When American Adults Do Move, How Do They Do So? Trends in Physical Activity Intensity, Type, and Modality: 1988–2017

in Journal of Physical Activity and Health

Background: Few Americans accumulate enough physical activity (PA) to realize its benefits. Understanding how and why individuals use their discretionary time for different forms of PA could help identify and rectify issues that drive individuals away from certain physical activities, and leverage successful strategies to increase participation in others. Methods: The authors analyzed approximately 30 years of changes in PA behavior by intensity, type, and mode, using data from the Behavioral Risk Factor Surveillance System. Results: Since 1988, the proportions of adults most frequently engaging in exercise, sport, or lifestyle physical activity have changed noticeably. The most apparent changes from 1988 to 2017 were the proportions most frequently engaging in Exercise and Sport. In addition, the proportion of time reportedly spent in vigorous-intensity PA decreased over time, particularly among male respondents. Moreover, the proportion of Americans reporting an “Other” PA mode increased substantially, suggesting a growing need for a greater variety of easily accessible options for adult PA. Conclusions: Over time, a smaller proportion of American adults reported participating in sport and exercise modalities and reported engaging more frequently in low-intensity physical activities.

Few American adults accrue sufficient weekly moderate- to vigorous-intensity physical activity (MVPA).1 Despite a seemingly unending stream of data suggesting the benefits of regular physical activity (PA), innumerable public health campaigns, and other attempts to intervene, PA behavior at the population-level remains resistant to meaningful change.2 With no clear end to this epidemic in sight, novel approaches are essential to address the problem of physical inactivity.

Especially over the last 2 decades, a growing body of evidence suggests that, among the many correlates and determinants of PA, some of the most meaningful may be repeated experiences of pleasure and enjoyment during PA.3,4 At the same time, a paradoxical argument often put forward by exercise researchers and professionals is that exercise and PA “make people feel better.” However, if this were true, the current epidemic of physical inactivity would probably not be occurring in the first place, as people tend to seek out behaviors that make them feel better.5 This logical contradiction has led to a critical reevaluation in recent years,6 suggesting that, perhaps, not all exercise and PA leads to all people feeling better. It is possible, instead, that the interplay of several individual biopsychosocial characteristics (eg, preferences and predispositions, weight status) could influence how people experience and later positively or negatively reflect on their participation in PA. As a consequence, increased attention is being afforded to understanding how these individual differences7 can impact the experience of PA, and how PA promotion may be more effective by considering and adapting to these differences (eg, choosing to engage in light- vs vigorous-intensity PA, individual vs team PA810), as opposed to a one-size-fits-all approach. When individuals participate in physical activities that match their preferences, they may be more likely to derive pleasure and enjoyment from them—the experience of which could perpetuate further PA.

However, little is currently known about what physical activities American adults most commonly choose to do during their discretionary time and whether these inclinations have changed over time. If, for example, the proportion of time Americans devote to sport or vigorous-intensity physical activities has consistently changed over time, it could suggest that systematic mechanisms are responsible. Previous work11 has broadly examined temporal trends in PA among American adults, suggesting, for example, that PA during discretionary time has increased while occupation-related PA has decreased12 and that women tend to be less active than men.13 The American College of Sports Medicine (ACSM) has also tracked trends in the fitness industry since 2007,14 suggesting that PA preferences and “fads” ebb and flow over time. At the same time, these ACSM data were sampled among health and fitness professionals, rendering it unlikely that they would generalize to typical American adults (ie, likely sedentary, overweight, and/or obese). As a result, limited data among the general American adult population are available to understand how they use their discretionary time for PA and whether it may be changing over time.

Identifying whether the proportion of time people allocate to physical activities of differing intensities, types, and modalities is evolving could serve as a first step to understanding and addressing the mechanisms driving engagement in certain types of physical activities and, perhaps, leveraging these findings to increase engagement in activities showing reduced participation. Using this evidence, exercise professionals may have additional tools to better serve their clients by aiding them to discover more enjoyable physical activities (ie, those with a greater likelihood of adherence). Furthermore, researchers and policymakers might better direct both intellectual and financial resources to improve access to physical activities that are most likely to be adopted and adhered to, or, in contrast, develop strategies to reengage individuals in activities that have become less popular. As a first step in this direction, we analyzed trends in the frequency of self-reported PA intensity, type, and modality among American adults using 3 decades of data derived from the Behavioral Risk Factor Surveillance System (BRFSS).

Methods

Participants

We analyzed data from the BRFSS, a yearly state-based, random digit-dialed telephone survey of American citizens 18 years of age or older administered by the Centers for Disease Control (CDC). In the present study, 5 approximately equally spaced time points were considered: 1988, 1994, 2000, 2011, and 2017 (note that our question of interest was not included from 2001 to 2010). Participation in the BRFSS has increased over time, ranging from 56,448 in 1988 to 450,016 in 2017. Table 1 provides a more detailed illustration of participant characteristics. Depending on the year the questionnaire was administered, participants responded to up to 7 questions about their PA behavior during the past month. Of these questions, we were primarily interested in the item: “What type of physical activity or exercise did you spend the most time doing during the past month?” The BRFSS provided between 53 and 67 individual physical activities to choose from 1988 to 2017. Participants also had the option to choose “Other” as their most frequent PA type over the previous month.

Table 1

Participant Biological and Demographic Characteristics

Participant characteristic19881994200020112017
Age
 18–55 (n)38,65872,422125,475230,387195,470
 55–99 (n)17,78133,07257,916276,113254,546
Sex
 Female (n)32,86762,176109,680307,655251,007
 Male (n)23,58143,72474,820198,812198,725
Race
 White (n)47,32189,980143,510391,068337,166
 Minority (n)7,03022,56930,83154,54056,022
Body mass index (mean [SD])24.7 (4.3)25.5 (4.8)26.4 (5.3)27.6 (6.0)28.2 (6.3)
 Normal weight, %53.048.141.734.231.2
 Overweight, %30.734.335.836.336.1
 Obese, %10.314.820.427.831.1
Education
 High school graduate or less (n)16,83529,99548,549115,44785,745
 Some college/technical school or more (n)20,57743,52881,694235,104212,885
Income
 Less than household median (n)11,92837,69749,711160,478116,401
 More than household median (n)21,49027,15764,057145,334140,200

Analytical Approach

In 2011, the CDC introduced significant changes to the design and administration of the BRFSS, including cellular phone data collection and new data weighting methodologies. These changes were intended to increase the representation of young adults, minorities, and respondents of lower socioeconomic status. Consequently, the CDC noted15 that data collected from the year 2011 should be treated as a new baseline, and that subsequent data are not directly comparable to previous data, as the frequencies of some health risk behaviors could be inflated. Therefore, we could not employ inferential statistical analyses to examine year-to-year changes in PA intensity, type, and modality. Instead, to make use of data both prior to and following 2011, we adopted a descriptive approach to summarize time trends. This approach was deemed appropriate, given that neither the direction nor magnitude of changes in PA differed markedly from other data collected among US adults over similar time periods16,17. At the same time, the 2011 changes did not preclude us from using inferential statistics to analyze within-year associations between the intensity of PA at which respondents participated in most frequently over the previous month and biological/demographic variables.

We classified each individual BRFSS PA modality (eg, swimming, running) into exercise, sport, or one of two lifestyle PA categories (ie, household and leisure lifestyle PA; see Table 2). Three authors (M.A.L., C.N.S., and B.J.A.) independently categorized each individual PA modality into its respective PA type using the following operational definitions. Exercise was defined as PA that is structured, routine, and specifically intended to sustain or improve one of the several facets of health and fitness.18 We considered Sport to be an activity involving physical exertion and skill in which an individual or team competes against another or teams.19 Finally, Lifestyle Physical Activities, which were considered to be all occupational, leisure, or household activities that could be planned or unplanned and part of everyday life,20 were further subdivided into “Household Lifestyle PA” and “Leisure Lifestyle PA” categories. Although occupational PA is often considered lifestyle PA, the BRFSS item of interest, “What type of physical activity or exercise did you spend the most time doing during the past month?” is framed to encompass all PA outside of occupation-related PA.

Table 2

Physical Activity Type Classifications and Estimated MET Expenditures

ExerciseMETsLifestyle Physical Activity—LeisureMETsLifestyle Physical Activity—HouseholdMETsSportMETs
Aerobics video or class4.1Active gaming devices (Wii Fit, Kinect, Dance Dance Revolution, etc)4.4Carpentry4.1Badminton6.3
Childcare2.5Basketball6.9
Bicycling machine exercise8.1Farm/Ranch work (caring for livestock, stacking hay, etc)3.9Bowling3.4
Backpacking7.4Boxing8.7
Bicycling7.2Canoeing/rowing in competition12.3
Calisthenics5.9Boating (canoeing, rowing, kayaking, sailing for pleasure)3.2Gardening (spading, weeding, digging, filling)5.4
Elliptical/EFX machine exercise5.0Golf4.2
Household Activities (vacuuming, dusting, home repair, etc)3.7Golf (with motorized cart)3.5
Dancing (Ballet, ballroom, Latin, hip hop, Zumba, etc)5.1
Health Club Exercise6.1Golf (without motorized cart)4.8
Fishing from river bank or boat3.2Mowing Lawn5.5
Home Exercise3.8Painting/papering house4.3Handball10.0
Jogging7.0Frisbee5.5Raking lawn/trimming hedges3.8Hockey8.6
Pilates3.0Gardening (spading, weeding, digging, filling)5.4Judo/Karate7.8
Rope skipping10.9Snow blowing2.5Karate/Martial Arts7.8
Rowing machine exercises7.7Hiking-cross country5.7Snow shoveling by hand6.4Lacrosse8.0
Horseback riding5.5Yard work (cutting/gathering wood, trimming, etc)4.3Paddleball8.0
Hunting large game–deer, elk, etc5.5Racquetball8.5
Running8.5Rugby7.3
Stair climbing/StairMaster7.5Hunting small game–quail, etc3.9  Soccer8.5
Softball/baseball5.0
Inline skating10.9Squash9.7
Swimming in laps7.8Mountain climbing8.0Table Tennis4.0
Rock climbing6.6Tennis6.2
Tai Chi2.3Scuba diving7.0Touch football6.0
Upper body cycle (wheelchair sports, ergometer)3.6Skateboarding5.5Volleyball5.3
Skating-ice or roller9.3Wrestling6.0
Sledding, tobogganing7.0  
Snorkeling5.0
Snow skiing7.0
Weight lifting4.8Snowshoeing7.7
Yoga2.9Stream fishing in waders6.0
  Surfing4.0
Swimming6.0
Walking3.3
Waterskiing6.0

Abbreviations: METs, metabolic equivalent of tasks; PA, physical activity.

M.A.L. provided a vote to settle any disagreements between raters. Given the inherent subjectivity of categorizing the modalities (see “Discussion” section for more detail), the agreement between C.N.S. and B.J.A. was substantial (κ = .65, 95% confidence interval, 0.50–0.80). Following categorization into PA type, each PA modality was assigned a metabolic equivalent of task (MET) value, using the intensity data provided by the most recent (ie, 2011) compendium of physical activities.21 When the compendium provided multiple MET values for differing levels of intensity of a PA modality (eg, bicycling, <10 mph, leisure vs bicycling 14–15.9 mph, racing or leisure, fast, vigorous effort), we summed and then averaged all MET values for that PA modality. The individual PA modalities categorized by PA type, along with MET values, are summarized in Table 2.

Following categorization of each PA into intensity, type, and modality, we summarized data at each time point cumulatively, by age (ie, less than 55 y, 55 y and older), biological sex (male and female), the race or ethnicity of the respondent (white and minority), body mass index (BMI; normal weight, overweight, and/or obese), education level (high school graduate or less and some college or technical school or more), and income level (less than the median household income for that year and more than median household income for that year). Correlations between within-year variables were analyzed using chi-square independence tests, and the Φ coefficient was used to estimate effect sizes. The Φ coefficient values of 0.1, 0.3, and 0.5, were considered small, medium, and large effects, respectively. Log odds and odds ratios were also calculated for each test of association. Because we conducted 180 tests of probability, to reduce the probability of committing a type I error, alpha was Bonferroni-corrected to P = .0002 (ie, .05/150).

Results

Time Trends in Physical Activity Intensity During the Previous Month

Overall, the proportion of respondents who reported engaging in a moderate-intensity PA most frequently over the previous month increased over time, while the proportion reporting a vigorous-intensity PA decreased. Though there were apparent differences in the proportions between respondents with different biological and demographic characteristics, these trends (ie, moderate-intensity increase, vigorous-intensity decrease) remained consistent over time. A larger proportion of respondents who reported being a male, a minority, younger than 55 years of age, completing some college/technical school or more, a household income greater than the national median, and who were normal weight reported most frequently engaging in a vigorous-intensity PA during the previous month. Conversely, a larger proportion of respondents who reported being a female, white, 55 years of age or older, completing only high school, a household income less than the national median, and who were overweight and/or obese reported most frequently engaging in a moderate-intensity PA. Tables 3 and 4 display year-by-year changes in most frequent PA intensity overall and between groups.

Table 3

Proportions of Individuals Reporting Moderate-Intensity Physical Activity Most Frequently Over Previous Month

Group19881994200020112017
All77.3%78.0%79.1%82.9%82.0%
Male67.1%68.8%70.6%76.3%77.3%
Female85.0%84.8%85.3%87.3%86.0%
White78.2%79.1%79.9%83.4%82.6%
Minority71.7%75.1%76.4%81.4%78.9%
Less than 55 y72.4%74.5%74.9%73.5%75.2%
55 y or older90.9%89.0%89.7%89.6%90.1%
High school or less83.5%83.8%84.4%87.9%86.6%
Some college/tech school or more72.2%74.8%76.0%81.5%82.1%
Less than median79.5%81.0%81.9%86.8%85.9%
More than median75.4%74.5%76.4%79.4%80.8%
Normal weight75.3%76.7%76.0%79.6%79.0%
Overweight and/or obese79.7%80.0%81.2%85.5%85.3%
Table 4

Proportions of Individuals Reporting Vigorous-Intensity Physical Activity Most Frequently Over Previous Month

Group19881994200020112017
All22.7%22.0%20.9%16.2%16.4%
Male32.9%31.1%29.4%23.3%22.0%
Female15.0%15.2%14.7%11.5%11.7%
White21.8%20.9%20.1%15.7%15.8%
Minority28.3%24.9%23.6%18.6%21.1%
Less than 55 y27.6%25.5%25.1%26.5%24.8%
55 y or older9.1%11.0%10.3%10.4%9.9%
High school or less16.5%16.2%15.6%12.1%13.4%
Some college/technical school or more27.8%25.2%24.0%18.5%17.9%
Less than median20.5%19.0%18.1%13.2%14.1%
More than median24.6%25.5%23.6%20.6%19.2%
Normal weight24.7%23.3%24.0%20.4%21.0%
Overweight and/or obese20.3%20.0%18.8%14.5%14.7%

Associations Between PA Intensity During the Previous Month and Biological/Demographic Variables

At each time point, the likelihood of reporting engaging in a vigorous-intensity PA most frequently over the previous month was significantly greater among respondents who were male, a minority, younger than 55 years of age, completed some college/technical school or more, had a household income above the national median, and normal weight. The Φ coefficients indicated a small effect size for biological sex, education level, and age group, but did not meet the threshold for a small effect for race or ethnicity, household income, or BMI. These data are further summarized in Table 5.

Table 5

Odds Ratios for PA Intensity by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member0.34**0.27 to 0.420.051.40
Is female−1.02**−1.07 to −0.670.212.77
Some college/technical school or more0.66**0.61 to 0.720.131.93
Household income is more than median0.24**0.18 to 0.290.051.27
Is 55 y of age or older−0.91**−0.96 to −0.860.192.48
Is overweight and/or obese−0.25**−0.31 to −0.200.051.28
1994Is a minority group member0.19**0.14 to 0.240.031.21
Is female−0.99**−1.02 to −0.950.202.69
Some college/technical school or more0.55**0.51 to 0.590.111.73
Household income is more than median0.38**0.34 to 0.420.081.46
Is 55 y of age or older−0.71**−0.74 to −0.670.142.03
Is overweight and/or obese−0.19**−0.23 to −0.160.041.21
2000Is a minority group member0.19**0.15 to 0.230.031.21
Is female−0.88**−0.91 to −0.850.182.41
Some college/technical school or more0.53**0.50 to 0.560.101.69
Household income is more than median0.34**0.31 to 0.370.071.40
Is 55 y of age or older−1.08**−1.11 to −1.040.172.94
Is overweight and/or obese−0.31**−0.34 to −0.280.061.36
2011Is a minority group member0.08**0.05 to 0.110.011.08
Is female−0.84**−0.86 to −0.820.162.32
Some college/technical school or more0.49**0.47 to 0.510.081.63
Household income is more than median0.54**0.52 to 0.560.091.72
Is 55 y of age or older−1.06**−1.08 to −1.040.192.89
Is overweight and/or obese−0.41**−0.43 to −0.390.081.51
2017Is a minority group member0.24**0.21 to 0.260.031.27
Is female−0.74**−0.76 to −0.720.142.10
Some college/technical school or more0.34**0.32 to 0.370.081.40
Household income is more than median0.37**0.35 to 0.390.071.45
Is 55 y of age or older−1.10**−1.12 to −1.070.203.00
Is overweight and/or obese−0.43**−0.45 to −0.410.081.54

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is PA intensity most frequently during previous month (0 = moderate and 1 = vigorous).

**P < .000001.

Time Trends in Physical Activity Type During the Previous Month

Overall, the PA type reported most frequently by respondents over the previous month was Lifestyle PA—Leisure, and the proportion reporting it most frequently increased from 1988 to 2017. In 1988, the proportion of individuals reporting Sport most frequently represented the third largest out of the 5 PA type categories, but its proportion was the smallest in 2011 and 2017. The proportion of respondents who engaged in exercise most frequently over the previous month decreased over time, while the proportion of those reporting an “Other” PA increased. The proportion reporting Lifestyle PA—Household most frequently remained essentially the same over time.

Among respondents who reported engaging in Exercise most frequently during the previous month, the largest proportion who did so were those who were male, a minority, younger than 55 years of age, had completed some college/technical school or more, had a household income greater than the national median for that year, and were normal weight.

The largest proportion of those who reported engaging in Lifestyle PA—Household most frequently during the previous month were female, white, 55 years of age or older, had a high school education or less, had a household income less than the national median for that year, and were overweight and/or obese.

In respondents who reported engaging in Lifestyle PA—Leisure most frequently during the previous month, the largest proportion were those who were female, white, 55 years of age or older, had a high school education or less, had a household income less than the national median for that year, and were overweight and/or obese.

The largest proportion of individuals who reported engaging in a sport most frequently during the previous month were those who were male, a minority, younger than 55 years of age, had completed some college/technical school or more, had a household income greater than the national median for that year, and were overweight and/or obese.

Among respondents who reported engaging in an “Other” PA most frequently during the previous month, the proportions differed little between groups and no apparent patterns were noted.

The year-by-year time trends for PA type are further summarized in Figures 1A1M.

Figures 1
Figures 1
Figures 1

—(A–M) Proportions of respondents reporting each PA type over the previous month by year. PA indicates physical activity.

Citation: Journal of Physical Activity and Health 18, 10; 10.1123/jpah.2020-0424

Associations Between PA Type During the Previous Month and Biological/Demographic Variables

At each time point, the likelihood of reporting engaging in Exercise most frequently during the previous month was significantly higher for respondents who were male, a minority, younger than 55 years of age, had completed some college/technical school or more, had a household income more than the national median for that year, and normal weight. The Φ coefficients suggested a small effect size for age group at all time points, education level in 1988 and 2000, income level in 2000, and BMI in 1988. All other tested relationships did not meet the threshold for a small effect. These data are further summarized in Table 6.

Table 6

Odds Ratios for Exercise PA Type by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member0.39**0.32 to 0.460.061.48
Is female−0.22**−0.26 to −0.170.051.25
Some college/technical school or more0.52**0.47 to 0.570.111.68
Household income is more than median0.14**0.09 to 0.190.031.15
Is 55 y of age or older−0.96**−1.01 to −0.910.212.61
Is overweight and/or obese−0.51**−0.56 to −0.460.111.67
1994Is a minority group member0.33**0.29 to 0.380.051.39
Is female−0.26**−0.29 to −0.230.061.30
Some college/technical school or more0.48**0.44 to 0.510.091.62
Household income is more than median0.26**0.22 to 0.290.061.30
Is 55 y of age or older−0.75**−0.79 to −0.720.162.12
Is overweight and/or obese−0.33**−0.36 to −0.290.071.40
2000Is a minority group member0.08**0.06 to 0.110.011.25
Is female−0.34**−0.35 to −0.320.071.51
Some college/technical school or more0.61**0.59 to 0.630.111.65
Household income is more than median0.60**0.59 to 0.620.121.38
Is 55 y of age or older−0.87**−0.89 to −0.860.172.18
Is overweight and/or obese−0.40**−0.42 to −0.390.081.36
2011Is a minority group member0.08*0.05 to 0.110.011.08
Is female−0.84*−0.86 to −0.820.162.32
Some college/technical school or more0.49*0.47 to 0.510.081.63
Household income is more than median0.54*0.52 to 0.560.091.72
Is 55 y of age or older−1.06*−1.08 to −1.040.192.89
Is overweight and/or obese−0.41*−0.43 to −0.390.081.51
2017Is a minority group member0.18**0.15 to 0.200.031.19
Is female−0.37**−0.39 to −0.350.081.45
Some college/technical school or more0.49**0.48 to 0.520.091.63
Household income is more than median0.47**0.45 to 0.490.091.60
Is 55 y of age or older−0.98**−1.00 to −0.960.192.66
Is overweight and/or obese−0.39**−0.41 to −0.370.081.48

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is respondents who reported exercise most frequently during previous month (0 = no and 1 = yes).

*P < .0002. **P < .000001.

At each time point, the likelihood of reporting engaging in a Lifestyle PA—Household most frequently during the previous month was significantly higher for respondents who were white, 55 years of age or older, had a high school education or less, had a household income less than the national median for that year, and were overweight and/or obese. Female respondents were significantly more likely to report Lifestyle PA—Household in 2000, 2011, and 2017, but not 1988 or 1994. The Φ coefficients suggested a small effect size for age group in 1988, 1994, and 2017. All other relationships did not meet the threshold for a small effect. These data are further summarized in Table 7.

Table 7

Odds Ratios for Lifestyle PA—Household by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member−0.36**−0.49 to −0.230.031.43
Is female−0.04−0.11 to −0.040.011.04
Some college/technical school or more−0.52**−0.59 to −0.450.071.68
Household income is more than median−0.17*−0.25 to −0.090.021.19
Is 55 y of age or older1.03**0.94 to 1.120.122.80
Is overweight and/or obese0.27*0.19 to 0.340.041.31
1994Is a minority group member−0.47**−0.57 to −0.380.041.60
Is female0.060.01 to 0.120.011.06
Some college/technical school or more−0.45**−0.50 to −0.390.061.57
Household income is more than median−0.19**−0.25 to −0.130.021.21
Is 55 y of age or older1.16**1.08 to 1.240.113.19
Is overweight and/or obese0.24**0.18 to 0.290.031.27
2000Is a minority group member−0.54**−0.61 to −0.470.041.72
Is female0.19**0.16 to 0.240.031.21
Some college/technical school or more−0.32**−0.36 to −0.280.041.38
Household income is more than median−0.16**−0.21 to −0.120.021.17
Is 55 y of age or older0.74**0.69 to 0.780.092.09
Is overweight and/or obese0.15**0.11 to 0.190.021.16
2011Is a minority group member−0.57**−0.62 to −0.530.041.77
Is female0.09**0.06 to 0.110.011.09
Some college/technical school or more−0.31**−0.33 to −0.280.041.36
Household income is more than median−0.31**−0.34 to −0.280.041.36
Is 55 y of age or older0.75**0.73 to 0.780.092.12
Is overweight and/or obese0.16**0.13 to 0.180.021.17
2017Is a minority group member−0.57**−0.62 to −0.530.051.27
Is female0.08**0.05 to 0.100.012.10
Some college/technical school or more−0.33**−0.35 to −0.290.041.40
Household income is more than median−0.35**−0.37 to −0.320.051.45
Is 55 y of age or older0.85**0.82 to 0.880.113.00
Is overweight and/or obese0.18**0.15 to 0.210.021.54

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is respondents who reported lifestyle PA—household most frequently during previous month (0 = no and 1 = yes).

*P < .0002. **P < .000001.

At each time point, the likelihood of reporting engaging in a Lifestyle PA—Leisure most frequently during the previous month was significantly higher among those who were female, 55 years of age or older, had a high school education or less, had a household income less than the national median for that year, and were overweight and/or obese. Minority respondents were significantly less likely to report a Lifestyle PA—Leisure in 1988 and 1994 and significantly more likely than white respondents to do so in 2011. The Φ coefficients indicated a small effect size for being female across all time points and being 55 years of age or older in 1988, 1994, and 2017. All other relationships did not meet the threshold for a small effect. These data are further summarized in Table 8.

Table 8

Odds Ratios for Lifestyle PA—Leisure by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member−0.24**−0.31 to −0.170.041.27
Is female0.75**0.71 to 0.790.182.12
Some college/technical school or more−0.38**−0.42 to −0.340.091.46
Household income is more than median−0.15**−0.19 to −0.100.041.16
Is 55 y of age or older0.77**0.73 to 0.810.192.16
Is overweight and/or obese0.21**0.17 to 0.260.051.23
1994Is a minority group member−0.14**−0.18 to −0.100.021.15
Is female0.75**0.72 to 0.780.182.12
Some college/technical school or more−0.32**−0.35 to −0.290.081.38
Household income is more than median−0.31**−0.34 to −0.280.081.36
Is 55 y of age or older0.55**0.52 to 0.580.131.73
Is overweight and/or obese0.10**0.07 to 0.130.031.11
2000Is a minority group member−0.02−0.06 to 0.010.001.02
Is female0.73**0.71 to 0.750.182.08
Some college/technical school or more−0.31**−0.33 to −0.290.071.36
Household income is more than median−0.31**−0.33 to −0.280.081.36
Is 55 y of age or older0.43**0.41 to 0.460.091.54
Is overweight and/or obese0.14**0.12 to 0.160.041.15
2011Is a minority group member0.09**0.08 to 0.120.021.09
Is female0.49**0.48 to 0.510.121.63
Some college/technical school or more−0.31**−0.33 to −0.290.071.36
Household income is more than median−0.38**−0.39 to −0.370.091.46
Is 55 y of age or older0.34**0.33 to 0.360.081.40
Is overweight and/or obese0.21**0.19 to 0.220.051.23
2017Is a minority group member−0.01−0.03 to 0.020.001.01
Is female0.46**0.45 to 0.480.111.58
Some college/technical school or more−0.25**−0.27 to −0.240.061.28
Household income is more than median−0.29**−0.31 to −0.280.071.34
Is 55 y of age or older0.47**0.46 to 0.490.121.60
Is overweight and/or obese0.22**0.20 to 0.230.051.25

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is respondents who reported lifestyle PA—leisure most frequently during previous month (0 = no and 1 = yes).

**P < .000001.

The likelihood of reporting engaging in a sport most frequently during the previous month was significantly higher for respondents who were male, younger than 55 years of age, completed some college/technical school or more, had a household income more than the national median for that year, and were overweight and/or obese at each time point. Minority respondents were significantly more likely than white respondents to report engaging in a sport most frequently during the previous month in 2000 and 2017. The Φ coefficients suggested a small effect size for being female across all time points and being 55 years of age or older in 1988. All other relationships did not meet the threshold for a small effect. These data are further summarized in Table 9.

Table 9

Odds Ratios for Sport by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member0.130.03 to 0.220.011.14
Is female−1.48**−1.55 to −1.410.234.39
Some college/technical school or more0.36**0.29 to 0.420.061.43
Household income is more than median0.22**0.15 to 0.290.031.25
Is 55 y of age or older−0.73**−0.79 to −0.670.122.08
Is overweight and/or obese0.16**0.09 to 0.220.031.17
1994Is a minority group member0.080.01 to 0.150.011.08
Is female−1.57**−1.63 to −1.520.224.81
Some college/technical school or more0.25**0.20 to 0.300.041.28
Household income is more than median0.39**0.34 to 0.440.061.48
Is 55 y of age or older−0.61**−0.66 to −0.560.091.84
Is overweight and/or obese0.17**0.12 to 0.220.031.19
2000Is a minority group member0.11*0.06 to 0.170.011.12
Is female−1.49**−1.53 to −1.450.194.44
Some college/technical school or more0.16**0.12 to 0.200.021.17
Household income is more than median0.42**0.38 to 0.460.061.52
Is 55 y of age or older−0.62**−0.67 to −0.570.071.86
Is overweight and/or obese0.13**0.09 to 0.180.021.14
2011Is a minority group member0.060.01 to 0.110.001.06
Is female−1.63**−1.66 to −1.590.165.10
Some college/technical school or more0.19**0.16 to 0.230.021.21
Household income is more than median0.50**0.47 to 0.540.051.65
Is 55 y of age or older−0.35**−0.39 to −0.320.041.42
Is overweight and/or obese0.14**0.10 to 0.170.011.15
2017Is a minority group member0.15**0.10 to 0.200.011.16
Is female−1.52**−1.56 to −1.470.144.57
Some college/technical school or more0.09**0.06 to 0.140.011.09
Household income is more than median0.49**0.45 to 0.530.051.63
Is 55 y of age or older−0.40**−0.44 to −0.370.041.49
Is overweight and/or obese0.11**0.07 to 0.150.011.12

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is respondents who reported sport most frequently during previous month (0 = no and 1 = yes).

*P < .0002. **P < .000001.

The likelihood of reporting engaging in an “Other” PA most frequently during the previous month varied from year to year with no consistent pattern. Though several relationships were statistically significant, none of the Φ coefficients for the relationships met the threshold of a small effect size. These data are further summarized in Table 10.

Table 10

Odds Ratios for Other PA Type by Biological and Demographic Group

YearEffectLogit95% CIΦOdds ratio
1988Is a minority group member−1.16**−1.65 to −0.670.033.19
Is female0.48**0.28 to 0.680.021.62
Some college/technical school or more0.08−0.11 to 0.270.001.08
Household income is more than median0.08−0.14 to 0.290.001.08
Is 55 y of age or older0.65**0.44 to 0.870.031.92
Is overweight and/or obese0.270.08 to 0.460.021.31
1994Is a minority group member−0.33**−0.46 to −0.210.021.39
Is female−0.24**−0.32 to −0.170.021.27
Some college/technical school or more0.04−0.04 to 0.120.001.04
Household income is more than median0.19**0.11 to 0.270.021.21
Is 55 y of age or older0.090.01 to 0.170.011.09
Is overweight and/or obese0.090.02 to 0.180.011.09
2000Is a minority group member−0.33**−0.43 to −0.240.021.39
Is female−0.28**−0.34 to −0.220.031.32
Some college/technical school or more0.01−0.05 to 0.070.001.01
Household income is more than median−0.06−0.13 to 0.020.011.06
Is 55 y of age or older0.14*0.07 to 0.200.011.15
Is overweight and/or obese0.01−0.05 to 0.070.001.01
2011Is a minority group member−0.04−0.08 to 0.010.001.04
Is female−0.07**−0.09 to −0.040.011.07
Some college/technical school or more−0.01−0.04 to 0.020.001.01
Household income is more than median−0.09**−0.12 to −0.060.011.09
Is 55 y of age or older0.27**0.24 to 0.290.031.31
Is overweight and/or obese−0.01−0.04 to 0.020.001.01
2017Is a minority group member0.010.01 to 0.100.011.01
Is female0.040.01 to 0.080.011.01
Some college/technical school or more0.10**0.07 to 0.140.011.11
Household income is more than median−0.01−0.04 to 0.030.001.01
Is 55 y of age or older0.08**0.05 to 0.120.011.08
Is overweight and/or obese−0.06−0.09 to −0.020.011.06

Abbreviations: CI, confidence interval; PA, physical activity. Note: Reference for the effect (coded 0 = no and 1 = yes) is respondents who reported other PA most frequently during previous month (0 = no and 1 = yes).

*P < .0002. **P < .000001.

Time Trends in Physical Activity Mode Over the Previous Month

Table 11 summarizes the change in the proportion of respondents reporting each PA mode most frequently over the previous month at each time point, as well as the percentage change in the proportion when comparing 1988 to 2017. Over the period from 1988 to 2017, among the 70 modes of PA that had enough data to establish a trend, 14 (20%) increased in proportion, 29 (41%) remained stable, and 27 (39%) decreased. For the sake of brevity, we will not summarize each individual PA mode here in detail. Instead, we will outline the most apparent trends we observed while exploring these data.

Table 11

PA Mode Across All Participants

PA typePA mode%Change19881994200020112017
ExerciseYoga+71.4%0.7%1.2%
ExerciseWeight lifting+34.4%3.2%3.3%4.2%2.5%4.3%
ExerciseBicycling machine exercise+23.5%1.7%1.0%2.3%2.1%
ExerciseRunning+11.95.9%4.8%6.3%6.2%6.6%
ExercisePilates0.2%0.2%
ExerciseRope skipping0.0%0.0%0.0%0.0%0.0%
ExerciseTai Chi0.1%0.1%
ExerciseSwimming in laps−81.5%2.7%1.8%1.6%0.4%0.5%
ExerciseAerobics class−75.4%6.1%6.6%3.3%2.9%1.5%
ExerciseJogging−62.5%2.4%1.8%1.3%1.0%0.9%
ExerciseRowing machine exercise−50%0.2%0.1%0.1%0.1%
ExerciseElliptical/EFX machine exercise−11.8%1.7%1.5%
ExerciseCalisthenics−4.3%2.3%1.1%1.2%1.3%2.2%
ExerciseHealth club exerciseN/A0.9%1.1%1.8%
ExerciseHome exerciseN/A2.3%2.1%2.5%
ExerciseUpper body cycleN/A0.1%
ExerciseStair climbing/StairMasterN/A0.0%0.5%0.2%0.3%0.3%
HouseholdMowing lawn+100%0.2%0.4%0.4%0.7%0.4%
HouseholdSnow shoveling by hand+100%0.1%0.4%0.1%0.3%0.2%
HouseholdCarpentry0.1%0.1%0.1%0.1%0.1%
HouseholdRaking lawn0.1%0.1%0.1%0.1%0.1%
HouseholdSnow blowing0.0%0.0%0.0%0.0%0.0%
HouseholdPainting/papering house−100%0.1%0.0%0.0%0.0%0.0%
HouseholdGardening−31.6%7.9%6.4%7.1%7.1%5.4%
HouseholdChildcareN/A0.2%
HouseholdFarm/ranch workN/A0.4%
HouseholdHousehold activitiesN/A0.6%
HouseholdYard workN/A1.5%
LeisureHiking (cross-country)+175%0.4%0.8%0.8%0.7%1.1%
LeisureWalking+20.1%44.8%46.8%50.3%55.6%53.8%
LeisureBackpacking0.0%0.0%0.0%0.0%0.0%
LeisureBoating0.1%0.1%0.1%0.1%0.1%
LeisureDancing (aerobic/ballet)0.6%0.6%0.6%0.7%0.6%
LeisureFrisbee0.0%0.0%
LeisureInline skating0.0%0.0%
LeisureMountain climbing0.0%0.0%0.0%0.0%0.0%
LeisureScuba diving0.0%0.0%0.0%0.0%0.0%
LeisureSledding and tobogganing0.0%0.0%0.0%0.0%0.0%
LeisureSnorkeling0.0%0.0%0.0%0.0%0.0%
LeisureSnow shoeing0.0%0.0%0.0%0.0%0.0%
LeisureStream fishing in waders0.0%0.0%0.0%0.0%0.0%
LeisureSurfing0.1%0.1%0.1%0.1%0.1%
LeisureSwimming1.1%1.1%
LeisureActive gaming devices−100%0.2%0.0%
LeisureHunting small game−100%0.1%0.0%
LeisureSkating (ice or roller)−100%0.2%0.3%0.3%0.1%0.0%
LeisureWater skiing−100%0.2%0.1%0.1%0.1%0.0%
LeisureHunting large game (deer and elk)−80%0.5%0.3%0.3%0.1%0.1%
LeisureSnow skiing−80%0.5%0.6%0.4%0.2%0.1%
LeisureHorseback riding−75%0.4%0.3%0.3%0.2%0.1%
LeisureFishing from river bank or boat−66.7%0.3%0.2%0.2%0.1%0.1%
LeisureBicycling for pleasure−43.5%4.6%3.8%3.1%2.5%2.6%
LeisureRock climbingN/A0.0%0.1%
LeisureSkateboardingN/A0.0%0.1%
SportSoccer+50%0.2%0.3%0.4%0.3%0.3%
SportBadminton0.0%0.0%0.0%0.0%0.0%
SportGolf (with motorized cart)1.3%1.3%
SportHandball0.0%0.0%0.0%0.0%0.0%
SportHockey0.1%0.1%
SportLacrosse0.0%0.0%
SportPaddleball0.0%0.0%0.0%0.0%0.0%
SportRugby0.0%0.0%
SportSquash0.0%0.0%0.0%0.0%0.0%
SportTable tennis0.0%0.0%0.0%0.0%0.0%
SportWrestling0.0%0.0%
SportCanoeing/rowing (in competition)−100%0.1%0.0%0.1%0.0%0.0%
SportTouch football−100%0.2%0.1%0.1%0.0%0.0%
SportSoftball/baseball−86.7%1.5%1.1%0.6%0.2%0.2%
SportRacquetball−85.7%0.7%0.4%0.2%0.1%0.1%
SportVolleyball−83.3%0.6%0.6%0.4%0.1%0.1%
SportBowling−77.8%0.9%0.5%0.4%0.2%0.2%
SportBasketball−69.6%2.3%2.0%1.9%0.7%0.7%
SportTennis−69.2%1.3%0.9%0.7%0.5%0.4%
SportBowling−77.8%0.9%0.5%0.4%0.2%0.2%
SportGolf (without motorized cart)−28.6%0.7%0.5%
SportBoxingN/A0.0%0.0%0.1%0.1%0.1%
SportJudo/karateN/A0.2%0.2%0.4%
SportGolfN/A3.5%3.3%3.0%
SportKarate/martial artsN/A0.2%

Abbreviations: —, no change between 1988 and 2017; N/A, too few data points to establish a trend or response option no longer available; PA, physical activity.

Time Trends in Exercise Mode

Of 14 response options for Exercise mode, 5 (36%) increased in proportion over time, 3 (21%) remained stable, and 6 (43%) decreased over time. Yoga showed the largest percentage change, though it was only a response option in 2011 and 2017. Among the PA modes with data at each time point, the proportion of respondents engaging in weight lifting most frequently in the past month increased the most. The proportions of respondents reportedly engaging in swimming in laps, aerobics class, and jogging each fell by over 50% from 1988 to 2017. Supplementary Tables 1–6 (available online) further summarize these PA mode data by each biological and demographic characteristic group over time.

Time Trends in Lifestyle Physical Activity—Household Mode

For Lifestyle PA—Household mode, 2 (29%) increased in proportion over time, 3 (43%) remained stable, and 2 (29%) decreased. The proportion of respondents reporting mowing lawn and snow shoveling by hand increased over time, while the proportions of those reporting painting/papering house and gardening decreased.

Time Trends in Lifestyle Physical Activity—Leisure Mode

Of the 26 Lifestyle PA—Leisure response options, the proportions of 4 (15%) increased, 13 (50%) were stable, and 9 decreased (35%). The proportion of individuals reporting hiking cross-country most frequently over the previous month increased by over 100% from 1988 to 2017. In contrast, the proportions of active gaming devices, hunting small game, skating (ice or roller), and water skiing each fell by 100% over the same period.

Time Trends in Sport Mode

The proportions of 3 of the response options for sport modality increased (13%), 10 (43%) remained stable, and 10 (43%) decreased over time. The proportion of respondents engaging in soccer most frequently during the past month increased by approximately 50% during the period from 1988 to 2017. The proportions of several sport mode response options decreased by more than 50%, and the largest changes were for canoeing/rowing (in competition) and touch football.

Discussion

The purpose of this study was to examine temporal trends in the frequencies at which BRFSS respondents reported participating in physical activities of differing intensities, types, and modalities from 1988 to 2017. The overall data suggested that the proportion of individuals reportedly engaging in vigorous-intensity PA decreased over time being replaced by a larger proportion reporting moderate- or light-intensity PA. In addition, the proportions of all respondents who reported engaging in a Lifestyle PA—Leisure and an “Other” PA most frequently grew over time while the proportions engaging in Sport and Exercise most frequently became smaller.

Due to the size and representativeness of the BRFSS, the PA data contained within may help to provide a more generalizable representation of how American adults use their discretionary time for PA and how their allocation of time for different physical activities ebb and flow over time, compared with surveys such as the one conducted annually by the American College of Sports Medicine (ACSM).14 As an illustration of what these data could provide, in the 1970s and 1980s, the concept of aerobics became popularized among the general public after the actress Jane Fonda released a line of aerobics exercise videos primarily marketed to women and intended to be performed at home. Our results suggest that the proportion of respondents, especially females, engaging in aerobics videos or classes most frequently over the past month peaked from 1988 to 1994 and decreased by around 75% by 2017. Another trend that may have been highlighted during our analyses was the “jogging boom” of the 1970s and 1980s. Around the same time that Cooper published Aerobics,22 the book Jogging23 hit store shelves, selling over a million copies and inspiring many to jog regularly. Over time, however, it is possible that the term “jogging” has fallen out of colloquial use, with the term “running” becoming more prevalent. Indeed, by 2017, less than 1% of respondents reported jogging most frequently over the previous month (down from 2.4% in 1988), while the percentage reporting “running” was mostly unchanged over time.

Disengagement From Sport

Disengagement from Sport physical activities over time among American adults is unsurprising given reports from youth sport researchers for decades.2426 For instance, in a clinical report for the American Academy of Pediatrics, Brenner27 outlined how the culture and structure of youth sport in the United States has evolved over the past 40 years. While it was common in the past for children and adolescents to engage in unorganized “pick-up” style games with their peers, mostly free from adult influence, organized sport has become more widespread over time. Furthermore, children are now more likely to be single-sport versus multisport athletes, with some reasoning that early specialization could lead to better odds of future success in that sport.28 While from a performance standpoint, considerable debate among sport scientists remains over specializing in a single sport,29 it is clear that dropout from youth sport has increased over time.

In some of the earliest available research on youth sport during the 1970s and 1980s, it was estimated that the rate of youth sport attrition was about 35%.24,26 Since then, it has been estimated that 70% or more children over 13 years of age will drop out of youth sport.25 Along with increased specialization leading to burnout and subsequent dropout, others have suggested that dropout may be, at least partly, driven by psychological factors such as perceptions that sport is not fun and enjoyable,30 that winning is overemphasized, and that many coaches assume individual motivations for sport participation are the same (eg, to win “at all costs” as opposed to having fun or task-related goal motivations31).

Many of the factors that influence disengagement from sport during youth may continue to influence PA behavior into adulthood. For instance, opportunities for sport during adulthood are often provided by so-called “recreational” or “adult” leagues. Although branded as “recreational,” these leagues may often emphasize extrinsic motivational factors, such as the need to win and perform better than others.32 Because emphases on extrinsic motivational factors in recreational leagues may be similar to those factors reported as influential by children who drop out of youth sport, adults who participate in recreational sport leagues may represent a unique segment of the population. Specifically, those who enjoyed the motivational climate during youth sport and did not drop out may be more likely to continue with recreational sport or adult leagues.

While whether it is possible to modify perceptions of previous negative experiences with sport and PA is unclear,33,34 one potential approach to address decreased sport participation during adulthood may be to offer attractive sport alternatives that do not foster the same motivational climates present in many organized youth sports and adult recreational sport leagues. Adult sport opportunities where fun, enjoyment, limited competition, and no contact are emphasized may appeal to a broader audience than do many existing alternatives.34,35 Consequently, implementing these options for both adults and children may reduce dropout over time by developing an automatic association between sport and enjoyment, in contrast to associations between sport and aversive emotions, such as embarrassment and/or performance anxiety.34,36

Disengagement From Exercise and Increased Engagement in Lifestyle Physical Activities

While the proportion of respondents who reported engaging in sport and exercise most frequently over the previous month decreased over time, those Americans who are physically active may have become more likely to choose to engage with Lifestyle Physical Activities. Among the most apparent explanations for these trends could include lack of access to and/or interest in fitness centers, recreational/adult sport leagues, the expenses associated with many sports and exercise (ie, equipment, membership costs), and, as outlined above, a lack of alternatives beyond more traditional recreational sport leagues. Indeed, these data suggest a growing proportion of those participating in sport and exercise most frequently are among those with the highest income level. However, several alternative explanations for these changes may be less clear.

For example, Americans are becoming heavier over time. A 2018 National Center for Health Statistics study estimated nearly 40% of American adults are obese,37 and the prevalence of severe obesity (ie, BMI ≥ 50 kg/m2) has increased from around one-in-two-thousand in 1980 to one-in-four-hundred in 2008.38 For many individuals, as body weight increases, even “moderate-intensity” PA can become more strenuous and more likely to be perceived as unpleasant and unenjoyable.38,39 Moreover, among those with excess weight, prolonged periods of physical inactivity, driven by attempts to avoid the difficulty and unpleasantness of PA, are associated with physical deconditioning,40 which could lead to a vicious circle where physical deconditioning further exacerbates the difficulty and discomfort of PA.

As American adults become more sedentary and physically deconditioned, exercise professionals and researchers may increasingly encounter apparently healthy, but overweight and/or obese clients who exhibit aerobic capacities similar to patients with heart failure (ie, VO2peak ≤ 14 mL·kg−1·min−14143). Many exercise and sport modalities, particularly when played competitively or to maximize performance gains, are likely to be of vigorous intensity (ie, ≥6.0 METs), while many lifestyle physical activities are often lower intensity.21 Because of the tendency for many adults with overweight and/or obesity to experience vigorous-intensity PA as aversive, it is perhaps unsurprising that, as average BMI increases, fewer adults report engaging in sport and exercise most frequently. These trends in PA behavior toward lower-intensity PA, such as those reported here or, more concerning, increased physical inactivity, may continue if Americans continue to become heavier. Therefore, it is critical that exercise professionals and researchers continue to develop their understanding of how average overweight and/or obese American adults feel during exercise44 as they may have increasing difficulty moving while, at the same time, experiencing pleasure and enjoyment.43

The difficulty and discomfort of performing PA with high body weight could also account for the changes in reported walking behavior over the past 3 decades. For example, in 1988, 44.8% of Americans adults responded that walking was their most frequent PA over the previous month, while in 2017, it was 53.8%. This trend was most apparent among men, whose proportion reporting it most frequently increased from 32.8% in 1988 to 46.2% in 2017. Besides imparting less impact on the joints and expending less energy,21 walking may be, for many, among the physical activities most likely to elicit feelings of pleasure.45 Consequently, further attention should be afforded toward fostering opportunities where safe places to walk are among the easiest to access or even default mode of transportation (ie, by designing walkable cities and towns46) may be of great value to increase PA. Similarly, among all PA modes, hiking cross-country displayed, by far, the largest change in the proportion of individuals reporting it over time, increasing nearly 200% from 1988 to 2017. Although, depending on the terrain, hiking is often more metabolically demanding than walking, doing so in a “green” environment with the opportunity to get away from the “hustle and bustle” of city life may render the experience pleasant and enjoyable enough where individuals are increasingly attracted to it.47

It is also becoming clear that youth PA experiences may shape adult PA preferences.48 For example, retrospective reports from adults of lack of enjoyment and embarrassment during physical education and youth sport, due to factors such as being chosen last for teams, embarrassment over poor performance, and the perception that their bodies were on display to peers,34 are associated with reduced PA and increased sedentary behavior during adulthood. Interestingly, many lifestyle physical activities can be done alone or with only a few others, which could help reduce feelings of embarrassment due to performance and/or social physique anxiety.49 Consequently, exercise scientists and recreational professionals may have a unique opportunity to capitalize on these factors to mitigate decreasing exercise and sport participation by addressing the underlying mechanisms that cause individuals to associate these PA types with nonenjoyment, displeasure, and negative emotions.

The Rise of the “Other” Physical Activity

The response option “Other” has been included in the BRFSS every year since its first administration. Despite its constant inclusion and the number of response options growing over time from 53 to 67, the proportion of individuals responding “Other” grew substantially (ie, approximately 400%) from 1988 to 2017 compared with the other PA types. Moreover, these changes were consistent across all biological and demographic groups. It is possible that other, less common or popular physical activities may not have been captured with these response options. Furthermore, although the BRFSS provides respondents with the option to respond “other” to their most frequent PA mode over the past month, those qualitative data, if collected, are not readily accessible in the public data sets. This is unfortunate. For example, in 2015, the Sports and Fitness Industry Association reported 2.46 million Pickleball players,50 yet Pickleball was not provided as a response option. To further illustrate the scope of this issue, in 2017, around 34,000 individuals responded “other” to describe their most frequent PA modality over the past month. This trend toward seeking out alternative PA could highlight the need for increased variety in PA offerings for adults, beyond the most common fitness center and recreational sport offerings.51

Limitations

While the results reported here are based on large, nationally representative data sets, the present study has several limitations. First, due to the 2011 BRFSS changes in sampling methodology and weighting, we were precluded from employing inferential statistical analyses to analyze year-to-year differences in PA behavior. According to the CDC,14 these changes may have increased the likelihood that certain risky health-related behaviors, such as physical inactivity, were better accounted for than had been previously. However, given the very large sample sizes, even small differences between variables over time would be statistically significant, and these small differences may not be clinically or practically relevant.52 We were not, however, prevented from analyzing associations within each BRFSS administration. Changes in the strength of association between relevant variables over time may indicate that their influence is also changing over time even without tests of statistical significance. Next, despite attempts to improve representation, the BRFSS continues to underrepresent non-white individuals and those of lower socioeconomic status.53,54 PA behavior among racial and ethnic minorities may not align well with those derived from predominantly white samples.

In addition, despite the growing body of evidence attesting to its benefits for health,55,56 the BRFSS includes few light-intensity PA mode options for respondents to choose from. These data also do not provide a good understanding of the overall underlying PA levels of the respondents. For example, a respondent may participate in multiple physical activities at nearly the same frequency, but this variety would not be captured by the BRFSS, as it does not ask respondents to try to list all physical activities engaged in over the previous month. Moreover, several of the PA mode response options are seasonal (eg, snow skiing), and the proportions of those engaging in them may vary by time of year and geographical location.

Finally, our categorization of each PA modality into its respective type categories is not free from subjectivity. For instance, to classify a PA modality as exercise, it is important to consider its intended purpose. A PA modality, such as walking, could be categorized as a lifestyle PA, exercise, or both, depending on whether the activity is regular and structured and/or intended to lead to improvements in one or more facets of physical fitness (eg, muscular strength, flexibility17). At the same time, regular engagement in lifestyle physical activities could lead to improvements in fitness, suggesting that it could also be classified as exercise. Moreover, walking and many other lifestyle physical activities or exercises may be considered sport if competition is introduced. For example, USA Cycling, the governing body for competitive cycling in the United States, reported 61,631 members in 201057. It has been estimated that around 12%, or 39,000,000 Americans ride a bicycle on a regular basis. Using these data, less than two-tenths of 1% of Americans who cycle regularly do so competitively. With these limitations in mind, we classified each PA based on the default, or most common manner that each activity is performed, based on the operational definitions provided, experiences in the fields of sports medicine and PA behavior, as well as our best judgment.

Conclusions

Physical activity remains one of the most difficult health-related behaviors to modify, despite numerous attempts at intervention and millions of dollars allocated to research. It is, therefore, critical that novel, evidence-based approaches to its promotion continue to be investigated. We suggest that one encouraging avenue for the future is through understanding and leveraging individual and population-level differences in the choices people make to engage in physical activities of varying intensities, types, and modalities. These results suggest that the influence of income inequality on the variety of PA with which individuals engage may be growing over time. Increased focus on providing safe, accessible, and inexpensive opportunities for a variety of PA experiences may be necessary to increase the likelihood of PA among American adults. Moreover, because the trends in obesity among Americans are expected to continue, exercise researchers and professionals must be prepared to consider the influence of weight status on the experience of PA. Frequently reporting and acting on these continuously updated data from nationwide surveys such as the BRFSS may help improve the likelihood that individuals have opportunities to experience a wide range of activities (and can discover those that are most personally pleasant and enjoyable) during youth and into adulthood, improving PA adherence across the life span.

Acknowledgments

C.N.S. has an investment, such as stock, in a company which has begun to investigate the possibility of creating a business that provides exercise programs. No sources of support were used in the preparation of this work.

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Ladwig, Sciamanna, Auer, and Stine are with the Department of Medicine, Penn State College of Medicine, Hershey, PA, USA. Oser is with the Department of Family Medicine, University of Colorado School of Medicine, Aurora, CO, USA. Agans is with the Department of Recreation, Park, and Tourism Management, Penn State University, University Park, PA, USA.

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  • 1.

    Tucker JM, Welk GJ, Beyler NK. Physical activity in US: adults compliance with the Physical Activity Guidelines for Americans. Am J Prev Med. 2011;40(4):454461. PubMed ID: 21406280 doi:10.1016/j.amepre.2010.12.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 2.

    Hallal PC, Bo Andersen L, Bull FC, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247257. PubMed ID: 22818937 doi:10.1016/S0140-6736(12)60646-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Williams DM, Papandonatos GD, Napolitano MA, Lewis BA, Whiteley JA, Marcus BH. Perceived enjoyment moderates the efficacy of an individually tailored physical activity intervention. J Sport Exerc Psychol. 2006;28(3):300309. doi:10.1123/jsep.28.3.300

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Williams DM, Dunsiger SI, Ciccolo JT, Lewis BA, Albrecht AE, Marcus BH. Acute affective response to a moderate-intensity exercise stimulus predicts physical activity participation 6 and 12 months later. Psychol Sport Exerc. 2008;9(3):231245. PubMed ID: 18496608 doi:10.1016/j.psychsport.2007.04.002

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Cabanac M. Pleasure: the common currency. J Theor Biol. 1992;155(2):173200. PubMed ID: 12240693 doi:10.1016/S0022-5193(05)80594-6

  • 6.

    Ekkekakis P, Parfitt G, Petruzzello SJ. The pleasure and displeasure people feel when they exercise at different intensities: decennial update and progress towards a tripartite rationale for exercise intensity prescription. Sports Med. 2011;41(8):641671. PubMed ID: 21780850 doi:10.2165/11590680-000000000-00000

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    De Moor MH, Beem AL, Stubbe JH, Boomsma DI, De Geus EJ. Regular exercise, anxiety, depression and personality: a population-based study. Prev Med. 2006;42(4):273279. PubMed ID: 16439008 doi:10.1016/j.ypmed.2005.12.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 8.

    Ekkekakis P, Hall EE, Petruzzello SJ. Some like it vigorous: measuring individual differences in the preference for and tolerance of exercise intensity. J Sport Exerc Psychol. 2005;27(3):350374. doi:10.1123/jsep.27.3.350

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 9.

    Salmon J, Owen N, Crawford D, Bauman A, Sallis JF. Physical activity and sedentary behavior: a population-based study of barriers, enjoyment, and preference. Health Psychol. 2003;22(2):178188. PubMed ID: 12683738 doi:10.1037/0278-6133.22.2.178

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Wilcox S, King AC, Brassington GS, Ahn DK. Physical activity preferences of middle-aged and older adults: a community analysis. J Aging Phys Act. 1999;7(4):386399. doi:10.1123/japa.7.4.386

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 11.

    Knuth AG, Hallal PC. Temporal trends in physical activity: a systematic review. J Phys Act Health. 2009;6(5):548559. PubMed ID: 19953831 doi:10.1123/jpah.6.5.548

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12.

    Moore LV, Harris CD, Carlson SA, Kruger J, Fulton JE. Trends in no leisure-time physical activity—United States, 1988–2010. Res Q Exerc Sport. 2012;83(4):587591. PubMed ID: 23367822 doi:10.1080/02701367.2012.10599884

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13.

    Carlson SA, Densmore D, Fulton JE, Yore MM, Kohl HW, 3rd. Differences in physical activity prevalence and trends from 3 U.S. Surveillance systems: NHIS, NHANES, and BRFSS. J Phys Act Health. 2009;6(suppl):S18S27. doi:10.1123/jpah.6.s1.s18

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Thompson WR. Worldwide survey reveals fitness trends for 2007. ACSMs Health Fit J. 2006;10(6):814. doi:10.1249/01.FIT.0000252519.52241.39

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Centers for Disease Control and Prevention. Methodologic changes in the behavioral risk factor surveillance system in 2011 and potential effects on prevalence estimates. Morb Mortal Weekly Rep. 2012;61(22):410413.

    • Search Google Scholar
    • Export Citation
  • 16.

    Brownson RC, Boehmer TK, Luke DA. Declining rates of physical activity in the United States: what are the contributors? Annu Rev Public Health. 2005;26(1):421443. doi:10.1146/annurev.publhealth.26.021304.144437

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Ng SW, Popkin B. Time use and physical activity: a shift away from movement across the globe. Obes Rev. 2012;13(8):659680. PubMed ID: 22694051 doi:10.1111/j.1467-789X.2011.00982.x

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Centers for Disease Control and Prevention. Glossary of terms, 2017. https://www.cdc.gov/nchs/nhis/physical_activity/pa_glossary.htm. Accessed December 14, 2020. Published 2017.

    • Search Google Scholar
    • Export Citation
  • 19.

    Deaner RO, Geary DC, Puts DA, et al . A sex difference in the predisposition for physical competition: males play sports much more than females even in the contemporary U.S. PLoS One. 2012;7(11):e49168. PubMed ID: 23155459 doi:10.1371/journal.pone.0049168

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Dunn AL, Andersen RE, Jakicic JM. Lifestyle physical activity interventions. History, short- and long-term effects, and recommendations. Am J Prev Med. 1998;15(4):398412. PubMed ID: 9838980 doi:10.1016/S0749-3797(98)00084-1

    • Crossref
    • Search Google Scholar
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