{"paper_id":"c4904afe-46f1-45bb-90aa-7ee542af5f32","body_text":"1 / 14\n1Association of physical activity and the risk of COVID-19 hospitalization: \n2a dose–response meta-analysis\n3Dan Li 1☯, Shengzhen Jin 2,1☯ and Songtao Lu 1,3* \n41 School of Sports, Wuhan University of Science and Technology, Wuhan 430081, China.; \n5lidanscerlett@126.com\n62 Tennis College, Wuhan sports university, Wuhan 430079, China; whtyjinsz@163.com\n73 School of Physical Education and Sports, Central China Normal University, Wuhan 430079, China.\n8☯ These authors contributed equally to this work. \n9* songtaozhenzhenl@sina.com\n10Abstract\n11Background\n12Many people have experienced a high burden due to the spread of the coronavirus disease (COVID-19) and its serious \n13consequences for health and everyday life. Prior studies have reported that physical activity (PA) may lower the risk of COVID-\n1419 hospitalization. The present meta-analysis (PROSPERO registration number: CRD42022339672) explored the dose–\n15response relationship between PA and the risk of COVID-19 hospitalization. \n16Methods\n17Epidemiological observational studies on the relationship between PA and the risk of COVID-19 hospitalization were included. \n18Categorical dose–response relationships between PA and the risk of COVID-19 hospitalization were assessed using random \n19effect models. Robust error meta-regression models assessed the continuous relationship between PA (metabolic equivalent \n20[MET]–h/week) and COVID-19 hospitalization risk across studies reporting quantitative PA estimates. \n21Results\n22Seventeen observational studies (cohort\\case–control\\cross-section) met the criteria for inclusion in the meta-analysis. \n23Categorical dose-relationship analysis showed a 40% (risk ratio (RR) 0.60, 95% confidence intervals (CI): 0.48–0.71) reduction \n24in the risk of COVID-19 hospitalization compared to the lowest dose of PA. The results of the continuous dose–response \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \nNOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.\n\n 2 / 14\n25relationship showed a non-linear inverse relationship (Pnon-linearity < 0.05) between PA and the risk of COVID-19 \n26hospitalization. When total PA was less than or greater than 10 Met-h/week, an increase of 4 Met-h/week was associated with \n27a 14% (RR = 0.83, 95%CI: 0.85–0.87) and 11% (RR = 0.89, 95%CI: 0.87–0.90) reduction in the risk of COVID-19 \n28hospitalization, respectively. \n29Conclusions\n30There was an inverse non-linear dose–response relationship between PA level and the risk of COVID-19 hospitalization. \n31Doses of the guideline-recommended minimum PA levels by WTO may be required for more substantial reductions in the \n32COVID-19 hospitalization risk. \n33\n341. Introduction\n35The coronavirus disease 2019 (COVID-19) outbreak continues worldwide. As of 7 May 2022, COVID-19 has caused 51,587,3758 \n36infections and 6,272,357 deaths worldwide [1]. It is essential to identify high-risk groups that require special attention under these \n37conditions [2]. For non-communicable disease outcomes, lifestyle risk factors have been consistently associated with morbidity, \n38mortality, and loss of disease-free life [3,4]. For example, physical inactivity and smoking appear to be independently associated \n39with a higher risk of community-acquired pneumonia morbidity and mortality [5,6].\n40It is also well established that the risk of developing respiratory disease is much higher in people with low physical activity (PA), \n41whereas COVID-19 patients with a physically inactive lifestyle (e.g., sedentary behavior) are more likely to be hospitalized and \n42have a greater likelihood of poor clinical outcomes [7]. Moreover, it has previously been shown that regular physical activity and \n43higher physical fitness levels enhance immune function and, therefore, might reduce susceptibility to COVID-19 infection and \n44infection severity [8,9]. Recent studies retrospectively evaluating cohorts of COVID-19 positive adults have described the benefit \n45of regular physical activity in decreasing the incidence of adverse outcomes in confirmed cases of COVID-19 [10,11,12]. \n46However, research on such topics is just emerging, and the impact of PA on the infectious and clinical outcomes of COVID-19 \n47remains unclear. The protective effects of different levels of physical activity against COVID-19 are controversial. Rahmati et al. \n48[13] conducted a meta-analysis on this topic, which did not address the controversy regarding the protective effects of different \n49levels of physical activity. In addition, Rahmati et al. [13] classified the case–control group as a cross-sectional study. In a meta-\n50analysis, they assumed cardiopulmonary function as physical activity, which inevitably led to unconvincing results. Finally, the \n51study only used the binary variables of physical activity included in the literature to analyze the outcome variables, ignoring the \n52moderate dose in the multi-level doses, making it difficult to explain the heterogeneity generated by the meta-analysis studies. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 3 / 14\n53Furthermore, no systematic review or meta-analysis has reported the exact dose–response relationship between pre-diagnosis \n54PA and COVID-19 hospitalization. Consequently, there is still substantial uncertainty regarding the association between pre-\n55diagnosis PA levels and hospitalization due to COVID-19 among the general population. To precisely quantify the association \n56between pre-diagnosis PA and the risk of COVID-19 hospitalization, we conducted a systematic review and meta-analysis of \n57observational studies published up to May 8, 2022.\n582. Materials and Methods\n592.1. Inclusion Criteria\n60The criteria for inclusion, and each article determined for inclusion, were discussed by three authors, and the discussions on \n61inclusion and exclusion occurred more than three times. The inclusion criteria were as follows: (1) studies published as \n62epidemiological observational cohort studies, case controls, and cross-sectional design investigation studies; (2) studies providing \n63at least an odds ratio, relative risk (RR or HR), and 95% confidence interval (95%CI) between the level of physical activity and the \n64risk of hospitalization for COVID-19, or raw data provided to calculate these indicators. The repeated literature was excluded. Only \n65the latest studies were selected if they were conducted at different time points in the same cohort. Additionally, if multiple articles \n66were published in the same group, we chose articles where subjects were followed for a longer time or with a larger sample size. \n672.2. Search Strategy \n68We searched PubMed (1980 to the present) and the Web of Science database (1980 to the present) for literature on the \n69relationship between physical activity and the risk of COVID-19 hospitalization. The search strategy used keywords such as \n70\"exercise or physical activity or sport or walking or motor activity\", \"COVID-19 or SARS-CoV-2\", and \"severe or hospitalization\". \n71These searches were screened for cohort studies, case controls, and cross-sectional design studies. The latest search date was \n72April 2022, and there was no language limit. The reference lists of the selected and related review articles were screened to \n73identify potentially relevant studies. All searches were conducted independently by two authors, and the differences were \n74resolved by group discussion.\n752.3. Quality Assessment\n76The Newcastle–Ottawa Scale (NOS) was used to evaluate literature quality, and scores of 0–3, 4–6, and 7–9 were determined as \n77low, moderate, and high quality, respectively [14]. Each article was evaluated independently by two authors and cross-checked. \n78In the group meeting, the results were publicized, and the reasons for the score of each item were specified. If the evaluation of \n79the literature quality was inconsistent, the group focused on solving the final score of its quality. \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 4 / 14\n802.4. Synthesis Methods\n81Stata16.0 software was used for the meta-analysis. The p-value was set at p < 0.05, and all tests were double-sided. The effect \n82value-adjusted risk ratio (RR) and 95% confidence interval (CI) of the group with the highest dose of PA (physical activity) compared \n83to the control group with the lowest dose of PA in each study were combined. The combined effect values were calculated using \n84a random-effects model. Heterogeneity was assessed and described using I2 statistics as the percentage of variation in the study; \n85I2 values of 25%, 50%, and 75% represented low, moderate, and high levels of heterogeneity, respectively [15]. Egger and Begg \n86tests were used to determine publication bias. During the sensitivity analysis, each study was deleted one by one to check whether \n87the combined effect of the remaining studies had changed [15]. The subgroup meta-analysis was conducted according to PA \n88intensity classification (LPA-light intensity physical activity, VPA-vigorous-intensity physical activity, MVPA-moderate-to-vigorous \n89physical activity, MPA-moderate-intensity physical activity), sex, age, study area, study quality, and adjustment for confounding \n90factors. Meta-regression was used to examine the heterogeneity among studies.\n91According to categorical and continuous dose PA, this study analyzed the dose–response relationship with the risk of COVID-19 \n92hospitalization. Categorical doses were divided into dichotomous and multi-classified doses shown in the study, and the combined \n93effect value RR was generated by comparing the highest and lowest doses. To analyze the continuous dose–response relationship, \n94we calculated the total weekly dose of PA for each effect value RR based on the PA intensity, duration, and weekly frequency of \n95the baseline survey provided in the literature. Furthermore, we assumed that the dose remained at this level during the follow-\n96up survey. To determine the exposure value of the included dose, the median was set as the determined dose. If the development \n97interval was < 0.5, it was set to 0.25. If the upper open interval was ≥ 1, the difference between the intermediate dose intervals \n98was 0.25, and the exposure value was set to 1.25 [16]. Met-h/week was considered as the final unit of analysis. These are combined \n99absolute indices of intensity, duration, and frequency used to calculate exposures to Met units not directly reported in the \n100literature. Met, a physiological measure of PA energy, is defined as energy expenditure per kilogram of body weight per hour: 1 \n101Met = 1 kcal/kg ∗h. To address the differences in PA units in different studies, we used Ainsworth et al. 's classification [16], \n102classifying PA into low-intensity LPA (3 Mets, such as walking exercise), moderate-intensity MPA(4 Mets), and high-intensity VPA \n103(8 Mets). We then converted the duration of a particular PA intensity (h/week) to Met-h/week in combination with the frequency \n104of the week [17].\n105To establish the dose–response relationship between PA and the risk of hospitalization for COVID-19, robust error meta-\n106regression (REMR) was used for model fitting [18]. The REMR approach is based on a \"one-stage\" framework that treats each \n107study as a cluster and fits the revised regression to the average PA dose across the entire dataset. In addition, the method also \n108uses the inverse variance method to weight each dose-specific effect in the data and balances heteroscedasticity in the REMR \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 5 / 14\n109model to ensure unbiased estimation of parameters. Finally, we used restricted cubic splines as connection functions to fit the \n110linear and non-linear dose–response models. Based on the dose-centralization treatment, the independent variable PA dose of \n111the model was set as three nodes (0, 6.75, and 21), including two regression splines. The χ2 test was used to test the hypothesis \n112that the regression coefficient of the second regression spline is significant (p < 0.05), indicated by a linear or non-linear dose–\n113response relationship. A dose–response relationship curve was drawn using the Stata software XBLC command [16].\n1143. Results\n1153.1. Study Selection and Characteristics\n116In total, 170 articles were preliminarily identified. According to the literature inclusion and exclusion criteria formulated in this \n117study, 17 studies, including seven cohort studies, five case–control studies, and five cross-sectional design studies, were finally \n118included. There were 1,038,768 subjects and about 3022 hospitalized COVID-19 cases (some studies had not reported the number \n119of cases). The steps for retrieval and inclusion are shown in Fig 1. The characteristics of the literature are listed in Table 1. Among \n120the 17 studies, 3 were from North America [10,19,20], 7 were from Asia [11,12,21,22,23,24,25], 6 were from Europe \n121[26,27,28,29,30,31], and 1 was from Oceania [32]. The NOS was used to score the included studies. Eight studies were considered \n122high quality, with a score greater than or equal to seven, and the other nine were considered medium quality.\n123\n124Fig 1. Flow diagram of studies considered for inclusion in the systematic review.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 6 / 14\n125Table 1. Summary of the extracted studies.\n126\nAuthor（year） Country Study type Case\\total Age（SD） female % Measurement and Categories of PA adjustment * QA\nAlKetbi（2021） AUH Cohort 135\\641 44(NP) 36% Self-report PA: five Categories(times\\w) NP* 5\nBrandenburg（2021） CA Case-control NP\\263 86%<65 57% PA-R: No, Moderate, <1 h of vigorous, >1 h of vigorous 2 5 6 8 9 12 15 7\nBielik(2021） SLK Cohort 104\\2343 18 to 65 49% physically active(≥3 times\\w), cold-water swim NP 5\nEkblom-Bak(2021） SWE Case-control 172/407131 49.9(NP) 30% Never/irregular, 1–2 times/w, ≥ 3 times/w 1 2 5 16 7\nKatsoulis（2021） UK Cohort NP\\ 85308 18-69(NP) NP Self-report PA: Low, Gentle, Moderate, Vigorous 1 2 7 9 7\nHamer(2020) UK Cohort 760\\387109 56.2± 8.0 55.1% IPAQ: Sufficient\\ Insufficient\\ None 1 2 6 8 9 7\nHalabchi（2021） Iran. Cross-Section 60\\4694 36.45±9.77 55% Regular sports participation (yes/no) NP 6\nHamdan(2021） PLE Cross-Section 59\\300 30.5±12.2 55.0% PA questionnaire: yes\\no NP 5\nLee (2021) KR Cohort 277\\118768 NP 51.2% Self-report PA: None, Gentle, Moderate, Vigorous 1 2 5-9 14 16\nLatorre-Roman(2021)  \nal.,2021\nSpanish Cross-Section NP\\420 33 (20–54) 52.6% IPAQ: Moderate PA>150 min\\w, 30–150 min\\w, None 1 6\nMalisoux(2022） LUX Cohort 106\\452 42 (31–51) 48% PA questionnaire: yes\\no 1 2 3 5 16 18 19 7\nMaltagliati(2021) France Case-control 66\\3139 69.3 ± 8.5 53% four-point PA scale ranging (>1, 1; <1; 0\\week) 1 2 5 7 8 12 19 9\nSouza et al(2021） Brazil Cross-Section 91\\938 NP(NP) 33.4% IPAQ: Sufficient>150 min/w(moderate), Insufficient 1 2 19 7\nSallis（2021） USA Cohort 1199\\2970 47±16.97 61.9% UPAG: active, inactive, some activity 1-15 9\nSteenkamp（2021） ZA Cohort NP\\65361 41±12.1 48.2% Low activity, Moderate activity, High activity 1 2 6 8 9 14 15 8\nTavakol(2021) Iran Cross-Section 64\\188 18-75(NP) 52.7% GPAQ:  Low, Moderate to high NP 6\nYuan(2021) CN Case-control 29\\164 61.8 ± 13.6 48.8% Self-report PA: Inactivity, activity NP 6\n127Case \\ total: Number of cases and total sample size; Age characteristics: Single value indicates mean age, others are age range.* Adjustment factors: 1 age, 2 gender, 3 socioeconomic status, 4 race, 5BMI, 6 cardiovascular disease, 7 cancer, \n1288 diabetes, 9 hypertension, 10 use of antihypertensive drugs, 11 corticosteroids, 12 chronic lung / respiratory disease, 13 liver disease, 14 HIV, 15 end-stage renal disease and immune disease, 16 smoking, 17 alcohol,18 sedentary behaviour \n129, 19 comorbidities.NP *: Not reported. AUH: AUH , ZA: South Africa, UK: UK, AUH: UAE, CA: Canada, SWE: Switzerland, Iran: Iran, KR: Korea, ESP: Spain, USA: US, ZA: New Zealand, CN: China, SLK: Slovak, LUX: Luxembourg, PLE: Palestine. \n130IPAQ: International Physical Activity Questionnaire. PA-R: physical activity rating questionnaire. UPAG: US Physical Activity Guidelines. GPAQ: Global physical activity questionnaire.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 7 / 14\n1313.2. Categorical analysis between PA and COVID-19 hospitalization\n132Compared with the lowest PA dose, the highest PA in the included studies reduced the risk of COVID-19 hospitalization by 40% \n133(RR = 0.60, 95%CI: 0.48, 0.71). The heterogeneity test result was I2 = 66.22% (p < 0.01), indicating significant heterogeneity of the \n134study results (Fig 2). The pooled effect size results did not change significantly after excluding each study from the sensitivity \n135analysis (Supporting information S1 Fig). Published bias analysis with Begg’s test p = 0.54 > 0.05, Egger test p = 1.330 > 0.05, and \n136funnel diagram also showed no significant published bias (Supporting information S2 Fig). The effect values of the cohort study, \n137case–control study, and cross-sectional design study were 0.63 (95%CI:0.54,0.71), 0.59 (95%CI: 026, 0.91), and 0.58 (95%CI: 0.42, \n1380.74), respectively.\n139Fig 2. Forest plot showing categorical analysis between PA and COVID-19 hospitalization\n140As for the source of heterogeneity (presented in Table 2), between-group heterogeneity only appeared in the comparative \n141analysis of the relationship between PA at different dose levels and the risk of hospitalization for COVID-19 (P b < 0.01), indicating \n142that PA at different doses significantly reduces the risk of hospitalization. Within-group heterogeneity appeared in the multi-dose \n143PA, case–control study, high quality, European, adjustment for age, sex, adjustment for high blood pressure, adjustment for \n144diabetes, adjustment for cancer, and cardiovascular diseases subgroup, illustrating that the subgroup study effect value of the \n145results may not be stable. The results may be affected by other factors.\n146\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 8 / 14\n147\n148\n149Table 2. Subgroup analysis\n150\n151Multi-dose comparison *: The highest dose is higher than the 10 h-met / week and the lowest dose, and the moderate dose is lower than 10 h-met / week. The binary dose \n152is generally expressed as exercise and non-exercise; * multiple dose grouping represents the highest, relative and lowest dose comparison,at least three lever; P a and P b \n153represent heterogeneity within and between subgroups, respectively. \n1543.3. Continuous dose-response relationship between PA and COVID-19 hospitalization\n155Fig 3 shows the continuous dose–response relationship. The results showed a negative non-linear relationship between PA and \n156the risk of hospitalization for COVID-19 (Pnon-linearity < 0.01). When PA < 10 Met-h/week, an increase of 4 Met-h/week (1 h of \nSubgroup N RR(95% CI) I2  (%) pa* Pb*\nTotal effect size (highest vs. lowest) 17 0.60（0.48,0.71） 66.22 0.01\nbinary does * 7 0.51(0.33,0.69) 45.43 0.09 0.24PA Categories\nmulti-dose * 10 0.65(0.51.0.79) 68.34 0.02\nhighest vs. lowest 12 0.59(0.55,0.63) 0 0.86multi-class dose\ncomparison* moderate vs. lowest 13 0.75(0.65,0.85) 26.88 0.33\n0.01<\ncohort 7 0.63(0.54,0.71) 20.83 0.53 0.85\nCase-control 5 0.59(026,0.91) 82.07 0.00\nStudy type\nCross-section 5 0.58(0.42,0.74) 2.05 0.28\nStudy quality 》7 8 0.67(0.53,0.81) 70.32 0.02 0.1\n7< 9 0.49(0.32,0.66) 38.93 0.15\nEurope 6 0.68(0.43,0.94) 68.68 0.02 0.46\nAsia 7 0.49(0.30,0.68) 46.31 0.11\nDifferent continent\nothers (America) 4 0.59(0.54,0.64) 0 0.96\nAdjusted confunding factor\nyes 10 0.63(0.51,0.75) 66.73 0.01 0.36age\nno 7 0.51(0.29,0.74) 43.20 0.14\nyes 10 0.65(0.54,0.78) 59.93 0.03 0.15sex\nno 7 0.47(0.25,0.69) 44.89 0.12\nyes 7 0.66(0.48,0.84) 67.76 0.02 0.29BMI \nno 10 0.54(0.40,0.87) 43.12 0.15\nAdjusted baseline disease\nyes 8 0.58(0.31,0.85) 10.28 0.57 0.83hypertension\nno 9 0.61(0.55,0.67) 71.22 0.00\ndiabetes yes 8 0.61(0.55,0.87) 11.67 0.49 0.91\nno 9 0.59(0.34,0.84) 67.09 0.00\ncardiovascular yes 8 0.61(0.55,0.87) 11.67 0.49 0.91\nno 9 0.59(0.34,0.84) 67.09 0.00\ncancer yes 6 0.57(0.45,0.69) 0 0.70 0.75\nno 11 0.61(0.43,0.79) 80.95 0.00\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 9 / 14\n157moderate-intensity or 1/2 h of high-intensity) was associated with a 14% reduction in the risk of hospitalization for COVID-19 (p < \n1580.01, RR = 0. 86, 95%CI: 0.85–0.87). When PA > 10 Met-h/week, the risk of hospitalization for COVID-19 decreased by 11% for each \n159additional 4 Met-h/week (p < 0.01, RR = 0.89, 95%CI: 0.87–0.90).\n160Fig 3 Continuous dose-response relationship between PA and COVID-19 hospitalization\n1614. Discussion\n162This study is the first dose–response meta-analysis of the relationship between PA and the risk of COVID-19 hospitalization. The \n163literature included observational studies on the relationship between PA and the risk of COVID-19 hospitalization. Through a meta-\n164analysis of the categorical dose, our main conclusion is that the risk of COVID-19 hospitalization is reduced by 40% compared with \n165the lowest dose of PA. For continuous dose–response analysis, we confirmed that the relationship between PA and the risk of \n166COVID-19 hospitalization is non-linear and inverse. For every four Met-h\\week PA increase, the risk of COVID-19 hospitalization \n167decreased by 11–14%. Sensitivity and published bias analyses further support these results, and these main quantitative features \n168have important clinical significance. \n169The dose–response association between PA and the risk of COVID-19 hospitalization has been previously reported. As for the \n170categorical analysis, Rahmati et al. observed that PA was significantly associated with a reduction in COVID-19 hospitalization \n171compared with control (RR = 0.58) by meta-analysis [13]. In the present study, we observed a similar RR of 0.60. However, we \n172included multiple categorical analysis data with different PA dose levels to analyze the relationship between PA and the risk of \n173COVID-19 hospitalization. We observed significantly different protective effects of varying PA levels on the hospitalization risk of \n174COVID-19 by heterogeneity analysis. Compared to the results obtained by Rahmati et al., our observations are more abundant.\n175As for the continuous analysis, Malisoux et al. observed an inverse dose–response association between PA and the risk of \n176moderate COVID-19 illness, and increased PA was associated with a slightly lower risk of moderate illness (OR:0.99) [25].  In the \n177present study, we observed a similar inverse dose–response association between PA and the risk of COVID-19 hospitalization. An \n178increase of 4 Met-h/week PA was associated with a 12–17% (RR = 0.83–0.88) reduction in the risk of COVID-19 hospitalization. A \n1794 Met-h/week PA is equivalent to one hour MPA or half an hour high VPA; our results are more specific and close to a practical \n180exercise. Otherwise, the observed dose–response relationship between PA and the risk of COVID-19 hospitalization was non-linear. \n181We assumed that after 10 Met-h/week, the degree of enhancement in lowering the risk of moderate COVID-19 illness when PA \n182increase is weakened. However, the threshold is 30 Met-h/week according to the j-shaped association of PA and risk of moderate \n183illness by Malisoux et al.. [25]. Meanwhile, our meta-analysis findings are more robust and specific, and we observed increasing \n184PA benefits for the inpatient burden due to COVID-19.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 10 / 14\n185This difference in the magnitude of risk reduction for COVID-19 hospitalization could be related to differences in the mechanism \n186through which PA modifies the risk of respiratory viral infections. This is supported by previous studies that showed a stronger \n187association between maximal fitness exercise capacity and the risk of hospitalization due to COVID-19 [33]. Exercise capacity is an \n188important index for measuring overall health and the ability of the body to cope with external stressors. More specifically, it is an \n189important index to bear the burden on the heart and lungs [33]. However, PA greatly influences exercise capacity; more specifically, \n190regular moderate-intensity to vigorous-intensity aerobic exercise daily can improve exercise capacity. In addition, the beneficial \n191effects of regular PA on the immune system may involve several mechanisms, including enhanced immunosurveillance, reduced \n192systemic inflammation, improved regulation of the immune system, and delayed onset of immunosenescence [34,35]. A recent \n193meta-analysis investigated the effects of regular PA on the immune system [36]. This study showed that moderate to vigorous \n194intensity exercise (e.g., walking, running, cycling) is overall beneficial with a lower concentration of neutrophils and a higher \n195concentration of CD4 T helper cells and salivary IgA. These biochemical indicator changes may be the critical mechanism for regular \n196PA to lower the risk of hospitalization due to COVID-19.\n197As for the analysis of the confounding factors, results on between-subgroup heterogeneity showed significant heterogeneity in \n198all subgroups adjusted for underlying disease. Thus, confounding factors of underlying disease significantly affected the \n199association between PA and the risk of hospitalization. However, contrary to expectations, heterogeneity was observed in the \n200within-subgroup analysis adjusted for age, sex, and BMI. This indicated that our data failed to demonstrate significant differences \n201in the impact of PA on the risk of hospitalization for COVID-19 according to age, sex, and body weight. This may be because the \n202overall heterogeneity of this meta-analysis was precisely concentrated in these subgroups. The other possible reason is that the \n203effect of PA on reducing the risk of COVID-19 hospitalization is very stable among these different demographic characteristics.\n204The WHO global recommendation on the health benefits of PA states that adults have at least 150 minutes of moderate-\n205intensity aerobic PA per week, at least 75 minutes of high-intensity aerobic PA per week, or a combination of moderate and high-\n206intensity activities; this is equivalent to 10 met hour/week [1]. Our analyses also show that when the PA level is >10 met \n207hours/week, the risk of hospitalization for COVID-19 is reduced, but the degree of reduction becomes significantly smaller. The \n208practical significance of this conclusion is that PA or exercise within 10 met hours/week has the most apparent effect on reducing \n209the risk of hospitalization for COVID-19, with an additional benefit for reducing the risk when PA level exceeds 10 met hours/week. \n210Increased hospitalization due to COVID-19 is a threat to health and a heavy disease burden on all aspects, such as individuals \n211and the country. We found that increased physical activity significantly reduces the hospitalization risk of COVID-19, and physical \n212activity should be a positive factor in decreasing the COVID-19 disease burden. However, the Global Burden of Diseases (GBD) \n2132019 ranked low physical activity 19th out of 20 risk factors in terms of disability-adjusted life years, down from 10th in the \n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 11 / 14\n214equivalent 2010 GBD publication [37,38]. Moreover, PA decreased in all age groups, independent of sex, due to the COVID-19 \n215pandemic, according to a recent meta-analysis [39]. In the face of the global spread of COVID-19, we must regain the vital role of \n216physical activity in reducing the burden of the disease. The conclusions of our study will undoubtedly have significant implications \n217for public health.\n218Finally, our study is the first meta-analysis on the dose–response relationship between PA and the hospitalization risk of COVID-\n21919. The results of this study are based on a large sample cohort study, case–control group study, and the advantages of a long \n220period of follow-up investigation by cohort study; therefore, the results are relatively stable. However, this study may have \n221limitations. First, the literature we included may be insufficient in the continuous dose–response analysis. One possible reason for \n222this is that we set strict inclusion criteria. In addition, cohort studies require long-term follow-up surveys and extensive sample \n223data. Therefore, few cohort studies have been conducted on related topics. Second, the methods of PA evaluation included in the \n224literature of this study are subjective measurements, which may lead to inaccurate doses, and different measurement and \n225observation methods of physical activities and hospitalized cases may have a significant impact on the research conclusion. In \n226addition, exercise habit is based on the assumption that there is no change in the long-term follow-up; that is, the dose of PA is \n227constant in the long-term observational study. This assumption may make the results inaccurate. Moreover, the results of some \n228cohort studies included were not adjusted by the confounding factors such as sex, age, and other concomitant medical conditions, \n229which should be paid attention to in future studies.\n2305. Conclusions\n231There was an inverse non-linear dose–response relationship between PA levels and the risk of COVID-19 hospitalization. An \n232increase in the physical activity dose significantly reduced the hospitalization risk of COVID-19. The degree of risk reduction is \n233weakened when PA is higher than 10 Met-h/week. Doses of the guideline-recommended minimum PA levels by the WTO may be \n234required for more substantial reductions in the COVID-19 hospitalization risk. Future studies with different doses of PA or exercise \n235interventions are needed to determine the optimum PA dose required for COVID-19 prevention.\n236\n237Supporting information\n238S1 Checklist. PRISMA checklist of the meta-analysis.(DOC)\n239S1 Fig. Sensitivity analysis result. (TIF)\n240S2 Fig. Published bias funnel plot. (TIF)\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n 12 / 14\n241Acknowledgements \n242We would like to acknowledge the assistance of Chang Xu in helping us to develop an dose-response model with \n243REMR approach.\n244Author Contributions\n245Conceptualization: Dan Li, Songtao Lu.\n246Data curation: Dan Li, Shenzhen Jin, Songtao Lu.\n247Formal analysis: Shenzhen Jin, Songtao Lu.\n248Methodology: Dan Li, Shenzhen Jin.\n249Software: Dan Li, Shenzhen Jin.\n250Supervision: Shenzhen Jin, Songtao Lu.\n251Visualization: Dan Li, Shenzhen Jin.\n252Writing – original draft: Dan Li, Shenzhen Jin, Songtao Lu.\n253Writing – review & editing: Shenzhen Jin, Songtao Lu.\n254References\n1. World Health Organization, Physical Activity Guidelines. 2021. Available from: \nhttps://www.who.int/emergencies/diseases/novel-coronavirus-2019.\n2. 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PubMed PMID: 35206434; PubMed \nCentral PMCID: PMC8871718.\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint \n\n . CC-BY 4.0 International licenseIt is made available under a \n is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)\nThe copyright holder for this preprintthis version posted June 23, 2022. ; https://doi.org/10.1101/2022.06.22.22276789doi: medRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}