The Journal of School Nursing2023, Vol. 39(6) 475–486© The Author(s) 2021Article reuse guidelines:sagepub.com/journals-permissionsDOI:10.1177/10598405211038962journals.sagepub.com/home/jsn
The study purpose was to examine whether adolescents who participated in organized physical activity (PA) programs differed from nonparticipants in motivation, social support, and self-efficacy related to PA; PA (min/hr); and sedentary screen time behavior. Thirty-nine 5th–7th grade adolescents participated in organized PA programs; 41 did not. Approximately 56.3% were Black, and 52.5% had annual family incomes <$20,000. Compared to nonparticipants, those who participated reported significantly higher social support (M = 2.32 vs. 3.13, p < .001) and fewer hours watching television or movies on a usual weekend day (M = 2.49 vs. 1.59, p = .016); and had higher accelerometer-measured vigorous PA (M = 0.58 vs. 1.04, p = .009) and moderate-to-vigorous PA (M = 2.48 vs. 3.45, p = .035). Involving adolescents in organized PA programs may be important for improving their moderate-to-vigorous PA, vigorous PA, and related psychosocial factors, as well as reducing sedentary screen time behavior.
Keywordsexercise, schools, motivation, social support, self-efficacy, perception, sedentary behavior, screen time
Evidence supports that adequate physical activity (PA) during adolescence is associated with health benefits (U.S. Department of Health and Human Services [USDHHS], 2018). Yet, ∼81.0% of 11- to 17-year-old adolescents (Guthold et al., 2020) worldwide do not meet World Health Organization (WHO, 2010) and USDHHS (2018) guidelines calling for at least 60 min of moderate-to-vigorous physical activity (MVPA) daily. Overall, PA declines as age increases across adolescence (Armstrong et al., 2018; Dumith et al., 2011). This problem indicates a need for research with young adolescents, defined by the WHO (2021) as those 10–14 years of age, to examine contributing factors. Moreover, adolescents who live in predominately minority and low-income areas, as compared to those who do not, exhibit lower levels of PA (Gill et al., 2018). A large United States (U.S.) study showed Black and Hispanic 12- to 17-year-old adolescent females had lower levels of MVPA than their White counterparts; and low-income adolescents of the same age reported less PA than their higher-income peers (Armstrong et al.,2018). These findings not only underscore the low levels of MVPA among adolescents but also highlight existing disparities by race/ethnicity and income.
In the U.S., most children and young adolescents, 8- to 12-years old, spend substantial amounts of time using digital media devices to engage in recreational behaviors that involve no or minimal PA (Parent et al., 2016). These sedentary behaviors are likely to carry forward into adulthood (USDHHS, 2018), ultimately resulting in chronic health conditions, such as Type 2 diabetes and cardiovascular disease (Katzmarzyk et al., 2019). Participation in organized PA programs, including engaging in sports or taking lessons involving PA, is one possible solution that may help to reverse the disconcerting trend (Howie et al., 2018; Oosterhoff et al., 2017). Organized PA programs are specifically defined as those including PA directed by adult or youth leaders and involving rules and formal practice (Logan et al., 2020).
Participation in organized PA programs contributes to adolescents’ overall positive development (Mahoney et al., 2005) by providing them with opportunities to learn (Oosterhoff et al., 2017). Therefore, perceptions regarding PA among young adolescents who engage in organized PA programs may differ from those who do not participate. Understanding the differences between these two groups is important because positive PA perceptions among adolescents are associated with increased PA in this age group and are reported to favorably influence PA attitudes and PA in adulthood (Thompson et al., 2003).
Although research has identified several psychosocial factors as being associated with adolescents’ PA, the following three have received increased attention due to their relatively consistent positive relationships with the behavior: perceived PA self-efficacy, social support for PA, and motivation for PA. Strategies to enhance these psychosocial factors are encouraged to both increase adolescents’ MVPA and decrease their sedentary screen time behavior (Hill et al., 2019; Huffman et al., 2018).
PA self-efficacy involves confidence adolescents have in their ability to participate in PA when faced with barriers (Bandura, 1986). PA self-efficacy has been identified as a positive psychosocial correlate with PA among children and adolescents, aged 9–17 years (Hill et al., 2019), and has been shown to predict their future PA (Dishman, Dunn, et al., 2010). Previous experiences with a behavior are identified as the major source for developing behavior-specific self-efficacy (Bandura, 1997). Increasing adolescents’ PA self-efficacy to improve their PA has been a prime objective in programs (Ashford et al., 2010), including those designed for young urban adolescents from minority and/or low-income backgrounds who face many social and environmental barriers to engaging in the behavior (Wieland et al., 2020). Regardless, continued examination of young adolescents’ PA self-efficacy is important because the barriers to PA that they face likely change over time, particularly due to new technologies that promote sedentary activity. To avoid a downward spiral that leads to a physically inactive lifestyle (Hill et al., 2019), identifying ways to assist young adolescents to bolster their PA self-efficacy during this critical developmental period is essential.
Considerable literature supports the positive relationship between social support for PA and adolescents’ PA (Mendonça et al., 2014). Social support from parents and teachers has been found to exert a positive influence on MVPA among adolescents whose mean age was 16.4 years old (Pluta et al., 2020). Parents and other family members can serve as role models of PA and provide logistical (e.g., transportation) and emotional support (e.g., encouragement or praise; Duncan et al., 2005). Friend support is particularly salient for young adolescents who have limited resources for PA (Gill et al., 2018). Gill et al. (2018) found that both family and friend social support predicted the number of days urban ethnically diverse (68.5% Hispanic), low-income adolescents in the 7th grade attained at least 60 min/day of PA during a previous week. This information indicates that social support for PA, which can be received from varied individuals, is a key determinant of PA among adolescents (Sterdt et al., 2014).
Motivation for PA is another important psychosocial factor consistently associated with adolescents’ PA (Nogg et al., 2020). Self-determination theory (SDT; Deci & Ryan, 1985) posits that motivation lies on a continuum ranging from amotivation (completely lacking motivation to engage in an activity) to the highest level of self-determination, which is intrinsic motivation (engaging because an activity is inherently enjoyable). Between these two forms are four forms of external motivation that are ordered as follows to reflect decreasing self-determination along the continuum: integrated (engaging because the activity itself reflects personal values [i.e., who the individual is]), identified (engaging because an outcome of the activity is valued; therefore, making the effort to engage is important), introjected (engaging in activity due to feeling guilty), and external regulation (engaging to achieve something [e.g., reward]). The latter two forms are referred to as controlled motivation (Deci & Ryan, 2012; Ryan & Deci, 2000). Regarding PA, engagement most likely occurs in the presence of autonomous motivation (Ryan & Deci, 2007), which is reflected in the literature by either intrinsic and identified regulation (Duncan et al., 2017) or intrinsic, integrated, and identified regulation (Whitehead, 1995). When motivation was assessed in predominately low-income 11- to 16-year-old African American adolescents with a survey that was highly correlated with measures of PA enjoyment and intrinsic motivation, results showed that motivation was positively associated with accelerometer-measured MVPA (Huffman et al., 2018). Research to understand motivation for PA in terms of the behavioral regulations among young adolescents, especially those living in underserved areas (Jones et al., 2021), is needed so that interventions can be targeted to increase the possibility of lifelong PA (Duncan et al., 2017).
Although participation in organized PA programs is recommended (Shull et al., 2020) and may help adolescents form positive PA perceptions, some negative psychosocial outcomes can occur (Petitpas et al., 2005). For example, sports participation has been found to be associated with high stress and negative peer interactions among racially and ethnically diverse adolescents in high school (Larson et al., 2006). Whether those who do participate have more positive perceptions related to PA than those who do not is unknown, and no study was found that focused on young adolescents living in underserved (predominately minority and low-income) urban areas to examine any differences in perceptions and health behaviors (i.e., PA and sedentary screen time behavior) between these two groups. Therefore, the purpose of this cross-sectional study was to examine whether young adolescents living in a racially diverse, predominately low-income urban area who are currently participating in organized PA programs differed from those not participating in motivation, social support, and self-efficacy related to PA; PA (min/hr); and sedentary screen time behavior. The specific research question is: Compared to young adolescents not currently participating in organized PA programs offered at or outside their school, do those currently participating differ in the various forms of motivation, including autonomous motivation; social support; self-efficacy related to PA; moderate PA, vigorous PA, and MVPA; and hours of sedentary screen time behavior.
SDT (Deci & Ryan, 1985; Ryan & Deci, 2000) provided the theoretical foundation for the study. The theory postulates that an increased sense of relatedness (including social support), competence (including self-efficacy), and motivation related to a healthy behavior may contribute to greater behavioral enactment. One assumption of the SDT is that learning different skills and mastering tasks associated with a specific behavior, while feeling supported and connected to others, motivates individuals with resultant increases in the behavior (Deci & Ryan, 1985; Ryan & Deci, 2000). Based on the SDT assumption, participation in organized PA programs may have the potential to enhance social support, self-efficacy, and motivation related to PA and result in increased overall PA and decreased sedentary screen time behavior. This line of reasoning was tested in the current study.
This cross-sectional study involved a secondary analysis of baseline data obtained from a 2017 pilot study, which is described elsewhere (Robbins et al., 2020). The current study included 80 10- to 13-year-old adolescents from two urban (i.e., city schools) kindergarten to 8th-grade public schools located in racially and ethnically diverse, low-income communities in the same Midwestern U.S. school district. The University Institutional Review Board provided approval, and each school’s administrators gave their permission to conduct the study. Adolescents were included in the study if they were in 5th–7th grade. They were excluded from participating if they had a mental or physical health condition that prevented them from safely participating in PA.
Adolescents in 5th–7th grade in each school were called to an assembly at their respective schools. Researchers informed adolescents about the study and invited them to participate. All interested adolescents received a packet that included a brief overview of the study, consent and assent forms, and a screening tool to determine their eligibility status. At School 1, 115 were enrolled, and 101 were present at the assembly and took packets. At School 2, 173 were enrolled, and 151 attended and accepted packets. Adolescents were asked to take the packet home to share with their parents/guardians. Adolescents were told to return the packet with completed forms showing whether they were interested in participating or not to researchers who would be available at their school during the next 2 days. Sixty-seven and 96 adolescents returned packets at Schools 1 and 2, respectively. Twenty-two in School 1 and 43 in School 2 declined to participate, 5 in School 1 and 0 in School 2 were unable to participate for personal reasons, and the remainder did not meet inclusion and exclusion criteria. Prior to any study participation, all adolescents and their parents/guardians provided written assent and consent, respectively. Of 84 participating adolescents, 80 were included in the data analysis for the current study (one of two twins from each of three families was randomly selected for exclusion, and one participant did not complete the surveys measuring the psychosocial factors of interest).
Demographic Characteristics. Each parent responded to items listed on the consent form that asked about the adolescent’s age in years, sex (male, female, other), and race (Asian; Native Hawaiian or other Pacific Islander; Black or African American; American Indian, Alaskan Native, or Native American; White or Caucasian); and total annual family income in the last year (<$20,000; $20,000–$29,999; $30,000–$49,999; $50,000 or above). An income <$20,000 was below the U.S. federal poverty level for a family with three or more members living in a household during the year that the study was conducted (USDHHS, 2017).
To estimate weight status, each adolescent’s height and weight were obtained. Behind a privacy screen at school, two trained research assistants (RAs) asked each adolescent to remove shoes and any heavy clothing. RAs used a Shorr Board (Weigh and Measure, LLC, Olney, MD) to measure each adolescent’s height (to level of scalp) twice to the nearest 0.10 cm, and a Tanita BC-534 foot-to-foot bioelectric impedance scale (Tanita Corporation, Tokyo, Japan) to assess weight in kg twice to nearest 0.10 kg. If the difference between the two measurements was ≥0.5 cm for height or ≥0.5 kg for weight, RAs obtained another measurement. The two closest height and weight measures were averaged. Adolescents were categorized as having a healthy weight (<85th percentile) or being overweight/obese (>85th percentile) based on body mass index (BMI) for age and sex, which was estimated from height and weight and determined via the online SAS program for Centers for Disease Control and Prevention (CDC, 2015) Growth Charts.
Organized PA Program Participation. Adolescents responded to the following two survey items asking whether or not they were currently: (1) on a sports or cheerleading team, including any team at their school or outside their school, such as those run by community groups or other organizations; and (2) in any physically active programs at their school or outside their school, such as those run by community groups or other organizations (NOT including team sports or cheerleading teams). Some examples provided for the latter item included: attending health/exercise clubs or taking lessons that involve PA, such as those for gymnastics, dance, martial arts, or tennis. Adolescents who responded “yes” to at least one of the two items were included in the organized PA program participation group, whereas those who responded “no” to both items were included in the nonparticipating group.
Motivation for PA. The 24-item Behavioral Regulation in Exercise Questionnaire-3 (BREQ-3; Markland & Tobin, 2004; Wilson et al., 2006) was used to assess reasons underlying adolescents’ decisions to engage or not engage in PA. Some items were slightly modified to remove double negatives (in item statement and response choice) to enhance comprehension by this young age group. Consistent with SDT definitions (Deci & Ryan, 1985; Ryan & Deci, 2000), the BREQ-3 includes six subscales (four items/subscale) reflecting different motivational regulations: (1) intrinsic motivation (e.g., I exercise because it’s fun), (2) integrated regulation (e.g., I consider exercise a big part of who I am), (3) identified regulation (e.g., I think it is important to make the effort to exercise every day), (4) introjected regulation (e.g., I would feel bad about myself if I failed to make the time for exercise), (5) external regulation (e.g., I exercise because others say I should), and (6) amotivation (e.g., I think exercising is a waste of time; Markland & Tobin, 2004; Wilson et al., 2006). For this study, amotivation or the lack of motivation was not examined due to our interest in only positive perceptions. Integrated regulation was also not examined because this advanced form, which develops from aligning PA with the sense of self and personal life goals (Standage & Ryan, 2012), is not typically displayed in young adolescents (Deci et al., 1996).
The BREQ has acceptable validity when used with adolescents (McDavid et al., 2012). Cronbach’s α values for the subscales have been ≥0.72 in 11- to 15-year-old adolescents (White et al., 2018). In this study, all subscales, except the one for identified regulation (α = 0.57), had Cronbach’s α values ≥0.78. Deleting one item (e.g., I get restless if I fail to get regular exercise) in the identified regulation subscale increased the Cronbach’s α to 0.65. To assess autonomous motivation, a composite score was calculated by averaging scores for the four intrinsic motivation items and three identified regulation items (Duncan et al., 2017). This approach resulted in a Cronbach’s α of 0.81 for the autonomous motivation subscale.
Social Support for PA. An 8-item Social Support Scale assessed instrumental assistance and emotional encouragement to exercise, be active, or do sports that each adolescent received from others. Test–retest reliability, internal consistency (Cronbach’s α = 0.83), and validity have been established (Ling et al., 2014). An item example was: someone takes me to play sports or exercise. Four response choices were: never (0), rarely (1), sometimes (2), and often (3). Higher scores indicated greater social support. In this study, Cronbach’s α was 0.80.
PA Self-Efficacy. A six-item Perceived PA Self-Efficacy Scale measured confidence in overcoming barriers to PA during free time. Test–retest reliability, internal consistency (Cronbach’s α = 0.79), and validity have been established (Dishman, Hales, et al., 2010). One survey item was: I can be active in my free time on most days even when I am busy. Four response choices were: not at all sure (0), not very sure (1), somewhat sure (2), and very sure (3). A higher score indicated greater PA self-efficacy. In this study, Cronbach’s α was 0.74.
Sedentary Screen Time Behavior. Four single items were used to assess number of hours not involving PA that were spent watching television (TV) or movies and the number of hours playing video or computer games or using computers or phones for something that is not schoolwork on a usual weekend day and usual school day. Items were adapted from those used in the CDC Division of Adolescent and School Health (2017) Youth Risk Behavior Surveys.
Physical activity. Minutes per hour of moderate, vigorous, and MVPA, were assessed via the ActiGraph GT3X+ (Version 3.2.1; ActiGraph, LLC, Pensacola, FL), a triaxial accelerometer that is reliable and valid for detecting levels of PA intensity (Hänggi et al., 2013; Trost et al., 1998). RAs present at the schools helped adolescents place the ActiGraph, which was attached to an elastic belt around the waist, on the right hip. Every accelerometer worn by an adolescent had an identification (ID) number created by the researchers and a monitor serial number listed on it. In an electronic database file located on a secure password-protected university server having regular backups, a confidential record was kept that included each adolescent’s study ID number with the corresponding worn accelerometer ID number. RAs asked adolescents to wear the accelerometer for 7 days, except when bathing or sleeping at night. The accelerometer is water resistant and cannot be immersed in water. Therefore, any PA attained via water-based activities was not included in the data.
The monitors were initialized to collect data beginning at 5 a.m. on the day after adolescents received the monitors. Adolescents received a text message or phone call every morning to remind them to wear the accelerometer. After the data collection was completed and monitors were collected from the adolescents at their school, RAs uploaded the data from each monitor and entered the monitor’s corresponding ID into a password-protected study computer. ActiLife software (Version 6.13.2) was used to determine mean minutes per hour of activity based on the following age-appropriate counts per minute cut points established by Evenson et al. (2008): moderate: 574–1,002; and vigorous: ≥1,003. Activity counts were stored in 15 s intervals.
Due to variable wear time by adolescents, average minutes per hour were reported. The time periods of the day (9 p.m. to 7 a.m. on weekdays; 9 p.m. to 11 a.m. on weekend days) when most adolescents are sleeping (not physically active) or not wearing the monitor were filtered out (Catellier et al., 2005). Time not wearing the accelerometer during the expected awake time (periods showing ≥60 consecutive zeros) was removed (Troiano et al., 2008). Adolescents having ≥8 hr of wear time on ≥3 days were included in the analysis to determine minutes per hour of MVPA (Penpraze et al., 2006). Each adolescent’s relevant PA data were transferred to another file that was used for analyzing all study data. PA data were matched to the respective adolescent’s study ID. To estimate minutes per day of MVPA and note whether adolescents are meeting PA recommendations, minutes per hour had to be multiplied by 14 hr or the maximum number of hours adolescents are expected to be awake per day (Catellier et al., 2005). All data were stored on the password-protected university server. The file connecting each adolescent’s name to an ID was deleted.
IMB SPSS statistics software (Version 26) was used to analyze the data. Means, standard deviations, frequencies, and percentages were calculated for the demographic data. Regarding the demographic differences between adolescents participating in organized PA programs and those not participating, chi-square tests were used for categorical demographic variables and independent t-test was applied for the age continuous variable. Shapiro–Wilk tests were performed to analyze if the data followed normal distribution. Variance inflation factors (VIFs) were applied to examine multicollinearity among independent variables in linear regression models. Two-way analysis of variance (ANOVA) was used to examine group differences in psychosocial factors and behaviors by adjusting for any identified significant demographic characteristics. Hierarchical linear regression was performed to examine the relationships between psychosocial factors (social support, self-efficacy, autonomous motivation) and MVPA after adjusting for covariates.
Thirty-nine adolescents were currently participating in organized PA programs, and 41 were not. The adolescents ranged in age from 10 to 13 years old, and slightly over half were female (51.2%). Close to half of the adolescents were overweight or obese (47.5%). The majority were Black (56.3%), 22.5% were White, and 21.3% were multiracial or other races. Of 11 Hispanic/Latinx adolescents, only two were in an organized PA program. Most had annual family incomes <$20,000 (52.5%). Table 1 depicts the demographic characteristics for the total sample and by participation or not in organized PA programs. Due to the small sample size, race was collapsed into three categories, and annual family income was collapsed into two categories as follows: Black, White, and multiracial; and <$20,000 and > $20,000, respectively. Results from the chi-square tests indicated that adolescents participating in organized PA programs had a higher proportion of Black adolescents (χ2 = 9.57, p = .008) and a lower proportion of Hispanics (Fisher’s exact p = .048) than those not participating. Participation in the organized PA programs did not vary significantly by school (χ2 = 2.49, p = .114), age (t = −0.35, p = .725), sex (χ2 = 0.21, p = .650), annual family income (χ2 = 0.99, p = .320), or weight status (χ2 = 0.46, p = .496).
Based on results from the Shapiro–Wilk tests, only the social support data followed a normal distribution. Log transformation was performed for the nonnormally distributed data. Compared to adolescents who did not participate in organized PA programs, those who did reported significantly higher social support for PA (M = 2.32 vs. 3.13, p < .001). No significant differences occurred in any of the other psychosocial factors. However, small–medium effect sizes were noted for PA self-efficacy, intrinsic motivation, external regulation, and autonomous motivation; and all of the respective mean scores were higher for adolescents participating in organized PA programs than for those not participating (see Table 2).
Statistically significant between-group differences in some behaviors occurred. Compared to adolescents who did not participate, those who did had fewer hours watching television or movies on a usual weekend day (M = 2.49 vs. 1.59, p = .016); and higher minutes per hour of accelerometer-measured vigorous PA (M = 0.58 vs. 1.04, p = .009), and MVPA (M = 2.48 vs. 3.45, p = .035). No significant differences were noted in hours watching television or movies on a usual school day or in playing video or computer games or using computers or phones for something that was not schoolwork on a usual weekend day or school day (see Table 2).
The VIF values ranged from 1.03 to 1.52, indicating absence of multicollinearity. Race and organized PA program participation entered at Step 1 explained 44.3% of the variance in MVPA. After entry of social support, self-efficacy, and autonomous motivation at Step 2, the total variance explained by the whole model was 49.8%. The three psychosocial factors explained close to an additional 6% of the variance in MVPA, after controlling for race and participation in organized PA programs. Although no significant relationships emerged between the psychosocial factors and MVPA, associations of social support and self-efficacy with MVPA were slightly stronger than the relationship between autonomous motivation and MVPA. The results of this analysis, which are presented in Table 3, lend support for the findings displayed in Table 2.
The current study showed that social support for PA, vigorous PA, and MVPA were significantly higher among adolescents participating in organized PA programs, compared to those not participating. Moreover, participants reported fewer hours watching television or movies on a usual weekend day than nonparticipants. No other significant between-group differences emerged for the other psychosocial factors. The small sample size may have contributed to this latter finding (Serdar et al., 2021). However, a small–medium effect size occurred for self-efficacy and external regulation with mean scores being higher for adolescents participating in organized PA program than for those not participating. The findings show some promise that participation in organized PA programs may have potential to improve some psychosocial factors in diverse adolescents living in low-income urban areas.
Several reasons may underlie the higher social support for PA perceived by adolescents who participate in organized PA programs, as compared to those who do not, and the positive relationship between social support and MVPA. For example, participation increases adolescents’ ability to cultivate supportive adult relationships and connect with others their age who share similar PA interests. Participation facilitates interactions with nonparental adults in the community who can answer questions or assist adolescents in improving their PA (Howie et al., 2018; Oosterhoff et al., 2017). Also, the current study’s sample was comprised of predominately Black adolescents. Compared to those of other races, young Black athletes perceive greater levels of encouragement from family members and others, especially coaches or instructors (Shakib & Veliz, 2013). This situation may be occurring because Black athletes view coaches or instructors as role models or “parent figures” who provide a support system for PA (Stodolska et al., 2014). Based on this information, coaches or instructors may want to regularly meet or at least talk informally with adolescents, especially those who are Black, about their PA and life in general to build a rapport (Ahrens & Chu, 2021).
Gill et al. (2018) found that both family and friend social support predicted the number of sports teams that predominately Hispanic adolescents from 16 U.S. urban middle schools (grades 6–8) had played on during the past 12 months. Gill et al. suggested that perceived social support for PA among adolescents may be enhanced through participation in PA programs that incorporate group activity with peers and involve the parent or family in some way. Oosterhoff et al. (2017) concurred that frequent and supportive interactions occurring from participation can promote positive perceptions of the self and encourage health-promoting behaviors, such as increased PA and decreased sedentary screen time behavior.
Although mean scores for PA self-efficacy were higher for adolescents who participated in organized PA programs than for nonparticipants, the finding of no significant between-group differences or relationship with MVPA was unexpected. When adolescents participate in organized PA programs, they can learn new PA skills and increase perceptions of competence in performing them, resulting in greater confidence in their ability to engage in PA (Peers et al., 2020). Also, observing peers and others model PA and achieve success through sustained effort can strengthen adolescents’ perceptions that they too are capable of mastering various PAs (Voskuil & Robbins, 2015). Although the current study’s findings conflicted with results identifying PA self-efficacy as a significant predictor of PA among racially and ethnically diverse urban adolescents in grades 6–12 (18.9% White; Graham et al., 2014), they were consistent with those from a recently conducted study with 11- to 18-year-old Black adolescents. Unfortunately, the researchers who conducted the latter study did not explain the unanticipated finding other than to state that self-efficacy may not be directly associated with PA in this population (Shaver et al., 2019). In a qualitative study involving interviews, 13- to 15-year old boys (2 Latino; 11 Black) participating in an urban sport program stated that the program did help them to improve their skills, but some pessimistic cultural and stereotypical societal beliefs about their ability to do well in certain sports negatively affect their perceived competence or PA self-efficacy (Stodolska et al., 2014). Perhaps, additional effort is needed from influential people in the lives of minority adolescents participating in organized PA programs to encourage and persuade them that they possess the abilities to successfully perform various types of PA (Thompson et al., 2003). For example, coaches and instructors can assist adolescents in performing self-assessments and tracking their own personal improvements over time. When adolescents’ PA self-efficacy increases, they are more likely to sustain their efforts when problems arise or priorities change (Inchley et al., 2011); therefore, building adolescents’ PA self-efficacy within learning contexts may be an important step toward promoting lifelong PA participation.
Although mean scores for autonomous and intrinsic motivation, as well as identified regulation, for PA were higher for adolescents participating in organized PA programs than for nonparticipants, the nonsignificant between-group differences in these three forms of motivation and lack of a significant relationship between autonomous motivation and MVPA was surprising. Contrary to these results, intrinsic motivation and identified regulation, both of which reflect autonomous motivation, were found to be positively associated with MVPA in a predominately minority sample of 7th grade adolescents; and intrinsic motivation was related to MVPA in a similar sample of 6th grade adolescents (Dishman et al., 2018). The lack of between-group differences in the current study was concerning because the findings indicate that increased effort may need to be directed in organized PA programs toward helping adolescents to inherently enjoy PA and understand that engaging in PA is important for various reasons, many of which may be of value for this young age group. Gaining competence or increasing self-efficacy and experiencing positive feelings in the context of PA may aid in increasing these forms of motivation for PA and the behavior itself (Ryan & Deci, 2000; Wigfield & Eccles, 2000). In the current study, PA self-efficacy was not significantly higher for adolescents participating than for those not involved in organized PA programs; this occurrence may have contributed to the lack of between-group differences in these forms of motivation.
Akin to the autonomous forms of motivation for PA, small–medium effect sizes emerged for external regulation with means being higher for adolescents participating in organized PA programs than for nonparticipants, but no significant between-group differences were noted. These results suggest that adolescents participating in organized PA programs may be more driven than those not participating by achievement of outcomes that do not reflect the behavior itself, such as receiving tangible rewards. External regulation was evident among Black and Hispanic boys, who stated they were participating in an urban baseball program so that they could be recruited into the major league. Whether this perspective offers a way for them to achieve success and advance racial justice or limits mobility and reinforces racial stereotypes is debatable (Stodolska et al., 2014). According to Nogg et al. (2020), higher levels of controlled motivation, such as external regulation, lead to less persistence of behaviors, such as PA.
The maximum mean minutes of MVPA per day based on time awake (Catellier et al., 2005) was estimated as 48.30 for adolescents participating in organized PA programs and 34.72 for nonparticipants, indicating neither group met MVPA recommendations (USDHHS, 2018; WHO, 2010). However, consistent with Shull et al. (2020) investigation that included a diverse sample (44.1% Black), adolescents in the current study who participated in organized PA programs had higher MVPA than nonparticipants. Shull et al. also reported that MVPA was higher among sport participants than nonparticipants in 7th grade. Moreover, somewhat aligned with the current study’s findings showing adolescents who participated in organized PA programs had fewer hours watching television or movies on a usual weekend day, Shull et al. noted that sedentary behavior was lower among sport participants than nonparticipants in 7th grade. Collectively, the findings imply that participation in sports or organized PA programs has potential to positively impact diverse adolescents’ MVPA and sedentary or screen time behavior. The current study’s findings were not completely surprising because young urban adolescents, especially those in low-income neighborhoods, who are not involved in organized PA programs may have few opportunities for unstructured PA due to limited green space, safety issues, and other barriers related to the built environment (Duck et al., 2020).
The study had strengths and limitations. Some strengths were the use of accelerometers to assess PA and the inclusion of a racially diverse sample from predominately low-income families. Although the cross-sectional data provided information to describe the adolescents and identify group differences, no causal relationships can be determined. Self-report could be sensitive to social desirability and recall biases. No distinctions were made based on the type of organized PA program or the frequency of participation by the adolescents. Future examination of these program characteristics and the relationships between adolescents’ perceptions and behaviors in longitudinal studies may be warranted. Examining psychosocial factors other than those of interest in the current study may also be a fruitful approach. Experimental study designs are needed to test the effects of different organized PA programs on various psychosocial factors, PA, and sedentary behavior or screen time. Generalizability may be limited because the sample was comprised of adolescents from only two schools in one urban school district. Future research would benefit from repeating these analyses with large samples from varied geographical areas and investigating if the between-group differences hold across a broad range of academic grades.
One issue that needs to be mentioned is that using BMI as an indicator of body fat, weight status, or health is problematic because BMI does not distinguish between body lean mass and body fat mass (Nuttall, 2015). A muscular athlete can have a high BMI, but low fat mass (Witt & Bush, 2005). Also, for an equivalent BMI, White children and adolescents have higher body fat than their Black peers (Daniels et al., 1997). Using the recommended BMI cut-scores, which have been based on data from White men and women, to indicate overweight and obesity status has overestimated overweight and obesity prevalence in Black men and women (Jackson et al., 2009). This information highlights the need not only to understand the personal characteristics influencing BMI but also to consider the inherent racial/ethnic bias of using BMI to indicate obesity in diverse groups (Jackson et al., 2009).
This study’s results suggest that efforts to increase MVPA, decrease sedentary screen time, and promote positive perceptions toward PA among adolescents may need to be directed toward increasing their organized PA program participation. Due to school nurses’ direct access to adolescents, parents, school personnel, and community members, school nurses are in a prime position to share findings from this study. They can also offer educational sessions for school administrators, coaches, teachers, PA instructors, and adolescents about cultural differences and the need for acceptance to promote a positive climate (Chang et al., 2017). School nurses can discuss the benefits of adolescent participation in organized PA programs with parents and encourage parents to support their adolescents in these extracurricular activities. School nurses can teach parents how to effectively communicate to motivate their adolescents to continue participation. As age increases across adolescence, dropout becomes more common (Shull et al., 2020). This unfortunate situation may be avoided if programs are perceived by adolescents to be enjoyable and helpful in building skills to increase their competence. Assisting adolescents to manage their time effectively may also be important. Dropout may be prevented if adolescents feel a sense of connectedness to peers and others instead of social pressures, the former of which can be promoted through team building and group social activities (Nogg et al., 2020). School nurses can advocate for legislative and policy changes that expand organized PA programs in schools and communities, especially in racially diverse, low-income urban areas, to provide an increased variety of opportunities or choices for participation by adolescents of all levels of ability to reduce existing disparities.
Although continued research is needed with a larger sample, findings suggest that involving adolescents in organized PA programs is important for not only increasing their MVPA and reducing sedentary screen time, but also improving some psychosocial factors related to PA. However, strategies are needed to enhance positive perceptions associated PA among adolescents participating in organized PA programs. Longitudinal studies are needed to examine whether these positive perceptions continue over time and influence future behaviors.
The authors appreciate the support received from school administrators, school nurses, teachers, and other staff members who assisted us in implementing this study. Their commitment toward promoting the health of their students is admirable. We also acknowledge the following Michigan State University College of Nursing PhD in Nursing students for their assistance with various phases of the research: Muna Alali, Eakachai Kantawong, and Karla Palmer. We also thank the following undergraduate students for their assistance with the research: Maria Cotts, Kendall Piper, Christina Vu, and Maddie Young. Lastly, we thank the adolescents for their interest in participating.
All authors contributed to the design of the study. L. Robbins initially drafted and then revised the manuscript. J. Ling and M. W. Chang critically reviewed and revised drafts of the paper. L. Robbins contributed to data acquisition. J. Ling assisted with the data analysis and interpretation. All authors gave final approval of the paper and agreed to be accountable for all aspects of the work.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Michigan State University College of Nursing.
Lorraine B. Robbins https://orcid.org/0000-0003-2914-3630
Ahrens, A. D., & Chu, T. L. (2021). Motivation is not always Black and White: Attending to African American athletes’ psychological needs for sport involvement. Strategies, 34(2), 9–12. https://doi.org/10.1080/08924562.2020.1867269
Armstrong, S., Wong, C. A., Perrin, E., Page, S., Sibley, L., & Skinner, A. (2018). Association of physical activity with income, race/ethnicity, and sex among adolescents and young adults in the United States: Findings from the National Health and Nutrition Examination Survey, 2007–2016. JAMA Pediatrics, 172(8), 732–740. https://doi.org/10.1001/jamapediatrics.2018.1273
Ashford, S., Edmunds, J., & French, D. P. (2010). What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. British Journal of Health Psychology, 15(2), 265–288. https://doi.org/10.1348/135910709X461752
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice Hall.
Bandura, A. (1997). Self-efficacy: The exercise of control. W. H. Freeman and Company.
Catellier, D. J., Hannan, P. J., Murray, D. M., Addy, C. L., Conway, T. L., Yang, S., & Rice, J. C. (2005). Imputation of missing data when measuring physical activity by accelerometry. Medicine and Science in Sports and Exercise, 37(11 Suppl), S555–S562. https://doi.org/10.1249/01.ms.0000185651.59486.4e
Centers for Disease Control and Prevention. (2015). A SAS program for the 2000 CDC growth charts (ages 0 to <20 years). https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm
Centers for Disease Control and Prevention Division of Adolescent and School Health. (2017). Youth risk behavior surveillance system. https://www.cdc.gov/healthyyouth/data/yrbs/index.htm
Chang, E. C., Najarian, A. S., Chang, O. D., Hill, G. B., & Lee, J. (2017). Athletic competence as a central facet of sport orientation among collegiate athletes. Journal of Sport Behavior, 40(3), 269.
Daniels, S. R., Khoury, P. R., & Morrison, J. A. (1997). The utility of body mass index as a measure of body fatness in children and adolescents: Differences by race and gender. Pediatrics, 99(6), 804–807. https://doi.org/10.1542/peds.99.6.804
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum. https://doi.org/10.1007/978-1-4899-2271-7
Deci, E. L., & Ryan, R. M. (2012). Motivation, personality, and development within embedded social contexts an overview of self-determination theory. In R. M. Ryan (Ed.), The Oxford handbook of human motivation (pp. 85–110). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780195399820.013.0006
Deci, E. L., Ryan, R. M., & Williams, G. C. (1996). Need satisfaction and the self-regulation of learning. Learning and Individual Differences, 8(3), 165–183. https://doi.org/10.1016/S1041-6080(96)90013-8
Dishman, R. K., Dunn, A. L., Sallis, J. F., Vandenberg, R. J., & Pratt, C. A. (2010). Social-cognitive correlates of physical activity in a multi-ethnic cohort of middle-school girls: Two-year prospective study. Journal of Pediatric Psychology, 35(2), 188–198. https://doi.org/10.1093/jpepsy/jsp042
Dishman, R. K., Hales, D. P., Sallis, J. F., Saunders, R., Dunn, A. L., Bedimo-Rung, A. L., & Ring, K. B. (2010). Validity of social-cognitive measures for physical activity in middle-school girls. Journal of Pediatric Psychology, 35(1), 72–88. https://doi.org/10.1093/jpepsy/jsp031
Dishman, R. K., McIver, K. L., Dowda, M., & Pate, R. R. (2018). Declining physical activity and motivation from middle school to high school. Medicine and Science in Sports and Exercise, 50(6), 1206–1215. https://doi.org/10.1249/MSS.0000000000001542
Duck, A. A., Robinson, J. C., & Stewart, M. W. (2020). Adults’ and children’s perceptions of barriers and facilitators of school-aged children’s physical activity in an inner-city urban area. Journal for Specialists in Pediatric Nursing, 25(1), e12278. https://doi.org.10.1111/jspn.12278
Dumith, S. C., Gigante, D. P., Domingues, M. R., & Kohl, H. W. III III, H. W. (2011). Physical activity change during adolescence: A systematic review and a pooled analysis. International Journal of Epidemiology, 40(3), 685–698. https://doi.org/10.1093/ije/dyq272
Duncan, M. J., Eyre, E. L., Bryant, E., Seghers, J., Galbraith, N., & Nevill, A. M. (2017). Autonomous motivation mediates the relation between goals for physical activity and physical activity behavior in adolescents. Journal of Health Psychology, 22(5), 595–604. https://doi.org/10.1177/1359105315609089
Duncan, S. C., Duncan, T. E., & Strycker, L. A. (2005). Sources and types of social support in youth physical activity. Health Psychology, 24(1), 3. https://doi.org/10.1037/0278-6133.24.1.3
Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., & McMurray, R. G. (2008). Calibration of two objective measures of physical activity for children. Journal of Sports Sciences, 26(14), 1557–1565. https://doi.org/10.1080/02640410802334196
Gill, M., Chan-Golston, A. M., Rice, L. N., Roth, S. E., Crespi, C. M., Cole, B. L., Koniak-Griffin, D., & Prelip, M. L. (2018). Correlates of social support and its association with physical activity among young adolescents. Health Education & Behavior, 45(2), 207–216. https://doi.org/10.1177/1090198117714826
Graham, D. J., Wall, M. M., Larson, N., & Neumark-Sztainer, D. (2014). Multicontextual correlates of adolescent leisure-time physical activity. American Journal of Preventive Medicine, 46(6), 605–616. https://doi.org/10.1016/j.amepre.2014.01.009
Guthold, R., Stevens, G. A., Riley, L. M., & Bull, F. C. (2020). Global trends in insufficient physical activity among adolescents: A pooled analysis of 298 population-based surveys with 1·6 million participants. The Lancet Child & Adolescent Health, 4(1), 23–35. https://doi.org/10.1016/S2352-4642(19)30323-2
Hänggi, J. M., Phillips, L. R., & Rowlands, A. V. (2013). Validation of the GT3X ActiGraph in children and comparison with the GT1M ActiGraph. Journal of Science and Medicine in Sport, 16(1), 40–44. https://doi.org/10.1016/j.jsams.2012.05.012
Hill, C. R., Feltz, D. L., Samendinger, S., & Pfeiffer, K. A. (2019). The relationship between barrier self-efficacy and physical activity in children and adolescents: A meta-analysis. Kinesiology Review, 9(2), 122–137. https://doi.org/10.1123/kr.2019-0002
Howie, E. K., Daniels, B. T., & Guagliano, J. M. (2018). Promoting physical activity through youth sports programs: It’s Social. American Journal of Lifestyle Medicine, 14(1), 78–88. https://doi.org/10.1177/1559827618754842
Huffman, L. E., Wilson, D. K., Van Horn, M. L., & Pate, R. R. (2018). Associations between parenting factors, motivation, and physical activity in overweight African American adolescents. Annals of Behavioral Medicine, 52(2), 93–105. https://doi.org/10.1007/s12160-017-9919-8
Inchley, J., Kirby, J., & Currie, C. (2011). Longitudinal changes in physical self-perceptions and associations with physical activity during adolescence. Pediatric Exercise Science, 23(2), 237–249. https://doi.org/10.1123/pes.23.2.237
Jackson, A. S., Ellis, K. J., McFarlin, B. K., Sailors, M. H., & Bray, M. S. (2009). Body mass index bias in defining obesity of diverse young adults: The training intervention and genetics of exercise response (TIGER) study. The British Journal of Nutrition, 102(7), 1084–1090. https://doi.org/10.1017/S0007114509325738
Jones, D., Feigenbaum, P., & Jones, D. F. (2021). Motivation (constructs) made simpler: Adapting self-determination theory for community-based youth development programs. Journal of Youth Development, 16(1), 7–28. https://doi.org/10.5195/jyd.2021.1001
Katzmarzyk, P. T., Powell, K. E., Jakicic, J. M., Troiano, R. P., Piercy, K., Tennant, B., & 2018 Physical Activity Guidelines Advisory Committee. (2019). Sedentary behavior and health: Update from the 2018 physical activity guidelines advisory committee. Medicine and Science in Sports and Exercise, 51(6), 1227. https://doi.org/10.1249/MSS.0000000000001935
Larson, R. W., Hansen, D. M., & Moneta, G. (2006). Differing profiles of developmental experiences across types of organized youth activities. Developmental Psychology, 42(5), 849–863. https://doi.org/10.1037/0012-1649.42.5.849
Ling, J., Robbins, L. B., Resnicow, K., & Bakhoya, M. (2014). Social support and peer norms scales for physical activity in adolescents. American Journal of Health Behavior, 38(6), 881–889. https://doi.org/10.5993/AJHB.38.6.10
Logan, K., Lloyd, R. S., Schafer-Kalkhoff, T., Khoury, J. C., Ehrlich, S., Dolan, L. M., Shah, A. S., & Myer, G. D. (2020). Youth sports participation and health status in early adulthood: A 12-year follow-up. Preventive Medicine Reports, 19, 101107. https://doi.org/10.1016/j.pmedr.2020.101107
Mahoney, J. L., Larson, R. W., Eccles, J. S., & Lord, H. (2005). Organized activities as developmental contexts for children and adolescents. In J. L. Mahoney, R. W. Larson, & J. S. Eccles (Eds.), Organized activities as contexts of development: Extracurricular activities, after-school and community programs (pp. 3–22). Erlbaum.
Markland, D., & Tobin, V. (2004). A modification to the behavioural regulation in exercise questionnaire to include an assessment of amotivation. Journal of Sport and Exercise Psychology, 26(2), 191–196. https://doi.org/10.1123/jsep.26.2.191
McDavid, L., Cox, A. E., & Amorose, A. J. (2012). The relative roles of physical education teachers and parents in adolescents’ leisure-time physical activity motivation and behavior. Psychology of Sport and Exercise, 13(2), 99–107. https://doi.org/10.1016/j.psychsport.2011.10.003
Mendonça, G., Cheng, L. A., Mélo, E. N., & de Farias Júnior, J. C. (2014). Physical activity and social support in adolescents: A systematic review. Health Education Research, 29(5), 822–839. https://doi.org/10.1093/her/cyu017
Nogg, K. A., Vaughn, A. A., Levy, S. S., & Blashill, A. J. (2020). Motivation for physical activity among U.S. adolescents: A self-determination theory perspective. Annals of Behavioral Medicine, 55(2), 133–143. https://doi.org/10.1093/abm/kaaa037
Nuttall, F. Q. (2015). Body mass index: Obesity, BMI, and health: A critical review. Nutrition Today, 50(3), 117–128. https://doi.org/10.1097/NT.0000000000000092
Oosterhoff, B., Kaplow, J. B., Wray-Lake, L., & Gallagher, K. (2017). Activity-specific pathways among duration of organized activity involvement, social support, and adolescent well-being: Findings from a nationally representative sample. Journal of Adolescence, 60, 83–93. https://doi.org/10.1016/j.adolescence.2017.07.012
Parent, J., Sanders, W., & Forehand, R. (2016). Youth screen time and behavioral health problems: The role of sleep duration and disturbances. Journal of Developmental and Behavioral Pediatrics, 37(4), 277–284. https://doi.org/10.1097/DBP.00000000000002
Peers, C., Issartel, J., Behan, S., O’Connor, N., & Belton, S. (2020). Movement competence: Association with physical self-efficacy and physical activity. Human Movement Science, 70, 102582. https://doi.org/10.1016/j.humov.2020.102582
Penpraze, V., Reilly, J. J., MacLean, C. M., Montgomery, C., Kelly, L. A., Paton, J. Y., Aitchison, T., & Grant, S. (2006). Monitoring of physical activity in young children: How much is enough? Pediatric Exercise Science, 18(4), 483–491. https://doi.org/10.1123/pes.18.4.483
Petitpas, A. J., Cornelius, A. E., Van Raalte, J. L., & Jones, T. (2005). A framework for planning youth sport programs that foster psychosocial development. The Sport Psychologist, 19(1), 63–80. https://doi.org/10.1123/tsp.19.1.63
Pluta, B., Korcz, A., Krzysztoszek, J., Bronikowski, M., & Bronikowska, M. (2020). Associations between adolescents’ physical activity behavior and their perceptions of parental, peer and teacher support. Archives of Public Health, 78(1), 1–8. https://doi.org/10.1186/s13690-020-00490-3
Robbins, L. B., Ling, J., Clevenger, K., Voskuil, V. R., Wasilevich, E., Kerver, J. M., Kaciroti, N., & Pfeiffer, K. A. (2020). A school- and home-based intervention to improve adolescents’ physical activity and healthy eating: A pilot study. The Journal of School Nursing, 36(2), 121–134. https://doi.org/10.1177/1059840518791290
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037//0003-066x.55.1.68
Ryan, R. M., & Deci, E. L. (2007). Active human nature: Self-determination theory and the promotion and maintenance of sport, exercise, and health. In M. S. Hagger & N. L. D. Chatzisarantis (Eds.), Intrinsic motivation and self-determination in exercise and sport (pp. 1–19). Human Kinetics.
Serdar, C. C., Cihan, M., Yücel, D., & Serdar, M. A. (2021). Sample size, power and effect size revisited: Simplified and practical approaches in pre-clinical, clinical and laboratory studies. Biochemia Medica, 31(1), 27–53. https://doi.org/10.11613/BM.2021.010502
Shakib, S., & Veliz, P. (2013). Race, sport and social support: A comparison between African American and white youths’ perceptions of social support for sport participation. International Review for the Sociology of Sport, 48(3), 295–317. https://doi.org/10.1177/1012690212439172
Shaver, E. R., McGlumphy, K. C., Gill, A. K., & Hasson, R. E. (2019). Application of the transtheoretical model to physical activity and exercise behaviors in African-American adolescents. American Journal of Health Behavior, 43(1), 119–132. https://doi.org/10.5993/AJHB.43.1.10
Shull, E. R., Dowda, M., Saunders, R. P., McIver, K., & Pate, R. R. (2020). Sport participation, physical activity and sedentary behavior in the transition from middle school to high school. Journal of Science and Medicine in Sport, 23(4), 385–389. https://doi.org/10.1016/j.jsams.2019.10.017
Standage, M., & Ryan, R. M. (2012). Self-determination theory and exercise motivation: Facilitating self-regulatory processes to support and maintain health and well-being. In G. C. Roberts, & D. C. Treasure (Eds.), Advances in motivation in sport and exercise (3rd ed., pp. 233–270). Human Kinetics.
Sterdt, E., Liersch, S., & Walter, U. (2014). Correlates of physical activity of children and adolescents: A systematic review of reviews. Health Education Journal, 73(1), 72–89. https://doi.org/10.1177/0017896912469578
Stodolska, M., Sharaievska, I., Tainsky, S., & Ryan, A. (2014). Minority youth participation in an organized sport program: Needs, motivations, and facilitators. Journal of Leisure Research, 46(5), 612–634. https://doi.org/10.1080/00222216.2014.11950345
Thompson, A. M., Humbert, M. L., & Mirwald, R. L. (2003). A longitudinal study of the impact of childhood and adolescent physical activity experiences on adult physical activity perceptions and behaviors. Qualitative Health Research, 13(3), 358–377. https://doi.org/10.1177/1049732302250332
Troiano, R. P., Berrigan, D., Dodd, K. W., Mâsse, L. C., Tilert, T., & McDowell, M. (2008). Physical activity in the United States measured by accelerometer. Medicine and Science in Sports and Exercise, 40(1), 181–188. https://doi.org/10.1249/mss.0b013e31815a51b3
Trost, S. G., Ward, D. S., Moorehead, S. M., Watson, P. D., Riner, W., & Burke, J. R. (1998). Validity of the computer science and applications (CSA) activity monitor in children. Medicine and Science in Sports and Exercise, 30(4), 629–633. https://doi.org/10.1097/00005768-199804000-00023
U.S. Department of Health and Human Services. (2017). Poverty Guidelines. https://aspe.hhs.gov/2017-poverty-guidelines
U.S. Department of Health and Human Services. (2018). 2018 Physical activity guidelines advisory committee scientific report. https://health.gov/sites/default/files/2019-09/PAG_Advisory_Committee_Report.pdf
Voskuil, V. R., & Robbins, L. B. (2015). Youth physical activity self-efficacy: A concept analysis. Journal of Advanced Nursing, 71(9), 2002–2019. https://doi.org/10.1111/jan.12658
White, R. L., Parker, P. D., Lubans, D. R., MacMillan, F., Olson, R., Astell-Burt, T., & Lonsdale, C. (2018). Domain-specific physical activity and affective wellbeing among adolescents: An observational study of the moderating roles of autonomous and controlled motivation. International Journal of Behavioral Nutrition and Physical Activity, 15(1), 87. https://doi.org/10.1186/s12966-018-0722-0
Whitehead, J. R. (1995). A study of children’s physical selfperceptions using an adapted physical self-perception profile questionnaire. Pediatric Exercise Science, 7(2), 132–151. https://doi.org/10.1123/pes.7.2.132
Wieland, M. L., Biggs, B. K., Brockman, T. A., Johnson, A., Meiers, S. J., Sim, L. A., Tolleson, E., Hanza, M. M., Weis, J. A., Rosenman, J. R., Novotny, P. J., Patten, C. A., Clark, M. M., Millerbernd, J., & Sia, I. G. (2020). Club Fit: Development of a physical activity and healthy eating intervention at a boys & girls club after school program. The Journal of Primary Prevention, 41(2), 153–170. https://doi.org/10.1007/s10935-020-00582-4
Wigfield, A., & Eccles, J. S. (2000). Expectancy—value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68–81. https://doi.org/10.1006/ceps.1999.1015
Wilson, P. M., Rodgers, W. M., Loitz, C. C., & Scime, G. (2006). It’s who I am… really!’’ The importance of integrated regulation in exercise contexts. Journal of Applied Biobehavioral Research, 11(2), 79–104. https://doi.org/10.1111/j.1751-9861.2006.tb00021.x
Witt, K. A., & Bush, E. A. (2005). College athletes with an elevated body mass index often have a high upper arm muscle area, but not elevated triceps and subscapular skinfolds. Journal of the American Dietetic Association, 105(4), 599–602. https://doi.org/10.1016/j.jada.2005.01.008
World Health Organization. (2010). Global recommendations on physical activity for health. WHO Press. https://www.who.int/publications/i/item/9789241599979
World Health Organization. (2021). Adolescent and young adult health. https://www.who.int/news-room/fact-sheets/detail/adolescentshealth-risks-and-solutions
1 College of Nursing, Michigan State University, East Lansing, MI, USA
2 College of Nursing, The Ohio State University, Columbus, OH, USA
Corresponding Author:Lorraine B. Robbins, PhD, RN, FNP-BC, FAAN, College of Nursing, Michigan State University, 1355 Bogue Street, East Lansing, MI 48824, USA.Email: robbin76@msu.edu
