SELF Research Centre, University of Western Sydney, Australia

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1 THO008 Adoption and Maintenance of Physical Activity amongst Australian Adolescents: Implications for Schooling and Learning Kathryn Thorpe, Martin Dowson, Andrew J. Martin, Rhonda G. Craven, Garry E. Richards, Herbert W. Marsh, and Melinda R. Williams SELF Research Centre, University of Western Sydney, Australia Whilst regular physical activity is commonly associated with numerous physical health benefits, the literature suggests that physical activity may also have favourable effects on academic performance, classroom behaviour, interpersonal relationships, and psychological well-being. Despite this, sedentary lifestyles, particularly amongst school aged students, are increasing. Simplistic interventions addressing physical activity have had limited success because participation in physical activity is a complex phenomenon with social, environmental and psychological antecedents and consequences. The transtheoretical model (TTM) of change (Prochaska & Velicer, 1997) has, however, been successfully applied to the physical activity domain because it accounts for the psychological complexity of involvement in physical activity. The present study analyses the applicability of the TTM to the physical activity of adolescents in Australian school settings. A total of 7 high school students from the Greater Western Sydney region were surveyed using a newly devised measure of physical activity based on the TTM of change. Findings indicated support for both the reliability of the new instrument, and the applicability of the transtheoretical model for physical activity in an Australian adolescent sample. The research will assist educators to develop suitable physical education programs that establish life-long positive health behaviours amongst Australian school students. For thousands of years, physical activity was an essential part of our daily life. Over time, increases in the use of technology in home life, school, work, and leisure has resulted in a reduced need for daily human movement (Sherwood & Jeffery, 000). As such, large proportions of the population in many countries have sedentary lifestyles (Great Britain Sports Council & Health Education Council, 199; Irving, Adlaf, Allison, Paglia, Dwyer, & Goodman, 00; Sherwood & Jeffery, 000; U.S. Department of Health and Human Services, 1996). Disturbingly, international literature suggests that approximately 0% of children and adolescents are not physically active on a regular basis. Additionally, physical activity may decline with age among adolescents (Irving et al., 00; U.S. Department of Health and Human Services, 1996). This is indeed unfortunate as a physically active lifestyle has many health benefits for all age groups including a lower risk of cardiovascular disease, non-insulin-dependent diabetes mellitus, and obesity (Biddle, Gorely, & Stensel, 00; NIH Concensus Development Panel on Physical Activity and Cardiovascular Health, 1996; Taylor, Cable, Faulkner, Hillsdon, Narici, & Van Der Bij, 00; U.S. Department of Health and Human Services, 1996; U.S. Department of Health and Human Services, 00). Physical activity is also related to lower levels of stress, anxiety, and depression (Biddle et al., 00; Taylor et al, 00; U.S. Department of Health and Human Services, 00). Less well publicised is the positive effect physical activity may have on educational outcomes for school students. For example, improved maths grades (Dwyer, Coonan, Leitch, Hetzel, & Baghurst, 198; Maynard, Coonan, Worsley, Dwyer, & Baghurst, 1987), higher reading grades (Dwyer et al., 198; Maynard et al., 1987), enhanced academic achievement in general (Kidd, 1999; Sallis, McKenzie, Kolody, Lewis, Marshall, & Rosengard, 1999; Shephard, 1996), better attention in the classroom (Shepherd, 1996), increased educational aspirations (Kerr, 1996), better teacher ratings of classroom behaviour (Dwyer et al., 198; Maynard et al., 1987), and greater motivation to learn (Kidd, 1999) have been linked with increased physical activity. In light of these important outcomes for schooling and associated health benefits, it is important to examine the processes that enhance both adoption and maintenance of physical activity levels in young populations (Sherwood & Jeffery, 000; U.S. Department of Health and Human Services, 1996). Research has indicated that physical activity participation is a complex behaviour that appears to be influenced by many factors (Sparling, Owen, Lambert, & Haskell, 000; Speck & Harrell, 00). Several behaviour change theories have been utilised in the promotion of health related physical activity with some promising results. The transtheoretical model (TTM) of change (Prochaska & DiClemente, 198) has been utilised to assist individuals in achieving their intention to modify a particular behaviour including physical activity behaviour.

2 An Overview of the TTM The TTM was developed through an in-depth analysis of many existing theories of psychotherapy, and focuses largely on an individual s intention to change a behaviour (Marshall & Biddle, 001; Prochaska & Velicer, 1997). The model proposes two main events: firstly, when an individual intends to change a particular behaviour, they will move through a progression of stages before that behaviour is modified. Secondly, as the individual progresses through the stages of change, there are three significant constructs that mediate the individual s development as they advance through the stages: the processes of change (Prochaska & DiClemente, 198), self-efficacy (Bandura, 1977) and decisional balance (Janis & Mann, 1968). The Stages of Change The five distinct stages people move through whilst attempting to change their behaviour are precontemplation, contemplation, preparation, action, and maintenance. Precontemplation is characterised by either not engaging in a particular behaviour a person would like to, or engaging in an activity he/she would prefer not to (Cardinal & Kosma, 00; Marttila & Nupponen, 00).. Contemplation is the second stage of change. This stage is characterised by seriously considering behaviour change (Cardinal & Kosma, 00; Marttila & Nupponen, 00). The third stage is preparation, whereby people make plans to change, and try small or irregular changes to their behaviour (Cardinal & Kosma, 00; Marttila & Nupponen, 00). In the fourth stage of change labelled action, behaviour modifications are obvious and individuals are eagerly practising the modified behaviour (Cardinal & Kosma, 00; Marttila & Nupponen, 00). Relative to the next stage (maintenance), this is the stage with the highest risk of relapse, usually occurring in the first six months (Dishman, 199). Maintenance is the fifth stage. People are in this stage when they have maintained the behaviour change for six months or longer (Cardinal & Kosma, 00; Marttila & Nupponen, 00). Initially, researchers suggested that individuals progressed through the stages linearly. However, it has since been found that the stages of change are commonly cyclical in nature, whereby individuals regress from the latter stages to the early stages repeatedly before absolute behaviour change is achieved (Marshall & Biddle, 001). Measurement Many different algorithms can be used to assess the stages of change construct (Reed, Velicer, Prochaska, Rossi & Marcus 1997; Norman, Benisovich, Nigg & Rossi, 1998). Due to the range of dynamic algorithms available, it has been suggested that further investigation of algorithms is needed in order to gain better understanding of their validity and reliability. This would assist researchers to select an algorithm that is suitable to their particular study (Hausenblas, Dannecker, Connaughton & Lovins, 1999). The present investigation aims to address this need by comparing and contrasting two of the available algorithms: one developed by Marcus, Selby, Niaura and Rossi (199), and one developed by Nigg and Courneya (1998). The Marcus algorithm was selected as it appears to be the most commonly used in the literature. The Nigg algorithm was selected as it utilizes a different method of calculating the stages of change to that of the Marcus algorithm and it is the most recently developed staging measure. The Processes of Change The processes of change are covert and overt activities, events and strategies that individuals utilise to move through the stages of change (Cardinal & Kosma, 00; Grimley, Prochaska, Velicer, Blais & DiClemente, 199; Marcus and Simkin, 199). Prochaska, Velicer, DiClemente and Fava (1988) suggested that processes of change were based upon two higher order constructs which they identified based upon Confirmatory Factor Analysis (CFA) and defined as experiential and behavioural in nature. Each of these higher order factors was hypothesised to contain a number of lower order change processes. The experiential higher order construct was hypothesised to be comprised of five change processes: consciousness raising, dramatic relief, environmental reevaluation, self-reevaluation, and social liberation. The behavioural higher order construct was hypothesised to be comprised of five behavioural processes: counter conditioning, helping relationships, reinforcement management, self-liberation, and stimulus control (see Table 1). Narrative reviews have also concluded that a two-factor model is appropriate, and stage-specific trends exist for these higher order constructs (Buxton, Wyse & Mercer, 1996; Reed, 1999; Marcus & Simkin, 199;

3 Prochaska & Marcus, 199) whereby experiential processes are more important during the early stages, and behavioural processes more important at later stages (Reed, 1999; Biddle & Nigg, 000). However, these conclusions are offered with little empirical evidence to support them. Table 1. Processes of Change Definitions Process Definition Experiential Consciousness Raising Dramatic Relief Environmental Reevaluation Self-Reevaluation Social Liberation Behavioural Finding and learning new facts, ideas, and tips that support participating in exercise. Experiencing the negative emotions that go along with the risks of being physically inactive. Realising the negative impact of not exercising or the positive impact of exercising on one s proximal social and physical environment. Realising that exercising is an important part of one s identity as a person. Realising that social norms are changing in the direction of supporting exercise. Counter Conditioning Substituting behaviour and cognitions related to exercise for those related to not exercising. Helping Relationships Seeking and using social support for exercise. Reinforcement Management Finding ways to reward oneself for engaging in exercise and changing the contingencies related to not exercising. Self-Liberation Making a firm commitment to change. Stimulus Control Removing reminders or cues that encourage sedentary behaviours and adding cues or reminders to engage in exercise. Source: Nigg, Norman, Rossi, Sarkin & Benisovich, 000. Related Constructs Other variables that have been postulated to be important during behaviour change are decisional balance (Herrick, Stone, & Mettler, 1997; Marcus, Eaton, Rossi & Harlow, 199) and self-efficacy (Clark, Abrams, Niaura, Eaton, & Rossi, 1991; Marcus et al., 199; Redding & Rossi, 1999). Whilst social support is not hypothesised to be a primary related construct of the TTM, it is often examined in conjunction with this model. Therefore, the present investigation has included social support as a new expansion within the instrumentation. Decisional Balance Decisional balance originates from Janis and Mann s (1977) decision-making theory and relates to a person s perception of benefits (pros) compared to negative aspects (cons) associated with the behaviour change (Marcus & Owen, 199). For example, some people may recognise the pros of participating in physical activity such as higher fitness levels, while others are more likely to consider the negative aspects of physical activity such as having limited time. Self-Efficacy The self-efficacy construct was adapted from Bandura s self-efficacy theory (Bandura, 1977; 198). Marshall and Biddle (001) describe self-efficacy as the confidence that an individual has for performing a behaviour in challenging or tempting situations (p. 9). Research on addictive behaviour (DiClemente, 1981, 1986; DiClemente & Hughes, 1990; DiClemente, Prochaska, & Gibertini, 198; Velicer, DiClemente, Rossi & Prochaska, 1990) has shown self-efficacy to be an important variable in understanding and predicting behaviour change. According to social cognitive theory, self-efficacy is the most important moderator of behaviour change (Bandura, 198). As such, self-efficacy beliefs are closely connected to

4 exercise whereby improved self-efficacy is hypothesised to result in greater levels of physical activity (Sallis, Haskell, Fortman, Vranizan, Taylor, & Soloman, 1986; Sonstroem, Harlow, Gemma, & Osborne, 1991). Social Support Research comparing social support in general with social support specific to physical activity has demonstrated that there is a clear distinction between the two (Sallis, Grossman, Pinski, Patterson & Nader, 1987). For example, Sallis et al. (1987) found that individuals with high test scores for social support were more devoted to changing their behaviour and that success rates were higher. Hence social support has been identified as a strong behaviour change mediator (Sarason & Sarason, 198) whereby it is considered important for people to be able to share interest related to physical activity in addition to receiving advice and positive feedback. The TTM and Physical Activity The TTM has been employed in physical activity behavior research in a diverse range of settings and with various populations. A meta-analysis conducted by Marshall and Biddle (001) synthesised findings from studies that applied the TTM to physical activity. The studies that were examined consisted of 91 independent samples. Across the total sample (N = 68,80) 1% of participants were in precontemplation, 16% in contemplation, % in preparation, 11% in action and 6% in maintenance stages of change. Effect sizes were calculated using Cohen s d values of 0., 0., and 0.8 respectively reflecting small, medium, and large effects. Findings indicated that physical activity intensity increased as the stages progressed. The largest effect was for precontemplation to action (d = 0.8, 9% CI = 0.6 to 1.07), the position at which individuals start to establish set criterion for physical activity. Results also displayed small to moderate increments in physical activity from precontemplation to contemplation (d = 0., 9% CI = 0.1 to 0.), suggestive of physical activity change between inactive stages. Results also suggested that moderators were functioning because three of the four stage transitions displayed heterogeneous effect sizes. Consistent with the TTM, self-efficacy was found to increase with each stage of change. However, the pattern of increase was non-linear which disagrees with theoretical predictions. The smallest increase in selfefficacy was from contemplation to preparation (d = 0.6, 9% CI = 0. to 0.7) and the largest was from action to maintenance (d = 0.7, 9% CI = 0.6 to 0.8). The behavioural pros of behaviour change appeared to increase with each increasing stage of change as all effect sizes of behavioural pros (excluding contemplation to preparation) were positive and significant. This finding is in accordance with the TTM prediction. Precontemplation to contemplation had the largest effect size (d = 0.97, 9% CI = 0.66 to 1.8) which again conforms to model predictions. Effect sizes for behavioural cons were small to moderate, negative and significant. This suggests the perceived disadvantages of physical activity reduce with increasing stages as predicted by the TTM. Precontemplation to contemplation displayed the most distinct decline (d = -0.6, 9% CI = to ) and action to maintenance stages displaying the smallest decline (d = -0., 9% CI = -0. to -0.1). Results indicated that all 10 processes of change are used when exercise behaviour modification is taking place. The largest effect sizes across all the processes of change was from precontemplation to contemplation stages (d range = 0. to 1.18). Limited support is also given to the predictions for higher order constructs with experiential processes generally highest in the action stage and behavioural processes predominantly highest in the maintenance stage. In summarising their findings, Marshall and Biddle (001) acknowledged findings support the application of the TTM to the physical activity domain. Further research was called for to examine the moderators and mediators of stage transition, and to standardize and improve reliability of instruments. There is also an expressed need in the literature for additional studies to examine the TTM in relation physical activity in adolescent populations as much of the research to date has focused on adult and older adult populations in the United States of America (USA). In light of this paucity in the research, and given that physical activity is linked to important educational outcomes for students, it is essential to examine the application of the TTM to adolescent physical activity behaviour. The present investigation breaks new theoretical ground to address these research needs by extending the TTM to the physical activity of Australian adolescents.

5 Age and Gender Differences in Physical Activity Patterns of physical activity have been shown to vary according to age and gender (Pate et al., 199). For example, some evidence suggests men are more likely to participate in regular physical activity than women, and that the total amount of time spent engaging in physical activity declines with age (U. S. Department of Health and Human Services, 1996). In relation to adolescent physical activity behaviour, lower levels of activity are reported for females and older adolescents when compared to males and younger adolescents respectively (Allison & Adlaf, 1997; Allison, Dwyer, & Makin, 1999; Heath, Pratt, Warren, & Kann, 199; Irving et al., 00; Kemper, 199; Sallis, 199). Some evidence suggests that physical activity patterns may be stable over the life stages in that a sedentary lifestyle adopted in childhood or adolescence could last across the lifespan (Allison et al., 1999; Dishman & Dunn, 1988; Kelder, Perry, Klepp, & Lytle, 199; Wu & Jwo, 00). It is therefore important to establish physically active lifestyles early in life for the immediate health and school-related benefits, as well as the long-term benefits (Allison et al., 1999; Dishman & Dunn, 1988). Factors influencing the adoption and maintenance of physical activity may be different for males and females. For example, Sallis, Hovell, and Hofstetter (199) found adoption of vigorous physical activity by sedentary men was predicted by self-efficacy, younger age, and safe neighborhood environment. Education, self-efficacy, and friend and family support for exercise were the predictors of vigorous physical activity for sedentary women. Furthermore, maintenance of vigorous physical activity was predicted by self-efficacy and younger age for active men, and by education for active women. Research examining the gender differences regarding the TTM stages of change and physical activity have revealed more males in the later stages of change (action and maintenance) than females (Leenders, Silver, White, Buckworth, & Sherman, 00; O Hea, Wood, & Brantley, 00). The present study seeks to build on this research regarding age and gender differences in physical activity patterns and the adoption and maintenance of physical activity. Additionally, findings from this study are expected to further enhance current literature within the physical activity domain. Furthermore, as schools offer an almost population-wide setting for promoting physical activity to young people, findings from this study will assist in developing school-based physical activity interventions for Australian high school students. Focus of Current Study Specifically, the present investigation aims to: 1) Evaluate the reliability of a new instrument for adolescents that measures the TTM of change (stages of exercise, self-efficacy, decisional balance and processes of change); ) Elucidate the relations between increase in stage level and increase in variable means of the TTM, together with the intensity of physical activity; ) Make a comparison between the two algorithms of Nigg and Marcus to test whether one algorithm is superior to the other; and ) Test the TTM in relation to physical activity for participating age and gender cohorts. Method Participants Participants consisted of secondary school students from Year 7 to Year 11 drawn from two state high schools in the Greater Western Sydney Region. Both of the schools predominantly encompass working class suburban children. A total of 9.1% of the participants were male and 0.9% female. Participants ages ranged from 11 years to 18 years (m = 1., SD = 1.18). However, the most common age was 1 years, comprising % of all participants. Instrumentation The instrument utilised in this study comprised a total of 78 items measuring a number of scales pertaining to the TTM in relation to physical activity levels. The scales measured by this instrument were stages of change, processes of change, self-efficacy, decisional balance, social support, and amount and frequency of physical activity participation in the past week.

6 Stages of Change At the beginning of the instrument six questions were asked in order to assess each participant s stage of readiness for physical activity participation (e.g., Do you currently do regular physical activity or exercise? ). Three items were adopted from research studies that have previously employed the Marcus algorithm (Marcus et al., 199) and three items were adopted from research studies that have utilised the Nigg stage of change algorithm (Nigg et al., 1998). For each of these questions the respondent was required to answer yes or no. Items were scored in accordance with which questions were answered yes and which questions were answered no in relation to the Marcus or Nigg algorithm. Hence two variables were developed to indicate stage of change, one reflecting the Marcus algorithm and one reflecting the Nigg algorithm. Processes of Change The instrument consisted of 0 items with three items for each scale of the processes of change. Participants responded to this instrument by firstly reading a declarative sentence (e.g. for consciousness raising, I read articles to learn more about physical activity or exercise ), followed by responding on a - point Likert scale. The response options were: 1 - never, - seldom, - occasionally, - often, - repeatedly. Self-Efficacy A 6-item short form self-efficacy instrument developed by Rossi, Benisovich, Norman, & Nigg, (000) was selected for inclusion in the present investigation. This instrument assesses which situations particular individuals find challenging when they are progressing from a sedentary lifestyle into a more active one (Rossi et al., 000). Rossi et al. (000) define this self-efficacy instrument as measuring a single global construct (p. ). Participants were required to read a short declarative sentence (e.g., I am confident I can participate in physical activity when, it is raining or bad weather ) then rate how confident they would be participating in physical activity under that particular circumstance on a point Likert response scale: 1 - not at all confident, - somewhat confident, - moderately confident, - very confident, and - completely confident. Decisional Balance The decisional balance instrument utilised in the present investigation was adopted from the decisional balance sheet developed by Janis and Mann (1977). This instrument assesses the diligence and precision that an individual demonstrates in determining the available benefits along with the associated consequences of partaking in physical activity (Janis & Mann, 1977). As such, assessing decisional balance measures the extent to which an individual s behaviour changes, dependent upon their perceptions of proportional benefits (pros) and losses (cons) of participating in physical activity (Janis & Mann, 1977). A 10-item scale comprising items to reflect pros and items to reflect cons was administered to participants. An example of a pros item is, I would feel less stressed if I was physically active or exercised regularly, and an example of a cons item is, I would feel embarrassed if people saw me be physically active or exercising. Participants were required to read each item and note two responses to each item by utilising two -point Likert response scales. The first response scale was 1 - false, - mostly false, - sometimes false sometimes true, - mostly true, - true. Participants were asked to utilise this scale to rate the extent that each item was false or true about them. The second response scale was: 1 - not at all important, - somewhat important, - moderately important, - very important, - extremely important. Participants were asked to utilise this scale to rate the extent that they felt this statement was important to them in relation to participating in physical activity. Social Support Sallis et al., (1987) developed a 1-item instrument devised to assess the amount of physical activity partaken by an individual in relation to the level of social support they receive from both family and friends. Each of the 1 items relates to two constructs within the scale: (1) household support, and () support from friends. Participants were asked to read each item (e.g. Did physical activity with me ) and respond to two

7 -point Likert response scales. The first response scale was designed to measure how often someone living in the participants household had said or done what is described in the statement within the last three months. The second response scale was designed to measure how often any of the participants friends, acquaintances or classmates had said or done what is described in the statement within the past three months. Options for both of the -point response scales are: 1 - none or never, - rarely, - a few times, - often, - very often. Active Australia A series of questions were asked in order to assess the amount and frequency of physical activity participation in the past week. These questions were adopted from the Active Australia Survey (Australian Institute of Health and Welfare, 00). The first four questions related to any type of physical activity undertaken in the past week (e.g. What do you estimate was the total time that you spent doing vigorous gardening or heavy work around the yard in the last week? ). The second set of four questions focused on non-household physical activity (e.g. In the last week, how many times did you do any vigorous physical activity which made you breathe harder or puff or pant? (e.g. jogging, cycling, aerobics, competitive tennis). Respondents were asked to note the number of times or the amount of minutes and or hours they spent in relation to each item. Procedures Consent was obtained from the University of Western Sydney Ethics Review Committee and the New South Wales (NSW) Department of Education and Training. After this initial approval, permission was acquired from the principals of each participating high school. Teachers administered the questionnaires during Personal Development Health and Physical Education (PD/H/PE) classes over a period of 0 to 0 minutes. Teachers began the session by explaining the purpose of the study to students, alerting students to the confidentiality of their responses, advising students that participation was voluntary, and seeking students informed consent to participate. Reliability Data Analysis The reliability of the TTM instrument used in the present investigation was tested using Cronbach s Alpha, which ranges from 0 to 1 (Griffiths, Stirling & Weldon, 1998; Hinkle, Wiersma & Jurs, 1998). Reliability results that are close to 1 specify greater reliability of the instrument being tested. Results that are 0. and greater specify that the instrument being tested provides a sufficient level of reliability (George & Mallery, 199). Reliabilities for each subscale are presented in Table. Analysis of Variance (ANOVAs) ANOVAs were conducted in order to determine physical activity participation levels across the stages of change. The dependent variable was the amount of time spent exercising per week whilst the independent variable was the stages students belonged to as calculated by either the Marcus of Nigg staging algorithm. Pairwise comparisons between groups were conducted using the Student Neuman Keuls (SNK) test for post hoc significances. Multivariate Analysis of Variance (MANOVAs) A series of MANOVAs were conducted to examine cross stage differences in the means of the TTM variables (processes of change, self-efficacy, decisional balance and social support) with the TTM variables as the dependent variables and the stages to which students belonged as the independent variable. The age and gender differences in self-efficacy and processes of change were also analysed using MANOVAs with age and gender as the independent variables and self-efficacy and the mean of the processes of change as the dependent variables.

8 Table. Descriptive Statistics and Reliabilities for TTM Variables Across Stages of Change Stage 1 Stage Stage Stage Stage Scale Reliability Algorithm M SD M SD M SD M SD M SD Self-efficacy.68 M N Pros.7 M Pros (Importance) N M N Cons.8 M N Cons (Importance) Consciousness Raising. M N M N Dramatic Relief.6 M N Environmental Re-evaluation.8 M Self Re-evaluation Social Liberation Counter Conditioning Helping Relationships Reinforcement Management N M N M N M N M N M N Self-Liberation.7 M Stimulus Control Social Support (Family) Social Support (Friends) N M N M N M N Note. M = Marcus algorithm, N = Nigg algorithm.

9 Cross Stage Differences in Means of TTM Variables Results Stages of Change Assessment The first MANOVA indicated that there were significant overall cross stage differences in the means of the TTM facilitating variables for the Marcus stages [F (8, 100) =., p <.001, = 0.7]. The associated univariate F-tests indicated that self-efficacy, four of the pros items, four of the cons items, social support (family) and social support (friends) showed significant cross stage differences. Significant cross stage differences in the means of the TTM variables for the Nigg stages were indicated in the second MANOVA [F (8, 1000) =.86, p <.001, = 0.88]. The univariate F tests indicated significant cross stage differences for self-efficacy, four of the pros items, social support (family) and social support (friends). Table displays the stage-by-stage significant differences for each of the facilitating variables. The third MANOVA revealed significant cross stage differences in the means of the TTM processes of change for the Marcus stages [F (0, 10) =.1, p <.001, = 0.9]. The univariate F tests indicated that all the processes of change measures (except social liberation) showed significant cross stage differences. Results from the fourth MANOVA indicated that there were significant cross stage differences in the means of the TTM processes of change for the Nigg stages [F (0, 17) =.0, p <.001, = 0.86]. The univariate F tests indicated that that all the processes of change measures (except social liberation) showed significant cross stage differences, which was consistent with results based upon the Marcus algorithm. Table displays the stage-by-stage significant differences for each of the processes of change. Physical Activity Participation To determine physical activity participation across the Marcus stages, an ANOVA was conducted. Results indicated there was a significant difference in reported participation and exercise (F = 6.86, p <.001, = 0.11). Pairwise comparisons indicated that participants in Marcus stages and reported significantly greater participation in exercise than participants in Marcus stage (Stage v Stage, p <.01, Stage v Stage, p <.001). Also, students in stage reported significantly higher participation in exercise than students in Stage (Stage v Stage, p <.0). Overall, students in the higher stages reported more participation in physical activity than students in the lower stages. A second ANOVA was conducted to determine physical activity participation across the Nigg stages. A significant difference in reported participation and exercise was also found for the Nigg stages (F =.8, p <.001, = 0.11). Pairwise comparisons indicated that participants in Nigg stage reported significantly greater participation in exercise than participants in stage (Stage v Stage, p <.0) and stage (Stage v Stage, p <.001). In general, students in the higher stages reported more time spent in physical activity than students in the lower stages. Self-Efficacy and Processes of Change Age and Gender Relations A MANOVA was performed to investigate age and gender differences in self-efficacy and processes of change. Results indicated the main effects for gender [F (, 0) =.998, p =.1, = 0.1], age [F (6, 680) =.96, p >.0, = 0.], and the interaction effect for gender by age [F (6, 680) =.979, p =.09, = 0.10] were not significant. The associated univariate F tests indicated that there was only one significant age difference in participants responses. This related to the processes of change in relation to age (F = 7.1, p <.001, = 0.9). Pairwise comparisons indicated that participants in the age groups 1 and under, 1, and 1 and over displayed significant differences among the processes of change (age group under 1 vs. age group 1, p <.001, age group under 1 vs. age group 1 and over, p <.0). There was also a significant difference between the 1 years age group and 1 years age group for the processes of change (age group 1 vs. age group 1, p <.001). For self-efficacy there was a significant difference between the age group under 1 and the 1 year-old age group (age group under 1 vs. age group 1 years, p <.0).

10 Table. Stage-By-Stage Decomposition of the Significant Cross-Stage Differences for the Facilitating Variables Self-efficacy 1 M MN MN MN MN Pros 1 MN MN MN MN Cons 1 M M N Pros (Importance) 1 MN M M Cons (Importance) 1 M MN Social-Support (Household) 1 N N N M M M N Social-Support (Family) 1 N M M N M = Marcus algorithm, N = Nigg algorithm.

11 Table. Stage-by-Stage Decomposition of the Significant Cross-Stage Differences for the Processes of Change. Consciousness Raising 1 M MN MN M Dramatic Relief 1 MN MN MN N N Environmental Re-evaluation 1 M MN MN MN Self-Re-evaluation 1 M MN MN MN N N MN Counter Conditioning 1 MN MN MN MN MN N N Helping Relationships 1 M M MN M Reinforcement Management 1 M MN MN MN N N MN Self-Liberation 1 M MN MN MN N N MN M N Stimulus Control 1 M MN MN Social Liberation 1 M N N MN M = Marcus algorithm, N = Nigg algorithm.

12 Patterns of Physical Activity In order to determine gender differences in physical activity participation across the stages, two ANOVAs were conducted. In the first ANOVA, the overall F tests for Marcus stages (F = 6.7, p <.001) was significant, whilst the F tests for gender (F =.01, p =.90) and for Marcus stages by gender (F =.670, p =.61) were not significant. The associated univariate F test indicated that there were no significant differences between the amount of time spent exercising for males and females across the Marcus stages (F =.01, p =.90). In the second ANOVA, the overall F test for Nigg stages (F =.01, p <.001) was significant, whilst F tests for gender (F =.09, p =.809) and for Nigg stages by gender (F =.19, p =.968) were not significant. The associated univariate F test indicated there were no significant differences between the amount of time spent exercising for males and females across the Nigg stages (F =.09, p =.809). Discussion Stages of Change Assessment As the stages of change increased, means of the TTM variables were found to also increase. Improved self-efficacy was associated with greater levels of physical activity, decisional balance pros increased and cons decreased across the stages, and increased social support was found with an increase in stage of change. These findings are consistent with previous research. Of particular interest is that the means for social support (family) were higher than the means for social support (friends) across each of the stages. Perhaps even during adolescence social support from ones family is more highly regarded and important than social support from ones friends and peers. The findings in regard to processes of change do not correspond with previous findings. Marshall and Biddle (001) reported experiential processes to be highest during the action stage and behavioural processes to peak during maintenance. However, the present investigation found the experiential processes to peak during maintenance and the behavioural processes to plateau during the final stages. Marshall and Biddle (001) also reported behavioural processes sharply increased between stage 1 and and further between stages and. The present investigation found the behavioural processes increase linearly until preparation stage and then plateau off to maintenance. Given the paucity of research available that is process specific, this area warrants additional research. It was found that general physical activity intensified as stage levels escalated. These findings are consistent with the findings from Marshall and Biddle s (001) meta-analysis and lend support for the usefulness of applying the TTM to levels of physical activity. Age and Gender Relations In exploring gender differences in self-efficacy and the processes of change across the stages, findings from the present investigation demonstrated that the differences reported were not statistically significant. These results agree with those of O Hea et al. (00), who also found that differences in gender responses to self-efficacy were not statistically significant. These results suggest that there is little difference between male and female adolescents in relation to self-efficacy and the processes of change across the stages. However, given the paucity of research examining these issues in relation to adolescents and that the current investigation is based on a small sample size, future research is needed to elucidate these findings. The present investigation found there were no significant differences in physical activity participation across the stages for male or female adolescents. This is in contrast to previous research with adult populations which found males were more often in the later stages of change (action and maintenance) than females (Leenders et al., 00; O Hea et al., 00). Perhaps this reflects that adolescents have similar amounts of available time for, and interest in, engaging in physical activity. There is a need for further studies to clarify these results.. Implications for Education and Research Given the health-related benefits and potential educational yields that increases in physical activity may bring, analysis of the TTM in relation to the physical activity of Australian adolescents is both timely and significant. The findings of this study and previous research support the application of the TTM to school and community-based interventions designed to increase physical activity participation in adolescents.

13 Thus strategies for increasing self-efficacy, perceived benefits, and social support from family and friends, and decreasing perceived negative aspects should be incorporated into such interventions. For example, promoting successful experiences by moving from easier to more complex physical activity tasks may assist adolescents to increase self-efficacy for physical activity. Furthermore, disseminating information regarding physical activity resources and opportunities within schools to families and the general community may increase adolescents support networks for physical activity. This study has highlighted the need for further research investigating age and gender differences and the TTM in relation to physical activity participation for adolescents. Firstly, additional studies are warranted to test whether the findings emanating from the present investigation hold across other samples. In addition, longitudinal studies would be useful in order to examine the critical life-stage transition to adolescence, developmental trends across adolescent age spans, and the causal impact of stages of change on physical activity levels over time. Experimental studies with stage-matched interventions addressing the TTM variables and processes of change are also required, as are qualitative studies examining the subjective experiences of participants from each of the five stages of change. Conclusion Daily physical activity patterns have changed dramatically over the years due to increases in technology and sedentary lifestyles appear to be increasing even amongst children and adolescents. The health benefits of an active lifestyle are well-recognised and some research suggests positive educational outcomes may be linked with increased physical activity. This study offers support for the application of the TTM to the physical activity of adolescents which may assist educators and health care professionals provide interventions that aim to increase activity levels of Australian adolescents. As such, the present investigation makes an important contribution to addressing a significant health issue of our time. About the Authors Kathryn Thorpe is a PhD candidate at the SELF Research Centre, University of Western Sydney. Her current research focus is on examining the psychological determinants of physical activity. Dr. Martin Dowson is an active researcher within the SELF Research Centre. His primary research interests are in student motivation and cognition, and the psychology of religion and the religious experience. Dr. Andrew Martin is Postdoctoral Research Fellow at the SELF Research Centre. His primary research interests are in student motivation and self-concept and their application to teaching and learning processes. Associate Professor Rhonda Craven is Deputy Director of the SELF Research Centre ranked 7 th in the world in educational psychology, is Associate-Professor in the School of Education and Early Childhood Studies, University of Western Sydney. As an Educational Psychologist her research focuses on large-scale quantitative research studies in educational settings. Garry Richards is the Research Programs Coordinator of the SELF Research Centre. His research interests are in the areas of self-concept (particularly physical self-concept), human development, outdoor and experiential education and sports. Professor Hebert Marsh is Director of the SELF Research Centre. His primary research interests are in selfconcept, psychometrics, and student evaluation. Melinda R. Williams is an Honours student at the SELF Research Centre. Her research into physical activity participation in schools formed the basis of this investigation.

14 Contact details Kathryn Thorpe, PhD Candidate SELF Research Centre University of Western Sydney Locked Bag 1797 Penrith South DC NSW 1797 Australia. Phone: Fax: References Allison, K. R., & Adlaf, E. (1997). Age and gender differences in physical activity among Ontario teenagers. Canadian Journal of Public Health, 88, Allison, K. R., Dwyer, J. J. M., & Makin, S. (1999). Self-efficacy and participation in vigorous physical activity by high school students. Health Education & Behavior, 6(1), 1-. Australian Institute of Health and Welfare. (00). The Active Australia Survey: A guide and manual for implementation and reporting. Canberra: Author. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behaviour change. Psychological Review, 8, Bandura, A. (198), Self-efficacy mechanism in human agency. American Psychologist, 7, Biddle, S. J. H., Gorely, T., & Stensel, D. J. (00). Health-enhancing physical activity and sedentary behaviour in children and adolescents. Journal of Sports Science, (8), Biddle, S, J., H., & Nigg, C. R., (000). Theories of exercise behaviour. International Journal of Sport Psychology, 1, Buxton, K. E., Wyse, J., & Mercer, T. (1996). How applicable is the stages of change model to exercise behavior? A review. Health Education Journal,, 9-7. Cardinal, B. J., & Kosma, M. (00). Self-efficacy and the stages and processes of change associated with adopting and maintaining muscular fitness-promoting behaviours. Research Quarterly for Exercise and Sport, 7(), Clark, M. M., Abrams, D. B., Niaura, R. S., Eaton, C. A., & Rossi, J. S. (1991). Self-efficacy in weight management. Journal of Consulting and Clinical Psychology, 9, DiClemente, C. C. (1981). Self-efficacy and smoking cessation maintenance: A preliminary report. Cognitive Therapy and Research,, DiClemente, C. C. (1986). Self-efficacy and addictive behaviours. Journal of Social and Clinical Psychology,, 0-1. DiClemente, C. C., & Hughes, S. (1990). Stages of change profiles in alcoholism treatment. Journal of Substance Use,, 19-. DiClemente, C., Prochaska, J., & Gibertini, M. (198). Self-efficacy and the stages of self-change of smoking. Cognitive Therapy and Research, 9,

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16 Leenders, N. Y. J. M., Silver, L. W., White, S. L. Buckworth, J., & Sherman, W. M. (00). Assessment of physical activity, exercise self-efficacy, and stages of change in college students using a street-based survey method. American Journal of Health Education, (), Marcus, B. H., Eaton, C. A., Rossi, J.S., & Harlow, L. L., (199). Self-efficacy, decision-making, and stages of change: An integrative model of physical exercise. Journal of Applied Social Psychology,, Marcus, B. H. & Owen, N. (199). Motivational readiness, self-efficacy and decision- making for exercise. Journal of Applied Social Psychology,, -16. Marcus, B.H., Selby, V. C., Niaura, R.S., & Rossi, J.S., (199). Self-efficacy and the stages of exercise behaviour change. Research Quarterly for Exercise and Sport, 6, Marcus, B. H., & Simkin, L. R., (199). The transtheoretical model: Applications to exercise behavior. Medicine and Science in Sports and Exercise, 6, Marshall, S. J., & Biddle, S. J. (001). The transtheoretical model of behavior change: A meta-analysis of application to physical activity and exercise. Annals of Behavioral medicine,, 9-6. Marttila, J., & Nupponen, R. (00). Assessing stage of change for physical activity: How congruent are parallel methods? Health Education Research, 18(), Maynard, E. J., Coonan, W. E., Worsley, A., Dwyer, T., & Baghurst, P. A. (1987). The development of the lifestyle education program in Australia. In B. S. Hetzel & G. S. Berenson (Eds.), Cardiovascular risk factors in children: Epidemiology and prevention (pp. 1-19). Amsterdam: Elsevier. Nigg, C. R., & Courneya, K. S. (1998). Transtheoretical model: Examining adolescent exercise behavior. Journal of Adolescent Health,, 1-. Nigg, C., Norman, G., Rossi, J., Sarkin, J., & Benisovich, S. (000). The processes of exercise behavior change. Cancer Prevention Research Center, University of Rhode Island. NIH Consensus Development Panel on Physical Activity and Cardiovascular Health. (1996). Physical activity and cardiovascular health. Journal of the American Medical Association, 76, 1-6. Norman, G. J., Benisovich, S. V., Nigg, C. R., & Rossi, J. S. (1998). Examining three exercise staging algorithms in two samples. Annals of Behavioral Medicine, 0, 11. O Hea, E. L., Wood, K. B., & Brantley, P. J. (00). The transtheoretical model: Gender differences across three health behaviors. American Journal of Health Behavior, 7(6), Pate, R. R., Pratt, M., Blair, S. N., Haskell, W. L., Macera, C. A., Bouchard, C., et al. (199). Physical activity and public health: A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. Journal of the American Medical Association, 7(), Prochaska, J. O, & DiClemente, C. C. (198). Common processes of self change in smoking, weight control and psychological distress. In S. Shiffman & T. A. Willis (Eds.), Coping and Substance Abuse (pp. -6). New York: Academic Press. Prochaska, J. O., & Marcus, B. H. (199). The transtheoretical model: Application to exercise. In R. K. Dishman (Ed.), Advances in exercise adherence (pp ). Champaign, IL: Human Kinetics. Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of behaviour change. American journal of Health Promotion, 1, 8-8.

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