6. Among high school students, enrollment in physical education remained unchanged during the first half of the 1990s. However, daily atten- dance in physical education declined from ap- proximately 42 percent to 25 percent. The percentage of high school students who were enrolled in physical education and who reported being physically active for at least 20 minutes in physical education classes declined from approxi- mately 81 percent to 70 percent during the first half of this decade. Only 19 percent of all high school students report being physically active for 20 minutes or more in daily physical education classes. Research Needs 1, Develop methods to monitor patterns of regular, moderate physical activity. 2. Improve the validity and comparability of self- reported physical activity in national surveys. 3. Improve methods for identifying and tracking physical activity patterns among people with disabilities. 4. Routinely monitor the prevalence of physical activity among children under age 12. 5. Routinely monitor school policy requirements and of students' participation in physical educa- tion classes in elementary, middle, and high schools. Patterns and Trends in Physical Activity Appendix A: Sources of National Survey Data National Health Interview Survey (NHIS) This analysis used data from the 1991 NHIS to determine current prevalences of physical activity, and from 1985, 1990, and 1991 to determine physi- cal activity trends, among U.S. adults aged 18 years and older (National Center for Health Statistics [NCHS] 19881993; NCHS unpublished data). Since 1957, NCHS has been collecting year-round health data from a probability sample of the civilian, noninstitutionalized adult population of the United States. The design included oversampling of blacks to provide more precise estimates. For the 1985, 1990, and 1991 special supplement on health promo- tion and disease prevention, one adult aged 18 years or older was randomly selected from each family for participation from the total NHIS sample. Interviews were conducted in the homes; self-response was re- quired for this special supplement, and callbacks were made as necessary. The sample was poststratified by the age, sex, and racial distribution of the U.S. population for the survey year and weighted to provide national estimates. The overall response rate for the NHIS has been 83 to 88 percent. Behavioral Risk Factor Surveillance System (BRFSS) The Centers for Disease Control and Prevention (CDC) initiated the BRFSS in 1981 to help states obtain prevalence estimates of health behaviors, in- cluding physical activity, that were associated with chronic disease. The BRFSS conducts monthly, year- round, telephone interviews of adults aged 18 years of age and older sampled by random-digit dialing (Remington et al. 1988; Siegel et al. 1991; Frazier, Franks, Sanderson 1992). Physical activity ques- tions have been consistent since 1986, except for a minor change from 1986 to 1987. In 1994, the most recent survey available, 49 states and the District of Columbia participated. Only 25 states and the District of Columbia have participated continuously since 1986. For 1986-1991, sample sizes ranged from approximately 35,000 to 50,000, and response rates from 62 to 7 1 percent; for 1992, the sample size was 96,343, and the response rate 71 percent; for 1994, the sample size was 106,030, and the response rate 201 Physical Activity and Health 70 percent. For examination of trends, analysis was restricted to the 25 states and the District of Colum- bia, that had consistently participated from 1986 through 1994. For 1992 cross-sectional analyses, data were included from all 48 states that had participated that year and from the District of Columbia. For 1994 cross-sectional analyses, data were included from the 49 participating states and from the District of Columbia. Third National Health and Nutrition Examination Survey (NHANES III) NHANES 111 is the seventh in a series of national health examination surveys that began in the 1960s. The sample for NHANES 111 (NCHS 1994a) was selected from 81 counties across the United States. The survey period covered 1988-1994 and consisted of two phases of equal length and sample size. Both Phase 1(1988-1991) and Phase 11(1992-1994) used probability samples of the U.S. civilian noninstitu- tionalized population. Black and Mexican American populations were oversampled to obtain statistically reliable estimates for these minority groups. Phase II data were not available at the time this report was prepared. In Phase I, the selected population was 12,138 adults 18 years of age or older, of which 82 percent (9,901) underwent a home interview that included questions on physical activity. Participants in NHANES III also underwent a detailed medical examination in a mobile examination center. NHANES III data were weighted to the 1990 U.S. civilian noninstitutionalized population to provide national estimates. Youth Risk Behavior Survey (YRBS) The CDC developed the YRBS (Kolbe 1990; Kolbe, Kant-t, Collins 1993) to measure six categories of priority health-risk behaviors among adolescents: 1) behaviors that contribute to intentional and unin- tentional injuries; 2) tobacco use; 3) alcohol and other drug use; 4) sexual behaviors that result in unintended pregnancy and sexually transmitted dis- eases, including HIV infection 5) unhealthy dietary behaviors; and 6) physical inactivity. Data were collected through national, state, and local school- based surveys of high school students in grades 9-12 during the spring of odd-numbered years and through a 1992 national household-based survey of young people aged 12-21 years. The 1991,1993, and 1995 national school-based YRBS (Kann et al. 1993; CDC unpublished data) used three-stage cluster sample designs. The targeted population consisted of all public and private school students in grades 9-12 in the 50 states and the District of Columbia. Schools with substantial numbers of black and Hispanic students were sampled at relatively higher rates than all other schools. Survey procedures were designed to protect stu- dent privacy and allow anonymous participation. The questionnaire was administered in the classroom by trained data collectors, and students recorded their responses on answer sheets designed for scanning by computer. The school response rates ranged from 70 to 78 percent, and the student response rate ranged from 86 to 90 percent. The total number of students who completed questionnaires was 12,272 in 1991, 16,296 in 1993, and 10,904 in 1995. The data were weighted to account for nonresponse and for oversampling of black and Hispanic students. National Health Interview Survey-Youth Risk Behavior Survey (NHIS-YRBS) To provide more information about risk behaviors among young people, including those who do not attend school, the CDC added a youth risk behavior survey to the 1992 National Health Interview Survey (CDC 1993; NCHS 1994b). The survey was con- ducted as a follow-back from April 1992 through March 1993 among 12- through 2 I-year-olds from a national probability sample of households. School- aged youths not attending school were oversampled. NHIS-YRBS interviews were completed for 10,645 young people, representing an overall response rate of 74 percent. The questionnaire for this survey was adminis- tered through individual portable cassette players with earphones. After listening to questions, respon- dents marked their answers on standardized answer sheets. This methodology was designed to help young people with reading problems complete the survey and to enhance confidentiality during household administration. Data from this report were weighted to represent the U.S. population of 12- through 2 1 -year-olds. 202 Patterns and Trends in Physical Activity Appendix B: Measures of Physical Activity in Population Surveys There is no uniformly accepted method of assessing physical activity. Various methods have been used (Stephens 1989); unfortunately, estimates of physi- cal activity are highly dependent on the survey instrument. The specific problems associated with using national surveillance systems-such as those employed here- to monitor leisure-time physical activity have been reviewed previously (Caspersen, Merritt, Stephens 1994). All of the population surveys cited have em- ployed a short-term recall of the frequency, and in some cases the duration and intensity, of activities that either were listed for the participant to respond to or were probed for in an open-ended manner. The validity of these questions is not rigorously estab- lished. Estimates of prevalence of participation are influenced by sampling errors, seasons covered, and the number and wording of such questions; gener- ally, the more activities offered, the more likely a participant will report some activity. Besides defin- ing participation in any activity or in individual activities, many researchers have found it useful to define summary indices of regular participation in vigorous activity or moderate activity (Caspersen 1994; Caspersen, Merritt, Stephens 1994). These summary measures often require assumptions about the intensity of reported activities and the frequency and duration of physical activity required for health benefits. National Health Interview Survey (NHIS) Participants in the NHIS were asked in a standard- ized interview whether they did any of 22 exercises, sports, or physically active hobbies in the previous 2 weeks: walking for exercise, jogging or running, hiking, gardening or yard work, aerobics or aerobic dancing, other dancing, calisthenics or general exer- cise, golf, tennis, bowling, bicycling, swimming or water exercises, yoga, weight lifting or training, basketball, baseball or softball, football, soccer, volley- ball, handball or racquetball or squash, skating, and skiing (National Center for Health Statistics [NCHS] 1992). They were also asked, in an open-ended fashion, for other unmentioned activities performed in the previous 2 weeks. For each activity, the inter- viewer asked the number of times, the average min- utes duration, and the perceived degree to which heart rate or breathing increased (i.e., none or small, moderate, or large). The physical activity patterns were scored by using data for frequency and duration derived di- rectly from the NHIS. To estimate the regular, vigorous physical activity pattern, a previously pro- posed convention was followed (Caspersen, Pol- lard, Pratt 1987). One of two sex-specific regression equations was used to estimate the respondent's maximum cardiorespiratory capacity (expressed in metabolic equivalents [METS]) (Jonesand Campbell 1982): [60-0.55 o age (years)]/3.5 for men, and [48-0.37 o age (years)]/3.5 for women. One MET is the value of resting oxygen uptake relative to total body mass and is generally ascribed the value of 3.5 milliliters of oxygen per kilogram of body mass per minute (for example, 3 METS equals 3 times the resting level; walking at 3 miles per hour on a level surface would be at about that intensity). Indi- vidual activity intensity was based on reported values (Taylor et al. 1978; Folsom et al. 1985; Stephens and Craig 1989). The final activity intensity code for a specific activity was found by selecting one of three condi- tions corresponding to the perceived level of effort associated with usual participation. The perceived effort was associated with none or small, moderate, or large perceived increases in heart rate or breath- ing. For example, the activity intensity code for three levels of volleyball participation would be 5, 6, and 8 METS as the perceived effort progressed from none or small to large increases in heart rate or breathing. In some cases, a single intensity code was averaged for several types of activity participa- tion that were not distinguished in the NHIS. This averaging was done for such activities as golf, calis- thenics or general exercise, swimming or water exercises, skating, and skiing. To determine if an activity would qualify a person to meet the intensity criterion of vigorous physical activity, each inten- sity code had to meet or exceed 50 percent of the estimated age- and sex-specific maximum cardio- respiratory capacity. 203 Physical Activity and Health For this report, three patterns of leisure-time activity were defined (Caspersen 1994): o No pltysical activity: No reported activity during the previous 2 weeks. o Rcgnlar, sirstnincd activity: 2 5 times per week and 2 30 minutes per occasion of physical activ- ity of any type and at any intensity. o Regular, vigorous activity: 2 3 times per week and 2 20 minutes per occasion of physical activity involving rhythmic contractions of large muscle groups (e.g., jogging or running, racquet sports, competitive group sports) performed at > 50 percent of estimated age- and sex-specific maxi- mum cardiorespiratory capacity. Behavioral Risk Factor Surveillance System (BRFSS) The BRFSS questionnaire first asks, "During the past month, did you participate in any physical activities or exercises such as running, calisthenics, golf, gar- dening, or walking for exercise?" If yes, participants were asked to identify their two most common physical activities and to indicate the frequency in the previous month and duration per occasion (Caspersen and Powell 1986; Caspersen and Merritt 1995). If running, jogging, walking, or swimming were mentioned, participants were also asked the usual distance covered. The reported frequency and duration of activity were used for scoring. Intensity of physical activity was assigned by using the same intensity codes as the NHIS, and a correction procedure (explained later in this section) based on speeds of activities was used to create intensity codes for walking, running/jogging, and swimming (Caspersen and Powell 1986; Caspersen and Merritt 1995). The estimate of speed was made by dividing the self-reported distance in miles by the duration in hours. The speed estimate was entered into specific regression equations to refine the intensity code for these four activities, because the application of a single intensity code is likely to underestimate or overestimate the intensity. Based on previously pub- lished formulae (American College of Sports Medi- cine 1988), five equations were constructed for predicting metabolic intensity of walking, jogging, and running at various calculated speeds: Equation 1 METS = 1.80 (Speeds < 0.93 mph) Equation 2 METS = 0.72 x mph + 1.13 (Speeds 2 0.93 but < 3.75 mph) Equation 3 METS = 3.76 x mph - 10.20 (Speeds L 3.75 but < 5.00 mph) Equation 4 METS = 1.53 x mph + 1.03 (Speeds 2 5.00 but < 12.00 mph) Equation 5 METS = 7.0 or 8.0 (Speeds 2 1200,mph) Below 0.93 mph, an intensity code of 1.8 METS (Equation 1) wasused, to beconsistentwithMontoye's intensity code for residual activities like those associ- ated withslow movements (Montoye 1975). Equation 2 is extrapolated to include speeds as slow as 0.93 mph-the point at which metabolic cost was set at 1.8 METS. Persons whose calculated speeds fell between 0.93 and 12.0 mph were assigned an intensity from equations 2, 3, or 4, regardless of whether they said they walked, jogged, or ran. Equation 3 was created by simply connecting with a straight line the last point of equation 2 and the first point of equation 4. This interpolation was seen as a reasonable way to deter- mine intensity within the range of speed where walk- ing or jogging might equally occur. This assignment method was considered to be more objective, specific, and generally conservative than assigning an intensity code based solely on the self-reported type of activity performed. Thus, as a correction procedure for self- reported speeds judged likely to be erroneously high, an intensity of 2.5 METS was assigned for walking speeds above 5.0 mph, 7.0 METS for jogging speeds above 12.0 mph, and 8.0 METS for running speeds above 12.0 mph. Another set of regression equations predicted metabolic intensity from swimming velocity: Equation 6 METS = 1.80 (Speeds < 0.26 mph) Equation 7 METS = 4.19 x mph - 0.69 (Speeds 2 0.26 but < 2.11 mph) Equation 8 METS = 8.81 x mph - 9.08 (Speeds 2 2.11 but < 3.12 mph) Equation 9 METS = 5.50 (Speeds 2 3.12 mph) These equations were set forth in a Canadian mono- graph of energy expenditure for recreational activi- ties (Groupe d'etude de Kino-Quebec sur le systcme de quantification de la depense energetique 1984). However, swimming speeds up to 3.12 mph for the crawl and backstroke, in the derivation of equations 7 and 8, were obtained from published research (Holmer 1974a; Holmer 1974b; Passmore and Durnin 1955). Default intensity codes were assigned as fol- lows: 1.8 METS for swimming speeds less than 0.26 mph, and 5.5 METS for velocities greater than 3.12 mph, because such speeds are improbable and likely reflected errors in self-report. Definitions used for leisure-time physical activ- ity were the same as those described for the NHIS earlier in this appendix, Third National Health and Nutrition Examination Survey (NHANES III) The NHANES III questions that addressed leisure- time physical activity (NCHS 1994a) were adapted from the NHIS. Participants firstwere asked how often they had walked a mile or more at one time in the previous month. They were then asked to specify their frequency of leisure-time physical activity during the previous month for the following eight activities: jogging or running, riding a bicycle or an exercise bicycle, swimming, aerobics or aerobicdancing, other dancing, calisthenics or exercises, gardening or yard work, and weight lifting. An open-ended question asked for information on up to four physical activities not previously listed. Information on duration of physical activity was not collected. Northern sites selected for NHANES III tended to be surveyed in warm rather than cold months, which might have led to a greater prevalence of reported physical activity than would otherwise be obtained from a year-round survey. No physical activity was defined as no re- ported leisure-time physical activity in the previous month. Regular, sustained activity and regular, vigor- ous activity were not defined for NHANES III because of the lack of information on activity duration. Youth Risk Behavior Survey (YRBS) In the YRBS questionnaire (Kann et al. 1993), stu- dents in grades 9-12 were asked eight questions about physical activity. The question on vigorous physical activity asked, "On how many of the past Patterns and Trends in Physical Activity 7 days did you exercise or participate in sports activities for at least 20 minutes that made you sweat and breathe hard, such as basketball, jogging, fast dancing, swimming laps, tennis, fast bicycling, or similar aerobic activities?" The questionnaire asked separately about the frequency of three specific ac- tivities in the previous 7 days: 1) stretching exer- cises, such as toe touching, knee bending, or leg stretching; 2) exercises to strengthen or tone the muscles, such as push-ups, sit-ups, or weight lifting; and 3) walking or bicycling for at least 30 minutes at a time. Participants were asked about physical edu- cation, "In an average week when you are in school, on how many days do you go to physical education (PE) classes?" and "During an average physical edu- cation (PE) class, how many minutes do you spend actually exercising or playing sports?" Students were also asked, "During the past 12 months, on how many sports teams run by your school did you play? (Do not include PE classes.)" and "During the past 12 months, on how many sports teams run by orga- nizations outside of your school did you play?" National Health Interview Survey-Youth Risk Behavior Survey (NHIS-YRBS) The NHIS-YRBS questionnaire (NCHS 1994b) ascer- tained the frequency of vigorous physical activity among U.S. young people aged 12-21 years by asking, "On how many of the past 7 days did you exercise or take part in sports that made you sweat and breathe hard, such as basketball, jogging, fast dancing, swim- ming laps, tennis, fast bicycling, or other aerobic activities?" Ten other questions asked about the prc- vious 7 days' frequency of participating in the fol'.: . - ing specific activities: 1) stretching exercises. P h as toe touching, knee bending, or leg strc' _ nir . . 2) exercises to strengthen or tone muscles ,uch as push- ups, sit-ups, or weight lifting; 3) house cleaning or yard work for 2 30 minutes at a ttmt : ) walking or bicycling for 2 30 minutes at a I` Le; 5) baseball, softball, or Frisbee@`; 6) bask< ! &, football, or soc- cer; 7) roller skating, ice st aLmg, skiing, or skate- boarding; 8) running, jogging, or swimming for exercise; 9) tennis. ry.,quetball, or squash; and 10) aerobics or danc-,-. Questions about duration and intensity werL not asked. `Use cl! mde names is for identification only and does not imply end! (rsement by the U.S. Department of Health and Human Services. LO5 Physical Activity and Health References American College of Sports Medicine. Guidelinesfor exer- cise testing and prescription. 3rd ed. Philadelphia: Lea and Febiger, 1988168-169. Brener ND, Collins JL, Kann L, Warren CW, Williams Bl. Reliability of the Youth Risk Behavior Survey question- naire.AmericanJoumaf ofEpidemioIogy1995;141:575580. Caspersen CJ. What are the lessons from the U.S. ap- proach for setting targets. In: Killoran AJ, Fentem P, Caspersen C, editors. LMoving on: international perspec- tives on promoting physical activity. London: Health Education Authority, 1994:35-55. Caspersen CJ, Merritt RK. Physical activity trends among 26 states, 1986-1990. Medicine and Science in Sports and Exercise 1995;27:713-720. Caspersen CJ. Merritt RK, Stephens T. Internationalphysi- cal activity patterns: a methodological perspective. In: Dishman RK, editor. Advances in exercise adherence. Champaign, IL: Human Kinetics, 1994: 73-l 10. Caspersen CJ, Pollard RA, Pratt SO. Scoring physical activity data with special consideration for elderly populations. In: Data for an aging population. Proceed- ings OJ the 21s~ national meeting of the Public Health Conference on Records and Statistics. Washington, DC: U.S. Government Printing Office, 1987:30-4. DHHS Publication No. (PHS)SS-1214. Centers for Disease Control. 1992 BRFSS Summary Preva- lencc Report. Atlanta: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Chronic Disease Prevention and Health Promotion, 1992. Centers for Disease Control. Youth Risk Behavior Sur- vey, 1991 data tape. Atlanta: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control. National Center for Chronic Disease Prevention and Health Promotion, 1991. National Technical Information Service Order No. PB93-500121. Centers for Disease Control and Prevention. 1994 BRFSS Summary Prevalence Report. Atlanta: U.S. Department of Health and Human Services, Public'Health Service, Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, 1994. Centers for Disease Control and Prevention. 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Folsom AR, Caspersen CJ, Taylor HL, Jacobs DR Jr, Luepker RV, Gomez-Marin 0, et al. Leisure-time physi- cal activity and its relationship to coronary risk factors in a population-based sample: the Minnesota Heart Sur- vey. AmericanJournal o/Epidemiology 1985;121:570-579. Frazier EL, Franks AL, Sanderson LM. Behavioral risk factor data. In: Using chronic disease data: a handbook forpublic health practitioners. Atlanta: U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Center for Chronic Disease Prevention and Health Promotion, 1992:4-l-4-17. Groupe d'ctude de Kino-Quebec sur le systeme de quan- tification de la depense energetique (GSQ). Rapport final. Quebec: Government du Quebec, 1984. Heath GW, Chang MH, Barker ND. Physical activity among persons with limitations-United States, 1991. Paper presented at the annual meeting of the Society for Disability Studies, June 17-19, 1995, Oakland, California. Holmer 1. Energy cost of arm stroke, leg kick, and the whole stroke in competitive swimming styles, Euro- peanJourna1 of Applied Physiology 1974a:33:105-118. Holmer 1. Propulsive efficiencyofbreaststrokeand freestyle swimming. European Journal of Applied Physiology 1974b;33:95-103. Jacobs DR Jr, Hahn LP, Folsom AR, Hannan PJ, Sprafka JM, Burke GL. Time trends in leisure-time physical activity in the upper Midwest, 1957-1987: University of Minnesota Studies. Epidemiology 1991;2:8-15. Jones NL, Campbell EJM. Clinical exercise testing. 2nd ed. Philadelphia: W.B. Saunders, 1982:249. Kann L, Warren W, Collins JL, Ross J, Collins B, Kolbe LJ. Results from the national school-based 1991 Youth Risk Behavior Survey and progress toward achieving related health objectives for the nation. Public Health Reports 1993;108(Suppl 1):47-67. Patterns and Trends in Physical Activity Kolbe LJ, An epidemiological surveillance system to monitor the prevalence of youth behaviors that most affect health. 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DHHS Publica- tion No. (PHS)91-50212. 207 CHAPTER 6 UNDERSTANDING AND PROMOTING PHYSICAL ACTIVITY Contents tntroduction . . . . . . . . . . . . . . . . . . . . . . . .._....._......_._._................_... 211 Fhcories and Models Used in Behavioral and Social Science Research on Physical Activity ... 211 LcarningTheories ........................................................ 211 HcalthBehefModel ....................................................... 213 Transtheoretical Model .................................................... 213 Relapse Prevention Model .................................................. 213 Theory of Reasoned Action and Theory of Planned Behavior ...................... 213 Social Learning/Social Cognitive Theory ...................................... 214 SocialSupport ........................................................... 214 EcologicalApproaches ..................................................... 214 Summary ............................................................... 215 I1ehavioral Research on Physical Activity among Adults ............................. 215 Factors Influencing Physical Activity among Adults ............................. 215 Modifiable Determinants ................................................ 215 Determinants for Population Subgroups .................................... 216 Summary ............................................................ 217 lnterventions to Promote Physical Activity among Adults ......................... 217 Individual Approaches .................................................. 217 Interventions in Health Care Settings ...................................... 226 Community Approaches ................................................ 227 Worksite Programs .................................................... 229 Communications Strategies .............................................. 23 1 Special Population Programs ............................................. 232 Racial and Ethnic Minorities .......................................... 232 People Who Are Overweight .......................................... 232 Contents, continued Older Adults .................................................... . . . . . 233 People with Disabilities ........................................... . 233 Summary ...................................................... . . . . 234 Behavioral Research on Physical Activity among Children and Adolescents ....... . . . . 234 Factors Influencing Physical Activity among Children and Adolescents ........ . . . . 234 Modifiable Determinants .......................................... . . . 234 Determinants for Population Subgroups .............................. . . . . . 235 Summary ...................................................... . . 236 Interventions to Promote Physical Activity among Children and Adolescents ... . . . . . 236 SchoolPrograms ................................................ . . . 236 School-Community Programs ...................................... . . . . . 242 Interventions in Health Care Settings ................................ . . 242 Special Population Programs ....................................... . . 243 Summary ...................................................... . . . , . 243 Promising Approaches, Barriers, and ,Resources ......................... Environmental and Policy Approaches .............................. Community-Based Approaches .................................... Societal Barriers ................................................ Societal Resources .............................................. Summary ..................................................... . . 243 . 244 245 . 246 . . 247 . 248 Chapter Summary __.............,,..,,_............._....................... 248 Conclusions ............................................................... 249 ResearchNeeds ............................................................ 249 Determinants of Physical Activity ............................................ 249 Physical Activity Interventions .............................................. 249 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .._.. 249 CHAPTER 6 UNDERSTANDING AND PROMOTING PHYSICAL ACTIVITY introduction A s the benefits of moderate, regular physical activity have become more widely recognized, the need has increased for interventions that can promote this healthful behavior. Because theories and models of human behavior can guide the development and refinement of intervention efforts, this chapter first briefly examines elements of be- havioral and social science theories and models that have been used to guide much of the research on physical activity. First for adults, then for children and adolescents, the chapter reviews factors influ- cncing physical activity and describes interven- [tons that have sought to improve participation in regular physical activity among these two age groups. To put in perspective the problem of increasing individual participation in physical activity, the chapter next examines societal barri- ers to engaging in physical activity and describes csisting resources that can increase opportunities for activity. The chapter concludes with a sum- mary of what is knouln about determinant and intervention research on physical activity and makes recommendations for research and practice. Theories and Models Used in Behavioral and Social Science Research on Physical Activity Numerous theories and models have been used in behavioral and social science research on physical activity. These approaches vary in their applicability to physical activity research. Some models and theo- ries were designed primarily as guides to under- 5tanding behavior, not as guides for designing Interventions. Others were specifically constructed \vith a view toward developing interventions, and some of these have been applied extensively in inter- vention research as well. Because most were devel- oped to explain the behavior of individuals and to guide individual and small-group intervention pro- grams, these models and theories may have only limited application to understanding the behavior of populations or designing communitywide interven- tions. Key elements most frequently used in the behavioral and social science research on physical activity are described below and summarized in Table 6-1. Learning Theories Learning theories emphasize that learning a new, complex pattern of behavior, like changing from a sedentary to an active lifestyle, normally requires modifying many of the small behaviors that compose anoverall complex behavior (Skinner 1953). Principles of behavior modification suggest that a complex- pattern behavior, such as walking continuously for 30 minutes daily, can be learned by first breaking it down into smaller segments (e.g., walking for 10 minutes daily). Behaviors that are steps toward a final goal need to be reinforced and established first, with rewards given for partial accomplishment if necessary. Incremental increases, such as adding 5 minutes to the daily walking each week, are then made as the complex pattern ofbehaviors is "shaped" toward the targeted goal. A further complication to the change process is that new patterns of physical activity behavior must replace or compete with former patterns of inactive behaviors that are often satisfy- ing (e.g., watching television), habitual behaviors (e.g., parking close to the door), or behaviors cued by the environment (e.g., the presence of an elevator). Reinforcement describes the consequences that motivate individuals either to continue or discon- tinue a behavior (Skinner 1953; Bandura 1986). Physical Activity and Health Table 6-l. Summary of theories and models used in physical activity research Theory/model Level Key concepts Classic learning theories Individual Reinforcement Cues Shaping Health belief model Individual Perceived susceptibility Perceived severity Perceived benefits Perceived barriers Cues to action Self-efficacy Transtheoretical model Relapse prevention Individual Social cognitive theory Interpersonal Theory of planned behavior Social support Individual Interpersonal Interpersonal Environmental Precontemplation Contemplation Preparation Action Maintenance Skills training Cognitive reframing Lifestyle rebalancing Reciprocal determinism Behavioral capability Self-efficacy Outcome expectations Observational learning Reinforcement Attitude toward the behavior Outcome expectations Value of outcome expectations Subjective norm Beliefs of others Motive to comply with others Perceived behavioral control Instrumental support Informational support Emotional support Appraisal support Ecological perspective Multiple levels of influence Intrapersonal interpersonal Institutional Community Public policy Source: Adapted from Glanz K and Rlmer BK. Theory at-a-glance: a guide ior health promotion practice, U.S. Department of Health and Human Services, 1995. 212 ltost behaviors, including physical activity, are learned and maintained under fairly complex sched- ules of reinforcement and anticipated future re- il,ards. Future rewards or incentives may include physica1 consequences (e.g., looking better), extrin- sic rewards (e.g., receiving praise and encourage- ,ncnt from others, receiving a T-shirt), and intrinsic rc\vards (e.g., experiencing a feeling of accomplish- I11ent or gratification from attaining a personal mile- stone). lt is important to note that although providing praise, encouragement, and other extrinsic rewards may help people adopt positive lifestyle behaviors, >uch external reinforcement may not be reliable in ,ustaininglong-termchange(GlanzandRimer 1995). Health Belief Model The health belief model stipulates that a person's hcaith-related behavior depends on the person's per- ccption of four critical areas: the severity of a poten- teal illness, the person's susceptibility to that illness, the benefits of taking a preventive action, and the h;\rritrs to taking that action (Hochbaum 1958; iioscnstock 1960, 1966). The model also incorpo- rates cues to action (e.g., leaving a written reminder to oneself to walk) aS important elements in eliciting or maintaining patterns of behavior (Becker 1974). fhc construct of self-efficacy, or a person's confi- tlcncc in his or her ability to successfully perform an .Ic`tlon (discussed in more detail later in this chap- rcr), has been added to the model (Rosenstock 19901, I)crhaps allowing it to better account for habitual Ochaviors, such as a physically active lifestyle. Transtheoretical Model In this model, behavior change has been conceptual- l=cd as a five-stage process or continuum related to `1 person's readiness to change: precontemplation, contemplation, preparation, action, and maintenance prochaska and DiClemente 1982.1984). People are thought to progress through these stages at varying rates, often moving back and forth' along the con- `muurn a number of times before attaining the goal of maintenance. Therefore, the stages of change are better described as spiraling or cyclical rather than linear (Prochaska, DiClemente, Norcross 1992). In rtlls model, people use different processes of change 1s they move from one stage of change to another. Efficient self-change thus depends on doing the right Understanding and Promoting Physical Activity thing (processes) at the right time (stages) (Prochaska, DiClemente, Norcross 1992). According to this theory, tailoring interventions to match a person's readiness or stage of change is essential (Marcus and Owen 1992). For example, for people who are not yet contemplating becoming more active, encourag- ing a step-by-step movement along the continuum of change may be more effective than encouraging them to move directly into action (Marcus, Banspach, et al. 1992). Relapse Prevention Model Some researchers have used concepts of relapse prevention (Marlatt and Gordon 1985) to help new exercisers anticipate problems with adherence. Fac- tors that contribute to relapse include negative emo- tional or physiologic states, limited coping skills, social pressure, interpersonal conflict, limited social support, low motivation, high-risk situations, and stress (Brownell et al. 1986; Marlatt and George 1990). Principles of relapse prevention include iden- tifying high-risk situations for relapse (e.g., change in season) and developing appropriate solutions (e.g., finding a place to walk inside during the winter). Helping people distinguish between a lapse (e.g., a few days of not participating in their planned activity) and a relapse (e.g., an extended period of not participating) is thought to improve adherence (Dishman 1991; Marcus and Stanton 1993). Theory of Reasoned Action and Theory of Planned Behavior The theory of reasoned action (Fishbein and Ajzen 1975; Ajzen and Fishbein 1980) states that indi- vidual performance of a given behavior is primarily determined by a person's intention to perform that behavior. This intention is determined by two major factors: the person's attitude toward the behavior (i.e., beliefs about the outcomes of the behavior and the value of these outcomes) and the influence of the person's social environment or subjective norm (i.e., beliefs about what other people think the person should do, as well as the person's motivation to comply with the opinions of others). The theory of planned behavior (Ajzen 1985, 1988) adds to the theory of reasoned action the concept of perceived control over the opportunities, resources, and skills necessary to perform a behavior. Ajzen's concept of 213 Physical Activity and Health perceived behavioral control is similar to Bandura's (1977a) concept of self-efficacy-a person's percep- tion of his or her ability to perform the behavior (Ajzen 1985, 1988). Perceived behavioral control over opportunities, resources, and skills necessary to perform a behavior is believed to be a critical aspect of behavior change processes. Social Learning/Social Cognitive Theory Social learning theory (Bandura 1977b), later re- named social cognitive theory (Bandura 1986), proposes that behavior change is affected byenviron- mental influences, personal factors, and attributes of the behavior itself (Bandura 1977b). Each may affect or be affected by either of the other two. A central tenet of social cognitive theory is the concept of self- efficacy. A person must believe in his or her capability to perform the behavior (i.e., the person must possess self-efficacy) and must perceive an incentive to do so (i.e., the person's positive expectations from perform- ing the behavior must outweigh the negative expecta- tions). Additionally, a person must value the outcomes or consequences that he or she believes will occur as a result of performing a specific behavior or action, Outcomes may be classified as having immediate benefits (e.g., feeling energized following physical activity) or long-term benefits (e.g., experiencing improvements in cardiovascular health as a result of physical activity). But because these expected out- comes are filtered through a person`s expectations or perceptions of being able to perform the behavior in the first place, self-efficacy is believed to be the single most important characteristic that determines a person's behavior change (Bandura 1986). Self-efficacy can be increased in several ways, among them by providing clear instructions, ptovid- ing the opportunity for skill development or training, and modeling the desired behavior. To be effective, models must evoke trust, admiration, and respect from the observer; models must not, however, appear to represent a level of behavior that the observer is unable to visualize attaining (Bandura 1986). Social Support Often associated with health behaviors such as physical activity, social support is frequently used in behavioral and social research. There is, how- ever, considerable variation in how social support is conceptualized and measured (Israel and Schurman 1990). Social support for physical activity can be instrumental, as in giving a nondriver a ride to an exercise class; informational, as in telling someone about a walking program in the neighborhood; emo- tional, as in calling to see how someone is faring with a new walking program; or appraising, as in provid- ing feedback and reinforcement in learning a new skill (Israel and Schurman 1990). Sources of support for physical activity include family members, friends, neighbors, co-workers, and exercise program lead- ers and participants. Ecological ApproacheS A criticism of most theories and models of behavior change is that they emphasize individual behavior change processes and pay little attention to sociocul- tural and physical environmental influences on be- havior (McLeroy et al. 1988). Recently, interest has developed in ecological approaches to increasing participation in physical activity (McLeroy et al. 1988; CDC 1988; Stokols 1992). These approaches place the creation of supportive environments on a par with the development of personal skills and the reorientation of health services. Stokols (1992) and Simons-Mortonandcolleagues (CDC 1988; Simons- Morton, Simons-Morton, et al. 1988) have illus- trated thisconceptofa health-promotingenvironment by describing how physical activity could be pro- moted by establishing environmental supports, such as bike paths, parks, and incentives to encourage walking or bicycling to work. An underlying theme of ecological perspectives is that the most effective interventions occur on multiple levels. McLeroy and colleagues (1988), for example, have proposed a model that encompasses several levels of influences on health behaviors: intrapersonal factors, interpersonal and group fac- tors, institutional factors, community factors, and public policy. Similarly, a model advanced by Simons- Morton and colleagues (CDC 1988) has three levels (individual, organizational, and governmental) in four settings [schools, worksites, health care institu- tions, and communities). Interventions that simulta- neously influence these multiple levels and multiple settings may be expected to lead to greater and longer-lasting changes and maintenance of existing health-promoting habits. This is a promising area for 214 the design of future intervention research to pro- mote physical activity. summary some similarities can be noted among the behavioral .,& social science theories and models used to un- , and adherence to structured physical activity programs (Howze, Smith, DiGilio 1989; Mirotznik et al. 1995; Robertson and Keller 1992). Additionally, attitude toward the behavior (outcome expectations and their values) has been consistently and positively related to physical activity (Courneya and McAuley 1994; Dishman and Steinhardt 1990; Godin et al. 1987, 1991; Kimiecik 1992; Yordy and Lent 1993) and stage of change (Courneya 1995). Social support from family and friends has been consistently and positively related to .adult physical activity (Felton and Parsons 1994; Horne 1994; Minor andBrown 1993;Sallis,Hovell,Hofstetter 1992;Treiber et al. 19911, stage of change (Lee 1993), and adher- ence to structured exercise programs (Duncan and McAuley 1993; Elward, Larson, Wagner 1992). Be- havioral intention, a construct from the theory of reasoned action and the theory of planned behavior, also has consistently been associated with adult physi- cal activity (Courneya and McAuley 1994; Godin et al. 1987, 1991; Godin, Valois, Lepage 1993; Kimiecik 1992;YordyandLent 1993),stageofchange(Courneya 1995), and adherence to structured exercise programs (CourneyaandMcAuley 1995;DuCharmeandBrawley 1995). Conversely, the construct of subjective norm from these theories has been both positively associ- ated(Courneya 1995;Godinetal. 1987,199l;Hawkes and Holm 1993; Kimiecik 1992; Yordy and Lent 1993) and not associated (Courneya and McAuley 1995; Godin et al. 1995; Hofstetter et al. 1991) with adult physical activity, stage ofchange, and adherence to structured exercise programs. There is also mixed evidence regarding the posi- tive relationship between the health belief model's construct of perceived severity of diseases and either physical activity (Godin et al. 1991) or adherence to structured exercise programs (Lynch et al. 1992; Mirotznik, Feldman, Stein 1995; Oldridge and Streiner 1990; Robertson and Keller 1992). Addi- tionally, that model's construct of perceived suscep- tibility to illness has been unrelated to adult adherence to structured exercise programs (Lynch et al. 1992; Mirotznik et al. 1995; Oldridge and Streiner 1990). The cumulative body of determinants research consistently reveals that .exercise enjoyment is a determinant that has been positively associated with adult physical activity (Courneya and McAuley 1994; Horne 1994; McAuley 1991), stage of change (Calfas et al. 1994), and adherence to structured exercise programs (Wilson et al. 1994). Conversely, there has been no relationship between locus of control beliefs (i.e., perceptions of personal control over health, fitness, or physical activity) and either adult physical activity (Ali and Twibell 1995; Burk and Kimiecik 1994; Dishman and Steinhardt 1990; Duffy and MacDonald 1990) or adherence to structured exer- cise programs (Lynch et al. 1992; Oldridge and Streiner 1990). Although previous physical activity during adulthood has been consistently related to physical activity among adults (Godin et al. 1987, 1993; Minor and Brown 1993; Sharpe and Connell 1992) and stage of change (Eaton et al. 1993), history of physical activity during youth has been unrelated to adult physical activity (Powell and Dysinger 1987; Sallis, Hovell, Hofstetter 1992). Determinants for Population Subgroups Few determinants studies of heterogeneous samples have examined similar sets of characteristics in sub- groups. Self-efficacy is the variable with the stron- gest and most consistent association with physical activity in different subgroups from the same large study sample. Self-efficacy has been positively re- lated to physical activityamongmen, women, younger adults, older adults (Salliset al. 19891, Latinos (Hovel] et al. 19911, overweight persons (Hovel1 et al. 19901, and persons with injuries or disabilities (Hofstetter et al. 1991). The generalizability of the self-efficacy associations is extended by studies of universit) students and alumni (Calfas et al. 1994; Courneya and McAuley 1994; Yordy and Lent 19931, employed 216 \vomcn (Marcus, Pinto, et al. 1994), participants in structured exercise programs (Duncan and McAuley 1993; McAuley, Lox, Duncan 1993; Poag-DuCharme ,,nd Brawley 1993), and people with coronary heart dlscase (CHD) (Robertson and Keller 1992). Summary ldcally. theories and models of behavioral and social s;cIcncc could be used to guide research concerning the factors that influence adult physical activity. In .ictuality, the application of these approaches to deter- nlinants research in physical activity has generally \Jccn limited to individual and interpersonal theories .Ind models. Social support and some factors from s~jcial cognitive theory, such as confidence in one's .lbiIity to engage in physical activity (i.e., self-efficacy) .tnd beliefs about the outcome of physical activity, tl;l\,c been consistently related to physical activity .Irnong adults. Factors from other theories and mod- ~15. however, have received mixed support. Although pcrccptionsof the benefits of, and barriers to, physical .lctlvity have been consistently related to physical ,~c.[~vity amongadults, other constructs from the health hcllcf model, such as perceptions of susceptibility to, ,111d the severity of, disease, have not been related to x1~1lt physical activity. Further, constructs from the 1 hco7 of reasoned action and the theory of planned I~chavior, including intentions and beliefs about the rlutcomes of behavior, have been consistently related 10 ,ldult physical activity, whereas there has been r.qulvocal evidence of this relationship for normative hcllcfs and perceptions of the difficulty of engaging in, the behavior. Exercise enjoyment, a determinant that ciocs not derive directly from any of the behavioral rhcorics and models, has been consistently associated \vlth adult physical activity. Few studies have specifically contrasted physi- ( .lI activity determinants among different sex, age. .~ciaVcthnic, geographic location, or health status \llbgroups. Many studies contain relatively homoge- n~)us samples of groups, such as young adults, cldcrly persons, white adults, participants in weight 10~s groups, members of health clubs, persons with heart disease, and persons with arthritis. Because the nllmbers of participants in the studies that include lhcse subgroups are small, and because the studies \.aluated different factors, making comparisons be- i\veen studies is problematic. Understanding and Promoting Physical Activity Interventions to Promote Physical Activity among Adults This section reviews intervention studies in which the measured outcome was physical activity, adher- ence to physical activity, or movement in stage of change (Table 6-2). It does not include intervention studies designed to assess the effect of physical activity on health outcomes or risk factors (see Chapter 4). Further, this review places special em- phasis on experimental and quasi-experimental stud- ies, which are better able to control the influence of other factors and thus to-determine if the outcomes were due to the intervention itself (Weiss 1972). Individual Approaches Individual behavioral management approaches, in- cluding those derived from learning theories, relapse prevention, stages of change, and social learning theory, have been used with mixed success in nu- merous intervention studies designed to increase physical activity (Table 6-2). Behavioral manage- ment approaches that have been applied include self- monitoring, feedback, reinforcement, contracting, incentives and contests, goal setting, skills training to prevent relapse, behavioral counseling, and prompts or reminders. Applications have been car- ried out in person, by mail, one-on-one, and in group settings. Typically, researchers have employed these in combination with other behavioral management approaches or with those derived from other theo- ries, such as social support, making it more difficult to ascertain their specific effects. In numerous in- stances, physical activity was only one of several behaviors addressed in an intervention, which also makes it difficult to determine the extent that physi- cal activity was emphasized as an intervention com- ponent relative to other components. Self-monitoring of physical activity behavior has been one of the most frequently employed behavioral management techniques. Typically, it has involved individuals keeping written records of their physical activity, such as number of episodes per week, time spent per episode, and feelings during exercising. In one study, women who joined a health club were randomly assigned to a control condition or one of two intervention conditions-self-monitoring of at- tendance or self-monitoring plus extra staff attention (Weber and Wertheim 1989). Overall, women in the 217 Physical Activity and Health Table 6-2. Studies of interventions to increase physical activity among adults Study Design Theoretical approach Population Individual approaches Weber and Wertheim (1989) King, Haskell, et al. (1995) Lombard, Lombard, Winett (1995) Cardinal and Sachs (1995) Belisle (1987) Gossard et al. (1986) 3 month experimental 2 year experimental 24 week experimental 12 week experimental 10 week quasi-experimental with 3-month follow-up 12 week experimental King, Carl, et al. (1988) 16 week pretest-posttest King and Frederiksen 3 month (1984) experimental King, Taylor, et al. (1988) Self-monitoring Behavioral management Stages of change Stages of change Relapse prevention Behavioral management Behavioral management Relapse prevention, social support, behavioral management 55 women who joined a gym; mean age = 27 269 white adults aged SO-65 years 155 university faculty and staff; mostly women 113 clerical staff at a university; mean age = 37; 63% black 3.50 people enrolled in beginning exercise groups 64 overweight healthy men aged 40-60 years 38 blue-collar university employees; mean age = 45 58 college women aged 18-20 years Study 1: 6 month experimental Relapse prevention, behavioral management 152 Lockheed employees aged 42-55 years Study 2: 6 month experimental I = intervention; C = control or comparison group. Behavioral management Lockheed employees from Study 1 218 Understanding and Promoting Physical Activity Intervention Findings and comments l-1 : Self-monitoring of attendance, fitness exam l-7: Self-monitoring, staff attention, fitness exam c: Fitness exam l-l had better attendance than l-2 overall; interest in self- monitoring waned after 4 weeks I.1 : Self-monitoring, telephone contact, vigorous exercise at home I-2: Self-monitoring, telephone contact, moderate exercise at home Better exercise adherence at 1 year in home-based groups; at year 2 better adherence in vigorous home-based group; 5 times per week schedule may have been difficult to follow l-3: Self-monitoring, vigorous exercise in group I- I : Weekly calls, general inquiry Frequent call conditions had 63% walking compared with I-?: Weekly calls, structured inquiry 26% and 22% in the infrequent condition; frequent call and 1.3: Call every 3 weeks, general inquiry structured inquiry had higher rate of walking than other I--J: Call every 3 weeks, structured inquiry groups 1-1 : Mail-delivered lifestyle packet based on stages of change No difference in stage of change status among or within groups I-.!: Mail-delivered structured exercise packet with exercise prescription (-: Mail-delivered fitness feedback packet I: Exercise class and relapse prevention training (1: Exercise class Higher attendance in relapse prevention group over 10 weeks and at 3 months; high attrition and inconsistent results across experimental groups I- 1 : Vigorous self-directed exercise, staff telephone calls, self-monitoring I 2: Moderate self-directed exercise, staff telephone calls, self-monitoring (.. Staff telephone calls Better adherence in the moderate-intensity group at 12 weeks compared with vigorous (96% vs. 90%) (no statistical tests reported); travel, work schedule conflicts, and weather were noted as barriers to physical activity I: 90-minute classes 2 times/week after work, parcourse, self-monitoring, contests (1: None Twofold increase in bouts of exercise compared with nonparticipants. Participants different from nonparticipants at baseline t-1 : Team building, relapse prevention training; sroup exercise i-2: Team building, group exercise I- i: Relapse prevention training and jogging alone I`: logging alone l-2 and 1-3 had twice the jogging episodes as l-l and C at 5 weeks; at 3 months, 83% of l-3 were jogging compared with 38% of l-l and l-2 and 36% of C 1-l : Home-based moderate exercise, self- monitoring with portable monitor, relapse prevention training, telephone calls from staff i-2: Same as l-l without telephone calls from staff NO difference in number of sessions and duration reported at 6-month follow-up i-1 : Daily self-monitoring -2: a'eekly self-monitoring I-1 had more exercise bouts per month (11 vs. 7.5) 219 Physical Activity and Health Table 6-2. Continued Study Marcus and Stanton (1993) Design 18 week experimental Theoretical approach Relapse prevention, social learning theory Population 120 female university employees, mean age = 35 McAuley et al. (1994) 5 month experimental Social learning theory 114 sedentary middle- aged adults Owen et al. (1987) 12 week quasi-experimental Robison et al. (1992) 6 month quasi-experimental Interventions in health care settings Logsdon, Lazaro, Meier (1989) (INSURE) 1 year quasi-experimental Calfas et al. (in press) Community approaches Luepker et al. (1994) (Minnesota Heart Health Project) Young et al. (in press) (Stanford Five-City Project) 2 week quasi-experimental 5 to 6 year quasi-experimental; 3 matchedpairs 7 year quasi-experimental Behavioral management Behavioral management, social support None mentioned Stage of change Diffusion of innovations, Community longitudinal social learning theory, cohort (n = 7,097), community organization, independent survey communication theory (n = 300-500) Social learning theory, communication theory, community organization 2 sets of paired, medium- sized cities (5th city used for surveillance only) . 343 white-collar and pro- fessional workers, mean age = 36, mostly women 137 university staff at 6 campus worksites, mean age = 40 2,218 patients from multi- specialty group practice sites 212 patients Macera et al. (1995) 4 year quasi-experimental (2 matched communities) None specified Community residents 2 18 years; 24% African American (I), 35% African American (Cl Brownson et al. (1996) 4 year quasi-experimental Social learning theory, Rural communities; largely stage theory of innovation African American I = intervention; C = control or comparison group. 220 Understanding and Promoting Physical Activity Intervention Findings and comments t-1 : Relapse prevention training and exercise 1-2: Scheduled reinforcement for attendance and exercise i: Exerc.ise only 1: Modeling of exercise, provision of efficacy- based information (mastery accomplishments, social modeling, social persuasion, physiological response), walking program (1: Biweekly meetings on health information, walking program 1. Self-management instruction, exercise class (.: Exercise class I: Weekly group meetings, contracts, cash incentives, social support, exercise (`: Exercise, diary ~ Screening and counseling from physicians who received continuing education; preventive visits at no charge I: Physician counseling; booster call from a health educator r`: Nothing Screening and education; mass media; com- munity participation; environmental change; professional education; youth and adults (:: Nothing I: Print materials; workshops and seminars; organized walking; organized walking events; "Heart & Sole" groups; worksite programs; TV spots 1. Community cardiovascular risk reduction activities (1: None specified `1 Community organization; development of 6 coa- litions; exercise classes and walking classes and walking clubs; demonstrations; sermons; news- paper articles; community improvements; $5,000 to each coalition from the state health department Better attendance in f-1 at 9 weeks; no difference at 18 weeks or 2-month follow-up Better class attendance (67% vs. 55%) and more minutes and miles walked among intervention group than controls I No difference in activity levels at 6 months Higher attendance among experimental groups than comparison groups (93-99% vs. 19%) Increase in starting to exercise among intervention patients (34% to 24%) Intervention patients increased walking (37 minutes vs. 10 minutes per week) Percent physically active higher in independent survey at 3 years; higher in the cohort at 7 years Men increased participation in vigorous activities; men and women in the intervention communities increased their overall number of physical activities; significant differences between intervention and comparison communities at baseline No difference in physical activity prevalence, physican counseling for exercise, or exercise knowledge Increased physical activity levels in coalition communities, declining levels in communities without; net effect was 7%. Planned Approach to Community Health education planning model 221 Physical Activity and Health Table 6-2. ConGnued Study Design Theoretical approach Population 6 week pretest-posttest uncontrolled Stages of change 610 sample of community residents, mean age = 42 Marcus, Banspach, et al. (1992) (Pawtucket Heart Health Program: imagine Action) Worksites Blair et al. (1986) (Live for Life) Fries et a). (1993) Heirich et al. (1993) Communication Osler and Jespersen (1993) Owen et al. (1995) Brownell, Stunkard, Albaum (1980) 2 year quasi-experimental 24 month experimental 3 year experimental 2 year quasi-experimental 2 year pretest-posttest Study 1: 8 week quasi-experimental Study 2: 4 month quasi-experimental None None 4,300 Johnson & Johnson employees 4,712 Bank of America retirees None specified 1,300 automobile plant workers Social learning theory, Rural communities in communications Denmark (n = 8,000 [II) (diffusion of innovations); community organization Social learning theory, social marketing theory 2 national physical activity campaigns in Australia None specified None specified 21,091 general public observations at a mall, train station, bus terminal 24,603 general public observations at a train station Blarney, Mutrie, Aitchison (1995) 16 week quasi-experimental None 22,275 subway users observations I = intervention; C = control or comparison group 222 Understanding and Promoting Physical Activity Intervention Written materials, resource manual, weekly fun ,yalks, and activity nights Findings and comments Participants more active after intervention with movement toward action and low relapse to earlier stage; suggests stage-based community intervention can result in movement toward action; study uncontrolled 1: Screening; lifestyle seminar; exercise programs; 20% of women and 30% of men began vigorous exercise newsletters; contests; health communications; of 2 years no smoking policies C: Screening only I-I : Health risk appraisal; feedback letter; behavioral management materials; personalized health promotion program t-2: Health risk appraisal; no feedback; full program in year 2 C: No intervention I- 1 : Fitness facility I-L: Outreach and counseling to high risk employees I- 1: Outreach and counseling to all employees C: Health education events I: Heart Week with assessments, health education, weekly community exercise, TV, radio, newspaper community messages (`: Not specified I: Messages to promote walking and readiness to become active; modeling activity; radio and TV PSAs; T-shirts; special scripting of soap operas I: Sign reading "Your heart needs exercise- here's your chance" 1. Sign reading "Your heart needs exercise- Number of people using the stairs increased from 12% to here's your chance" 18%; effect remained for 1 month after sign was removed 1: Sign reading "Stay Healthy, Save Time, Use the Stairs" . No difference in physical activity year 1; l-l greater physical activity in year 2 over l-2 Percent exercising 3 times per week: I-1 = 30%, , l-2 = 44%, l-3 = 45%, c = 37% No difference in self-reported physical activity, but intervention community expressed more interest in becoming active; low response rate to surveys (59%); became mainly a media campaign with little community involvement 1 st campaign-increase in percent who walked for exercise (70% to 74%), greatest impact on 50+ age group (twofold increase in reported walking-not significant) 2nd campaign-small declines in reported walking and in intentions to be more active Number of people using the stairs increased from 5% to 14% when sign was up. Use declined to 7% when sign was removed Baseline stair use increased to 15-l 7% when sign was up; persisted at 12 weeks after sign removal; larger increase among men 223 Physical Activity and Health Table 6-2. Conhued Stdy Design Special populations: ethnic minorities Heath et al. (1991) 2 year quasi-experimental Theoretical approach None specified Population 86 Native Americans with diabetes Lewis et al. (1993) Nader et al. (1989) (San Diego Family Health Project) Baranowski et al. (1990) 3 year quasi-experimental 3 month experimental 9 month maintenance 14 weeks Special populations: persons at risk for chronic disease Perri et al. (1988) 18 month experimental Jeffery (1995) King et al. (1989) 7 year uncontrolled 2 year experimental Special Populations: older adults Mayer et al. (1994) 2 year experimental Constituency-based model African American residents of 6 public housing units . Social learning theory 623 Mexican and Anglo- American families with 5th grade children None specified 94 black families (63 adults, 64 children) Behavioral management 123 overweight adults None mentioned 280 community members trying to lose weight None mentioned 96 men trying to maintain weight loss Social learning theory 1,800 Medicare beneficiaries in HMO, mostly white, high SES I = intervention; C = control or comparison group. 224 Understanding and Promoting Physical Activity intervention Findings and comments I: Exercise class c-: Nonparticipants I.1 : Basic exercise program I-.!: Basic exercise program; social; goal setting; attention; information; barrier reduction I: Family newsletter; telephone; mail; personal contact; feedback; family behavior manage- ment; physical activity; nutrition education (-: Periodic evaluation I. Individual counseling, small group education, aerobic activity, incentives (babysitting, transportation), telephone prompts, assessment (.: Assessment only I- I : Behavior therapy I-.!: Behavior therapy, maintenance I- 1: Behavior therapy, maintenance, social influence I--$: Behavior therapy, maintenance, exercise I-5: Behavior therapy, maintenance, exercise, social influence ! 1: Diet management ). Weight management, including exercise _ in i: Physical activity I. Monthly mailings, advice and tips for coping, staff telephone calls (`: No intervention Health risk appraisal, feedback, health No change in physical activity (3+ times a week) at 1 year, education sessions, medical tests, immuniza- but 21% vs.1 4% moved from sedentary to active tions, goal setting, self-monitoring (no statistical test reported); attrition 16% in experimental (1: Not specified group at 1 year - Participants in the exercise program lost 4 kg of weight on average, compared with 0.9 kg among nonparticipants; improvements occurred in fasting blood glucose levels and medication requirements Communities that were better organized and had more committed leaders had better program attendance and higher physical activity levels No difference in physical activity at 1 year No difference in energy expenditure; low participation (20%) Difference adherence in high exercise groups at 6 months; no differences at 12 and 18 months; high attrition (24"/0) t-2 resulted in greater weight loss at end, but no differences were observed at 1 year Men who exercised and received the intervention regained less weight in year 2 than exercisers who did not get the intervention or dieters who were exposed to the intervention 225