Journal of Physical Activity and Health, 2015, 12, 1414  -1420 http://dx.doi.org/10.1123/jpah.2014-0412 © 2015 Human Kinetics, Inc.

ORIGINAL RESEARCH

Changes in Physical Activity Domains During the Transition Out of High School: Psychosocial and Environmental Correlates Javier Molina-García, Ana Queralt, Isabel Castillo, and James F. Sallis Background: This study examined changes in multiple physical activity domains during the transition out of high school and psychosocial and environmental determinants of these changes. Methods: A 1-year prospective study was designed. The baseline sample was composed of 244 last-year high school students (58.6% female) from Valencia, Spain. Follow-up rate was 46%. Physical activity and potential determinants were measured by the Global Physical Activity Questionnaire and other evaluated scales in 2 waves. Results: Total physical activity and active commuting (AC) decreased, respectively, by 21% and 36%, only in males. At time 1, access to car/motorbike (inverse), planning/psychosocial barriers (inverse), street connectivity (positive) and parental education (inverse) were significantly associated with AC (P < .05). Prospectively, the increase in distance to school/workplace was associated with AC decrease among males (P < .001). In both genders, there was a decrease in leisure-time physical activity (LTPA; –35% in males, –43% in females). At time 1, self-efficacy and social support were positive correlates of LTPA (P < .05). Social support decreases were associated with reductions in LTPA for males (P < .05). Conclusions: Several psychosocial and environmental correlates of physical activity change were identified, and these are promising targets for interventions. Keywords: adolescent, exercise, active transport, built environment, health promotion

A lack of physical activity is related to a higher incidence of noncommunicable diseases such as coronary heart disease, type 2 diabetes, breast and colon cancers, and reduced life expectancy.1 One hour of moderate-intensity physical activity for adolescents and half an hour of activity for adults on most days are recommended to achieve health benefits.2 Physical activity declines across most of the lifespan, but one of the most significant decreases occurs in late adolescence and young adulthood.3–5 These declines in physical activity have been linked to weight gain6 and poorer psychological well-being.7 Most studies of physical activity changes after high school are based on retrospective measures and are open to recall bias.3,7,8 The few follow-up data available confirm a decline in overall or leisure-time physical activity during the transition out of high school.9–11 The main limitation of the prospective studies is that they usually do not examine other physical activity domains such as active travel or occupational activity. Studies of the correlates of physical activity have examined biological/demographic (eg, age, gender), psychosocial (eg, family, friends, social support) and environmental factors (eg, walking and cycling barriers, distance to facilities, street connectivity), building on multilevel ecological models.12 However, few studies have examined multilevel correlates of prospective changes in physical activity. In a retrospective study in Australia,8 correlates of being persistently active during the transition out of high school were perceived sports competency in females and having active fathers in males. The current study improved methodological rigor with a prospective design and improved the application of ecological models by including potential correlates from multiple levels. It Molina-García ([email protected]) is with the Dept of Teaching of Musical, Visual, and Corporal Expression; Queralt is with the Dept of Nursing; Castillo is with the Dept of Social Psychology; University of Valencia, Valencia, Spain. Sallis is with the Dept of Family & Preventive Medicine, University of California at San Diego. 1414

is useful to examine the changes in correlates among late adolescents and young adults to provide an empirical basis for effective interventions to prevent or minimize the documented decline in physical activity. The first purpose of the present prospective study was to examine changes in multiple physical activity domains (ie, active commuting, work-related and leisure-time physical activities) during the transition out of high school. The second purpose was to analyze if changes in psychosocial and environmental factors can explain changes in physical activity domains during the study period. This study also aimed to identify cross-sectional correlates of physical activity domains when students are in the last-year of high school.

Methods Participants and Recruitment A 1-year follow-up study was carried out in a sample of last-year high school students. The baseline sample was composed of 244 students (58.6% female; 17.6 years, SD 0.7) recruited through 15 high schools selected randomly from the census register of Valencia, Spain. Five selected schools declined to participate. The study protocol was approved by the Human Research Ethics Committee of the University of Valencia and conformed to the provisions of the Declaration of Helsinki. Written informed consent was obtained from the parents of the students, or from the students if older than 18 years of age.

Data Collection The first data collection (T1; April 2011) was performed in high schools, using a paper questionnaire, requiring about 15 minutes. Students provided their e-mail addresses and phone numbers. One year later (T2; April 2012), an e-mail with a link to an online version of the survey was sent to students using Google Docs. The online version of the questionnaire was piloted previously with 10

Physical Activity and Transition From High School   1415



volunteer university students. In cases in which participants did not answer the online questionnaire, they were contacted by phone to ask for their participation. At T2, the response rate was 45.5% (n = 111; 18.6 years, SD 0.7). One week after T1, a subsample of 33 high school students completed again the physical activity social support scales to assess test-retest reliability.

Questionnaire Measures Self-Efficacy.  Self-efficacy was measured by the Spanish version

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of Perceived Physical Ability subscale of the Physical Self-Efficacy scale of Ryckman and associates.13 The 10 items were scored on a Likert scale ranging from “strongly disagree” = 1 to “strongly agree” = 6. Example item: “I can’t run fast.” In the present sample, Cronbach’s alpha was 0.84. Access to Car and Motorbike.  Car and motorbike access was assessed using 2 items: “Do you have a motorbike for personal use?”; “Do you have a car for personal use?” Response options were: “never” = 1, “sometimes” = 2, “always” = 3. The highest score from the 2 items was used in the data analysis. Parental Education.  Students were asked to indicate the highest

level of education of their parents. Response options were: “none” = 1, “primary school” = 2, “secondary school” = 3, “completed high school/technical training” = 4, “university training” = 5. The highest educational level of parents was used.

Physical Activity Social Support.  Before data collection, the Physical Activity Family and Friends Support Scales of Norman et al14 were translated into Spanish using a back-translation procedure. For both scales, a response format from 1 (“never”) to 5 (“every day”) was used. The 4-item Family Support Scale asked participants to rate, during a typical week, how often a member of their household, for example, encouraged them to do sports or physical activity. Test–retest ICCs for the 4 items ranged from 0.67 to 0.91. The Cronbach’s alpha coefficient was 0.70. The Friends Support Scale version used in this study was composed of 4 items. Item number 4 from original 5-item scale was deleted based on the results from the internal consistency analysis. This scale asked participants to rate, during a typical week, how often, for example, their friends asked them to walk or bike to school or university/ workplace or to a friend’s house. ICCs ranged from 0.84 to 0.99. Cronbach’s alpha was 0.70. Results from the confirmatory factor analysis (CFA) showed that the 2 scales had satisfactory fit indexes. Interested readers may contact the corresponding author to obtain the Spanish version of the scales. Distance to High School and University or Workplace.  Partici-

pants reported their home and university/workplace addresses, and the Spanish version of Google Maps was used to measure streetnetwork distance (kilometers) from home.

Physical Activity.  Physical activity was assessed by the Spanish

version of the GPAQ survey (Global Physical Activity Questionnaire).15 Frequency and duration for 3 domains of physical activity were reported. A weekly estimation of energy expenditure (MET·minutes/week) was calculated for active commuting (AC), leisure-time physical activity (LTPA) and work-related physical activity (WRPA). Total physical activity (TPA) was also calculated. Modes of Transport to High School and University or Workplace.

Transport was measured by: “How often do you use each of the following ways to go to and from the high school or university/

workplace?” Response options were bike, bus, car, train/metro/ tram, motorbike and walking. The sample reported the number of trips per week (to or from) and habitual minutes per trip in each mode of transport. The main mode of transport among participants who used mixed mode trips (eg, walk to bus) was assigned based on the longest portion of their trip. Test-retest reliability for each item (modes of transport) was good in previous studies.16 Barriers to Active Commuting to High School and University or Workplace.  The 2-factor scale of Molina-García et al17 was used.

The factors are: environment and safety barriers (7 items) and planning and psychological barriers (7 items). An example item was: “It is unsafe because of crime to walk or bike.” A 4-point Likert scale was used to score each item from “strongly disagree” = 1 to “strongly agree” = 4. Cronbach’s alphas for the current study were 0.71 for planning/psychosocial barriers and 0.66 for environment/ safety barriers. Neighborhood Environment Perceptions.  The long Spanish

version of ALPHA survey (Assessing Levels of Physical Activity environmental questionnaire)18,19 was used to measure neighborhood environment perceptions in relation to physical activity. In the current study, 3 neighborhood indicators were used: residential density, street connectivity, and distance to destinations, as a land mixed use indicator, based on the construct of walkability.20–22 Density was assessed by 3 items that required participants to rate how common different types of residences were in their neighborhood. Answers ranged from “none” = 1 to “all” = 5. An example item is: “Apartment buildings or blocks of flats.” Connectivity of the street network was assessed by 3 items, such as: “There are many shortcuts for walking in my neighborhood.” A Likert scale was used from “strongly disagree” = 1 to “strongly agree” = 4. Finally, distance to local destinations was assessed by asking participants to report the usual walking minutes from their homes to the nearest destinations from a list of 8 facilities or businesses (eg, “Bus stop, tram, metro, or train station”). Answer options were: “1–5” = 1, “6–10” = 2, “11–20” = 3, “21–30” = 4, “More than 30” = 5. The ALPHA environmental questionnaire manual was used to calculate all the neighborhood indicators (www.ipenproject.org).

Statistical Analyses To examine the factor structure of the physical activity social support scales, we carried out CFA with the LInear Structural RELations program.23 Internal consistency of the study scales was assessed using Cronbach’s alpha. Test–retest reliability was measured with 1-way, single measure intraclass correlations (ICC). Percentage, mean and standard deviation were calculated. Differences between means were evaluated by paired and unpaired Student’s t-test. McNemar test was used for testing differences in proportions of modes of transport use. Residualized change scores were calculated for all variables to obtain a measure of change. They can be interpreted as the amount of increase or decrease and are preferable to raw change scores because they are free of autocorrelated error and eliminate regression to the mean effects.24,25 Finally, stepwise multiple linear regression analyses were performed to examine the associations of changes in psychosocial and environmental factors with changes in physical activity domains. Regression analyses were also used to identify correlates of domains at T1. In preliminary analyses, Pearson’s correlations between study variables were conducted to identify candidate variables

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for inclusion in multiple regression models. Only the correlations between physical activity domains and variables that had significantly changed during the 1-year follow-up period were examined, with the aim of determining which variables had to be included in the regression analyses. Statistical analyses were conducted using SPSS 19.0 software (SPSS, Chicago, Illinois, USA).

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Results Table 1 presents descriptive and inferential statistics of the study variables at T1 and T2 by gender. It is notable that neighborhood perceptions did not change between T1 and T2 in males and females. Results at T2 indicated that 97.3% of participants continued to live in the same type of residence and neighborhood as at T1. Most high school students (98.8%) did not work at T1. Figure 1 shows the distribution of university or college students and workers at T2. The results indicated no differences in AC and LTPA levels between the 2 groups. As expected, higher levels of WRPA were found among workers compared with students (P < .001; 1470.9 vs. 189.2 MET·minutes/week).

Changes in Physical Activity Behavior and Main Modes of Transport With regard to males, the results of the paired t tests identified a significant decrease of 21.3% in TPA (P = .01), AC (–36%; P < .001) and LTPA (–34.6%; P = .001) between T1 and T2 (Figure 2). However, WRPA increased by 2728.1% (P < .001) between the 2 phases. The female results showed lower levels of LTPA (–42.7%; P = .03) and higher levels of WRPA (4471.7%; P = .04) at T2, but no significant differences in AC and TPA longitudinally. The independent t test to assess possible differences between genders showed that in T1 males had higher levels of TPA (P < .001) and LTPA (P < .001). The levels of AC and WRPA were similar between males and females. At T2, males also had higher levels of LTPA (P = .001), but females had higher levels of AC (P < .001). There were no significant differences between genders in WRPA or TPA in T2. As shown in Figure 3, walking for commuting decreased dramatically during the study period in both males (76.2% for T1, 19.6% for T2; P < .001) and females (78.3% for T1, 32.7% for T2; P < .001). In contrast, in both groups, the use of public transport (ie, bus and train) increased around 20% from T1 to T2 (P < .05).

Correlates of Physical Activity Domains at T1 Regarding males, access to car/motorbike (β = –0.346, P < 0.001), planning/psychosocial barriers (β = –0.252, P = .007), street connectivity (β = 0.211, P = .025) and parental education (β = –0.190, P = .036) were significantly associated with AC, and explained 27.5% of the variance. As regards females, planning/psychosocial barriers (β = –0.233, P = .005) was the only predictive variable of AC (explained variance = 5.4%). Regression analysis showed that self-efficacy was a positive correlate of LTPA, both for males (β = 0.230, P = .017) and females (β = 0.378, P < 0.001). Besides self-efficacy in males, friends (β = 0.306, P = .003) and family support (β = 0.238, P = .010) were positive predictors of LTPA (explained variance = 37.3%). In the female group, family support (β = 0.269, P = .001) was also associated with LTPA (explained variance = 27.3%).

Associations of Changes in Psychosocial and Environmental Factors With Changes in Physical Activity Domains For change in AC for males, changes in access to car/motorbike and distance to university/workplace were included in the regression analysis. Regression analysis showed that changes in distance to school/workplace (β = –0.546, P < .001) were associated with a decrease in AC, and explained 29.8% of the variance for males. For change in LTPA for males, changes in family and friend support were included in the regression analysis. A decrease in family support (β = 0.280, P = .011), together with a decrease in friends support (β = 0.541, P < .001), were predictors of the decline in LTPA only among males (explained variance = 43.1%). For change in LTPA for females, no candidate variables were found to be included in the regression analysis.

Discussion This 1-year study examined physical activity behavior during the transition to early adulthood. Consistent with the prospective literature,10 a significant decrease of 21% in overall physical activity was found in males. In contrast to males, women’s TPA did not decrease significantly, but this may be due to the fact that there was a decrease in LTPA and an increase in WRPA, which may compensate each other. In males, there was an additional decrease in AC, which may explain the net decrease in TPA. In the current study, females had lower TPA levels compared with males at the beginning of the study, whereas these levels were not different between genders at T2. Girls may experience the largest declines in physical activity before late adolescence.9 These gender differences among adolescents have been shown in other studies,26,27 and are usually connected with differences in LTPA, mainly in competitive sport participation, and stereotyped attitudes which consider this participation as masculine. The present findings are consistent with the idea that the transition to adulthood is related to new demands at the university or workplace affecting modes of transportation, work activities, and leisure activities.10,28,29 The use of active modes of transport in the current study corroborates previous studies in Spanish samples of adolescents30 and university students.31 Walking declined significantly whereas public transport use increased in both genders. Interestingly, AC levels only decreased in males and remained the same in females. These gender differences are inconsistent with previous studies,31,32 which have shown that public transport use is associated with active transport because users have to walk to and from transit stops. In the current study, public transport use increased in both genders; however, AC decreased in men. Further research is therefore needed to explore reasons for gender differences in AC changes during the transition to adulthood. Environmental factors were associated with AC among males. The increase in distance to school/workplace during the study period, more than 5 km on average, was associated with decreases in AC. This finding is consistent with literature31,33 that showed distance to school or workplace less than 5 km were the most related to AC behavior. Street connectivity was one of the main correlates at the beginning of the current study. A recent study in Belgian adolescents34 showed that different neighborhood perceptions, including connectivity, were correlates of AC to school but not of AC for other aims. Considering the study by De Meester et al,34 neighborhood perceptions would influence active transport differently, depending on the destination. Future research should

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1417

1–5

0–30

1–4

1–4

Friend support

Distance to high school or university/workplace (km)

Planning and psychosocial barriers

Environment and safety barriers

4.2

8.6

4–12

8–39

 Connectivity

  Distance to facilities

Note. Bold indicates statistically significant values.

14.7

242.2

2.3

1.7

1.2

3.0

1.8

67–288

 Density

Neighborhood perceptions

1–5

1–5

Parental education

Family support

4.4

1–6

1–3

Self-efficacy

Access to car/motorbike

1.3

Mean

Range

4.4

1.6

27.4

0.5

0.6

2.3

0.9

0.7

0.8

0.6

0.9

SD

Male (n = 101)

14.6

8.7

245.8

2.3

1.7

1.3

2.8

1.8

4.0

1.2

3.7

Mean

4.2

1.6

31.6

0.6

0.5

2.6

0.7

0.8

0.8

0.5

0.8

SD

Female (n = 143)

Time 1

4.6

0.966

0.529

0.354

0.387

0.516

0.778

14.4

8.4

239.4

2.3

1.9

5.3

2.8

0.006

– 1.5

0.982

0.024

1.5

< 0.001 0.112

Mean

gender

P for

5.1

1.8

42.5

0.5

0.6

5.9

0.7

0.6

–

0.8

1.0

SD

Male (n = 56)

14.5

8.8

246.8

2.5

2.1

3.8

2.6

1.5

–

1.4

3.7

Mean

4.7

1.6

33.5

0.6

0.7

2.7

0.6

0.7

–

0.7

1.0

SD

Female (n = 55)

Time 2

Table 1  Descriptives of the Physical Activity Correlate Variables at Time 1 and Time 2 by Gender

P for

0.916

0.268

0.311

0.066

0.144

0.974

0.881

0.010

–0.2

–0.2

0.4

–0.1

–0.1

0.6

< 0.001 0.468

–0.1

0.001

– 0.02

– 0.016

0.1 0.01

0.062

Mean

3.1

1.6

33.9

0.5

0.5

4.4

0.5

0.4

–

0.6

0.6

SD

0.669

0.860

0.328

0.2

0.2

–0.4

0.1

0.1

< 0.001 0.014

–0.6

0.1

–0.02

–

–0.01

–0.1

Mean

3.7

1.4

34.1

0.6

0.7

3.2

0.5

0.5

–

0.4

0.6

SD

Residualized change score

Female

< 0.001

0.112

0.009

–

0.006

0.392

change

P for

1-year change Residualized change score

Male

0.010

0.133

0.143

0.219

0.822

–

0.357

< 0.001

gender change

P for

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Figure 1 — Distribution of university students and workers at time 2. Note. Students who have a part-time job (n = 4) have been included in the category “University or college student.”muting; LTPA, leisure-time physical activity; WRPA, work-related physical activity; TPA, total physical activity.

Figure 2 — Changes in physical activity domains (MET·min/week) during the study period. * P < .05; ** P < .001. Abbreviations: AC, active commuting; LTPA, leisure-time physical activity; WRPA, work-related physical activity; TPA, total physical activity.

Figure 3 — Main modes of transport to high school (time 1) and to university/workplace (time 2). * P < .05; ** P < .001.

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differentiate between AC to school/workplace and AC to leisure activities. Furthermore, research should focus on analyzing gender differences in correlates of AC. As in previous studies,16,17 barriers to AC were negative correlates of active transport in males and females. Planning and psychological barriers were the most supported barriers of AC. These types of barriers have been shown to be the strongest correlate of AC in university populations,17,35 mainly associated with travel time which is related not only with planning barriers but also with environmental elements such as land use or the public transportation system. In contrast to previous findings,17 access to car and motorbike for personal use was a negative correlate of AC only in male adolescents. A reason for the lack of association in females is not apparent. Additional research is required to determine the role that personal motorized transport has on active transport by gender and in different life periods. Another factor associated with AC only in males was parental educational level. Present data concur with findings from Chillón et al30 in which male adolescents whose parents had lower educational level had higher AC compared with male adolescents whose parents achieved a university degree. Possibly, families with higher levels of education may be able to afford cars to drive their sons to diverse facilities. With respect to LTPA results and coinciding with other longitudinal studies,9,11 there was a decrease in this behavior in both genders. This decrease is likely related to the transition into university or work environments.9 It could be due to the fact that leisure activities become voluntary and must compete for time with increased academic or work demands.29,36 In the current study, family and friends support and physical self-efficacy were the main correlates of LTPA, similar to findings reported by diverse studies.8,37,38 High schools and universities can play a relevant role in promoting physical activity and other health behaviors.29,39 It is necessary therefore to design school programs in which participants have a good experience and develop positive physical self-perceptions, not only in adolescence but also in adulthood. Considering the importance of social support in physical activity and the results of previous studies,4,29 sports associations (ie, sports clubs or teams) should be promoted in educational settings as a strategy to enhance LTPA participation. Family members and friends should be involved in these sports structures. Furthermore, current literature indicates that school variables such as physical education experience during high school could influence physical activity levels at university.40 In the future, the relationship between school context during adolescence and LTPA in adulthood should be analyzed in depth. Overall, the present findings support the principle of ecological models that correlates and determinants of physical activity are expected to differ by domain of physical activity. Environmental factors would be expected to be more related to the active transport domain34,41 (eg, to school/workplace), and psychosocial variables would be more related to the LTPA domain.21 This pattern was generally true for cross-sectional and prospective findings, which tends to support the value of cross-sectional finding when prospective results are not available. As is common, fewer significant prospective determinants were identified than cross-sectional correlates.42 The finding that increased distance to work/school was related to males’ reduced AC support the promise of interventions to systematically support active travel modes and encourage employers and universities to provide proximal affordable housing. The finding that changes in social support were related to males’ LTPA changes supports the need to develop effective social support interventions targeting young adults. Though several correlates were common to males and

females, it is important for future studies to explore explanations for the present findings of differential correlates by gender.

Strengths and Limitations This is one of just a few longitudinal studies to analyze multiple physical activity domains during the transition to early adulthood. Strengths included the use of multiple validated measures, the prospective design, multilevel analyses based on ecological models, and gender-specific analyses. The current study had 4 main limitations. First, measures were based on self-report, and participants tend to overreport physical activity behaviors. Secondly, because the majority of participants continued to live in the same households, there were not changes in neighborhood perceptions that could explain the changes in physical activity domains. Third, the sample size was low, and attrition rate was high for this prospective study. High loss of participants was expected because the second data collection was carried out through an online questionnaire. Fourth, the study was conducted in 1 city, so results may not generalize.

Conclusions The transition from high school to young adulthood was marked by large changes in the patterns of physical activity across domains. Interventions to retain AC among males and to assist males and females to maintain LTPA in the face of increase study and work demands are recommended based on current results. Interventions delivered at school and work settings should be tailored on the basis of gender and the physical activity domains. Environmental interventions may be most effective for AC, and interventions to change psychosocial mediators may be most effective for LTPA. Acknowledgments This work was supported by the University of Valencia (grant no. UV-INVAE11-41997) and the Generalitat Valenciana (grant no. GV-2013-087).

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