Kuwaiti Adults' Physical Activity Levels during COVID Lockdown

Abstract

Introduction

The Coronavirus disease (COVID-19) pandemic has led to unprecedented global restrictions, prompting concerns regarding physical inactivity.

Methods

This study investigated physical activity (PA) levels among adults in Kuwait during the COVID pandemic using the International PA Questionnaire Short Form (IPAQ-SF) distributed via an online survey through social media between June and July 2020.

Results

A total of 679 Kuwaiti adults completed the IPAQ-SF. Results show that 28.9% were in the high PA category, 35.8% in the moderate PA category, and 35.3% in the low PA category. While no significant differences between genders were detected (P=0.460), PA levels were significantly associated with age (P=0.022) among adults in Kuwait.

Discussions

This study emphasizes the necessity for a targeted public health initiative to encourage PA during pandemics in Kuwait.

Conclusion

During viral pandemics, national health awareness programs to encourage PA should focus on adults over 45 years.

Keywords: PA, COVID, Kuwait.

1. INTRODUCTION

The COVID pandemic caused a global health crisis and unprecedented restrictions. As of March 2023, there were over 600 million confirmed cases and 6.8 million deaths reported [1]. To relieve stress on health services and mitigate transmission, countries introduced several measures, including lockdowns, causing changes in daily life. These changes limited movement and social interactions, which led to reduced PA levels and an increase in the incidence of sedentary behavior [2, 3]. Physical inactivity and sedentary behavior are linked to a range of negative health outcomes, including cardiovascular disease, obesity, mental health problems, and type 2 diabetes [4-14].

In Kuwait, over 630,000 confirmed cases and 2,555 deaths were recorded [15]. To contain the spread of the virus, Kuwait implemented several measures, including full and partial lockdowns and restrictions on public gatherings. These measures may have contributed to changes in PA levels in the Kuwaiti population. Understanding these changes is crucial for public health purposes, given PA health benefits including increasing immunity and reducing the risk of viral respiratory infections (16-19). The fundamental factors behind these health benefits may be attributed to enhanced immunity by increases in white blood cells following regular exercise [20-22]. Therefore, public awareness of the benefits of PA during a viral pandemic is important.

During viral epidemics, the recommendation is to perform moderate PA at home using personal equipment, as well as brisk walking outdoors while avoiding crowded gyms and sports with close physical contact [23-25]. While some countries were weighing the risks and benefits of continuing major competitions such as the 2020 Olympics, the advice for competitive athletes during viral outbreaks was to follow strict isolation procedures or postpone events [26-28]. Both public health and individual well-being benefit from following expert guidance on maintaining regular PA during viral outbreaks in Kuwait.

To date, one study investigated the effects of the COVID lockdown on PA in Kuwait [29]. In this study 33% of the participants reported performing less than 30 minutes of PA per week during June and July of 2020. Of the sample, 33% did not meet the WHO’s recommended guideline of at least 150 minutes of PA per week [30]. However, PA levels were not categorized according to the specific PA categories (low, moderate, and high), and the effects by gender or age were not examined. Therefore, to address these limitations, the IPAQ-SF was used to assess PA levels among a sample of Kuwaiti adults during the pandemic [31]. This study aimed to investigate the levels of PA during the COVID pandemic in Kuwait and factors that are related to performing PA. The findings of the study are aimed at generating public health interventions to promote PA during pandemics.

2. MATERIALS AND METHODS

2.1. Study design and Sample Demographic information

This cross-sectional study used an online survey that was distributed using social media platforms such as X, formerly known as Twitter, and WhatsApp during June and July 2020. Participants were provided with a link to the survey with an electronic consent page preceding the survey to obtain their consent. Participation was voluntary, and no incentives were offered to complete the survey. Participants self-reported demographic information including age, height, weight, gender, governorate, marital status, number of children, employment, education level, whether they are smoking, exercising, and if they have any chronic illnesses such as diabetes or hypertension.

2.2. Inclusion and Exclusion Criteria

To take part in the study, participants had to be 21 years or older and living in Kuwait. The exclusion criteria involve participants younger than 21 years and living outside of Kuwait.

Data Collection Methods

2.3.1. IPAQ-SF Analysis

The IPAQ-SF was used to assess PA levels among study participants. The IPAQ-SF-short form is a well-established self-administered questionnaire that assesses PA spent in vigorous, moderate, and walking activities during the previous seven days [32, 33]. It is the most widely used tool to monitor PA levels worldwide [33] and it has demonstrated significant uses in policy change and decision-making in Europe [34]. The questionnaire comprises 7 items assessing frequency (number of days) and duration (minutes) spent in each PA category in the previous 7 days to avoid recall bias, in addition to an item related to the time spent sitting over the last 7 days. According to the IPAQ-SF, moderate PA is any PA that causes a moderate increase in heart rate and breathing rate, and is represented by 3-6 metabolic equivalents (METS) or the amount of oxygen consumed during rest [35]. Physical activities that are considered vigorous are those that result in rapid increases in breathing and heart rate and represent a value greater than 6 METS.

2.3.2. Sample Size Calculations

The sample size required for the study was at least 384 participants, calculated using the SurveyMonkey Inc [36]. using a 95% confidence interval and a 5% margin of error, and a population size of 706,000 [37].

2.4. Ethics

The study protocol was approved by the Medical and Health Ethics Committee of the Ministry of Health in Kuwait (2020/1501) and was conducted under the declaration of Helsinki.

2.5. Data Analysis

The data were inspected, cleaned, and analyzed according to IPAQ-SF guidelines [31]. Missing data or incomplete responses were removed. To begin data analysis, the duration was converted from hours to minutes. The total amount of time, expressed in minutes, was then calculated for each category: walking, moderate, and engaging in vigorous PA. METS were calculated as follows: minutes from each category were then multiplied by the number of days and the METS value to determine the METS-minutes per week for each category using the typically assigned METS values of 3.3 for walking, 4.0, and 8.0 for moderate PA and vigorous PA, respectively. To calculate the total METS-minutes per week, all categories are added together (Total METS= walking (METS) + moderate (METS) + vigorous (METS)). Based on whether respondents met the required standards for total METS-minutes per week and days per week of PA, respondents were then classified into a low, moderate, or high PA level category. Low PA refers to those who report some PA but do not meet moderate or high PA criteria. Moderate PA is considered as the following: three or more days of vigorous PA (>20 minutes each), five or more days of moderate PA, walking more than 30 minutes, or any combination of walking, moderate or vigorous intensity of more than 600 METS-minutes. The high PA category was defined as performing three or more days of vigorous PA with at least 1500 METS-minutes per week, or performing any PA (walking, moderate, or vigorous) for 7 or more days with a minimum of 3000 METS-minutes per week.

2.6. Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics version 28 (IBM Corp., Armonk, NY, USA). Numerical Data are presented as mean and standard deviation or as median and interquartile range and were analysed using one-way ANOVA. Categorical data were presented as frequencies and percentages and analysed using the Chi-square test or Fisher’s exact test, as appropriate. Ordinal logistic regression analysis was performed to assess factors associated with PA level. Significance was reached if P-value <0.05.

3. RESULTS

Table 1 represents the socio-demographic characteristics of the respondents. The study consisted of 679 participants, of which 45.2% were males and 54.8% were females, with an average body mass index is 27.8 ± 7.44 kg/m2. Most of the sample lived in Kuwait City (24.2%), Hawally (23.7%), and Mubarak Alkabeer (21.1%), while fewer participants participated from Farwaniya (13%), Alahmadi (10.6%), and Aljahra (7.5%). Most of the participants are married (54.2%), while 41.1% are single, and 4.7% either divorced or widowed. Of the sample, 47.4% had no children, 20.3% had 3 or 4 children, 19.3% had 1 or 2 children, and 13% had more than 4 children. Concerning employment status, 69.7% are employees, 17.7% are students, 6.5% are retired, and 6.2% are other. Participants who held an undergraduate degree were 62.7%, followed by doctoral and postgraduate degrees (19.1%), diploma (11.8%), and high school or less (6.3%). Furthermore, 21.8% of the sample were smokers, 13.5% had chronic illnesses, and 62.9% reported that they exercise. Fig. (1 and Table 2) represent PA levels among respondents; 35.8% were in the moderate PA group, 35.3% were in the low PA group, and 28.9% were in the high PA group.

Table 1.
Socio-demographic characteristics of respondents (n=679).
Item N %
Age (years)
21 – 35 395 58.2
36 – 45 102 15.0
>45 182 26.8
BMI (kg/m2)
Mean ± SD 27.8 ± 7.44
Gender
Male 307 45.2
Female 372 54.8
Governate
Kuwait City 164 24.2
Alahmadi 72 10.6
Farwaniya 88 13.0
Aljahra 51 7.5
Hawaly 161 23.7
Mubarak Alkabeer 143 21.1
Marital status
Married 368 54.2
Single 279 41.1
Other 32 4.7
Number of children
No children 322 47.4
1 or 2 131 19.3
3 or 4 138 20.3
More than 4 88 13.0
Employment
Student 120 17.7
Employee 473 69.7
Retired 44 6.5
Other 42 6.2
Educational level
High school education or less 43 6.3
Diploma 80 11.8
Undergraduate 426 62.7
Doctoral and post-doctoral degree 130 19.1
Smoking 148 21.8
Chronic illness 92 13.5
Exercise 427 62.9
Fig. (1).

Distribution of respondents according to PA levels.

Table 2.
PA levels among respondents (n=679).
Item N % 95%CI
Physical Activity level
Low 240 35.3 31.02 to 40.11
Moderate 243 35.8 31.43 to 40.58
High 196 28.9 24.97 to 33.2
METS
Median (IQR) 1308 (495, 2542.5)
CI: Confidence interval, METS: Metabolic equivalents.

Our investigation revealed a statistically significant relationship between the age of respondents and their PA level (P=0.022), as respondents in the high PA category were significantly younger than those in the moderate PA category, with 65.3% of the first vs. 55.6% of the latter being in the 21–35 year range, 10.2% vs. 19.8% in the 36–45 range and 24.5% vs 24.7% over the age of 45 years. BMI significantly differed among groups (P=0.031), being significantly lower among participants with high PA levels than among those with low PA (26.9 ± 4.67 vs 28.75 ± 10.5 kg/m2). Additionally, exercise prevalence was 36.7% in the group with low PA levels, 64.2% in those with moderate PA, and 93.4% in those with high PA, showing a statistically significant difference among the three groups (P<0.001). Notably, 42.5% of the low PA group, 45.3% of the moderate PA group, and 48.5% of the high PA group were males, and these percentages were comparable among the groups (Table 3, Fig. 24).

In univariate regression analysis, BMI, marital status, having chronic illnesses, and exercising were significantly associated with PA level according to the IPAQ-SF as follows: Each 1 unit increase in BMI resulted in a decrease in the odds of moderate or high PA levels by 0.97 (95%CI: 0.95 to 0.99, P=0.011). Single participants showed significantly higher odds of having moderate or high PA levels than married participants (OR=1.52, 95%CI: 1.14 to 2.02, P=0.005). As for chronic illnesses, participants suffering from them showed significantly lower odds of engaging in moderate or high PA than others (OR=0.6, 95%CI: 0.4 to 0.91, P=0.016). Further, participants who exercised showed significantly higher odds of having moderate or high PA levels than those who did not exercise (OR=7.13, 95%CI: 5.15 to 9.87, P<0.001).

After adjusting for the included factors, BMI, nationality, marital status, number of children, and exercise were significantly associated with PA level as follows:

Table 3.
Relation between socio-demographic characteristics of respondents and Physical Activity levels.
Item Physical Activity level P-value
Low
(n=240)
Moderate (n=243) High
(n=196)
Age (years)
21 – 35 132 (55%) ab 135 (55.6%) a 128 (65.3%) b 0.022
36 – 45 34 (14.2%) 48 (19.8%) 20 (10.2%)
>45 74 (30.8%) 60 (24.7%) 48 (24.5%)
BMI (kg/m2) 28.75 ± 10.5 a 27.6 ± 5.18 ab 26.9 ± 4.67 b 0.031
Gender
Male 102 (42.5%) 110 (45.3%) 95 (48.5%) 0.460
Female 138 (57.5%) 133 (54.7%) 101 (51.5%)
Marital status
Married 146 (60.8%) 129 (53.1%) 93 (47.4%) 0.076
Single 84 (35%) 101 (41.6%) 94 (48%)
Other 10 (4.2%) 13 (5.3%) 9 (4.6%)
Number of children
No children 107 (44.6%) 113 (46.5%) 102 (52%) 0.056
1 or 2 41 (17.1%) 56 (23%) 34 (17.3%)
3 or 4 57 (23.8%) 38 (15.6%) 43 (21.9%)
More than 4 35 (14.6%) 36 (14.8%) 17 (8.7%)
Employment
Student 45 (18.8%) 38 (15.6%) 37 (18.9%) 0.342
Employee 168 (70%) 166 (68.3%) 139 (70.9%)
Retired 17 (7.1%) 19 (7.8%) 8 (4.1%)
Other 10 (4.2%) 20 (8.2%) 12 (6.1%)
Educational level
High school education or less 17 (7.1%) 16 (6.6%) 10 (5.1%) 0.360
Diploma 21 (8.8%) 30 (12.3%) 29 (14.8%)
Undergraduate 160 (66.7%) 152 (62.6%) 114 (58.2%)
Doctoral and post-doctoral degree 42 (17.5%) 45 (18.5%) 43 (21.9%)
Smoking 53 (22.1%) 53 (21.8%) 42 (21.4%) 0.987
Chronic illness 42 (17.5%) 31 (12.8%) 19 (9.7%) 0.055
Exercise 88 (36.7%) a 156 (64.2%) b 183 (93.4%) c <0.001
Fig. (2).

Comparison of age among the three Physical Activity groups.

Fig. (3).

Comparison of BMI among the three Physical Activity groups.

Each 1 unit increase in BMI resulted in a decrease in the odds of having moderate or high PA levels by 0.97 (95%CI: 0.95 to 1, P=0.046). Single participants showed significantly higher odds of having moderate or high PA levels than married participants (OR=2.33, 95%CI: 1.19 to 4.58, P=0.014). Participants with one or two children elicited significantly higher odds of having moderate or high PA than those with no children (OR=2.07, 95%CI: 1.04 to 4.09, P=0.037). Furthermore, participants who exercised showed significantly higher odds of moderate or high PA levels than those who did not exercise (OR=7.18, 95%CI: 5.1 to 10.09, P<0.001) Table 4.

Fig. (4).

Comparison of exercising rate among the three Physical Activity groups.

Table 4.
Ordinal logistic regression analysis for factors associated with higher PA level.
Item Univariate analysis Multivariable analysis
Unadjusted OR 95%CI P-value Adjusted OR 95%CI P-value
Age (years)
21 – 35 Ref Ref
36 – 45 0.76 0.52 to 1.13 0.177 0.88 0.55 to 1.39 0.574
>45 0.73 0.53 to 1.01 0.061 1.17 0.71 to 1.91 0.538
BMI (kg/m2) 0.97 0.95 to 0.99 0.011 0.97 0.95 to 1 0.046
Gender
Male Ref Ref
Female 0.84 0.63 to 1.11 0.214 0.85 0.59 to 1.21 0.363
Marital status
Married Ref Ref
Single 1.52 1.14 to 2.02 0.005 2.33 1.19 to 4.58 0.014
Other 1.3 0.68 to 2.51 0.428 1.18 0.57 to 2.46 0.654
Number of children
No children Ref Ref
1 or 2 0.92 0.64 to 1.33 0.665 2.07 1.04 to 4.09 0.037
3 or 4 0.81 0.55 to 1.18 0.267 1.72 0.82 to 3.59 0.151
More than 4 0.66 0.43 to 1.01 0.057 1.42 0.63 to 3.19 0.400
Employment
Student Ref Ref
Employee 1.02 0.7 to 1.48 0.931 1.11 0.69 to 1.8 0.656
Retired 0.75 0.4 to 1.41 0.369 1.05 0.45 to 2.43 0.913
Other 1.3 0.69 to 2.43 0.420 1.71 0.86 to 3.42 0.127
Educational level
High school education or less Ref Ref
Diploma 1.82 0.92 to 3.6 0.084 0.85 0.4 to 1.78 0.662
Undergraduate 1.13 0.64 to 2.01 0.678 0.54 0.28 to 1.04 0.064
Doctoral and post-doctoral degree 1.48 0.78 to 2.79 0.229 0.7 0.34 to 1.44 0.335
Smoking 0.97 0.7 to 1.36 0.870 0.95 0.62 to 1.44 0.800
Having a chronic illness 0.6 0.4 to 0.91 0.016 0.95 0.6 to 1.5 0.813
Exercising 7.13 5.15 to 9.87 <0.001 7.18 5.1 to 10.09 <0.001
OR: Odds ratio, CI: Confidence interval, statistical significance at P-value<0.05.

4. DISCUSSION

We sought to determine PA levels among Kuwaiti adults during the COVID pandemic by using the IPAQ-SF. This is the first attempt to both quantify and categorize PA levels using the IPAQ-SF in Kuwait during the pandemic. The main findings of this study were twofold. First, there were no gender differences in PA levels during the pandemic in Kuwait. Secondly, age and BMI were contributing factors to PA levels. These findings indicate that promotion of PA levels in Kuwait is necessary for specific age groups during the pandemic, and multidimensional strategies should be pursued at the national level to increase levels of PA.

During the pandemic, unprecedented measures such as social distancing caused interruptions to the ‘normal’ daily activities. This was a global attempt to contain the virus and to limit its transmission. However, one of the strict social distancing measures, such as ‘lockdowns’ and quarantines, came at a huge cost [5]. People’s daily living, travel, education, and leisure were impacted and had an enormous effect on their daily PA levels [5]. Indeed, a recent systematic review found that PA levels were reduced during the pandemic, especially in countries with lockdowns [38]). This is important because PA can promote health and well-being, and Exercise can be prescribed as a therapy for various chronic diseases, highlighting its role as a subcategory of PA [39-41]. It is well established that weekly moderate-to-vigorous PA can reduce all-cause mortality [42-45]. Increased PA levels have been shown to provide immunity against viruses such as COVID-19 [16, 17, 24]. Frequent moderate-intensity PA reduces the incidence of respiratory tract infection by a value of 40-50% as reported elsewhere [19]. Moreover, individuals with chronic conditions such as diabetes and hypertension can benefit from exercising due to its positive immune effects, a conclusion supported by several studies [17, 46, 47]. Therefore, given the evidence on the benefits of PA levels, monitoring them at a national level is crucial for implementing policies to promote PA.

WHO suggested PA guidelines for the general population. The recommendation is that the minimum PA needed to have the benefits is engaging in a minimum of 150 minutes of moderate or 75 minutes of vigorous PA weekly [30]. Due to the pandemic restrictions and the ‘stay at home’ message that was disseminated during this period, vigorous activities such as running or playing soccer, which are very common in Kuwait, are not possible. However, other vigorous intensity physical activities can be performed at home, such as stair climbing or moving heavy loads, which can be considered as a vigorous PA at home [48]. In the present study, most participants engaged in regular physical activities. The results showed that 35.8% and 28.9% of the sample were in the moderate or high PA category, respectively, during the pandemic. These results are consistent with those of other studies that have assessed PA levels using the IPAQ-SF during the pandemic. Antunes, Frontini [49] and Maugeri, Castrogiovanni (50) found that the majority of participants recruited from Portugal and Italy belonged to the high and moderate categories of the IPAQ-SF. Maugeri, Castrogiovanni [50] investigated the impact of COVID on PA levels and whether it had psychological effects (50). They found that most participants were in either the moderate (50%) or high (19%) PA category. They also reported that ~40% of their participants were in the low PA category. In the present study, 35% of the participants were in the low PA category, which is higher than the figure reported by Antunes, et al. [49] (32%), but lower than that reported by Maugeri, Castrogiovanni [50] (40%). Antunes, Frontini [49] explored PA levels during the pandemic. They also found that most participants had either moderate (30%) or vigorous PA (31%). Discrepancies in the results may be attributed to the level of restrictions that occurred during these different settings of the studies compared to Kuwait or other social and cultural factors. However, ~30-40% physical inactivity levels among studies can reveal that work needs to be done to curb the burden of inactivity.

The finding of low PA in our sample (35.3%) is consistent with concerns across the Gulf Cooperation Council (GCC) region regarding insufficient PA levels before the pandemic. A systematic review conducted before the pandemic revealed low levels of PA, particularly among women [51]. Furthermore, our findings during the COVID lockdown are also comparable to those of neighboring countries. A recent scoping review concluded that PA was reduced consistently across all ages and in both genders during the COVID lockdown period in Saudi Arabia [52]. Kuwait's response to the pandemic during June and July of 2020 included a 12-hour curfew (6 am-6 pm) and closure of non-essential business, where gyms and public outdoor areas were closed off, limiting PA engagement. In addition to extreme heat (>45°C daytime) during June and July making prolonged PA is dangerous, especially for older adults, due to heat-related immunosuppression effects [53]. Nevertheless, the proportion of individuals maintaining moderate to high activity levels (64.7%) during the pandemic may indicate adoption of home exercise, a shift reinforced by a worldwide increase in digital fitness platforms and applications during the pandemic [54].

In the present study, no significant gender differences were found between males and females in Kuwait. This finding is interesting as it contrasts with studies conducted before the pandemic in Kuwait and the surrounding region. For example, a study on Kuwaiti adolescents found that males were significantly more physically active than females [55]. This is consistent with studies conducted in the region across the Arabian Peninsula, where a systematic review reported that males were significantly more active than females [51]. Several factors may have contributed to this finding. Pandemic restrictions limit access to sports venues such as soccer fields, where males were more often accessed before the pandemic. Traditionally, men are more likely to exercise in gyms, and societal norms make females more likely to engage in PA at home [56]. In addition, restrictions might have restricted females’ PA to segregated venues that they often accessed before the pandemic. Limited access to male sports outdoor venues may have reduced PA of males, and possibly females, increased activity levels at home, which might lead to convergence in overall PA levels. However, these explanations require further investigation.

Our study also showed that age was a contributing factor in PA levels in Kuwait during the pandemic. As expected, PA decreased as age increased in all participants. It is well known that as age increases, PA levels decrease [57, 58]. In an older study conducted in Saudi Arabia [59], a region close to Kuwait, PA levels decreased as age increased from 15-29 years old to 60-78 years old. The 15-29 years old group was 15% more active than the older group in the study 60-78-year-old group. Similarly, the largest age group with physical inactivity was the older group with 41%, compared to over 50% in the other study [59]. Therefore, as aging can play a role in PA levels, it is necessary to focus on these aging groups towards PA level participation and surveillance.

5. RECOMMENDATIONS AND POLICY IMPLICATIONS

Based on the findings of this study, we propose several recommendations and policy implications. First, public health efforts should focus on getting older adults (45+ years old) more active, especially during viral pandemics. This study indicated that this age group was at risk of not being active during the viral pandemics. In addition, public health messaging should encourage a wide range of home-based physical activities, using little or no equipment. This would ensure that individuals are active even during periods of lockdown and restrictions. Furthermore, Kuwait's policy makers should monitor trends in PA and evaluate national health interventions. Integrating the IPAQ-SF as a validated evaluation tool is important for achieving this goal.

6. STUDY LIMITATIONS

This study has several limitations regarding PA assessment. Although the IPAQ-SF is a validated tool to assess PA levels, it is subject to self-reporting biases, including an over-reporting bias and potential recall bias regarding PA levels during the previous 7 days. Therefore, future studies could incorporate accelerometers or wearable fitness trackers to support these findings. Second, recruitment was through an online survey that could have introduced a selection bias and may have over-represented individuals with more digital literacy and under-represented older adults and others who have less access to digital online platforms. However, recruitment through social media remains an efficient strategy [60, 61], and during lockdowns commuting is restricted; therefore, it is difficult to assess PA levels in any other means such as accelerometers or wearable fitness trackers. Furthermore, the IPAQ-SF can provide an acceptable estimate of PA levels, and its validity has been demonstrated and confirmed [33, 62, 63]. Another limitation in this study is the lack of a comparative analysis, which limits the generalizability of the findings to Kuwait. Also, the cross-sectional design of the study may identify relationships between variables, but it does not allow for inference of the causes of these relationships. For example, we detected a relationship between age and PA; however, it’s difficult to conclude that age causes reductions in PA according to the design of this study. Future studies should consider longitudinal studies to study the causes of reductions in PA during the pandemic and use these limitations and investigate how the pandemic affected PA levels using objective measures such as accelerometers.

CONCLUSIONS

In conclusion, the findings of this study provide a valuable snapshot of PA levels among adults in Kuwait during the COVID lockdown. This study indicated that older adults and adults with higher BMI were associated with lower PA; therefore, during viral pandemics, PA promotion strategies should focus on older adult age groups and those with higher BMI. These strategies can be implemented at the community level by establishing a social environment where older adults can engage and perform low-impact activities in collaboration with healthcare providers, such as physical therapists. Future studies should consider longitudinal studies to study the causes of reductions in PA during the pandemic and investigate PA levels using objective measures such as accelerometers.

AUTHOR CONTRIBUTIONS

The authors confirm contribution to the paper as follows: Study conception and design; S.A., A.A., M.M.:, Analysis and interpretation of results; S.A., A.A, M.M., A.A.A., A.H.A., M.A., and N.A.; and A.S.H.: Draft Manuscript; S.A., A.A., M.M., A.A.A., A.H.A., M.A., and N.A.; and A.S.H.:, Given final approval of the version to be published;. All authors reviewed the results and approved the final version of the manuscript S.A., A.A., M.M., A.A.A., A.H.A., M.A., and N.A.; and A.S.H.

LIST OF ABBREVIATIONS

ANOVA = Analysis of Variance
BMI = Body Mass Index
CI = Confidence Interval
COVID = Coronavirus Disease
GCC = Gulf Cooperation Council
IPAQ-SF = International Physical Activity Questionnaire Short Form
METs = Metabolic Equivalents
OR = Odds Ratio
PA = Physical Activity
WHO = World Health Organization

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The study was approved by the Medical and Health Ethics Committee of the Ministry of Health in Kuwait (2020/1501).

HUMAN AND ANIMAL RIGHTS

No animals were used in this research. All procedures performed in studies involving human participants were under the ethical standards of institutional and/or research committee and with the 1975 Declaration of Helsinki, as revised in 2013.

CONSENT FOR PUBLICATION

Informed consent was obtained from all subjects involved in the study.

STANDARDS OF REPORTING

STROBE guidelines were followed.

AVAILABILITY OF DATA AND MATERIALS

The data presented in this study are available on request from the corresponding author.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

We extend our heartfelt thanks to all the participants whose invaluable contributions made this study possible.

REFERENCES

1
The Centre for Systems Science and Engineering (CSSE) at Johns Hopkins University. 2023. Available from: https://coronavirus.jhu.edu/map.html
2
Iacobucci G. Covid-19: UK lockdown is "crucial" to saving lives, say doctors and scientists. BMJ 2020; 368: m1204.
3
Runacres A, Mackintosh KA, Knight RL, et al. Impact of the COVID-19 Pandemic on Sedentary Time and Behaviour in Children and Adults: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health 2021; 18(21): 11286.
4
Sallis JF, Adlakha D, Oyeyemi A, Salvo D. An international physical activity and public health research agenda to inform coronavirus disease-2019 policies and practices. J Sport Health Sci 2020; 9(4): 328-34.
5
Stockwell S, Trott M, Tully M, et al. Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: a systematic review. BMJ Open Sport Exerc Med 2021; 7(1): e000960.
6
Hossain MM, Sultana A, Purohit N. Mental health outcomes of quarantine and isolation for infection prevention: A systematic umbrella review of the global evidence. Epidemiol Health 2020; 42: e2020038.
7
Fox KR, Hillsdon M. Physical activity and obesity. Obes Rev 2007; 8(s1)(Suppl. 1): 115-21.
8
Lakka T, Bouchard C. Physical activity, obesity and cardiovascular diseases. Handbook of experimental pharmacology 2005; 170: 137-63.
9
Andersen RE, Crespo CJ, Bartlett SJ, Cheskin LJ, Pratt M. Relationship of physical activity and television watching with body weight and level of fatness among children: results from the Third National Health and Nutrition Examination Survey. JAMA 1998; 279(12): 938-42.
10
Andersen RE, Wadden TA, Bartlett SJ, Zemel B, Verde TJ, Franckowiak SC. Effects of lifestyle activity vs structured aerobic exercise in obese women: a randomized trial. JAMA 1999; 281(4): 335-40.
11
Ashton W, Nanchahal K, Wood DA. Body mass index and metabolic risk factors for coronary heart disease in women. Eur Heart J 2001; 22(1): 46-55.
12
Baik I, Ascherio A, Rimm EB, et al. Adiposity and mortality in men. Am J Epidemiol 2000; 152(3): 264-71.
13
Ballor DL, Keesey RE. A meta-analysis of the factors affecting exercise-induced changes in body mass, fat mass and fat-free mass in males and females. Int J Obes 1991; 15(11): 717-26.
14
Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012; 380(9838): 219-29.
15
The Central Agency for Information Technology. COVID 19 updates: Sate of kuwait—live 2023. 2023. Available from: https://corona.e.gov.kw/En/
16
Nieman D, Nehlsen-Cannarella S, Markoff P, et al. The effects of moderate exercise training on natural killer cells and acute upper respiratory tract infections. Int J Sports Med 1990; 11(6): 467-73.
17
Nieman DC, Wentz LM. The compelling link between physical activity and the body’s defense system. J Sport Health Sci 2019; 8(3): 201-17.
18
da Silveira MP, da Silva Fagundes KK, Bizuti MR, et al. Physical exercise as a tool to help the immune system against COVID-19: an integrative review of the current literature. Clin Exp Med 2021; 21(1): 15-28.
19
Nieman DC, Henson DA, Austin MD, Sha W. Upper respiratory tract infection is reduced in physically fit and active adults. Br J Sports Med 2011; 45(12): 987-92.
20
Gupta P, Bigley AB, Markofski M, Laughlin M, LaVoy EC. Autologous serum collected 1 h post-exercise enhances natural killer cell cytotoxicity. Brain Behav Immun 2018; 71: 81-92.
21
Nieman DC, Henson DA, Gusewitch G, et al. Physical activity and immune function in elderly women. Med Sci Sports Exerc 1993; 25(7): 823-31.
22
Nieman DC, Henson DA, Austin MD, Brown VA. Immune response to a 30-minute walk. Med Sci Sports Exerc 2005; 37(1): 57-62.
23
Chen P, Mao L, Nassis GP, Harmer P, Ainsworth BE, Li F. Coronavirus disease (COVID-19): The need to maintain regular physical activity while taking precautions. J Sport Health Sci 2020; 9(2): 103-4.
24
Harris MD. Infectious disease in athletes. Curr Sports Med Rep 2011; 10(2): 84-9.
25
Martin SA, Pence BD, Woods JA. Exercise and respiratory tract viral infections. Exerc Sport Sci Rev 2009; 37(4): 157-64.
26
Halabchi F, Ahmadinejad Z, Selk-Ghaffari M. COVID-19 epidemic: exercise or not to exercise; that is the question! 2020.
27
Hammami A, Harrabi B, Mohr M, Krustrup P. Physical activity and coronavirus disease 2019 (COVID-19): specific recommendations for home-based physical training. Manag Sport Leis 2022; 27(1-2): 26-31.
28
Eirale C, Bisciotti G, Corsini A, Baudot C, Saillant G, Chalabi H. Medical recommendations for home-confined footballers’ training during the COVID-19 pandemic: from evidence to practical application. Biol Sport 2020; 37(2): 203-7.
29
Salman A, Sigodo KO, Al-Ghadban F, et al. Effects of COVID-19 lockdown on physical activity and dietary behaviors in Kuwait: A cross-sectional study. Nutrients 2021; 13(7): 2252.
30
Bull FC, Al-Ansari SS, Biddle S, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med 2020; 54(24): 1451-62.
31
Committee IR.. Guidelines for data processing and analysis of the International Physical Activity Questionnaire (IPAQ)-short and long forms. 2005. Available from: https://www.researchgate.net/file.PostFileLoader.html?id=5641f4c36143250eac8b45b7&assetKey=AS%3A294237418606593%401447163075131
32
Shahnazi H, Ahmadi-Livani M, Pahlavanzadeh B, Rajabi A, Hamrah MS, Charkazi A. Assessing preventive health behaviors from COVID-19: a cross sectional study with health belief model in Golestan Province, Northern of Iran. Infect Dis Poverty 2020; 9(1): 157.
33
Sember V, Meh K, Sorić M, Starc G, Rocha P, Jurak G. Validity and reliability of international physical activity questionnaires for adults across EU countries: Systematic review and meta analysis. Int J Environ Res Public Health 2020; 17(19): 7161.
34
European Commission D-GfC. Special Eurobarometer 472: Sport and physical activity. 2018. Available from: https://sport.ec.europa.eu/sites/default/files/special-eurobarometer-472_en.pdf
35
Jetté M, Sidney K, Blümchen G. Metabolic equivalents (METS) in exercise testing, exercise prescription, and evaluation of functional capacity. Clin Cardiol 1990; 13(8): 555-65.
36
SurveyMonkey Inc. Sample Size Calculator. 2025. Available from: https://www.surveymonkey.com/mp/sample-size-calculator/
37
Bureau CS. Estimated population of kuwait. 2024. Available from: https://www.csb.gov.kw/
38
Ng TKY, Kwok CKC, Ngan GYK, et al. Differential effects of the COVID-19 pandemic on physical activity involvements and exercise habits in people with and without chronic diseases: A systematic review and meta-analysis. Arch Phys Med Rehabil 2022; 103(7): 1448-1465.e6.
39
Pedersen BK, Saltin B. Exercise as medicine – evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports 2015; 25(S3): 1-72.
40
Durstine JL, Gordon B, Wang Z, Luo X. Chronic disease and the link to physical activity. J Sport Health Sci 2013; 2(1): 3-11.
41
Thornton JS, Frémont P, Khan K, et al. Physical activity prescription: A critical opportunity to address a modifiable risk factor for the prevention and management of chronic disease: A position statement by the canadian academy of sport and exercise medicine. Br J Sports Med 2016; 50(18): 1109-14.
42
Arem H, Moore SC, Patel A, et al. Leisure time physical activity and mortality: A detailed pooled analysis of the dose-response relationship. JAMA Intern Med 2015; 175(6): 959-67.
43
Paffenbarger RS Jr, Hyde R, Wing AL, Hsieh C. Physical activity, all-cause mortality, and longevity of college alumni. N Engl J Med 1986; 314(10): 605-13.
44
Bembom O, van der Laan M, Haight T, Tager I. Leisure-time physical activity and all-cause mortality in an elderly cohort. Epidemiology 2009; 20(3): 424-30.
45
Almeida OP, Khan KM, Hankey GJ, Yeap BB, Golledge J, Flicker L. 150 minutes of vigorous physical activity per week predicts survival and successful ageing: A population-based 11-year longitudinal study of 12 201 older australian men. Br J Sports Med 2014; 48(3): 220-5.
46
Booth FW, Roberts CK, Laye MJ. Lack of exercise is a major cause of chronic diseases. Compr Physiol 2012; 2(2): 1143-211.
47
Stewart AL, Hays RD, Wells KB, Rogers WH, Spritzer KL, Greenfield S. Long-term functioning and well-being outcomes associated with physical activity and exercise in patients with chronic conditions in the medical outcomes study. J Clin Epidemiol 1994; 47(7): 719-30.
48
Teh KC, Aziz AR. Heart rate, oxygen uptake, and energy cost of ascending and descending the stairs. Med Sci Sports Exerc 2002; 34(4): 695-9.
49
Antunes R, Frontini R, Amaro N, et al. Exploring lifestyle habits, physical activity, anxiety and basic psychological needs in a sample of Portuguese adults during COVID-19. Int J Environ Res Public Health 2020; 17(12): 4360.
50
Maugeri G, Castrogiovanni P, Battaglia G, et al. The impact of physical activity on psychological health during Covid-19 pandemic in Italy. Heliyon 2020; 6(6): e04315.
51
Mabry R, Koohsari MJ, Bull F, Owen N. A systematic review of physical activity and sedentary behaviour research in the oil-producing countries of the arabian peninsula. BMC Public Health 2016; 16(1): 1003.
52
Evenson KR, Alothman SA, Moore CC, et al. A scoping review on the impact of the COVID-19 pandemic on physical activity and sedentary behavior in saudi arabia. BMC Public Health 2023; 23(1): 572.
53
Awad R, Suberry A, Abu-Akel A, Ayalon L. Heat stress effects on the immune system of older adults: A systematic literature review. Exp Gerontol 2025; 206: 112777.
54
Budd J, Miller BS, Manning EM, et al. Digital technologies in the public-health response to COVID-19. Nat Med 2020; 26(8): 1183-92.
55
Allafi A, Al-Haifi AR, Al-Fayez MA, et al. Physical activity, sedentary behaviours and dietary habits among Kuwaiti adolescents: Gender differences. Public Health Nutr 2014; 17(9): 2045-52.
56
Nash EA, Critchley JA, Pearson F, et al. A systematic review of interventions to promote physical activity in six gulf countries. PLoS One 2021; 16(10): e0259058.
57
Payette H, Gueye NR, Gaudreau P, Morais JA, Shatenstein B, Gray-Donald K. Trajectories of physical function decline and psychological functioning: The Quebec longitudinal study on nutrition and successful aging (NuAge). J Gerontol B Psychol Sci Soc Sci 2011; 66B(Suppl. 1): i82-90.
58
Milanović Z, Jorgić B, Trajković N, Sporiš G, Pantelić S, James N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clin Interv Aging 2013; 8: 549-56.
59
Al-Hazzaa HM. Health-enhancing physical activity among saudi adults using the international physical activity questionnaire (IPAQ). Public Health Nutr 2007; 10(1): 59-64.
60
Ellington M, Connelly J, Clayton P, et al. Use of facebook, instagram, and twitter for recruiting healthy participants in nutrition-, physical activity–, or obesity-related studies: A systematic review. Am J Clin Nutr 2022; 115(2): 514-33.
61
Darko EM, Kleib M, Olson J. Social media use for research participant recruitment: Integrative literature review. J Med Internet Res 2022; 24(8): e38015.
62
Helou K, El Helou N, Mahfouz M, Mahfouz Y, Salameh P, Harmouche-Karaki M. Validity and reliability of an adapted arabic version of the long international physical activity questionnaire. BMC Public Health 2018; 18(1): 49.
63
Craig CL, Marshall AL, Sjöström M, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc 2003; 35(8): 1381-95.