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. 2023 Sep 19;18(9):e0291667. doi: 10.1371/journal.pone.0291667

Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic

Madison Hooper 1, Morgan Reinhart 2, Stacie B Dusetzina 3, Colin Walsh 4,5,6, Kevin N Griffith 3,7,*
Editor: Yohannes Kebede8
PMCID: PMC10508610  PMID: 37725598

Abstract

Importance

The COVID-19 pandemic represents a unique stressor in Americans’ daily lives and access to health services. However, it remains unclear how the pandemic impacted perceived health status and engagement in health-related behaviors.

Objective

To assess changes in self-reported health outcomes during the COVID-19 pandemic, and to explore trends in health-related behaviors that may underlie the observed health changes.

Design

Interrupted time series stratified by age, gender, race/ethnicity, educational attainment, household income, and employment status.

Setting

United States.

Participants

All adult respondents to the 2016–2020 Behavioral Risk Factor Surveillance System (N = 2,146,384).

Exposure

Survey completion following the U.S. public health emergency declaration (March-December 2020). January 2019 to February 2020 served as our reference period.

Main outcomes and measures

Self-reported health outcomes included the number of days per month that respondents spent in poor mental health, physical health, or when poor health prevented their usual activities of daily living. Self-reported health behaviors included the number of hours slept per day, number of days in the past month where alcohol was consumed, participation in any exercise, and current smoking status.

Results

The national rate of days spent in poor physical health decreased overall (-1.00 days, 95% CI: -1.10 to -0.90) and for all analyzed subgroups. The rate of poor mental health days or days when poor health prevented usual activities did not change overall but exhibited substantial heterogeneity by subgroup. We also observed overall increases in mean sleep hours per day (+0.09, 95% CI 0.05 to 0.13), the percentage of adults who report any exercise activity (+3.28%, 95% CI 2.48 to 4.09), increased alcohol consumption days (0.27, 95% CI 0.18 to 0.37), and decreased smoking prevalence (-1.11%, 95% CI -1.39 to -0.83).

Conclusions and relevance

The COVID-19 pandemic had deleterious but heterogeneous effects on mental health, days when poor health prevented usual activities, and alcohol consumption. In contrast, the pandemic’s onset was associated with improvements in physical health, mean hours of sleep per day, exercise participation, and smoking status. These findings highlight the need for targeted outreach and interventions to improve mental health in individuals who may be disproportionately affected by the pandemic.

Introduction

The novel coronavirus (COVID-19) began to spread across the world in December 2019 and on March 13, 2020, the United States declared a national public health emergency [1,2]. Efforts to mitigate the spread of the virus included social distancing, stay-at-home orders, closure of nonessential business, travel restrictions, and quarantines [3]. Additionally, the Centers for Disease Control (CDC) and Centers for Medicare and Medicaid Services advised hospitals across the United States to prioritize urgent visits and delay elective or noncritical medical services to diminish the spread of COVID-19 in health care settings [4,5]. According to a Kaiser Family Foundation poll conducted in May 2020, 48% of U.S. adults reported that they or someone in their household skipped or postponed medical care because of the virus, and 11% of them reported that their health deteriorated as a result [6,7].

Anecdotal evidence for the pandemic’s deleterious impacts on mental health have been covered widely in national media, and survey research suggests changes in mental health both overall and for distinct population subgroups [8–11]. For instance, 40% of U.S. adults reported that stress related to COVID-19 has negatively impacted their mental health [6,7]. However, these findings relied on the Census Bureau’s Household Pulse Survey which had very low response rates nearing 7%. Young adults, college students, low-income households, and Hispanics were also more likely to report psychological distress during the pandemic compared to other population subgroups [8,11].

Prior work on the pandemic’s health impacts generally focused on the acute or long-run health effects of COVID-19 infection. To our knowledge, there has been no prior work documenting population-level health effects during the pandemic. Certain features of the pandemic may mitigate against negative health impacts due to deferred/avoided care. Prior work suggests that individuals might adopt healthier lifestyles during periods of unemployment or reduced economic activity [12]. Additionally, the pandemic triggered a recession that may lead to concomitant reductions in job-related stress and hazardous working conditions for some adults [12–14]. Thus, the net effect of the pandemic on population-level physical and mental health is unclear. We addressed these gaps and assessed overall changes in population-level mental and physical health during the COVID-19 pandemic, described changes in the mental and physical health of key population subgroups, and explored related trends in engagement in health-related behaviors (sleep and exercise) that may underlie the observed changes in health outcomes.

Methods

Study sample

Data for this study were obtained from the 2016–2020 Behavioral Risk Factor Surveillance System (BRFSS). The BRFSS is an annual telephone survey of approximately 450,000 households that is conducted by the states in partnership with the CDC. Respondents provide information on their health behaviors, physical and mental health conditions, use of health services, and demographic characteristics. The BRFSS includes weights to account for differential rates of survey non-response across varying population subgroups, thereby ensuring the weighted sample is representative of the U.S. adult noninstitutionalized population.

Study variables

We examined seven self-reported outcomes for individual’s health status and health behaviors. These included the number of days during the past month (range 0–30) that the respondent spent in poor mental health, poor physical health, or when poor health prevented their usual daily activities. We also examined the number of hours slept per day (range 0–24), number of days where alcohol was consumed during the past month (range 0–30), participation in any exercise within the past month (coded as 0 if no, 1 if yes), and smoking status (coded as 0 if non-smoker, 1 otherwise). The exact text and response format of each survey question are listed in Table 1.

Table 1. List of study outcomes.

Outcome Name Survey Question Response Format
Mental Health (N = 2,108,270) “For how many days during the past 30 days was your mental health not good?” 0–30
Physical Health
(N = 2,099,610)		 “For how many days during the past 30 days was your physical health not good?” 0–30
Poor Health
(N = 2,119,907)		 “During the past 30 days, for about how many days did poor physical or mental health keep you from doing your usual activities, such as self-care, work, or recreation?” 0–30
Sleep Hours1 (N = 1,326,579) "On average, how many hours of sleep do you get in a 24-hour period?" 0–24
Exercise Participation
(N = 2,088,459)		 "During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?" Yes/No
Alcohol Consumption (N = 2,026,525) “During the past 30 days, how many days per week or per month did you have at least one drink of any alcoholic
beverage such as beer, wine, a malt beverage or liquor?”		 Week: 0–72
Month: 0–30
Smoking Status (N = 2,052,550) “Do you now smoke cigarettes every day, some days, or not at all? Every day, Some days, Not at all3
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1This question is not asked in most states during odd years and was excluded from the 2019 BRFSS

2Converted to monthly for consistency

3Binary coded where 1 = every day or some days, 0 = not at all.

Self-reported covariates included binary indicators for respondent sex, primary race (categorized in the BRFSS as White non-Hispanic, Black non-Hispanic, Other race non-Hispanic, Multiracial non-Hispanic, Hispanic), household income category (less than $15,000, $15,000 to $25,000, $25,000 to $35,000, $35,000 to $50,000, $50,000 to $75,000, and over $75,000), age category (18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64, and 65-plus), veteran status, educational attainment (less than college graduate, current college student, college graduate), whether the respondent is currently married, pregnancy status, presence of children in the household, whether the respondent owns their home, and employment status (categorized as employed, unemployed, or not in the labor force). We also included dummy variables for survey language (English or non-English), whether the survey was conducted via cellphone or land line, and state of residence. We excluded potential covariates that may change due to the pandemic’s onset (e.g., insurance coverage, household income), as these may bias our effect estimates.

Analytic approach

Our analysis proceeded in three steps. First, we estimated unadjusted means for each calendar month to illuminate trends in outcomes throughout the study period. Second, we estimated covariate-adjusted interrupted time series regression models to identify overall changes in self-reported health before and after the pandemic’s onset. We included dummy variables for whether each survey response was collected in 2016, 2017, 2018. Our key variable of interest was a binary indicator taking on a value of one if the response was collected after the US public health emergency declaration (March 2020 to December 2020), 0 otherwise. Thus, January 2019 to February 2020 served as our reference period. Due to the large sample size and our interest in population average effects, we opted to conduct linear probability models using ordinary least squares to aid in interpretability of results [15].

Third, we re-estimated our regression models stratified into one of 26 different population subgroups based on sex, income, race/ethnicity, age, educational attainment, employment status, or student status. We employed heteroskedasticity-robust standard errors in all regression models. Errors were also clustered by state to account for pandemic-related policies that may affect their residents. All data preparation and analyses were conducted in Microsoft R Open version 4.0.2. Our analytic dataset and R scripts are available within an open access Mendeley Data Repository, accessible at https://data.mendeley.com/datasets/nfvpcd9wpf. This project was considered exempt by the Vanderbilt University Medical Center Institutional Review Board. Informed consent was waived because the study involved only secondary analyses of existing data.

Results

Our final sample included 2,146,384 unweighted survey respondents. After weighting, our sample demographics were reflective of the U.S. general population (Table 2). Covariate-level missingness ranged from 0–20%; we used multiple imputation using additive regression, bootstrapping, and predictive mean matching to reduce the potential for non-response bias [16,17]. Respondents with missing data for individual outcomes were excluded for those specific analyses. Baseline rates for all outcomes during our pre-pandemic reference period (January 2019 to February 2020) are contained in Table 3.

Table 2. Characteristics of the Study Sample (N = 2,146,384).

Variable N Weighted %1
Sex    
Female 1,185,025 51.3
  Male 961,359 48.7
Income Group    
  Less than $15,000 206,912 10.8
  $15,000 to $25,000 353,727 16.9
  $25,000 to $35,000 227,076 10.3
  $35,000 to $50,000 301,525 13.2
  $50,000 to $75,000 340,247 14.8
  $75,000 + 716,897 34.1
Race    
  White 1,665,833 63.5
  Black 172,350 11.8
  Hispanic 159,701 16.2
  Other race 105,897 7.2
  Multiracial 42,603 1.4
Age    
  18 to 24 127,310 12.4
  25 to 34 227,259 17.5
  35 to 44 254,871 16.2
  45 to 54 318,698 15.8
  55 to 64 448,482 16.8
  65+ 769,764 21.3
Education Group    
  College grad 809,004 72.0
  Not a college grad 1,337,380 28.0
Employment Group    
  Unemployed 92,807 5.7
  Employed 1,080,504 57.4
  Not in labor force 973,073 36.9
Student Status    
  Student 56,756 5.5
  Not a student 2,089,628 94.5
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Source: Authors’ analysis of data from the 2016–2020 Behavioral Risk Factor Surveillance System (BRFSS). Notes

1Incorporates BRFSS post-stratification weights.

Table 3. Baseline outcomes prior to the onset of the COVID-19 pandemic.

Strata Poor mental health days (#) Poor physical health days (#) Days when health prevented activities (#) Sleep hours per day (#) Any exercise during the past month (%) Days when alcohol was consumed (#) Smoke cigarettes (%)
Overall National Rate 4.30 4.08 5.07 6.97 73.7 4.65 15.4
Sex
  Female 4.90 4.41 5.12 7.01 71.9 3.53 13.5
  Male 3.67 3.72 5.01 6.93 75.5 5.84 17.3
Income Group
  Less than $15,000 6.92 7.63 9.14 6.99 60.0 2.51 24.6
  $15,000 to $25,000 5.58 5.77 6.91 6.92 63.3 3.07 21.7
  $25,000 to $35,000 4.62 4.51 5.18 7.04 67.8 3.73 18.4
  $35,000 to $50,000 4.37 3.87 4.69 6.96 72.4 4.27 16.0
  $50,000 to $75,000 3.82 3.29 3.97 6.94 76.7 5.01 13.8
  $75,000 + 2.99 2.51 3.05 6.98 83.5 6.28 9.2
Race
  White 4.37 4.21 5.09 7.00 75.6 5.39 15.8
  Black 4.54 4.10 5.34 6.78 68.8 3.55 17.8
  Hispanic 3.98 3.89 4.96 6.96 67.7 3.20 12.8
  Other race 3.63 3.16 4.48 7.02 76.5 3.14 11.9
  Multiracial 6.41 5.01 5.84 6.72 77.5 4.30 22.6
Age
  18 to 24 6.34 2.41 3.52 6.96 80.4 3.59 10.3
  25 to 34 5.25 2.71 3.76 6.84 77.4 4.89 18.9
  35 to 44 4.47 3.25 4.31 6.78 75.7 4.83 19.7
  45 to 54 4.21 4.42 5.78 6.85 72.6 4.78 17.4
  55 to 64 3.91 5.50 6.87 6.91 71.0 4.90 17.7
  65+ 2.63 5.38 6.14 7.30 68.4 4.66 9.2
Education Group
  College grad 3.07 2.65 3.29 7.04 85.2 6.13 6.1
  Not a college grad 4.78 4.64 5.73 6.94 69.1 4.06 19.1
Employment Group
  Unemployed 7.36 5.68 8.05 6.92 70.0 4.04 27.0
  Employed 3.74 2.51 2.90 6.84 77.1 5.32 15.4
  Not in labor force 4.75 6.32 7.63 7.16 68.9 3.72 13.8
Student Status                  
  Student 5.99   2.21 3.36   7.05 85.2 3.01 5.6
  Not a student 4.20   4.18   5.20   6.96   73.0 4.75 15.9
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Source: Authors’ analysis of data from the 2016–2020 Behavioral Risk Factor Surveillance System (BRFSS). Notes: The exhibit displays mean outcomes during our pre-pandemic baseline period of January 2019 through February 2020, after accounting for BRFSS post-stratification weights.

Changes in self-reported health

Trends in self-reported health outcomes and engagement in health behaviors were generally stable prior to the onset of the COVID-19 pandemic (Fig 1, Appendix A2-A5 in S1 File). In adjusted regression models, the national rate of days spent in poor mental health did not change significantly during the pandemic (-0.03 days, 95% CI: -0.16 to 0.09) (Table 4). However, certain population subgroups experienced significant increases in poor mental health days; the largest increases were observed for respondents who were not college graduates (+0.39 days, 95% CI: 0.31 to 0.47), lived in households earning more than $75,000 per year (+0.29 days, 95% CI: 0.20 to 0.37), or were currently employed (+0.11 days, 95% CI: 0.02 to 0.20). In contrast, days spent in poor mental health decreased among those who were unemployed (-1.00 days, 95% CI: -1.39 to -0.60), had household incomes between $15,000 to $25,000 (-0.39 days, 95% CI: -0.68 to -0.11), college graduates (-0.21 days, 95% CI: -0.37 to -0.05), or those aged 18–24 (-0.19 days, 95% CI: -0.31 to -0.06).

Fig 1. National trends in poor physical and mental health days, 2016–2020.

Fig 1

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Source: Authors’ analysis of data from the 2016–2020 BRFSS. Notes: The figure displays monthly unadjusted trends in outcomes, accounting for BRFSS post-stratification weights. The vertical dashed line indicates February 2023.

Table 4. Adjusted regression estimates for changes in poor health days during the COVID-19 pandemic.

Variable Poor mental health days (#) Poor physical health days (#) Days when poor health prevented activities (#)
ẞ 95% CI ẞ 95% CI ẞ 95% CI
Overall National Rate -0.03 (-0.16, 0.09) -1.00*** (-1.10, -0.90) -0.03 (-0.12, 0.06)
Sex            
  Female 0.11 (-0.05, 0.28) -1.08*** (-1.17, -0.98) 0.03 (-0.10, 0.16)
  Male -0.19** (-0.31, -0.06) -0.92*** (-1.04, -0.79) -0.11 (-0.24, 0.03)
Income Group            
  Less than $15,000 -0.30 (-0.67, 0.06) -1.57*** (-1.87, -1.28) -0.03 (-0.45, 0.39)
  $15,000 to $25,000 -0.39** (-0.68, -0.11) -1.26*** (-1.56, -0.95) -0.38* (-0.67, -0.09)
  $25,000 to $35,000 -0.12 (-0.35, 0.10) -0.92*** (-1.07, -0.77) 0.58* (0.11, 1.06)
  $35,000 to $50,000 0.04 (-0.33, 0.41) -0.90*** (-1.13, -0.67) 0.07 (-0.17, 0.31)
  $50,000 to $75,000 -0.02 (-0.20, 0.16) -0.73*** (-0.88, -0.59) -0.01 (-0.27, 0.26)
  $75,000 + 0.29*** (0.20, 0.37) -0.77*** (-0.90, -0.64) 0.09 (-0.08, 0.27)
Race            
  White -0.01 (-0.13, 0.11) -1.06*** (-1.15, -0.96) -0.22*** (-0.32, -0.12)
  Black -0.19 (-0.45, 0.06) -0.91*** (-1.15, -0.68) 0.32 (-0.21, 0.85)
  Hispanic 0.07 (-0.33, 0.48) -0.89*** (-1.28, -0.50) 0.38* (0.04, 0.73)
  Other race -0.22 (-0.68, 0.24) -0.95*** (-1.20, -0.70) 0.13 (-0.24, 0.51)
  Multiracial -0.06 (-0.53, 0.40) -0.76* (-1.46, -0.06) 0.30 (-0.42, 1.01)
Age            
  18 to 24 -0.33** (-0.58, -0.09) -0.75*** (-0.93, -0.56) 0.36** (0.12, 0.61)
  25 to 34 -0.05 (-0.28, 0.18) -0.78*** (-0.95, -0.62) 0.28** (0.09, 0.46)
  35 to 44 0.07 (-0.16, 0.30) -0.89*** (-1.07, -0.72) 0.02 (-0.29, 0.33)
  45 to 54 0.19 (-0.02, 0.41) -1.10*** (-1.26, -0.93) -0.15 (-0.43, 0.13)
  55 to 64 -0.12 (-0.36, 0.11) -1.32*** (-1.62, -1.03) -0.39 (-0.81, 0.03)
  65+ 0.00 (-0.14, 0.14) -1.02*** (-1.22, -0.82) -0.24* (-0.48, 0.00)
Education Group            
  College grad -0.21* (-0.37, -0.05) -1.07*** (-1.24, -0.91) -0.07 (-0.19, 0.05)
  Not a college grad 0.39*** (0.31, 0.47) -0.82*** (-0.92, -0.71) 0.08 (-0.03, 0.20)
Employment Group            
  Unemployed -1.00*** (-1.39, -0.60) -2.05*** (-2.32, -1.77) -1.39*** (-1.68, -1.09)
  Employed 0.11* (0.02, 0.20) -0.71*** (-0.79, -0.64) 0.26*** (0.14, 0.38)
  Not in labor force -0.20 (-0.4, 0.00) -1.30*** (-1.47, -1.14) -0.35*** (-0.50, -0.21)
Student Status            
  Student 0.11 (-0.45, 0.68) -0.54*** (-0.74, -0.33) 0.70*** (0.30, 1.10)
  Not a student -0.04 (-0.15, 0.07) -1.02*** (-1.13, -0.92) -0.08 (-0.18, 0.01)
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Source: Authors’ analysis of data from the 2016–2020 Behavioral Risk Factor Surveillance System (BRFSS). Notes: The exhibit displays regression-adjusted estimates for the number of days during the past 30 that were spent in poor mental health, poor physical health, or when poor health prevented usual activities. All models were accounted for BRFSS post-stratification weights, with standard errors clustered by state. Significance codes

*p< .05

**p < .01

***p< .001.

The number of days spent in poor physical health significantly decreased after the start of the pandemic (-1.00 days, 95% CI: -1.10 to -0.90). After stratifying by demographics, we found that physical health improved significantly (i.e., number of days spent in poor physical health declined) for every single demographic group after the start of the pandemic. The groups that reported the largest improvements were those who were unemployed (-2.05 days, 95% CI: -2.32 to -1.77), not in the labor force (-1.30 days, 95% CI: -1.47 to -1.14), and those in households earning an income less than $15,000 (-1.57 days, 95% CI: -1.87 to -1.28) or between $15,000 and $25,000 (-1.26 days, 95% CI: -1.56 to -0.95).

Nationally, the number of days when poor health prevented usual activities did not change significantly after the start of the pandemic (-0.03 days, 95% CI: -0.12 to 0.06). However, several groups reported significant increases in poor health days including students (+0.70 days, 95% CI: 0.30 to 1.10), those aged 18–24 (+0.36 days, 95% CI: 0.12 to 0.61) or 25–34 (+0.28 days, 95% CI: 0.09 to 0.46), Hispanic respondents (+0.38 days, 95% CI: 0.04 to 0.73), and households earning between $25,000 and $35,000 per year (+0.58 days, 95% CI: 0.11 to 1.06). In contrast, we observed significant decreases in the number of poor health days were those who were unemployed (-1.39 days, 95% CI: -1.68 to -1.09) or not in the labor force (-0.35 days, 95% CI: -0.50 to -0.21), White respondents (-0.22 days, 95% CI: -0.32 to -0.12), those aged 65+ (-0.24 days, 95% CI: -0.48, -0.00), and households earning between $15,000 and $25,000 (-0.38 days, 95% CI: -0.67 to -0.09).

Changes in self-reported health behaviors

The adjusted regression models revealed that Americans’ mean hours of sleep per day significantly increased after the pandemic’s onset (+0.09 hours, 95% CI: 0.05 to 0.13) (Table 5). Most population groups experienced significant increases in sleep hours per day; the largest increases were observed for those aged 18 to 24 years (+0.21 hours, 95% CI: 0.11 to 0.31), the unemployed (+0.18 hours, 95% CI: 0.07 to 0.29), households earning between $15,000 and $25,000 (+0.17, 95% CI: 0.07 to 0.27), or Black respondents (+0.15 hours, 95% CI: 0.05 to 0.26). In contrast, mean sleep hours did not change significantly for respondents in households earning between $25,000 and $35,000, those who identified as multiracial or other race, and those aged 45+.

Table 5. Adjusted regression estimates for the changes in sleep and exercise behavior during the COVID-19 pandemic.

    Sleep hours per day (#) Any exercise during the past month (%) Days consuming alcohol (#) Current smoker (%)
Variable ẞ 95% CI ẞ 95% CI ẞ 95% CI ẞ 95% CI
Overall National Rate 0.09*** (0.05, 0.13) 3.28*** (2.48, 4.09) 0.27*** (0.18, 0.37) -1.11*** (-1.39, -0.83)
Sex                
  Female 0.09*** (0.05, 0.12) 3.35*** (2.57, 4.12) 0.40*** (0.31, 0.48) -1.12*** (-1.41, -0.82)
  Male 0.09*** (0.04, 0.15) 3.22*** (2.25, 4.19) 0.15* (0.01, 0.29) -1.08*** (-1.51, -0.66)
Income Group                
  Less than $15,000 0.13** (0.05, 0.22) 1.25 (-0.48, 2.98) 0.21** (0.07, 0.35) -0.76 (-1.92, 0.40)
  $15,000 to $25,000 0.17** (0.07, 0.27) 4.05*** (2.73, 5.38) 0.16 (-0.03, 0.34) -1.93*** (-2.55, -1.31)
  $25,000 to $35,000 0.04 (-0.06, 0.14) 3.22*** (1.79, 4.66) 0.11 (-0.18, 0.40) -1.08* (-1.98, -0.19)
  $35,000 to $50,000 0.06** (0.02, 0.10) 3.35*** (2.18, 4.52) 0.30*** (0.15, 0.45) -0.30 (-1.03, 0.42)
  $50,000 to $75,000 0.09** (0.02, 0.16) 2.78*** (1.41, 4.14) 0.25** (0.10, 0.39) -1.22*** (-1.82, -0.62)
  $75,000 + 0.06** (0.02, 0.11) 3.36*** (2.55, 4.17) 0.33*** (0.17, 0.49) -0.71*** (-1.11, -0.31)
Race                
  White 0.07*** (0.04, 0.11) 4.06*** (3.35, 4.77) 0.38*** (0.27, 0.49) -0.86*** (-1.12, -0.59)
  Black 0.15** (0.05, 0.26) 3.46*** (2.58, 4.34) 0.18 (-0.02, 0.39) -1.58*** (-2.40, -0.75)
  Hispanic 0.14** (0.05, 0.24) 1.64 (-0.44, 3.72) 0.11 (-0.03, 0.26) -2.14*** (-3.09, -1.18)
  Other race -0.03 (-0.16, 0.11) 0.65 (-1.09, 2.40) -0.05 (-0.27, 0.17) -0.56 (-1.67, 0.56)
  Multiracial 0.13 (-0.02, 0.28) 1.27 (-0.78, 3.31) 0.49** (0.17, 0.80) -1.56 (-3.21, 0.08)
Age                
  18 to 24 0.21*** (0.11, 0.31) 2.22** (0.71, 3.72) -0.02 (-0.21, 0.17) -1.98*** (-2.81, -1.14)
  25 to 34 0.10** (0.02, 0.17) 4.04*** (2.66, 5.43) 0.25* (0.04, 0.46) -2.34*** (-2.92, -1.77)
  35 to 44 0.12*** (0.08, 0.15) 3.92*** (2.68, 5.16) 0.41** (0.14, 0.68) -1.47** (-2.34, -0.59)
  45 to 54 0.06 (-0.01, 0.12) 4.56*** (3.52, 5.60) 0.38** (0.15, 0.60) -0.24 (-1.12, 0.63)
  55 to 64 0.07 (0.00, 0.14) 3.53*** (2.34, 4.72) 0.36** (0.12, 0.59) -0.21 (-0.90, 0.48)
  65+ 0.04 (-0.02, 0.10) 1.58*** (0.68, 2.48) 0.20*** (0.10, 0.31) -0.45 (-1.05, 0.14)
Education Group                
  College grad 0.11*** (0.06, 0.16) 3.40*** (2.49, 4.31) 0.23*** (0.13, 0.33) -1.30*** (-1.70, -0.89)
  Not a college grad 0.04** (0.02, 0.07) 3.05*** (2.22, 3.88) 0.40*** (0.28, 0.52) -0.62*** (-0.93, -0.31)
Employment Group                
  Unemployed 0.18** (0.07, 0.29) 5.73*** (4.03, 7.42) 0.40 (-0.04, 0.84) -3.55*** (-5.17, -1.93)
  Employed 0.10*** (0.05, 0.15) 3.89*** (2.84, 4.94) 0.32*** (0.19, 0.46) -1.37*** (-1.80, -0.94)
  Not in labor force 0.06** (0.02, 0.10) 1.89*** (0.96, 2.82) 0.18** (0.06, 0.31) -0.62* (-1.15, -0.08)
Student Status                
  Student 0.13** (0.03, 0.23) 1.21 (-0.22, 2.65) -0.06 (-0.3, 0.17) -0.99** (-1.74, -0.25)
  Not a student 0.09*** (0.05, 0.13) 3.42*** (2.61, 4.23) 0.29*** (0.2, 0.38) -1.15*** (-1.42, -0.88)
Open in a new tab

Source: Authors’ analysis of data from the 2016–2020 Behavioral Risk Factor Surveillance System (BRFSS). Notes: The exhibit displays regression-adjusted estimates for the number of sleep hours per day, and whether the respondent reported participating in any physical activities or exercise during the past month. All models were accounted for BRFSS post-stratification weights, with standard errors clustered by state

*p< .05

**p < .01

***p< .001.

Exercise participation significantly improved after the start of the pandemic, both overall (+3.28 percentage points (pp), 95% CI: 2.48 to 4.09) and most population subgroups. The largest improvements in exercise participation accrued to the unemployed (+5.73 pp, 95% CI: 4.03 to 7.42), those between the ages of 45 and 54 years (+4.56 pp, 95% CI: 3.52 to 5.60), and White respondents (+4.06 pp, 95% CI: 3.35 to 4.77). In contrast, mean sleep hours did not change significantly for respondents in households earning under $15,000, those who identify as Hispanic, multiracial, or other race, and students.

The mean number of days during the past thirty when alcohol was consumed increased at a national level (+0.27 days, 95% CI: 0.18 to 0.37). Most subgroups also experienced increases, especially respondents who are female (+0.40 days, 95% CI: 0.31 to 0.48), identify as White (+0.38 days, 95% CI: 0.27 to 0.49) or multiracial (+0.49 days, 95% CI: 0.17 to 0.80), those aged 35–44 (+0.41 days, 95% CI: 0.14 to 0.68), and those who did not graduate college (+0.40 days, 95% CI: 0.28 to 0.52). In contrast, alcohol consumption days did not change for households earning between $15,000 and $35,000 per year; respondents who identify as Black, Hispanic, or other race; those aged 18–24; and those who were unemployed or students.

Lastly, the percentage of Americans who identify as current smokers decreased during the pandemic (-1.11 pp, 95% CI: -1.39 to -0.83). Most population subgroups experienced declines in smoking, especially among respondents who were unemployed (-3.55pp, 95% CI: -5.17 to -1.93), aged 25–34 (-2.34pp, 95% CI: -2.92 to -1.77) or 18–24 (-1.98pp, 95% CI: -2.81 to -1.14), identify as Hispanic (-2.14pp, 95% CI: -3.09 to -1.18) or Black (-1.58pp, 95% CI: -2.40 to -0.75), and households earning $15,000 to $25,000 (-1.93pp, 95% CI: -2.55 to -1.31). No significant changes were observed for respondents who were aged 45+, identify as other race or multiracial, or households earning either less than $15,000 or $35,000 to $5,000.

Discussion

The COVID-19 pandemic dramatically changed the day-to-day lives of Americans, and our results expand our knowledge about the impact of the COVID-19 pandemic on self-reported health status and engagement in healthy behaviors. At a national level, we observed a mean increase of 0.14 days per person per month spent in poor mental health during March-December 2020. These findings are in line with studies on the negative psychological consequences of previous infectious disease outbreaks [18–22]. We also observed substantial heterogeneity by population subgroup. For instance, women experienced +0.29 days in poor mental health, while no significant effect was observed for men. Several of our results disagree with previously published findings; respondents who were White, employed, higher income, middle-aged, and did not have a college degree experienced the largest increases in poor mental health days. A discussion of the shared and unique challenges faced by each population group during the pandemic is outside the scope of this work. However, prior studies have found that employed individuals experienced heightened fears of COVID-19 exposure and contagion of themselves and their families, inability to secure childcare due to school closures, job insecurity, or difficulty transitioning to remote work during shelter-in-place orders [23–26]. Furthermore, prior studies often assessed mental health of population subgroups at a single point in time, without controlling for pre-existing trends. For instance, our results comport with prior research using Healthy Minds study data from 2013 to 2021; the authors concluded that the increased prevalence of mental health problems among college students during COVID-19 represented a continuation of pre-existing time trends rather than a unique spike [9,10]. Indeed, our results in Fig 1 suggests the decline in poor mental health days was a temporary deviation from an increasing national trend.

Despite the widespread disruption of in-person medical services during the COVID-19 pandemic, the population-level number of days spent in poor physical health also decreased during the pandemic. In fact, every demographic group experienced a decrease in poor physical health days. Correspondingly, we found that the number of days when poor health prevented usual activities decreased both overall and among most population subgroups. These results may seem counterintuitive; however, our findings comport with prior work documenting positive changes in health and health behaviors during economic downturns. Unemployment spells and economic downturns are associated with temporary reductions in smoking, alcohol consumption, obesity, physical inactivity, and air pollution [12–14]. We found that sleep and exercise behaviors improved for nearly every subgroup; increases in time spent at home due to COVID-19 mitigation efforts (e.g., shelter in place orders) may have allowed individuals to devote more time to self-care activities [27]. Smoking status also decreased during the pandemic for most groups, although alcohol consumption increased. However, these changes may lack durability in the face of extended lockdowns and associated social isolation. Future research should examine whether our findings replicate in international contexts or represent idiosyncrasies of U.S. workplace culture.

Study limitations

The results of this study should be interpreted in the context of the following limitations. First, the BRFSS is a cross-sectional survey. We cannot infer causality and all findings should be interpreted as associations. Second, our outcomes are self-reported; however, prior work has validated the BRFSS responses for physical activity self-reported health against data sources [28]. Third, our outcomes do not capture the severity of health challenges (e.g., minor versus major depression) or the quality of health behaviors (e.g., exercise duration). Fourth, while the BRFSS has a high response rate for phone surveys (47.9% in 2020), there is also a potential for nonresponse bias. However, we utilized BRFSS survey weights based on the age, gender, and racial distribution of the targeted population and multiple imputation using additive regression, bootstrapping, and predictive mean matching to adjust for the potential non-response bias [16,17,29]. Lastly, the BRFSS core survey does not contain measures of social engagement, nutrition or access to behavioral health services that may have important implications for physical and mental health. Despite these limitations, our findings have important policy implications and emphasize the need for sustained social and financial assistance to mitigate the pandemic’s adverse impacts on mental health.

Conclusion

In conclusion, we find the onset of COVID-19 was associated with positive effects on self-reported physical health, hours of sleep per day, exercise participation, and smoking status. In contrast, alcohol consumption increased, and the pandemic had heterogeneous effects on the number of days in poor mental health or when poor health prevented usual activities. Taken together, our results also emphasize the importance of targeted outreach and interventions to improve mental health in those who may be disproportionately affected by the pandemic and to experience significant distress.

Supporting information

S1 File

(DOCX)

Click here for additional data file. (123.2KB, docx)

Data Availability

Our analytic dataset and R scripts are available within an open access Mendeley Data Repository, accessible at https://data.mendeley.com/datasets/nfvpcd9wpf.

Funding Statement

KNG's effort was supported in part by a grant from the U.S. Agency for Healthcare Research & Quality (https://www.ahrq.gov/, K12 HS026395). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Yohannes Kebede

6 Jun 2023

PONE-D-22-34054Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemicPLOS ONE

Dear Dr. Griffith,

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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Additional Editor Comments (if provided):

My additional comments

Clear indicate in the limitation subsection the following points:

1. Measurements of outcomes are only based on days without considering "extents: e.g. perceieved severity and frequency were one of the missed good measurement indicator for health outcomes like sleep, exercise, etc

2. Self reported health outcomes overly represented by sleep and exercise while omitting access to counseling and communication services, nutrition, engagement in discussions, etc during Covid-19

3. Measurements of self reports should have better done by scales

Add the following in the discussion section:

1. Why high income is associated to poor health outcome? Check them against two things: 1) whether or not measurement gaps exist, 2) if there has been extra expectation experienced by those who have higher income that could link to poor health outcomes

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: Partly

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: Review: PONE-D-22-34054

Reviewer comments to the authors

Summary

The authors conduct a study of changes in health and health behaviors during the first year of the COVID-19 pandemic in the United States using the Behavior Risk Factor Surveillance System (BRFSS) as the data source. The authors examine mental health, days in poor physical health, and activity days to determine how the pandemic, associated shutdowns, and stress might have changed responses to BRFSS questions using two-tailed t-tests and interrupted time series regression models. The authors find that respondents reported increases in the days in poor mental health but also increases in sleep and exercise. Results were heterogenous and varied based several socioeconomic indicators including race, income, and education. The paper is very well-written and is a suitable fit for PLOS One. I have a few questions and comments for the authors.

1.In the discussion, I think the authors could discuss their findings a bit more and perhaps the implications for mental health and physical health of the US population. Findings might also be linked to public health initiatives or policy. In addition, were any findings surprising or unexpected (such as increase in physical health among all respondents, which seems to be the only consistent demography response).

2.Also in the discussion, the authors might discuss how the increases in sleep and physical activity might be related to the increase in time available for such activities for those populations. What does that say about workplace culture and life in the US? While the authors are correct in restricting their discussion to associations only, I think some speculation or perhaps a further research section might allow them to engage with the “why did we find this?” portion of this paper that is somewhat missing.

3.Figure 1 is very good and shows a distinct trend in poor mental health days increasing dramatically in 2020, but the increase is linked to an initial decrease in early 2020. Further, looking backward, from 2016 to 2019, there was a steady rise in poor mental health days. If the pandemic had not happened, would poor mental health days still have increased (it seems like, visually at least, that the increase was happening prior to the pandemic). It might be worth considering a comparison of the slopes for the change over time before and after the pandemic. Perhaps COVID-19 merely brought attention to an issue that has been building in the US behind the scenes.

4.To me, Figure one shows the strongest association for changes in physical health, which is a very interesting finding since many people speculate that physical health deteriorated for many during the pandemic. Your study would suggest otherwise and the discussion should state this and suggest why.

Reviewer #2: This is a valuable article and can add input to the existing literature. However, there are some points that need clarity. Please find the comments below:

Comments

Title:

1. The term ‘self-reported health’ is not informative. Do the researchers want to say ‘self-reported health impacts’? Please revise it. In addition, it is also attractive to say ‘engagement in healthy behaviors’ instead of ‘self-care behaviors’.

2. It is not good to use abbreviations or acronyms in the title. Hence, instead of the Acronym ‘U.S.’ write in full words ‘United States’.

Abstract:

Importance: However, it remains unclear how the COVID-19 pandemic impacted self-reported mental and physical health and influence human behaviors.

Objective: please revise it. To assess changes in self-reported health outcomes/impacts during the COVID-19 pandemic and trends of engagement in health behaviors.

In abstract section; good to include the summary of analyses that researchers carried out.

Introduction:

1. The first sentence needs revision. The novel coronavirus (COVID-19) began to spread across the world in December 2019 and on March 13, 2020. In response to the pandemic, different countries of the world had been taking different prevention and control measures. Similarly, the United States declared a national public health emergency.

2. The last paragraph of the introduction needs revision. Indeed, the researchers should explain the gaps in detail and convince why this study is needed and its input to the existing literature.

3. Our objectives were........no need of making separate paragraphs for the objective. In addition, no need of listing them. Rather write it in a single sentence and merge it with the last paragraph of the introduction section.

Methods:

1. Some variables need Operational definitions and explain how they were measured. Please define poor mental health, poor physical health, and exercise. Explain in detail how these variables were measured.

2. Did the researchers check assumptions for the statistical tests carried out? What are the findings of assumptions? Any violation?

Results

1. The researchers showed the trends in poor physical and mental health days (figure 1). That is good. Would you please explain the trends of self-care behaviors/engagement in health behaviors during the COVID-19 pandemic?

Discussion

The discussion is shallow. The researchers should discuss the pertinent findings from both theoretical and empirical perspectives.

**********

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Reviewer #1: Yes: Dustin T. Hill

Reviewer #2: No

**********

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Attachment

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Decision Letter 1

Yohannes Kebede

22 Aug 2023

PONE-D-22-34054R1Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemicPLOS ONE

Dear Dr. Griffith,

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.

Please submit your revised manuscript by Oct 06 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Methods section:

1. Some variables such as poor mental health, poor physical health and exercise need to be defined (operational definition) and explain in detail how they were measured.

2. Please describe the assumptions that the researchers checked for the statistical tests carried out? Please incorporate the findings of assumptions in the method section.

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PLoS One. 2023 Sep 19;18(9):e0291667. doi: 10.1371/journal.pone.0291667.r004

Author response to Decision Letter 1

31 Aug 2023

Dear Dr. Kebede,

Thank you for the opportunity to revise and resubmit our manuscript, “Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic” for consideration at PLOS ONE. The reviewers’ and editor’s comments have been very helpful and strengthened the paper. Our responses are below.

Sincerely,

Kevin N. Griffith, Ph.D.

Department of Health Policy

Vanderbilt University School of Medicine

Reviewer #2 comments:

1) Some variables such as poor mental health, poor physical health and exercise need to be defined (operational definition) and explain in detail how they were measured.

Thanks for the opportunity to clarify. All outcomes were defined in Appendix A1 including the exact survey text and response format; we have moved this to the main text (Table 1) to aid the reader.

2) Please describe the assumptions that the researchers checked for the statistical tests carried out. Please incorporate the findings of assumptions in the method section.

Thank you for this question. We estimated ordinary least squares regressions (OLS) for all analyses. We estimated a total of 182 regression models (26 population groups x 7 outcomes) and it would not be possible to present all of the diagnostic regression output. There are 5 key assumptions for OLS:

i) Linearity of regressors: All of our independent variables are binary, thus this assumption does not need to be checked.

ii) Normality of model residuals. As stated in the Methods section: “Due to the large sample size and our interest in population average effects, we opted to conduct linear probability models using ordinary least squares to aid in interpretability of results.” Further, we include the following citation which describes how the normality assumption is not important for analyses of large public health datasets: Lumley T, Diehr P, Emerson S, Chen L. The importance of the normality assumption in large public health data sets. Annu Rev Public Health. 2002;23. doi:10.1146/annurev.publhealth.23.100901.140546

iii) Homoscedasticity of model residuals. We also state in the Methods section: “We employed heteroskedasticity-robust standard errors in all regression models.” This obviates the need to check this assumption.

iv) Independence of observations. The BRFSS is a repeated cross-sectional survey with a new sample of respondents each year. The primary risk here is that residents in the same state are subjected to the same set of laws and policies (e.g., shelter in place orders). Thus, our Methods section includes the following which addresses this concern: “Errors were also clustered by state to account for pandemic-related policies that may affect their residents.”

v) No multicollinearity. Multicollinearity is usually obvious to diagnose because the standard errors of your regression explode if it is present. However, we also checked the variance inflation factor (VIF) for each regression specification. The VIF did not exceed 10 in any of our models, thus multicollinearity was not an issue.

We defer to the editor for whether this information should be included in the manuscript text versus the peer review tab, since the former is unusual.

Attachment

Submitted filename: PLOS Response Letter 2023 08 31.pdf

Click here for additional data file. (168KB, pdf)

Decision Letter 2

Yohannes Kebede

4 Sep 2023

Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic

PONE-D-22-34054R2

Dear Dr. Griffith,

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,

Yohannes Kebede, Ph.D.

Guest Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Yohannes Kebede

11 Sep 2023

PONE-D-22-34054R2

Trends in U.S. self-reported health and self-care behaviors during the COVID-19 pandemic

Dear Dr. Griffith:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Yohannes Kebede

Guest Editor

PLOS ONE

Associated Data

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

    Supplementary Materials

    S1 File

    (DOCX)

    Click here for additional data file. (123.2KB, docx)
    Attachment

    Submitted filename: Reviewers comments, June, 2023.docx

    Click here for additional data file. (14.5KB, docx)
    Attachment

    Submitted filename: PLOS Response Letter 2023 08 06.pdf

    Click here for additional data file. (211.2KB, pdf)
    Attachment

    Submitted filename: PLOS Response Letter 2023 08 31.pdf

    Click here for additional data file. (168KB, pdf)

    Data Availability Statement

    Our analytic dataset and R scripts are available within an open access Mendeley Data Repository, accessible at https://data.mendeley.com/datasets/nfvpcd9wpf.

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