Submitted by Jennifer Kwak1 MA*, Michael Amrhein2*, Harald Barkhoff2*, and Elaine M. Heiby1*
1* Department of Psychology, University of Hawai’i at Mānoa
2* Department of Kinesiology and Exercise Sciences, University of Hawai’i at Hilo
Purpose: Being physically active during leisure time is a positive contributor to overall physical and mental health, while sedentariness is a risk factor for several diseases. Minority students are at-risk of physical inactivity during leisure time and more research is needed to better understand how this affects health outcomes and its dynamical nature.
Methods: Computer Assisted Mobile Interview (CAMI) cell phone technology was used to prospectively collect daily self-monitoring of physical leisure activity and the outcomes of six health practices (eating habits, feeling hassled, mood, alcohol and cigarette consumption, and use of sun protection) and mental health indicators of self-concept and quality-of-life, over four months with 28 multi-ethnic college students in Hawaiʻi, U.S.
Results: Correlational and multiple regression analyses yielded significant positive relationships among daily physical leisure activity, self-concept, and feeling less hassled. Daily sedentary leisure activity was significantly associated with poorer health practices. Very-Physically-Active participants reported significantly more positive self-concept than Not-Very-Physically-Active participants. Self-concept and quality-of-life were significantly related to more positive daily health practices.
Conclusions: These results provide preliminary evidence for the positive and dynamical effects of active physical leisure activity on health practices and mental health indicators, and demonstrate cell phones as an effective tool for daily self-monitoring.
Applications in Sport: Health professionals, coaches, and educators may better understand the temporal health effects of physical leisure activities in student minorities. The use of cell phone technology, particularly text-messaging, can be an effective tool to self-monitor daily activities to improve health and fitness during leisure time.
Key words: physical leisure activities, health practices, self-monitoring, self-concept, quality-of-life
Within the study of fitness and health, there is an increased appreciation of the benefits of leisure activity reflected in an increase in exercise participation and a positive response to public health promotion efforts (10, 15, 32). The concept of leisure time exercise is related, but different from physical activity (27, 39). There have also been differentiations between physical leisure activities (e.g., running, walking, swimming, bicycling) versus sedentary ones. Sedentariness or sedentary leisure activities are risk factors for increased mortality and other major diseases, such as cardiovascular disease and coronary artery disease (30-32, 42). Whereas,physical leisure activities are preventive of negative health outcomes (14, 25, 33, 40). These beneficial effects are not only limited to physical health, but also extend to mental health and psychological development (30-31).
Engagement in physical leisure activities has demonstrated positive mental health outcomes, including higher quality-of-life, alleviation of anxiety, increased well-being, positive identity development and self-concept, increased self-efficacy, and stress-coping benefits (12, 23,32, 42). Moreover, improved quality-of-life and self-concept have been shown to be associated with not only physical activities, but also elite sports performance, specifically in situations of training and competition (2-5).
Given that health practices and indicators are unstable, such as physical activity and mood, it is important to inspect their relationship over time (18, 37). There are also differential patterns of behavioral response based on the dynamical nature of physical leisure activity (17, 42). For example, intensity (moderate vs. vigorous) and location (indoor vs. outdoor) of physical leisure activity have been found to differentially predict behavioral engagement, maintenance, and emotional response. Students who engaged in vigorous physical activity during leisure time had increased self-efficacy and were better able to overcome barriers associated with exercise, compared to students who engaged in moderate activity levels. Maintaining these higher levels of physical leisure activity is another component that has been shown to shift and evolve over time. Prior research generally has relied on static modeling using retrospective self-reports of leisure activity (15, 22, 24, 26, 44).
Pagano et al. (28) demonstrated the relation between leisure activity and several health behaviors over time as an extension of the maladaptive behavior determinism (MBD) theory. MBD theory is derived from what has been referred to as systems, dynamical, complexity, and chaos theory. It states that ordered behavior patterns over time are suggestive of disease states due to endogenous (internally-driven) factors, whereas adaptive behaviors lead to more randomly occurring health behaviors due to exogenous (externally-driven) factors. Thus, it can be hypothesized that sedentary behaviors lead to deterministic health outcomes based on the premise that these individuals perseverate, even in the presence of environmental change. Correspondingly, increased physical activity would be related to more randomly occurring health behaviors (16). Heiby et al. (16) showed some support for the MBD hypothesis, particularly with respect to the health practice of feeling less hassled. The findings underscored the importance of daily self-monitoring and also suggest that being physically active during leisure time might have health benefits that are dynamical over time and not necessarily captured by retrospective reports.
The present study examined the relationship between physically active versus sedentary leisure activities and its association with daily health practices and mental health indicators among minority college students. With the known benefits of physical leisure activity on mental and health outcomes, young ethnic minorities are an at-risk population. Minorities in the U.S. have been found to participate in less physical activity during leisure time and have disproportionally higher rates of heart disease and other chronic diseases caused in part by physical inactivity, compared to their Caucasian counterparts (19, 39, 41). Furthermore, physical activity rates decline consistently during adolescent years and it has been found that approximately one third of college students do not engage in adequate amounts of physical activity (8, 13, 35, 38, 42). Minority college students, in particular, were almost twice as severe as that in other college student populations and Asian-American students were found to have significantly lower physical activity compared to other ethnic minorities (Mexican-American) (13, 36).
Previous studies have shown that college students who have better health habits perceive fewer constraints on leisure activities and are therefore, more physically active (12). Additionally, college students who engage in regular exercise have increased self-efficacy, derive greater enjoyment from multiple pursuits, and are better able to self-monitor and balance multiple goals and pursuits (e.g., academic, occupational, interpersonal, etc.). While, non-exercisers have been shown to have motivational deficits (20).
The primary objectives in this study were to examine the relations between daily engagement and intensity level of physical activity during minority students’ leisure time, daily health practices, and mental health indicators. Comparisons were made between those who were physically active versus sedentary during leisure time, as well as differentiating between intensity levels of leisure time physical activity (Very-Physically-Active vs. Not-Very-Physically-Active). Outcome variables included six daily health practices (eating habits, feeling hassled, mood, alcohol and cigarette consumption, and use of sun protection) and self-reported mental health indicators of self-concept and quality-of-life. Given the subtropical climate and ethnic diversity of Hawai’i, U.S., where the participants resided, the use of sun protection was deemed to be an important daily health practice. Pre- and post-test self-concept and quality-of-life were also examined to determine any relationship between these constructs with daily health practices. We hypothesized the following:
- More daily leisure time spent being physically active relates to more positive health outcomes:
- More positive self-concept at pre- and post-test
- Higher quality-of-life at pre- and post-test
- More positive daily health practices (i.e., daily reports of healthier eating, feeling less hassled, more positive mood, less alcohol and cigarette consumption, and greater use of sun protection);
- More negative self-concept at pre- and post-test
- Lower quality-of-life at pre- and post-test
- More negative daily health practices (i.e., daily reports of unhealthier eating, feeling more hassled, less positive mood, more alcohol and cigarette consumption, and less use of sun protection);
- More daily leisure time spent being sedentary relates to more negative health outcomes:
- Greater level of physical activity during leisure time relates to more positive health outcomes:
- Being Very-Physically-Active relates to more positive self-concept at pre- and post-test
- Being Very-Physically-Active relates to higher quality-of-life at pre- and post-test
- Being Very-Physically-Active relates to more positive daily health practices;
- More positive self-concept relates to higher quality-of-life at pre- and post-test;
- More positive self-concept and higher quality-of-life at pre- and post-test relate to more positive daily health practices.
Based on the dynamic nature of physical leisure activity on health outcomes, CAMI technology (7) with the use of cellular phones was utilized for the present study. This allowed for the prospective collection of daily individualized data on the level and type of leisure activity, enabling a differentiation between physically active versus sedentary leisure activities, as well as changes in health practices and mental health indicators. Numerous studies have reported on the successful use of cell phones in daily self-monitoring, as well as its improved accuracy in self-reported information (vs. retrospective questionnaires) in relation to athlete training results, mood, well-being, and health practices (1, 7, 11, 17, 21, 29).
The participants of this study included 28 undergraduate student volunteers who were attending the University of Hawai’i at Hilo (UHH) on Hawai’i Island in the State of Hawai’i in the U.S. Participants ranged in age from 18 to 47, with a mean age of 23.26 years (SD = 6.3). The sample also represented a multi-ethnic population, reporting diverse ethnicities: Mixed/Other (n = 12, 42.9%), Caucasian (n = 9, 32.1%), Hawaiian/ Part Hawaiian (n = 5, 17.9%), or Asian (n = 2, 7.1%). The majority of participants were unmarried (n = 25, 89.3%), and gender was relatively equally represented (males, n = 13, 46.4%). Of the 28 participants, 100% completed the study.
Materials and Procedure
Upon obtaining informed consent, participants were asked to fill out three questionnaires using the “paper and pencil” method at the beginning of the study (pre-test) and the end (post-test) four months later. These measurements included a Demographics Questionnaire (age, gender, marital status, and ethnicity) and self-reported measures of quality-of-life and self-concept.The 31 item World Health Organization Quality of Life Assessment – Brief Instrument (WHOQOL-BREF; 43) and the 10 item subscale of self-esteem on the measure of Self-Concept, Die Frankfurter Selbstkonzeptskalen (translated from German to English) (FSKN; 9) were used at the onset and end of the self-monitoring data collection for the present study to provide additional information about the relationship between these mental health indicators and daily physical leisure activity and health practices.Participants were also introduced and instructed on CAMI cell phone technology.
In between the pre- and post-test questionnaires, participants were asked to report daily on the following eight items: (1) amount of leisure time spent physically active indicated on an open-ended item, (2) amount of leisure time spent sedentary also indicated on an open-ended item, (3) degree of healthy eating practices rated from very healthy to very unhealthy, (4) degree of feeling hassled rated from very much to not at all, (5) mood level rated from very good to very bad, (6) number of cigarettes smoked, (7) number of alcoholic drinks consumed, and (8) whether sun protection was used, determined by “yes/no” answers. The rated items were on a 6-point scale. Lower scores on eating practices indicate healthier eating. Lower scores on feeling hassled indicate feeling more hassled. Lower scores on mood level indicate a better mood.
Using the CAMI cell phone technology, participants reported their responses to these eight items daily over four months. The CAMI is a patented technology, which uses the infrastructure of the internet. The programmed University Server, set up by the researchers, sent participants a Short Messaging Service (SMS) text-message containing a link, which opens the self-monitoring questionnaire on a daily basis (at 7 p.m.). Participants then responded to the questions on cell phones with the use of a Wireless Application Protocol on Wireless Markup Language-sides. Responses were saved on the University server and transferred into coded data for statistical analyses. Participants were prompted two times to respond if they failed to do so at 7 p.m. The first prompt was sent out one hour after the initial SMS text-message and if necessary, a second prompt was sent out after another hour. After two prompts, the systems were closed (at 10 p.m.) and were not able to receive later messages. During the data collection process, the data were handled anonymously and in a confidential manner. However, participants could be linked by their individualized cell phone numbers, but this document was only accessible to trained research personnel. All participants were given a new cell phone with a new telephone number for daily self-monitoring. They could keep the cell phone as an incentive to participate in the study. The study was IRB approved, and funded by the UHH Seed Money Program.
Descriptive statistics for the demographic characteristics of the sample are reported above in the Participants subsection. Summary statistics for each of the other measures are reported in Table 1. Cronbach alpha internal consistency and test-retest stability reliability estimates were calculated for measures of self-concept, quality-of-life, and health practices. Zero-order correlations and multiple regression analyses were conducted to determine the relationships between daily physical leisure activity and pre- and post-test measures of self-concept (Hypothesis 1a), quality-of-life (Hypothesis 1b), and the means of six daily health practices (healthy eating habits, feeling less hassled, mood, alcohol and cigarette consumption, and use of sun protection) (Hypothesis 1c). Additionally, zero-order correlational and multiple regression analyses were conducted to determine the relationships between daily reports of sedentary leisure activity and pre- and post-test measures of self-concept (Hypothesis 2a) and quality-of-life (Hypothesis 2b), and the means of six daily health practices (Hypothesis 2c). Scores for each quantitative predictor variable were centered in order to determine if interaction effects were present, and results indicated no significant interactions between the predictors.
Independent sample t-tests were performed to determine if there were significant differences in participants based on reported daily level of physical leisure activity. Participants were divided into one of two groups, being Very-Physically-Active (n = 13) or Not-Very-Physically-Active (n = 15). Determination of the groups was based upon participants’ mean score on one item of the SMS, “number of hours spent being physically active,” over the four-month study period. Those who reported greater than average (1.25 hours; SD = 0.15) engagement in daily physical leisure activity were considered Very-Physically-Active (i.e., on average, more than 1.25 hours of daily physical leisure activity), while the remaining participants were considered Not-Very-Physically-Active. Multiple regression analyses were conducted to determine the effect of being Very-Physically-Active on post-test measures of self-concept (Hypothesis 3a) and quality-of-life (Hypothesis 3b), and the means of six daily health practices (Hypothesis 3c).
Paired sample t-tests were performed on the FSKN (9) and WHOQOL-BREF (43) scores to determine significant mean differences between pre- and post-measures of self-concept and quality-of-life, respectively (Hypothesis 4). Finally, correlational and multiple regression analyses were conducted to determine the relationships between pre- and post-test measures of self-concept and quality-of-life, with the means of six daily health practices (Hypothesis 5).
Internal consistency analysis for the pre-test measurement of self-concept resulted in a Cronbach’s alpha of 0.79. Internal consistency analysis for the post-test measurement of self-concept resulted in a Cronbach’s alpha of 0.74. Test-retest reliability analysis revealed a stability estimate of 0.61 over a four-month period.
Internal consistency analysis for the pre-test measurement of quality-of-life resulted in a Cronbach’s alpha of 0.89. Internal consistency analysis for the post-test measurement of quality-of-life resulted in a Cronbach’s alpha of 0.87. Test-retest reliability analysis revealed a stability estimate of 0.65 over a four-month period.
For the initial report of health practices, internal consistency analysis resulted in a Cronbach’s alpha of 0.37. For the final report of health practices, internal consistency analysis resulted in a Cronbach’s alpha of 0.48. The increase in internal consistency measurements may reflect a greater inter-relatedness of health practices following a period of self-monitoring.
Physical Leisure Activity and Positive Daily Health Practices, Self-Concept, and Quality-of-Life
Results from the correlational analyses (see Table 2) indicated a significant positive correlation between the amount of daily physical leisure activity and the post-test measure of self-concept (r = .38; p < .05), partially supporting Hypothesis 1a. Correlational analyses also indicated amount of daily physical leisure activity was not significantly related to the pre-test measure of self-concept or to the pre- and post-test measures of quality-of-life, partially failing to support Hypothesis 1a and failing to support Hypothesis 1b. However, more daily physical leisure activity was significantly correlated to feeling less hassled (r = .48; p < .01), partially supporting Hypothesis 1c. Results from the multiple regression analysis indicated that pre- and post-test measures of self-concept and quality-of-life, and mean daily health practices (healthy eating habits, feeling less hassled, mood, alcohol and cigarette consumption, and use of sun protection) were not significantly associated with more leisure time being physically active, F(11,16) = 1.46, p > .05, R2= .50, failing to support Hypothesis 1a and 1b. However, within this model, the average daily health practice of feeling less hassled was significantly related to daily physical leisure activity (β = .58, p < .05), partially supporting Hypothesis 1c.
Sedentary Leisure Activity and Negative Daily Health Practices, Self-Concept, and Quality-of-Life
Daily leisure time spent being sedentary was significantly correlated to unhealthier eating habits (r = .70; p < .001) and lower use of sun protection (r = .44; p < .05), partially supporting Hypothesis 2c. Sedentary leisure activity was not found to be significantly related to pre- or post-test measures of self-concept or quality-of-life, failing to support Hypotheses 2a and 2b. However, results from the multiple regression indicated that all predictor variables (pre- and post-test measures of self-concept and quality-of-life, and mean daily health practices), accounted for 66.1% of the variance, F(10, 17) = 3.32, p < .05, R2= .66. Within the model, the average daily practice of unhealthy eating (β = .67, p < .01) contributed significantly to the variance in sedentariness, partially supporting Hypotheses 2a, 2b, and 2c.
Level of Daily Physical Leisure Activity and Positive Daily Health Practices, Self-Concept, and Quality-of-Life
Independent sample t-tests demonstrated that participants who were categorized as Very-Physically-Active scored significantly higher on the post-test measure of self-concept, compared to participants who were categorized as Not-Very-Physically-Active, t(26) = 2.27, p < .05, partially supporting Hypothesis 3a. There was no significant difference in quality-of-life at pre- and post-test measures based on the level of daily physical leisure activity, failing to support Hypothesis 3b. In the multiple regression model, with eight predictor variables (post-test measures of self-concept and quality-of-life, and six mean daily health practices), the eight health outcome variables accounted for 40.2% of the variance, F(9, 18) = 1.35, p > .05, R2= .40, but this was not significant. However, within the model, the average daily health practice of feeling less hassled (β = .54, p < .05) was significantly associated with having a higher level of daily physical leisure activity, partially supporting Hypothesis 3c.
Self-Concept and Quality-of-Life and Their Relationships to Six Positive Daily Health Practices
Paired sample t-tests demonstrated a significant positive increase in scores of self-concept from pre- to post- test, t (27) = 2.41, p < .05. A significant positive correlation was also found between the post-test measure of self-concept and the pre- (r = .45; p < .05) and post-test measures of quality-of-life (r = .46; p < .05), partially supporting Hypothesis 4.
Additional results from the correlational analyses further elucidated the relationship between self-concept and quality-of-life, with mean daily health practices. More positive pre-test self-concept was significantly correlated to healthier eating habits (r = -.53, p < .01) and more positive mood (r = -.44; p < .05), but post-test self-concept was not found to be significantly related to the means of six daily health practices, partially supporting Hypothesis 5. Results from the multiple regressions indicated that mean daily health practices accounted for 39.8% of the variance in pre-test self-concept, F(7, 20) = 1.89, p > .05, R2= .40, and 30.4% of the variance in post-test self-concept, F(7, 20) = 1.25, p > .05, R2 = .30, but neither were found to be significant, partially failing to support Hypothesis 5.
Higher pre-test quality-of-life was significantly correlated with healthier eating habits (r = -.41; p < .05) and greater use of sun protection (r = -.39; p < .05), and no significant correlations between post-test quality-of-life and mean daily health practices were found, partially supporting Hypothesis 5. Multiple regression analyses indicated that mean daily health practices accounted for 37.9% of the variance in pre-test quality-of-life, F(7, 20) = 1.74, p > .05, R2= .38, and 38.7% of the variance in post-test quality-of-life, F(7, 20) = 1.81, p > .05, R2= .39, but both were not significant. However, within these models, less use of sunscreen was found to be significantly associated with both, pre-test quality-of-life (β = -.48, p < .05) and post-test quality-of-life (β = -.51, p < .05), partially supporting Hypothesis 5.
The results of this study indicate that more time spent engaging in daily physical leisure activities was significantly related to a more positive self-concept after four months of daily self-monitoring (post-test), but not at pre-test. This suggests that the self-monitoring scores were not accounted for by the initial degree of positive self-concept. Our findings support the importance of monitoring the dynamic nature of physical leisure activity level, health practices, and mental health indicators over time (28). Alternatively, there may have been a “Hawthorne effect;” in other words, simply instructing individuals to follow the CAMI cell phone procedure may have potentially increased participants’ daily level of physical leisure activity, and thereby, improving self-concept at the end of the study (34). As such, future research that utilizes cell phone technology may want to include a control group to further explore the effect of daily self-monitoring on health practices and its changes over time.
By utilizing cell phone technology, results also indicate that, on average over a four-month period, feeling less hassled was significantly associated with more daily physical leisure activity. Additionally, level of physical leisure activity was significantly related to several positive health outcomes. College students who engaged in more daily physical leisure activity (more than 1.25 hours on average) tended to have significantly more positive self-concept and felt less daily hassle than students who, on average, engaged in fewer hours being physically active during their leisure time. Feeling less hassled was also found to be significantly associated with a greater level of engagement in daily physical leisure activity. Furthermore, more leisure time spent being sedentary was found to have a significant relationship with some negative daily health practices. Sedentary leisure activities were significantly related to unhealthier eating habits and less use of sun protection (in Hawai’i, it is easy to be in the sun outdoors while being sedentary). Less positive self-concept, lower quality-of-life, unhealthier eating habits, feeling more hassled, lower mood, greater alcohol and cigarette consumption, and less use of sun protection were found to significantly contribute to the variance in explaining sedentary leisure activity. Lastly, a more positive self-concept was significantly related to a higher degree of quality-of-life, healthier eating habits, and more positive mood. Higher levels of quality-of-life were significantly correlated to greater use of sun protection and healthier eating, and less use of sunscreen was significantly associated with lower quality-of-life.
Some of the limitations of this study include the following considerations that may restrict generalizability. The sample size (N = 28) is small and did not include a control group. The participants were multi-ethnic college students and the sample size precluded inspection of ethno-cultural differences. The sample was young (M = 23.6 years), so the use of cell phone technology and the findings may not apply to children or older adults. The self-concept questionnaire was translated from German to English (FSKN; 9). Although standard back-translation procedures for the FSKN were used in prior studies (6, 28), the content of the items may still have a different meaning for our participants. While the study took place over four months, the subsequent effects of self-monitoring are unknown. Finally, our study failed to account for cigarette and alcohol consumption, presumably due to the sample’s low levels of smoking (0.03 cigarettes smoked daily on average; SD = 0.08) and drinking (0.04 alcoholic drinks consumed daily on average; SD = 0.05).
Findings from this study provide preliminary evidence for the positive and dynamic effects of physical leisure activity on daily health practices and mental health indicators, and demonstrate CAMI cell phone technology as an effective tool for daily self-monitoring. Daily self-monitoring of minority students’ leisure activity indicate that being physically active versus being sedentary has positive effects on self-concept and health practices (feeling less hassled, healthy diet, and use of sun protection), as well as elucidates the average degree of health practices over time. Positive effects of daily self-monitoring on the number of cigarettes smoked or the number of alcoholic drinks consumed were not found.
These results add to the field’s current understanding of the effects of leisure activities, by not only differentiating daily leisure time spent being physically active versus being sedentary, but by also highlighting the importance of the average amount of time spent being physically active each day and its relationship to various health practices and psychological outcomes over time within multi-ethnic college students. In the face of the increasing number of health problems that require self-care (e.g., diet and exercise to treat cardiovascular disease; 14), precise information regarding mental health and health-related practices in daily life are crucial to fostering physical leisure activity, which may lead to more effective prevention and treatment programming.
Furthermore, the use of cell phone technology appears to be an effective and efficient approach to collecting prospective data from individuals, particularly when investigating health practices and mood outcomes that are highly variable over an extended period of time. Additionally, application of cell phone technology can provide researchers, practitioners, and coaches the unique opportunity to reach research participants, clients, and athletes, regardless of their location, at any given time. Another distinctive feature is that time of measurement also can be easily determined for a large sample of participants in research studies, health promotion programs, or within a sports setting to test athletes’ daily training habits and competition readiness.
APPLICATIONS IN SPORT
Based on the study’s findings, health and fitness professionals, coaches, and educators
may better understand the temporal health effects of physical leisure activities in young minorities. With current technological advancements and the growing interest in daily self-monitoring, the use of cell phones is an effective tool to more accurately self-monitor and analyze daily leisure activities, health practices, and psychological health (e.g., mood) to inform health and fitness interventions. It has been shown that physical education in higher education combats physical inactivity. As such, integrating the use of cell phones within health education and sports training for youths can help identify daily barriers to promoting and maintaining fitness by monitoring dynamic health activities that lead to positive physical and mental health outcomes.
This research was supported in part by a grant from the University of Hawai’i at Hilo Seed Money Program.
- Anderson, I., Maitland, J., Sherwood, S., Barkhuus, L., Chalmers, M., Hall, M., Brown, B., & Muller, H. (2007). Shakra: Tracking and sharing daily activity levels with unaugmented mobile phones. Mobile Networks and Applications, 12(2-3), 185-199.
- Barkhoff, H. (2000). Handlungskontrolle und Selbstkonzept(e) von Hochleistungssportlern im Roll- und Eiskunstlauf in Trainings- und Wettkampfsituationen [Action control and self-concept(s) of top level roller and figure skaters in training and competition]. Egelsbach: Hänsel-Hohenhausen.
- Barkhoff, H. (2001). In search of the art in roller- and figure-skating. Sports efficiency and the ability of artistic expression. IRSTA- Newsletter, 2, 3-4.
- Barkhoff, H., & Heiby, E.M. (2004a). Differences in mood among elite athletes in artistic roller, artistic figure and inline speed skating before and after competition. European Journal of Sport Science.
- Barkhoff, H., & Heiby, E.M. (2004b). Differences in self-concept, body-concept, and mood between training champion and competitor type athletes in artistic roller and figure skating. Athletic Insight, 6, 74-92.
- Barkhoff, H. & Heiby, E.M. (2010). The relations between self-efficacy, competition success, delight, self-esteem, and body care among elite artistic roller skaters. Journal of Behavioral Health and Medicine, 1, 43-51.
- Barkhoff, H., Hochwald, H. & Germone, M. (2006). CAMI – Computer Assisted Mobile Interview: A pilot study of three elite athletes in artistic roller and figure skating. Refeld (Revista Brasileira de Educação Física, Esporte, Lazer e Dança) – The Brazilian Journal of Physical Education, Sport, Leisure, and Dance, 1, 4,88-94.
- Corbin, C.B. (2002). Physical education as an agent of change. Quest, 54, 182-195.
- Deusinger, I. M. (1986). Die Frankfurter Selbstkonzeptskalen (FSKN), Handanweisungen. Göttingen: Hogrefe.
- Di Bona, L. (2000). What are the benefits of leisure? An exploration using the Leisure Satisfaction Scale. British Journal of Occupational Therapy, 63, 50-58.
- Dishman RK, Washburn RA, Schoeller DA. Measurement of physical activity. Quest. 2001 Aug;53(3):295–309. doi:10.1080/00336297.2001.10491746.
- Drakou, A., Tzetzis, G., & Mamantzi, K. (2008). Leisure constraints experienced by university students in Greece. The Sport Journal, 11(1), 55-73.
- Fennell, R. (1997). Health behaviors among students attending historically black colleges and universities: Results from the National College Health Risk Behavior Survey. Journal of American College Health, 46(3): 109-117.
- Folsom, A., Arnett, D., Hutchinson, R., Liao, F., Clegg, L., & Cooper, L. (1997). Physical activity and incidence of coronary heart disease in middle-aged men and women. Medicine and Science in Sports and Exercise, 29, 901-909.
- Frankish, C. J., Milligan, C. D., & Reid, C. (1998). A review of relationships between active living and determinants of health. Social Science & Medicine, 47, 287-301.
- Heiby, E. M., Pagano, I. S., Blaine, D. D., Nelson, K., & Heath, R. A. (2003). Modeling unipolar depression as a chaotic process. Psychological Assessment, 15, 426-434.
- Hekler, E.B., Buman, M.P., Poothakandiyil, N., Rivera, D.E., Dzierzewski, J.M., Morgan, A.A., McCrae, C.S., Roberts, B.L., Marsiske, M., & Giacobbi, P.R. (2013). Exploring behavioral markers of long-term physical activity maintenance: A case study of system identification modeling within a behavioral intervention, Health Education & Behavior, 40(IS), 51S-62S.
- Hill, C. M., & Hill, D. W. (1991). Influence of time of day on responses to the profile of mood states. Perceptual and Motor Skills, 72, 434-439.
- Jasti, S., Siega-Riz, A.M., & Bentley, M.E. (2003). Dietary supplement use in the context of health disparities: Cultural, ethnic and demographic determinants of use. Journal of Nutrition, 133(6), 2010s-2013s.
- Karoly, P., Ruehlman, L.S., Okun, M.A., Lutz, R.S., Newton, C., & Fairholme, C. (2005). Perceived self-regulation of exercise goals and interfering goals among regular and irregular exercisers: A life space analysis, Psychology of Sport and Exercise, 6, 427-442.
- Kauer, S. D., Reid, S. C., Crooke, A. H. D., Khor, A., Hearps, S. J. C., Jorm, A. F., et al. (2012). Self-monitoring using mobile phones in the early stages of adolescent depression: Randomized controlled trial. Journal of Medical Internet Research, 14(3), e67.
- Kleiber, D. A., Hutchinson, S. L., & Williams, R. (2002). Leisure as a resource in transcending negative life events: self-protection, self-restorations and personal transformation. Leisure Sciences, 24, 219-235.
- Lustyk, M., Widman, L., Paschane, A., & Olson, K. (2004). Physical activity and quality of life: assessing the influence of activity frequency, intensity, volume, and motives. Behavioral Medicine, 30(3), 124-131.
- Mahoney, J. L., & Stattin, H. (2000). Leisure activities and adolescent antisocial behavior: The role of structure and social context. Journal of Adolescence, 23, 113-127.
- Mensink, G., Deketh, M., Mul, M., Schuit, A., & Hofmeister, H. (1996). Physical activity and its association with cardiovascular risk factors and mortality. Epidemiology, 7, 391-397.
- Mota, J., & Esculas, C. (2002). Leisure-time physical activity behavior: Structured and unstructured choices according to sex, age and level of physical activity. International Journal of Behavioral Medicine, 9, 111-121.
- National Institute of Health. (2004). Strategic plan for NIH obesity research. Retrieved March 28, 2015 from http://obesityresearch.nih.gov.eres.library.manoa.hawaii.edu/About/strategic-plan.htm
- Pagano, I., Barkhoff, H., Heiby, E., & Schlicht, W. (2006). Dynamical modeling of the relations between leisure activities and health indicators. Journal of Leisure Research, 38(1), 61-77.
- Pfaeffli, L. Maddison, R., Jiang, Y., Dalleck, L., & Lof, M. (2013). Measuring Physical Activity in a Cardiac Rehabilitation Population Using a Smartphone Based Questionnaire. J Med Internet Res, 15(3), e61.
- Phillips, W.T., Kiernan, M., & King, A.C. (2001). The effects of physical and psychological health. In A. Baum, T.A. Revenson, & J.E. Singer (Eds.), Handbook of Health Psychology (pp. 627-657). Mahwah, NJ: Erlbaum.
- Salmon, J., Owen, N., Crawford, D. Bauman, A., & Sallis, J.F. (2003). Physical activity and sedentary behavior: A population-based study of barriers, enjoyment, and preference. Health Psychology, 22, 178-188.
- Schroeder, S.A. (2007). We can do better: Improving the health of the American people. New England Journal of Medicine, 357,1221-1228.
- Schlicht, W. (2002). Physical activity and health promotion. In N.J.Smelser & P.Baltes (Eds.), International Encyclopedia of the Social and Behavioral Sciences (pp. 11415-11418). Oxford: Elsevier.
- Steele-Johnson, D., Beauregard, R.S., Hoover, P.B., & Schmidt, A. (2000). Goal orientation and task demand effects on motivation, affect, and performance. Journal of Applied Psychology, 85(5), 724-738.
- Sullivan, S. D. (2008). Physical education and general health courses and minority community college student risk levels for poor health and leisure-time exercise patterns, College Student Journal, 42(1), 132-151.
- Suminski, R.R., Petosa, R., Utter, A.C., & Zhang, J.J. (2002). Physical activity among ethnically diverse college students. Journal of American College Health, 51(2), 75-80.
- Thayer, R. E. (1996). The Origin of Everyday Moods. Managing Energy, Tension, and Stress. New York: Oxford University Press, Inc.
- U.S. Department of Health and Human Services [USDHHS]. (1996). Physical activity and health: A report of the Surgeon General. Atlanta, GA: U.S. department of health and human services, center for chronic disease prevention and health promotion.
- U.S. Department of Health and Human Services [USDHHS]. (2000). Healthy People 2010: Understanding and improving health (2nd ed.). Washington, DC: U.S. Government Printing Office.
- Wenger, N. (1996). Physical inactivity and coronary heart disease in elderly patients. Clinics in Geriatric Medicine, 12, 79-88.
- Winkleby, M.A., Kraemer, H.C., Ahn, D.K., & Varady, A.N. (1998). Ethnic and socioeconomic differences in cardiovascular disease risk factors: Findings for women from the Third National Health and Nutrition Examination Survey: 1988-1994. The Journal of American Medical Association, 280, 356-362.
- Winters, E.R., Petosa, R.L., & Charlton, T.E. (2003). Using social cognitive theory to explain discretionary, “leisure time” physical exercise among high school students, Journal of Adolescent Health, 32, 436-442.
- World Health Organization (1998). The World Health Organization quality of life assessment (WHOQOL): Development and general psychometric properties. Social Science & Medicine, 46(12), 1569-1585.
- Zeijl, E., te Poel, Y., du Pois-Reymond, M., Ravesloot, J., & Meulman, J. J. (2000). The role of parents and peers in the leisure activities of young adolescents. Journal of Leisure Research, 32, 281-302.