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Open Access

Peer-reviewed

Research Article

Temporal trends in aerobic physical activity guideline adherence among nationally representative samples of U.S adults between 2011 and 2019: Cross-sectional findings from a sample of over 2 million adults

  • David Abernethy ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

    David.abernethy@hdr.qut.edu.au

    Affiliation School of Exercise and Nutrition Science, Queensland University of Technology, Brisbane, Australia

    ⨯
  • Jason Bennie,

    Roles Conceptualization, Methodology, Supervision, Writing – review & editing

    Affiliation Murrumbidgee Primary Health Network, Wagga Wagga, Australia

    ⨯
  • Toby Pavey

    Roles Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Writing – review & editing

    Affiliation Faculty of Health, Queensland University of Technology, Brisbane, Australia

    ⨯

Abstract

Background

Physical inactivity is a significant public health concern associated with numerous adverse health outcomes and substantial economic costs. This study describes the prevalence, trends and correlates for adherence to moderate to vigorous physical activity (MVPA) guidelines among a large sample of U.S. adults.

Methods

Data from the 2011, 2013, 2015, 2017, and 2019 Behavioral Risk Factor Surveillance System surveys were analyzed. Self-reported MVPA was assessed by the same item across each survey. Population-weighted prevalence was calculated for meeting MVPA guidelines (150+ mins/wk). Adjusted prevalence ratios for reporting sufficient MVPA across sociodemographic, behavioral and health variables were calculated by multivariate Poisson regression.

Results

Data was available for 2,052,288 respondents (≥ 18 years). Across the surveys, the prevalence of sufficient MVPA fluctuated but remained between 49.5% and 51.1%. Among those aged 18 to 24, the prevalence of sufficient MVPA declined between surveys, from 56.5% in 2011 to 49.7% in 2019. Notable correlates of adhering to guidelines included male sex, higher education, former and never smokers, normal body mass index and increased fruit and vegetable consumption.

Conclusion

From 2011 to 2019, approximately half of US adults reported sufficient MVPA, with a steady decline observed among young adults. While many identified correlates of adhering to PA guidelines were observed, this study has provided further evidence for correlates that had previously provided inconsistent or inconclusive results. These findings emphasize the complexity of addressing physical inactivity and the importance of multifaceted public health strategies tailored to diverse populations.

Citation: Abernethy D, Bennie J, Pavey T (2025) Temporal trends in aerobic physical activity guideline adherence among nationally representative samples of U.S adults between 2011 and 2019: Cross-sectional findings from a sample of over 2 million adults. PLoS ONE 20(1): e0316051. https://doi.org/10.1371/journal.pone.0316051

Editor: Henri Tilga, University of Tartu, ESTONIA

Received: June 24, 2024; Accepted: December 4, 2024; Published: January 8, 2025

Copyright: © 2025 Abernethy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All BRFSS data files are publicly available from the BRFSS index webpage https://www.cdc.gov/brfss/index.html.

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

Competing interests: The authors have declared that no competing interests exist.

Introduction

The public health burden of physical inactivity has become increasingly apparent, with approximately 6–10% of all coronary heart disease, type two diabetes and breast cancer cases attributable to this modifiable risk factor alone [1]. In addition to contributing to more than three million deaths and 32 million disability-adjusted life years every year, physical inactivity has been identified as the fourth leading risk factor for mortality [2]. Moreover, the economic and healthcare-related costs associated with inactivity are high. For example, recent projections suggest that almost 500 million new cases of noncommunicable diseases will occur worldwide by 2030, accompanied by direct health-care costs of $520 billion dollars if the prevalence of inactivity remains the same [3].

Epidemiological evidence has indicated the protective benefits of physical activity (PA) against chronic disease, regardless of intensity, frequency and dosage [4–6], with risk reductions of between 20%-30% for at least twenty-five chronic conditions associated with regular engagement in PA [7]. A similar relationship exists for mortality, with a dose-response relationship between cardiovascular- and all-cause mortality and PA [6, 7]. Improvements in brain, muscle, bone and mental health have also been attributed to regular engagement in PA [4]. While at present, there is no exact dose of PA for optimal health benefits and longevity, the recently updated 2020 World Health Organization (WHO) guidelines on PA and sedentary behavior for adults aged 18 to 64 recommend adults engage in at least 150 to 300 minutes of moderate-intensity or 75 to 150 minutes of vigorous-intensity aerobic PA or a combination of the two and incorporate muscle-strengthening activities on two or more days per week [8]. These guidelines were developed by synthesizing high-quality evidence from longitudinal cohort studies, that have been shown to reduce the risk of adverse health outcomes and increase longevity [8].

Although many studies have identified the association between regular PA and health benefits, others have investigated how increasing PA doses from currently low or no activity can largely reduce risks of adverse health outcomes. One systematic review concluded that clinically relevant health benefits could be experienced from half or less than half of current MVPA recommendations, with the largest relative health benefits experienced at the lowest doses of MVPA [6]. While limited evidence exists suggesting a U-shaped association between PA and the risk of adverse events [9], the evidence above highlights the benefits of PA, regardless of intensity, frequency and dosage, and signals the importance of regular engagement in PA behaviors.

Despite the benefits of PA being well known, experts have stated that the issue of physical inactivity has reached pandemic proportions [10]. Public health estimates have shown that adults are not engaging in sufficient PA, with little improvement made within the last decade and insufficient PA trends persisting [11]. In response to global concerns regarding insufficient levels of activity, the WHO developed a global action plan promoting PA. The primary aim of this plan is to increase activity levels by 10 and 15% by 2025 and 2030, respectively [8]. However, experts have stated that goals and targets set by governments and international agencies are more prone to be amended than accomplished [12]. While the prevalence of inactivity varies considerably between countries and geographical regions, recent global estimates suggest that 27.5% of adults worldwide are not meeting the recommended dose of PA to improve health and protect against adverse effects [8]. The prevalence of insufficient activity is among the greatest in the United States, with current adult adherence to PA guidelines estimates based on self-report ranging from 50.5% [13] to 65.2% [14].

The current state of evidence about correlates of aerobic PA in adults has underscored the multifaceted nature of factors that influence consistent engagement in PA, as many studies have highlighted the significance of sociodemographic factors (age, sex, level of education, poor health status), behavioral and psychosocial factors (self-efficacy, perceived behavioral control, social support) [7, 15]. While many correlates of activity have been repeatedly identified in various studies, others have provided results that were conflicting or inconclusive. For example, many individual-level factors such as sex, level of education and health status have been frequently assessed, with increasing male sex, increased education and poorer health status repeatedly identified as having an association with PA [15, 16]. While age is largely considered to be inversely associated with PA [15, 17, 18], limited evidence has suggested that no association exists between the variables [19]. Some behavioral and health variables have also provided mixed results about their association with PA. Smoking status has been included as a potential correlate in a range of studies investigating potential associations with PA, however, studies have continually concluded the available evidence is limited and/or inconclusive [16, 20]. Inconclusive or inconsistent research findings in this context can contribute to difficulties in determining areas for public health intervention and which factors are most influential in promoting PA.

Ongoing research of PA trends in populations and identifying correlates of sufficient activity help to refine the understanding of shifts in behavior, priority areas and populations requiring intervention and the impact of public health campaigns targeting physical inactivity. Therefore, this study aims to (i) describe the proportion of US adults adhering to current aerobic PA guidelines from 2011 to 2019; and (ii) identify significant sociodemographic, behavioral, and health-related correlates for guideline adherence in a representative sample of two million adults living in the United States.

Methods

The Behavioral Risk Factor Surveillance System BRFSS is an annual cross-sectional public health survey conducted in the United States that collects data from residents about health-related risk behaviors, chronic health conditions and the use of preventive health services [21]. Developed in 1984, the BRFSS is the largest continuously conducted health survey of its kind in the world. For the present analysis, data from the 2011, 2013, 2015, 2017 and 2019 individual surveys were analyzed. This is due to the BRFSS protocol, which collects comprehensive PA data biennially. Each BRFSS survey is approved by the National Center for Health Statistics Research Ethics Review Board [21]. The response rates were 49.7%, 45.9%, 47.2%, 45.9%, and 49.4% for the 2011, 2013, 2015, 2017 and 2019 BRFSS surveys, respectively [21].

The same survey and methodology were used across all the BRFSS surveys used in this research to assess self-reported data for PA, height and weight, sociodemographic and lifestyle variables [21]. Due to the BRFSS’s large scope and depth of questioning, it is commonly used in health research to explore relationships between various exposures and their impact on health [21]. This survey has been repeatedly assessed to ensure that its items are valid and reliable, which produces data that allows for study results to be replicated and accurate. Several studies have previously shown that the assessment items used in the BRFSS display evidence of moderate or high levels of reliability and validity [22–24].

Data were available for 2,307,980 respondents; however, for the analyses performed in this study, participants were excluded if they were missing data for PA (n = 255,692) (final sample = 2,052,288). Individuals missing data for the explanatory variables were excluded during the analysis. To enhance the generalizability of this research, no other inclusion or exclusion criteria were applied to the initial sample.

Physical activity assessments

Moderate to vigorous physical activity (MVPA) of BRFSS respondents was assessed using previously validated questionnaires [25]. The survey questions have acceptable test-retest reliability and concurrent validity for classifying groups to levels of recommended MVPA when compared to accelerometry as the gold standard [25].

When examining an individual’s PA habits, the BRFSS surveyors provided participants with the following statement: “The next few questions are about exercise, recreation or physical activities other than your regular job duties.” This was followed by “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?”. If respondents replied yes, further questions were asked: “What type of physical activity or exercise did you spend the most time doing during the past month?”, “How many times per week or per month did you take part in this activity during the past month?”, and “When you took part in this activity, for how many minutes or hours did you usually keep at it?”

The same set of questions was asked about a second activity when respondents indicated that they engaged in more than one PA. All reported time spent being physically active was reported in hours and minutes, with all activities coded as being either “aerobic” or “nonaerobic” according to a list of 56 activities [26, 27]. Aerobic activities included exercises such as walking, running, swimming, and cycling, whereas nonaerobic activities included activities such as bowling, golf, house chores and weight training. MVPA included only aerobic activities and was classified as moderate or vigorous based on the estimated metabolic equivalent (MET) [21]. Activities classified as moderate intensity had a MET value greater than 3.0 but lower than the cutoff for vigorous activities. Activities classified as vigorous intensity had an allocated MET value of 6.0 or higher, to reflect at least 60% of an individual’s maximal cardiorespiratory capacity, based on sex and age [27]. Total MVPA was calculated by multiplying weekly minutes of vigorous-intensity activity by two and then adding to moderate-intensity weekly minutes, to reflect the higher MET from vigorous activities.

Physical activity categories

Two mutually exclusive PA categories were formed according to participant adherence to the World Health Organization’s aerobic MVPA guidelines. Individuals who failed to record 150 or more minutes of MVPA per week were categorized as “not meeting guidelines” (MVPA = 0.00–149.99 min/wk), and individuals who recorded 150 minutes or more were categorized as “meeting guidelines” (MVPA = 150 ≥ min/wk).

Explanatory variables

Sociodemographic, lifestyle and health characteristics were assessed using standardized survey items. All of the explanatory variables included have been recognized as having an association with PA behaviors [15]. Sociodemographic variables included age (18–24, 25–34, 35–44, 45–54, 55–64, 65 or older), sex (male, female), level of education (did not graduate high school, graduated high school, attended technical school or college, graduated technical school or college), race (white, black, multiracial, Hispanic, other), employment status (employed, unemployed, homemaker, student, retired, unable to work), marital status (married/member of an unmarried couple, divorced/widowed/separated, never married), and number of children (zero, one, two, three or more). Lifestyle variables included smoking status (current smokers, former smokers and never smoked) and fruit and vegetable consumption. The BRFSS measures daily fruit and vegetable consumption with six items assessing the frequency of consuming 100% fruit juice, fruit, beans (legumes), dark green vegetables, orange vegetables and other vegetables for the month before respondent interviews [28]. The daily number of fruit and vegetable servings was created by adding the total calculated number of daily fruit servings to the total calculated number of vegetable servings. Health-related variables included self-rated health (SRH) (excellent, very good, good, fair, poor), number of poorer mental health days, number of chronic health conditions and body mass index (BMI). Days with poor mental health were assessed by asking respondents “now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?” (zero, one to two, three to six, seven to fourteen, fifteen to thirty) [29]. The twelve chronic health conditions assessed include hypertension, high cholesterol, heart attack, coronary heart disease, stroke, asthma, cancer (non-skin), chronic obstructive pulmonary disorder, depression, kidney disease, diabetes and arthritis. These conditions were included due to their recognized association with morbidity and mortality [30] and assessed by asking respondents “Has a doctor, nurse or other health professionals ever told you that you had any of the following?” [21]. BMI was calculated from self-reported height and weight data using the standardized formula (kg/m2).

Statistical analysis

All statistical analyses were completed using IBM’s SPSS (version 29). Each participant was given an individual weighting factor to be used for analyses, effected to help correct for non-response, stratification, and clustering, which increased population representativeness and reduced the risk of bias. More detailed information concerning the weighting of BRFSS respondents is available elsewhere [31]. Weighted prevalence levels and 95% confidence intervals for adhering to MVPA guidelines in each BRFSS survey were reported for the whole sample and all subcategories of the various explanatory variables included.

Multivariate Poisson log-linear regression analyses with a robust-error variance were used to calculate adjusted prevalence ratios (APRs) for meeting the PA guidelines for each category of the explanatory variables compared to reference categories, while also adjusting for all other explanatory variables. The use of prevalence ratios obtained from Poisson regression has been identified as a more statistically robust method in cross-sectional epidemiology studies compared to general logistic regression [32]. P-values were based on two-sided tests and were considered statistically significant at p < 0.05. Before performing analyses, collinearity between the various explanatory variables was examined using variance inflation factor (VIF), with VIFs  ≥ 2.5 indicating multicollinearity [33]. VIFs ranged from 1.05–2.14, demonstrating no evidence of collinearity.

Results

Characteristics of the sample

Data were available for 2,052,288 individuals (≥ 18 years old) and are shown in Table 1. In summary, 19.6% were aged 65 years or older, 51.4% were female, and most identified as either white, black, or Hispanic (92.2%). Over half of the sample reported never having smoked (58.3%), and 18.3% self-reported having excellent health.

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Table 1. BRFSS sample characteristics by survey year (2011, 2013, 2015, 2017 and 2019).

https://doi.org/10.1371/journal.pone.0316051.t001

Intensity of reported PA

Across each survey, more than 60% of respondents engaged in only moderate PA (range = 62.6%– 63.3%), while approximately 17.5% (range = 16.9% - 18.6%) engaged in only vigorous activities. The remaining 19.5% of respondents (range = 18.1% - 20.5) engaged in a combination of moderate and vigorous activities.

Most common activities

The two most common aerobic activities for BRFSS respondents for all surveys were walking (range = 60.5% - 63.3%) and running (range = 15.4% - 16.1%). Cycling on a bicycle or machine (range = 8.5% - 9.6%) and gardening (range = 7.7% - 10.7%) were the next most reported activities by respondents.

Adherence to aerobic PA guidelines

Weight-adjusted adherence (95% CI) to the aerobic PA guidelines for each of the explanatory variables is available in Table 2. Guideline adherence for the total sample was 51.1% (50.8–51.4), 49.5% (49.2–49.8), 50.6% (50.2–50.9), 49.7% (49.3–50.0) and 50.1% (49.7–50.4) for the 2011, 2013, 2015, 2017 and 2019 BRFSS surveys. The prevalence of reporting sufficient PA declined for respondents aged 18 to 24 across the surveys, dropping from 56.5% (55.3–57.6) in 2011, to 49.7% (48.6–50.8) in 2019. Declines in sufficient PA were also observed across the surveys for single individuals and those with excellent self-reported health, dropping from 52.5% (51.7–53.3) to 47.6% (46.9–48.3) and 63.1% (62.4–63.8) to 60.9% (60.1–61.7), respectively. The prevalence of reporting sufficient PA increased for respondents aged 65 and above across the surveys, climbing from 51.9% (51.4–52.4) in 2011, to 53.9% (53.4–54.5) in 2019. Retired individuals also reported increased guideline adherence from 2011 to 2019, increasing from 55.4% (54.9–55.9) to 56.6% (55.9–57.1).

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Table 2. Weighted proportions for reporting sufficient physical activity among BRFSS samples: By sociodemographic, behavioral and health characteristics.

https://doi.org/10.1371/journal.pone.0316051.t002

Correlates of physical activity guideline adherence

Adjusted prevalence ratios (APRs) and 95% confidence intervals for meeting the MVPA guidelines by categories of each individual explanatory variable are presented in Table 3.

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Table 3. Adjusteda prevalence ratiosb and 95% confidence intervals for reporting sufficient physical activity for BRFSS samples.

https://doi.org/10.1371/journal.pone.0316051.t003

Sociodemographic variables

The prevalence of MVPA guideline adherence was similar for all age groups across each of the surveys included, with individuals aged between 25–34 and 35–44 slightly less likely to meet guidelines. In the last data collection (2019), respondents aged 55 or older reported a notably higher prevalence of engaging in 150 or more minutes of MVPA compared to the 18-24-year-old age category (APR ≥ 1.09). Throughout all surveys, women were less likely to meet guidelines than men (APRs ≤ 0.95). Compared to those identifying as white, those in all other race/ethnicity categories were less likely to meet PA guidelines (APRs ≤ 0.95), aside from those who were multiracial (APRs ≥ 1.07).

Students, retirees, homemakers, and unemployed individuals (APR range = 1.05–1.16) were more likely to adhere to PA guidelines than those who were employed for wages (reference category), or unable to work (APRs ≥ 0.93). Respondents who graduated college or technical school were most likely to meet guidelines (APRs ≥ 1.24). Sufficient MVPA did not notably differ between individuals who were single, married/partnered or widowed/separated/divorced (APR range = 0.95–1.02). Aside from 2017 (APR range = 0.96–0.98), the number of children under the age of eighteen living in a household showed to have little effect on the prevalence of adhering to MVPA guidelines for each survey (APR range = 0.98–1.01).

Behavioral variables

Compared to current smokers, former smokers were more likely to meet guidelines (APR range = 1.02–1.08). Fruit and vegetable consumption (FVC) were positively correlated with meeting PA guidelines, with each additional serving of fruit or vegetables associated with a slightly increased likelihood of guideline adherence (APR range = 1.01–1.03).

Health variables

Decreases from ‘excellent’ SRH resulted in a reduced likelihood of engaging in sufficient PA, with ‘poor’ SRH being least associated with adherence (APR range = 0.50–0.52). Reporting fifteen to thirty days of poor mental health in a month resulted in the lowest guideline adherence (APRs = ≤ 0.96). BMI and guideline adherence were negatively linked, with normal-weight individuals most likely to meet guidelines and obesity class III individuals least likely (APR range = 0.71–0.75). The presence of one or two chronic health conditions was associated with a slightly increased likelihood of meeting guidelines (APR range = 1.00–1.04), whereas having three or more conditions was associated with a slightly decreased likelihood of meeting guidelines (APR range = 0.95–1.00).

Discussion

This study described the temporal trends of adults adhering to current aerobic PA guidelines and identified significant sociodemographic, behavioral, and health-related correlates for guideline adherence in representative samples of U.S. adults. The key findings were that approximately half of each sample reported sufficient PA across each survey and guideline adherence among young adults (18–24 years) declined over time. Noteworthy correlates of adhering to guidelines included male sex, identifying as white, higher levels of education, former and never smokers, greater fruit and vegetable consumption and normal body mass index.

Previous self-reported prevalence estimates of MVPA guideline adherence in U.S adults ranged from 50.5% to 65.2% [13, 14]. Adherence to MVPA guidelines among BRFSS respondents between 2011 and 2019 was lower than expected, as approximately half of each sample (range 49.5–51.1%) reported 150 or more minutes of MVPA per week during the month before their interview in each of the surveys. This suggests the true prevalence of sufficient aerobic activity may be in the lower range of current population estimates [13, 14] and closer to those reported in this study.

Sociodemographic variables

One sociodemographic correlate of MVPA guideline adherence was being male, with the contrast between the sexes in this study corroborating others that have shown that men are generally more physically active than women [11, 15, 34, 35]. While the differences between males and females in this study are significant, previous literature has suggested that the variation in adherence to PA guidelines is due to many factors. Some evidence has indicated that varying types, frequencies and intensities of PA are a driver behind the discrepancy between guideline adherence between males and females [36]. Other evidence has suggested that interpersonal and social-cultural factors within the social-ecological model for health are notable contributors to reduced PA participation for females that are not commonly experienced by males [37]. Gender norms often reinforce expectations that prioritize female caregiving duties associated with mother/wife identity, causing PA participation to result in feelings of guilt as it is not viewed as a ‘feminine’ activity [37]. Societal norms such as perpetuated stereotypes and lack of a female-specific PA culture often discourage engagement in activities due to the perception that PA will be viewed as masculine or result in unwanted judgement from others. Beauty and media standards are considerable factors that act as a barrier to engaging in PA, as it is viewed that various activities will promote unwanted muscle development which will be counterproductive to the commonly promoted slender physique [37].

Differences between guideline adherence were observed among racial/ethnicity groups, with white individuals more likely to meet MVPA guidelines compared to other groups, aside from multiracial individuals. For example, previous studies have reported that individuals identifying as white were more likely to engage in non-occupational PA compared to other racial groups [15, 38, 39], with limited contrasting evidence [19, 40]. Other sociodemographic correlates of MVPA guideline adherence were level of education and employment status, as those who attended or graduated college or technical school and were not employed were more likely to report adequate MVPA. The evidence pertaining to education level and PA behaviors is consistent and has indicated higher levels of education are associated with increased PA [15, 41, 42]. The current study aligns with evidence suggesting that employed individuals are at risk of being inactive [43], although other evidence has suggested that the two variables are positively related, with those employed more likely to be active [15]. A potential explanation for this finding is that employed individuals have less free time to engage in PA compared to the other employment subgroups on account of their employment.

The relationship between age and MVPA guideline adherence in this study was unexpected, as evidence has largely indicated age is inversely related to engagement in PA [15, 17, 18], or that no association exists between the variables [19]. While the APRs for sufficient PA were higher for individuals aged 55 or older in 2019, this can likely be attributed to the decline in PA among the 18-to-24-year-old reference group. This decline in sufficient PA among young adults was unexpected, particularly as prior evidence has suggested that increasing age is generally associated with declines in PA [44]. A potential explanation for this decline is the increased accessibility and reliance on sedentary behaviors associated with technological advancements, resulting in decreased PA and increased sedentary behaviors among young adults. An analysis of high school students in the United States found that TV screen time significantly decreased and other screen time significantly increased between 2007 and 2015 [45]. The authors concluded that an increasing proportion of young people were not engaging in sufficient PA due to excessive screen time. It is therefore plausible that these behaviors continued into young adulthood for a large proportion of United States adults. However, the reduced likelihood of guideline adherence for respondents aged between 25 and 44 is supportive of some previous research investigating the impact of major life events and transitions on PA behaviors. Major life events such as marriage, pregnancies and increasing parental responsibilities, as well as career development, are very common during this life stage and have been attributed to reduced PA behaviors [46]. The associations with PA previously reported between relationship [46–48] and parental status [46, 48, 49] were observed in the current study but to a lesser extent. Married/partnered and divorced/widowed/separated were less likely to adhere to guidelines compared to single individuals (APR range = 0.95–1.00), aside from 2019, while the presence of one or more children in a given household was associated with slightly lower guideline adherence only in 2017 (APR range = 0.96–0.98).

Behavioral variables

The findings from this study support previous associations identified among former smokers and individuals who had never smoked, as they were more likely to adhere to MVPA guidelines when compared to current smokers. Previous literature about smoking and PA behaviors has shown the two behaviors are incongruent [50, 51], with limited contrasting evidence [16]. Prior investigations into the relationship between fruit and vegetable consumption (FVC) and PA have provided conflicting results. While some studies have concluded that an association between FVC and engaging in PA exists [52–55], others have suggested the behaviors are unrelated [54, 56]. Though a temporal link could not be explored between the variables in this study, the results suggest that FVC is only modestly correlated with sufficient PA as each additional serving from zero was associated with a 1–3% increased likelihood of meeting guidelines (APR range = 1.01–1.03).

Health variables

The protective effects of PA on the risk of poorer mental health symptoms [57, 58] and mental health disorders [4, 59] have already been well-established, among individuals with and without relevant disorders. In the current study, although very minimal differences were observed between most groups, a reduced likelihood of adherence was experienced for individuals with fifteen or more days of poorer mental health days. The relationship between SRH and PA in this study also showed an inverse association, confirming results from previous studies. In these studies, an association between SRH and PA were present in a dose-response manner [60–62]. A study of older adults [62] found that PA was 51% higher among individuals with very good SRH compared to those with poor or very poor SRH.

Current evidence has shown that chronic disease status [4] and obesity [15] are notable correlates of insufficient activity. This study provides mixed results about chronic disease status and being sufficiently active. Individuals who reported one or two conditions had a slightly higher prevalence of adhering to guidelines compared to the disease-free reference group. While it is plausible that the association between chronic disease and PA observed for individuals with one or two conditions could be explained by increases in activity in individuals who were previously insufficiently active after receiving advice from a health professional, this is unlikely. A study of Australian women concluded that being diagnosed with a chronic disease did not affect PA behaviors [63].

The association between disease status and guideline adherence in this study for individuals with three or more conditions were expected, as prior evidence has indicated that guideline adherence is low for individuals with multimorbidity [64]. The relationship between obesity and PA behaviors has been heavily investigated, with a systematic review [65] of obese adults highlighting significant associations with physical inactivity. Studies comparing PA behaviors across BMI classes have yielded similar results, with PA behaviors decreasing with increasing levels of adiposity [13, 66–68]. The findings from the current study align with the aforementioned evidence, reinforcing the importance of a healthy body weight and prevention of unhealthy weight gain.

Strengths and limitations

Strengths of this study include the large, nationally representative sample of U.S. adults, standardized processes of recruitment and data collection that allow comparison of results with further BRFSS studies and similar explorations. Another strength was the inclusion of a weighting variable that helped to correct for non-response, stratification, and clustering, improving the generalizability of the findings to the total U.S. adult population.

While interpreting these results, some limitations need to be considered. Firstly, the reliance on self-report measures of MVPA is prone to recall bias and may not encapsulate habitual PA behaviors [25]. The limiting cross-sectional nature of this study also does not allow for causal exploration between the various exposure variables and guideline adherence. Future studies exploring correlates and determinants of PA that include objective assessments of MVPA would help to verify temporal associations between PA behaviors with currently ambiguous factors. Additionally, the potential influence of seasonality on PA behaviors was not accounted for in the analysis. The seasonality distribution of BRFSS interviews was relatively consistent across all surveys included in the study, suggesting that seasonal variations are likely to average out in such a large sample. For the seasonality distribution of BRFSS survey interviews refer to S1 Table.

Conclusion

The findings from this large study suggest that the prevalence of adherence to MVPA guidelines in the adult United States population falls within the lower range of previous estimates, with approximately half of the population reporting sufficient aerobic PA. Many previously identified correlates of sufficient activity were also observed in this study. Notable findings from this study were that employed individuals were noticeably less likely to adhere to MVPA guidelines, while modest increases in likelihood were associated with a single-serving increase of fruit and vegetables. Sustained levels of population inactivity identified in this study will continue to burden healthcare systems due to the identified associations with morbidity and mortality, further emphasizing the importance of continued public health approaches to tackle physical inactivity. The identified associations between sociodemographic, behavioral and health-related variables and guideline adherence in this study emphasize the complexity surrounding tackling physical inactivity. Innovative approaches promoting PA tailored to both general populations and at-risk populations are imperative to help reduce the incidence of non-communicable diseases and contribute to advancements in achieving the WHO’s goal for reducing inactivity by 15% by 2030.

Supporting information

S1 Table. Seasonality distribution of conducted BRFSS interviews for 2011, 2013, 2015, 2017 and 2019 surveys.

https://doi.org/10.1371/journal.pone.0316051.s001

(DOCX)

Acknowledgments

All authors are grateful to the respondents of the 2011, 2013, 2015, 2017 and 2019 Behavioral Risk Factor Surveillance System for their time in participating in this study.

References

  1. 1. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29. pmid:22818936
  2. 2. World Health Organization. Physical inactivity. 2024. Available from: https://www.who.int/data/gho/indicator-metadata-registry/imr-details/3416#:~:text=Approximately%203.2%20million%20deaths%20and,attributable%20to%20insufficient%20physical%20activity.
  3. 3. Santos AC, Willumsen J, Meheus F, Ilbawi A, Bull FC. The cost of inaction on physical inactivity to public health-care systems: a population-attributable fraction analysis. The Lancet Global Health. 2023;11(1):e32–e9. pmid:36480931
  4. 4. Anderson E, Durstine JL. Physical activity, exercise, and chronic diseases: A brief review. Sports Medicine and Health Science. 2019;1(1):3–10. pmid:35782456
  5. 5. Posadzki P, Pieper D, Bajpai R, Makaruk H, Könsgen N, Neuhaus AL, et al. Exercise/physical activity and health outcomes: an overview of Cochrane systematic reviews. Bmc Public Health. 2020;20(1):1724. pmid:33198717
  6. 6. Warburton DER, Bredin SSD. Health benefits of physical activity: a systematic review of current systematic reviews. Current Opinion in Cardiology. 2017;32(5). pmid:28708630
  7. 7. Rhodes RE, Janssen I, Bredin SSD, Warburton DER, Bauman A. Physical activity: Health impact, prevalence, correlates and interventions. Psychology & Health. 2017;32(8):942–75. pmid:28554222
  8. 8. World Health Organization. WHO GUIDELINES ON PHYSICAL ACTIVITY AND SEDENTARY BEHAVIOUR 2022. Available from: https://www.who.int/publications/i/item/9789240015128.
  9. 9. Franklin BA, Thompson PD, Al-Zaiti SS, Albert CM, Hivert M-F, Levine BD, et al. Exercise-Related Acute Cardiovascular Events and Potential Deleterious Adaptations Following Long-Term Exercise Training: Placing the Risks Into Perspective–An Update: A Scientific Statement From the American Heart Association. Circulation. 2020;141(13):e705–e36. pmid:32100573
  10. 10. Kohl HW 3rd, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380(9838):294–305. pmid:22818941
  11. 11. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. The Lancet Global Health. 2018;6(10):e1077–e86. pmid:30193830
  12. 12. Foley BC, McLaughlin M, Edney S, Islam SMS, Seymour J, Peralta LR, et al. "A 15% Reduction in Physical Inactivity Will Be Achieved in Australasia by 2030"-Audience Votes Negative in Online Debate. Journal of Physical Activity & Health. 2021;18(11):1321–4. pmid:34548424
  13. 13. Bennie JA, De Cocker K, Teychenne MJ, Brown WJ, Biddle SJH. The epidemiology of aerobic physical activity and muscle-strengthening activity guideline adherence among 383,928 U.S. adults. International Journal of Behavioral Nutrition and Physical Activity. 2019;16(1):34. pmid:30999896
  14. 14. Du Y, Liu B, Sun Y, Snetselaar LG, Wallace RB, Bao W. Trends in Adherence to the Physical Activity Guidelines for Americans for Aerobic Activity and Time Spent on Sedentary Behavior Among US Adults, 2007 to 2016. Jama Network Open. 2019;2(7):e197597. pmid:31348504
  15. 15. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJF, Martin BW. Correlates of physical activity: why are some people physically active and others not? The Lancet. 2012;380(9838):258–71. pmid:22818938
  16. 16. Choi J, Lee M, Lee JK, Kang D, Choi JY. Correlates associated with participation in physical activity among adults: a systematic review of reviews and update. Bmc Public Health. 2017;17. pmid:28438146
  17. 17. Milanović Z, Pantelić S, Trajković N, Sporiš G, Kostić R, James N. Age-related decrease in physical activity and functional fitness among elderly men and women. Clinical Interventions for Aging. 2013;8:549–56. pmid:23723694
  18. 18. Suryadinata RV, Wirjatmadi B, Adriani M, Lorensia A. Effect of age and weight on physical activity. Journal of Public Health Research. 2020;9(2):1840. pmid:32728579
  19. 19. Koeneman MA, Verheijden MW, Chinapaw MJM, Hopman-Rock M. Determinants of physical activity and exercise in healthy older adults: A systematic review. International Journal of Behavioral Nutrition and Physical Activity. 2011;8(1):142. pmid:22204444
  20. 20. Condello G, Puggina A, Aleksovska K, Buck C, Burns C, Cardon G, et al. Behavioral determinants of physical activity across the life course: a "DEterminants of DIet and Physical ACtivity" (DEDIPAC) umbrella systematic literature review. International Journal of Behavioral Nutrition and Physical Activity. 2017;14. pmid:28464958
  21. 21. Centers for Disease Control and Prevention. Behavioural Risk Factor Surveillance System. 2022. Available from: https://www.cdc.gov/brfss/index.html.
  22. 22. Hsia J, Zhao G, Town M, Ren J, Okoro CA, Pierannunzi C, et al. Comparisons of Estimates From the Behavioral Risk Factor Surveillance System and Other National Health Surveys, 2011−2016. American Journal of Preventive Medicine. 2020;58(6):e181-e90. pmid:32444008
  23. 23. Centers for Disease Control and Prevention. Methodologic changes in the Behavioral Risk Factor Surveillance System in 2011 and potential effects on prevalence estimates. MMWR Morbidity and Mortality Weekly Rep. 2012;61(22):410–3 pmid:22672976
  24. 24. Nelson DE, Holtzman D, Bolen J, Stanwyck CA, Mack KA. Reliability and validity of measures from the Behavioral Risk Factor Surveillance System (BRFSS). Sozial-Und Praventivmedizin. 2001;46:S3–S42 pmid:11851091
  25. 25. Yore MM, Ham SA, Ainsworth BE, Kruger J, Reis JP, Kohl HW, et al. Reliability and validity of the instrument used in BRFSS to assess physical activity. Medicine & Science in Sports & Exercise. 2007;39(8):1267–74. pmid:17762359
  26. 26. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR, Tudor-Locke C, et al. 2011 Compendium of Physical Activities: A Second Update of Codes and MET Values. Medicine & Science in Sports & Exercise. 2011;43(8):1575–81. pmid:21681120
  27. 27. Centers for Disease Control and Prevention. Physical activity trends—United States, 1990–1998. MMWR Morbidity and Mortality Weekly Rep. 2001;50(9):166–9
  28. 28. Prevention CfDCa. Surveillance of Fruit and Vegetable Intake Using the Behavioral Risk Factor Surveillance System 2011. Available from: https://www.cdc.gov/brfss/pdf/fruits_vegetables.pdf.
  29. 29. Boyer WR, Indelicato NA, Richardson MR, Churilla JR, Johnson TM. Associations between mental distress and physical activity in US adults: a dose-response analysis BRFSS 2011. Journal of Public Health. 2018;40(2):289–94. pmid:28985352
  30. 30. World Health Organization. World health statistics 2023: monitoring health for the SDGs, sustainable development goals. 2023 19 May 2023.
  31. 31. Centers for Disease Control and Prevention. Weighting the BRFSS Data July 2020. 2020 Available from: https://www.cdc.gov/brfss/annual_data/2019/pdf/weighting-2019-508.pdf.
  32. 32. Bastos LS, Oliveira Rde V, Velasque Lde S. Obtaining adjusted prevalence ratios from logistic regression models in cross-sectional studies. Cad Saude Publica. 2015;31(3):487–95. pmid:25859716
  33. 33. Johnston R, Jones K, Manley D. Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour. Quality and Quantity. 2018;52(4):1957–76. pmid:29937587
  34. 34. Sun F, Norman IJ, While AE. Physical activity in older people: a systematic review. Bmc Public Health. 2013;13:449. pmid:23648225
  35. 35. Saffer H, Dave D, Grossman M, Leung LA. Racial, Ethnic, and Gender Differences in Physical Activity. Journal of Human Capital. 2013;7(4):378–410. pmid:25632311
  36. 36. Azevedo MR, Araújo CL, Reichert FF, Siqueira FV, da Silva MC, Hallal PC. Gender differences in leisure-time physical activity. International Journal of Public Health. 2007;52(1):8–15. pmid:17966815
  37. 37. Peng B, Ng JYY, Ha AS. Barriers and facilitators to physical activity for young adult women: a systematic review and thematic synthesis of qualitative literature. International Journal of Behavioral Nutrition and Physical Activity. 2023;20(1):23. pmid:36849995
  38. 38. Carlson SA, Fulton JE, Schoenborn CA, Loustalot F. Trend and Prevalence Estimates Based on the 2008 Physical Activity Guidelines for Americans. American Journal of Preventive Medicine. 2010;39(4):305–13. pmid:20837280
  39. 39. Marquez DX, Neighbors CJ, Bustamante EE. Leisure Time and Occupational Physical Activity among Racial or Ethnic Minorities. Medicine & Science in Sports & Exercise. 2010;42(6) pmid:19997031
  40. 40. Hawkins MS, Storti KL, Richardson CR, King WC, Strath SJ, Holleman RG, et al. Objectively measured physical activity of USA adults by sex, age, and racial/ethnic groups: a cross-sectional study. International Journal of Behavioral Nutrition and Physical Activity. 2009;6(1):31. pmid:19493347
  41. 41. Pavey TG, Kolbe-Alexander TL, Uijtdewilligen L, Brown WJ. Which Women are Highly Active Over a 12-Year Period? A Prospective Analysis of Data from the Australian Longitudinal Study on Women’s Health. Sports Medicine. 2017;47(12):2653–66. pmid:28477211
  42. 42. Kari JT, Viinikainen J, Bockerman P, Tammelin TH, Pitkanen N, Lehtimaki T, et al. Education leads to a more physically active lifestyle: Evidence based on Mendelian randomization. Scand J Med Sci Sports. 2020;30(7):1194–204. pmid:32176397
  43. 43. Kirk MA, Rhodes RE. Occupation correlates of adults’ participation in leisure-time physical activity: a systematic review. American Journal of Preventive Medicine. 2011;40(4):476–85. pmid:21406284
  44. 44. Westerterp KR. Changes in physical activity over the lifespan: impact on body composition and sarcopenic obesity. Obesity Reviews. 2018;19 Suppl 1:8–13. pmid:30511504
  45. 45. Joshi P, Cole K, Overton M. Trends in sedentary behaviors among high school students: Analysis of television and other screen-time activities. Journal of Physical Education and Sport. 2016;16(4):1142–5.
  46. 46. Gropper H, John JM, Sudeck G, Thiel A. The impact of life events and transitions on physical activity: A scoping review. PLOS ONE. 2020;15(6):e0234794. pmid:32569282
  47. 47. Ortega FB, Brown WJ, Lee DC, Baruth M, Sui XM, Blair SN. In Fitness and Health? A Prospective Study of Changes in Marital Status and Fitness in Men and Women. American Journal of Epidemiology. 2011;173(3):337–44. pmid:21123852
  48. 48. Miller J, Nelson T, Barr-Anderson DJ, Christoph MJ, Winkler M, Neumark-Sztainer D. Life Events and Longitudinal Effects on Physical Activity: Adolescence to Adulthood. Medicine & Science in Sports & Exercise. 2019;51(4):663–70. pmid:30673690
  49. 49. Abell LP, Tanase KA, Gilmore ML, Winnicki AE, Holmes VL, Hartos JL. Do physical activity levels differ by number of children at home in women aged 25–44 in the general population? Womens Health (Lond). 2019;15:1745506519871186. pmid:31495288
  50. 50. Kaczynski AT, Manske SR, Mannell RC, Grewal K. Smoking and physical activity: a systematic review. American Journal of Health Behavior. 2008;32(1):93–110. pmid:18021037
  51. 51. Allen MS, Vella SA. Longitudinal determinants of walking, moderate, and vigorous physical activity in Australian adults. Preventive Medicine. 2015;78:101–4. pmid:26212630
  52. 52. van der Avoort CMT, ten Haaf DSM, de Vries JHM, Verdijk LB, van Loon LJC, Eijsvogels TMH, et al. Higher Levels of Physical Activity are Associated with Greater Fruit and Vegetable Intake in Older Adults. The journal of nutrition, health & aging. 2021;25(2):230–41. pmid:33491039
  53. 53. Dinger MK, Brittain DR, Hutchinson SR. Associations Between Physical Activity and Health-Related Factors in a National Sample of College Students. Journal of American College Health. 2014;62(1):67–74. pmid:24313698
  54. 54. Alakaam AAH, Lemacks JL. Fruit and Vegetable Consumption, Fat Intake, and Physical Activity Participation in Relation to Socio-demographic Factors Among Medically Underserved Adults. AIMS Public Health. 2015;2(3):402–10. pmid:29546116
  55. 55. Woolcott C, Dishman R, Motl R, Matthai C, Nigg C. Physical Activity and Fruit and Vegetable Intake: Correlations Between and Within Adults in a Longitudinal Multiethnic Cohort. American journal of health promotion: AJHP. 2013;28. pmid:23458370
  56. 56. Wilson OWA, Graupensperger S, Evans MB, Bopp M. The Temporal Association Between Physical Activity and Fruit and Vegetable Consumption: A Longitudinal Within- and Between-Person Investigation. Journal of Physical Activity & Health. 2019;16(4):274–80. pmid:30849929
  57. 57. Singh B, Olds T, Curtis R, Dumuid D, Virgara R, Watson A, et al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. British Journal of Sports Medicine. 2023. pmid:36796860
  58. 58. Chekroud SR, Gueorguieva R, Zheutlin AB, Paulus M, Krumholz HM, Krystal JH, et al. Association between physical exercise and mental health in 1.2 million individuals in the USA between 2011 and 2015: a cross-sectional study. Lancet Psychiatry. 2018;5(9):739–46. pmid:30099000
  59. 59. Wanjau MN, Möller H, Haigh F, Milat A, Hayek R, Lucas P, et al. Physical Activity and Depression and Anxiety Disorders: A Systematic Review of Reviews and Assessment of Causality. AJPM Focus. 2023;2(2):100074. pmid:37790636
  60. 60. Sodergren M, Sundquist J, Johansson SE, Sundquist K. Physical activity, exercise and self-rated health: a population-based study from Sweden. Bmc Public Health. 2008;8.Artn 352 pmid:18840294
  61. 61. Fass E, Schlesinger T. The Relation of Physical Activity and Self-Rated Health in Older Age-Cross Country Analysis Results from SHARE. Journal of Population Ageing. 2020;13(3):347–64.
  62. 62. Lohne-Seiler H, Hansen BH, Kolle E, Anderssen SA. Accelerometer-determined physical activity and self-reported health in a population of older adults (65–85 years): a cross-sectional study. Bmc Public Health. 2014;14(1):284. pmid:24673834
  63. 63. Dontje ML, Krijnen WP, de Greef MH, Peeters GG, Stolk RP, van der Schans CP, et al. Effect of diagnosis with a chronic disease on physical activity behavior in middle-aged women. Preventive Medicine. 2016;83:56–62. pmid:26656407
  64. 64. Hains-Monfette G, Atoui S, Needham Dancause K, Bernard P. Device-Assessed Physical Activity and Sedentary Behaviors in Canadians with Chronic Disease(s): Findings from the Canadian Health Measures Survey. Sports (Basel). 2019;7(5). pmid:31100779
  65. 65. Silveira EA, Mendonça CR, Delpino FM, Elias Souza GV, Pereira de Souza Rosa L, de Oliveira C, et al. Sedentary behavior, physical inactivity, abdominal obesity and obesity in adults and older adults: A systematic review and meta-analysis. Clinical Nutrition ESPEN. 2022;50:63–73. pmid:35871953
  66. 66. Tudor-Locke C, Brashear MM, Johnson WD, Katzmarzyk PT. Accelerometer profiles of physical activity and inactivity in normal weight, overweight, and obese U.S. men and women. Int J Behav Nutr Phys Act. 2010;7:60. pmid:20682057
  67. 67. Hansen BH, Holme I, Anderssen SA, Kolle E. Patterns of objectively measured physical activity in normal weight, overweight, and obese individuals (20–85 years): a cross-sectional study. PLOS ONE. 2013;8(1):e53044. pmid:23308135
  68. 68. Cassidy S, Chau JY, Catt M, Bauman A, Trenell MI. Low physical activity, high television viewing and poor sleep duration cluster in overweight and obese adults; a cross-sectional study of 398,984 participants from the UK Biobank. International Journal of Behavioral Nutrition and Physical Activity. 2017;14(1):57. pmid:28454540