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. 2023 Feb 10;18(2):e0279442. doi: 10.1371/journal.pone.0279442

The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factors

Hadii M Mamudu 1,2,*, David Adzrago 3, Emmanuel O Odame 4, Oluwabunmi Dada 5, Valentine Nriagu 1,2, Trishita Paul 6, Florence W Weierbach 1,7, Karilynn Dowling-McClay 1,8, David W Stewart 1,8, Jessica Adams 9, Timir K Paul 1,10
Editor: Taeyun Kim11
PMCID: PMC9916641  PMID: 36763672

Abstract

Background

There is a dearth of evidence on the relationship between COVID-19 and metabolic conditions among the general U.S. population. We examined the prevalence and association of metabolic conditions with health and sociodemographic factors before and during the COVID-19 pandemic.

Methods

Data were drawn from the 2019 (N = 5,359) and 2020 (N = 3,830) Health Information National Trends Surveys on adults to compare observations before (2019) and during (2020) the COVID-19 pandemic. We conducted weighted descriptive and multivariable logistic regression analyses to assess the study objective.

Results

During the pandemic, compared to pre-pandemic, the prevalence of diabetes (18.10% vs. 17.28%) has increased, while the prevalence of hypertension (36.38% vs. 36.36%) and obesity (34.68% vs. 34.18%) has remained similar. In general, the prevalence of metabolic conditions was higher during the pandemic (56.09%) compared to pre-pandemic (54.96%). Compared to never smokers, former smokers had higher odds of metabolic conditions (AOR = 1.38, 95% CI = 1.01, 1.87 and AOR = 1.57, 95% CI = 1.10, 2.25) before and during the pandemic, respectively. People with mild anxiety/depression symptoms (before: AOR = 1.52, 95% CI = 1.06, 2.19 and during: AOR = 1.55, 95% CI = 1.01, 2.38) had higher odds of metabolic conditions relative to those with no anxiety/depression symptoms.

Conclusion

This study found increased odds of metabolic conditions among certain subgroups of US adults during the pandemic. We recommend further studies and proper allocation of public health resources to address these conditions.

Introduction

The SARS-CoV-2 (Coronavirus diseases; COVID-19) has continued to affect many countries, including the United States, since the World Health Organization (WHO) declared a global pandemic in March 2020 [1, 2]. Before the declaration, metabolic conditions such as obesity, type 2 diabetes mellitus, and cardiovascular disease have continued to be the leading cause of morbidity and mortality in the U.S. and the world [3–5]. In 2018, approximately 13% of all U.S. adults had diabetes, with 2.8% of this population being unaware of their status but meeting laboratory criteria for diabetes [6]. Similarly, a national survey from 2017 to 2018 shows that 42.4% of U.S. adults had obesity [7]. These metabolic conditions are associated with severe health risks [8, 9]. Additionally, they predispose people to the risks of death and adverse health outcomes from COVID-19 [8–13]. However, studies on the effects of COVID-19 on these metabolic conditions are scarce. Indeed, the associations between cardiometabolic conditions in U.S. adults and the COVID-19 pandemic and risk factors such as physical inactivity, tobacco use, anxiety/depression, and sociodemographic characteristics remain understudied.

Not only did patients with diabetes or obesity have increased mortality due to COVID-19 infection [14–20], their overall health was also negatively impacted by the COVID-19 lockdown [17, 19]. Studies demonstrated poor glycemic control and increased body mass index (BMI) for patients with diabetes during the lockdown [20, 21] along with a deterioration in glucose regulation [16, 22]. The timings of lockdown orders in the target populations from these studies differ, as they were based on when the countries declared a lockdown [16, 20–22]. Other studies have shown that newly diagnosed diabetes is more prevalent in patients following COVID-19 infection, with one in every ten COVID-19 patients diagnosed with new onset diabetes mellitus [15, 23]. In addition to metabolic health outcomes secondary to diabetes, some subgroups, specifically those with cardiometabolic disease [24], were at an increased risk of poor mental health outcomes, had depressive symptoms [25], and poor sleep quality [26]. Other negative behavioral activities included reduced physical activity [27, 28] and increased alcohol consumption [26].

While the metabolic conditions such as diabetes and obesity are well documented, there is a paucity of research on the association between conditions such as hypertension and COVID-19 [29]. Given the high prevalence of medical conditions such as hypertension and the potential for negative health outcomes secondary to COVID-19, this study explores the relationships between COVID-19 and metabolic conditions before and during the COVID-19 pandemic.

We utilize a nationally representative sample of U.S. adults to estimate the prevalence of metabolic conditions (diabetes, hypertension, and obesity) before and during the COVID-19 pandemic declaration. Further, this study examines the association between these metabolic conditions among U.S. adults and the ramifications of the COVID-19 pandemic, including physical inactivity, tobacco use, anxiety/depression, and sociodemographic characteristics. Considering the higher prevalence of these metabolic conditions in the U.S. adult population and throughout the world, it is imperative to understand which populations to target for public health interventions to decrease COVID-19 related morbidities for high-risk populations.

Methods

The 2019 and 2020 Health Information National Trends Surveys (HINTSs) de-identified public-use datasets were combined for this study. HINTS is a cross-sectional survey that assesses health-related information (e.g., diabetes, hypertension, and obesity) and behaviors (e.g., tobacco use) among a nationally representative sample of U.S. adults aged ≥18 years. It uses a random sampling technique to select a sample of the U.S. civilian, noninstitutionalized adult population. Details of the methods, questionnaire, and survey administration have been published [30, 31]. The 2019 survey (HINTS 5 Cycle 3) was conducted from January through April 2019, and the 2020 survey (HINTS 5 Cycle 4) was conducted from February through June 2020. These surveys are the recent publicly available HINTS datasets. The combined HINTS 5 Cycles 4 (N = 3,865) and 3 (N = 5,438) datasets consist of a total sample of 9,303 adults. The study was reviewed by the Institutional Review Board (IRB) of East Tennessee State University and exempted as the HINTS datasets are de-identified and publicly available.

Measures and variables

Dependent variable

The dependent variable is "metabolic condition", derived from three distinct questions about diabetes, hypertension, and obesity. For diabetes, the participants were asked, "Has a doctor or other health professional ever told you that you have diabetes or high blood sugar?" (yes/no). Hypertension was assessed with the question, "Has a doctor or other health professional ever told you that you have high blood pressure or hypertension?" (yes/no). Obesity status was determined using body mass index (BMI), and defined as underweight = <18.5, healthy/normal = 18.5–24.9, overweight = 25.0–29.9, and obese ≥ 30.0. Obese was defined as BMI ≥ 30.0 and not obese as BMI < 30 [32, 33]. Thus, the variable "metabolic condition" in this study was ascertained as participants who had diabetes, hypertension, or were obese.

Independent variables

The main independent variable is the HINTS survey year, which was based on the 2019 and 2020 surveys, given that COVID-19 cases were widespread globally by January 2020 [31, 34, 35]. The 2019 HINTS data were used as the pre-COVID-19 pandemic cohort, while the 2020 HINTS data were used as the COVID-19 pandemic cohort for stratified analysis.

Other independent variables analyzed in this study included self-reported sociodemographic characteristics, moderate physical activity intensity, cigarette smoking status, e-cigarette use status, and anxiety/depression symptoms [36–38]. The sociodemographic variables included age (18–25, 26–34, 35–49, 50–64, and 65+), sex (male/female), race/ethnicity (non-Hispanic White, non-Hispanic Black/African American, Hispanic, non-Hispanic Asian, and non-Hispanic others), gender identity (heterosexual/straight or sexual minorities [homosexual, lesbian, gay, or bisexual]), marital status (single/never married, married/living as married, divorced/separated, or widowed), level of education completed (less than high school, high school graduate, some college, and college graduate or higher), and total family income (<$20,000, $20,000 to < $35,000, $35,000 to < $50,000, $50,000 to < $75,000, or  ≥ $75,000). General health status was based on self-ratings of overall health as excellent, very good, good, fair, or poor. Due to limited samples, we dichotomized general health status into excellent/very good/good or fair/poor. The number of days per week of moderate intensity physical activity (none and at least one day per week), cigarette smoking status (never/non-smoker, former smoker, and current daily or some days smoker), and e-cigarette use status (never used, former user, and current daily/some days user) were also included.

The anxiety/depression symptoms variable was constructed from Patient Health Questionnaire-4 (PHQ-4) in the HINTS 5 survey. The PHQ-4 assesses symptoms/signs of anxiety and depression, with total scores from 0–12 (0–2 = normal/negative, 3–5 = mild, 6–8 = moderate, and 9–12 = severe) [39, 40]. Thus, anxiety/depression symptoms were categorized into normal or no anxiety/depression, mild, moderate, and severe.

Statistical analyses

The HINTS sampling weight was applied to the analysis to achieve population estimates and offset nonresponse. We estimated the weighted prevalence of each component of metabolic condition before and during the COVID-19 pandemic. The weighted prevalence and unweighted frequencies of metabolic conditions by the sociodemographic characteristics, moderate-intensity physical activity, cigarette smoking status, e-cigarette use status, and anxiety/depression symptoms were computed to characterize the survey sample before and during the COVID-19 pandemic. Additionally, two logistic regression analyses represented by two models were conducted. While Model 1 assessed the association between the metabolic conditions and independent variables using the data before the pandemic, Model 2 utilized the data during the pandemic. All analyses were weighted using the HINTS sampling weight and replicate weight to offset non-response bias and to achieve nationally representative estimates [30, 31]. The weighted percentages, adjusted odds ratios (AOR), 95% 2-tailed confidence intervals (CI), and statistically significant p-value (< 0.05) have been reported.

Results

The prevalence of each metabolic condition (diabetes, hypertension, and obesity) before and during the COVID-19 pandemic is presented in Fig 1. The results showed that the prevalence of diabetes was higher during the COVID-19 pandemic (18.10%) than before the pandemic (17.28%). However, the prevalence of hypertension (36.36% vs. 36.38%) and obesity (34.68% vs. 34.18%) was similar during and before the pandemic.

Fig 1. The prevalence of diabetes, obesity, and hypertension before (2019) and during (2020) the COVID-19 pandemic.

Fig 1

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Table 1 presents the prevalence of metabolic conditions by independent variables before and during the COVID-19 pandemic. Overall, the prevalence of metabolic conditions (54.96% vs. 56.09%) was higher during the pandemic than before the pandemic. The distribution of the prevalence of metabolic conditions within the sociodemographic groups, moderate intensity physical activity, cigarette smoking status, and e-cigarette use status before and during the COVID-19 pandemic varied. The prevalence of metabolic conditions during the COVID-19 pandemic, compared to before the pandemic increased for individuals aged 35–49 and 50–64 years but decreased for those aged 18–25, 26–34, and ≥65 years. The prevalence also increased for all non-Hispanic racial/ethnic groups but decreased for Hispanic individuals. The prevalence of metabolic condition was higher for individuals who did not engage in moderate-intensity physical activity compared to those who engaged in at least one moderate-intensity physical activity per week. Individuals who were former cigarette smokers or current smokers had an increased prevalence of metabolic conditions. For e-cigarette use groups, the prevalence had increased for those who had never used e-cigarettes and those who currently used e-cigarettes; however, it decreased for former e-cigarette users.

Table 1. Demographic characteristics of U.S. adults before and during the COVID-19 pandemic and association with metabolic conditions (N = 9,189).

Before the COVID-19 pandemic (2019) During the COVID-19 pandemic (2020)
Metabolic conditions Metabolic conditions
Total, n n(%) Total, n n(%)
Characteristics (n = 5,359) 3,287 (54.96) P value (n = 3,830) 2,353 (56.09) P value
Age < .001 < .001
    18–25 187 (11.78) 51 (32.54) 147(13.32) 44 (29.31)
    26–34 493 (12.49) 170 (38.38) 337 (12.97) 118 (37.98)
    35–49 964 (24.61) 444 (47.75) 701 (25.47) 342 (55.97)
    50–64 1,654 (31.11) 1,067 (61.25) 1,137 (27.78) 746 (65.53)
    65+ 1,930 (20.02) 1,470 (76.64) 1,396 (20.45) 1,042 (73.34)
Sex .577 .288
    Female 2,795 (50.77) 1,677 (54.53) 2,038 (50.88) 1,242 (55.07)
    Male 2,091 (49.23) 1,320 (55.95) 1,486 (49.12) 934 (57.75)
Race/ethnicity .002 < .001
    Non-Hispanic White 3,030 (63.49) 1,740 (53.04) 2,125 (63.33) 1,245 (55.55)
    Non-Hispanic Black 669 (11.26) 505 (64.76) 478 (11.145) 369 (70.58)
    Hispanic 724 (16.83) 444 (59.00) 595 (16.96) 343 (49.20)
    Non-Hispanic Asian 223 (5.35) 111 (37.57) 160 (5.23) 81 (44.75)
    Non-Hispanic other 165 (3.07) 100 (48.46) 119 (3.34) 72 (67.55)
Gender identity .610 .658
    Heterosexual 4,747 (95.65) 2,896 (54.96) 3,389 (94.57) 2,083 (56.45)
    Homosexual or lesbian/gay or bisexual 194 (4.35) 113 (51.12) 163 (5.43) 92 (53.50)
Marital status < .001 < .001
    Single/never married 876 (30.52) 468 (45.59) 644 (30.87) 354 (49.31)
    Married/living as married 2,827 (55.69) 1,654 (56.43) 1,972 (54.77) 1,177 (58.39)
    Divorced/separated 936 (8.94) 623 (63.57) 679 (9.75) 445 (58.45)
    Widowed 581 (4.85) 461 (78.85) 410 (4.61) 310 (77.46)
Level of education completed < .001 < .001
    Less than High School 327 (6.88) 239 (65.38) 273 (8.05) 202 (66.33)
    High School graduate 932 (23.36) 675 (63.18) 702 (22.47) 509 (66.69)
    Some college 1,580 (40.26) 1,052 (57.32) 1,075 (39.16) 707 (56.87)
    College graduate or higher 2,394 (29.50) 1,243 (43.09) 1,658 (30.32) 865 (45.42)
Total annual family income < .001 < .001
    <$20,000 883 (18.35) 636 (62.99) 619 (15.09) 455 (67.22)
    $20,000 - $34,999 608 (11.05) 417 (62.90) 450 (11.48) 298 (61.18)
    $35,000 - $49,999 623 (13.48) 412 (59.33) 459 (12.70) 300 (62.05)
    $50,000 - $74,999 845 (17.46) 524 (55.54) 589 (18.20) 351 (54.86)
    ≥$75,000 1,795 (39.66) 915 (46.42) 1,319 (42.54) 695 (48.49)
General health status < .001 < .001
    Excellent/very good/good 4,478 (84.84) 2,555 (50.08) 3,187 (85.89) 1,818 (51.72)
    Fair/poor 853 (15.16) 713 (81.98) 626 (14.11) 525 (82.99)
Moderate physical activity intensity < .001 < .001
    None 1,421 (25.78) 1,065 (68.23) 1,041 (27.12) 773 (69.39)
    At least one day per week 3,861 (74.22) 2,169 (50.26) 2,739 (72.88) 1,545 (51.01)
Anxiety/depression symptoms .001 .229
    None 3,771 (68.35) 2,252 (52.23) 2,669 (68.57) 1,594 (53.65)
    Mild 865 (18.08) 533 (55.43) 629 (17.48) 398 (59.62)
    Moderate 334 (7.40) 225 (57.83) 258 (7.93) 174 (61.98)
    Severe 236 (6.18) 174 (74.62) 173 (6.02) 113 (58.89)
Cigarette smoking status < .001 < .001
    Never 3,261 (64.22) 1,892 (51.74) 2,407 (63.11) 1,403 (50.98)
    Former smoker 1,406 (23.27) 984 (63.86) 931 (23.02) 628 (67.91)
    Current smoker 611 (12.52) 364 (55.43) 436 (13.87) 287 (59.39)
E-cigarette use status < .001 .005
    Never 4,591 (80.77) 2,882 (57.15) 3,297 (80.91) 2,070 (58.40)
    Former user 526 (13.83) 282 (53.61) 381 (12.71) 205 (52.51)
    Current user 173 (5.40) 86 (28.66) 114 (6.39) 55 (37.24)
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Data source: 2019 and 2020 Health Information National Trends Surveys, HINTS 5 Cycles 3 and 4, respectively.

Unweighted N = 9,189 and weighted N = 501,680,570.

Before the COVID-19 pandemic data (HINTS 5 Cycles 3) were collected from January through April 2019, and during the COVID-19 pandemic data were collected from February through June 2020.

Frequencies were not weighted, while percentages were weighted. Differences in total numbers in categories may be due to missing data.

Table 2 shows metabolic conditions and their associated factors before (Model 1) and during (Model 2) the COVID-19 pandemic, respectively. Before the pandemic, compared to age 18–25 years, only two groups of individuals aged 50–64 (AOR = 2.64, 95% CI = 1.20, 5.77) and 65 years or older (AOR = 4.82, 95% CI = 2.25, 10.32) had significantly higher odds of metabolic conditions. During the pandemic, the likelihood of metabolic conditions was significantly higher for four groups: individuals aged 26–34 (AOR = 1.95, 95% CI = 1.04, 3.67), 35–49 (AOR = 4.13, 95% CI = 2.12, 8.04), 50–64 (AOR = 6.19, 95% CI = 3.03, 12.65), and 65 years or older (AOR = 7.82, 95% CI = 3.92, 15.57) compared to age 18–25 years. During the pandemic, males had significantly higher odds of metabolic conditions (AOR = 1.28, 95% CI = 1.01, 1.64) relative to females, whereas the odds were not different before the pandemic. Compared to non-Hispanic White people, the odds were significantly higher for non-Hispanic Black people before (AOR = 2.01, 95% CI = 1.26, 3.22) and during (AOR = 2.09, 95% CI = 1.22, 3.58) the pandemic. Engaging in at least one moderate-intensity physical activity per week was associated with a lower likelihood of metabolic conditions before (AOR = 0.64, 95% CI = 0.46, 0.88) and during (AOR = 0.58, 95% CI = 0.42, 0.79) the pandemic as compared to no physical activity. Having mild (AOR = 1.52, 95% CI = 1.06, 2.19) or severe (AOR = 2.44, 95% CI = 1.27, 4.69) anxiety/depression symptoms, compared to no anxiety/depression symptoms, was associated with higher metabolic conditions before the pandemic. Only mild anxiety/depression symptoms (AOR = 1.55, 95% CI = 1.01, 2.38) were associated with higher metabolic conditions during the pandemic. Compared to people who never smoked cigarettes, former cigarette smokers had significantly higher odds of metabolic conditions before (AOR = 1.38, 95% CI = 1.01, 1.87) and during (AOR = 1.57, 95% CI = 1.10, 2.25) the pandemic, but not current smokers. Before the pandemic, the likelihood of metabolic conditions was significantly lower for current e-cigarette users (AOR = 0.44, 95% CI = 0.23, 0.85) compared to those who had never used e-cigarettes, with no difference observed during the pandemic.

Table 2. The odds of metabolic conditions among U.S. adults before and during the COVID-19 pandemic (N = 9,189).

Before the COVID-19 (Model 1) During the COVID-19 (Model 2)
AOR 95% CI AOR 95% CI
Age
    18–25 Ref. - Ref. -
    26–34 1.04 (0.44, 2.45) 1.95 * (1.04, 3.67)
    35–49 1.46 (0.66, 3.26) 4.13 *** (2.12, 8.04)
    50–64 2.64 * (1.20, 5.77) 6.19 *** (3.03, 12.65)
    65+ 4.82 *** (2.25, 10.32) 7.82 *** (3.92, 15.57)
Sex
    Female Ref. - Ref. -
    Male 1.11 (0.85, 1.44) 1.28 * (1.01, 1.64)
Race/ethnicity
    Non-Hispanic White Ref. - Ref. -
    Non-Hispanic Black 2.01 ** (1.26, 3.22) 2.09 ** (1.22, 3.58)
    Hispanic 1.33 (0.93, 1.89) 0.91 (0.64, 1.28)
    Non-Asian 0.74 (0.44, 1.25 0.72 (0.42, 1.25)
    Non-Hispanic other 1.37 (0.56, 3.35) 1.25 (0.57, 2.72)
Gender identity
    Heterosexual Ref. - Ref. -
    Homosexual/lesbian/gay/bisexual 1.43 (0.67, 3.06) 1.30 (0.58, 2.93)
Marital status
    Single/never married Ref. - Ref.
    Married/living as married 1.68 ** (1.18, 2.39) 0.74 (0.49, 1.13)
    Divorced/separated 1.40 (0.92, 2.14) 0.46 ** (0.27, 0.78)
    Widowed 1.89 (0.97, 3.66) 0.71 (0.36, 1.40)
Level of education completed
    Less than High School Ref. - Ref. -
    High school graduate 1.40 (0.72, 2.71) 1.23 (0.611, 2.49)
    Some college 1.31 (0.67, 2.55) 0.93 (0.45, 1.92)
    College graduate/higher 1.07 (0.54, 2.14) 0.72 (0.35, 1.47)
Total family annual income
    <$20,000 Ref. Ref. -
    $20,000 - $34,999 1.02 (0.61, 1.68) 0.77 (0.38, 1.56)
    $35,000 - $49,999 0.94 (0.54, 1.65) 0.78 (0.42, 1.45)
    $50,000 - $74,999 0.89 (0.52, 1.53) 0.67 (0.39, 1.17)
    ≥$75,000 0.73 (0.45, 1.17) 0.63 (0.37, 1.07)
General health status
    Excellent/very good/good Ref - Ref -
    Fair/poor 2.82 *** (1.99, 3.99) 2.99 *** (1.97, 4.53)
Moderate physical activity intensity
    None Ref Ref
    At least one day per week 0.64 ** (0.46, 0.88) 0.58 *** (0.42, 0.79)
Anxiety/depression symptoms
    None Ref. Ref. -
    Mild 1.52 * (1.06, 2.19) 1.55 * (1.01, 2.38)
    Moderate 1.31 (0.83, 2.08) 1.76 (0.87, 3.57)
    Severe 2.44 ** (1.27, 4.69) 1.53 (0.72, 3.22)
Cigarette smoking status
    Never smoker Ref. Ref. -
    Former smoker 1.38 * (1.01, 1.87) 1.57 ** (1.10, 2.25)
    Current smoker 0.93 (0.59, 1.46) 1.02 (0.54, 1.95)
E-cigarette smoking status
    Never user Ref. Ref. -
    Former user 1.03 (0.63, 2.68) 1.03 (0.60, 1.78)
    Current user 0.44 * (0.23, 0.85) 0.63 (0.30, 1.30)
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Data source: 2019 and 2020 Health Information National Trends Surveys, HINTS 5 Cycles 3 and 4 respectively.

Before the COVID-19 pandemic data (HINTS 5 Cycles 3) were collected from January through April 2019, and during the COVID-19 pandemic data were collected from February through June 2020.

AOR = Adjusted odds ratio. 95% CI = 95% confidence interval. Ref = Reference group.

*p ≤0.05

**p ≤0.01

***p ≤ 0.001.

Discussion

This study assessed the prevalence of metabolic conditions among U.S. adults and the underlying associated factors before and during the COVID-19 pandemic using the HINTS 2019 and 2020 survey data. To the best of our knowledge, this is the first study to use nationally representative U.S. adult data to highlight associations between metabolic outcomes, sociodemographic factors, and the COVID-19 pandemic.

There was an increase in the overall prevalence of metabolic conditions, especially among certain subgroups during the COVID-19 pandemic. This is consistent with a systematic review that assessed the impact of disasters, including pandemics, on metabolic conditions and reported increased incidence and mortality for diabetes and obesity [41]. Our findings indicate that being elderly (aged 50+), non-Hispanic Black person, former smoker, having fair/poor health status, and having mild anxiety significantly increased the likelihood of metabolic conditions pre- and during the pandemic. However, the disparities in these health and sociodemographic factors were greater during the pandemic.

Previous studies have established that the COVID-19 pandemic exacerbated and further unmasked existing disparities in metabolic outcomes [42–46]. For instance, we found that the odds of metabolic outcomes were significantly higher only among the elderly age groups (50–64 and 65+) compared to young adults before the pandemic. However, these increases in odds almost doubled among these age groups during the pandemic. Significantly higher odds were also noted in the middle age groups (26–34 and 35–49), where the odds almost tripled for the 35–49 age group during the pandemic. Consistent with the literature, our results indicated age was the strongest factor associated with an increased likelihood of adverse metabolic conditions during the pandemic [46–48] with higher age range conferring a higher risk for metabolic conditions. The association between age and metabolic conditions during the pandemic and the risks for adverse health conditions from COVID-19 suggests that health interventions targeted at high-risk groups such as the elderly could optimize outcomes, particularly during disasters such as this global pandemic.

Moreover, this study provides additional evidence that individual health behaviors played a critical role in developing metabolic conditions before and during the pandemic. For example, while being a former smoker increased the odds of metabolic conditions, engaging in moderate physical activity decreased the odds. In light of pandemic-related restrictions associated with increased social isolation and psychological distress [49], people are more likely to smoke and engage in sedentary behaviors such as screen time which limits physical activity [50, 51] and increases the risk of metabolic diseases. A recent systematic review assessing screen-based sedentary behavior among adolescents during the COVID-19 pandemic reported a dose-response association between increased levels of screen time and components of metabolic syndrome [28]. Our findings are consistent with the existing literature on smoking as a risk factor for metabolic conditions [52–55] and increased physical activity as a protective factor [56–58]. As such, both smoking cessation and physical activity should be encouraged to reduce the risk of metabolic conditions, especially during this pandemic.

Implications to practice and research

The COVID-19 pandemic has placed extraordinary demand on public health systems and essential services, while individuals with underlying health conditions such as diabetes, hypertension, and cardiovascular diseases are at higher risk of hospitalization and death [8, 59]. Thus, the association between COVID-19 and metabolic conditions alongside the disparities highlighted in this study suggests the need for further research and fair allocation of medical resources to address these conditions during and after the pandemic.

Although this study provides additional evidence to the literature on the effects of the COVID-19 pandemic related to metabolic conditions among the generally representative U.S. population, some limitations should be considered. These limitations include self-reporting bias and potential underestimation of chronic health problems, such as metabolic conditions, that develop over time. Given the cross-sectional data and lack of temporal sequence information on the variables, we could not make causal inferences. Longitudinal follow-up should be continued for future research to further validate this study’s findings. Additionally, public health assessment tools specifically validated for chronic diseases, such as metabolic conditions, that could be used for national observatory datasets would allow researchers to more rapidly evaluate data in real time in future public health crises, such as this recent global pandemic. Standardized validated tools would provide more meaningful assessments and results nationally and internationally. Moreover, there are probable effects of confounders not considered in this study such as sedentary behavior, sleep pattern, eating habits, and employment that might give rise to inaccurate estimates of the true association. Furthermore, the HINTS datasets do not contain responses specific to the COVID-19 pandemic. Additionally, because metabolic conditions and lifestyle behavioral risk factors take time to accumulate and change health conditions, the results of this study might be biased and under-estimated given the durations of the data before (January through April 2019) and during (February through June 2020) the COVID-19 pandemic. Therefore, future studies comparing the rates and prevalence of metabolic conditions before and during the pandemic are needed considering the longer of the data.

Despite these limitations, these data validate that high-risk groups, such as advanced age, should be targeted for interventions to protect against the negative effects of COVID-19. Another gap in the literature that could be addressed with future research is the health consequences of a public lockdown, which was the mitigation strategy for a global pandemic, compared to the consequences of the infectious disease itself. Development of tools designed to measure outcomes secondary to each of these distinctly different effects would benefit future research and resultant health policy. For example, it would be helpful to know if depressive symptoms were a direct effect of the disease, such as suffering from long-COVID, or from the social isolation secondary to the public lockdown. Overall, the HINTS dataset provides an efficient means to evaluate important public health questions in a rapidly evolving situation such as the COVID-19 pandemic.

Conclusion

In this nationally representative sample of U.S. adults, the prevalence of metabolic conditions increased during the COVID-19 pandemic in certain subgroups of individuals. Specifically, there was an increased risk of metabolic outcomes associated with older age. Other groups with signals for increased risk included: non-Hispanic Black people, former smokers, individuals with poor health status, and mild anxiety. Thus, there is a need for proper rationing of resources to address these conditions during the pandemic.

Data Availability

The data is publicly available at HINTS (https://hints.cancer.gov/).

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Taeyun Kim

15 Sep 2022

PONE-D-22-21818The prevalence of cardiometabolic conditions before and during COVID-19 and its association with health and sociodemographic factorsPLOS ONE

Dear Dr. Mamudu,

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Reviewer #1: This paper evaluates the population prevalence of diabetes, obesity and hypertension in the US during 2019 and 2020. The paper concludes that the prevalence of these conditions increased during the Covid-19 pandemic.

1. The title seems to imply that observations were made on Covid-19 status of individuals, which is not the case. Please adjust the wording of the title to make this clear.

2. In the Abstract, make clear that the study compares 2019 and 2020 and no observations were made on Covid-19 as such. We know that diabetes has been increasing over many years, so this paper does not demonstrate any deviation from the underlying trend, consquently the conclusions that can be darwn are quite limited.

3. Introduction, lines 68-69, there needs to be more discussion of references 16 and 21 - what do these studies show? Also, how widespread and what were the tiomings of lockdown orders in the target population for this study.

4. The outcome comprises hypertension, obesity and diabetes. No cardiac conditions are included, so this might be equated with the 'metabolic syndrome'.

5. In the analysis, explain what weights were employed.

6. Using a cut-off of P <0.05 is an out of date approach to interpretation. Please follow the ASA guidelines on P values. https://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108

7. Figure 1 may be better as a Table. If included it will be preferred to show the results for 2019 and 2020 side by side so these can be more readily compared.

8. Table 1. More appropriate column headers may be '2019' and '2020'.

9. Table 2: if the intention is to see whether associations differed in 2019 and 2020, it would be better to include all the data in a one model and test for the interaction of each variable with study year. Most of the associations appear to be quite similar across years.

10. In the Discussion, what can be concluded is very limited because the study has not evaluated secular trends over time. The difference between 2019 and 2020 could be accounted for by the underlying trend.

11. In the Limitations section, mention that no caulsa inferences can be drawn.

12. In the conclusion, where it says 'Disparities in cardiometabolic conditions became more evident after the pandemic', there does not appear to be sufficient evidence from the analysis to support this conclusion.

13. Where it says ' the prevalence of cardiometabolic conditions increased during the COVID-19 pandemic', only diabetes increased not the other conditions, and we do not know whether the increase exceeded pre pandemic expectations. Please address this text in teh Abstract also.

Reviewer #2: Review of Manuscript Number: PONE-D-22-21818

Introduction:

The introduction is lacking the part linking the risk factors especially mental health status during pandemic to cardiometabolic diseases and how COVID-19 pandemic has led to an increased adoption of such behavioral risk factors e.g., physical inactivity and smoking.

� Line 62-64

COVID-19 may have an exacerbating effect on glycemic control for patients with diabetes [14, 20], and there may be risk of increased body mass index (BMI) as well as a deterioration in glucose regulation due to COVID-19 [16, 21].

Reviewer comments:

Will you clarify if COVID-19 infection or the implications of COVID-19 related lockdown have led to exacerbation of glycemic control and increased BMI?

Methods:

� Line 90-91

Briefly, the 2019 survey (HINTS 5 Cycle 3) was conducted from January through April 2019, and the 2020 survey (HINTS 5 Cycle 4) was conducted from February through June 2020.

Reviewer comments:

COVID-19 was declared pandemic on March 11th, 2020, and lockdown has followed in most of the world regions. Therefore, if you are aiming to compare the rates and prevalence of cardiometabolic diseases, that are mainly linked to lifestyle behavioral risk factors taking time to accumulate and changing health conditions, before and during COVID-19, the results of this study might be biased and under-estimated.

� Line 124-125

The number of days per week of moderate intensity physical activity (none and at least one day per week)

Reviewer comments:

Why have you chosen to ask none or at least one day per week? Physical activity significance should hit the international recommendations of 150 mins/week. Therefore, performing one time/week or so will not add significant information, hence correlation. If you are not using a valid tool to measure your variables, you will be subjected to bias. It is better to question about the days and minuets and calculate the mean.

Results

Line 147-151

prevalence of diabetes was higher during the COVID-19 pandemic (18.10%) than before the 150 pandemic (17.28%). However, the prevalence of hypertension (36.36% vs. 36.38%) and obesity 151 (34.68% vs. 34.18%) was similar during and before the pandemic.

Reviewer comments:

Again, the slight variations in the prevalence of cardiometabolic diseases between before and during the pandemic is most likely due to early trials on lifestyle related behavior that work in cumulative effects manner (developing over longer period) developing chronic diseases.

� Line 163-166

Individuals who were former cigarette smokers or current smokers had increased prevalence of cardiometabolic conditions. For e cigarette use groups, the prevalence had increased for those who had never used e-cigarettes and those who currently used e-cigarettes however decreased for former e-cigarette users.

Reviewer comments:

The prevalence was increased for those who never smoked e-cigarettes and those who currently use e cigarette, is bringing so much confusion for the reader and later to decision makers. You need to revisit your analysis or at least explain your odd findings in the discussion section comparing to previous research.

Discussion

� Line 200-202

Reviewer comments:

Discussion needs to be improved.

1. Correlation of age and risk of cardiometabolic conditions is not well highlighted. First, your study revealed that age group from 26- ≥65 year are at increased risk when compared to 18-25 years. I can notice that the risk is doubled for 26-34 year and tripled for 35-49 year. Therefore, in your recommendations, you need to focus not only on older age group but middle-aged as well.

2. You need to enrich discussion further to add new implications of your results. Since the findings are not adding additional knowledge to the literature, you need to discuss your odd findings and give explanations. You may discuss how to improve future research in the same area e.g., by adopting validated tools to measure variables, by using more reliable source of data like registry or objective measures as compared to self-reported. Suggestions in how to improve the internal validity of the results are important and show your understanding of pitfalls. Therefore, add a section of “implications to practice & research’.

Limitations:

Kindly add the limitations discussed above and the probable effect of confounders not considered in this study such as sedentary behavior, sleep, eating patterns, employment and etc., that might give rise to inaccurate estimates of the true association.

Overall, the manuscript needs improvement in English writing, linking ideas, relating results to previous findings, and most importantly providing explanation of each finding that disagree with the existing knowledge or literature.

Since the study design is cross-sectional, the main finding we are looking for is the prevalence of cardiometabolic conditions before and compare it to during the pandemic. However, your findings are not impressive and not reflecting the actual impact of COVID-19 on NCDs burden due to methodology reasons highlighted above. For this, you have to enrich your paper with additional values such as using the analysis of predictors and justifications of such findings.

Add the following article in your referencing (linking COVID-19 to behavioral risk factors which increase the risk of cardiometabolic conditions)

COVID-19 and screen-based sedentary behaviour: Systematic review of digital screen time and metabolic syndrome in adolescents | PLOS ONE

Available in PubMed also: COVID-19 and screen-based sedentary behaviour: Systematic review of digital screen time and metabolic syndrome in adolescents - PubMed (nih.gov)

**********

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Reviewer #1: Yes: Martin Gulliford

Reviewer #2: Yes: Sarah Rashid Musa

**********

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PLoS One. 2023 Feb 10;18(2):e0279442. doi: 10.1371/journal.pone.0279442.r002

Author response to Decision Letter 0

16 Nov 2022

Response to Review of Manuscript Number: PONE-D-22-21818

Reviewer #1: This paper evaluates the population prevalence of diabetes, obesity and hypertension in the US during 2019 and 2020. The paper concludes that the prevalence of these conditions increased during the Covid-19 pandemic.

Comment

1. The title seems to imply that observations were made on Covid-19 status of individuals, which is not the case. Please adjust the wording of the title to make this clear.

Response

We appreciate your comment. Our title implies the observations made before and during the COVID-19 pandemic:

“The prevalence of cardiometabolic conditions before and during COVID-19 and its association with health and sociodemographic factors.”

We have revised the title by adding “pandemic” to further clarify it as:

“The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factors.”

Comment

2. In the Abstract, make clear that the study compares 2019 and 2020 and no observations were made on Covid-19 as such. We know that diabetes has been increasing over many years, so this paper does not demonstrate any deviation from the underlying trend, consequently the conclusions that can be drawn are quite limited.

Response

We appreciate your comment. We have stated our study’s aim as:

“We examined the prevalence and association of cardiometabolic conditions with health and sociodemographic factors before and during the COVID-19 pandemic.”

We have revised the methods to further incorporate your suggestion as:

“Data were drawn from the 2019 (N= 5,359) and 2020 (N= 3,830) Health Information National Trends Surveys on adults to compare observations before (2019) and during (2020) the COVID-19 pandemic.”

Comment

3. Introduction, lines 68-69, there needs to be more discussion of references 16 and 21 - what do these studies show? Also, how widespread and what were the timings of lockdown orders in the target population for this study.

Response

Thank you for this observation. We have revised the entire paragraph to incorporate your suggestion. For the lockdown timings, it varies for all studies in that paragraph and was summarized to prevent making that paragraph too long. Below is the explanation of the lockdown timings in the study.

The timing of lockdown orders in reference 16 compared was before the lockdown and the 6th month of lockdown. The study population were a research cohort in the Istanbul Research and Education Hospital, Turkey, from March 2019 to October 2020.

In reference 20, the study population included all patients with diabetes mellitus who visited the Tohoku Medical and Pharmaceutical University Hospital in Sendai, from January 1, 2019, to August 31, 2020. Japan declared a state of emergency on April 7, 2020, so we presume this is the date their lockdown order started, although they think a state of emergency differs from lockdown orders established by other nationals.

In reference 21, the study population included outpatients at the Diabetology Unit of Humanitas Clinical and Research Center, IRCCS in Italy at baseline, between December 15, 2019, and March 1, 2020, and at the resumption of clinical activities, between May 15 and June 30, 2020.

In reference 22, the study population included Type 2 diabetes mellitus patients unable to attend clinic follow-up visits due to the lockdown order in Turkey between March 16 and June 1, 2020, but attended follow-ups in July and August 2020 after the restriction had been lifted.

Comment

4. The outcome comprises hypertension, obesity and diabetes. No cardiac conditions are included, so this might be equated with the 'metabolic syndrome'.

Response

We agree with the reviewer; therefore, we have changed all “cardiometabolic” to “metabolic,” from the title to the conclusion. This is because we do not have data specifically on waist circumference, triglycerides, and HDL, therefore, using the term “metabolic syndrome” is not appropriate. As such, we will use the term “metabolic” instead of “cardiometabolic” as the reviewer suggested.

Comment

5. In the analysis, explain what weights were employed.

Response

Thank you. We have included weight information as:

“The HINTS sampling weight was applied to the analysis to achieve population estimates and offset nonresponse.” (see the first sentence in the statistical analysis section)

Comment

6. Using a cut-off of P <0.05 is an out of date approach to interpretation. Please follow the ASA guidelines on P values. https://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108

Response

We appreciate your suggestion. We would like the reviewer to know that our decision on whether a result is statistically significant or not was informed by the p-value and the Confidence Interval (CI). As such, our decision-making approach is consistent with the recommendation by the American Statistical Association [https://www.tandfonline.com/doi/full/10.1080/00031305.2016.1154108]. Further, as recommended by Leo and Sardanelli [https://eurradiolexp.springeropen.com/articles/10.1186/s41747-020-0145-y], we have shown the actual p-value so that the reader could determine the extent of the association. Thus, our reporting of the results addresses the statistical issues raised by the reviewer.

Comment

7. Figure 1 may be better as a Table. If included it will be preferred to show the results for 2019 and 2020 side by side so these can be more readily compared.

Response

Thank you for the suggestion. We believe that a figure could better depict the patterns of the outcomes than a table and, as such, we would like to retain the figure. However, if the reviewer still feels that we should replace the figure with the table, we would be pleased to do.

Comment

8. Table 1. More appropriate column headers may be '2019' and '2020'.

Response

Thank you. We have included the 2019 and 2020 in the column headers.

Comment

9. Table 2: if the intention is to see whether associations differed in 2019 and 2020, it would be better to include all the data in a one model and test for the interaction of each variable with study year. Most of the associations appear to be quite similar across years.

Response

We appreciate your comment. Given the differences in the population in 2019 and 2020 based on the outcomes, we could not include all the data in one model. Respectfully, we would like to maintain the table in its current form to avoid any statistical falsification.

Comment

10. In the Discussion, what can be concluded is very limited because the study has not evaluated secular trends over time. The difference between 2019 and 2020 could be accounted for by the underlying trend.

Response

We agree with you. As such, we have toned down on our interpretations and reasoning by not making causal inferences or making inferences beyond our results.

Comment

11. In the Limitations section, mention that no causal inferences can be drawn.

Response

Thank you. We have included your suggestion in the limitation section.

Comment

12. In the conclusion, where it says 'Disparities in cardiometabolic conditions became more evident after the pandemic', there does not appear to be sufficient evidence from the analysis to support this conclusion.

Response

Thank you for pointing this out. We have revised the statement as:

“The prevalence of metabolic conditions increased during the COVID-19 pandemic in certain subgroups of individuals. Specifically, there was an increased risk of metabolic conditions associated with older age. Other groups with signals for increased risks include non -Hispanic Black people, former smokers, individuals with poor health status, and mild anxiety.”

Comment

13. Where it says ' the prevalence of cardiometabolic conditions increased during the COVID-19 pandemic', only diabetes increased not the other conditions, and we do not know whether the increase exceeded pre pandemic expectations. Please address this text in the Abstract also.

Response

We have revised this in the abstract also as suggested:

“This study found increased odds of metabolic conditions among certain subgroups of U.S. adults during the pandemic”

Reviewer #2:

Introduction:

The introduction is lacking the part linking the risk factors especially mental health status during pandemic to cardiometabolic diseases and how COVID-19 pandemic has led to an increased adoption of such behavioral risk factors e.g., physical inactivity and smoking.

� Line 62-64

COVID-19 may have an exacerbating effect on glycemic control for patients with diabetes [14, 20], and there may be risk of increased body mass index (BMI) as well as a deterioration in glucose regulation due to COVID-19 [16, 21].

Response:

Thank you for your comment. This comment has been incorporated into the introduction section of the revised manuscript.

Reviewer comments:

Will you clarify if COVID-19 infection or the implications of COVID-19 related lockdown have led to exacerbation of glycemic control and increased BMI?

Response:

Thank you for your comment. This statement has been clarified in the introduction of the revised manuscript.

Methods:

� Line 90-91

Briefly, the 2019 survey (HINTS 5 Cycle 3) was conducted from January through April 2019, and the 2020 survey (HINTS 5 Cycle 4) was conducted from February through June 2020.

Reviewer comments:

COVID-19 was declared pandemic on March 11th, 2020, and lockdown has followed in most of the world regions. Therefore, if you are aiming to compare the rates and prevalence of cardiometabolic diseases, which are mainly linked to lifestyle behavioral risk factors taking time to accumulate and changing health conditions, before and during COVID-19, the results of this study might be biased and under-estimated.

Response:

Thank you for pointing out this information. We agree with the reviewer and have added the recommendation as a limitation of the study (see the limitation section).

� Line 124-125

The number of days per week of moderate intensity physical activity (none and at least one day per week)

Reviewer comments:

Why have you chosen to ask none or at least one day per week? Physical activity significance should hit the international recommendations of 150 mins/week. Therefore, performing one time/week or so will not add significant information, hence correlation. If you are not using a valid tool to measure your variables, you will be subjected to bias. It is better to question about the days and minutes and calculate the mean.

Response

We appreciate your comments. This variable is a standardized measure in HINTS and based on the number of days per week of moderate intensity physical activity. The “150 mins/week” is used to derive the “moderate physical activity” either per day or week, hence the choice for this study.

Results

Line 147-151

prevalence of diabetes was higher during the COVID-19 pandemic (18.10%) than before the 150 pandemic (17.28%). However, the prevalence of hypertension (36.36% vs. 36.38%) and obesity 151 (34.68% vs. 34.18%) was similar during and before the pandemic.

Reviewer comments:

Again, the slight variations in the prevalence of cardiometabolic diseases between before and during the pandemic is most likely due to early trials on lifestyle related behavior that work in cumulative effects manner (developing over longer period) developing chronic diseases.

Response

Thank you for noting this. This suggestion has been included in the limitations of the revised manuscript (see the limitation section).

� Line 163-166

Individuals who were former cigarette smokers or current smokers had increased prevalence of cardiometabolic conditions. For e-cigarette use groups, the prevalence had increased for those who had never used e-cigarettes and those who currently used e-cigarettes however decreased for former e-cigarette users. The prevalence was increased for those who never smoked e-cigarettes and those who currently use e-cigarette, is bringing so much confusion for the reader and later to decision makers. You need to revisit your analysis or at least explain your odd findings in the discussion section comparing to previous research.

Response

Thank you! We revisited the analysis and found the same results. This finding is consistent with the tobacco literature: former tobacco users, including former e-cigarette users, are less likely to engage in unhealthy behaviors; therefore, they are less likely to develop health conditions such as cardiometabolic conditions (https://nida.nih.gov/publications/research-reports/tobacco-nicotine-e-cigarettes/what-are-physical-health-consequences-tobacco-use; https://www.health.ny.gov/prevention/tobacco_control/; Lowe et al., 2009; U.S. Department of Health and Human Services, 2014). However, the pandemic has altered many lifestyles, including tobacco use behaviors that might have also affected their risks for cardiometabolic conditions.

Citations:

Lowe, F. J., Gregg, E. O., & McEwan, M. (2009). Evaluation of biomarkers of exposure and potential harm in smokers, former smokers, and never-smokers. Clinical chemistry and laboratory medicine, 47(3), 311-320.

U.S. Department of Health and Human Services (2014). The Health Consequences of Smoking—50 Years of Progress: A Report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health. https://www.hhs.gov/sites/default/files/consequences-smoking-exec-summary.pdf

Discussion

� Line 200-202

Reviewer comments:

Discussion needs to be improved.

1. Correlation of age and risk of cardiometabolic conditions is not well highlighted. First, your study revealed that age group from 26- ≥65 year are at increased risk when compared to 18-25 years. I can notice that the risk is doubled for 26-34 year and tripled for 35-49 year. Therefore, in your recommendations, you need to focus not only on older age group but middle-aged as well.

Response

Thank you! We have included discussions on these middle-aged groups as suggested in the revised manuscript. For instance, we found that the odds of cardiometabolic outcomes were significantly higher only among the elderly age groups (50-64, and 65+) compared to young adults before the pandemic. However, these increases in odds almost doubled among these age groups during the pandemic. Significantly higher odds were also noted in the middle age groups (26-34 and 35-49), where the odds almost tripled for the 35-49 age group during the pandemic.

Comment:

2. You need to enrich discussion further to add new implications of your results. Since the findings are not adding additional knowledge to the literature, you need to discuss your odd findings and give explanations. You may discuss how to improve future research in the same area e.g., by adopting validated tools to measure variables, by using more reliable source of data like registry or objective measures as compared to self-reported. Suggestions in how to improve the internal validity of the results are important and show your understanding of pitfalls. Therefore, add a section of “implications to practice & research.”

Response

Thank you for this suggestion. We have added a new section of “Implications to Practice and Research,” including limitations of the study, in the discussion section.

Regarding the issue of validity and reliability, as in most national surveys, the measures in HINTS have been validated and widely used since 2002/2003. We have, however, noted the limitation of using self-reported measures and recommended using objective measures in future studies.

Regarding the internal validity of the results, we followed the analytical recommendations of HINTS in computing accurate estimates. As such, we believe our estimates are accurate and valid.

Limitations:

Comment

Kindly add the limitations discussed above and the probable effect of confounders not considered in this study such as sedentary behavior, sleep, eating patterns, employment and etc., that might give rise to inaccurate estimates of the true association.

Response

Thank you. We have incorporated your suggestions in the revised manuscript (see the limitation section).

Comment

Overall, the manuscript needs improvement in English writing, linking ideas, relating results to previous findings, and most importantly providing explanation of each finding that disagree with the existing knowledge or literature.

Response

We have thoroughly reviewed the entire paper by doing line-by-line editing. Additionally, we asked our colleague not familiar with the study to review the paper for language.

Comment

Since the study design is cross-sectional, the main finding we are looking for is the prevalence of cardiometabolic conditions before and compare it to during the pandemic. However, your findings are not impressive and not reflecting the actual impact of COVID-19 on NCDs burden due to methodology reasons highlighted above. For this, you have to enrich your paper with additional values such as using the analysis of predictors and justifications of such findings.

Response

We agree with the reviewer that the cross-sectional data is a limitation of this study. As such, we have included this limitation in the limitations section of the revised manuscript. In future studies, we will consider longitudinal data for the analysis of predictors and justifications of such findings. Additionally, we have enhanced the discussion by relating our study to the extant literature and illuminating its added value to the growing literature on COVID-19.

Comment

Add the following article in your referencing (linking COVID-19 to behavioral risk factors which increase the risk of cardiometabolic conditions)

COVID-19 and screen-based sedentary behaviour: Systematic review of digital screen time and metabolic syndrome in adolescents | PLOS ONE

Available in PubMed also: COVID-19 and screen-based sedentary behaviour: Systematic review of digital screen time and metabolic syndrome in adolescents - PubMed (nih.gov)

Response

The article has been incorporated in the revised manuscript. Specifically, we have incorporated the following in the discussion section:

“Sedentary behaviors such as screen time which limits physical activity [50, 51], increases the risk of cardiometabolic diseases. A recent systematic review assessing screen-based sedentary behavior among adolescents during the COVID-19 pandemic reported a dose-response association between increased level of screen time and components of metabolic syndrome [Musa et al]”

Attachment

Submitted filename: PONE-D-22-21818-Response-to-reviews-11.15.2022.docx

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Decision Letter 1

Taeyun Kim

28 Nov 2022

PONE-D-22-21818R1The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factorsPLOS ONE

Dear Dr. Mamudu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Taeyun Kim

Academic Editor

PLOS ONE

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

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The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1. In the Abstract, suggest changing 'rationing' to 'allocation'.

2. In the Abstract, where it refers to ' older adults, non-Hispanic Black people... individuals with poor health status', no data are presented to support this statement in the Abstract. Eitehr include supporting evidence in the Abstract or omit.

Reviewer #2: Thank you for addressing every point and incorporating them into your revised manuscript.

The paper sounds much better indeed with feedback of reviewer 1 as well.

**********

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Reviewer #1: No

Reviewer #2: Yes: Dr. Sarah Musa

**********

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PLoS One. 2023 Feb 10;18(2):e0279442. doi: 10.1371/journal.pone.0279442.r004

Author response to Decision Letter 1

30 Nov 2022

Reviewer #1:

Comment

1. In the Abstract, suggest changing 'rationing' to 'allocation'.

Response

We have changed it as suggested.

Comment

2. In the Abstract, where it refers to ' older adults, non-Hispanic Black people... individuals with poor health status', no data are presented to support this statement in the Abstract. Either include supporting evidence in the Abstract or omit.

Response

We have deleted the emphasis as suggested.

Reviewer #2:

Comment

Thank you for addressing every point and incorporating them into your revised manuscript.

The paper sounds much better indeed with feedback of reviewer 1 as well.

Response

Thank you for helping to improve the paper substantively and stylistically.

Attachment

Submitted filename: PONE-D-22-21818R2_R2R.docx

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Decision Letter 2

Taeyun Kim

7 Dec 2022

The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factors

PONE-D-22-21818R2

Dear Dr. Mamudu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Taeyun Kim

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Taeyun Kim

2 Feb 2023

PONE-D-22-21818R2

The prevalence of metabolic conditions before and during the COVID-19 pandemic and its association with health and sociodemographic factors

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: PLOS ONE-Reviewer comments.docx

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    Submitted filename: PONE-D-22-21818-Response-to-reviews-11.15.2022.docx

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    Attachment

    Submitted filename: PONE-D-22-21818R2_R2R.docx

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    Data Availability Statement

    The data is publicly available at HINTS (https://hints.cancer.gov/).

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