses of the Pfizer / Moderna / AstraZeneca vaccine or one dose of Johnson & Johnson) were considered to have completed a full vaccination course. The patient also completed the section of the survey regarding lifestyle risk factors: smoking, stress, sleep disorders and lack of regular physical activity.

These parameters were defined as follows:

• a)Smoking—a smoker was considered someone who, in the past 12 months, has used any tobacco products;
• b)Stress/feeling nervous—subjective assessment of being anxious, on edge, unable to stop or control worrying for more than half a day within the 4 weeks prior to COVID-19 infection;
• c)Sleep disorders—sleep disorder was diagnosed when the patient subjectively assessed at least one of the following three parameters persisting for at least half of the 4 weeks prior to COVID-19 infection: difficulties in falling asleep or staying asleep, or somnolence during the next day;
• d)Lack of regular physical activity—less than 150 min of any physical activity per week adjusted to abilities in the last 3 months before COVID-19 (in accordance with European Society of Cardiology guidelines [26]).

Patients subjectively assessed the severity of COVID-19 symptoms during the course of the disease (on a 0–3 scale where: 0—asymptomatic; 1—mild; 2—moderate; 3—severe) and were then categorized as asymptomatic/mildly symptomatic patients or those having moderate/severe symptoms. Asymptomatic patients were those with no symptoms, despite having positive a Real-Time PCR test; their subjective evaluation on a 0–3 scale was “0”. The mild group included those who stayed at home during infection, who subjectively evaluated disease severity as a light course ("1" on a 0–3 scale) and whose duration of symptoms was up to 7 days. Patients were classified as having a moderate course of disease if their subjective evaluation was “2” or “3” on a 0–3 scale or if they had fever> 38 °C or dyspnea or symptoms of any severity lasting 7–14 days. A severe course of disease included those who were hospitalized with a diagnosis of one of the following: pneumonia, respiratory failure, intensive care unit, assisted breathing or thromboembolic complications during hospitalization. Alternatively, it also comprised a home course with symptoms lasting> 14 days, subjective evaluation by the patient as severe ("3" on a scale of 0–3), with temperature> 38 °C, dyspnea or saturation below 94 lasting more than 3 days.

2.2. Statistical analysis

Statistical analysis was performed using the PQStat software v1.8.4 and PAST v4.09. The analyzed variables were quantitative, variable and dichotomous. Quantitative variables were characterized by providing basic descriptive statistics, such as mean values, medians, first and third quartiles, and standard deviation (SD). The normality of the distribution was verified using the Shapiro–Wilk test. Due to the fact that the normality criterion was not met, the non-parametric Mann–Whitney U test was used for the comparison of the two variables. In the case of dichotomous variables, the relationship between them was verified with the Chi2 independence test. If the Cochran assumption was not met in the Chi2 test, the Yates correction was used. To determine the effect of factors on the severity of COVID-19, a complex logistic regression model was built (both full and reduced with the backward stepwise method), where the dependent variable was the assessment of disease severity, and the independent variables were sociodemographic variables, i.e., chronic diseases, vaccination status, period of illness (pandemic wave), smoking, stress, sleep disorders and lack of physical activity. Linear regression analysis (both full model and reduced model with backward stepwise method) was used to assess the impact of the above variables on the duration of the disease and the number of symptoms. In the case of continuous variables, the relationships between them were determined using the Spearman correlation. Risk factors for the occurrence of symptoms up to 4 weeks after the onset of COVID-19 were assessed through logistic regression analysis using the backward stepwise method, where the dependent variable was the presence of at least one symptom and the independent variables were chronic conditions, sociodemographic variables, vaccination status, pandemic wave and lifestyle. The effect of the above variables on the number of symptoms present up to 4 weeks was assessed through linear regression analysis using the backward stepwise method. In each case, the results with p < 0.05 were considered statistically significant.

3. Results

The study included 568 senior citizens; the mean age was 70.41 ± 4.64 years (minimum: 65; maximum: 89); however, no impact of age on the severity of COVID-19 was noted (p = 0.431). The vast majority were women (64.1%). Furthermore, 502 patients (88.4%) had at least one chronic disease, the most common of which was arterial hypertension (66.7%) and diabetes (22.5%). Of all subjects, 145 senior citizens had a verified vaccination status, of which 100 (68.9%) were fully vaccinated. The largest group was the one with senior citizens from the third wave of the pandemic in Poland (37.4%). The median duration of the disease for the entire study group was 10 days (min.: 7; max.: 14) and the number of symptoms was 7 (min.: 5; max.: 10). In the assessment of the respondents’ lifestyle, as many as 68.1% were not physically active in the last 3 months before the disease, and 36.4% suffered from sleep disorders. Most of the patients were non-smokers (93.1%). There was no effect of vaccinations on the severity of COVID-19 (p = 0.223) and the number of symptoms (p = 0.972), while the median duration of the disease was significantly lower (p = 0.042). Furthermore, a much more severe course of COVID-19 was observed (72.1% vs. 44.2%) among patients who were not considered to be physically active (p < 0.001). Moreover, patients who were exposed to increased stress within the 4 weeks prior to infection had a greater number of clinical symptoms (p < 0.001) and a longer duration of the disease (p = 0.010). Table 1 summarized the study group and its lifestyle.

Table 1.

Characteristics of the study group: lifestyle, taking into account the severity of COVID-19, its duration and the number of present clinical symptoms.

Variable		COVID-19 severity				The sum of the symptoms during COVID-19		The duration of the symptoms (days) during COVID-19
		The entire group (N = 568)	Asymptomatic and mild (N = 209)	Moderate and severe (N = 359)	p	Median [Q1; Q3]	p	Median [Q1; Q3]	p
Age	Mean± SD	70.41 ± 4.64	70.46 ± 4.79	70.38 ± 4.57	0.431	–	–	–	–
	Median [Q1; Q3]	69 [67; 73]	69 [67; 73]	69 [67; 73]		–	–	–	–
BMI	Mean± SD	28.77 ± 5.22	29.10 ± 5.74	28.58 ± 5.23	0.775	–	–	–	–
	Median [Q1; Q3]	28.1 [25.6; 31.3]	28.4 [25.8; 31.6]	28.6 [25.5; 31.2]		–	–	–	–
Sex	Female	364 (64.1%)	127 (34.9%)	237 (65.1%)	0.243	8 [5; 10]	0.001	10 [7; 14]	0.928
	Male	204 (35.9%)	82 (40.2%	122 (59.8%)		7 [4; 9]		10 [6; 14]
Diabetes Mellitus	Yes	128 (22.5%)	41 (32.0%)	87 (68.0%)	0.204	8 [5.5; 10]	0.171	10 [7; 14]	0.315
	No	440 (77.5%)	168 (38.2%)	272 (61.8%)		7 [5; 10]		10 [7; 14]
Coronary Artery Disease	Yes	101 (17.8%)	2322.8%)	78 (77.2%)	0.001	7 [5; 10]	0.016	10 [7; 14]	0.131
	No	467 (82.2%)	186 (39.8%)	281 (60.2%)		7 [5; 10]		10 [7; 14]
Heart Failure	Yes	23 (4.1%)	6 (26.1%)	17 (73.9%)	0.281	8 [5; 12]	0.234	10 [7; 14]]	0.857
	No	544 (95.9%)	202 (37.1%)	342 (62.9%)		7 [5; 10]		10 [8; 14]
Hypertension	Yes	379 (66.7%)	137 (36.2%)	242 (63.8%)	0.650	8 [5; 10]	0.015	10 [7; 14]	0.735
	No	189 (33.3%)	72 (38.1%)	117 (61.9%)		7 [4; 9]		10 [7; 14]
COPD	Yes	36 (6.3%)	11 (30.6%)	25 (69.4%)	0.532	10.5 [7; 12]	0.001	14 [8; 14]	0.150
	No	532 (93.7%)	198 (37.2%)	334 (62.8%)		7 [5; 10]		10 [7; 14]
Asthma	Yes	67 (11.8%)	20 (29.9%)	47 (70.1%)	0.209	8 [6; 11]	0.003	9 [7; 14]	0.883
	No	501 (88.2%)	189 (37.7%)	312 (62.3%)		7 [5; 10]		10 [7; 14]
Comorbidities	Yes	502 (88.4%)	181 (36.1%)	321 (63.9%)	0.382	8 [5; 10]	0.012	10 [7; 14]	0.651
	No	66 (11.6%)	28 (42.4%)	38 (57.6%)		6 [4; 9]		10 [6; 14]
Any sleep disorders	Yes	207 (36.4%)	79 (38.2%)	128 (61.8%)	0.673	8 [6; 11]	< 0.001	10 [7; 14]	0.208
	No	361 (67.3%)	130 (36.0%)	231 (64.0%)		7 [4; 10]		10 [6; 14]
Smoking	Yes	39 (6.9%)	21 (53.9%)	18 (46.2%)	0.034	6 [4; 10]	0.449	7 [5; 10]	0.013
	No	529 (93.1%)	188 (35.5%)	341 (64.5%)		8 [5; 10]		10 [7; 14]
Stress in the last 4 weeks before COVID-19	Yes	83 (14.6%)	23 (27.7%)	60 (72.3%)	0.082	9 [5; 11]	0.001	10 [7; 14]	0.010
	No	485 (85.4%)	186 (38.4%)	299 (61.7%)		7 [5; 10]		10 [7; 14]
Regular activity 3 months before COVID-19	Yes	181 (31.9%)	101 (55.8%)	80 (44.2%)	< 0.001	7 [4; 9]	0.128	8 [6; 14]	0.022
	No	387 (68.1%)	108 (27.9%)	279 (72.1%)		8 [5; 10]		10 [7; 14]
COVID-19 Vaccination (n = 145)	Yes	100 (68.9%)	44 (44.0%)	56 (56.0%)	0.223	8 [5; 10]	0.972	8 [7; 14]	0.042
	No	45 (31.1%)	15 (33.3%)	30 (66.7%)		8 [6; 0]		10 [7; 14]
Pandemic Wave	II	187 (32.9%)	80 (42.8%)	107 (57.2%)	0.017	7 [4; 10]	0.497	10 [7; 14]	0.005
	III	212 (37.4%)	61 (28.8%)	151 (71.2%)		7 [5; 10]		10 [8; 14]
	IV	104 (18.3%)	40 (38.5%)	64 (61.5%)		8 [6; 9]		10 [7; 14]
	V	65 (11.4%)	28 (43.1%)	37 (56.9%)		7 [5; 9]		7 [5; 10]
COVID-19 Severity	Asymptomatic/ mild	–	–	–	–	6 [3; 8]	< 0.001	7 [4; 10]	< 0.001
	Moderate / severe	–	–	–	–	8 [6; 10]		12 [9; 14]

SD – Standard deviation; Q1; Q3 – first and third quartile; COPD – chronic obstructive pulmonary disease; BMI – Body Mass Index. Statistically significant values are in bold with the significance level set at p < 0.05.

3.1. Risk factors of the severe COVID-19 course, the disease duration and the quantity of present clinical symptoms

Among the potential risk factors in both the full and the reduced models with a backward stepwise method, it was shown that a lack of physical activity increased the risk of severe COVID-19 in senior citizens by more than 3.4 times and the severity of stress by 1.85-fold. In addition, a history of coronary artery disease also increased the risk of severe COVID-19 in patients ≥ 65 years old by 2.3-fold. The detailed results of the full and the reduced models are shown in Table 2.

Table 2.

Full and reduced backward stepwise logistic regression analysis model assessing the impact of risk factors on the severity of COVID-19.

Variable	OR	-95% CI	+ 95% CI	p
Full logistic regression analysis model
Age	0.991	0.952	1.033	0.699
Any sleep disorders	0.896	0.595	1.348	0.599
Smoking	0.485	0.233	1.011	0.054
Stress in the last 4 weeks before COVID-19	1.851	1.055	3.248	0.032
No regular activity 3 months before COVID-19	3.571	2.410	5.291	< 0.001
Female	1.103	0.747	1.629	0.619
BMI	0.966	0.931	1.022	0.070
Diabetes Mellitus	1.223	0.759	1.987	0.403
Coronary Artery Disease	2.151	1.247	3.710	0.005
Heart Failure	0.771	0.278	2.130	0.615
Hypertension	0.920	0.576	1.471	0.728
COPD	1.128	0.489	2.598	0.777
Asthma	1.403	0.754	2.612	0.284
COVID-19 Vaccination	0.636	0.305	1.327	0.228
III pandemic wave	1.920	1.223	3.014	0.004
IV pandemic wave	1.395	0.851	2.377	0.221
V pandemic wave	1.084	0.569	2.065	0.805
Reduced backward stepwise logistic regression analysis model
No regular activity 3 months before COVID	3.441	2.349	5.041	< 0.001
Coronary Artery Disease	2.283	1.348	3.867	0.002
Stress in the last 4 weeks before COVID-19	1.858	1.075	3.212	0.026
III pandemic wave	1.849	1.193	3.865	0.002

OR – Odds ratio; 95% CI – 95% Confidence interval; COPD – chronic obstructive pulmonary disease; BMI – Body Mass Index. Statistically significant values are in bold with the significance level set at p < 0.05.

3.2. Factors affecting the duration and number of symptoms during COVID-19

In the constructed linear regression models with applied backward stepwise analysis, it was demonstrated that both the severe course of COVID-19 (value 2.712; p = 0.004) and COPD (chronic obstructive pulmonary disease) (value 3.722; p = 0.004) increased the number of clinical symptoms. In addition, sleep disturbances were also associated with a greater number of symptoms during SARS-CoV-2 infection (value 1.532; p = 0.001). For disease duration, the severity of COVID-19 was associated with longer-lasting symptoms (value 6.368; p < 0.001). The Spearman correlation analysis showed no relationship between BMI and age in relation to the number of symptoms (rBMI = 0.015; p = 0.807; rage = 0.031; p = 0.582) and disease duration (rBMI = −0.102; p = 0.100; rage = 0.043; p = 0.492). A detailed comparison of the full and reduced models is presented in Table 3.

Table 3.

Full and reduced backward stepwise linear regression analysis model assessing the effect of COVID-19 severity, sociodemographic variables, vaccination status, pandemic wave, chronic conditions and lifestyle on disease duration and the number of clinical symptoms.

Variable	The sum of the symptoms during COVID-19				The duration of the symptoms (days) during COVID-19
	Value	SD	t	p	Value	SD	t	p
Full linear regression analysis model
COVID-19 Severity	2.647	0.508	5.209	< 0.001	6.429	1.105	5.815	< 0.001
Age	-0.093	0.057	-1.612	0.109	0.042	0.124	0.342	0.732
Any sleep disorders	1.134	0.489	2.319	0.022	2.310	1.065	2.169	0.032
Smoking	-0.756	0.986	-0.767	0.444	-1.413	2.147	-0.658	0.511
Stress in the last 4 weeks before COVID-19	0.152	0.640	0.238	0.812	1.133	1.137	0.998	0.321
No regular activity 3 months before COVID	0.807	0.522	1.546	0.124	-1.823	1.163	-1.567	0.112
Sex, female	0.807	0.514	1.569	0.119	-1.701	1.120	-1.519	0.131
BMI	0.075	0.052	1.501	0.135	-0.004	0.124	-0.0.044	0.965
Diabetes Mellitus	1.009	0.652	1.547	0.124	0.098	1.419	0.069	0.944
Coronary Artery Disease	0.204	0.6851	0.297	0.766	0.234	1.491	0.157	0.875
Heart Failure	-1.742	2.057	-0.847	0.398	-4.707	4.478	-1.051	0.295
Hypertension	-0.254	0.557	-0.456	0.649	-1.098	1.213	-0.904	0.367
COPD	4.363	1.379	3.161	0.001	3.382	3.003	1.125	0.262
Asthma	0.431	0.697	0.617	0.537	-0.043	1.518	-0.029	0.997
COVID-19 vaccination	0.058	0.534	0.109	0.912	-1.823	1.162	-1.567	0.112
Pandemic wave	-0.505	0.444	-1.136	0.257	-2.537	0.968	-2.620	0.009
Reduced backward stepwise linear regression analysis model
COVID-19 Severity	2.712	0.474	2.919	0.004	6.368	1.005	6.334	< 0.001
Pandemic wave	–	–	–	–	-2.366	0.914	-2.586	0.011
Any sleep disorders	1.532	0.467	3.318	0.001	–	–	–	–
COPD	3.722	1.275	2.919	0.004	–	–	–	–

SD – Standard deviation; BMI – Body Mass Index; COPD – chronic obstructive pulmonary disease. Statistically significant values are in bold with the significance level set at p < 0.05.

3.3. Symptoms up to 4 weeks after COVID-19

In assessing the presence of at least one symptom up to 4 weeks after COVID-19 onset, 513 patients (90.3%) were included, of which 308 (60.0%) reported such symptoms. The median of symptoms up to 4 weeks among the analyzed patients is 7 (min.: 5; max.: 10). The logistic regression analysis showed that the severity of the disease (value 1.733; p = 0.002), sleep disturbance (value 1.638; p = 0.004), COPD (value 4.571; p = 0.017) and coronary artery disease (value 1.836; p = 0.039) had an effect on the number of symptoms present up to 4 weeks after COVID-19. In contrast, the occurrence of any symptoms in the group of patients of ≥ 65 years old was associated with the severity of the disease (Odds Ratio (OR) = 1.816; p = 0.006) and the female sex (OR 1.747; p = 0.004). Patients infected in the fourth and fifth waves of the pandemic showed a much lower risk of persistent symptoms for up to 4 weeks than senior citizens at the beginning of the pandemic. Pre-disease lifestyle has not been shown to significantly affect the risk of symptom persistence. The comparison of the exact results of the linear and logistic regression model with regard to the number and occurrence of symptoms is presented in Table 4.

Table 4.

Influence of sociodemographic variables, lifestyle, vaccination status, pandemic wave and chronic diseases on the persistence of symptoms up to 4 weeks after the onset of COVID-19 and the number of symptoms.

Variable	At least one symptom 4 weeks after COVID-19#				The number of symptoms 4 weeks after COVID-19*
	OR	-95% CI	+ 95% CI	p	Value	SD	t	p
Full model
COVID-19 severity	1.993	1.323	3.003	< 0.001	1.490	0.641	2.236	0.022
Age	0.967	0.929	1.001	0.643	-0.129	0.065	-1.999	0.049
Any sleep disorders	1.072	0.702	1.636	0.746	0.745	0.641	1.116	0.249
Smoking	1.809	0.803	4.075	0.153	-0.363	1.032	-0.352	0.726
Stress in the last 4 weeks before COVID-19	0.879	0.504	1.533	0.651	0.334	0.752	0.444	0.658
No regular activity 3 months before COVID	0.953	0.624	1.454	0.823	0.707	0.631	1.121	0.265
Sex, female	1.762	1.178	2.634	0.005	0.979	0.611	1.601	0.114
BMI	0.991	0.955	1.028	0.643	0.076	0.058	1.205	0.232
Diabetes Mellitus	0.974	0.601	1.573	0.914	0.897	0.827	1.085	0.282
Coronary Artery Disease	1.748	1.022	2.988	0.041	2.038	0.958	2.213	0.037
Heart Failure	0.922	0.299	2.848	0.889	-0.087	2.756	-0.032	0.974
Hypertension	0.897	0.585	1.368	0.614	0.194	0.708	0.273	0.785
COPD	0.856	0.390	1.882	0.700	5.443	2.043	2.665	0.009
Asthma	1.001	0.540	1.856	0.995	0.132	00.843	0.156	0.876
COVID-19 vaccination	1.398	0.636	3.071	0.403	0.001	0.609	0.003	0.998
III pandemic wave	1.048	0.662	0.715	0.841	–	–	–	–
IV pandemic wave	0.416	0.242	0.715	0.001	–	–	–	–
V pandemic wave	0.248	0.120	0.510	< 0.001	–	–	–	–
Reduced backward stepwise model
COVID-19 severity	1.816	1.118	2.783	0.006	1.733	0.563	3.075	0.002
Sex, female	1.747	1.189	2.567	0.004	–	–	–	–
IV pandemic wave	0.421	0.249	0.711	0.001	–	–	–	–
V pandemic wave	0.265	0.133	0.527	< 0.001	–	–	–	–
Coronary Artery Disease	–	–	–	–	1.836	0.880	2.087	0.039
COPD	–	–	–	–	4.571	1.884	2.425	0.017
Any sleep disorders	–	–	–	–	1.638	0.562	2.912	0.004

# – Linear regression analysis; * – Logistic regression analysis; OR – Odds ratio; COPD – chronic obstructive pulmonary disease; BMI – Body Mass Index. Statistically significant values are in bold with the significance level set at p < 0.05.

4. Discussion

The COVID-19 pandemic caused lifestyle changes on a global scale [27]. It is forecasted that living in such an unhealthy environment will increase the presence of unhealthy populations, which leads to unhealthy offspring [28]. Elderly people tend to be less active compared to younger people and are more prone to chronic diseases [29]; they are now the most burdened individuals that are losing years of life due to pandemic-related premature mortality [30]. Using patients’ data from the STOP-COVID registry of the PoLoCOV-Study, the current research focused on the assessment of the lifestyle factors that can affect the course of COVID-19 in patients of ≥ 65 years old. This group is of particular importance since older adults are the most susceptible to severe medical complications and death due to COVID-19 [31]. In brief, results present that disease severity, physical inactivity, stress, coronary artery disease, COPD, sleeping disturbance, female sex, pandemic wave and vaccination status were of significance in terms of the COVID-19 course.

The pre-infection lifestyle analysis showed that both physical inactivity and feeling nervous significantly contributed to the occurrence of severe disease, increasing the risk by 3.5 and 1.85 times, respectively. There are many reports on physical inactivity in the literature; among others, Yuan et al. observed pre-existent physical inactivity to be associated with an increased risk of severe COVID-19 [32]. Similarly, another source indicates that a physically active lifestyle might decrease the rate of acute respiratory infection incidence and the severity of COVID-19 symptoms [33]. Hamer et al. characterized the risk profile of patients that were hospitalized during study follow-up; the lack of physical activity was one of the variables that contribute to the risk profile [34]. The mitigation of infection progress could be achieved by both home- and outdoor-based exercise, regardless of age and chronic conditions [35]. Sallis et al. recommended promoting physical activity by public health agencies and incorporating it into routine medical care, since constantly inactive COVID-19 patients had a greater risk of hospitalization and admission to the intensive care unit or even death, compared to patients who were consistently meeting physical activity guidelines. The same applies to the comparison of constantly inactive patients to individuals who were somewhat physically active, in favor of the latter group [36]. It has been summarized that various mechanisms involving redox-sensitive transcription factors, cytokines, and molecules associated with cellular stress or fatty acid oxidation are responsible for the beneficial effects of exercise [37]. Similarly, inflammatory markers were reported to correlate with symptoms related to cognitive deficits [38]. In the Schou et al. study, disease severity and duration of symptoms were identified as risk factors of psychiatric sequelae, while neuroimmune alterations were supported by the fact that: (1) Angiotensin-converting enzyme 2 (ACE2) is expressed on neurons and glial cells; (2) SARS-CoV-2 can be detected in the brain; and (3) astrocytes and microglia cells are activated during COVID-19 [38]. Recently, a longitudinal prospective observational cohort study by Ayling et al. revealed that more psychological distress during the early pandemic phase was significantly associated with further reports of SARS-CoV-2 infection, as well as with more severe disease and a greater number of symptoms [39]. Sjöberg et al. concluded that for future pandemics or waves of COVID-19, the appropriate strategies are required to counteract physical inactivity, especially among older individuals [40]. Regarding COVID-related stress, Hadjistavropoulos and Asmundson conceptualized several ways in which the pandemic may uniquely impact stress levels among the elderly [41]. In the review of Grolli et al., the cause–effect scheme was proposed, in which the stress triggers the hypothalamus–pituitary–adrenal axis and the inflammatory processes that are related to immunosenescence in advanced age. This scenario can culminate in a higher degree of chronic inflammation, predisposing the elderly to psychiatric sequelae, e.g., anxiety or major depressive disorder [42].

Of the chronic conditions, a history of coronary artery disease not only 2.2-fold increased the risk of a severe disease course but also increased the number of symptoms up to 4 weeks after COVID-19. Other studies have demonstrated an increased risk of complications and mortality in people with COVID-19 and pre-existing cardiovascular disease (CVD), as well as in people with one or more comorbidities, such as hypertension, diabetes, hypercholesterolemia or obesity [43], [44], [45]. A meta-analysis performed on the Chinese population has shown an increased mortality in people with cardiovascular diseases infected with COVID-19; the mortality rate was approximately 11% [46]. Similar conclusions were drawn from the review by Xintao Li et al. [47]. Several mechanisms of action have been proposed; first, patients with cardiac burden are more prone to the deterioration of hemodynamic status after infection with the SARS-CoV-2 virus [48]. ACE-2 plays a key role in the development of cardiovascular complications [49], and the virus enters the host cells through this receptor, causing damage to the lung tissue. It also binds to vascular endothelial cells of other organs, such as the kidneys and the heart [50]. Severe pneumonia puts a significant strain on the ventricles, which may aggravate pre-existing left ventricular dysfunction and even cause cardiogenic shock [51]. Another mechanism may be an infection-induced inflammatory reaction that may transform chronic coronary artery disease into acute coronary syndrome [52], [53]. It has been observed that the occurrence of cardiovascular events in people with COVID-19 is associated with vascular inflammation and remodeling resulting from endothelial dysfunction [50]. Systemic inflammation along with local inflammatory infiltration can lead to the hypercoagulability and rupture of atherosclerotic plaque [54]. In addition, prioritizing COVID-19 treatment while neglecting other comorbidities may predispose patients with cardiovascular disease to adverse clinical outcomes. Recently, Karadavut and Altintop’s study on elderly individuals demonstrated that long-term cardiovascular complications were more frequent in patients with severe COVID-19 [55]. Similarly, Napoli et al. highlighted that cardiometabolic comorbidities and aging are associated with a higher frequency and severity of disease in the elderly [56].

Furthermore, the current study found that COPD increases the number of clinical symptoms during SARS-CoV-2 infection, as well as up to 4 weeks after COVID-19. Gerayeli et al. performed a systematic review and meta-analysis, which indicated that the diagnosis of COPD is related to poorer clinical outcomes in COVID-19 patients; COPD patients were considered a high-risk group that should be targeted for preventative measures and vaccination [57]. COPD was also found to be a risk factor in the study of Higham et al. [58], and another study reported an increased risk of over 5-fold for severe COVID-19 due to COPD [59]. The mechanisms that could explain COPD-related poorer COVID-19 outcomes include ACE2 upregulation in the airways and lungs of individuals with COPD, which facilitates progression [60], [61] or impaired innate immune response among COPD patients [62]. The latter may be due to altered interferon responses that have been associated with an increased risk of severe COVID-19 [63]. The presence of other risk factors that are common in people with COPD (e.g., older age, CVD, hypertension, and diabetes) might also be associated with more severe COVID-19 [64]. In COPD patients with SARS-CoV-2 infection, respiratory failure-related symptoms increased, and the clinical condition worsened, affecting the complications and mortality [65].

Moreover, we found sleep disturbances to be associated with a greater number of symptoms during viral infection. This complements the findings of Kim et al., where a lack of sleep at night and severe sleep problems were proposed as COVID-19 risk factors, based on data from six countries [66]. Another report indicates that in a group of patients exhibiting poor sleep quality, the duration of hospitalization and the depression rate were higher [67]. Pataka et al. observed that compared with dyspnea or depression, insomnia symptoms were more frequently reported in acute COVID-19 patients [68]. Recently, Bhat and Chokroverty underlined that poor sleep is indeed associated with a greater susceptibility to COVID-19 infection and a worse disease course, but the exact cause-and-effect relationship remains undefined [69]. In another study, a group of patients was divided into good and poor sleepers, which allowed the identification of the lower absolute lymphocyte count and increased neutrophil-to-lymphocyte ratio in the latter subgroup [70]. Another scientific group mentions that prolonged exposure to proinflammatory mediators and innate immune molecules may modulate neuroinflammation and causes clinical symptoms of insomnia, arousal, and diminished sleep efficiency [71]. It is worth mentioning that the relation between sleep disturbances and symptom quantity can be a double-edged sword, as COVID-19 patients report sleep problems due to illness-related symptoms that make rest difficult. Pataka et al. mentioned that elderly patients and those with comorbid chronic diseases were more likely to report sleep problems [68].

The occurrence of any symptoms was also associated with the female sex. A study by Pelà et al. indicated that females were more symptomatic than males not only in the acute phase but also at follow-up. Gender was also found to be a significant predictor of persistent symptoms, such as dyspnea, fatigue, chest pain, and palpitations among females [72]. In contrast, other researchers concluded that the female sex is not a risk factor associated with COVID-19 symptoms but is associated with long-term COVID symptoms [73]. In the authors’ opinion, a systematic review will be of relevance in the case of literature contradictions; one of such data collections was prepared by Sylvester et al. [74]. They found that COVID-19 sequelae related to psychiatric, ear/nose/throat (ENT), musculoskeletal, and respiratory complications were more frequent among females, whereas renal sequelae were prevalent among males. Moreover, the likelihood of having long-COVID was also greater among females, with ENT, gastrointestinal, psychiatric, neurological, and dermatological disorders being more prevalent among females, while those related to endocrine and renal complications were more frequent among males. It appears that any complications except for endocrine or renal complications are related to the female sex, which verifies our observations. It has been proposed that such differences between females and males could be due to immune system function [75], [76] or hormone regulation [77]. Although there are reports that are in line with our findings (e.g., Jacobs noticed that not only older participants aged 65–75 years, but also women, experienced more persistent symptoms for up to 35 days after the acute phase [78]), Doerre and Doblhammer present an interesting discussion with regard to gender-specific diagnoses taking into account many additional aspects [79]. Nevertheless, it is imperative to not disregard all significant symptoms among women and profoundly evaluate the findings on a larger scale.

The severity of the course was significantly associated with longer persistent symptoms and a greater number of symptoms; in addition, those with a more severe course were at greater risk of persistent symptoms for up to 4 weeks after the acute phase of the disease. The meta-analysis of He et al. revealed that fever, cough, dyspnea, expectoration, hemoptysis, abdominal pain, diarrhea, anorexia, and fatigue occurred more frequently in patients with severe COVID-19 than in mild cases, while chest pain, pharyngalgia, nausea, vomiting, headache and myalgia did not show such tendency [80]. It appears that symptoms differentiate the groups. Furthermore, Tenforde et al. found that a prolonged symptom duration and disability are more common in hospitalized COVID-19 patients [81], while the study of Lane et al. demonstrated that the only factors associated with a prolonged duration of symptoms are the presence of lower respiratory symptoms or neurologic symptoms at disease onset [82]. Our findings verify the former study and are of particular importance for the elderly as they tend to be susceptible to a more severe course of COVID‐19 [83].

Senior citizens with illness during wave III of the pandemic were most likely to undergo severe disease, while illness during wave IV and wave V were associated with a lower risk of persistent symptoms up to 4 weeks after isolation. It is challenging to find appropriate data for comparison with our findings since much research related to COVID-19 do not distinguish pandemic waves (which was the advantage of our previous research [15]), and many studies outside Poland assume different period for each pandemic wave. However, in Jassat et al. study the waves’ timeframe was similar to our research, i.e., from 0 to 3 months of difference [84]. Compared with each preceding wave, fewer patients were admitted to the hospital during the fourth wave, with less clinically severe illness and a lower case-fatality ratio. Another report by Matsunaga et al., despite having greater disparities regarding timeframe, indicated that each wave had different characteristics. Namely, the first wave was characterized by a more severe disease and the worst case-fatality rate, the second wave included young patients and mild disease, while the third wave had older patients with comorbidities. Clinically significant differences concerned age, the severity of illness at admission, and hypertension [85]. Definitely, our observations are in line with the connection of older patients with a third wave, as well as alleviating the clinical manifestation from the fourth wave onwards.

In addition, there was no effect of vaccination on the severity of disease course or persistence of symptoms up to 4 weeks after COVID-19. Interestingly, the duration of disease was significantly shorter, similar to research by Thompson et al., where partially or fully vaccinated patients spent 2.3 fewer days sick in bed compared to unvaccinated participants [86]. Likewise, Ronchini et al. recently concluded that the probability of infection after vaccination is not only lower compared to natural infection, but is also associated with a shorter duration of infection (than that of first infection and reinfection) and is inversely correlated with circulating immunoglobulins G [87]. Lytras et al. evaluated vaccine effectiveness against severe COVID-19 and found that efficacy gently declined after two doses, but a third restored the protection [88]. In the case of our research, the observation of vaccines shortening disease duration should now be only considered as a hint, as the vaccination status was verified only in a quarter of all patients included in this study. The slightly modest percentage is due to the inclusion of second-wave elderly patients that were not able to be vaccinated as no programme was available in that period for this group in Poland. Nevertheless, it seems reasonable to direct the profound investigation to a larger group of patients in the future.

The findings of this study are subject to at least three other limitations. First, there is a lack of data regarding the use of pharmacotherapy in the course of the disease. While specific guidelines for treating COVID-19 were provided during the pandemic, physicians often executed their own regimens, including antibiotic therapy, antiviral medications, and amantadine or ivermectin. However, based on worldwide reports [89], [90], [91], [92], these medications do not affect the disease course or severity; however, it cannot be ruled out that their heterogeneous usage did not influence the disease course for the individual patient. Another drawback is the retrospective nature of data collection, which entails a risk of memory error, influencing the reliability of estimated symptoms frequency. In addition, recording data on all ailments in a given period may result in underestimating or overestimating the frequency of individual symptoms. Moreover, the group of patients analyzed in our study are those who self‐referred to the health centre due to persistent symptoms after recovery from COVID‐19. Obviously, these are not all individuals who are COVID-19 survivors, and thus, the findings should not be extrapolated to the entire population.

In conclusion, elderly COVID-19 patients should re-think their lifestyle habits to consider a physical activity that is adjusted to their abilities, in order to decrease the risk of severe disease course and to further limit both the number and duration of symptoms. This will facilitate repose and further reduce stress, improving well-being. Exercise is especially an ally for risk groups having comorbidities (e.g., coronary artery disease or COPD) to combat the severity of disease and/or number of clinical symptoms, the latter especially among females. The vaccines require further investigation but are promising with regard to decreases in disease duration among Polish elderly patients. From the fourth wave onwards, it is encouraging to observe a reduction in the risk of symptoms up to 4 weeks after isolation.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.