The effects of persuasive communication and planning on intentions to be more physically active and on physical activity behaviour among low-active adolescents.

This article was downloaded by: [New York University] On: 03 May 2015, At: 06:40 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Psychology & Health Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/gpsh20

The effects of persuasive communication and planning on intentions to be more physically active and on physical activity behaviour among low-active adolescents a

a

Damien Tessier , Philippe Sarrazin , Virginie Nicaise

ab

& Jean-

c

Philippe Dupont a

Univ. Grenoble Alpes, SENS, Grenoble, France

b

Click for updates

Laboratoire Centre de Recherche et d’Innovation sur le Sport, Université Claude Bernard, Lyon, France c

Physical Education Department, Parnasse-ISEI, Haute Ecole Leonard de Vinci, Brussels, Belgium Accepted author version posted online: 10 Dec 2014.Published online: 03 Feb 2015.

To cite this article: Damien Tessier, Philippe Sarrazin, Virginie Nicaise & Jean-Philippe Dupont (2015) The effects of persuasive communication and planning on intentions to be more physically active and on physical activity behaviour among low-active adolescents, Psychology & Health, 30:5, 583-604, DOI: 10.1080/08870446.2014.996564 To link to this article: http://dx.doi.org/10.1080/08870446.2014.996564

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Downloaded by [New York University] at 06:40 03 May 2015

This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/termsand-conditions

Psychology & Health, 2015 Vol. 30, No. 5, 583–604, http://dx.doi.org/10.1080/08870446.2014.996564

The effects of persuasive communication and planning on intentions to be more physically active and on physical activity behaviour among low-active adolescents Damien Tessiera*, Philippe Sarrazina, Virginie Nicaisea,b and Jean-Philippe Dupontc Univ. Grenoble Alpes, SENS, Grenoble, France; bLaboratoire Centre de Recherche et d’Innovation sur le Sport, Université Claude Bernard, Lyon, France; cPhysical Education Department, Parnasse-ISEI, Haute Ecole Leonard de Vinci, Brussels, Belgium


a

(Received 13 March 2014; accepted 5 December 2014) The purpose of the present study was to examine, using the theory of planned behaviour (TPB) combined with a self-regulatory behaviour change approach, whether persuasive communication based on adolescents’ salient beliefs (SBCondition) and planning (PCondition) could promote the intention and physical activity (PA) behaviour of low-active adolescents participating in less than 1 h/day of moderate-to-vigorous physical activity. The protocol tested the effectiveness of two strategies used separately (i.e. SBC or PC) or in combination (i.e. CC = NSBC-SBC-PC) compared to a group receiving a message based on non-salient beliefs (NSBCondition). The 116 low-active students from ten 10th and 11th grade classes were assigned, using a cluster randomisation, into one of the four conditions (i.e. NSBC, SBC, PC and CC). Baseline data were collected two weeks before the intervention. The post-test data collection occurred directly after the intervention, and the follow-up took place two weeks later. Results showed that (1) the NSBC was the least effective strategy, (2) the SBC had no significant effect on PA behaviour and the TPB variables, (3) the PC had no significant effect on PA behaviour but increased the intention and perceived behavioural control and (4) the effects of the PC and the CC were not significantly different. Keywords: persuasive communication; planning; low-active adolescent; physical activity; health

Although adolescence is believed to be the age period for peak health, the transition between adolescence and young adulthood is a critical period in terms of sedentary behaviours (Sallis, 2000). Large proportions of young people (i.e. 95.4% of girls and 83.3% of boys) across different European countries do not meet moderate-to-vigorous physical activity (MVPA) recommendations of at least 60 min/day, and spend a lot of time sedentary (Verloigne et al., 2012). On average, European 10–12 year-olds spend 487 min/day sedentary, and only 37 min/day in MVPA (Verloigne et al., 2012). Hence, there is a strong need for effective interventions promoting physical activity (PA) that *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

584

D. Tessier et al.

prevent early development of a sedentary lifestyle and overweight in adolescence (Marcus et al., 2000). Nevertheless, numerous previous trials have failed to use evidence-based theory to identify key determinants of behaviour change as targets for interventions (Demetriou & Höner, 2012; Marcus et al., 2000). Therefore, the present study is a theory-based intervention, which aims to increase the intention to be more physically active and increases PA behaviour among low-active adolescents.


Extending the theory of planned behaviour (TPB) Many health psychology theories – such as the TPB – consider intention as a key variable in the behavioural change process. The TPB (Ajzen, 1991) assumes that a person’s intention to perform a given behaviour, such as PA, is a central determinant of that behaviour. Intention is an indicator of how hard people are willing to try, and of how much effort they are planning to exert towards performance of a behaviour (Ajzen, 1991). Intention is determined by three conceptually distinct variables: attitude towards behaviour, subjective norm and perceived behavioural control (Ajzen, 1991). Attitude reflects a summary evaluation of a given behaviour, captured in evaluative dimensions such as good–bad, harmful–beneficial and pleasant–unpleasant. Subjective norm reflects the perceived social pressure that individuals may feel to perform or not a given behaviour. Perceived behavioural control describes the perceived ease or difficulty associated with execution of future behaviour. Whether the vast amount of research using correlational design has provided strong evidence for the overall predictive validity of the TPB on PA with adults and adolescents (for a review, see Hagger, Chatzisarantis, & Biddle, 2002; Symons Downs & Hausenblas, 2005), experimental tests of the TPB have been rarer, and those that have been conducted revealed equivocal findings (Hardeman et al., 2002). The results of these experimental studies tend to demonstrate a gap between intention and action, meaning that good intentions do not necessarily guarantee corresponding actions. These results lead some researchers to criticise the validity of the TPB, in particular, the assumption that a strong intention will result in engagement in the behaviour (Sniehotta, Presseau, & Araujo-Soares, 2014). In the present study, to overcome this limitation, the TPB framework has been reinforced with a self-regulatory behaviour change strategy (i.e. planning), in order to improve its predictive validity. Persuasive communication strategies and attitude towards PA behaviour Among the wide variety of techniques that can be successfully implemented to change accessible beliefs, persuasive communication is one of the more common techniques (Hardeman et al., 2002). According to Ajzen (2006), for persuasive communication to effectively change attitudes and thus increase intention, belief-targeted messages that target antecedents of attitudes have to be provided. Using an expectancy × value model, the TPB proposes that attitudes arise from a combination of beliefs that behaviour will lead to certain consequences (i.e. behavioural beliefs) and the evaluation of the value of these consequences (Ajzen, 1991). Development of belief-targeted messages involves the selection of statements that ultimately affect the beliefs that serve as the foundation for attitudes held by the group targeted to receive the communication (Ajzen, 1991).


Psychology & Health

585

However, further studies (e.g. Gagne & Godin, 2000) demonstrated the limited additional utility of the value component. Consequently, the persuasive communication delivered to change adolescents’ attitudes in the present study only targeted their behavioural beliefs. Theoretical considerations support three possible bipolar dimensions along which behavioural beliefs might be grouped: positive (e.g. PA makes me feel good) vs. negative (e.g. PA makes me feel bad), affective (e.g. PA is fun) vs. instrumental (e.g. PA prevents disease) and proximal (e.g. PA permits stress management) vs. distal (e.g. PA provides weight control) dimensions (Rhodes & Conner, 2010). In a study aiming to compare behavioural belief structures, Rhodes and Conner (2010) showed that positive affective beliefs (e.g. fun, accomplishment) were the best predictors of intention to be physically active, and that the proximal–distal division of beliefs is not necessary after consideration of affective–instrumental and positive–negative dimensions. In addition, recent systematic reviews revealed that gain-framed messages (i.e. positive beliefs) were significantly more likely than loss-framed messages (i.e. negative beliefs) to encourage intention to be physically more active (e.g. Latimer, Brawley, & Basset, 2010). These results are in accordance with the few studies that investigated the youth-salient behavioural beliefs towards PA (e.g. Berkowitz et al., 2008; Hagger, Chatzisarantis, & Biddle, 2001). These studies showed that the best salient reasons for adolescents to practice PA are ‘it helps you get fit/stay in shape’, ‘it is good fun’, ‘it helps you improve your skills’, ‘to have fun with friends’, ‘to explore something new’, ‘to fulfil a fantasy or dream’, ‘to have a sense of escape or adventure’ and ‘that no one will judge how well they perform’. By contrast, these studies revealed that health is a non-salient behavioural belief for adolescents. This point is in accordance with studies showing that under 35 years of age, individuals do not interpret PA as health behaviour (Renner, Spivak, Kwon, & Schwarzer, 2007). Commonly, people develop higher intention of performing PA as a health-promoting behaviour when they start experiencing health problems and disease as they age. Thus, to change adolescents’ attitudes and promote PA, it seems more effective to deliver a message targeting PA benefits in terms of fun, affiliation, success, challenge, skills development and fitness rather than health. To the best of our knowledge, only two studies based on the TPB tested the effects of persuasive communication congruent with youth-salient behavioural beliefs on attitudes, intention and PA behaviour (Chatzisarantis & Hagger, 2005; Huhman et al., 2010). These two studies revealed the effectiveness of persuasive communication congruent with the adolescents’ salient behavioural beliefs on attitude. In addition, the Chatzisarantis and Hagger (2005) study showed that participants who were presented with the persuasive message targeting salient behavioural beliefs reported stronger intention. However, neither study showed an influence on PA behaviour. In addition, it should be noted that both studies produced small-size effects on attitude. To address the difficulty of changing psychosocial outcomes, designing multi-component interventions targeting other pre-intention determinants than attitude, could be proposed. As such, it is assumed that the small changes induced by persuasive communication may contribute to the additive effects of the other intervention components. In the present study, the effects of persuasive messages on attitude were examined in combination with those on the individuals’ planning on perceived behavioural control.

586

D. Tessier et al.


Planning and perceived behavioural control towards PA Planning, as a broad concept, incorporates implementation intentions and action planning (Hagger & Luszczynska, 2014). While most researchers use these two terms synonymously, Hagger and Luszczynska (2014) identified divergent attributes. Implementation intentions are ‘if-then’ plans aimed at forging a link between a critical, unconditional situation and a goal-directed behavioural response (Gollwitzer, 1999). The if-then plans specified in implementation intentions tend to target a single cueto-action response (Hagger & Luszczynska, 2014). In contrast, action planning tends to focus on a broader perspective, and it may include multiple cues and complex behavioural responses (Hagger & Luszczynska, 2014). The action planning approach assumes that cues-to-action should make reference to time-related cues (‘when’), the complex external environment (‘where’), and the specification of ‘how’ the behaviour should be done. Besides forming action plans about when, where and how to act, the action planning approach is generally accompanied by additional coping plans, that is, the anticipation of barriers and the generation of alternative behaviours to overcome them (Sniehotta, Schwarzer, Scholz, & Schüz, 2005). Planned self-regulatory strategies are used in the post-intention phase to assist people to enact their motivation in order to reduce the gap between intention and behaviour (Gollwitzer, 1999). This well-elaborated action plan greatly eases the attainment of the goal because the mental representation of the anticipated situation becomes highly activated and thus easily accessible – leading to perceptual, attentional and mnemonic advantages (Gollwitzer, 1999). Action initiation becomes swift and efficient and does not require conscious intent, because the direct control of the behaviour passes into the environment (Gollwitzer, 1999). In a recent meta-analysis, Belanger-Gravel, Godin, and Amireault (2013) showed that implementation intentions yielded small-to-medium significant effect size on PA. Moreover, they revealed that the integration of coping plans leads to greater increase in PA behaviours when compared to only having implementation intentions. Planning is also an effective strategy in the pre-intention phase. Indeed, when people do not form high intention, planning can support their pre-intention belief in themselves and their abilities (Gollwitzer, Wieber, Meyers, & McCrea, 2010). As Gollwitzer et al. (2010) claimed, ‘People may use implementation intentions to favourably modulate the moderators of implementation intention effects’ (p. 157). One explanation is that planning replaces an implemental mindset (i.e. a certain kind of cognitive orientation) with a deliberative mindset (Achtziger & Gollwitzer, 2010). A deliberative mindset is associated with low feeling of control over future events and induces people to process all the available information to balance the pros and the cons of committing to the pursuit of the goal. In contrast, an implemental mindset is associated with a high degree of personal control and higher estimations of the probability of success, inducing people to focus on information that will help to promote the chosen goal. Accordingly, inducing an implemental mindset is assumed to support people in overcoming the doubts encountered in the pre-intention phase, and thus increase perceptions such as self-efficacy and perceived behavioural control. However, research that has investigated the impact of implementation intentions on self-efficacy has produced equivocal findings. In their meta-analysis, Webb and Sheeran (2008) identified only three studies that investigated the impact of implementation

Psychology & Health

587


intentions of PA behaviour on self-efficacy, and two of these studies revealed a medium-to-large effect size. Murray, Rodgers, and Fraser (2005), and Latimer, Ginis, and Arbour (2006) found that participants who formed implementation intentions were more confident that they would be able to fit exercise into their everyday activities than participants who did not make a plan. However, Milne, Orbell, and Sheeran (2002) found that participants who formed implementation intentions did not feel significantly more confident in their ability to undertake at least one 20-min session of vigorous exercise during the following week than participants who did not form a plan. Otherwise, the participants of these three studies were asked to form implementation intentions but not action and coping plans. Therefore, no previous study has tested whether action planning and coping planning, could be efficient in pre-intention phases in increasing perceived behavioural control towards PA behaviour.

Present study The purpose of this study is to examine whether a message targeting salient behavioural beliefs (SB Condition) and individuals’ plans (P Condition) to be more physically active could promote the intention and PA behaviour of low-active adolescents carrying out less than 1 h/day of MVPA. More specifically, the effectiveness of the two intervention strategies used separately (i.e. SBC or PC) or the two strategies combined (CC = SBC followed by PC) are compared to a non-salient behavioural belief condition (NSBC), that is a group receiving a PA health message. Four hypotheses were made. Firstly, that the SBC, the PC and the CC would have a greater effect than the NSBC on PA behaviour, intention, attitude and perceived behavioural control (Hypothesis 1). Secondly, that the SBC would be more effective than the PC in reinforcing attitude, but not more effective than the CC (Hypothesis 2). Thirdly, that the PC would be more effective than the SBC in increasing perceived behavioural control and PA behaviour, but not more effective than the CC (Hypothesis 3). Finally, due to additive effect, it was hypothesised that the CC would be the most effective strategy for increasing intention and PA behaviour (Hypothesis 4). However, given the scarcity of empirical studies, no hypothesis was made concerning the effectiveness of the SBC compared to the PC on intention, and no hypothesis was made concerning subjective norm because neither of the two strategies targeted the antecedents of this variable.

Method Participants and procedure The present study is a proof of principle study. The school administrators of 25 high schools in a middle-size town in S.E. France were contacted by mail. Twelve physical education (PE) teachers and their low-active students (n = 141) from four high schools with similar socio-demographic variables volunteered to participate in this study. The sample size was determined by the availability of the schools and the teachers rather than by formal power analyses. To be considered as a low-active student, the eligibility criterion was to carry out less than 1 h/day of MVPA (WHO, 2010). All the parents were informed and authorised their child to complete the measures.


588

D. Tessier et al.

The design was a cluster-randomised trial (stratified randomisation): clusters (in this case, classes) of individuals rather than the individuals themselves were randomised to participate. Using a random-number table, the study’s main researcher assigned three classes of 10th and/or 11th grade to each of the four conditions of the trial (blocked randomisation): (a) the NSBC, (b) the SBC, (c) the PC and (d) the CC. However, two teachers dropped out before baseline measures. The first dropped out because of personal health concerns. For the second, the PE classes took place at a sports field away from the school where it was not possible to implement the intervention. The participants were blind of the condition in which their class was assigned, of the participation to the study of other classes from the same school, and no reference was made to the participants about the objective of the study (i.e. promoting intention and PA behaviour). Instead, they were told that the researchers were carrying out a survey about adolescents and PA. The study was conducted in three steps by a researcher within the students’ usual PE classes. Baseline data were collected in late-February 2010. The post-test data collection was conducted just after the intervention in mid-March 2010. Finally, the follow-up took place two weeks later, at the end of March 2010. At baseline, the adolescents completed the TPB survey (i.e. attitude, perceived behavioural control, subjective norm and intention) and the PA behaviour questionnaire. At post-test, they completed the TPB survey, and at follow-up, they completed the TPB survey, the PA behaviour questionnaire and the planning questionnaire. At baseline, the self-reported measure of PA behaviour referred to a normal week and allowed the lowactive adolescents – carrying out less than 1 h/day of MVPA (WHO, 2010) in each class to be selected. At follow-up, PA referred to the previous week (i.e. between posttest and follow-up). The flow diagram of progress through the trial is depicted in Figure 1. The baseline sample of 10 classes represented 116 low-active adolescents (M age = 15.07; N Girls = 74). At post-test, 110 students (M age = 15.03; N Girls = 70) completed the second wave of data collection. The retention rate at post-test was 94%. The 110 persisting students did not differ significantly from the six students who dropped out at post-test, on the dependent measures (ts < 1.5). Finally, at follow-up, 105 students (M age = 15.12; N Girls = 67) completed the third wave of data collection. The retention rate at follow-up was 95%. The 105 persisting students did not differ significantly from the five students who dropped out at follow-up on the dependent measures (ts < 1.5).

Measures Translation into French Before the study, each measure was translated into French following the guidelines recommended by Brislin (1980). Each English measure was translated into French using a professional English–French translator. Once done, two native French graduate students who were fluent in both languages then carried out separate back-translations into English. Any discrepancy that emerged between the translators was discussed until a consensus translation was found.

Psychology & Health

589

Participants randomized (n= 141)

Allocated to group 1 (NSBC) (n= 32)

Allocated to group 2 (SBC) (n= 34)

Allocated to group 3 (PC) (n= 40)

Allocated to group 4 (CC) (n= 35)

n= 32 Absent on day of the measure n = 2

n= 30 Absent on day of the measure n = 10 (class dropped out)

n= 34 Absent on day of the measure n = 1

n= 28 Absent on day of trial n= 4



n= 26 Absent the day of the measure n= 2




Analyzed at baseline (n= 116) n= 20 Absent on day of the measure n = 12 (class dropped out)

Received intervention Analyzed at post-test (n= 110)


Analyzed at follow-up (n= 105) n= 15 Absent the day of the measure n= 3

Figure 1. Flow diagram of progress though the trial.

Self-reported PA An adaptation of the International Physical Activity Questionnaire for Adolescents (IPAQ-A, Hagströmer et al., 2008) was used to assess the adolescents’ PA behaviour. This questionnaire covered four domains of PA: (1) school-related PA, including activity during PE classes and breaks, (2) transportation, (3) household and (4) leisure time, including sports in clubs and unstructured sport activity. Practical examples of culturally relevant moderate and vigorous activities were given. To avoid overreporting, the household domain was shortened to include only one question about PA in the garden or at home. Also, the order of intensity of the activities was changed – so that walking was investigated first, followed by moderate and then vigorous activities – because more overreporting has been shown with the original order (vigorous, moderate and walking) than with this revised order (see, Hagströmer et al., 2008). Outcome measures were minutes per day reported in walking, moderate and vigorous activities. Time spent in moderate and vigorous activities were summed to obtain an MVPA score for each participant. The IPAQ-A was found to be valid for assessing activities of different

590

D. Tessier et al.


intensities and for total PA in healthy European adolescents aged 15–17 years (Hagströmer et al., 2008) (for more detailed information, see the questionnaire in supplemental material).

The TPB The items drawn from Rhodes and Courneya (2005) were used to measure intention, subjective norm and perceived behavioural control. The items used to measure intention were: ‘Over the next 2 weeks, I intend to do PA’, which was anchored by (1) ‘never’ to (7) ‘every day’, and ‘I intend to do moderate to vigorous PA for at least 5 h/week, over the next 2 weeks’, which was anchored by (1) ‘definitely no’, to (7) ‘definitely yes’. Subjective norm was assessed with two items: ‘Most people in my social network want me to exercise at least 5 h/week in the next 2 weeks’, and ‘Most people in my social network would approve if I exercised at least 5 h/week in the next 2 weeks’. Both questions were measured with Likert scales ranging from (1) ‘strongly disagree’ to (7) ‘strongly agree’. Perceived behavioural control was measured by three items: ‘How confident are you that over the next 2 weeks that you could exercise at least 5 h/week if you wanted to do so’, which was anchored by (1) ‘very unconfident’, to (7) ‘very confident’; ‘How much personal control do you feel you have over exercising at least 5 h/week in the next 2 weeks’, which was anchored by (1) ‘very little control’, to (7) ‘complete control’; and ‘How much I exercise in the next 2 weeks is completely up to me’, which was anchored by (1) ‘strongly disagree’, to (7) ‘strongly agree’. Attitude was measured using responses to one open-ended statement: ‘For me, exercising 5 h/week over the next 2 weeks would be’. This statement was paired with five bipolar seven-point adjective scales (useless–useful, bad–good, harmful–beneficial, unenjoyable–enjoyable and boring–interesting) as previously utilised by Chatzisarantis, Hagger, and Brickelll (2008). Planning Self-reported planning was assessed at follow-up using action planning and coping planning subscales (Sniehotta et al., 2005). Responses were made on seven-point scales ranging from (1) not at all true to (7) exactly true. Five items measured action planning. The item stem ‘During the last seven days, I have made a detailed plan regarding’ was followed by items such as ‘… when to do my exercise’. Four items measured coping planning. The item stem ‘During the last seven days, I have made a detailed plan regarding …’ was followed by items such as ‘… what to do if something intervenes’. In line with previous studies (e.g. Renner et al., 2007), action and coping planning scores were used jointly to give an overall planning score. Intervention Interventions in the four conditions were made in person by the main researcher. The interventions were delivered in person to the class at the beginning of the usual PE lesson. For each condition, the message was standardised and rehearsed to ensure the intervention be delivered in the same way in the different classes.


Psychology & Health

591

The NSBC consisted of delivering a 10-slide Power-Point message entitled ‘physical activity is good for my health’ (for details, see the slides in supplemental material). The duration of the NSBC intervention was 15 min. After the title slide, two slides were devoted to describing how the amount of adolescents’ PA decreased. The fourth slide focus on the health benefits of doing MVPA regularly. Examples included, ‘By participating in regular physical exercise you will reduce your risk of coronary heart disease and other chronic diseases such as cancer, osteoporosis, obesity, and diabetes’. The next three slides presented guidelines in terms of the intensity and frequency of PA considered good for health: ‘PA is good for your health when you accumulate at least 1 h or more of MVPA on most or preferably every day of the week. PA can be done in one go or in 10–30 min sessions’. The last three slides were dedicated to explaining that, ‘you can reach these recommendations by playing sports in a club (e.g. football, judo, tennis), by doing sport outside of a club (e.g. swimming, jogging), but also in your everyday life (e.g. going to school on foot or by bike)’. The NSBC was the initial and common message; the three other intervention conditions all started with these 10 slides. The SBC (i.e. NSBC plus SBC) consisted of persuasive communication framed to present the positive consequences of doing PA. Therefore, after the presentation of the 10 NSBC slides, eight additional slides were devoted to explaining that participating in PA is related to fun, affiliation, success, challenge, skills development and fitness (for details, see the slides in supplemental material). The delivery of these eight additional slides lasted 10 min. Examples included, in doing physical activity regularly, you will: feel good in yourself; have the satisfaction of improving your performance, motor skills, and reflexes; enjoy interacting with friends, feel in good shape; feel more efficient/able to concentrate in your work and feel liberated.

The PC (i.e. NSBC plus PC) was based on the classic study performed by Gollwitzer and Brandstätter (1997). After the presentation of the 10 NSBC slides, three slides were devoted to informing students on the beneficial impact of planning (for details, see the slides in supplemental material): often, it is not easy to organise oneself to successfully reach ones goals. Many reasons can interfere with your wish to do more. PA planning has been found to facilitate the enactment of the individuals’ desired actions. PA planning is essential for meeting daily recommendations and overcoming potential barriers. For example, if one week you have too much homework that does not allow you to be sufficiently active, you may plan to participate in PA during the weekend.

Then, participants were instructed to fill out the week’s timetable. Firstly, they had to write in the timetable slots how long their past PA behaviours lasted in terms of transportation (e.g. walking 10 min to go to school), sport club training/competitions and PE classes (e.g. 2 h football training every Wednesday afternoon, and 1 h PE class each Monday morning and Thursday afternoon) and unstructured leisure time activities (e.g. swimming on Sunday morning for 1 h). Then, if the sum of the usual weekly PA was less than 5 h, they were invited to plan how to achieve the 5 h of MVPA by adding supplementary PA that they felt able to carry out over the next 2 weeks. Using the if-then format, they had to write the type of activity, the day, the time and the duration

592

D. Tessier et al.


of this activity (e.g. If, on Tuesday I get back home at 5 pm, I will go for a 30 min run). Finally, the students who planned to do more activities were invited to indicate three possible distractions that could occur during pursuit of their goal (i.e. doing at least 5 h of MVPA a week), and three strategies for managing those distractions (i.e. ‘When situation x occurs, I will respond with y’). The duration of the PC intervention was 10–15 min. Participants were given a copy of their written plan and were advised to keep this sheet so that it was accessible daily. The CC (i.e. NSBC plus SBC plus PC) consisted of combining the three previous conditions.

Data analysis To compare the effects of each condition on each variable, we conducted multilevel analyses using Predictive Analytics SoftWare (PASW; previously SPSS) Version 18.0.02. (2009). Because the nesting of students within classes raised the possibility of dependencies in the data, multilevel analyses are more appropriate than ordinary least squares models (Bryk & Raudenbush, 1992). For the TPB socio-cognitive variables, data were treated as a three-level hierarchical model, consisting of three occasions of measure at level 1, students at level 2 and classes at level 3. Association between experimental conditions (i.e. NSBC, SBC, PC and CC) and the TPB socio-cognitive variables (i.e. intention, attitude, perceived behavioural control and subjective norm) were examined in series of growth curve analyses. These analyses consisted of the following four steps. The first step was to use a fully unconditional three-level hierarchical model – with only one intercept and no explanatory variable – to partition the variance of each TPB socio-cognitive variable into between-class, between-student and within-student components. In step 2, the variable time was included in the models (Model 1) as a fixed, and as a random parameter. By including time as a random parameter, it is assumed that the intercepts of the TPB variables (i.e. the level of the TPB variables at baseline) and slopes (i.e. the change of the TPB variables over time) may vary randomly between students. In addition, this allows the covariance between intercepts and slopes to be estimated, that is whether the rate of change of the TPB variables over time vary as a function of the initial level of these variables. In step 3, experimental conditions (i.e. NSBC, SBC, PC and CC) were entered as predictors of each TPB variable (Model 2). This allows for the possibility that there were differences between experimental conditions at baseline to be tested. In a final model (Model 3), we added the interaction between experimental condition and time as a predictor of the TPB variables. This allows the effect of the experimental conditions on the TPB variables over time to be compared. For PA behaviour and planning variables, data were treated as a two-level hierarchical model, consisting of students at level 1 and classes at level 2. Association between experimental conditions (i.e. NSBC, SBC, PC and CC) and PA and planning variables were examined in a series of multilevel regression analyses. For PA behaviour, the first step was to use a fully unconditional two-level hierarchical model. In the second step, the level of PA behaviour assessed at baseline was added to the model, and in the third step, the experimental conditions (i.e. NSBC, SBC, PC and CC) were entered as predictors. For planning, the first step was to use a fully unconditional two-level


Psychology & Health

593

hierarchical model, and in the second step, the experimental conditions (i.e. NSBC, SBC, PC and CC) were entered as predictors. To compare the effects of the four experimental conditions, we computed two sets of three orthogonal contrasts (Judd & McClelland, 2008). In the first set, one contrast compared the NSBC with the three other conditions (using −3, 1, 1 and 1, respectively, for NSBC, SBC, PC and CC), and was labelled ‘NSBC vs. SBC-PC-CC’; a second contrasted the SBC with the PC and the CC (using 0, −2, 1 and 1), and was labelled ‘SBC vs. PC-CC’; and the third contrast tested whether the PC was significantly different from the CC (using 0, 0, −1 and 1), and was labelled ‘PC vs. CC’. As this first set of contrasts did not allow the comparison of the PC and the SBC, we computed a second set of three orthogonal contrasts: the first contrast compared, as in the previous set, ‘NSBC vs. SBC-PC-CC’; the second tested whether the CC was significantly different from the PC and the SBC (by using 0, 1, 1 and −2), and was labelled ‘CC vs. PC-SBC’; and the third contrasted the SBC and the PC (by using, 0, 1, −1 and 0), and was labelled ‘SBC vs. PC’. Results Descriptive statistics and correlations Table 1 presents descriptive statistics for the measures; all measures reached satisfactory levels of internal consistency (α > .70). Correlations indicated that all the TPB measures were positively intercorrelated, except subjective norm measured at follow-up. Planning was positively correlated with PA behaviour measures and the TPB measures, except post-test attitude measures and follow-up subjective norm measures. Finally, PA behaviour measures were positively correlated with most of the TPB variables, except subjective norm. This is in accordance with previous studies, which showed that subjective norm is a weaker predictor of the PA behaviour than attitude and perceived behavioural control (Hagger et al., 2002).

Effects of each experimental intervention condition For PA behaviour, the results are presented in Table 4. The results of the unconditional model show that 7% of PA behaviour variance was explained at the inter-class level, which justify the use of multilevel analysis. The results of model 1 show that none of the four conditions significantly predicted the students’ level of MVPA at post-test, controlling for the baseline level (model 2) (see Figure 1 in supplemental material). For intention, the results are presented in Table 2. The results of the unconditional model show that both levels 2 and 3 explain more than 5% of the variance of intention (i.e. 65.07% and 5.7% for levels 2 and 3, respectively), and then require the variance to be partitioned into three levels. In model 1, the results show that the level of intention increased over time. In addition, the significant covariance between intercepts and slopes reveals that the growth rhythm vary as a function of the students’ baseline level of intention. The lower the students’ baseline intention, the more they increased their intention level over time. Model 2 demonstrates that there were no differences between the four experimental conditions on intention at baseline. Finally, model 3 shows that the SBC, the PC and the CC were more effective than the NSBC in increasing intention to be

1.62 1.20 1.55 1.31 1.21

1.64 1.34 1.28 1.47

1.53 1.30 1.22 1.79 1.13 1.51

4.45 5.58 5.12 5.38 4.34

5.32 5.64 5.59 5.26

5.31 5.59 5.47 4.88 4.20 3.61

SD

.58*** .51*** .50*** .28* .24* .34***

.78*** .53*** .68*** .38**

(.74) . 61*** .71*** . 54*** .42***

1

3

4

.30** .32** .31** .37*** .22* .28**

.61*** .55*** .58*** .40*** .51*** .43*** .59*** .16 .27** .36***

.71*** .38*** .79*** .28** .29** .21 .17 .57*** .11 .17*

.46*** .27** .40*** .58***

(.80) .67*** (.88) .54*** .46*** (.73) .42*** .42*** .33***

2

.20* .12 .15 .20* .51*** .25*

.36*** .26** .39*** .14

5

7

8

9

.71*** .54*** .62*** .21 .27** .40***

.29** .44*** .35** .21* .26** .12

.56*** .55*** .64*** .13 .38** .40***

.33** .21* .34** .62*** .12 .30**

(.76) .53*** (.84) .83*** .52*** (.87) .47*** .33** .44*** (.76)

6

Notes: PBC = Perceived behavioral control, SN = Subjective norm, PA = Physical activity. The unit of the physical activity variables is in hour per week. Cronbach’s α reliabilities are in the diagonal. *p < .05; **p < .01; ***p < .001.

Baseline 1. Intention 2. Attitude 3. PBC 4. SN 5. PA Post-test 6. Intention 7. Attitude 8. PBC 9. SN Follow-up 10. Intention 11. Attitude 12. PBC 13. SN 14. PA 15. Planning

M

Table 1. Descriptive statistics and correlations.


(.81) .46*** .80*** .17 .22** .51***

10

(.83) .45*** .06 .21* .25*

11

13

14

15

(.86) .18 (.74) .25* .04 .50*** .10 .24* (.89)

12

594 D. Tessier et al.

1172.37

1.12 (.10)*** 1.41 (.25)*** .16 (.15)

4.95 (.18)***

Unconditional model Estimate (SE)

.74 (.10)*** .07 (.12) 2.67 (.66)*** .20 (.10)* −.48 (.22)* 1126.77 2.64(3)

.17 (.15) 2.61 (.64)*** .20 (.10)* −.47 (.22)* 1129.41 42.96(3)***

4.13 (.21)*** .41 (.07)*** .11 (.10) −.00 (.11) −.30 (.21) .15 (.11) −.15 (.21)

Model 2 Estimate (SE)

.74 (.10)***

4.13 (.24)*** .42 (.07)***


.05 2.43 .14 −.36 1116.71 1.06 (3)*

(.11) (.63)*** (.09) (.21)

.74 (.10)***

4.22 (.21)*** .37 (.07)*** −.10 (.14) −.26 (.16) −.38 (.27) .32 (.15) .21 (.28) .10 (.04)* .12 (.05)* .03 (.09) −.8 (.05) −.17 (.08)*


1028.92

.86 (.08)*** .53 (.12)*** .15 (.10)

5.62 (.16)***


.15 (.11) .92 (.44)* .09 (.08) −.18 (.18) 1027.45 1.47(3)

.76 (.10)***

5.56 (.19) .03 (.06)


(.15)*** (.06) (.06) (.07) (.12) (.07)* (.13)

.85 (.08) .54 (.12) .01 (.04)

5.55 .03 .09 −.16 −.35 .26 .07


1019.69 7.76(1)

Attitude

(.16)*** (.06) (.11) (.13) (.24) (.14) (.24) (.05) (.04) (.08) (.05) (.08)

1017.08 2.61(3)

.84 (.08) .53 (.12) .01 (.04)

5.53 .04 .12 −.13 −.10 .11 .14 −.02 −.02 −.12 .07 −.04


Notes: NSBC = Non-salient beliefs condition, SBC = salient beliefs condition, PC = planning condition, CC = combined condition. In models 2 and 3, the two sets of contrasts were tested separately, but for space reason the results were merged in the table. *p < .05; ***p < .001.

Random effect Measure level variance Student level variance Class level variance Intercept variance Slope variance Covariance intercept x slope Deviance test model χ2 (df)

Fixed effect Intercept Time NSBC vs. SBC-PC-CC SBC vs. PC-CC PC vs. CC CC vs. SBC-PC SBC vs. PC NSBC vs. SBC-PC-CCxtime SBC vs. PC-CCxtime PC vs. CCxtime CC vs. SBC-PCxtime SBC vs. PCxtime

Parameter

Intention

Table 2. Results of multilevel analysis for intention and attitude.


Psychology & Health 595


596

D. Tessier et al.

more physically active (NSBC vs. SBC-PC-CCxtime, p < .05); the PC and the CC were more effective than the SBC in promoting intention (SBC vs. PC-CCxtime, p < .05), the PC alone was more effective than the SBC in increasing intention (SBC vs. PCxtime, p < .05), and that the CC was not more effective than the PC in increasing adolescents’ intention (PC vs. CCxtime, ns). In addition, the results of model 3 show that the entry of the interaction between experimental conditions and time decreased the covariance between intercepts and slopes significantly (see Figure 2 in supplemental material). For attitude and subjective norm, the results are presented in Tables 2 and 3, respectively. The results of the unconditional models show that both levels 2 and 3, explain more than 5% of the variance (i.e. 38% and 14.8% for levels 2 and 3 of attitude, respectively; and 80.5% and 12.5% for levels 2 and 3 of subjective norm, respectively). The results of model 1 show that the level of attitude did not change over time, and that the level of subjective norm decreased over time. The results of models 2 and 3 show that in both cases, the experimental conditions had no influence on the students’ perceptions (see Figures 3 and 5 in supplemental material). For perceived behavioural control, the results are presented in Table 3. The results of the unconditional model, show that both levels 2 and 3, explain more than 5% of the variance of perceived behavioural control (i.e. 64.5% and 8.3% for levels 2 and 3, respectively), and then require the variance to be partitioned into three levels. In model 1, the results show that the level of perceived behavioural control increased over time. In addition, the significant covariance between intercepts and slopes reveals that the growth rhythm varies as a function of the students’ initial level of perceived behavioural control. The lower the students’ initial level of perceived behavioural control, the more it increased over time. Model 2 demonstrates that there were no differences between the four experimental conditions on perceived behavioural control at baseline. Finally, model 3 shows that the SBC, the PC and the CC were not more effective than the NSBC in increasing perceived behavioural control (NSBC vs. SBC-PC-CCxtime, p > .05); the PC and the CC were more effective than the SBC in promoting perceived behavioural control (SBC vs. PC-CCxtime, p < .05); the PC alone was more effective than the SBC in increasing perceived behavioural control (SBC vs. PCxtime, p < .05); and that the CC was not more effective than the PC in increasing the adolescents’ perceived behavioural control (PC vs. CCxtime, ns). In addition, the results of model 3 show that the entry of the interaction between experimental conditions and time decreased significantly the covariance between intercepts and slopes (see Figure 4 in supplemental material). For planning, the results are presented in Table 4. The results of model 1 show that the low-active adolescents in the PC and CC groups reported planning their PA significantly more than the low-active adolescents from the SBC group (SBC vs. PC-CC, p < .05); the adolescents from the PC group reported planning PA more than those from the SBC group (SBC vs. PC, p < .05); and that the CC and PC groups were not significantly different in terms of planning (PC vs. CC, ns) (see Figure 6 in supplemental material). Discussion The purpose of the present study was to examine whether persuasive communication targeting salient beliefs and planning could promote intention to be more physically

5.34 (.13)***

4.98 (.19)*** .18 (.06)**

Model 1 Estimate (SE) 5.00 .18 −.05 .00 −.19 .09 −.10

(.18)*** (.06)** (.08) (.09) (.14) (.09) (.17)

Model 2 Estimate (SE) 5.05 .16 −.12 −.66 −.13 −.14 .39 .03 .19 .05 .01 −.12

(.18)*** (.06)** (.11) (.24)* (.14) (.26) (.14)* (.04) (.07)* (.05) (.08) (.04)**

Model 3 Estimate (SE) 5.16 (.18)***

Unconditional model Estimate (SE) 5.67 (.18)*** −.26 (.08)**


5.63 (.18)*** −.26 (.07)** .12 (.07) −.13 (.08) −.11 (.15) .11 (.11) .15 (.20)


Subjective norm

5.67 −.29 .01 −.02 −.14 .08 −.02 .07 −.07 .03 .02 .12

(.16)*** (.08)*** (.10) (.12) (.21) (.13) (.25) (.05) (.05) (.10) (.05) (.09)


Notes: NSBC = Non-salient beliefs condition, SBC = salient beliefs condition, PC = planning condition, CC = combined condition. In models 2 and 3, the two sets of contrasts were tested separately, but for space reason the results were merged in the table. *p < .05; **p < .01; ***p < .001.

Random effect Measure level variance .66 (.06)*** .43 (.06)*** .43 (.06)*** .43 (.06)*** 1.12 (.11)*** .85 (.11)*** .88 (.09)*** .87 (.09)*** Student level variance 1.23 (.20)*** 1.14 (.21)*** Class level variance .04 (.07) .01 (.07) .04 (.11) .04 (.11) .16 (.14) .09 (.60) .08 (.13) .08 (.13) Intercept variance 3.02 (.55)*** 2.87 (.54)*** 2.68 (.51)*** .86 (.46) .65 (.21)** .66 (.21)** Slope variance .19 (.06)** .19 (.06)** .16 (.06)** .20 (.10)* .16 (.05)** .15 (.05)** Covariance intercept x slope −.62 (.17)*** −.59 (.17)*** −.51 (.15)** −.09 (.19) Deviance test model 1027.24 995.12 992.93 985.00 1155.10 1125.86 1121.57 1118.08 χ2 (df) 32.12(3)*** 2.19(3) 7.93(3)* 29.24(3)*** 4.29(2) 3.49(3)

Fixed effect Intercept Time NSBC vs. SBC-PC-CC SBC vs. PC-CC PC vs. CC CC vs. SBC-PC SBC vs. PC NSBC vs. SBC-PC-CCxtime SBC vs. PC-CCxtime PC vs. CCxtime CC vs. SBC-PCxtime SBC vs. PCxtime

Parameter


Perceived behavioural control

Table 3. Results of multilevel analysis for perceived behavioural control and subjective norm.


Psychology & Health 597

1.20 (.19)*** .09 (.11) 267.83

4.17 (.41)***


.84 (.14)*** .12 (.11) 239.29 28.54 (1)***

2.07 (.39)*** .47 (.08)**

Model 1 Estimate (SE) (.39)*** (.08)*** (.09) (.11) (.19) (.11) (.21)

.83 (.13)*** .10 (.11) 238.68 .61 (3)

2.05 .48 .07 .03 .05 −.04 −.02


1.77 (.26)*** .27 (.20) 357.43

3.15 (.21)***


Planning

(.11) (.10)* (.17) (.10) (.18)* 1.76 (.26)*** .09 (.13) 346.60 10.83 (3)*

.17 .39 .04 −.22 −.57

3.12 (.14)***


Notes: NSBC = Non-salient beliefs condition, SBC = salient beliefs condition, PC = planning condition, CC = combined condition. In models 2 for physical activity, and in model 1 for planning, the two sets of contrasts were tested separately, but for space reason the results were merged in the table. *p < .05; **p < .01; ***p < .001.

Random effect Student level Class level variance Deviance test model χ2 (df)

Fixed effect Intercept PA at baseline NSBC vs. SBC-PC-CC SBC vs. PC-CC PC vs. CC CC vs. SBC-PC SBC vs. PC

Parameter

Physical activity

Table 4. Results of multilevel analysis for physical activity and planning.


598 D. Tessier et al.


Psychology & Health

599

active and PA behaviour among low-active adolescents. This study was grounded in the TPB, reinforced by a planning approach in order to target the key determinants of the behavioural change process in a sample of physically low-active adolescents. A noteworthy result is that, contrary to the hypotheses, none of the four conditions influenced PA behaviour. Even the PC and the CC, which were more effective than the NSBC and the SBC in increasing intention, did not affect PA behaviour. This result challenges the utility of the intention–behaviour connection and echoes the recent debate about the validity of the TPB (Sniehotta et al., 2014). Indeed, recent metaanalyses with experimental-based designs showed that meaningful experimental changes in intention (d = .45) resulted in trivial changes in PA behaviour (d = .07) (Rhodes & Dickau, 2012), and revealed that 46% of intenders were not able to successfully perform that intended PA behaviour (Rhodes & de Bruijn, 2013). This is strong evidence that intention is a necessary, but often insufficient construct to produce behavioural enactment. While meta-analyses with correlation-based assessments and cross-sectional designs have situated the intention–behaviours relationship within a large effect size range (r = .50) (Hagger et al., 2002; Symons Downs & Hausenblas, 2005), experimental tests of the TPB have not supported the theory’s assumption according to which a strong intention will result in behaviour. Illustrating the gap between intention and action, the present study confirms the discordance between correlational and experimental studies and nurtures the more general criticism of the TPB concerning its limited potential utility. In this recent debate about the validity of the TPB, Sniehotta et al. (2014) claimed that it is time to retire the TPB and called for theoretical development testing new falsifiable hypotheses to explain behavioural phenomena. Rhodes (2014) emphasised that two decades of testing the TPB have shown that it has several failed propositions or missing concepts to enable understanding and changing of health behaviour, and Odgen (2014) stated that TPB appears to have no redeeming qualities at all. Others (e.g. Schwarzer, 2014) made more nuanced commentaries, but all suggested the need to move forward and go beyond the TPB limitations. To account for this intention–behaviour discordance, a contemporary line of enquiry places emphasis on a volitional phase of post-motivational constructs – such as self-regulatory, automatic and affective constructs – meant to bridge the intention–behaviour gap (Gollwitzer & Brandstätter, 1997; Schwarzer, 2008; Sniehotta, 2009). In accordance with the first hypothesis, the results revealed that the PC and the CC were more effective than the NSCB in increasing intention to be more physically active. As suggested in previous studies, it seems that low-active adolescents are not sensitive to PA health messages because they do not interpret PA as health behaviour (Renner et al., 2007). However, the SBC did not demonstrate more efficacy either. Contrary to Hypothesis 2, the SBC was not more effective than the NSBC and the PC in reinforcing attitude. To explain this result, a ceiling effect could be assumed. Indeed, the average baseline levels of low-active adolescents’ attitude are high, between 5.27 and 5.98 on a seven-point scale. Although low-active adolescents do less than 1 h/day of MVPA, they evaluate PA behaviour positively. An additional explanation is the use of mixed messages. Indeed, the presentation of the 10 NSBC-slides before the delivery of the SBC could have affected the adolescents’ perceptions; it may have been counterproductive. It could be assumed that this introductive non-adapted message has disrupted students’ attention and interest for the intervention. This could undermine the potential positive effect of the following salient beliefs message. Another trial would be needed to test this assumption.


600

D. Tessier et al.

The results also revealed the effectiveness of planning strategy and partially confirmed hypothesis 3. Used separately or in the CC, this strategy was more effective than the NSBC and the SBC in increasing perceived behavioural control. These results are in accordance with previous studies (Latimer et al., 2006; Murray et al., 2005), which showed that planning was an effective self-regulatory strategy in the pre-intention phase in strengthening self-efficacy. In this study, the positive effect of the intervention on perceived behavioural control has probably been favoured by the integration of a coping planning strategy in the PC. Indeed, adolescents were invited to identify three possible environmental distractions that could occur during the pursuit of their goal, and three strategies to deal with these distractions. It could be assumed that these adolescents probably felt more capable of overcoming external barriers. Therefore, this strategy enhanced their perceived behavioural control on external events. In addition, the results revealed that planning conditions were more effective than the NSBC and the SBC in increasing intention to be active. This effect on intention is probably due to the effectiveness of planning on perceived behavioural control. Thus, it can be assumed that perceived behavioural control played a central role in the change in intention observed in this study. This assumption is aligned with the longitudinal study by Motl et al. (2005), which showed that perceived behavioural control was an independent predictor of the PA behaviour change process over a one-year period, with a sample of adolescent girls. The effectiveness of planning conditions was confirmed by the self-reported measure of planning (i.e. action and coping planning). The low-active adolescents assigned to planning conditions (i.e. PC and CC) reported planning to do significantly more PA than their counterparts from the two other groups. Another interesting result was that planning conditions were all the more effective when the adolescents’ baseline levels of perceived behavioural control and intention were low. This is in agreement with the TPB, which states that the lower the baseline level, the more room there is for change (Ajzen, 2006). However, there is a relative dearth of research examining the effectiveness of planning interventions among groups with very low intentions. As claimed by Hagger and Luszczynska (2014), it seems that the conditions under which low intenders are likely to respond to planning interventions have yet to be elucidated. Contrary to Hypothesis 4, no additive effects were found when the SBC and the PC were combined. One explanation could be that the SBC seems to have no effect on the low-active adolescents’ attitude and intention. As such, the PC was the sole/only efficient strategy in this study. These results do not contradict the assumption that multicomponent interventions are more effective in increasing both PA and psychological mediators than single-component interventions (Demetriou & Höner, 2012). Indeed, other strategies targeting attitude (e.g. single message – not mixed-message – based only on salient behavioural beliefs) and subjective norm (e.g. strategies involving PE teachers targeting adolescents’ normative beliefs) need to be examined and combined with planning (which target perceived behavioural control) in order to have a greater impact on low-active adolescents’ PA behaviour and intention. Finally, the results revealed that the SBC and the PC did not affect adolescents’ subjective norm. This may be explained by the fact that the two strategies implemented did not target the adolescents’ antecedents of subjective norm (i.e. normative beliefs).


Psychology & Health

601

This study is not exempt from limitations. One limitation is the small sample size. Two teachers and their classes dropped out before the baseline measure, reducing the sample size and the statistical power. Future research with bigger samples is needed to examine the effects of multiple strategies on PA behaviour and intention. Another limitation is the short-term intervention length. The positive effect of the planning conditions on low-active adolescents’ intention is remarkable. However, longitudinal studies are needed to test longer term effects. For example, further studies could investigate whether the benefits of increasing the intention of low-active adolescents to be more physically active from the present intervention endure. Long-term longitudinal studies are needed to examine whether the low-active adolescents return to their pre-existing levels of intention, when the formal support is no longer provided, or whether the intervention has a sustained effect on participants’ intention to be more physically active. In addition, the use of self-reported measures of PA with a young population is also a limitation. It is now well established that indirect measures (i.e. self-reported measures of PA) report an overestimation of PA compared to direct measures (e.g. accelerometer, pedometer), and this all the more when the participants are young (Adamo, Prince, Tricco, Connor-Gorber, & Tremblay, 2009). Indeed, children and young people may have activity patterns that are much more variable and intermittent than those of adults, are more likely to suffer from recall bias and are less likely to be accurate (Adamo et al., 2009). This possible inaccurate measure of PA could explain why, in the present study, the PC and the CC affected intention but not PA behaviour. Thus, while in the validation study (i.e. Hagströmer et al., 2008), significant correlations between the IPAQ-A and an accelerometer were found, it would be necessary in future studies to use direct measures of PA – such as accelerometers – to ensure a more accurate assessment of the primary outcome. In conclusion, low-active adolescents are a population to consider in order to prevent future health issues. The results demonstrated that planning is an effective low-cost intervention in order to increase their intention to be physically more active. This study is innovative because it is the first in the PA context to use this strategy and to demonstrate its relevance in the pre-intention phase with an adolescent population. However, the main variable to target remains PA behaviour. As shown in this, as in many previous studies, there is a gap between intention and action. Therefore, there is a need, in future studies, not only to support low-active adolescents to develop strong intention, but also to help them to implement this intention into action. Supplemental material Supplemental data for this article can be accessed here: http://dx.doi.org/10.1080/ 08870446.2014.996564. References Achtziger, A., & Gollwitzer, P. M. (2010). Motivation and volition in the course of action. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (2nd ed., pp. 275–299). New York, NY: Cambridge University Press.


602

D. Tessier et al.

Adamo, K., Prince, S., Tricco, A., Connor-Gorber, S., & Tremblay, M. (2009). A comparison of indirect versus direct measures for assessing physical activity in the pediatric population: A systematic review. International Journal of Pediatric Obesity, 4, 2–27. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211. Ajzen, I. (2006). Behavioral Interventions based on the theory of planned behavior. Retrieved from http://people.umass.edu/~aizen/pdf/tpb.intervention.pdf Belanger-Gravel, A., Godin, G., & Amireault, S. (2013). A mete-analytic review of the effect of implementation intentions on physical activity. Health Psychology Review, 7(1), 23–54. doi:10.1080/17437199.2011.560095 Berkowitz, J. M., Huhman, M., Heitzler, C. D., Potter, L. D., Nolin, M. J., & Banspach, S. W. (2008). Overview of formative, process, and outcome evaluation methods used in the VERB™ campaign. American Journal of Preventive Medicine, 34, S222–S229. Brislin, R. (1980). Translation and content analysis of oral and written material. In H. C. Triandis & J. W. Berry (Eds.), Handbook of cross-cultural psychology (Vol. 2, pp. 389–444). Boston, MA: Allyn & Bacon. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models in social and behavioural research: Applications and data analysis methods (1st ed.). Newbury Park, CA: Sage. Chatzisarantis, N., & Hagger, M. (2005). Effects of a brief intervention based on the theory of planned behavior on leisure-time physical activity participation. Journal of Sport and Exercise Psychology, 27, 470–487. Chatzisarantis, N., Hagger, M., & Brickell, T. (2008). Using the construct of perceived autonomy support to understand social influence within the theory of planned behavior. Psychology of Sport and Exercise, 9, 27–44. Demetriou, Y., & Höner, O. (2012). Physical activity interventions in the school setting: A systematic review. Psychology of Sport and Exercise, 13, 186–196. Gagne, C., & Godin, G. (2000). The theory of planned behavior: Some measurement issues concerning belief-based variables. Canadian Journal of Applied Sport Sciences, 10, 141–146. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54, 493–503. Gollwitzer, P. M., & Brandstätter, V. (1997). Implementation intentions and effective goal pursuit. Journal of Personality and Social Psychology, 73, 186–199. Gollwitzer, P. M., Wieber, F., Meyers, A. L., & McCrea, S. M. (2010). How to maximize implementation intention effects. In C. R. Agnew, D. E. Carlston, W. G. Graziano, & J. R. Kelly (Eds.), Then a miracle occurs: Focusing on behavior in social psychological theory and research (pp. 137–161). New York, NY: Oxford Press. Hagger, M. S., Chatzisarantis, N., & Biddle, S. J. (2001). The influence of self-efficacy and past behaviour on the physical activity intentions of young people. Journal of Sports Sciences, 19, 711–725. Hagger, M., Chatzisarantis, N., & Biddle, S. (2002). A meta-analytic review of the theories of reasoned action and planned behavior: Predictive validity and the contribution of additional variables. Journal of Sport and Exercise Psychology, 24, 3–32. Hagger, M. S., & Luszczynska, A. (2014). Implementation intention and action planning interventions in health contexts: State of the research and proposals for the way forward. Applied Psychology: Health and Well-Being, 6, 1–47. Hagströmer, M., Bergman, P., Bourdeaudhuij, I., Ortega, F. B., Ruiz, J. R., Manios, Y., … Sjöström, M. (2008). Concurrent validity of a modified version of the International Physical activity Questionnaire (IPAQ-A) in European adolescents: The HELENA study. International Journal of Obesity, 32, S42–S48.


Psychology & Health

603

Hardeman, W., Johnston, M., Johnston, D. W., Bonetti, D., Wareham, N. J., & Kinmonth, A. L. (2002). Application of the theory of planned behaviour in behaviour change interventions: A systematic review. Psychology and Health, 17, 123–158. Huhman, M. E., Potter, L. D., Nolin, M. J., Piesse, A., Judkins, D. R., Banspach, S. W., & Wong, F. L. (2010). The influence of the VERB campaign on children’s physical activity in 2002 to 2006. American Journal of Public Health, 100, 638–645. Judd, C. M., & McClelland, G. H. (2008). Data analysis: A model comparison approach (2nd ed.). San Diego, CA: Harcourt, Brace, Jovanovich. Latimer, A. E., Brawley, L. R., & Bassett, R. L. (2010). A systematic review of three approaches for constructing physical activity messages: What messages work and what improvements are needed? International Journal of Behavioral Nutrition and Physical Activity, 7, 1–36. Latimer, A. E., Ginis, K. A., & Arbour, K. P. (2006). The efficacy of an implementation intention intervention for promoting physical activity among individuals with spinal cord injury: A randomized controlled trial. Rehabilitation Psychology, 51, 273–280. Marcus, B. H., Forsyth, L. A. H., Stone, E. J., Dubbert, P. M., McKenzie, T. L., Dunn, A. L., & Blair, S. N. (2000). Physical activity behavior change: Issues in adoption and maintenance. Health Psychology, 19, 32–41. Milne, S., Orbell, S., & Sheeran, P. (2002). Combining motivational and volitional interventions to promote exercise participation: Protection motivation theory and implementation intentions. British Journal of Health Psychology, 7, 163–184. Motl, R. W., Dishman, R. K., Ward, D. S., Saunders, R. P., Dowda, M., Felton, G., & Pate, R. R. (2005). Comparison of barriers self-efficacy and perceived behavioral control for explaining physical activity across 1 year among adolescent girls. Health Psychology, 24, 106–111. Murray, T., Rodgers, W., & Fraser, S. (2005). The effects of implementation intentions on exercise self-efficacy and behaviour. Psychology & Health, 20, S186. Odgen, J. (2014). Time to retire the theory of planned behaviour?: one of us will have to go! A commentary on Sniehotta, Presseau and Araújo-Soares. Health Psychology Review, 1–8. doi: 10.1080/17437199.2014.898679 Renner, B., Spivak, Y., Kwon, S., & Schwarzer, R. (2007). Does age make a difference? Predicting physical activity of South Koreans. Psychology and Aging, 22, 482–493. Rhodes, R. (2014). Will the new theories (and theoreticians!) please stand up? A commentary on Sniehotta, Presseau and Araújo-Soares. Health Psychology Review, 1–4. doi:10.1080/ 17437199.2014.882739 Rhodes, R. E., & De Bruijn, G. J. (2013). How big is the physical activity intention–behaviour gap? A meta-analysis using the action control framework. British Journal of Health Psychology, 18, 296–309. Rhodes, R. E., & Conner, M. (2010). Comparison of behavioral belief structures in the physical activity domain. Journal of Applied Social Psychology, 40, 2105–2120. Rhodes, R. E., & Courneya, K. S. (2005). Threshold assessment of attitude, subjective norm, and perceived behavioral control for predicting exercise intention and behavior. Psychology of Sport and Exercise, 6, 349–361. Rhodes, R. E., & Dickau, L. (2012). Experimental evidence for the intention–behavior relationship in the physical activity domain: A meta-analysis. Health Psychology, 31, 724–727. Sallis, J. (2000). Age related decline in physical activity: A synthesis of human and animal studies. Medicine and Science in Sports and Exercise, 32, 1598–1600. Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology: An International Review, 57, 1–29. Schwarzer, R. (2014). Some retirees remain active: A commentary on Sniehotta, Presseau and Araújo-Soares. Health Psychology Review, 1–3. doi:10.1080/17437199.2014.898407 Sniehotta, F. F. (2009). Towards a theory of intentional behaviour change: Plans, planning, and self-regulation. British Journal of Health Psychology, 14, 261–273.


604

D. Tessier et al.

Sniehotta, F. F., Presseau, J., & Araújo-Soares, V. (2014). Time to retire the theory of planned behaviour. Health Psychology Review, 8, 1–7. Sniehotta, F. F., Schwarzer, R., Scholz, U., & Schüz, B. (2005). Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35, 565–576. Symons Downs, D., & Hausenblas, H. A. (2005). Exercise behavior and the theories of reasoned action and planned behavior: A meta-analytic update. Journal of Physical Activity and Health, 2, 76–97. Verloigne, M., Van Lippevelde, W., Maes, L., Yildirim, M., Chinapaw, M., Manios, Y., … De Bourdeaudhuij, I. (2012). Levels of physical activity and sedentary time among 10- to 12year-old boys and girls across 5 European countries using accelerometers: An observational study within the ENERGY-project. International Journal of Behavioral Nutrition and Physical Activity, 9(34), 1–8. Webb, T. L., & Sheeran, P. (2008). Mechanisms of implementation intention effects: The role of goal intentions, self-efficacy, and accessibility of plan components. British Journal of Social Psychology, 47, 373–395. WHO. (2010). Global recommendations on physical activity for health. World Health Organization. Retrieved from http://whqlibdoc.who.int/publications/2010/9789241599979_eng.pdf

Effects of Epstein's TARGET on adolescents' intentions to be physically active and leisure-time physical activity.

Getting England to be more physically active: are the Public Health Responsibility Deal's physical activity pledges the answer?

Assessing the impact of different persuasive messages on the intentions and behaviour of cat owners: A randomised control trial.

Effects of Active Videogames on Physical Activity and Related Outcomes Among Healthy Children: A Systematic Review.

Risky sexual behaviour among adolescents may be related to ADHD.

Is the association between screen-based behaviour and health complaints among adolescents moderated by physical activity?

Effect of major school playground reconstruction on physical activity and sedentary behaviour: Camden active spaces.

Physical activity and sedentary behaviour among Asian and Anglo-Australian adolescents.

Estimation of leisure time physical activity and sedentary behaviour among school adolescents in Nepal.

Physical Activity and Beverage Consumption among Adolescents.

Associations between aspects of friendship networks, physical activity, and sedentary behaviour among adolescents.

A systematic review of school-based interventions targeting physical activity and sedentary behaviour among older adolescents.

Improving hand hygiene behaviour among adolescents by a planning intervention.

Effects of a persuasive communication on beliefs, attitudes, and career choice.

Active travel intervention and physical activity behaviour: an evaluation.

Normative influences on intentions to smoke among Greek adolescents: the moderating role of smoking status.

Habits predict physical activity on days when intentions are weak.

Long-term effects of physically active academic lessons on physical fitness and executive functions in primary school children.

Towards a physically more active lifestyle based on one's own values: the results of a randomized controlled trial among physically inactive adults.

Testing the bi-dimensional effects of attitudes on behavioural intentions and subsequent behaviour.

Longitudinal relationships between perceived stress, exercise self-regulation and exercise involvement among physically active adolescents.

Physical activity in overweight and obese adolescents: systematic review of the effects on physical fitness components and cardiovascular risk factors.

Effects of mortality salience and perceived vulnerability on HIV testing intentions and behaviour.

Using the Transtheoretical Model to Examine the Effects of Exergaming on Physical Activity Among Children.