Next Article in Journal
Mice Lacking the Systemic Vitamin A Receptor RBPR2 Show Decreased Ocular Retinoids and Loss of Visual Function
Next Article in Special Issue
Cutoff Points of Waist Circumference for Predicting Incident Non-Alcoholic Fatty Liver Disease in Middle-Aged and Older Korean Adults
Previous Article in Journal
Cardiovascular Safety and Effectiveness of Bisphosphonates: From Intervention Trials to Real-Life Data
Previous Article in Special Issue
Effect of Dietary and Lifestyle Interventions on the Amelioration of NAFLD in Patients with Metabolic Syndrome: The FLIPAN Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 14
Issue 12
10.3390/nu14122370
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impact of Physical Activity Differences Due to COVID-19 Pandemic Lockdown on Non-Alcoholic Fatty Liver Parameters in Adults with Metabolic Syndrome

by
Catalina M. Mascaró
1,2,
Cristina Bouzas
1,2,3,
Sofía Montemayor
1,2,
Silvia García
1,2,
David Mateos
1,2,
Miguel Casares
4,
Cristina Gómez
1,5,
Lucía Ugarriza
1,2,6,
Pere-Antoni Borràs
7,
J. Alfredo Martínez
8,9 and
Josep A. Tur
1,2,3,*
1
Research Group on Community Nutrition and Oxidative Stress, University of the Balearic Islands-IUNICS, 07122 Palma de Mallorca, Spain
2
Health Institute of the Balearic Islands (IDISBA), 07120 Palma de Mallorca, Spain
3
CIBEROBN (Physiopathology of Obesity and Nutrition CB12/03/30038), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain
4
Radiodiagnosis Service, Red Asistencial Juaneda, 07011 Palma de Mallorca, Spain
5
Clinical Analysis Service, University Hospital Son Espases, 07120 Palma de Mallorca, Spain
6
Camp Redó Primary Health Care Center, 07010 Palma de Mallorca, Spain
7
Area of Physical Education and Sports, Department of Pedagogy and Specific Didactics, University of the Balearic Islands, 07122 Palma de Mallorca, Spain
8
Cardiometabolics Precision Nutrition Program, IMDEA Food, CEI UAM-CSIC, 28049 Madrid, Spain
9
Department of Nutrition, Food Sciences, and Physiology, University of Navarra, 31008 Pamplona, Spain
*
Author to whom correspondence should be addressed.
Nutrients 2022, 14(12), 2370; https://doi.org/10.3390/nu14122370
Submission received: 5 May 2022 / Revised: 3 June 2022 / Accepted: 5 June 2022 / Published: 8 June 2022
(This article belongs to the Special Issue Management of Non-alcoholic Fatty Liver Disease from Primary Care: Prevention and Intervention through Nutrition and Physical Exercise)
Browse Figure
Versions Notes

Abstract

:
Background: A Mediterranean lifestyle with a Mediterranean diet and regular physical activity (PA) improves metabolic syndrome (MetS) characteristics and non-alcoholic fatty liver disease (NAFLD). The COVID-19 pandemic stopped healthy habits and increased NAFLD progression. Objectives: To assess how PA differences due to COVID-19 lockdown affected NAFLD parameters in adults with MetS. Design: Longitudinal 2-year analysis of data obtained between COVID-19 pre- and post-lockdown in a parallel-group randomized trial (n = 57, aged 40–60 years old, with MetS and NAFLD). Methods: NAFLD status and related parameters were assessed by magnetic resonance imaging (MRI), blood collection analysis and related indexes. PA and fitness status were assessed by an Alpha-Fit test battery, accelerometers, validated Minnesota questionnaire and functional fitness score. During lockdown, study personnel telephoned patients to motivate them. Participants were grouped according to PA levels. Results: The low PA group improved its fitness score tests (0.2) after lockdown more than the medium PA group, and it decreased its sedentary activity (−48.7 min/day), increased light (20.9 min/day) and moderate (32.3 min/day) PA intensities and improved sleep efficiency (0.6%) in comparison with the medium and high PA groups. The high PA group increased its steps per day more than the other groups. The low PA group was the only group that decreased its gamma glutamyl transferase (GGT) levels (−17.0 U/L). All groups increased their fatty liver index (FLI) after lockdown, but the medium PA group increased its FLI more than the low PA group. Participants in the high PA group decreased their HDL-cholesterol levels more than participants in the medium PA group (−0.4 mg/dL). Conclusions: Stopping regular PA together with an unhealthy lifestyle leads to a worsening of MetS and NAFLD. COVID-19 lockdown induced a decrease in PA in more active people, but inactive people increased their PA levels. Motivation seemed to be very important during lockdown.

Graphical Abstract

1. Introduction

Non-alcoholic fatty liver disease (NAFLD) is the most common hepatic disease in Western countries. Currently, it affects up to 30% of the population and it is also considered a metabolic syndrome (MetS) manifestation [1,2]. NAFLD results in the deposition of fat in hepatocytes and it can progress to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis and carcinoma [3,4] in the absence of excessive drug and/or alcohol consumption [5]. There is no pharmacological treatment for NAFLD, so the best existing treatment is lifestyle modification with a Mediterranean diet and physical activity (PA), as well as the treatment of each of the metabolic complications [6,7].
A Mediterranean diet is rich in unsaturated fatty acids, antioxidants and fiber; in addition, it provides a lower intake of saturated fats and animal proteins. This dietary composition prevents and/or improves type 2 diabetes mellitus, MetS and cardiovascular pathologies [8]. Besides that, PA decreases fatty liver content in people with NAFLD and improves MetS parameters (i.e., obesity, hypertension, high triglycerides and low plasma high-density lipoprotein (HDL) cholesterol levels) [9].
The COVID-19 pandemic increased sedentary activity and decreased regular PA, increased obesity and adversely affected mental health [10]. During lockdown, metabolic risk increased in people with NAFLD because adherence to a Mediterranean diet and regular PA decreased, so that in most cases, an increase in body weight and the progression of a fatty liver were observed [11].
There is considerable scientific evidence for health benefits of regular PA practice. In addition, more studies have demonstrated the benefits of PA and a Mediterranean diet on NAFLD. However, there is very little evidence assessing the impact of stopping a healthy lifestyle and regular PA on the different parameters of PA and NAFLD. Fortunately, COVID-19 has made it possible to assess this impact in the present manuscript. The aim of the current study was to assess how PA differences due to COVID-19 lockdown affected NAFLD parameters in adults with MetS.

2. Methods

2.1. Design

This research is a longitudinal analysis of pre- and post-COVID-19 lockdown data from a 2-year, randomized, parallel group trial. This trial was designed to assess the effect of changes in lifestyle with a Mediterranean diet and regular PA on preventing and/or reversing NAFLD in adults with MetS. Further details of the study protocol can be found at ClinicalTrials.gov (https://clinicaltrials.gov/ct2/show/NCT04442620; accessed on 25 February 2022) where the trial was registered in 2020 with number NCT04442620 [12].
During the development of this study, the COVID-19 pandemic and subsequent lockdown appeared, so the authors decided to study the impact of stopping a healthy lifestyle. Information and study variables remained the same, but the study time focused on the lockdown period and study groups were classified according to differences in METs/day measured by an accelerometer pre–post COVID-19 lockdown.

2.2. Subjects

Eligible participants were men and women aged 40–60 years with a body mass index (BMI) between 27 and 40 kg/m2 meeting at least three criteria for MetS according to the International Diabetes Federation (IDF) [13]), and with a diagnosis of NAFLD by magnetic resonance imaging (MRI) (Signa Explorer 1.5 T, General Electric Healthcare, Chicago, IL, USA) [14]. Exclusion criteria are available elsewhere [12]. We contacted 143 subjects for an initial screening between June of 2018 and January of 2020 in Mallorca (Balearic Islands), and a total of 57 subjects were eligible for the study. A total of 86 subjects were excluded because they did not meet inclusion criteria or declined their participation.
All participants were informed about the study before participating and they provided written consent. The study protocol and all procedures were performed and approved according to the ethical standards of the Declaration of Helsinki and Ethics Committee of Research of Balearic Islands (ref. IB 2251/14 PI).

2.3. Intervention Groups

Selected individuals were randomized by gender (men/women), type 2 diabetes mellitus (yes/no) and grade of steatosis into one of the three following groups (1:1:1 ratio): (1) Conventional diet (CD) group, based on the recommendations of the American Association for the Study of Liver Diseases (AASLD) [15]. PA recommendation was walking a minimum 10,000 steps per day. (2) Mediterranean diet-high meal frequency (MD-HMF) group, based on a RESMENA diet [16] with 7 meals a day. PA recommendation was walking minimum 10,000 steps per day. (3) Mediterranean diet-physical activity (MD-PA) group, based on a Mediterranean diet [17] with 5 meals a day. PA recommendation was performing 35 min interval training sessions (5 min warm-up, 20 min training, and 10 min breathing and stretching) 3 days per week (2 days with an instructor and 1 day a remote session). The 20 min training consisted of 5 moderate intensity tests, with recovery time in between each test. Each week, the tests changed to make the sessions more dynamic. Remote sessions were sent to participants by email and/or WhatsApp, and the PA was personalized to suit individual conditions. Aerobic PA in this group was equivalent to 10,000 steps per day referent to caloric expenditure, i.e., 400 kcal/70 kg.
The above PA recommendations were before lockdown. During COVID-19 confinement, all training sessions were cancelled, and people walking 10,000 steps daily had their route interrupted. Study personnel contacted each of the participants by telephone and encouraged them to continue with the respective interventions. Regarding to PA, subjects in the CD and MD-HMF groups were encouraged to complete a minimum of 10,000 steps per day walking around their home or garden (depending on the possibilities). Subjects in MD-PA group were informed to continue with all sessions remotely at least 3 days a week. Through email and WhatsApp, instructors sent them a large number of videos, photos and enough material to continue with sessions without an instructor’s presence, and even some video calls were made to follow the sessions remotely. In addition, a WhatsApp group was created so that they could exchange other ideas and motivate each other.
In the current paper, data analyzed comprised the latest available before lockdown and the first collected after lockdown, provided they were recorded within a period of four months before/after lockdown start/end. The lockdown period was 3 months, so pre-lockdown data were taken within a range of 4 months before, and post-lockdown data were also taken within a range of 4 months after.

2.4. Description of the Lockdown

The Spanish government ordered home lockdown for 3 months. Adults were forced to work from home and children had to study from home (schools were closed). Since it was a sudden order, no adaptation procedures were allowed. The situation forced some companies to shut down, however the government provided financial aid for affected workers. As people were not allowed to leave their houses, physical activity levels changed during confinement. Moreover, as a result of the increase in free time and boredom, people started to cook more, especially bakery [18,19].

2.5. Blood Collection Analysis

Biochemical analysis was performed on participants’ blood samples, which were collected from an antecubital vein after twelve hours overnight fast. All studied parameters were quantified by standard enzymatic methods [20]. These collections assessed aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), triglycerides, glucose and HDL-cholesterol.
In the original study, samples were to be collected at baseline, 6 months, 1 year and 2 years. For the present manuscript, samples used for the analysis were those closest to pre- and post-lockdown.

2.6. NAFLD Diagnosis

As previously mentioned, patients had to undergo an MRI (Signa Explorer 1.5T, General Electric Healthcare, Chicago, IL, USA) [14] to confirm a diagnosis of NAFLD, and later, to measure progression in the amount of liver fat (expressed as a percentage).
Again, in the original study, the MRI was to be performed at baseline, 6 months, 1 year and 2 years; for the present manuscript, the MRI results used for the analysis were those closest to pre- and post-lockdown.

2.7. NAFLD Related Indexes

In the current manuscript, two indexes were calculated to describe fatty liver status based on other measurements from non-invasive methods. Fatty liver index (FLI) allows individuals with NAFLD to be identified and to predict new cases. Patients with an FLI <30 were considered not to have NAFLD, whereas an FLI ≥60 indicated NAFLD [21]. To calculate FLI, triglycerides and GGT levels, BMI and waist circumference are entered into a previously defined formula [21]. Hepatic steatosis index (HSI) is a screening tool that identifies the presence of NAFLD. Patients with an HSI <30 were considered to not have NAFLD; HSI = 30–36 was considered an intermediate status; and HSI ≥36 indicated the presence of NAFLD [22]. To calculate HSI, ALT and AST levels, BMI, and the patient’s sex and diabetic status were entered into a formula previously published [22].

2.8. Physical Activity Assessment

PA was evaluated by three methods in order to achieve greater accuracy. Participants completed tests from “Fitness for Health: The Alpha-Fit Test Battery for Adults aged 18–69” [23] and the Chester step test [24]. The Alpha-Fit manual was used to assess postural control (one-leg balance test), static manual muscular strength (standing hand grip test), sitting manual muscular strength (sitting hand grip test), resistance of lower extremities (jump-and-reach test) and resistance of upper extremities and trunk (modified push-up test), as previously described [9]. The Chester step test was used to assess aerobic capacity and was calculated using software [25]. All mentioned tests were performed on the same day and in the same order that they were presented by trained personal. The best mark of each test was taken to compare with normal range of scores according to gender and age [23].
In addition, participants were asked to complete the Spanish version of the Minnesota Leisure Time Physical Activity Questionnaire, reporting their leisure and household activity as mean weekly time. Resulting energy expenditure was expressed as the metabolic equivalent of task (MET)·minute/week [26,27].
All patients had to wear an accelerometer (ActiGraph wGT3X-B; ActiGraph LLC, Pensacola, FL, USA) on their left wrist for one week. It measured sedentary, light, moderate and vigorous intensities of PA (the last category does not appear in the manuscript because nobody registered vigorous PA), weekly steps and sleep efficiency. Measured energy expenditure was expressed as metabolic equivalent of task (MET)·minute/week. All participants were then classified into three groups according to PA: Low PA/Medium PA/High PA. A grouping variable was calculated from the difference in METs/day measured by the accelerometer pre-post COVID-19 lockdown followed by tertiles for this pre-post-lockdown PA difference.

2.9. Functional Fitness Score

A previously defined functional fitness score was calculated [28] from a normal range for one-leg balance, standing hand grip, jump-and-reach and modified push-up test with a median defined as sex. Scores below the normal range and the median by sex were awarded 0 points. Scores above the normal range and the median by sex and were awarded 1 point. The best functional fitness score was obtained from 1 point for each test, i.e., a final score of 4 points. This fitness score described the physical function of the participants as a whole and were used to recommend alternatives to PA and better health promotion [28].

2.10. Other Health Outcomes

Information related to sociodemographic characteristics were obtained by questionnaires: age, gender, education level (years), marital status (single; married/coupled; divorced/separated/widow), socioeconomic status according to job (low, medium/high), smoking habit (no, ≥1 cigarette/day) and alcohol consumption (no, <7 drinks/week, ≥7 drinks/week).
Blood pressure was measured using a validated semi-automatic oscillometer (Omron HEM-705CP, Omron Healthcare Inc., Lake Forest, IL, USA) [20] and waist circumference was taken [20]. During lockdown, study personnel had telephonic contact with patients to help and motivate them to follow the diet and perform PA.

2.11. Statistics

Analyses were performed with the SPSS statistical software package version 27.0 (SPSSS Inc., Chicago, IL, USA). Data are shown as mean and standard deviation (SD), whereas prevalence was expressed in sample size and percentage. Differences in prevalence among groups was tested using χ2 (all p values were two-tailed). The normal distribution for continuous data was assessed with Kolmogorov–Smirnov. All variables with non-normal distribution were log-transformed before analysis; however, this data is presented as untransformed data in tables for easier interpretation.
Analysis of sociodemographic characteristics from baseline were tested with a one-way ANOVA and Bonferroni’s post-hoc analysis for continuous variables and χ2 for prevalence. On the other hand, paired sample t-tests were used to compare changes in PA and NAFLD parameters from COVID-19 pre-lockdown to COVID-19 post-lockdown within each of the three PA difference groups. A generalized linear model was used to assess changes in PA and NAFLD parameters during COVID-19 lockdown according to the three PA difference groups (between-group). The impact of the COVID-19 lockdown and PA differences was examined by using a repeated-measures ANCOVA with two factors: time (COVID-19 pre-lockdown vs. COVID-19 post-lockdown) as a repeated measure and group (low PA, medium PA, high PA). The COVID-19 pre- and post-lockdown period runs from 15 March to 4 May 2020. Physical activity levels were classified according to pre- and post-COVID-19 METs/day difference (tertiles). Bonferroni’s post-hoc test was performed to compare changes within and between groups. All p-values were two-tailed with p < 0.05.

3. Results

Table 1 shows the sociodemographic characteristics of participants at baseline according to PA level. Changes in PA parameters within and between PA difference groups pre- and post-COVID-19 are shown in Table 2. There was a significant increase in terms of jump-and-reach in the high PA group (2.7 cm). After COVID-19 lockdown, participants in the high PA group had increased their jumping height (2.7 cm) more than participants in the low PA group (0.4 cm) and the medium PA group, which had worsened (−0.7 cm) after lockdown. Regarding fitness score tests, participants in the medium PA group had a worse mark (−0.1) than participants in the low PA group (0.2), who had better fitness score tests after COVID-19. The low PA group decreased sedentary activity (−48.7 min/day) and increased light (20.9 min/day) and moderate (32.3 min/day) PA intensities after lockdown. The medium PA group increased moderate PA intensity (3.5 min/day), but the high PA group showed an increase in sedentary activity (23.0 min/day) and a significant decrease in moderate PA intensity (−50.8 min/day). Between groups, the low PA group had decreased sedentary PA intensity (−48.7 min/day) and increased moderate PA intensity (32.3 min/day) in comparison with the medium PA (1.3 min/day sedentary and 3.5 min/day moderate) and high PA (23.0 min/day sedentary and −50.8 min/day moderate) groups. At the same time, the high PA group had increased sedentary activity (23.0 min/day) more than the medium PA group (1.3 min/day) and decreased moderate PA intensity (−50.8 min/day) more than the medium PA group (3.5 min/day). According to light PA intensity, participants in the high PA group decreased their level (−15.5 min/day) more than the low PA (20.9 min/day) and medium PA (2.9 min/day) groups. The medium and high PA groups decreased their sleep efficiency after lockdown (−0.8% and −1.1% respectively), whereas participants in low PA group increased their sleep efficiency (0.6%). In addition, participants in the low PA group increased their steps per day after lockdown (2479.2 steps/day), but participants in the high PA group increased more steps per day (4288.4 steps/day) than the low and medium PA (−146.8 steps/day) groups. Nevertheless, the low PA group had more steps per day than the medium PA group. Regarding MET measured by accelerometer, after lockdown, the low PA group increased 0.1 MET/day, the medium PA group obtained no difference (0.0 MET/day) and the high PA group decreased −0.2 MET/day. All of these results were significantly different between groups.
Changes in NAFLD parameters within and between PA difference groups pre- and post-COVID-19 are showed in Table 3. Participants in the low PA group had lower GGT levels (−17.0 U/L) and participants in medium and high groups higher GGT levels (12.0 U/L and 17.1 U/L respectively). Low PA was the only group who decrease GGT levels in comparison with other two. All groups had an increased FLI after lockdown (low PA, 2.5; medium PA, 5.4; high PA, 5.7) and there was a bigger in increase in the FLI of the medium PA group than the low PA group. The low and high PA groups showed a reduction in HDL-c (−2.8 mg/dL and −1.6 mg/dL, respectively). Between groups, participants in the high PA group had decreased their HDL-c levels more than the participants in the medium PA group (−0.4 mg/dL).

4. Discussion

The current study showed that stopping regular PA together with an unhealthy lifestyle due to COVID-19 lockdown led to a worsening of MetS and NAFLD. In fact, COVID-19 lockdown induced a PA decrease in more active people, whereas inactive people increased their PA levels, contrary to logic. As a recent systematic review stated, studies and results for adults aged 45–65 years (which is the age range of the participants in the current study) are still lacking. However, some adults increased PA during the COVID-19 pandemic, which allowed them to obtain a better fitness score test [29]. The current results are aligned with previous literature on the impact of COVID-19 lockdown on PA. Adult populations in Spain, especially very active people, decreased PA and increased sedentary activity during COVID-19 lockdown [30]. The current results showed that people in the high PA group increased levels of sedentary activity more than people in the low and/or medium PA groups. Moreover, the high PA group suffered a considerable decrease in light and moderate PA intensities. Thus, the worst results were observed among participants who performed more PA before confinement. As confirmed by previous authors, it was more common for people with high PA to suffer greater changes in regular PA than people who were not very active [31]. The current results also showed an important fact: the low PA group decreased sedentary behavior and increased light and moderate PA, which seems contradictory to most of the existing literature. Nevertheless, one study previously obtained similar results [32]. These data reflect that a good health promotion aimed at inactive people can be essential for improving their lifestyle. During lockdown, domestic activities were considered a form of PA [33,34]. PA habits changed, but motivation helped to induce changes in the PA habits of inactive people [35].
Daily METs measured by accelerometry during lockdown reflected the same patterns of behavior in each group, i.e., a decrease in daily METs by people in the high PA group compared to an in the low PA group (although it was not a very large increase). Furthermore, people in the medium PA group experienced no change in daily METs. An explanation for these data is the same as above, because a sedentary lifestyle and light and moderate PA were also measured by accelerometry. It must be pointed out that in a Chinese population, there was a sharp decline in daily step count that increased as lockdown progressed [36]. Considering that the study population in this manuscript was Spanish, the current data also showed an increase in daily step counts for both the low and high PA groups, whereas the medium PA group showed a decreased daily step count during lockdown.
Regarding sleep efficiency, the most common trend in the population was a decrease in sleep quality due to the COVID-19 pandemic. The negative mood of lockdown has been associated with a decrease in sleep patterns, a delayed bedtime, presence of nightmares and insomnia [37,38]. Findings from this trial showed a decrease in sleep efficiency in the high and medium PA groups. This is consistent with previous literature, which showed that active people decreased PA levels and sleep quality during COVID-19 lockdown [39]. People in the low PA group increased their sleep efficiency a little, but it should be noted that they also increased PA levels during this period, which has a positive reinforcement on sleep quality [40].
After COVID-19 lockdown, people in the high PA group showed higher GGT levels, as did people in the medium PA group. Only the low PA group had decreased its GGT levels and they had the lowest levels compared to the other two groups. As already recorded, an active lifestyle based on a Mediterranean diet is associated with weight loss and improved MetS criteria in people with NAFLD. Thus, PA plays a fundamental role in the correct maintenance of GGT levels [5,41,42]. According to the current results, the high PA group decreased its active lifestyle and increased its sedentary lifestyle, whereas the low PA group increased light and moderate levels of PA and decreased their sedentary lifestyle. This event explains why the low PA group decreased GGT levels after confinement and not the high PA group.
Overall, FLI increased in all three groups, i.e., their NAFLD condition has worsened after lockdown. The low PA group had a smaller FLI increase than the medium PA group. This result is probably because the low PA group led a more active lifestyle. It is well known that steatosis status, and consequently FLI improves, when participants with NAFLD adopt a healthy lifestyle based on a Mediterranean diet and regular PA [43]. Although the low PA group had a more active lifestyle, it is shown that lockdown did not allow high enough levels of PA to improve their hepatic steatosis status.
All groups showed a decrease in HDL-cholesterol levels. This decrease was larger in the high PA group than the medium PA group. Low HDL-cholesterol level is one of the MetS parameters [44]. Regular PA can increase low HDL-cholesterol levels [44] and a Mediterranean lifestyle, with a Mediterranean diet and regular PA, is essential for improving MetS parameters [5]. It is well known that MetS is related to NAFLD, and the new tendency is to group both conditions under the one name of metabolic dysfunction-associated fatty liver disease (MAFLD) [45]. However, the COVID-19 lockdown caused an increase in the consumption of unhealthy foods and a decrease in PA levels in the population [19]. This could explain the current results, because not following a Mediterranean lifestyle intervention will worsen MetS and, in particular, low HDL-cholesterol levels.
Regarding intrahepatic fat content measured by nuclear magnetic resonance (IFC-NMR), it should be mentioned that results with significant differences were obtained with previous adjustments to the data in the current study (p = 0.001; data not shown). However, when socioeconomic and marital status were introduced in the current adjustment, the significance disappeared, which allows us to understand that these were two important factors during lockdown [46,47] that nullified the differences in liver fat according to PA groups.

Strengths and Limitations of the Study

Scientific evidence for the effect of the COVID-19 lockdown on NAFLD according to PA differences is limited. The first strength of the current study is that it contributes to increasing knowledge about the impact of PA differences due to the COVID-19 lockdown in adults with NAFLD and MetS. Secondly, it considers a large range of PA and liver profile parameters, all MetS criteria, different methods of PA measurement and an alternative tool to describe functional tests with a fitness score test. Other strengths of the current work are its longitudinal design, which adds more soundness than cross-sectional designs, and the standardized protocol adopted reduces the risk of information bias. On top of these strengths, the current results show the effect of a sudden change in a healthy lifestyle to one less healthy and not complying with the recommendations of a Mediterranean diet and regular PA. Most of the existing literature shows the benefits of PA, but not the effects of stopping PA, so the COVID-19 lockdown has allowed us to study what happens when PA is stopped. However, the present work has some limitations. The main limitation is the small sample size. Other limitations are the subjectivity of the FLI, HSI and Minnesota questionnaire, even if they are validated. The information provided by participants in telephone consultations during lockdown is also subjective. The different motivation, psychological state and physical condition of the participants are other limiting factors. The participants were aged 40–60 years, which makes it difficult to extrapolate results to the general population. In addition, in a human study it is difficult to know with precision and accuracy their adherence to lifestyle treatment. Participants were asked in this regard; nevertheless, it will always be a limitation, as researchers have to trust the participants. Lastly, the study was not initially designed for this purpose, which is also a limitation.

5. Conclusions

Stopping regular PA together with an unhealthy lifestyle due to the COVID-19 lockdown led to a worsening of MetS and NAFLD condition. The COVID-19 lockdown induced a decrease in PA by more active people, whereas inactive people increased their PA levels. Motivation seemed to be a very important factor for the practice of PA during lockdown.

Author Contributions

C.M.M., C.B. and J.A.T. designed the study and wrote the protocol; L.U. recruited the participants; C.M.M., S.M., S.G. and D.M. collected samples; P.-A.B. assessed physical activity measurements; C.G. conducted the biochemical analysis; M.C. conducted the MRI analysis; C.M.M. and C.B. conducted the statistical analysis; C.M.M., C.B. and J.A.T. wrote the first draft of the manuscript, and all authors (C.M.M., C.B., S.M., S.G., D.M., M.C., C.G., L.U., P.-A.B., J.A.M. and J.A.T.) commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

Fundació La Marató TV3 (Spain) project ref. 201630.10. Instituto de Salud Carlos III through the CIBEROBN CB12/03/30038, and Proyecto Intramural CIBER OBN18PI03, which are co-funded by the European Regional Development Fund. Other funding received: IDISBA Grants (FOLIUM, PRIMUS, SYNERGIA, and LIBERI). Catalina M. Mascaró received an FPU PhD Grant from the Spanish Ministry of Education. The funding sponsors had no role in the design of the study, in the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.

Institutional Review Board Statement

The study protocols followed the Declaration of Helsinki ethical standards, and all the procedures were approved by the Ethics Committee of the Balearic Islands (ref. IB 2251/14 PI). All participants were informed of the purpose and the implications of the study, and all provided the written informed consent to participate.

Informed Consent Statement

The results and writing of this manuscript followed the Committee on Publication Ethics (COPE) guidelines on how to deal with potential acts of misconduct, maintaining integrity of the research and its presentation following the rules of good scientific practice, the trust in the journal, the professionalism of scientific authorship, and the entire scientific endeavor. Written informed consent has been obtained from the patient(s) to publish this paper if applicable.

Data Availability Statement

There are restrictions on the availability of data for this trial, due to the signed consent agreements around data sharing, which only allow access to external researchers for studies following the project purposes. Researchers wishing to access the trial data used in this study can make a request to [email protected].

Acknowledgments

The authors especially thank the participants for their enthusiastic collaboration and the personnel for outstanding support and exceptional effort. The authors thank Octavio Barbero from Red Asistencial Juaneda, Palma de Mallorca, Spain, for technical assistance. CIBEROBN is an initiative of Instituto de Salud Carlos III, Spain.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ALT: alanine aminotransferase; AST: aspartate aminotransferase; BMI: Body mass index; CD: conventional diet; FLI: fatty liver index; GGT: gamma-glutamyl transferase; HDL-c: high density lipoprotein cholesterol; HSI: hepatic steatosis index; IDF: International Diabetes Federation; MET: metabolic equivalent of task; MetS: metabolic syndrome; MD-HMF: Mediterranean diet-high meal frequency; MD-PA: Mediterranean diet-physical activity; MRI: magnetic resonance imaging; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; PA: physical activity; SD: standard deviation; VO2max.: maximum oxygen volume.

References

  1. Abd El-Kader, S.M.; El-Den Ashmawy, E.M. Non-alcoholic fatty liver disease: The diagnosis and management. World J. Hepatol. 2015, 7, 846–858. [Google Scholar] [CrossRef]
  2. Vernon, G.; Baranova, A.; Younossi, Z.M. Systematic review: The epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Aliment. Pharmacol. Ther. 2011, 34, 274–285. [Google Scholar] [CrossRef] [PubMed]
  3. Than, N.N.; Newsome, P.N. Non-alcoholic fatty liver disease: When to intervene and with what. Clin. Med. (Lond.) 2015, 15, 186–190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Sherif, Z.A. The Rise in the Prevalence of Nonalcoholic Fatty Liver Disease and Hepatocellular Carcinoma. In Nonalcoholic Fatty Liver Disease—An Update; Gad, E.H., Ed.; IntechOpen: London, UK, 2019; Available online: https://www.intechopen.com/chapters/68253 (accessed on 4 May 2022).
  5. Mascaró, C.M.; Bouzas, C.; Tur, J.A. Association between Non-Alcoholic Fatty Liver Disease and Mediterranean Lifestyle: A Systematic Review. Nutrients 2021, 14, 49. [Google Scholar] [CrossRef]
  6. Issa, D.; Patel, V.; Sanyal, A.J. Future therapy for non-alcoholic fatty liver disease. Liver Int. 2018, 38 (Suppl. 1), 56–63. [Google Scholar] [CrossRef] [Green Version]
  7. Romero-Gómez, M.; Zelber-Sagi, S.; Trenell, M. Treatment of NAFLD with diet, physical activity and exercise. J. Hepatol. 2017, 67, 829–846. [Google Scholar] [CrossRef] [Green Version]
  8. Martínez-González, M.A.; Salas-Salvadó, J.; Estruch, R.; Corella, D.; Fitó, M.; Ros, E.; Predimed Investigators. Benefits of the Mediterranean Diet: Insights from the PREDIMED Study. Prog. Cardiovasc. Dis. 2015, 58, 50–60. [Google Scholar] [CrossRef] [Green Version]
  9. Mascaró, C.M.; Bouzas, C.; Montemayor, S.; Casares, M.; Llompart, I.; Ugarriza, L.; Borràs, P.-A.; Martínez, J.A.; Tur, J.A. Effect of a Six-Month Lifestyle Intervention on the Physical Activity and Fitness Status of Adults with NAFLD and Metabolic Syndrome. Nutrients 2022, 14, 1813. [Google Scholar] [CrossRef] [PubMed]
  10. Flanagan, E.W.; Beyl, R.A.; Fearnbach, S.N.; Altazan, A.D.; Martin, C.K.; Redman, L.M. The Impact of COVID-19 Stay-At-Home Orders on Health Behaviors in Adults. Obesity (Silver Spring) 2021, 29, 438–445. [Google Scholar] [CrossRef] [PubMed]
  11. Shanmugam, H.; Di Ciaula, A.; Di Palo, D.M.; Molina-Molina, E.; Garruti, G.; Faienza, M.F.; Vanerpecum, K.; Portincasa, P. Multiplying effects of COVID-19 lockdown on metabolic risk and fatty liver. Eur. J. Clin. Investig. 2021, 51, e13597. [Google Scholar] [CrossRef]
  12. NNCT04442620. Prevention and Reversion of NAFLD in Obese Patients with Metabolic Syndrome by Mediterranean Diet and Physical Activity (FLIPAN). 2020. Available online: https://clinicaltrials.gov/ct2/show/NCT04442620 (accessed on 5 May 2022).
  13. The International Diabetic Federation (IDF). The IDF Consensus Worldwide Definition of Definition of the Metabolic Syndrome. 2006. Available online: http://www.idf.org/webdata/docs/IDF_Meta_def_final.pdf (accessed on 5 May 2022).
  14. Abbate, M.; Mascaró, C.M.; Montemayor, S.; Barbería-Latasa, M.; Casares, M.; Gómez, C.; Angullo-Martinez, E.; Tejada, S.; Abete, I.; Zulet, M.A.; et al. Energy Expenditure Improved Risk Factors Associated with Renal Function Loss in NAFLD and MetS Patients. Nutrients 2021, 13, 629. [Google Scholar] [CrossRef]
  15. Chalasani, N.; Younossi, Z.; LaVine, J.E.; Charlton, M.; Cusi, K.; Rinella, M.; Harrison, S.A.; Brunt, E.M.; Sanyal, A.J. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases. Hepatology 2018, 67, 328–357. [Google Scholar] [CrossRef] [PubMed]
  16. de la Iglesia, R.; Lopez-Legarrea, P.; Abete, I.; Bondia-Pons, I.; Navas-Carretero, S.; Forga, L.; Alfredo Martinez, J.; Angeles Zulet, M. A new dietary strategy for long-term treatment of the metabolic syndrome is compared with the American Heart Association (AHA) guidelines: The MEtabolic Syndrome REduction in NAvarra (RESMENA) project. Br. J. Nutr. 2014, 111, 643–652. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Estruch, R.; Ros, E.; Salas-Salvadó, J.; Covas, M.-I.; Corella, D.; Arós, F.; Gómez-Gracia, E.; Ruiz-Gutiérrez, V.; Fiol, M.; Lapetra, J.; et al. Primary Prevention of Cardiovascular Disease with a Mediterranean Diet Supplemented with Extra-Virgin Olive Oil or Nuts. N. Engl. J. Med. 2018, 378, e34. [Google Scholar] [CrossRef]
  18. BOE.es—BOE-A-2020-3692 Real Decreto 463/2020, de 14 de marzo, por el que se declara el estado de alarma para la gestión de la situación de crisis sanitaria ocasionada por el COVID-19. 2020. Available online: https://www.boe.es/buscar/doc.php?id=BOE-A-2020-3692 (accessed on 5 May 2022).
  19. Sánchez-Sánchez, E.; Ramírez-Vargas, G.; Avellaneda-López, Y.; Orellana-Pecino, J.I.; García-Marín, E.; Díaz-Jimenez, J. Eating Habits and Physical Activity of the Spanish Population during the COVID-19 Pandemic Period. Nutrients 2020, 12, 2826. [Google Scholar] [CrossRef]
  20. Abbate, M.; Montemayor, S.; Mascaró, C.; Casares, M.; Gómez, C.; Ugarriza, L.; Tejada, S.; Abete, I.; Zulet, M.; Sureda, A.; et al. Albuminuria Is Associated with Hepatic Iron Load in Patients with Non-Alcoholic Fatty Liver Disease and Metabolic Syndrome. J. Clin. Med. 2021, 10, 3187. [Google Scholar] [CrossRef]
  21. Bedogni, G.; Bellentani, S.; Miglioli, L.; Masutti, F.; Passalacqua, M.; Castiglione, A.; Tiribelli, C. The Fatty Liver Index: A simple and accurate predictor of hepatic steatosis in the general population. BMC Gastroenterol. 2006, 6, 33. [Google Scholar] [CrossRef] [Green Version]
  22. Lee, J.-H.; Kim, D.; Kim, H.J.; Lee, C.-H.; Yang, J.I.; Kim, W.; Kim, Y.J.; Yoon, J.-H.; Cho, S.-H.; Sung, M.-W.; et al. Hepatic steatosis index: A simple screening tool reflecting nonalcoholic fatty liver disease. Dig. Liver Dis. 2010, 42, 503–508. [Google Scholar] [CrossRef] [PubMed]
  23. Suni, J.; Husu, P.; Rinne, M. Fitness for Health: The ALPHA-FIT Test Battery for Adults Aged 18–69—Tester’s Manual. UUK Inst. 2009. Available online: http://www.ukkinstituutti.fi/en/alpha (accessed on 4 May 2022).
  24. Bennett, H.; Parfitt, G.; Davison, K.; Eston, R. Validity of Submaximal Step Tests to Estimate Maximal Oxygen Uptake in Healthy Adults. Sports Med. 2016, 46, 737–750. [Google Scholar] [CrossRef]
  25. Sykes, K.; Cartwrightfitness Health and Fitness Measurement. Chester Step Test Kit (Official). 2016. Available online: https://www.cartwrightfitness.co.uk/product/chester-step-test-kit/ (accessed on 4 May 2022).
  26. Elosua, R.; Garcia, M.; Aguilar, A.; Molina, L.; Covas, M.I.; Marrugat, J. Validation of the Minnesota Leisure Time Physical Activity Questionnaire in Spanish Women. Investigators of the MARATDON Group. Med. Sci. Sports Exerc. 2000, 32, 1431–1437. [Google Scholar] [CrossRef]
  27. Elosua, R.; Marrugat, J.; Molina, L.; Pons, S.; Pujol, E. Validation of the Minnesota Leisure Time Physical Activity Questionnaire in Spanish men. The MARATHOM Investigators. Am. J. Epidemiol. 1994, 139, 1197–1209. [Google Scholar] [CrossRef] [PubMed]
  28. Mascaró, C.M.; Bouzas, C.; Montemayor, S.; Casares, M.; Gómez, C.; Ugarriza, L.; Borràs, P.-A.; Martínez, J.A.; Tur, J.A. Association between Physical Activity and Non-Alcoholic Fatty Liver Disease in Adults with Metabolic Syndrome: The FLIPAN Study. Nutrients 2022, 14, 1063. [Google Scholar] [CrossRef]
  29. Wunsch, K.; Kienberger, K.; Niessner, C. Changes in Physical Activity Patterns Due to the Covid-19 Pandemic: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2022, 19, 2250. [Google Scholar] [CrossRef]
  30. Castañeda-Babarro, A.; Arbillaga-Etxarri, A.; Gutiérrez-Santamaría, B.; Coca, A. Physical Activity Change during COVID-19 Confinement. Int. J. Environ. Res. Public Health 2020, 17, 6878. [Google Scholar] [CrossRef]
  31. Meyer, J.; McDowell, C.; Lansing, J.; Brower, C.; Smith, L.; Tully, M.; Herring, M. Changes in Physical Activity and Sedentary Behavior in Response to COVID-19 and Their Associations with Mental Health in 3052 US Adults. Int. J. Environ. Res. Public Health 2020, 17, 6469. [Google Scholar] [CrossRef]
  32. Pérez-Rodrigo, C.; Gianzo Citores, M.; Hervás Bárbara, G.; Ruiz-Litago, F.; Casis Sáenz, L.; Arija, V.; López-Sobaler, A.M.; de Victoria, E.M.; Ortega, R.M.; Partearroyo, T.; et al. Patterns of Change in Dietary Habits and Physical Activity during Lockdown in Spain Due to the COVID-19 Pandemic. Nutrients 2021, 13, 300. [Google Scholar] [CrossRef]
  33. Carvalho, V.O.; Gois, C.O. COVID-19 pandemic and home-based physical activity. J. Allergy Clin. Immunol. Pract. 2020, 8, 2833–2834. [Google Scholar] [CrossRef]
  34. Lesser, I.A.; Nienhuis, C.P. The Impact of COVID-19 on Physical Activity Behavior and Well-Being of Canadians. Int. J. Environ. Res. Public Health 2020, 17, 3899. [Google Scholar] [CrossRef] [PubMed]
  35. Maltagliati, S.; Rebar, A.; Fessler, L.; Forestier, C.; Sarrazin, P.; Chalabaev, A.; Sander, D.; Sivaramakrishnan, H.; Orsholits, D.; Boisgontier, M.P.; et al. Evolution of physical activity habits after a context change: The case of COVID-19 lockdown. Br. J. Health Psychol. 2021, 26, 1135–1154. [Google Scholar] [CrossRef]
  36. Ding, D.; Cheng, M.; Cruz, B.D.P.; Lin, T.; Sun, S.; Zhang, L.; Yang, Q.; Ma, Z.; Wang, J.; Jia, Y.; et al. How COVID-19 lockdown and reopening affected daily steps: Evidence based on 164,630 person-days of prospectively collected data from Shanghai, China. Int. J. Behav. Nutr. Phys. Act. 2021, 18, 40. [Google Scholar] [CrossRef]
  37. Petrov, M.E.; Pituch, K.A.; Kasraeian, K.; Jiao, N.; Mattingly, J.; Hasanaj, K.; Youngstedt, S.D.; Buman, M.P.; Epstein, D.R. Impact of the COVID-19 pandemic on change in sleep patterns in an exploratory, cross-sectional online sample of 79 countries. Sleep Health 2021, 7, 451–458. [Google Scholar] [CrossRef] [PubMed]
  38. Targa, A.D.S.; Benítez, I.D.; Moncusí-Moix, A.; Arguimbau, M.; de Batlle, J.; Dalmases, M.; Barbé, F. Decrease in sleep quality during COVID-19 outbreak. Sleep Breath 2021, 25, 1055–1061. [Google Scholar] [CrossRef] [PubMed]
  39. Martínez-de-Quel, Ó.; Suárez-Iglesias, D.; López-Flores, M.; Pérez, C.A. Physical activity, dietary habits and sleep quality before and during COVID-19 lockdown: A longitudinal study. Appetite 2021, 158, 105019. [Google Scholar] [CrossRef] [PubMed]
  40. Jurado-Fasoli, L.; De-la-O, A.; Molina-Hidalgo, C.; Migueles, J.H.; Castillo, M.J.; Amaro-Gahete, F.J. Exercise training improves sleep quality: A randomized controlled trial. Eur. J. Clin. Investig. 2020, 50, e13202. [Google Scholar] [CrossRef] [PubMed]
  41. Nivukoski, U.; Niemelä, M.; Bloigu, A.; Bloigu, R.; Aalto, M.; Laatikainen, T.; Niemelä, O. Impacts of unfavourable lifestyle factors on biomarkers of liver function, inflammation and lipid status. PLoS ONE 2019, 14, e0218463. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Gelli, C.; Tarocchi, M.; Abenavoli, L.; Di Renzo, L.; Galli, A.; De Lorenzo, A. Effect of a counseling-supported treatment with the Mediterranean diet and physical activity on the severity of the non-alcoholic fatty liver disease. World J. Gastroenterol. 2017, 23, 3150–3162. [Google Scholar] [CrossRef]
  43. Eslam, M.; Newsome, P.N.; Sarin, S.K.; Anstee, Q.M.; Targher, G.; Romero-Gomez, M.; Zelber-Sagi, S.; Wong, V.W.-S.; Dufour, J.-F.; Schattenberg, J.M.; et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J. Hepatol. 2020, 73, 202–209. [Google Scholar] [CrossRef]
  44. González-Gross, M.; Meléndez, A. Sedentarism, active lifestyle and sport: Impact on health and obesity prevention. Nutr. Hosp. 2013, 28 (Suppl. 5), 89–98. [Google Scholar] [PubMed]
  45. Wong, V.W.; Wong, G.L.; Woo, J.; Abrigo, J.M.; Chan, C.K.; Shu, S.S.; Leung, J.K.-Y.; Chim, A.M.-L.; Kong, A.P.-S.; Lui, G.C.-Y.; et al. Impact of the New Definition of Metabolic Associated Fatty Liver Disease on the Epidemiology of the Disease. Clin. Gastroenterol. Hepatol. 2021, 19, 2161–2171.e5. [Google Scholar] [CrossRef]
  46. Zhu, Y.; Duan, M.J.; Dijk, H.H.; Freriks, R.D.; Dekker, L.H.; Mierau, J.O.; Lifelines Corona Research Initiative. Association between socioeconomic status and self-reported, tested and diagnosed COVID-19 status during the first wave in the Northern Netherlands: A general population-based cohort from 49 474 adults. BMJ Open 2021, 11, e048020. [Google Scholar] [CrossRef] [PubMed]
  47. Purba, F.D.; Kumalasari, A.D.; Novianti, L.E.; Kendhawati, L.; Noer, A.H.; Ninin, R.H. Marriage and quality of life during COVID-19 pandemic. PLoS ONE 2021, 16, e0256643. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Baseline sociodemographic characteristics of study participants.
Table 1. Baseline sociodemographic characteristics of study participants.
Low PA
(n = 19)		Medium PA
(n = 19)		High PA
(n = 19)
Mean (SD)Mean (SD)Mean (SD)p
Age (years) #52.5 (7.7) a55.4 (5.7) a, c49.9 (6.2) c<0.001
Education (years) #14.3 (8.0)14.6 (9.7)13.7 (3.5)0.454
n (%)n (%)n (%)
Gender <0.001
Male16 (84.2)10 (52.6)11 (57.9)
Female3 (15.8)9 (47.4)8 (42.1)
Marital status 0.031
Single3 (15.8)1 (5.3)2 (10.5)
Married/coupled14 (73.7)13 (68.4)12 (63.2)
Divorced/separated/widower2 (10.5)5 (26.3)5 (26.3)
Socioeconomic status <0.001
Low16 (84.2)14 (73.7)10 (52.6)
Medium/high3 (15.8)5 (26.3)9 (47.4)
Smoking habit 0.014
No17 (89.5)17 (89.5)19 (100.0)
≥1 cigarette/day2 (10.5)2 (10.5)0 (0.0)
Alcohol consumption 0.827
No6 (31.6)5 (26.3)5 (26.3)
<7 drinks/week11 (57.9)11 (57.9)11 (57.9)
≥7 drinks/week2 (10.5)3 (15.8)3 (15.8)
Abbreviations: PA = physical activity. Physical activity levels are classified according to pre- and post-COVID-19 METs/day difference (terciles). # = log-transformed. Differences in means between groups were tested by ANOVA. Differences in prevalence across groups were examined using χ2. Different letters indicate significant differences between groups (a, c) according to Bonferroni’s post-hoc analysis where p < 0.05.
Table
Table 2. Changes in physical activity parameters within and between PA difference groups pre- and post-COVID-19.
Table 2. Changes in physical activity parameters within and between PA difference groups pre- and post-COVID-19.
Low PA
(n = 19)		Medium PA
(n = 19)		High PA
(n = 19)
Mean (SD)Mean (SD)Mean (SD)T*g†
Motor fitness tests
One-leg balance (s)Pre COVID44.8 (19.2)52.3 (14.2)49.4 (17.7)0.202
Post COVID49.7 (16.7)52.7 (14.5)53.0 (13.3)
Δ4.8 (15.6) *0.4 (10.9)3.6 (7.4) *
Standing hand grip (kg)Pre COVID44.1 (12.6)36.8 (12.1)37.9 (12.7)0.335
Post COVID44.6 (11.8)36.1 (10.8)37.9 (12.7)
Δ0.5 (5.8) −0.7 (3.1) *0.0 (5.4)
Jump-and-reach (cm)Pre COVID24.2 (13.5)23.7 (7.9)20.9 (9.1)0.002
Post COVID24.6 (9.7)22.9 (8.6)23.6 (6.4)
Δ0.4 (12.4) b−0.7 (4.2) c2.7 (5.1) * b, c
Modified push-up (reps)Pre COVID9.0 (5.5)6.8 (3.7)7.9 (4.7)0.502
Post COVID10.9 (6.4)7.1 (5.4)9.2 (8.1)
Δ1.9 (5.3) *0.3 (4.3)1.3 (7.1)
Fitness score testPre COVID1.9 (1.3)2.3 (0.9)2.5 (1.1)0.050
Post COVID2.1 (1.1)2.2 (1.0)2.5 (1.1)
Δ0.2 (0.7) * a−0.1 (0.9) a0.0 (0.7)
Sitting hand grip (kg)Pre COVID40.8 (13.1)33.6 (13.2)36.4 (11.6)0.970
Post COVID42.1 (12.3)34.6 (13.1)38.2 (12.5)
Δ1.3 (6.5)1.0 (4.2) *1.8 (5.2) *
Chester-step (ml O2/kg/min)Pre COVID37.4 (8.8)34.1 (7.5)35.4 (11.3)0.584
Post COVID35.2 (12.0)33.7 (8.4)38.2 (10.9)
Δ−2.2 (10.9)−0.4 (7.1)2.7 (9.2) *
Intensity PA (accelerometry)
Sedentary (min/day)Pre COVID678.7 (66.0)669.6 (103.9)609.3 (109.7)<0.001
Post COVID630.0 (83.9)670.9 (107.0)632.3 (105.3)
Δ−48.7 (78.7) * a, b1.3 (44.6) a, c23.0 (70.4) * b, c
Light (min/day)Pre COVID513.9 (80.9)489.4 (86.4)535.3 (93.3)<0.001
Post COVID534.8 (69.9)492.3 (83.2)519.8 (93.7)
Δ20.9 (53.1) * b2.9 (47.7) c−15.5 (75.8) b, c
Moderate (min/day)Pre COVID166.0 (49.6)196.7 (90.0)233.6 (107.3)<0.001
Post COVID198.3 (51.2)200.3 (87.8)182.8 (76.1)
Δ32.3 (20.1) * a, b3.5 (13.5) * a, c−50.8 (61.1) * b, c
Sleep efficiency (%)Pre COVID91.5 (3.3)92.8 (2.8)92.5 (3.0)<0.001
Post COVID92.0 (3.3)92.0 (3.4)91.4 (2.4)
Δ0.6 (2.6) a, b−0.8 (2.0) * a−1.1 (2.3) * b
Steps (steps/day)Pre COVID11,779.6 (3620.6)13,099.9 (3314.6)15,147.8 (3359.2)<0.001
Post COVID14,258.7 (3280.8)12,953.2 (3544.5)19,436.2 (29785.4)
Δ2479.2 (2587.5) * a, b−146.8 (1221.5) a, c4288.4 (29274.4) b, c
Energy expenditure
Measured accelerometer (MET/day)Pre COVID1.8 (0.2)1.9 (0.3)2.0 (0.3)<0.001
Post COVID1.9 (0.2)1.9 (0.3)1.8 (0.3)
Δ0.1 (0.1) * a, b0.0 (0.0) * a, c−0.2 (0.3) * b, c
Reported Minnesota (MET/day)Pre COVID0.7 (0.6)0.5 (0.4)0.5 (0.6)0.308
Post COVID0.6 (0.7)0.4 (0.5)0.4 (0.4)
Δ−0.1 (0.9)−0.1 (0.5)−0.1 (0.3) *
Measured-Reported (MET/day)Pre COVID1.1 (0.6)1.4 (0.4)1.5 (0.6)0.143
Abbreviations: Δ = change between pre- and post-COVID, cm = centimeter, kg = kilogram, MET = metabolic equivalents of task, min = minutes, ml = milliliter, No. = number, O2 = oxygen, PA = physical activity, reps = repetitions, s = seconds, T*g † = time*group after adjustment for age, gender, marital status and socioeconomic status according to job. Data were analyzed by two-way repeated measures ANCOVA. Different letters indicate statistically significant differences (p < 0.05) between time (*) time*group interaction after adjustment for age, gender, marital status and socioeconomic status according to job (a, b, c) by Bonferroni’s post-hoc test (p < 0.05). Physical activity levels are classified according to pre- and post-COVID METs/day difference (terciles). All variables were log-transformed.
Table
Table 3. Changes in NAFLD parameters within and between PA difference groups pre- and post-COVID-19.
Table 3. Changes in NAFLD parameters within and between PA difference groups pre- and post-COVID-19.
Low PA
(n = 19)		Medium PA
(n = 19)		High PA
(n = 19)
Mean (SD)Mean (SD)Mean (SD)T*g†
Liver profile
AST (U/L)Pre COVID31.3 (23.60)22.3 (6.6)22.6 (6.8)0.160
Post COVID25.3 (4.7)24.9 (10.5)30.0 (25.9)
Δ−6.0 (21.5)2.6 (9.7) *7.4 (21.2) *
ALT (U/L)Pre COVID44.6 (53.4)27.1 (13.5)28.3 (10.1)0.514
Post COVID35.4 (18.4)31.1 (19.8)37.6 (34.8)
Δ−9.2 (51.6)4.0 (17.3) *9.3 (30.2) *
GGT (U/L)Pre COVID56.9 (86.9)37.4 (16.6)38.7 (23.5)<0.001
Post COVID39.9 (28.4)49.4 (42.1)55.8 (62.0)
Δ−17.0 (61.4) * a, b12.0 (29.6) * a17.1 (45.8) * b
FLIPre COVID83.7 (13.8)77.1 (21.7)79.1 (21.2)0.014
Post COVID86.2 (9.2)82.5 (16.1)84.8 (12.1)
Δ2.5 (8.3) * a5.4 (22.2) * a5.7 (15.8) *
HSIPre COVID45.1 (5.6)43.4 (4.9)43.3 (5.0)0.747
Post COVID44.2 (3.9)43.9 (6.0)43.5 (5.6)
Δ−0.9 (4.8)0.5 (3.1)0.2 (2.4)
IFC-NMR (%)Pre COVID13.6 (8.5)12.7 (7.8)12.8 (8.0)0.119
Post COVID12.6 (9.2)13.7 (9.0)14.7 (8.4)
Δ−1.0 (4.9)1.0 (4.8)1.9 (5.8) *
Metabolic syndrome parameters
Abdominal obesity (WC ≥ 94 cm men; ≥80 cm women)Pre COVID112.9 (9.0)107.7 (9.4)109.7 (7.2)0.203
Post COVID112.6 (7.7)106.4 (8.8)110.2 (8.6)
Δ−0.3 (4.4)−1.3 (5.3) *0.5 (4.6)
High triglyceridemia ≥ 150 mg/dLPre COVID152.7 (66.3)193.1 (118.5)177.4 (119.1)0.561
Post COVID165.6 (62.0)189.7 (98.9)187.8 (121.3)
Δ12.9 (55.9) *−3.4 (78.1)10.4 (73.1)
Low HDL-c < 40 mg/dL men; <50 mg/dL womenPre COVID43.1 (9.6)44.8 (9.3)43.3 (10.3)0.014
Post COVID40.2 (7.7)44.4 (11.5)41.6 (9.9)
Δ−2.8 (4.9) *−0.4 (5.4) c−1.6 (5.1) * c
Hypertension (systolic pressure ≥ 130 mmHg)Pre COVID130.5 (12.5)128.1 (13.0)131.4 (10.4)0.718
Post COVID134.5 (12.7)132.4 (17.7)135.7 (19.1)
Δ4.0 (9.5) *4.3 (14.5) *4.3 (16.5) *
Hypertension (diastolic pressure ≥ 85 mmHg)Pre COVID80.6 (7.3)78.9 (9.3)79.8 (6.0)0.282
Post COVID80.4 (9.2)83.0 (13.0)83.2 (10.1)
Δ−0.2 (7.3)4.1 (10.3) *3.5 (9.3) *
High fasting glycemia ≥ 100 mg/dLPre COVID113.3 (36.2)104.4 (11.3)105.8 (31.1)0.712
Abbreviations: Δ = change between pre and post COVID, ALT = alanine aminotransferase, AST = aspartate aminotransferase, cm = centimeter, dL = deciliter, FLI = fatty liver index, GGT = gamma glutamyl transferase, HSI = hepatic steatosis index, HDL-c = high density lipoprotein-cholesterol, IFC-NMR = intrahepatic fat content by nuclear magnetic resonance, L = liter, mg = milligram, mmHg = millimeters of mercury, NAFLD = non-alcoholic fatty liver disease, PA = physical activity, U = units, T*g † = time*group after adjustment for age, gender, marital status and socioeconomic status according job. Data analyzed by two-way repeated measures ANCOVA. Different letters indicate statistically significant differences (p < 0.05) between time (*) and time*group interaction after adjustment for age, gender, marital status and socioeconomic status according to job (a, b, c) by Bonferroni’s post-hoc test (p < 0.05). Physical activity levels are classified according to pre- and post-COVID METs/day difference (terciles). All variables were log-transformed.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Mascaró, C.M.; Bouzas, C.; Montemayor, S.; García, S.; Mateos, D.; Casares, M.; Gómez, C.; Ugarriza, L.; Borràs, P.-A.; Martínez, J.A.; et al. Impact of Physical Activity Differences Due to COVID-19 Pandemic Lockdown on Non-Alcoholic Fatty Liver Parameters in Adults with Metabolic Syndrome. Nutrients 2022, 14, 2370. https://doi.org/10.3390/nu14122370

AMA Style

Mascaró CM, Bouzas C, Montemayor S, García S, Mateos D, Casares M, Gómez C, Ugarriza L, Borràs P-A, Martínez JA, et al. Impact of Physical Activity Differences Due to COVID-19 Pandemic Lockdown on Non-Alcoholic Fatty Liver Parameters in Adults with Metabolic Syndrome. Nutrients. 2022; 14(12):2370. https://doi.org/10.3390/nu14122370

Chicago/Turabian Style

Mascaró, Catalina M., Cristina Bouzas, Sofía Montemayor, Silvia García, David Mateos, Miguel Casares, Cristina Gómez, Lucía Ugarriza, Pere-Antoni Borràs, J. Alfredo Martínez, and et al. 2022. "Impact of Physical Activity Differences Due to COVID-19 Pandemic Lockdown on Non-Alcoholic Fatty Liver Parameters in Adults with Metabolic Syndrome" Nutrients 14, no. 12: 2370. https://doi.org/10.3390/nu14122370

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop