Effects on Mental Health After the COVID-19 Lockdown Period: Results From a Population Survey Study in Lima,Peru

Abstract

Background:

COVID-19 has created a rapid onset health crisis severely affecting different countries, such as Peru. This pandemic also involved social changes, such as the COVID-19 lockdown, which has had negative effects on different aspects of peoples’ mental health. For this reason, the main objective of this work is to establish a model that explains the effects of the COVID-19-lockdown period on the mental health of a population sample in Peru.

Methods:

In this sense, online questionnaires were carried out using the PHQ-9, GAD-7, and CPDI in 400 participants. To better explain the data, an ordinal logistic regression was carried out.

Results:

The model showed that the severity of stress due to COVID-19 is positively associated with the variables age (OR = 1.02; CI95 [1.01; 1.04]), depression (OR = 1.29; CI95 [1.14; 1.31]) and anxiety (OR = 1.49; CI95 [1.35; 1.66]), as well as with the presence of a deceased relative due to COVID-19 (OR = 3.53; CI95 [1.43; 8.82]). On the contrary, the presence of a family member who was hospitalized for COVID-19 is negatively correlated with COVID-19 related stress (OR = 0.30; CI95 [0.13; 0.69]).

Conclusion:

In conclusion, elderly people, having high levels of anxiety or depression, as well as having a deceased relative due to COVID-19 show higher levels of COVID-19 related stress. These factors play an important role in the intervention of future studies that plan to intervene in the mental health of the population affected by the COVID-19 lockdown.

Keywords

COVID-19 Anxiety Depression Psychological Distress Pandemics

Introduction

Coronavirus disease 2019 (COVID-19) has created a rapidly widening health crisis with negative consequences. Following the World Health Organization’s (WHO) reports, the Americas contain currently the highest number of cases of COVID-19,¹ with Peru being one of the most affected countries in this region. Initially, in March 2020, the Peruvian government declared an emergency health status, which included a prolonged lockdown with several social restrictions.² These policies included the restriction of free movement and the prohibition of any social or educational activity.³ Over time, the Peruvian authorities started to relax the proposed restrictive policies (eg, with the use of mouth-nose-protection⁴), and these policies terminated in July 2020.⁵

Due to long periods of isolation and restriction, many authors have reported the negative effects of these lockdown periods on mental health. For instance, Holmes et al reported that the COVID-19 pandemic might cause an impact on mental health with important consequences.⁶ Other reports indicate that pandemics, including COVID-19, are associated with higher depression,^7-9 anxiety,^10,11 and stress^12,13 in the general population. In addition, 3 studies pointed out that longer strictive restriction policies with social avoidance are related to higher anxiety scores.^14-16 Although the number of studies regarding the psychological effects of the COVID-19 pandemic lockdown is growing, few studies analyze the psychological effects after the COVID-19 pandemic lockdown period. There is agreement on the increase in psychological distress in the population during the pandemic¹⁷; however, the effects following the COVID-19 lockdown are not yet clearly described. The understanding of the effects of restrictive policies and lockdown periods on the population’s mental health suggests the need for further studies. Additionally, this topic gains more importance in the American continent, since it involves the most affected regions, and so far, there are no such studies regarding the mental health effects after COVID-19 lockdown policies.

To this end, the objective of the study is to analyze the effects after the COVID-19 lockdown period on mental health in a general population sample in Lima (Peru), identifying the role and predictive character of sociodemographic variables, the presence of physical symptoms and other health-related variables. For this purpose, a statistical model that best describes the results obtained from this population survey study in Peru will be established.

Materials and Methods

The information of this study comes from a population database of a Peruvian survey study that encompasses health personnel (eg, physicians, nurses, psychologists, etc.), medical students and the general population, which is not related to the other 2 groups. The information from this database covers the post-lockdown period due to COVID-19 in Peru

Study design and selection criteria

Information on recruited participants between the ages of 18 and 80 years old was used. The participants were recruited between 20th July and 18th August 2020. From this online survey database, the information of 400 participants was obtained. A complete description is shown in Table 1.

Table 1.

General sample descriptions.

	n = 400
Age	41.00 ± 13.60
Gender
Female	262 (65.50%)
Male	138 (34.50%)
Sexual orientation
Heterosexual	372 (93.00%)
Homosexual	12 (3.00%)
Bisexual	6 (1.50%)
Asexual	3 (0.80%)
Preferred not to answer	7 (1.80%)
Marital status
Single	174 (43.50%)
Married	144 (36.00%)
Cohabitation	41 (10.30%)
Separated	11 (2.80%)
Divorced	25 (6.30%)
Widowed	5 (1.30%)
Domicile
Central Lima	284 (71.00%)
Peripheral Lima	116 (29.00%)
Faith
None	98 (24.50%)
Catholicism	269 (89.40%)
Protestantism	25 (8.30%)
Judaism	2 (0.7%)
Others	5 (1.62%)
Occupation
Independent worker	41 (10.30%)
Housewife/Househusband	34 (8.54%)
Student	33 (8.29%)
Private concern and enterprises	28 (7.04%)
Unemployed	24 (6.03%)
Teaching	24 (6.03%)
Engineering	21 (5.28%)
Employee	19 (4.77%)
Business administration	18 (4.52%)
Law/Attorney	17 (4.27%)
Others	118 (29.65%)
Work not specified	23 (5.78%)
Medical diagnosis
Healthy participant	262 (65.50%)
Participant with only 1 medical diagnosis	89 (22.30%)
Participant with more than 1 medical diagnosis	49 (12.30%)
Medication intake
No medication intake	172 (43.00%)
Medication intake	228 (57.00%)

Participants younger than 18 years old, with insufficient knowledge of Spanish and with medical difficulties participating in the online survey were not included in this study. Additionally, the information from health personnel and medical students was also excluded from the analysis.

Each participant was fully informed of the study and gave their consent to participate. This study was approved by the ethics committee from the Faculty of Medicine of the Peruvian University Cayetano Heredia and carried out in accordance with the Helsinki Declaration and the ethical standards of the APA.

Data collection

Online survey

For the data collection, an online survey was carried out. Due to the restrictive policies for avoiding COVID-19 infection, all instruments and questions were digitalized and programmed into an internet survey free program (Google Forms). The questions included (1) informed consent and the declaration of not being under 18 years old, (2) age, gender, district, confession/faith, and occupation, (3) previous medical diagnosis and medication intake, (4) assessment of the COVID-19 peri-traumatic distress index (CPDI) for the COVID-19 pandemic, and (5) GAD-7 and PHQ-9 instruments.

Finally, additional questions were as follows: “in the last 14 days, did you have a cough, difficulty breathing, sore throat and fever?” (COVID_1), “do you have positive results for any sort of COVID-19 test?” (COVID_2), “have you been hospitalized (or are you hospitalized at the moment) due to COVID-19?” (COVID_3), “do you have relatives with positive results for any sort of COVID-19 test?” (COVID_4), “do you have relatives who were hospitalized due to COVID-19?” (COVID_5), and “do you have relatives who have passed away due to COVID-19?” (COVID_6).

COVID-19 peritraumatic distress index (CPDI)

The COVID-19 peritraumatic distress index (CPDI) was first applied in China¹⁸ and recently validated in other countries (ie, China), including Brazil, Iran, and Peru.^19,20 This instrument was designed for population evaluation of changes related to mood, behavior, cognitive skills, circadian rhythm, and other somatic symptoms due to the COVID-19 pandemic.

This instrument consists of 24 items, with a 4-factor design: negative mood, cognition, behavioral change, somatization and hyperarousal/exhaustion. Each item was evaluated by using Likert elements (from 0 to 4: never, occasionally, sometimes, often, and most of the time). The sum of each value per question results in the raw score. The display score is obtained by adding 4 to the raw score and used to calculate the CPDI severity degrees. For this reason, this instrument defines different categories for peritraumatic stress due to the COVID-19 pandemic: normal (0-28 display points), mild (29-52 display points), and severe (53-100 display points).

Depressive and anxiety symptoms

The Peruvian version of the PHQ-9²¹ was used to assess the severity of depressive symptoms. The PHQ-9 delivers values in the range between 0 and 27. The highest value indicates a higher depression score. This instrument was validated in Peru with a representative sample (n = 30446) and showed good internal consistency (Cronbach’s α = 0.87). This inventory defines different categories for depression scores: minimal (1-4 points), mild (5-9 points), moderate (10-14 points), and severe (15-27 points).

For anxiety symptoms, the Peruvian version of the GAD-7²² was used to assess the severity of anxiety symptoms. The GAD-7 delivers values in the range between 0 and 21 points. The highest value indicates a higher anxiety score. This instrument was also validated in Peru with a representative sample (n = 2978), showing good internal consistency (Cronbach’s α = 0.89). This inventory also defines different categories for anxiety scores: minimal (0-4 points), mild (5-10 points), moderate (11-15 points), and severe (16-21 points).

Statistical analysis

Statistical analyses were performed using SPSS version 26.0 (Statistical Package for the Social Sciences, International Business Machines Corporation, New York, United States of America) and jamovi 1.2.5.0.²³ For the choropleth map of metropolitan lima (Figure 1), the software CARTODB (CARTODB Inc., Denver, United States of America) was used.

Figure 1.

Percentages of cases with high CPDI values for each district of metropolitan Lima.

Descriptive data were managed with count data and percentages. To improve readability, the information is presented in tables. Quantitative variables approximately fitting a normal distribution are specified in the text as the mean ± standard deviation (M ± SD), and those with a non-normal distribution are expressed as the median (Me) with percentile 75 (Q3) and percentile 25 (Q1) and the interquartile range (Q3 – Q1; IQR). Categorical variables were specified with count data and percentages. Data were rounded to the next decimal to obtain results with 2 decimals. Values smaller than .001 were shown as <.001, and values greater than 1 million were expressed in scientific notation.

For the statistical model that explains the CPDI values of this sample size, an ordinal logistic regression was computed, considering the CPDI severity degrees as the dependent variable. The best model that explained the CPDI values was chosen by using the Akaike information criterion (AIC). The following were selected as predictor variables: age, GAD-7 scores, PHQ-9 scores, COVID_1, COVID_2, COVID_3, COVID_4, COVID_5, COVID_6, domicile, medication intake, and previous medical disease. The results of this statistical modeling are presented in Table 4. The odds ratio was flagged as “significant” if the 2-tailed P-value was smaller than .05. Ninety-five-percent confidence intervals were calculated for this model.

Results

General sample description

The first issue was to describe the data regarding the emotional impact after the COVID-19 lockdown period in a Peruvian sample of a survey study. The sample of this study consisted of 400 participants, with a mean age of 41.00 ± 13.60. Most of the participants were single (174 participants, 43.50%), were independent workers (41 participants, 10.30%) and lived in central Lima (284 participants, 71.00%). Of the 400 participants, the majority (262 participants, 65.50%) were healthy participants without any medical diagnosis. Within the diagnoses in participants with medical conditions, the most frequent diagnosis was arterial hypertension (13.96%), followed by major depressive disorder (9.91%), bronchial asthma (7.21%), hypothyroidism (7.21%), and diabetes mellitus (5.40%). Regarding medication intake, the majority of the participants used medicines (228 participants, 57.00%) and 126 of them used medicines for a medical condition. Of those participants who did not take medicines (172 participants, 43.00%), 12 of them have had a medical condition. More descriptive information and socio-demographic data are listed in Table 1. Previous information regarding COVID-19 infection, including COVID-19 infection on relatives, is represented in Table 2.

Table 2.

General previous information regarding COVID-19.

	n = 400
(COVID_1) In the last 14 days, did you have cough, difficulty to breath, sore throat and fever?
No	362 (90.50%)
Yes	38 (9.50%)
(COVID_2) Do you have positive results for any sort of COVID-19 test?
No	384 (96.00%)
Yes	16 (4.00%)
(COVID_3) Have you been hospitalized (or are you hospitalized at the moment) due to COVID-19?
No	399 (99.80%)
Yes	1 (0.30%)
(COVID_4) Do you have relatives with positive results for any sort of COVID-19 test?
No	269 (67.30%)
Yes	131 (32.80%)
(COVID_5) Do you have relatives who were hospitalized due to COVID-19?
No	334 (83.50%)
Yes	66 (16.50%)
(COVID_6) Do you have relatives who have passed away due to COVID-19?
No	346 (86.50%)
Yes	54 (13.50%)

Regarding anxiety descriptive data, 49.30% of the participants showed minimal values, followed by 40.00% with mild anxiety, 7.20% with moderate anxiety, and 3.50% with severe anxiety. Similar results were shown with depression scores: 48.50% of the participants showed minimal depression scores, followed by 29.50% with mild depression, 12.00% with moderate depression, and 10.00% with severe depression scores. Finally, the results of the CPDI scores (COVID-19-related stress) showed that 35.50% of the cases with mild stress and 9.30% with severe stress. More details of the descriptive data regarding anxiety, depression and CPDI scores are presented in Table 3. CPDI descriptive data sorted by Lima districts, which include percentages of participants with high CPDI values, are represented in Figure 1. Higher CPDI values are seen mostly in the southern and eastern districts of Lima (peripheral Lima). Additionally, part of central Lima showed higher CPDI values. Less concentration of higher CPDI values were seen in the northern peripheral Lima, some central areas of Lima as well as part of southwestern Lima.

Table 3.

Anxiety, depression, and CPDI scores.

	PHQ-9	GAD-7	CPDI	CPDI categories
N	400	400	400	Normal	221 (55.30%)
Missing	0	0	0	Mild	142 (35.50%)
Mean	6.21	5.06	29.50	Severe	37 (9.30%)
Standard deviation	6.07	4.39	14.30
25th percentile	1.00	1.75	19.00
Median	5.00	5.00	27.00
75th percentile	9.00	7.00	38.00
Minimum	0	0	6
Maximum	27	21	76
Skewness	1.29	1.15	0.71

Statistical modeling for emotional impact after COVID-19 lockdown

The second issue was to establish a plausible statistical model that could explain the obtained differences in emotional impact after the COVID-19 lockdown period in a Peruvian sample of a survey study. For this reason, an ordinal logistic regression was carried out, considering CPDI severity as the dependent variable (normal, mild, severe). The variables age, GAD-7 scores, PHQ-9 scores, COVID_1, COVID_2, COVID_3, COVID_4, COVID_5, COVID_6, district, medication intake, and previous medical disease were included as predictors in this model (χ² _{(df = 13)} = 324, P < .001, AIC = 439). Due to the AIC criterion, the variables gender, domicile, medication intake, previous medical disease, COVID_1, COVID_2, COVID_3, and COVID_4 were removed from the model, achieving a statistical model with the best AIC value (χ² _(df = 5) = 318, P < .001, AIC = 429). Therefore, the variables that remained in the model as predictors were age, GAD-7 scores, PHQ-9 scores, and COVID_6 (Do you have relatives who have passed away due to COVID-19?) and COVID_5 (Do you have relatives who were hospitalized due to COVID-19?). The results of this ordinal logistic regression are shown in Table 4.

Table 4.

Ordinal logistic regression model.

Model Specific Results—Omnibus Likelihood Ratio Tests
Predictor			χ²	df	P
GAD-7 scores			71.96	1	<.001
PHQ-9 scores			34.15	1	<.001
Age			5.63	1	.02
COVID_5			5.59	1	.02
COVID_6			7.97	1	.01
COVID_1			2.01	1	.16
COVID_2			0.58	1	.45
COVID_3			0.02	1	.90
COVID_4			2.95	1	.09
gender			0.14	1	.71
Domicile			0.56	1	.45
Previous medical disease			0.18	1	.67
Medication intake			1.50e-5	1	1.00
Best model coefficients (dependent variable: CPDI severity degrees)
Predictor	Estimate	SE	Z	P	OR, CI95
GAD-7 scores	0.40	0.05	7.80	<.001	1.49 [1.35;1.66]
PHQ-9 scores	0.20	0.04	5.62	<.001	1.29 [1.14;1.31]
age	0.02	0.01	2.40	.02	1.02 [1.01;1.04]
COVID_5	−1.19	0.42	−2.83	.01	0.30 [0.13;0.69]
COVID_6	1.26	0.46	2.73	.01	3.53 [1.43;8.82]

Abbreviations: COVID_1, “in the last 14 days, did you have cough, difficulty breathing, sore throat and fever?”, COVID_2, “do you have positive results for any sort of COVID-19 test?”, COVID_3, “Have you been hospitalized (or are you hospitalized at the moment) due to COVID-19?”, COVID_4, “Do you have relatives with positive results for any sort of COVID-19 test?”, COVID_5, “Do you have relatives who were hospitalized due to COVID-19?”, COVID_6, “Do you have relatives who have passed away due to COVID-19?”; CPDI, COVID-19 peritraumatic distress index; GAD-7, Generalized anxiety disorder—7 items, PHQ-9, Patient health questionnaire—9 items.

The dependent variable (CPDI severity degrees) had the following order: normal, mild, severe peritraumatic distress due to COVID-19.

Discussion

Obtained descriptive data

The following study was carried out in metropolitan lima, which actually concentrates the majority of COVID-19 cases in Peru. According to epidemiologic data of the Peruvian government and the Peruvian health ministry, Most of the COVID-19 infections are localized mostly in the northern districts of Lima (28.70%), followed by eastern Lima (24.40%), southern Lima (23.30%), and central Lima (23.10%).²⁴ During the COVID-19 lockdown in Peru, the government declared a sanitary emergency status, generating many restrictions for avoiding the spread of the infections. However, the government did not emphasize the possible complications related to mental health problems, such as depression, anxiety or stress related to events.

The following study reports the mental health effects after the COVID-19 lockdown period from a sample of Lima, Peru. To evaluate the stress levels due to COVID-19 pandemic lockdown, this study used the CDPI, which was used in different countries, and its Spanish translation was validated by experts in Peru. The results of this study showed 35.50% of the cases with mild COVID-19-related stress and 9.30% with severe COVID-19-related stress. In addition, higher CPDI values are seen mostly in the southern and eastern districts of Lima (peripheral Lima), as well as some districts of central Lima. Similar results with the CPDI were found in Chinese and Iranian populations,^18,19 but the frequencies were smaller when compared to those obtained in Brazilian populations.²⁰ Regarding the obtained values for PHQ-9 and GAD-7, the results of this study showed similar and comparable results with those obtained in the Chinese and Cypriot population.^25,26

Proposed statistical model—results of the ordinal logistic regression

The results of the ordinal logistic regression model showed that having a deceased relative due to COVID-19 is associated positively with CDPI severity scores. On the other hand, having a relative who was hospitalized due to COVID-19 was negatively associated with CPDI severity scores. These associations could be explained by the fact that the death of a relative due to COVID-19 generates a stronger negative emotional impact on individuals, whereas having a relative that was hospitalized due to COVID-19 could generate relief and positive emotions. Other variables, such as age, anxiety and depression scores, also showed positive associations with CPDI severity scores. The positive associations with age could be explained by the fact that an older age more likely represents a fragility state and is vulnerable to acquiring COVID-19.

Other studies reported similar results to the proposed statistical model in the Peruvian population from Lima. One study in Brazil performed by Zhang et al found different variables with positive associations with COVID-19-related stress, such as gender, education, sedentary life, and age.²⁰ This study, in comparison with the work done in Brazil, found that gender and education did not correlate with COVID-19-related stress. Instead, this study found a strong positive correlation between age and COVID-19-related stress severity.

Similar results were found in the study of Mazza et al in an Italian population. In this population, female gender, negative affect, and detachment were associated with higher levels of depression, anxiety, and stress. Mazza et al also found positive correlations with having relatives infected with COVID-19 and higher levels with stress. Similar results were found in this study, since positive associations between CPDI severity values and deceased relatives due COVID-19 were observed. Positive associations with gender were seen in the work of Mazza et al. However, in this study, no significant associations were observed between COVID-19 stress and gender.²⁷

Another example is the study of Losada-Baltar et al. In this study, different variables (gender, age, having negative self-perceptions about aging, being more time exposed to news about COVID-19, fewer positive emotions, lower quality of sleep, higher loneliness, etc.) correlated positively with psychological distress due to the COVID-19 lockdown crisis. The assessments of the study of Losada-Baltar et al were performed during the lockdown period at home, with similar conditions to those in this study. A similarity between both studies is seen in the positive associations with age. Although the study of Losada-Baltar et al found positive associations between gender and psychological distress, its results did not report significant positive associations between COVID-19-related distress and gender. Measurements of anxiety and depression scores were not carried out by the study of Losada-Baltar et al, and questions regarding contagiousness and relatives with COVID-19 were evaluated in the present study but not in the work Losada-Baltar et al.²⁸

Contradictory findings, which are also important to mention, are shown in 2 Spanish studies done by Ozamiz-Etxebarria et al and Picaza-Gorrochategi et al.^29,30 On the one hand, Ozamiz-Etxebarria et al found that young adults have higher levels of depression, anxiety, and stress than elderly participants.²⁹ Moreover, Picaza-Gorrochategi et al revealed that most of the participants over 60 years old do not show higher levels of stress, anxiety, and depression.³⁰ On the contrary, the results of this study reveal a positive association between COVID-19-related stress severity and the variable age, which means, that higher COVID-19-related stress severity is associated with older but not younger ages. The differences between both studies could be mostly related to different cultural perceptions, since the studies showed similar statistical distributions. In this case, the younger Peruvian population was more prone to breach the sanitary restrictions imposed by the government, carrying out their daily activities as usual and not perceiving, ultimately, a significant psychological burden compared to the elderly people. These mentioned factors in the younger Peruvian population involve mostly those with lower levels of education and lower civic awareness and that could also play a role in the lack of obedience exhibited toward the sanitary emergency laws imposed by the government. There is a lower correlation to a psychological burden in this group. An example of this phenomenon in the younger Peruvian population is the illegal party and nightclub attendance during the COVID-19 pandemic sanitary restriction in the Los Olivos District in Lima, reported also in different international news media.^31-33

The findings of this study contribute mostly to identifying important variables that are positively or negatively correlated with COVID-19-related stress severity. Future studies should take this into consideration, mostly the different correlations to possible mental health problems related to pandemics, to establish possible public health interventions.

Limitations

Although these findings reveal important correlations and describe the current panorama in a country with high COVID-19 rates, many limitations may be taken into consideration. The sample size could have been larger to generalize the results beyond the context of the study. However, the power obtained from this study with 400 participants was 1−β = 0.98, a value that overcame the 1−β = 0.80 threshold. The sample size for the study design used should therefore be sufficient to examine the expected effects. Then, the higher number of women compared to men could have influenced the results. However, studies related to COVID-19 lockdown distress have also reported a higher proportion of female participants, which is also reflected in this study. In addition, the proposed model did not show an association between gender and COVID-19 distress severity. As expected, there were many very low CPDI, PHQ-9, and GAD-7 scores in the evaluated participants, which led to skewed distributions. To overcome this limitation, an ordinal logistic regression was computed because there is no consideration regarding skewed statistical distributions. Finally, medication intake, previous medical condition and geographical district could affect COVID-19 distress scores. All these variables were included in the model to observe possible influences on the CPDI severity values. However, these variables did not appear to affect the results of the current study.

Conclusion

In conclusion, there is a higher prevalence of COVID-19-related distress, mostly in the eastern, central and southern parts of metropolitan lima, which are the areas mostly affected by COVID-19 infections. In addition, the variables age, depression scores, anxiety scores and the presence of a deceased relative explain higher COVID-19-related distress values. On the other hand, relatives who were hospitalized due to COVID-19 represent a protective factor in this model. Although most of the higher values of the CPDI are concentrated in the poorest districts of metropolitan Lima, no associations were seen between district or domicile and COVID-19 distress severity values.

Footnotes

Acknowledgements

The authors of this work would like to thank Dr Patrick McGowan (Anesthesiologist, private practice, London, UK) and Ms Katia Bravo Padilla (English teacher at the Language Center of the Catholic University, Lima, Peru), for supporting with the proofreading, spelling, and grammar correction of the English language.

Declaration of conflicting interests:

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding:

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Contributions

HKM: First and corresponding author. PI of this working group. HKM is responsible concerning the submission process on behalf of all the authors of the paper. Additionally, HKM contributed with the introduction, discussion, and conclusions. HKM corrected the manuscript and did the literature search. BPP: First author. BPP is also responsible for everything concerning the submission process on behalf of all authors of the paper. Additionally, BPP wrote the introduction, methods, results, discussion, and conclusions. Corrected the manuscript, did the data analysis and the literature search. MAF: Coauthor. MAF contributed with the literature search, introduction, and discussion. MAF helped with the paper proofreading, corrections and the interpretation of the results in the discussion. FSC: Coauthor. Corrected the manuscript and helped with the proofreading and paper mentoring. VAA: Coauthor. Helped with the data recollection and contributed with the proofreading. MDM: Coauthor. Helped with the data recollection and contributed with the proofreading.

Availability of Data and Materials

The datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.

Ethical Approval and Consent to Participate

ORCID iD

Bruno Pedraz-Petrozzi

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