Citation: Seyyed Abbas Hashemi, Sayeh Ghorbanoghli, Ali Asghar Manouchehri, Mahdi Babaei Hatkehlouei. Pharmacological effect of Allium sativum on coagulation, blood pressure, diabetic nephropathy, neurological disorders, spermatogenesis, antibacterial effects[J]. AIMS Agriculture and Food, 2019, 4(2): 386-398. doi: 10.3934/agrfood.2019.2.386
| [1] | Larrell L. Wilkinson, Saundra H. Glover, Janice C. Probst, Bo Cai, Lisa T. Wigfall . Psychological Distress and Health Insurance Coverage among Formerly Incarcerated Young Adults in the United States. AIMS Public Health, 2015, 2(3): 227-246. doi: 10.3934/publichealth.2015.3.227 |
| [2] | Shervin Assari . Differential protective effects of Family Income-to-Poverty-Ratio on electronic cigarette, depression, and obesity of Black and White Americans. AIMS Public Health, 2024, 11(4): 1157-1171. doi: 10.3934/publichealth.2024060 |
| [3] | Karent Zorogastua, Pathu Sriphanlop, Alyssa Reich, Sarah Aly, Aminata Cisse, Lina Jandorf . Breast and Cervical Cancer Screening among US and non US Born African American Muslim Women in New York City. AIMS Public Health, 2017, 4(1): 78-93. doi: 10.3934/publichealth.2017.1.78 |
| [4] | Alexios Batrakoulis, Oscar L Veiga, Susana Franco, Ewan Thomas, Antonios Alexopoulos, Manel Valcarce-Torrente, Rita Santos-Rocha, Fatima Ramalho, Andrea Di Credico, Daniela Vitucci, Liliana Ramos, Vera Simões, Alejandro Romero-Caballero, Isabel Vieira, Annamaria Mancini, Antonino Bianco . Health and fitness trends in Southern Europe for 2023: A cross-sectional survey. AIMS Public Health, 2023, 10(2): 378-408. doi: 10.3934/publichealth.2023028 |
| [5] | Alexios Batrakoulis, Saeid Fatolahi, Farnaz Dinizadeh . Health and fitness trends in Iran for 2024: A cross-sectional study. AIMS Public Health, 2023, 10(4): 791-813. doi: 10.3934/publichealth.2023053 |
| [6] | Pamila Sadeeka Adikari, KGRV Pathirathna, WKWS Kumarawansa, PD Koggalage . Role of MOH as a grassroots public health manager in preparedness and response for COVID-19 pandemic in Sri Lanka. AIMS Public Health, 2020, 7(3): 606-619. doi: 10.3934/publichealth.2020048 |
| [7] | Marybeth Gasman, Tiffany Smith, Carmen Ye, Thai-Huy Nguyen . HBCUs and the Production of Doctors. AIMS Public Health, 2017, 4(6): 579-589. doi: 10.3934/publichealth.2017.6.579 |
| [8] | Keith Elder, Keon Gilbert, Louise Meret Hanke, Caress Dean, Shahida Rice, Marquisha Johns, Crystal Piper, Jacqueline Wiltshire, Tondra Moore, Jing Wang . Disparities in Confidence to Manage Chronic Diseases in Men. AIMS Public Health, 2014, 1(3): 123-136. doi: 10.3934/publichealth.2014.3.123 |
| [9] | Quynh Nhu (Natasha) B. La Frinere-Sandoval, Catherine Cubbin, Diana M. DiNitto . Perceived neighborhood social cohesion and cervical and breast cancer screening utilization among U.S.-born and immigrant women. AIMS Public Health, 2022, 9(3): 559-573. doi: 10.3934/publichealth.2022039 |
| [10] | Casey T. Harris, Kevin Fitzpatrick, Michael Niño, Priya Thelapurath, Grant Drawve . Examining disparities in the early adoption of Covid-19 personal mitigation across family structures. AIMS Public Health, 2022, 9(3): 589-605. doi: 10.3934/publichealth.2022041 |
Physical activity is essential for overall good health and aids in the prevention and reduction of many diseases. Physical activity has been defined as, “any bodily movement produced by the contraction of skeletal muscle that increases energy expenditure above a basal level” [1]; resulting in enhanced health, if done regularly. In 2008, the U.S. Department of Health and Human Services (DHHS) issued the Physical Activity Guidelines (PAG) for Americans to foster appropriate levels of physical activity at various ages of development, which would help ensure optimal level of health for all citizens [2]. The Physical Activity Guidelines recommend that children and adolescents should perform at least 60 minutes or more of physical activity daily. Adults and older adults should do at least 150 minutes of moderate intensity aerobic physical activity each week [2]. Being physically active is one of the most effective measures that Americans of all ages can do to help promote and maintain good health. Daily physical activity is recognized by DHHS as significant to improving a person's health and quality of life, and it is recognized by Healthy People 2020 (HP 2020) as a leading health indicator [3]. HP 2020 goals and objectives for physical activity represent the latest evidence aimed at individuals to help them meet physical activity guidelines.
Despite the known health benefits of daily physical activity, some population subgroups, including Blacks, consistently fall short of meeting national Physical Activity Guidelines [4]. Given the high prevalence of poor health outcomes associated with low levels of physical activity among Blacks, understanding patterns of physical activity among this population subgroup is important for identifying targeted strategies to promote physical activity within population subcategories. Therefore, the purpose of this paper was to look at four national datasets [Youth Risk Behavior Survey (YRBS), Behavioral Risk Factor Surveillance System (BRFSS), The National Health and Nutrition Examination Survey (NHANES), and National Health Interview Survey (NHIS)] to identify any patterns and trends that could be used to improve physical activity behavior within this population.
Four national datasets (YRBS-2015; BRFSS-2015; NHANES, 2011-2014; and NHIS) were used to estimate the proportion of Black adults and youth meeting national physical activity recommendations overall—stratified by age, gender and other demographic characteristics, to help identify patterns. These surveys use different survey methods, questionnaire styles, and question formats to inquire about respondents' physical activity levels, which allows for an in-depth comparison of physical activity measurements. Compiling and evaluating data from multiple survey sources allowed for a more complete overall view of the physical activity levels and trends among Blacks than evaluating results from a single survey. Comparing results from these data sources will give a broader picture of physical activity levels.
These national surveys are supported by the U.S. Department of Health and Human Services (DHHS) and have been approved by the NCHS Research Ethics Review Board (ERB) and the Office of the IRB (OIRB), depending on the survey [5]. A brief overview of each survey is included in Table 1, with more details below regarding how physical activity guidelines were measured for each dataset. For each dataset, meeting regular physical activity requirements was defined as meeting the aforementioned 2008 PAG guidelines [2].
YRBS is one of the six categories under the Youth Risk Behavior Surveillance System (YRBSS), which monitors health-risk behaviors that contribute to the leading causes of death and disability among youth and young adults [6]. YRBS is a national school-based survey that is representative of all public and private high schools (9th-12th grades), conducted by the CDC bi-annually in all 50 states and the District of Columbia. To ensure a nationally representative sample of students in grades 9 to 12, a three-stage cluster sample design is utilized [6]. The 2015 YRBS sample size for Black students is 1,667. Youth physical activity levels were ascertained from the Youth Risk Behavior Survey 2015 (YRBS) [6]. Students were asked “During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day? (Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time)” [6]. Meeting physical activity recommendations for youth was defined as being physically active for at least 60 minutes on 5 or more days per week.
BRFSS is the nation's premier system of health-related telephone surveys that collects data in all 50 states as well as the District of Columbia and three U.S. territories regarding their health-related risk behaviors, chronic health conditions and use of preventive services [7]. The 2015 BRFFS sample size for Black adults was 34,346. Questions regarding physical activity for BRFSS were asked on odd years [7]. The BRFSS questionnaire asked respondents if they participated in physical activity outside of work. If the respondent answered yes, they were asked to identify the top 2 activities and the number of times per week or month, and the number of hours or minutes they typically did the activity. These activities were converted to metabolic equivalent (MET) levels and classified as moderate- or vigorous-intensity. The number of daily minutes at each intensity level was used to determine achievement of PAG recommendations.
NHANES is a national survey that combines one-on-one interviews with physical and laboratory examinations to assess the health and nutritional status of adults and children in the United States [5]. It is conducted annually and is representative of the country. The health interviews are conducted within a person's home, with medical examinations carried out in specially designed and equipped mobile examination centers [5]. A random subsample of NHANES participants were selected to be included in the medical examination. During the exam, their height and weight were measured, and used to calculate BMI. The 2011-2014 NHANES sample size for Black adults was 2,809. NHANES data asked physical activity questions in the personal interview section of the survey. The questions inquired whether the respondents participated in vigorous recreational activity, and if they answered yes, they were asked the number of days and minutes per day they performed the activity. The same questions were asked for moderate-intensity activity. A person was classified as meeting the recommended level of physical activity if participation included at least 150 minutes per week of moderate-intensity activity, or at least 75 minutes per week of vigorous- intensity activity, or a combination of at least 150 minutes of moderate- and vigorous-intensity activity. NHANES analyses were weighted using the interview weight. Because BMI measurements were for a subsample of the participants, BMI analyses were weighted using the exam weight. Although NHANES collects objective data of physical activity with use of accelerometers, objective measures were not considered for the current analysis [8].
NHIS is a cross-sectional household interview survey conducted by the National Center for Health Statistics (NCHS) [9]. It is an on-going survey that is updated every 10 years and drawn from each State and the District of Columbia. NHIS data is used to monitor trends in illness and disability and to track progress toward achieving national health objectives [9]. NHIS sample size for Black Adults was 5,057 in 2014. The NHIS physical activity questions were asked in the Sample Adult module. Respondents were asked how often they performed vigorous leisure-time activities that caused large increases in breathing or heart rate, and the length of time they performed these activities. Respondents were also asked how often they performed light or moderate leisure-time activities, and the length of time they did these activities. Achievement of PAG recommendations was classified as described above.
Each survey used a complex, sample design, which required weighted data analysis to calculate valid estimates of the population. NHIS used a complex, multi-stage sample design; which also included clustering, stratification, and over-sampling of specific populations. BRFSS uses a state-based, stratified random sample of both landline and cellular telephones. NHANES used a complex, multi-stage, probability sampling design with oversampling of certain groups (which changes with new survey cycles).
In an unweighted data analysis, each record has the same weight. Because of the survey designs, each survey had specific sampling weights which allow calculations to be generalizable to the larger population(s) and to enable appropriate calculations of variance according to the specific study design. STATA software [10] was used to incorporate the proper sample weights for variance estimation. As this paper was intended to be a descriptive analysis, physical activity prevalence estimates and 95% confidence intervals were calculated to indicate the percentage of people meeting the physical activity recommendations, overall and within population subgroups.
| Survey | Mode of Data Collection | Target Population | Total Sample Size | Conducted | Question to assess PA |
| Youth Risk Behavior Survey (YRBS), 2015 | Questionnaires administered in the classroom | High school students (9th-12th grade) | 1667 | Biennially | Consists of five PA questions; two questions are directly related to PA regarding frequency (times/day): During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day? (Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time.). One question is related to time spent in physical education (PE) classes and a question about involvement with a sport or a team |
| Behavioral Risk Factor Surveillance System (BRFSS). 2005 [*] | Telephone | Adults only | 34,346 | Annually | 1. 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? 2. What type of PA or exercise did you spend the most time doing during the past month? 3. How many times per week or per month did you take part in this activity during the past month? 4. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 5. What other type of physical activity gave you the next most exercise during the past month? 6. How many times per week or per month did you take part in this activity during the past month? 7. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 8. During the past month, how many times per week or per month did you do physical activities or exercises to strengthen your muscles? |
| National Health and Nutrition Examination Survey (NHANES), 2011-2014 | Face-to-face interviews in homes and mobile centers (labs and objective monitors) | Adults & Children | 2809 | Annually | 16 questions aimed at assessing PA and fitness. A sample question from the NHANES survey - During the past 7 days, on how many days were physically active for a total of at least 60 minutes per day? Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time? |
| The National Health Interview Survey (NHIS), 2014 | Face-to-face interviews | Adults & Children | 5057 | Annually | Includes a wide range of questions regarding PA from type and frequency of PA, for example, walking and for how long. Questions are asked about membership to fitness facilities and participation with sports and other type of exercises, as well as availability and access to parks or recreational areas. There is also a question regarding a doctor or other health professional recommending any type of exercise or PA, and if so what and how often? |
| PA: Physical Activity; All surveys are collected in the US & DC; [*] = as well as the territories. | |||||
DownLoad: CSVAn overview of participant characteristics and physical activity prevalence estimates is included in Table 2 for youth and Table 3 for adults. The YRBS shows that, overall, less than half of Black youth report meeting PAG recommendations. As evidenced by non-overlapping confidence intervals, boys reported significantly more physical activity than girls. Among both boys and girls, physical activity significantly declined by 12th grade (proxy for increasing age), and also declined with increasing body mass index levels.
Data for adults from all sources (BRFSS, NHANES & NHIS) showed that significantly higher percentages of men (45% to 52%) compared to women (27% to 41%) reported meeting PAG recommendations. Both men and women showed a significant decline in physical activity as they aged; for example, NHANES data show that the percentage of physical activity in men ages 25-44 was 66% and declined to 22% by age ≥ 65; and among women the decline was from 34% (ages 25-44) to 18% (age ≥ 65). Both men and women with higher educational levels reported higher percentages of physical activity; higher education usually equates to higher income levels, and as income levels increased, so did physical activity participation for both men and women. Participants who “were employed” were more physically active across all data sources and gender groups, although physical activity data on these surveys was assessing leisure time physical activity; inclusion of work-related physical activity by some participants cannot be ruled out. “Never married men” reported higher percentages (49% to 57%) of physical activity across all three datasets versus “married men” (39% to 49%). BRFSS and NHIS data showed that women who were “married/living with a partner” reported higher percentage (42%) of physical activity participation than women “No longer married,” or “Never married” (24% to 28%).
NHANES data stratified by weight status showed a steady decline in the proportion of Black women meeting national physical activity recommendations as weight status increased; normal weight (< 25.0 kg/m2) 34% compared to obese (25.0-29.9 kg/m2) 29%, and severely obese (≥ 30.0 kg/m2) women at 24%. Differences in physical activity participation by weight status were not as apparent in men.
Regional data from NHIS show that the Northeast region (Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island and Vermont) reported lower percentages (35% to 38%) of adults being physically active. The western region (Mountain States: Montana, Wyoming, Colorado, New Mexico, Idaho, Utah, Arizona, and Nevada); and Pacific States (Washington, Oregon, California, Alaska, and Hawaii) reported higher percentages (≥ 50%) of adults being physical activity.
| Total Black Children | |||||||
| Sampled (N = 1,667) | Boys (n = 837) | Girls (n = 821) | |||||
| 43.4 | (38.8-48.1) | 52.2 | (46.1, 58.3) | 33.4 | (28.1, 38.6) | ||
| Grade | N | % by Grade meeting PA Levels | |||||
| 9 | 424 | 47.5 | (38.0, 57.0) | 56.6 | (45.5, 67.7) | 35.3 | (26.1, 44.6) |
| 10 | 424 | 44.5 | (39.4, 49.7) | 51.8 | (44.3, 59.3) | 37.2 | (27.8, 46.7) |
| 11 | 423 | 46.1 | (37.7, 54.5) | 54.9 | (41.0, 68.7) | 35.5 | (26.7, 44.3) |
| 12 | 387 | 34.1 | (27.7, 40.5) | 44.0 | (33.3, 54.6) | 24.2 | (18.6, 29.8) |
| Body Mass Index (BMI) | N | % by BMI meeting PA Levels | |||||
| Normal weight | 986 | 47.0 | (42.5, 51.5) | 57.3 | (50.8, 63.7) | 35.2 | (29.2, 41.1) |
| Overweight | 242 | 41.5 | (33.0, 49.9) | 55.7 | (44.5, 66.8) | 31.1 | (20.3, 42.0) |
| Obese | 256 | 38.4 | (25.5, 51.3) | 43.2 | (23.9, 62.4) | 31.9 | (18.2, 45.7) |
| Source: Youth Risk Behavior Survey (YRBS)-2015; PA: Physical Activity. | |||||||
DownLoad: CSV| Total - Estimate % (95% CI) | Men - Estimate % (95% CI) | Women - Estimate % (95% CI) | |||||||
| BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | |
| Sample size N | 34,346 | 2,809 | 5,057 | n = 12,394 | n = 1354 | n = 2047 | n = 21,952 | n = 1455 | n = 3010 |
| Overall Pysical Activity | — | — | — | 47.7 (46.0-49.3) | 44.6 (41.6-47.6) | 51.6 (48.6-54.6) | 40.7 (39.4-42.1) | 27.4 (23.8-31.2) | 38.2 (35.7-40.7) |
| Age | |||||||||
| 18-24 | 2,164 (6.3%) | 457 (16.3%) | 472 (9.3%) | 56.3 (51.4-61.2) | 66.4 (60.2-72.7) | 61.9 (52.8-70.9) | 46.4 (41.8-51.1) | 34.3 (23.6-45.1) | 37.2 (29.1-45.3) |
| 25-44 | 8,349 (24.3%) | 795 (28.3%) | 1,714 (33.9%) | 46.8 (43.8-49.7) | 54.8 (49.0-60.6) | 62.3 (57.3-67.3) | 41.2 (38.8-43.6) | 29.9 (25.3-34.6) | 45.0 (41.2-48.7) |
| 45-64 | 13,690 (39.9%) | 1,011 (36.0%) | 1,838 (36.3%) | 44.6 (41.9-47.3) | 31.8 (27.3-36.2) | 41.4 (37.1-45.8) | 39.0 (36.9-41.2) | 25.0 (19.2-30.8) | 38.8 (35.0-42.6) |
| 65+ | 9,669 (28.2%) | 546 (19.4%) | 1,033 (20.4%) | 49.4 (45.8-53.0) | 21.9 (17.5-26.3) | 33.7 (27.8-39.5) | 38.9 (36.4-41.4) | 17.6 (12.8-22.5) | 21.5 (17.6-25.5) |
| Education | |||||||||
| < HIgh School | 4,243 (12.4%) | 578 (20.6%) | — | 42.7 (38.0-47.4) | 26.4 (20.0-32.7) | not available | 28.2 (24.7-31.6) | 12.3 (6.9-17.7) | not available |
| High School Graduate | 11,282(32.8%) | 703 (25.0%) | — | 45.4 (42.6-48.1) | 38.5 (32.7-44.4) | not available | 37.4 (34.9-39.9) | 21.2 (16.5-25.8) | not available |
| Some College | 9,488 (27.6%) | 885 (31.5%) | — | 50.3 (47.2-53.5) | 50.3 (44.1-56.6) | not available | 43.8 (41.3-46.2) | 28.4 (23.4-33.3) | not available |
| College Graduate | 9,189 (26.8%) | 459 (16.3%) | — | 51.6 (48.5-54.8) | 61.4 (52.5-70.2) | not available | 48.7 (46.2-51.2) | 41.5 (36.9-46.1) | not available |
| Income (BRFSS) | |||||||||
| < $15 ,000 | 5,629 (16.4%) | — | — | 42.1 (37.6-46.5) | — | — | 36.6 (33.5-39.8) | — | — |
| 15,000−25,000 | 6,901 (20.1%) | — | — | 46.2 (42.4-50.0) | — | — | 36.9 (34.2-39.7) | — | — |
| 25,000−35,000 | 3,528 (10.3%) | — | — | 47.9 (42.3-53.4) | — | — | 40.2 (36.1-44.3) | — | — |
| 35,000−50,000 | 3,845 (11.2%) | — | — | 47.7 (43.0-52.3) | — | — | 44.8 (40.4-49.1) | — | — |
| ≥ $50,000 | 8,180 (23.8%) | — | — | 50.9 (47.9-53.8) | — | — | 47.7 (44.9-50.5) | — | — |
| Income (NHIS - NHANES) | |||||||||
| 0−34,999 | — | 1,423 (50.7%) | 2,476 (49.0%) | — | 39.2 (34.9-43.5) | 43.2 (40.0-46.4) | — | 23.4 (19.0-27.7) | 31.0 (28.9-33.2) |
| 35,000−74,999 | — | 684 (24.4%) | 1,206 (23.8%) | — | 47.9 (41.4-54.3) | 51.2 (47.0-55.3) | — | 31.6 (27.3-35.9) | 43.7 (39.9-47.6) |
| 75,000−99,999 | — | 204 (7.3%) | 318 (6.3%) | — | 53.1 (41.3-64.8) | 55.9 (48.2-63.6) | — | 37.3 (23.5-51.2) | 44.4 (36.5-52.3) |
| $100,000+ | — | 274 (9.8%) | 371 (7.3%) | — | 52.4 (44.5-60.4) | 66.5 (59.7-73.3) | — | 35.2 (28.6-41.9) | 54.7 (47.4-62.0) |
| Employment Status | |||||||||
| Employed | 15,638 (45.5%) | 1,405 (50.0%) | 3,013 (59.6%) | 48.7 (46.5-51.0) | 47.6 (43.9-51.3) | 58.0 (54.3-61.7) | 42.5 (40.6-44.4) | 30.9 (26.7-35.0) | 45.0 (41.6-48.4) |
| Not Employed | 18,708 (54.5%) | 1,402 (49.9%) | 2,044 (40.4%) | 46.2 (43.7-48.7) | 40.5 (35.2-45.8) | 39.7 (35.1-44.3) | 38.8 (36.9-40.7) | 23.3 (18.5-28.0) | 27.4 (23.8-31.0) |
| Marital Status | |||||||||
| Married/Living With | 11,591 (33.7%) | 1,086 (38.7%) | 358 (7.1%) | 49.3 (46.7-51.8) | 39.3 (33.6-45.0) | 45.3 (35.1-55.6) | 41.8 (39.4-44.2) | 25.7 (19.5-32.0) | 42.0 (33.4-50.7) |
| No longer married | 12,261 (35.7%) | 713 (25.4%) | 2,757 (54.5%) | 41.1 (37.7-44.5) | 31.7 (26.5-36.9) | 49.0 (45.4-52.6) | 37.8 (35.7-39.9) | 23.9 (19.2-28.6) | 38.4 (35.3-41.5) |
| Never married | 10,262 (29.9%) | 831 (29.6%) | 1,921 (38.0%) | 49.1 (46.3-52.0) | 55.1 (50.3-60.0) | 57.3 (52.2-62.4) | 41.8 (39.5-44.2) | 29.4 (25.2-33.6) | 37.3 (32.9-41.7) |
| Body Mass Index (BMI) | |||||||||
| < 25.0 kg/m2 | 7,506 (21.9%) | 704 (25.1%) | 1,291 (25.5%) | 49.8 (46.6-53.0) | 46.0 (41.1-50.9) | 53.5 (47.6-59.4) | 45.2 (42.2-48.3) | 34.0 (27.4-40.7) | 37.5 (32.2-42.8) |
| 25.0-29.9 kg/m2 | 10,853 (31.6%) | 758 (27.0%) | 1,592 (31.5%) | 49.5 (46.7-52.4) | 47.3 (42.3-52.3) | 53.1 (48.7-57.5) | 45.7 (43.2-48.2) | 28.9 (22.6-35.1) | 46.3 (41.3-51.3) |
| ≥ 30.0 kg/m2 | 12,879 (37.5%) | 1,224 (43.6%) | 1,998 (39.5%) | 44.4 (41.6-47.2) | 41.0 (34.8-47.1) | 48.8 (43.7-53.8) | 36.6 (34.5-38.7) | 24.0 (21.2-26.9) | 34.3 (31.0-37.6) |
| Region of U.S. | |||||||||
| Northeast | — | — | 680 (13.4%) | — | — | 38.4 (29.7-47.1) | — | — | 34.8 (28.4-41.) |
| Midwest | — | — | 864 (17.1%) | — | — | 54.2 (47.1-61.3) | — | — | 34.3 (28.2-40.4) |
| South | — | — | 3,092 (61.1%) | — | — | 54.5 (50.6-58.4) | — | — | 38.7 (35.4-41.9) |
| West | — | — | 421 (8.3%) | — | — | 50.7 (41.5-60.0) | — | — | 50.8 (41.2-60.3) |
| Sources: Behavioral Risk Factor Surveillance System (BRFSS), 2015; The National Health and Nutrition Examination Survey (NHANES), 2011-2014; & National Health Interview Survey (NHIS), 2014. | |||||||||
DownLoad: CSVThe percentage of Black adults achieving physical activity guidelines has improved slightly over the last ten years, but physical activity participation is still low and continues to decline with age. Physical activity is a key factor for healthy aging; however, trends noted in this analysis showed that physical activity declines with age, beginning in youth. The current analysis shows that since 2013 there has been little or no detectable change in the proportion of students in grades 9 to 12 who met the physical activity guidelines for aerobic physical activity [11]. The targeted goal of 32% for adolescents meeting aerobic physical activity guidelines for HP 2020 at midcourse review has not been met [11]. Most programs aimed at youth are school-based, which are viewed as more universally accessible. Yet effective school-based programs are not readily available or accessible, especially to Black youth in low income communities [12]. A study by Sutherland et al. conducted in 2016 looked at the cost effectiveness of a multi-component intervention aimed at improving physical activity in adolescents in secondary schools located in low-income communities, and found them to be cost-effective [13]. The Physical Activity 4 Everyone (PA4E1) program, evaluated by Sutherland et al., may be one good example of a program that could be implemented more universally [13]. Data from the current analysis continue to show declines in physical activity as age increases among adults. Given the studies supporting the positive impact of regular physical activity in older adults, which can significantly reduce health problems associated with heart disease, arthritis and diabetes [14,15], studies are needed to understand how to begin physical activity at young ages and continue it across the lifespan.
Higher prevalence of regular physical activity was observed among boys/men compared with girls/women. This finding is consistent with the literature indicating a significant difference in physical activity levels between males and females regardless of age [16,17]. A study by Lenhart et al. showed that girls were less likely to be active than boys; and girls required more structured physical activity events than boys [18]. These differences noted in childhood continue into adulthood, with women reporting less physical activity than men. Factors that may influence higher levels of physical activity among boys/men compared with girls/women include perceptions of safety, concerns about personal appearance, cultural attitudes about the appropriateness of physical activity for boys/men vs. girls/women, and caregiving duties associated with girls/women that might preclude participation in leisure-time physical activity [17,19,20]. Based on data from the current analysis, only 27% to 41% of Black women are meeting the target physical activity goal of 48 % set by HP 2020, indicating significant improvement is needed.
The data showed that Black women who were “married/living with a partner” reported a higher percentage of physical activity participation than women “No longer married” or “Never married”. Studies show that Black women with a supportive partner who offers encouragement and financial support tend to engage in regular physical activity [21,22]. In contrast, single Black women are less physically active, which may be due to their increased family responsibilities [23]: Black women tend to be the main caregivers for children and others in the household, and therefore may have less time to engage in physical activity [24,25]. Cultural considerations may also prohibit some women from engaging in physical activity [26,27].
Consistent with the previous study, individuals with higher education and income (regardless of gender) also had higher prevalence of being physical activity [28]. Studies support that income level does have a bearing on physical activity [24,29]. A study by Van Domelen et al. in 2011 revealed that men employed full-time were more physically active [30], a finding that was also consistent with this analysis. Sun and colleagues revealed that higher socioeconomic status was positively associated with young Black women being physically active [31], suggesting that increased consideration of income status when designing physical activity programs may be warranted.
Regional data from NHIS show that the Northeast reported lower percentages of adults being physically active. The Western region and Pacific states reported higher percentages of adult being physical activity. The regions that are less physically active coincide with higher chronic disease indicators (diabetes, heart disease and obesity) [32]; supporting the importance of being physically active to help reduce chronic diseases.
Some limitations should be considered when interpreting multiply surveys. For example, the data collection processes varied from phone surveys, to one-on-one interviews, to questionnaires, thus contributing to differences in sample selection, participant responses and results. Likewise, all three-national datasets rely on self-reported measures of physical activity, which is subject to recall bias, and over or under reporting. Additionally, leisure-time physical activity is the primary focus of the national surveillance systems, which overlooks other forms or domains of physical activity (i.e., household, transportation, or occupational). Assessment of all domains of physical activity is needed to provide true estimates of overall physical activity prevalence and to determine if other domains of physical activity offer health benefits. Strengths of the current analysis include samples from national datasets, large sample sizes with which to draw point estimates, and consistency of the trends described in this paper.
It is well established that physical inactivity is not good for a person's health, and some physical activity is better than none. The benefits of regular physical activity in preventing and reducing risks for several chronic conditions, such as cardiovascular disease, hypertension, diabetes, obesity, and some forms of cancers, are well documented. Measures to promote regular daily physical activity are essential for reducing these risks and understanding gender and age difference would aid in the development of effective and sustainable physical activity programs. All people, regardless of age, can benefit from some form of daily physical activity, and older adults >65 years should continue to be as physically active as their abilities and conditions will allow.
Statistics indicate that Blacks are less physically active than Whites across the lifespan [4,6]. Examining national datasets helped identify trends that could be used to inform development of interventions aimed at promoting and maintaining regular physical activity among Blacks across generations and subgroups (women, youth and elderly). Although objective measures are now available from NHANES data, other objective data sources are also needed to obtain and compare more accurate and specific information regarding physical activity behavior and patterns, especially in racial/ethnic minority groups. Based on HP 2020 midcourse review some progress has been made in meeting physical activity guidelines; however, the results from this analysis suggest that more research is needed to gain an in-depth understanding of the psychosocial and economic factors that influence Blacks' participation in physical activity. Obtaining this information could help with developing and implementing effective preventive interventions aimed at getting people of all ages moving to a healthier lifestyle.
Funding to support this manuscript was received from the Rutgers University School of Nursing - Camden, Dean Summer Research Funds.
The manuscript has been approved by all authors and none of the authors has any relevant financial interests related to the research to disclose.
| [1] |
Rivlin RS (2001) Historical perspective on the use of garlic. J Nutr 131: 951–954. doi: 10.1093/jn/131.3.951S
|
| [2] |
Jiang Y, David B, Tu P, et al. (2010) Recent analytical approaches in quality control of traditional Chinese medicines-a review. Anal Chim Acta 657: 9–18. doi: 10.1016/j.aca.2009.10.024
|
| [3] |
Nagourney RA (1998) Garlic: Medicinal food or nutritious medicine? J Med Food 1: 13–28. doi: 10.1089/jmf.1998.1.13
|
| [4] | Tattelman E (2005) Health effects of garlic. Am Fam Physician 72: 103–106. |
| [5] | Avecina A (1991) Ghanoon in Medicine: 458–465. |
| [6] | Koch HP, Lawson LD (1995) Garlic: The Science and Therapeutic Application of Allium Sativum L. and Related Species. Baltimore: Williams & Wilkins, 34–108. |
| [7] | Mathew BC, Daniel RS, Augusti KT (1996) Hypolipidemic effect of garlic protein substituted for casein in diet of rats compared to those of garlic oil. Indian J Exp Biol 34: 337–340. |
| [8] |
Anwar MM, Meki AR (2003) Oxidative stress in streptozotocin-induced diabetic rats: Effects of garlic oil and melatonin. Comp Biochem Physiol A Mol Integr Physiol 135: 539–547. doi: 10.1016/S1095-6433(03)00114-4
|
| [9] | Pedraza-Chaverri J, Yam-Canul P, Chirino YI, et al. (2008) Protective effects of garlic powder against potassium dichromateinduced oxidative stress and nephrotoxicity. Food Chem Toxicol46: 619–627. |
| [10] |
Liu CT, Sheen LY, Lii CK (2007) Does garlic have a role as an antidiabetic agent? Mol Nutr Food Res 51: 1353–1364. doi: 10.1002/mnfr.200700082
|
| [11] |
El-Demerdash FM, Yousef MI, El-Naga NI (2005) Biochemical study on the hypoglycemic effects of onion and garlic in alloxan-induced diabetic rats. Food Chem Toxicol 43: 57–63. doi: 10.1016/j.fct.2004.08.012
|
| [12] |
Liu CT, Hse H, Lii CK, et al. (2005) Effects of garlic oil and diallyl trisulfide on glycemic control in diabetic rats. Eur J Pharmacol 516: 165–173. doi: 10.1016/j.ejphar.2005.04.031
|
| [13] |
Cruz C, Correa-Rotter R, Sánchez-González DJ, et al. (2007) Renoprotective and antihypertensive effects of S-allylcysteine in 5/6 nephrectomized rats. Am J Physiol Renal Physiol 293: F1691–F1698. doi: 10.1152/ajprenal.00235.2007
|
| [14] |
Mensah-Brown EP, Obineche EN, Galadari S, et al. (2005) Streptozotocin-induced diabetic nephropathy in rats: The role of inflammatory cytokines. Cytokine 31: 180–190. doi: 10.1016/j.cyto.2005.04.006
|
| [15] |
Yin MC, Hsu CC, Chiang PF, et al. (2007) Antiinflammatory and antifibrogenic effects of s-ethyl cysteine and s-methyl cysteine in the kidney of diabetic mice. Mol Nutr Food Res 51: 572–579. doi: 10.1002/mnfr.200600213
|
| [16] |
Maldonado PD, Barrera D, Rivero I, et al. (2003) Antioxidant S-allylcysteine prevents gentamicin-induced oxidative stress and renal damage. Free Radic Biol Med 35: 317–324. doi: 10.1016/S0891-5849(03)00312-5
|
| [17] |
Moreno FJ, Corzo-Martı´nez M, del Castillo MD, et al. (2006) Changes in antioxidant activity of dehydrated onion and garlic during storage. Food Res Int 39: 891–897. doi: 10.1016/j.foodres.2006.03.012
|
| [18] | Hamada Y, Fukagawa M (2007) A possible role of thioredoxin interacting protein in the pathogenesis of streptozotocin-induced diabetic nephropathy. Kobe J Med Sci 53: 53–61. |
| [19] | O'Bryan GT, Hostetter TH (1997) The renal hemodynamic basis of diabetic nephropathy. Semin Nephrol 17: 93–100. |
| [20] |
Kalantarinia K, Awad AS, Siragy HM (2003) Urinary and renal interstitial concentrations of TNF-alpha increase prior to the rise in albuminuria in diabetic rats. Kidney Int 64: 1208–1213. doi: 10.1046/j.1523-1755.2003.00237.x
|
| [21] |
Shiju TM, Rajesh NG, Viswanathan P (2013) Renoprotective effect of aged garlic extract in streptozotocin-induced diabetic rats. Indian J Pharmacol 45: 18–23. doi: 10.4103/0253-7613.106429
|
| [22] |
Moriguchi T, Takasugi N, Itakura Y (2001) The effects of aged garlic extract on lipid peroxidation and the deformability of erythrocytes. J Nutr 131: 1016S–1019S. doi: 10.1093/jn/131.3.1016S
|
| [23] |
Fukao H, Yoshida H, Tazawa Y, et al. (2007) Antithrombotic effects of odorless garlic powder both in vitro and in vivo. Biosci Biotechnol Biochem 71: 84–90. doi: 10.1271/bbb.60380
|
| [24] |
Yeh YY, Liu L (2001) Cholesterol lowering effect of garlic extracts and organosulphur compounds: Human and animal studies. J Nutr 131: 989S–993S. doi: 10.1093/jn/131.3.989S
|
| [25] |
Saravanan G, Ponmurugan P, Kumar GPS, et al. (2009) Anti-diabetic properties of S-allylcysteine, a garlic component on streptozotocin induced diabetes in rats. J App Biomed 7: 151–159. doi: 10.32725/jab.2009.017
|
| [26] | Wang X, Jiao F, Wang QW, et al. (2012) Aged black garlic extract induces inhibition of gastric cancer cell growth in vitro and in vivo. Mol Med Rep 5: 66–72. |
| [27] |
Bautista-Garcia P, Sanchez-Lozada LG, Cristobal-Garcia M, et al. (2006) Chronic inhibition of NOS-2 ameliorates renal inhury, as well as COX-2 and TGF-beta 1 overexpression in 5/6 nephrectomized rats. Nephrol Dial Transplant 21: 3074–3081. doi: 10.1093/ndt/gfl444
|
| [28] |
Fujihara CK, Antunes GR, Mattar AL, et al. (2007) Chronic inhibition of nuclear factor-kappa B attenuates renal injury in the 5/6 renal ablation model. Am J Physiol Ren Physiol 292: F92–99. doi: 10.1152/ajprenal.00184.2006
|
| [29] |
Maldonado PD, Barrera D, Medina-Campos ON, et al. (2003) Aged garlic extract attenuates gentamicin induced renal damage and oxidative stress in rats. Lif Sci 73: 2543–2556. doi: 10.1016/S0024-3205(03)00609-X
|
| [30] |
Wongmekiat O, Thamprasert K (2005) Investigating the protective effects of aged garlic extract on cyclosporine induced nephrotoxicity in rats. Fundam Clin Pharmacol 19: 555–562. doi: 10.1111/j.1472-8206.2005.00361.x
|
| [31] |
Kabasakal L, Sehiril O, Cetinel S, et al. (2005) Protective effect of aqueous garlic extract against renal ischemia/reperfusion injury in rats. J Med Food 8: 319–326. doi: 10.1089/jmf.2005.8.319
|
| [32] |
Seckiner I, Bayrak O, Can M, et al. (2014) Garlic supplemented diet attenuates gentamicin nephrotoxicity ın rats. Int Braz J Urol 40: 562–567. doi: 10.1590/S1677-5538.IBJU.2014.04.17
|
| [33] | Mahady GB, Matsuura H, Pendland SL (2004) Allixin, a phytoalexin from garlic, inhibits the growth of Helicobacter pylori in vitro. Am J Gastroenterol 96: 3454–3455. |
| [34] |
O'Gara E, Hill D, Maslin D (2000) Activities of garlic oil, garlic powder, and their diallyl constituents against helicobacter pylori. Appl Environl Microbiol 66: 2269–2273. doi: 10.1128/AEM.66.5.2269-2273.2000
|
| [35] | Cañizares P, Gracia I, Gómez LA, et al. (2004) Thermal degradation of allicin in garlic extracts and its implication on the inhibition of the in-vitro growth of helicobacter pylori. Biotechnol Prog 20: 32–37. |
| [36] |
Gail MH, You WC (2006) A factorial trial including garlic supplements assesses effect in reducing precancerous gastric lesions. J Nutr 136: 813S–815S. doi: 10.1093/jn/136.3.813S
|
| [37] |
Ernst E (1999) Is garlic an effective treatment for Helicobacter pylori infection? Arch Intern Med 159: 2484–2485. doi: 10.1001/archinte.159.20.2484
|
| [38] | Aydin A, Ersoz G, Tekesin O, et al. (2000) Garlic oil and Helicobacter pylori infection. Am J Gastroenterol 95: 563–564. |
| [39] | Prasad K, Laxdal VA, Yu M, et al. (1996) Evaluation of hydroxyl radical-scavenging property of garlic. Mol Cell Biochem 154: 55–63. |
| [40] |
Lai PK, Roy J (2004) Antimicrobial and chemopreventive properties of herbs and spices. Curr Med Chem 11: 1451–1460. doi: 10.2174/0929867043365107
|
| [41] |
Bakri IM, Douglas CWI (2005) Inhibitary effect of garlic extract on oral bacteria. Arch Oral Biol 50: 645–650. doi: 10.1016/j.archoralbio.2004.12.002
|
| [42] | Ferary S, Auger J (1996) What is the true odour of cut allium? Complementarity of various hyphenated methods: Gas chromatography-mass spectrometry and high-performance liquid chromatography-mass spectrometry with particle beam and atmospheric pressure ionization interfaces in sulphenic acids rearrangement components discrimination. J Chromatogr 750: 63–74. |
| [43] | Qiutang L, Inder MV (2002) NF-KB regulation in the immune system. Immunology 2: 725–734. |
| [44] |
Yamaoka Y, Kikuchi S, El–Zimaity HMT, et al. (2002) Importance of helicobacter pylori oipA in clinical presentation, gastric inflammation and mucosal interleukin 8 production. Gastroenterology 123: 414–424. doi: 10.1053/gast.2002.34781
|
| [45] | Crabtree JE (1996) Gastric mucosal inflammatory responses to Helicobacter pylori. Aliment Pharmacol Ther 10 Suppl 1: 29–37. |
| [46] | Reuter HD, Koch HP, Lawson LD (1996 ) Therapeutic effects and applications of garlic and its preparations. In: Garlic: The Science and Therapeutic Application of Allium Sativum and Related Species. 2 Eds., Baltimore: William & Wilkins,162–172. |
| [47] |
Rhodes MJC (1996) Physiologically-active compounds in plant foods: An overview. Proc Nutr Soc 55: 371–384. doi: 10.1079/PNS19960036
|
| [48] | Correa P (1992) Human gastric carcinogenesis: A multistep and multifactorial process-First American Cancer Society Award Lecture on Cancer Epidemiology and Prevention. Cancer Res 52: 6735–6740. |
| [49] | McNulty CAM, Wilson P, Havinga W, et al. (2008) A pilot study to determine the effectiveness of garlic oil capsules in the treatment of dyspeptic patients with helicobacter pylori.Helicobacter 6: 249–253. |
| [50] | Chowdhury AK, Ahsan M, Islam SN, et al. (1991) Efficacy of aqueous extract of garlic and allicin in experimental shigellosis in rabbits. J Med Res 93: 33–36. |
| [51] |
You WC, Zhang L, Gail MH, et al. (1998) Helicobacter pylori infection, garlic intake and precancerous lesions in a Chinese population at low risk of gastric cancer. Int J Epidemiol 27: 941–944. doi: 10.1093/ije/27.6.941
|
| [52] | Keiss HP, Dirsch VM, Hartung T (2003) Garlic (Allium sativum L.) modulates cytokine expression in lipopolysaccharide-activated human blood thereby inhibiting NF-kappaB activity.J Nutr 133: 2171–2175. |
| [53] | Salih BA, Abasiyanik FM (2003) Does regular garlic intake affect the prevalence of Helicobacter pylori in asymptomatic subjects? Saudi Med J 10: 1152. |
| [54] |
Cellini L, Di Campli E, Masulli M, et al. (1996) Inhibition of Helicobacter pylori by garlic extract (Allium sativum). FEMS Immunol Med Microbiol 13: 273–277. doi: 10.1111/j.1574-695X.1996.tb00251.x
|
| [55] |
Bozin B, Mimica-Dukic N, Samojlik I, et al. (2008) Phenolics as antioxidants in garlic (Allium Sativum L. Alliacea). Food Chem 111: 925–929. doi: 10.1016/j.foodchem.2008.04.071
|
| [56] | Song K, Milner JA (2001) The influence of heating on the anticancer properties of garlic. J Nutr131: 10545–10575. |
| [57] | Santos J, Almajano MP, Carbo R (2010) Antimicrobial and antioxidant activity of crude onion (Alliam cepa L.) extracts. Int J food Sci Technol: 403–409. |
| [58] | Dmotoso Go, Oyewopo AO, Kadir RE, et al. (2010) Effects of aqueous extracts of Allium Sativum (Garlic) on semen Parameters in Wistar rats. Int J urology 7: 35–42. |
| [59] |
Corzo-Martinez M, Corzo N, Villamiel M. (2007) Biological properties of onions and garlic.Trends Food Sci Technol 18: 609–625. doi: 10.1016/j.tifs.2007.07.011
|
| [60] |
Jakubowski H (2003) On the health benefits of Allium sp. Nutrition 19: 167–168. doi: 10.1016/S0899-9007(02)00953-X
|
| [61] |
Aitken RJ, Clar Kson JS, Fishel S (1989) Generation of reactive oxygen species lipid per oxidation and human sperm function. Biol Reprod 41: 183–197. doi: 10.1095/biolreprod41.1.183
|
| [62] | Chen CS, Chao HT, Pan RL, et al. (1997) Hydroxyl radicalinduesed decline in Motility and increase in lipid per oxidation and DNA modification in human sperm. Biochem Mol Biol Int43: 291–303. |
| [63] | Kaemmerer H, Mitzkat HJ (1985) Ion-exchange chromatography of amino acids in ejaculates of diabeties. Andrologia 17: 485–487. |
| [64] |
Ali BH (2003) Agents ameliorating or augmenting experimental gentamicin nephrotoxicity: Some recent research. Food Chem Toxicol 41: 1447–1452. doi: 10.1016/S0278-6915(03)00186-8
|
| [65] |
Agarwal A, Nallella KP, Allamaneni SS, et al. (2004) Role of antioxidants in treatment of male in fertility: An overview of the literature. Reprod Biomed Online 8: 616–627. doi: 10.1016/S1472-6483(10)61641-0
|
| [66] | Pal R, Vaiphei K, Arbab S, et al. (2006) The effect of garlic on isoniazid and rifam picin-induced hepatic in Jury in rats. World J Gastroenterol 28: 636–639. |
| [67] |
Pdraza-chaverri J, Maldonado PD, Medine-Campos ON, et al. (2000) Garlic ameliorates gentamicin nephrotoxicity: Relation to antioxidant enzymes. Free Radic Biol Med 29: 602–611. doi: 10.1016/S0891-5849(00)00354-3
|
| [68] |
SU D, Novoselov SV, Sun QA, et al. (2005) Mammalian selenoprotein thioredoxin-glutathione reductase roles in disulfide bond formation and sperm maturation. J Biol Chem 280: 26491–26498. doi: 10.1074/jbc.M503638200
|
| [69] |
Anwar MM, Meki AR (2003) Oxidative stress in streptozotocin-induced diabetic rats, effects of garlic oil and melatonin comp. Biochem physiol 135: 539–547. doi: 10.1016/S1096-4959(03)00139-8
|
| [70] | Chauhan NS, Raoch V, Dixit VK (2008) Effect of curculigo orchioides rhizomes on sexual behavior of made rats. Int J Appl Res Nat Products 1: 26–31. |
| [71] |
Lanzotti V (2006) The analysis of onion and garlic. J Chramatogr A 1112: 3–22. doi: 10.1016/j.chroma.2005.12.016
|
| [72] |
Guneli E, Tugyan K, Ozturk H, et al. (2008) Effect of melatonin on testicular damage in streptozotocin-induced diabetes rats. Eur Surg Res 40: 354–360. doi: 10.1159/000118032
|
| [73] |
Martiez-Cruz F, Guerrero IM, Osuna C (2002) Melatonin prevents the formation of pyrrolized proteins in human plasma induced by hydrogen peroxide. Neurosci Lett 326: 147–150. doi: 10.1016/S0304-3940(02)00020-4
|
| [74] | Mirfard M, Johari H, Mokhtari M, et al. (2011) The effect of Hydro-Alcoholic garlic extract on testis weight and spermatogenesis in mature male rats under chemotherapy with cyclophosphamide. J Fasa Univ Med Sci 3: 123–130. |
| [75] |
Borek C (2006) Garlic reduces dementia and heartdisease risk. J Nutr 136: 810–812. doi: 10.1093/jn/136.3.810S
|
| [76] |
Moriguchi T, Saito H, Nishiyama N (1997) Anti-ageing effect of aged garlic extract in the inbred brain atrophy mouse model. Clin Exp Pharmacol Physiol 24: 235–342. doi: 10.1111/j.1440-1681.1997.tb01813.x
|
| [77] |
Ushijima M, Sumioka I, Kakimoto M, et al. (1997) Effect of garlic and garlic preparations on physiological and psychological stress in mice. Phytother Res 11: 226–230. doi: 10.1002/(SICI)1099-1573(199705)11:3<226::AID-PTR85>3.0.CO;2-E
|
| [78] |
Chauhan NB, Sandoval J (2007) Amelioration of early cognitive deficits by aged garlic extract in Alzheimer's transgenic mice. Phytother Res 21: 629–640. doi: 10.1002/ptr.2122
|
| [79] |
Chauhan NB (2006) Effect of aged garlic extract on APP processing and tau phosphorylation in Alzheimer's transgenic model Tg2576. J Ethn Opharmacol 108: 385–394. doi: 10.1016/j.jep.2006.05.030
|
| [80] |
Yao M, Nguyen TV, Pike CJ (2005) Beta-amyloid-induced neuronal apoptosis involves c-Jun N-terminal kinasedependent downregulation of Bclw. J Neurosci 25: 1149–1158. doi: 10.1523/JNEUROSCI.4736-04.2005
|
| [81] |
Dhingra D, Kumar V (2008) Evidences for the involvement of monoaminergic and GABAergic systems in antidepressantlike activity of garlic extract in mice. Indian J Pharmacol 40: 175–179. doi: 10.4103/0253-7613.43165
|
| [82] | Diaz MR, Sembrano JM (1985) A comparative study of the efficacy of garlic and eugenol as palliative agents against dental pain of pulpal origin. J Philipp Dent Assoc 35: 3–10. |
| [83] | Kumar GR, Reddy KP (1999) Reduced nociceptive responses in mice with alloxan induced hyperglycemia after garlic (Allium sativum Linn.) treatment. Indian J Exp Biol 37: 662–666. |
| [84] |
Nagourney RA (1998) Garlic: Medicinal food or nutritious medicine? J Med Food 1: 13–28. doi: 10.1089/jmf.1998.1.13
|
| [85] |
Nishiyama N, Moriguchi T, Morihara N, et al. (2001) Ameliorative effect of S-allylcysteine, a major thioallyl constituent in aged garlic extract, on learning deficits in senescenceaccelerated mice. J Nutr 131: 1093–1095. doi: 10.1093/jn/131.3.1093S
|
| [86] |
Pérez-Severiano F, Salvatierra-Sánchez R, Rodríguez-Pérez M, et al. (2004) SAllylcysteine prevents amyloid-beta peptide-induced oxidative stress in rat hippocampus and ameliorates learning deficits. Eur J Pharmacol 489: 197–202. doi: 10.1016/j.ejphar.2004.03.001
|
| [87] | Kalantari H, Salehi M (2001) The protective effect of garlic oil on hepatototoxicity induced by acetaminophen in mice and comparison with N-acetylcysteine. Saudi Med J 22: 1080–1084. |
| [88] |
Tirranem LS, Borodina EV, Ushakova SA, et al. (2001) Effect of volatile metabolites of dill, radish and garlic on growth of bacteria. Acta Astronaut 49: 105–108. doi: 10.1016/S0094-5765(01)00006-6
|
| [89] | Senapati SK, Dey S, Dwivedi SK, et al. (2001) Effect of garlic (Allium sativum L.) extract on tissue lead level in rats. J Ethnopharmacol 76: 229–332. |
| [90] |
Kannar D, Wattanapenpaiboon N, Savage GS, et al. (2001) Hypocholestrolemic effect of an enteric-coated garlic supplement. J Am Coll Nut 20: 225–231. doi: 10.1080/07315724.2001.10719036
|
| [91] |
McNulty CA, Wilson MP, Havinga W, et al. (2001) A Pilot study to determine the effectiveness of garlic oil Capsules in the treatment of dyspeptic patients with Helicobacter Pylori.Helicobacter 6: 249–253. doi: 10.1046/j.1523-5378.2001.00036.x
|
| [92] | Cicero AF, Derosa G, Gaddi A (2004) What do herbalists suggest to diabetic patients in order to improve glycemic control? Evaluation of scientific evidence and potential risks. Acta Diabetol41: 91–98. |
| [93] |
Josling P (2001) Preventing the common cold with a garlic supplement: Adouble-blind, placebo-comtrolled survey. Adv Ther 18: 189–193. doi: 10.1007/BF02850113
|
| [94] |
Williamson EM (2005) Interactions between herbal and conventional medicines. Expert Opin Drag Sas 4: 355–378. doi: 10.1517/14740338.4.2.355
|
| [95] | Warren Grant Magnuson clinical center, National Institutes of Health Drug-Nutrient interaction Task force (2003) Maryland USA, Important information to know when you are taking: Coumadin and vitamin k. Acta Diabetol 41: 91–98. |
| [96] |
IZZO AA, Di-carlo G, Borrelli F, et al. (2005) Cardiovascular pharmacotherapy and herbal medicines: The risk of drug interaction. Int J Cardiol 98: 1–14. doi: 10.1016/j.ijcard.2003.06.039
|
| [97] | Tattelman E (2005) Health effects of Garlic. Am Fam Physician 72: 103–106. |
| [98] | Jabbari A, Argani H, Ghorbanihaghgo A, et al. (2005) Comparision between swallowing and chewing of garlic on levels of serum lipids, cyclosporine, creatinine and lipid peroxidation in renal tnansplant recipient. Lipids Health Dis: 72–77. |
| [99] | Hagdon Jane (2005) Garlic and organosulfur compounds. Plant Bioact Res Institute, orem, utah, USA, 1–8. |
| [100] |
Lee Moffitt (2005) Herbal supplements that modulate coagulation in cancer patients. Cancer Control Res Institute 12: 149–157. doi: 10.1177/107327480501200302
|
| [101] |
Ramsay NA, Kenny MW, Davies G, et al. (2005) Complimentary and alternative medicine use among patients starting warfarin. Br J Haematol 130: 777–780. doi: 10.1111/j.1365-2141.2005.05689.x
|
| [102] | Blumenthal M, Goldberg A, Brinckman J (2004) Herbal medicine: Expanded commission E Monographs. American Botanical Council. Newton: Integrative medicine Communication, 1029 Chestnut street, 139–148. |
| [103] | Lawson LD (2004) A review of its medicinal effect and indicated active compounds. In: Lawson, LD., Bauer, R., Eds. Phytomedicines of Europe: Chemistry and Biological Activity. Washington, Dc: American Chemical Society Symposium Series 691, 176–209. |
| [104] | Fakhar H, Hashemi Tayer A (2012) Effect of the garlic pill in comparison with Plavix on platelet aggregation and bleeding Time. Iran J Ped Hematol Oncol 2: 146–152. |
| [105] |
Al-Qattan KK, Thomson M, Al-Mutawa'a S, et al. (2006) Nitric oxide mediates the blood-pressure lowering effect of garlic in the rat two-kidney, one-clip model of hypertension. J Nutr 136: 774S–776S. doi: 10.1093/jn/136.3.774S
|
| [106] |
Sharifi AM, Darabi R, Akbarloo N (2003) Investigation of antihypertensive mechanism of garlic in 2K1C hypertensive rat. J Ethnopharmacol 86: 219–224. doi: 10.1016/S0378-8741(03)00080-1
|
| [107] |
Morihara N, Sumioka I, Moriguchi T, et al. (2002) Aged garlic extract enhances production of nitric oxide. Life Sci 71: 509–517. doi: 10.1016/S0024-3205(02)01706-X
|
| [108] |
Benavides GA, Squadrito GL, Mills RW, et al. (2007) Hydrogen sulfide mediates the vasoactivity of garlic. Proc Natl Acad Sci USA 104: 17977–17982. doi: 10.1073/pnas.0705710104
|
| [109] |
Chuah SC, Moore PK, Zhu YZ (2007) S-allylcysteine mediates cardioprotection in an acute myocardial infarction rat model via a hydrogen sulfide-mediated pathway. Am J Physiol Heart Circ Physiol 293: H2693–H2701. doi: 10.1152/ajpheart.00853.2007
|
| [110] |
Shouk R, Abdou A, Shetty K, et al. (2014) Mechanisms underlying the antihypertensive effects of garlic bioactives. Nutr Res 34: 106–115. doi: 10.1016/j.nutres.2013.12.005
|
| [111] | Karin Rie, Peter Fakler (2014) Potential of garlic (Allium sativum) in lowering high blood pressure: Mechanisms of action and clinical relevance. Integr Blood Press Control 7: 71–82. |
| [112] | Ried K (2014) Effect of garlic on blood pressure, serum cholesterol and immunity: Updated meta-analyses and review. J Nutr 71–82. |
| 1. | Melicia C. Whitt-Glover, David X. Marquez, Olivia Affuso, Timothy K. Behrens, Mina L. Liebert, 2019, 978-0-87553-309-4, 10.2105/9780875533100ch01 | |
| 2. | Yan Wang, Fang Sonh, Hai-hong Bian, 2020, Chapter 18, 978-3-030-63951-8, 204, 10.1007/978-3-030-63952-5_18 | |
| 3. | Amber W. Kinsey, Michelle L. Segar, Daheia J. Barr-Anderson, Melicia C. Whitt-Glover, Olivia Affuso, Positive Outliers Among African American Women and the Factors Associated with Long-Term Physical Activity Maintenance, 2019, 6, 2197-3792, 603, 10.1007/s40615-018-00559-4 | |
| 4. | Dawn M. Aycock, Patricia C. Clark, Aaron M. Anderson, Dhruvangi Sharma, Health Perceptions, Stroke Risk, and Readiness for Behavior Change: Gender Differences in Young Adult African Americans, 2019, 6, 2197-3792, 821, 10.1007/s40615-019-00581-0 | |
| 5. | Rodney P Joseph, Colleen Keller, Sonia Vega-López, Marc A Adams, Rebekah English, Kevin Hollingshead, Steven P Hooker, Michael Todd, Glenn A Gaesser, Barbara E Ainsworth, A Culturally Relevant Smartphone-Delivered Physical Activity Intervention for African American Women: Development and Initial Usability Tests of Smart Walk, 2020, 8, 2291-5222, e15346, 10.2196/15346 | |
| 6. | Rodney P Joseph, Barbara E Ainsworth, Kevin Hollingshead, Michael Todd, Colleen Keller, Results of a Culturally Tailored Smartphone-delivered Physical Activity Intervention among Midlife African American Women: A Feasibility Trial (Preprint), 2021, 2291-5222, 10.2196/27383 | |
| 7. | Cathy Antonakos, Ross Baiers, Tamara Dubowitz, Philippa Clarke, Natalie Colabianchi, Associations between body mass index, physical activity and the built environment in disadvantaged, minority neighborhoods: Predictive validity of GigaPan® imagery, 2020, 17, 22141405, 100867, 10.1016/j.jth.2020.100867 | |
| 8. | Loneke T. Blackman Carr, Brooke T. Nezami, Lucia A. Leone, Perceived Benefits and Barriers in the Mediation of Exercise Differences in Older Black Women with and Without Obesity, 2020, 7, 2197-3792, 807, 10.1007/s40615-020-00788-6 | |
| 9. | Owen Carmichael, Robert Newton, 2019, 165, 9780128183618, 3, 10.1016/bs.pmbts.2019.04.002 | |
| 10. | Rodney P. Joseph, Barbara E. Ainsworth, Sonia Vega-López, Marc A. Adams, Kevin Hollingshead, Steven P. Hooker, Michael Todd, Glenn A. Gaesser, Colleen Keller, Rationale and design of Smart Walk: A randomized controlled pilot trial of a smartphone-delivered physical activity and cardiometabolic risk reduction intervention for African American women, 2019, 77, 15517144, 46, 10.1016/j.cct.2018.12.011 | |
| 11. | John A. Bernhart, Gabrielle M. Turner‐McGrievy, Sarah Eustis, Mary J. Wilson, Brent Hutto, Sara Wilcox, Edward A. Frongillo, E. Angela Murphy, Sensor‐measured physical activity is associated with decreased cardiovascular disease risk in African Americans, 2020, 1, 2688-3740, 10.1002/lim2.16 | |
| 12. | Tori Alexis Justin, Shannon Jette, “That chart ain’t for us”: How Black women understand “obesity,” health, and physical activity, 2022, 26, 1363-4593, 605, 10.1177/13634593211046844 | |
| 13. | Guillermo M. Wippold, Kaylyn A. Garcia, Sarah Grace Frary, The role of sense of community in improving the health‐related quality of life among Black Americans, 2023, 51, 0090-4392, 251, 10.1002/jcop.22901 | |
| 14. | Samara R. Sterling, Shelly-Ann Bowen, Effect of a Plant-based Intervention Among Black Individuals in the Deep South: A Pilot Study, 2023, 55, 14994046, 68, 10.1016/j.jneb.2022.08.013 | |
| 15. | Natasha R. Brown, La’Marcus T. Wingate, The Influence of Memorable Message Receipt on Dietary and Exercise Behavior among Self-Identified Black Women, 2022, 37, 1041-0236, 1157, 10.1080/10410236.2021.1962587 | |
| 16. | Jean W. Davis, Suzanne Hyer, Rui Xie, Valerie C. Martinez, Jenna M. Wheeler, Dawn P. Misra, Carmen Giurgescu, Physical activity changes among non‐Hispanic Black pregnant women, 2022, 39, 0737-1209, 744, 10.1111/phn.13051 | |
| 17. | Olivia Affuso, Amber W. Kinsey, Melicia C. Whitt-Glover, Michelle Segar, Pamela Bowen, Social Environments and Physical Activity Among Active Black Women, 2022, 36, 0890-1171, 1275, 10.1177/08901171221102139 | |
| 18. | Ahmed M. Nahari, Leslie L. Davis, Debra C. Wallace, Stephanie Pickett, Paul G. Davis, Thomas P. McCoy, Intrapersonal and Interpersonal Factors Influencing Physical Activity in Arab Americans, 2022, Publish Ahead of Print, 1550-5049, 10.1097/JCN.0000000000000962 | |
| 19. | Rodney P. Joseph, Michael Todd, Barbara E. Ainsworth, Sonia Vega-López, Marc A. Adams, Kevin Hollingshead, Steven P. Hooker, Glenn A. Gaesser, Colleen Keller, Smart Walk: A Culturally Tailored Smartphone-Delivered Physical Activity Intervention for Cardiometabolic Risk Reduction among African American Women, 2023, 20, 1660-4601, 1000, 10.3390/ijerph20021000 | |
| 20. | David X. Marquez, Michelle A. Jaldin, Miguel Negrete, Melicia C. Whitt-Glover, Crystal M. Glover, Physical Activity and Health Equity for Middle-Aged and Older Adults, 2023, 12, 2163-0453, 66, 10.1123/kr.2022-0032 | |
| 21. | Willie M. Abel, Jimmy T. Efird, Patricia B. Crane, Keith C. Ferdinand, Capri G. Foy, Mark J. DeHaven, Use of coaching and technology to improve blood pressure control in Black women with hypertension: Pilot randomized controlled trial study, 2023, 25, 1524-6175, 95, 10.1111/jch.14617 | |
| 22. | Soohyun Nam, Sunyoung Jung, David Vlahov, Carl Latkin, Trace Kershaw, Robin Whittemore, Luca De Benedictis, Body mass index and obesity-related behaviors in African American church-based networks: A social network analysis, 2023, 18, 1932-6203, e0281145, 10.1371/journal.pone.0281145 | |
| 23. | Ashley Kuzmik, Marie Boltz, Kyungha Kim, Yining Ma, Xingran Weng, Li Wang, Physical Activity Level and Specific Type of Exercises Among US Middle-Aged and Older Adults: Findings From the Behavioral Risk Factor Surveillance Survey, 2023, 1543-3080, 1, 10.1123/jpah.2022-0308 | |
| 24. | Jolaade Kalinowski, Christie Idiong, Loneke Blackman-Carr, Kristen Cooksey Stowers, Shardé Davis, Cindy Pan, Alisha Chhabra, Lisa Eaton, Kim M Gans, Jay Ell Alexander, Sherry Pagoto, Leveraging the Black Girls Run Web-Based Community as a Supportive Community for Physical Activity Engagement: Mixed Methods Study, 2023, 7, 2561-326X, e43825, 10.2196/43825 | |
| 25. | Felicitas A. Huber, Rachel Carpenter, Burel R. Goodin, Stephen Bruehl, Cynthia Karlson, Uma Rao, Kerry Kinney, Subodh Nag, Matthew C. Morris, Physical activity, sitting time, and thermal quantitative sensory testing responses in African Americans, 2023, 8, 2471-2531, e1118, 10.1097/PR9.0000000000001118 | |
| 26. | Zachary Wahl-Alexander, Jennifer Jacobs, Christopher M. Hill, Gabrielle Bennett, Examining body mass index and health-related fitness marker progression of incarcerated minority youth engaged in a sport-leadership program, 2024, 2977-0254, 10.1108/IJOPH-01-2023-0005 | |
| 27. | Abiola O. Keller, Jennifer M. Ohlendorf, Engagement in physical activity among African American women caregivers: a cross-sectional study, 2024, 0895-2841, 1, 10.1080/08952841.2024.2325201 | |
| 28. | Delaney Beck, Joni Hersch, W. Kip Viscusi, Differences by Race and Ethnicity in Title IX’s Effect on Women’s Health, 2023, 14, 2194-5888, 437, 10.1017/bca.2024.2 | |
| 29. | Gurleen Kaur, Ofer Kobo, Purvi Parwani, Alaide Chieffo, Martha Gulati, Mamas A. Mamas, Sex Differences in Life's Essential Eight and its Association with Mortality Among US Adults Without Known Cardiovascular Disease, 2024, 26666677, 100685, 10.1016/j.ajpc.2024.100685 | |
| 30. | Tienna Fenton, Elena Bastida, Physical Activity and Sedentary Behaviors Trends Among Black High School Students, 2009–2019, 2024, 0270-1367, 1, 10.1080/02701367.2024.2351903 | |
| 31. | Tiwaloluwa A. Ajibewa, Ruth-Alma Turkson Ocran, Mercedes R. Carnethon, Faith E. Metlock, Xiaoyue Liu, Yvonne Commodore-Mensah, Physical Activity Engagement among Black Immigrants and African American Adults in the 2010 to 2018 NHIS Study, 2024, 34, 1945-0826, 165, 10.18865/EthnDis-2023-45 | |
| 32. | Bose Maposa, Emily Hill Guseman, Dawn Graham, Zelalem Haile, Physical activity engagement among Black African-born immigrant women in the United States: Results from the 2011 to 2018 National Health Interview Surveys, 2025, 00279684, 10.1016/j.jnma.2025.01.008 | |
| 33. | Grace Derboghossian, Janice B. Foust, Laura L. Hayman, Social and Religious Networks with Respect to the Health Behaviors of African American Women: A Systematic Review, 2024, 0022-4197, 10.1007/s10943-024-02147-9 |
Seyyed Abbas Hashemi, Sayeh Ghorbanoghli, Ali Asghar Manouchehri, Mahdi Babaei Hatkehlouei. Pharmacological effect of Allium sativum on coagulation, blood pressure, diabetic nephropathy, neurological disorders, spermatogenesis, antibacterial effects[J]. AIMS Agriculture and Food, 2019, 4(2): 386-398. doi: 10.3934/agrfood.2019.2.386
| Survey | Mode of Data Collection | Target Population | Total Sample Size | Conducted | Question to assess PA |
| Youth Risk Behavior Survey (YRBS), 2015 | Questionnaires administered in the classroom | High school students (9th-12th grade) | 1667 | Biennially | Consists of five PA questions; two questions are directly related to PA regarding frequency (times/day): During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day? (Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time.). One question is related to time spent in physical education (PE) classes and a question about involvement with a sport or a team |
| Behavioral Risk Factor Surveillance System (BRFSS). 2005 [*] | Telephone | Adults only | 34,346 | Annually | 1. 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? 2. What type of PA or exercise did you spend the most time doing during the past month? 3. How many times per week or per month did you take part in this activity during the past month? 4. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 5. What other type of physical activity gave you the next most exercise during the past month? 6. How many times per week or per month did you take part in this activity during the past month? 7. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 8. During the past month, how many times per week or per month did you do physical activities or exercises to strengthen your muscles? |
| National Health and Nutrition Examination Survey (NHANES), 2011-2014 | Face-to-face interviews in homes and mobile centers (labs and objective monitors) | Adults & Children | 2809 | Annually | 16 questions aimed at assessing PA and fitness. A sample question from the NHANES survey - During the past 7 days, on how many days were physically active for a total of at least 60 minutes per day? Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time? |
| The National Health Interview Survey (NHIS), 2014 | Face-to-face interviews | Adults & Children | 5057 | Annually | Includes a wide range of questions regarding PA from type and frequency of PA, for example, walking and for how long. Questions are asked about membership to fitness facilities and participation with sports and other type of exercises, as well as availability and access to parks or recreational areas. There is also a question regarding a doctor or other health professional recommending any type of exercise or PA, and if so what and how often? |
| PA: Physical Activity; All surveys are collected in the US & DC; [*] = as well as the territories. | |||||
DownLoad: CSV| Total Black Children | |||||||
| Sampled (N = 1,667) | Boys (n = 837) | Girls (n = 821) | |||||
| 43.4 | (38.8-48.1) | 52.2 | (46.1, 58.3) | 33.4 | (28.1, 38.6) | ||
| Grade | N | % by Grade meeting PA Levels | |||||
| 9 | 424 | 47.5 | (38.0, 57.0) | 56.6 | (45.5, 67.7) | 35.3 | (26.1, 44.6) |
| 10 | 424 | 44.5 | (39.4, 49.7) | 51.8 | (44.3, 59.3) | 37.2 | (27.8, 46.7) |
| 11 | 423 | 46.1 | (37.7, 54.5) | 54.9 | (41.0, 68.7) | 35.5 | (26.7, 44.3) |
| 12 | 387 | 34.1 | (27.7, 40.5) | 44.0 | (33.3, 54.6) | 24.2 | (18.6, 29.8) |
| Body Mass Index (BMI) | N | % by BMI meeting PA Levels | |||||
| Normal weight | 986 | 47.0 | (42.5, 51.5) | 57.3 | (50.8, 63.7) | 35.2 | (29.2, 41.1) |
| Overweight | 242 | 41.5 | (33.0, 49.9) | 55.7 | (44.5, 66.8) | 31.1 | (20.3, 42.0) |
| Obese | 256 | 38.4 | (25.5, 51.3) | 43.2 | (23.9, 62.4) | 31.9 | (18.2, 45.7) |
| Source: Youth Risk Behavior Survey (YRBS)-2015; PA: Physical Activity. | |||||||
DownLoad: CSV| Total - Estimate % (95% CI) | Men - Estimate % (95% CI) | Women - Estimate % (95% CI) | |||||||
| BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | |
| Sample size N | 34,346 | 2,809 | 5,057 | n = 12,394 | n = 1354 | n = 2047 | n = 21,952 | n = 1455 | n = 3010 |
| Overall Pysical Activity | — | — | — | 47.7 (46.0-49.3) | 44.6 (41.6-47.6) | 51.6 (48.6-54.6) | 40.7 (39.4-42.1) | 27.4 (23.8-31.2) | 38.2 (35.7-40.7) |
| Age | |||||||||
| 18-24 | 2,164 (6.3%) | 457 (16.3%) | 472 (9.3%) | 56.3 (51.4-61.2) | 66.4 (60.2-72.7) | 61.9 (52.8-70.9) | 46.4 (41.8-51.1) | 34.3 (23.6-45.1) | 37.2 (29.1-45.3) |
| 25-44 | 8,349 (24.3%) | 795 (28.3%) | 1,714 (33.9%) | 46.8 (43.8-49.7) | 54.8 (49.0-60.6) | 62.3 (57.3-67.3) | 41.2 (38.8-43.6) | 29.9 (25.3-34.6) | 45.0 (41.2-48.7) |
| 45-64 | 13,690 (39.9%) | 1,011 (36.0%) | 1,838 (36.3%) | 44.6 (41.9-47.3) | 31.8 (27.3-36.2) | 41.4 (37.1-45.8) | 39.0 (36.9-41.2) | 25.0 (19.2-30.8) | 38.8 (35.0-42.6) |
| 65+ | 9,669 (28.2%) | 546 (19.4%) | 1,033 (20.4%) | 49.4 (45.8-53.0) | 21.9 (17.5-26.3) | 33.7 (27.8-39.5) | 38.9 (36.4-41.4) | 17.6 (12.8-22.5) | 21.5 (17.6-25.5) |
| Education | |||||||||
| < HIgh School | 4,243 (12.4%) | 578 (20.6%) | — | 42.7 (38.0-47.4) | 26.4 (20.0-32.7) | not available | 28.2 (24.7-31.6) | 12.3 (6.9-17.7) | not available |
| High School Graduate | 11,282(32.8%) | 703 (25.0%) | — | 45.4 (42.6-48.1) | 38.5 (32.7-44.4) | not available | 37.4 (34.9-39.9) | 21.2 (16.5-25.8) | not available |
| Some College | 9,488 (27.6%) | 885 (31.5%) | — | 50.3 (47.2-53.5) | 50.3 (44.1-56.6) | not available | 43.8 (41.3-46.2) | 28.4 (23.4-33.3) | not available |
| College Graduate | 9,189 (26.8%) | 459 (16.3%) | — | 51.6 (48.5-54.8) | 61.4 (52.5-70.2) | not available | 48.7 (46.2-51.2) | 41.5 (36.9-46.1) | not available |
| Income (BRFSS) | |||||||||
| < $15 ,000 | 5,629 (16.4%) | — | — | 42.1 (37.6-46.5) | — | — | 36.6 (33.5-39.8) | — | — |
| 15,000−25,000 | 6,901 (20.1%) | — | — | 46.2 (42.4-50.0) | — | — | 36.9 (34.2-39.7) | — | — |
| 25,000−35,000 | 3,528 (10.3%) | — | — | 47.9 (42.3-53.4) | — | — | 40.2 (36.1-44.3) | — | — |
| 35,000−50,000 | 3,845 (11.2%) | — | — | 47.7 (43.0-52.3) | — | — | 44.8 (40.4-49.1) | — | — |
| ≥ $50,000 | 8,180 (23.8%) | — | — | 50.9 (47.9-53.8) | — | — | 47.7 (44.9-50.5) | — | — |
| Income (NHIS - NHANES) | |||||||||
| 0−34,999 | — | 1,423 (50.7%) | 2,476 (49.0%) | — | 39.2 (34.9-43.5) | 43.2 (40.0-46.4) | — | 23.4 (19.0-27.7) | 31.0 (28.9-33.2) |
| 35,000−74,999 | — | 684 (24.4%) | 1,206 (23.8%) | — | 47.9 (41.4-54.3) | 51.2 (47.0-55.3) | — | 31.6 (27.3-35.9) | 43.7 (39.9-47.6) |
| 75,000−99,999 | — | 204 (7.3%) | 318 (6.3%) | — | 53.1 (41.3-64.8) | 55.9 (48.2-63.6) | — | 37.3 (23.5-51.2) | 44.4 (36.5-52.3) |
| $100,000+ | — | 274 (9.8%) | 371 (7.3%) | — | 52.4 (44.5-60.4) | 66.5 (59.7-73.3) | — | 35.2 (28.6-41.9) | 54.7 (47.4-62.0) |
| Employment Status | |||||||||
| Employed | 15,638 (45.5%) | 1,405 (50.0%) | 3,013 (59.6%) | 48.7 (46.5-51.0) | 47.6 (43.9-51.3) | 58.0 (54.3-61.7) | 42.5 (40.6-44.4) | 30.9 (26.7-35.0) | 45.0 (41.6-48.4) |
| Not Employed | 18,708 (54.5%) | 1,402 (49.9%) | 2,044 (40.4%) | 46.2 (43.7-48.7) | 40.5 (35.2-45.8) | 39.7 (35.1-44.3) | 38.8 (36.9-40.7) | 23.3 (18.5-28.0) | 27.4 (23.8-31.0) |
| Marital Status | |||||||||
| Married/Living With | 11,591 (33.7%) | 1,086 (38.7%) | 358 (7.1%) | 49.3 (46.7-51.8) | 39.3 (33.6-45.0) | 45.3 (35.1-55.6) | 41.8 (39.4-44.2) | 25.7 (19.5-32.0) | 42.0 (33.4-50.7) |
| No longer married | 12,261 (35.7%) | 713 (25.4%) | 2,757 (54.5%) | 41.1 (37.7-44.5) | 31.7 (26.5-36.9) | 49.0 (45.4-52.6) | 37.8 (35.7-39.9) | 23.9 (19.2-28.6) | 38.4 (35.3-41.5) |
| Never married | 10,262 (29.9%) | 831 (29.6%) | 1,921 (38.0%) | 49.1 (46.3-52.0) | 55.1 (50.3-60.0) | 57.3 (52.2-62.4) | 41.8 (39.5-44.2) | 29.4 (25.2-33.6) | 37.3 (32.9-41.7) |
| Body Mass Index (BMI) | |||||||||
| < 25.0 kg/m2 | 7,506 (21.9%) | 704 (25.1%) | 1,291 (25.5%) | 49.8 (46.6-53.0) | 46.0 (41.1-50.9) | 53.5 (47.6-59.4) | 45.2 (42.2-48.3) | 34.0 (27.4-40.7) | 37.5 (32.2-42.8) |
| 25.0-29.9 kg/m2 | 10,853 (31.6%) | 758 (27.0%) | 1,592 (31.5%) | 49.5 (46.7-52.4) | 47.3 (42.3-52.3) | 53.1 (48.7-57.5) | 45.7 (43.2-48.2) | 28.9 (22.6-35.1) | 46.3 (41.3-51.3) |
| ≥ 30.0 kg/m2 | 12,879 (37.5%) | 1,224 (43.6%) | 1,998 (39.5%) | 44.4 (41.6-47.2) | 41.0 (34.8-47.1) | 48.8 (43.7-53.8) | 36.6 (34.5-38.7) | 24.0 (21.2-26.9) | 34.3 (31.0-37.6) |
| Region of U.S. | |||||||||
| Northeast | — | — | 680 (13.4%) | — | — | 38.4 (29.7-47.1) | — | — | 34.8 (28.4-41.) |
| Midwest | — | — | 864 (17.1%) | — | — | 54.2 (47.1-61.3) | — | — | 34.3 (28.2-40.4) |
| South | — | — | 3,092 (61.1%) | — | — | 54.5 (50.6-58.4) | — | — | 38.7 (35.4-41.9) |
| West | — | — | 421 (8.3%) | — | — | 50.7 (41.5-60.0) | — | — | 50.8 (41.2-60.3) |
| Sources: Behavioral Risk Factor Surveillance System (BRFSS), 2015; The National Health and Nutrition Examination Survey (NHANES), 2011-2014; & National Health Interview Survey (NHIS), 2014. | |||||||||
DownLoad: CSV| Survey | Mode of Data Collection | Target Population | Total Sample Size | Conducted | Question to assess PA |
| Youth Risk Behavior Survey (YRBS), 2015 | Questionnaires administered in the classroom | High school students (9th-12th grade) | 1667 | Biennially | Consists of five PA questions; two questions are directly related to PA regarding frequency (times/day): During the past 7 days, on how many days were you physically active for a total of at least 60 minutes per day? (Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time.). One question is related to time spent in physical education (PE) classes and a question about involvement with a sport or a team |
| Behavioral Risk Factor Surveillance System (BRFSS). 2005 [*] | Telephone | Adults only | 34,346 | Annually | 1. 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? 2. What type of PA or exercise did you spend the most time doing during the past month? 3. How many times per week or per month did you take part in this activity during the past month? 4. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 5. What other type of physical activity gave you the next most exercise during the past month? 6. How many times per week or per month did you take part in this activity during the past month? 7. And when you took part in this activity, for how many minutes or hours did you usually keep at it? 8. During the past month, how many times per week or per month did you do physical activities or exercises to strengthen your muscles? |
| National Health and Nutrition Examination Survey (NHANES), 2011-2014 | Face-to-face interviews in homes and mobile centers (labs and objective monitors) | Adults & Children | 2809 | Annually | 16 questions aimed at assessing PA and fitness. A sample question from the NHANES survey - During the past 7 days, on how many days were physically active for a total of at least 60 minutes per day? Add up all the time you spent in any kind of physical activity that increased your heart rate and made you breathe hard some of the time? |
| The National Health Interview Survey (NHIS), 2014 | Face-to-face interviews | Adults & Children | 5057 | Annually | Includes a wide range of questions regarding PA from type and frequency of PA, for example, walking and for how long. Questions are asked about membership to fitness facilities and participation with sports and other type of exercises, as well as availability and access to parks or recreational areas. There is also a question regarding a doctor or other health professional recommending any type of exercise or PA, and if so what and how often? |
| PA: Physical Activity; All surveys are collected in the US & DC; [*] = as well as the territories. | |||||
| Total Black Children | |||||||
| Sampled (N = 1,667) | Boys (n = 837) | Girls (n = 821) | |||||
| 43.4 | (38.8-48.1) | 52.2 | (46.1, 58.3) | 33.4 | (28.1, 38.6) | ||
| Grade | N | % by Grade meeting PA Levels | |||||
| 9 | 424 | 47.5 | (38.0, 57.0) | 56.6 | (45.5, 67.7) | 35.3 | (26.1, 44.6) |
| 10 | 424 | 44.5 | (39.4, 49.7) | 51.8 | (44.3, 59.3) | 37.2 | (27.8, 46.7) |
| 11 | 423 | 46.1 | (37.7, 54.5) | 54.9 | (41.0, 68.7) | 35.5 | (26.7, 44.3) |
| 12 | 387 | 34.1 | (27.7, 40.5) | 44.0 | (33.3, 54.6) | 24.2 | (18.6, 29.8) |
| Body Mass Index (BMI) | N | % by BMI meeting PA Levels | |||||
| Normal weight | 986 | 47.0 | (42.5, 51.5) | 57.3 | (50.8, 63.7) | 35.2 | (29.2, 41.1) |
| Overweight | 242 | 41.5 | (33.0, 49.9) | 55.7 | (44.5, 66.8) | 31.1 | (20.3, 42.0) |
| Obese | 256 | 38.4 | (25.5, 51.3) | 43.2 | (23.9, 62.4) | 31.9 | (18.2, 45.7) |
| Source: Youth Risk Behavior Survey (YRBS)-2015; PA: Physical Activity. | |||||||
| Total - Estimate % (95% CI) | Men - Estimate % (95% CI) | Women - Estimate % (95% CI) | |||||||
| BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | BRFSS | NHANES | NHIS | |
| Sample size N | 34,346 | 2,809 | 5,057 | n = 12,394 | n = 1354 | n = 2047 | n = 21,952 | n = 1455 | n = 3010 |
| Overall Pysical Activity | — | — | — | 47.7 (46.0-49.3) | 44.6 (41.6-47.6) | 51.6 (48.6-54.6) | 40.7 (39.4-42.1) | 27.4 (23.8-31.2) | 38.2 (35.7-40.7) |
| Age | |||||||||
| 18-24 | 2,164 (6.3%) | 457 (16.3%) | 472 (9.3%) | 56.3 (51.4-61.2) | 66.4 (60.2-72.7) | 61.9 (52.8-70.9) | 46.4 (41.8-51.1) | 34.3 (23.6-45.1) | 37.2 (29.1-45.3) |
| 25-44 | 8,349 (24.3%) | 795 (28.3%) | 1,714 (33.9%) | 46.8 (43.8-49.7) | 54.8 (49.0-60.6) | 62.3 (57.3-67.3) | 41.2 (38.8-43.6) | 29.9 (25.3-34.6) | 45.0 (41.2-48.7) |
| 45-64 | 13,690 (39.9%) | 1,011 (36.0%) | 1,838 (36.3%) | 44.6 (41.9-47.3) | 31.8 (27.3-36.2) | 41.4 (37.1-45.8) | 39.0 (36.9-41.2) | 25.0 (19.2-30.8) | 38.8 (35.0-42.6) |
| 65+ | 9,669 (28.2%) | 546 (19.4%) | 1,033 (20.4%) | 49.4 (45.8-53.0) | 21.9 (17.5-26.3) | 33.7 (27.8-39.5) | 38.9 (36.4-41.4) | 17.6 (12.8-22.5) | 21.5 (17.6-25.5) |
| Education | |||||||||
| < HIgh School | 4,243 (12.4%) | 578 (20.6%) | — | 42.7 (38.0-47.4) | 26.4 (20.0-32.7) | not available | 28.2 (24.7-31.6) | 12.3 (6.9-17.7) | not available |
| High School Graduate | 11,282(32.8%) | 703 (25.0%) | — | 45.4 (42.6-48.1) | 38.5 (32.7-44.4) | not available | 37.4 (34.9-39.9) | 21.2 (16.5-25.8) | not available |
| Some College | 9,488 (27.6%) | 885 (31.5%) | — | 50.3 (47.2-53.5) | 50.3 (44.1-56.6) | not available | 43.8 (41.3-46.2) | 28.4 (23.4-33.3) | not available |
| College Graduate | 9,189 (26.8%) | 459 (16.3%) | — | 51.6 (48.5-54.8) | 61.4 (52.5-70.2) | not available | 48.7 (46.2-51.2) | 41.5 (36.9-46.1) | not available |
| Income (BRFSS) | |||||||||
| < $15 ,000 | 5,629 (16.4%) | — | — | 42.1 (37.6-46.5) | — | — | 36.6 (33.5-39.8) | — | — |
| 15,000−25,000 | 6,901 (20.1%) | — | — | 46.2 (42.4-50.0) | — | — | 36.9 (34.2-39.7) | — | — |
| 25,000−35,000 | 3,528 (10.3%) | — | — | 47.9 (42.3-53.4) | — | — | 40.2 (36.1-44.3) | — | — |
| 35,000−50,000 | 3,845 (11.2%) | — | — | 47.7 (43.0-52.3) | — | — | 44.8 (40.4-49.1) | — | — |
| ≥ $50,000 | 8,180 (23.8%) | — | — | 50.9 (47.9-53.8) | — | — | 47.7 (44.9-50.5) | — | — |
| Income (NHIS - NHANES) | |||||||||
| 0−34,999 | — | 1,423 (50.7%) | 2,476 (49.0%) | — | 39.2 (34.9-43.5) | 43.2 (40.0-46.4) | — | 23.4 (19.0-27.7) | 31.0 (28.9-33.2) |
| 35,000−74,999 | — | 684 (24.4%) | 1,206 (23.8%) | — | 47.9 (41.4-54.3) | 51.2 (47.0-55.3) | — | 31.6 (27.3-35.9) | 43.7 (39.9-47.6) |
| 75,000−99,999 | — | 204 (7.3%) | 318 (6.3%) | — | 53.1 (41.3-64.8) | 55.9 (48.2-63.6) | — | 37.3 (23.5-51.2) | 44.4 (36.5-52.3) |
| $100,000+ | — | 274 (9.8%) | 371 (7.3%) | — | 52.4 (44.5-60.4) | 66.5 (59.7-73.3) | — | 35.2 (28.6-41.9) | 54.7 (47.4-62.0) |
| Employment Status | |||||||||
| Employed | 15,638 (45.5%) | 1,405 (50.0%) | 3,013 (59.6%) | 48.7 (46.5-51.0) | 47.6 (43.9-51.3) | 58.0 (54.3-61.7) | 42.5 (40.6-44.4) | 30.9 (26.7-35.0) | 45.0 (41.6-48.4) |
| Not Employed | 18,708 (54.5%) | 1,402 (49.9%) | 2,044 (40.4%) | 46.2 (43.7-48.7) | 40.5 (35.2-45.8) | 39.7 (35.1-44.3) | 38.8 (36.9-40.7) | 23.3 (18.5-28.0) | 27.4 (23.8-31.0) |
| Marital Status | |||||||||
| Married/Living With | 11,591 (33.7%) | 1,086 (38.7%) | 358 (7.1%) | 49.3 (46.7-51.8) | 39.3 (33.6-45.0) | 45.3 (35.1-55.6) | 41.8 (39.4-44.2) | 25.7 (19.5-32.0) | 42.0 (33.4-50.7) |
| No longer married | 12,261 (35.7%) | 713 (25.4%) | 2,757 (54.5%) | 41.1 (37.7-44.5) | 31.7 (26.5-36.9) | 49.0 (45.4-52.6) | 37.8 (35.7-39.9) | 23.9 (19.2-28.6) | 38.4 (35.3-41.5) |
| Never married | 10,262 (29.9%) | 831 (29.6%) | 1,921 (38.0%) | 49.1 (46.3-52.0) | 55.1 (50.3-60.0) | 57.3 (52.2-62.4) | 41.8 (39.5-44.2) | 29.4 (25.2-33.6) | 37.3 (32.9-41.7) |
| Body Mass Index (BMI) | |||||||||
| < 25.0 kg/m2 | 7,506 (21.9%) | 704 (25.1%) | 1,291 (25.5%) | 49.8 (46.6-53.0) | 46.0 (41.1-50.9) | 53.5 (47.6-59.4) | 45.2 (42.2-48.3) | 34.0 (27.4-40.7) | 37.5 (32.2-42.8) |
| 25.0-29.9 kg/m2 | 10,853 (31.6%) | 758 (27.0%) | 1,592 (31.5%) | 49.5 (46.7-52.4) | 47.3 (42.3-52.3) | 53.1 (48.7-57.5) | 45.7 (43.2-48.2) | 28.9 (22.6-35.1) | 46.3 (41.3-51.3) |
| ≥ 30.0 kg/m2 | 12,879 (37.5%) | 1,224 (43.6%) | 1,998 (39.5%) | 44.4 (41.6-47.2) | 41.0 (34.8-47.1) | 48.8 (43.7-53.8) | 36.6 (34.5-38.7) | 24.0 (21.2-26.9) | 34.3 (31.0-37.6) |
| Region of U.S. | |||||||||
| Northeast | — | — | 680 (13.4%) | — | — | 38.4 (29.7-47.1) | — | — | 34.8 (28.4-41.) |
| Midwest | — | — | 864 (17.1%) | — | — | 54.2 (47.1-61.3) | — | — | 34.3 (28.2-40.4) |
| South | — | — | 3,092 (61.1%) | — | — | 54.5 (50.6-58.4) | — | — | 38.7 (35.4-41.9) |
| West | — | — | 421 (8.3%) | — | — | 50.7 (41.5-60.0) | — | — | 50.8 (41.2-60.3) |
| Sources: Behavioral Risk Factor Surveillance System (BRFSS), 2015; The National Health and Nutrition Examination Survey (NHANES), 2011-2014; & National Health Interview Survey (NHIS), 2014. | |||||||||
