Long-term effectiveness and mediators of a need-supportive physical activity coaching

among Flemish sedentary employees

Ann-Sophie Van Hoecke, Christophe Delecluse, Joke Opdenacker, Luc Lipkens, Sofie

Martien, and Filip Boen

Submitted to Health Promotion International on May 26th

2011

Department of Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, KU

Leuven, Belgium

Corresponding author:

Ann-Sophie Van Hoecke

Ph.D. Fellow of Research Foundation Flanders, Belgium

Address: Tervuursevest 101, 3001 Heverlee, Belgium

Telephone number: 0032 16 32 91 80

Fax number: 0032 16 32 91 96

E-mail: AnnSophie.VanHoecke@faber.kuleuven.be

Abstract

This study examined the long-term and mediation effects of a need-supportive coaching program on

physical activity. Sedentary employees (n = 92) of the university of Leuven received four months of

physical activity coaching, based on the self-determination theory, by coaches with a bachelor’s

degree in kinesiology who are specializing in health-related physical activity (n = 30). The program

consisted of a limited number of individual contact moments (i.e. an intake session, three follow-up

contacts and an outtake session), either face-to-face, by phone or by e-mail. Self-reported physical

activity, social support, self-efficacy and autonomous motivation were assessed in the coaching group

(n = 92) and a control group (n = 34) at three moments: before the intervention (i.e. pre-test), after the

intervention (i.e. post-test) and one year after pre-test measurements (i.e. follow-up-test). Results

revealed significant 3 (time) x 2 (group) interaction effects on strenuous and total physical activity.

Moreover, whereas the control group remained stable from pre- to post-test, the coaching group

increased significantly in moderate, strenuous and total physical activity. Additionally, the coaching

group increased significantly in mild, moderate, strenuous and total physical activity from pre- to

follow-up-test, whereas the control group did not change. Bootstrapping analyses indicated that self-

efficacy and autonomous motivation significantly mediated the intervention effect on physical activity

from pre- to post-test, while social support significantly mediated the long-term effect. This study

provides evidence for the long-term effectiveness of a need-supportive physical activity program that

might be efficient at community level.

Key words: physical activity; behaviour change; intervention; longitudinal survey

Long-term effectiveness and mediators of a need-supportive physical activity coaching

among Flemish sedentary employees

Regular physical activity is considered to be a major contributor to physical and psychological

health and well-being (e.g., Kohl, 2001; Roglin et al., 2007). However, the majority of the population

in Western societies does not attain the physical activity health norm prescribed by the American

College of Sports Medicine (Gisle et al., 2010; Philippaerts et al., 2006, Varo et al., 2003). Therefore,

the promotion of physical activity has been identified as government priority (Golditz, 1999; Pate et

al., 1995). Numerous studies have demonstrated increased physical activity levels by implementing

lifestyle interventions, in which physical activity is incorporated into one’s daily routines (e.g., Dunn

et al., 1998; Opdenacker et al., 2008). Among the overall reported positive results, theory-based

lifestyle interventions were found to be most effective (Hillsdon et al. 2005; U.S. National Cancer

Institute, 2005).

In the past decade, the self-determination theory (SDT) has been postulated as a promising

theoretical approach to facilitate behavioural change, and in particular to increase physical activity

(e.g., Chatzisarantis and Hagger, 2009). SDT assumes that people possess three basic psychological

needs, i.e. autonomy, competence and relatedness. These needs are considered as fundamental to

obtain high quality motivated engagement in any given behaviour (Deci and Ryan, 1985). The need

for autonomy reflects the desire to be the origin of one’s own behaviour and can be satisfied by using

client-centred strategies, such as exploring options and letting the client make decisions. The need for

competence implies the pursuit of success experiences in producing desired outcomes and can be

fulfilled by appropriate goal-setting and providing positive feedback. Finally, the need for relatedness

refers to the feeling that one belongs to a given social environment and can be supported by

expressing empathy and avoiding judgement or criticism (Deci et al., 1985; Hardcastle and Hagger,

2011). A need-supportive environment, i.e. fulfilling the needs for autonomy, competence and

relatedness, has been shown to be effective in facilitating autonomous motivation. Because

autonomously motivated behaviour is initiated by the individual, and thus self-determined, it is more

likely to persist in the long term (Deci et al., 1985; Markland and Vansteenkiste, 2007; Silva et al.,

2010).

Within the psychology of health behaviour change, motivational interviewing (MI) is

recognized as an appropriate collaborative counselling technique to create such a need-supportive

environment (Markland et al., 2005; Markland et al., 2007; Miller and Rollnick, 2002). Furthermore,

MI has been shown to increase self-efficacy, which is the belief of one’s capability to perform a task

(Ajzen, 1991; Bandura, 1997). The concept of self-efficacy is related to perceived competence

(Biddle, 1999; Markland et al., 2005) and has been proposed as a crucial mediating factor in

enhancing behavioural change, e.g. physical activity (e.g., Dishman et al., 2004; Markland et al.,

2005; Taymoori and Lubans, 2008). In addition to self-efficacy, social support has been identified as

an important psychosocial variable in increasing physical activity in sedentary populations (e.g., De

Bourdeaudhuij et al., 2002; Eyler et al., 1999). A need-supportive climate, focusing on increasing

self-efficacy and advancing a supportive social environment by significant others, is thus assumed to

be beneficial to physical activity promotion.

Although numerous studies have linked need-supportive coaching to increased physical

activity levels (e.g., Chatzisarantis et al., 2009; Wilson et al., 2003), previous research has been less

conclusive on the long-term effectiveness of such intervention (Fortier et al., 2007; National Heart,

Lung and Blood Institute, 1998; Silva et al., 2010). Furthermore, although knowledge of mediation

effects can contribute to the effectiveness of physical activity interventions, relatively few studies

have examined the mediating influence of theoretical constructs on the relation between a SDT-based

intervention and physical activity, especially in the long term (Edmunds et al., 2006; Fortier et al.,

2011; Haerens et al., 2008). Finally, the majority of intervention studies involve intensive counselling

procedures, which are rather difficult to implement on a large-scale community level (Haerens et al.,

2008; Silva et al., 2010).

Therefore, the present study was designed to examine (1) the (long-term) physical activity

effects of a need-supportive coaching program, consisting of a limited number of contact moments,

among sedentary employees; and (2) the mediation effects of social support, self-efficacy and

autonomous motivation within this coaching process. It was hypothesized that the intervention group

(COACH) would increase their physical activity level, while no changes would emerge in the control

group (CONTR). Moreover, because the need-supportive intervention was intended to change the

underlying psychosocial constructs, changes in physical activity were expected to be mediated by

changes in social support, self-efficacy and autonomous motivation.

Method

Recruitment and participants

Data were derived from a physical activity coaching project at the university of Leuven.

Participants (n = 92) were sedentary employees of the university, who had been offered the

opportunity to be guided by a personal physical activity coach (n = 30). Recruitment took place via

personal mailing and an announcement in a monthly internal newsletter, and resulted in 382

volunteers. A two-stage procedure was followed to reduce the total number of volunteers to 90

participants of COACH, i.e. three per coach: (1) Volunteers were asked to indicate whether they

already attained the physical activity health norm prescribed by the American College of Sports

Medicine (Haskell et al., 2007) (yes/no-question) and to specify their weekly physical activity pattern.

Those who already attained the norm of 30 minutes of moderate-intensity physical activity on at least

five days a week or 20 minutes of strenuous-intensity physical activity on at least three days a week

were refused (n = 36); (2) The ‘first come – first served’ principle was applied, meaning that

volunteers who replied first on the recruitment message were invited to participate in the intervention.

Two participants who dropped-out shortly after the intake session (see section below) were

immediately replaced, which resulted in 92 participants in COACH. The CONTR consisted of the 34

subsequent volunteers who met the inclusion criteria. Because the majority of volunteers replied on

the recruitment message within two days, the selected participants for COACH are not considered to

be more motivated than volunteers selected for CONTR. At post-test, drop-out rates were 6.5% in

COACH and 0.0% in CONTR (2 = 2.261, p = .152), while follow-up drop-out rates were 13.0% and

11.8% respectively (2 = 0.019, p = .580). Lack of motivation was the most frequent mentioned

reason for dropping-out. There were no significant differences between drop-outs and adherers in

COACH and CONTR for demographic variables, self-reported physical activity, social support, self-

efficacy and autonomous motivation at pre-tests.

Procedures and intervention

The 4-month intervention took place from November 2009 to March 2010 and consisted of five

contact moments between the coach and participants of COACH (i.e. an intake session, three follow-

up contacts and an outtake session). Pre-, post- and follow-up-tests were completed respectively

before, immediately after, and one year after the beginning of the intervention in COACH and

CONTR. No contact occurred between coach and participants between post- and follow-up-tests.

Participants of CONTR completed all measurements under the supervision of a coach, but did not

receive physical activity instructions or program.

Coaches were recently graduated with a bachelor’s degree in kinesiology and now specializing in

health-related physical activity, to get their master’s degree. Prior to the coaching, they attended a

course in psychology of behavioural motivation and physical activity promotion, to improve their

coaching proficiency according to SDT and MI. Additionally, regular feedback from experts during

the coaching procedure ensured high-quality coaching.

During a face-to-face intake session of approximately one hour, an individualized physical

activity program was set up in accordance with the participant’s preferences, habits and opportunities.

The physical activity goals, specified by physical activity type, location, time frame, company,

barriers and solutions were written down in an agreement. The main goal was to attain the physical

activity health norm as prescribed by the American College of Sports Medicine (Haskell et al., 2007).

The three follow-up contacts and the outtake session took place three to four weeks after the previous

contact. According to earlier research, face-to-face support is as efficacious as support by phone or

computer-based support in targeting behavioural changes, e.g. physical activity (Opdenacker and

Boen, 2008; Portnoy et al., 2008). Therefore, the second and third follow-up contact occurred face-to-

face, by phone or by e-mail, depending on the participant’s need. Follow-up contacts occurred face-

to-face, by phone and by e-mail in respectively 98.5%, 30.7% and 67.7% of the clients. Face-to-face

and follow-up contacts by phone lasted approximately 30 and ten minutes respectively, while

conversations by e-mail consisted of four e-mails. During follow-up contacts, the student-coaches

asked for the degree of adherence to the program (i.e. did the participant reach his or her physical

activity goals), identified possible barriers and if necessary, modified goals. The coaches motivated

the clients to persist in their program by using behaviour change techniques (Abraham and Michie,

2008) and, more specifically, by coaching according to the principles of MI and SDT, i.e. autonomy

support (e.g., by facilitating rather than prescribing physical activity), competence support (e.g., by

providing positive feedback) and relatedness support (e.g., by expressing empathy through active

listening). The outtake session was planned to evaluate behaviour change among the participants, to

make sure the participants mastered the required techniques to continue engagement in physical

activity (e.g., barrier identification, using prompts and cues) and to discuss future challenges.

Questionnaires

Self-reported physical activity was assessed with a modified version of the Godin Leisure-Time

Exercise Questionnaire (GLTEQ) (Godin and Shephard, 1985). The GLTEQ was preferred because

this questionnaire is brief, easy to administer and considered to be clearly comprehensible for

university employees. The validity and test-retest reliability have already been demonstrated in

Western populations (Godin et al., 1985; Jacobs et al., 1993; Karvinen et al., 2007). Furthermore, the

GLTEQ has been successfully employed with different populations in Western societies (e.g., De

Bacquer et al., 2010; Karvinen et al., 2007; Rhodes and Pfaeffli, 2010). Participants reported the

number of times they engaged for at least 20 minutes in mild, moderate and strenuous intensity

physical activity in a typical week during the past month. An overall measure of self-reported physical

activity was obtained by summing the frequencies weighted by the metabolic equivalents for each

intensity category, i.e. three, five and nine, respectively.

Social support (SOSU) was measured by a validated five-point Likert scale from De

Bourdeaudhuij and Sallis (2002). Participants were asked how strongly they believed that family,

friends and colleagues wanted them to be physically active (3 items) and how frequently they

perceived support (i.e. accompany, propose, remind and encourage) from these significant others with

respect to physical activity (12 items). Cronbach’s alpha coefficients for pre-, post- and follow-up

tests ranged from .82 to .86, indicating a high internal consistency.

Self-efficacy (SE) was measured by a multidimensional Self-Efficacy Questionnaire (Opdenacker

et al., 2008). Participants indicated their confidence level for being physically active in various

situations, on a five-point Likert scale, ranging from ‘not at all confident’ to ‘very confident’. Internal

consistency was very high with Cronbach’s alpha coefficients for pre-, post- and follow-up-tests

ranging from .90 to .93.

Autonomous motivation (AUM) was assessed with eight items of the Dutch version of the

Behavioural Regulations for Exercise Questionnaire (Markland and Tobin, 2004). This self-

determined type of motivation comprises the subscales of identified regulation (4 items) and intrinsic

motivation (4 items) (Fortier and Kowal, 2007) and is positively associated with long-term physical

activity behaviour (Chatzisarantis et al., 2003; Pelletier et al., 2001; Vansteenkiste et al., 2004).

Cronbach’s alpha coefficients for pre-, post- and follow-up-tests ranged from .88 to .90, indicating a

high internal consistency.

Statistical analysis

All data were analysed with SPSS 16.0 (SPSS Inc, Chicago, IL, USA). In order to assess

differences between COACH and CONTR, One-Way ANOVA and chi-square (2) analyses were

used. One person with a pre-test GLTEQ-score more than three standard deviations above the pre-test

mean was excluded from all analyses. Effects on physical activity were analyzed with Repeated

Measures ANOVAs. Multivariate analyses (Phillai’s Trace coefficient) were conducted to test several

dependent variables simultaneously. Mediation effects were analyzed with the bootstrapping macro

provided by Preacher and Hayes (2008). The primary advantage of bootstrapping is that no

assumptions are made about the shape of sampling distribution of the indirect effect (Preacher and

Hayes, 2008). Change scores of SOSU, SE and AUM were included simultaneously in a multiple

mediator model, because this (1) ‘purifies’ the indirect effects by controlling for all the other

mediators, and (2) allows the researcher to determine which mediators are more successful than others

(Preacher and Hayes, 2008). Mediation analyses were conducted for change scores from pre- to post-

test (∆prepo) and from pre- to follow-up-test (∆prefu). Significance level was set at p < .05.

Results

Demographic profile

Mean age was 41.3 years (SD = 13.6). There were 52.0% women and most participants were

married or lived together with their life partner (71.2%). Twenty-seven percent were technical

employees, while professors and research assistants represented respectively 20.8% and 37.0%. A

minority (e.g., emeritus professors, partners of university employees) was not employed at the

university (15.2%). No significant differences in the abovementioned demographic variables emerged

between COACH and CONTR.

Effect analyses

Table 1 depicts the physical activity means (SD) at pre-test, pre- to post-test and pre- to

follow-up-test change scores for physical activity, 3 (time) x 2 (groups) interaction effects and One-

Way-ANOVAs for the physical activity change scores. Significant 3 (time) x 2 (groups) interaction

effects emerged on strenuous and total physical activity, indicating that the reported strenuous and

total physical activity in COACH changed differently over time compared with CONTR.

Furthermore, while no changes occurred in CONTR, participants of COACH showed significant

increases in moderate, strenuous and total physical activity from pre- to post-test and in all intensity

physical activities from pre- to follow-up-test (all p = .000). One-Way-ANOVA analyses revealed

significant differences in ∆prepo for moderate, strenuous and total physical activity (Pillai’s Trace: F

= 9.463, p = .000) and in ∆prefu for strenuous and total physical activity (Pillai’s Trace: F = 3.715, p

= .014) between COACH and CONTR. Despite their lower pre-test levels of strenuous and total

physical activity (strenuous physical activity: F = 7.419, p = .007; total physical activity: F = 7.965, p

= .006), participants in COACH reported higher levels of physical activity at post-test (strenuous

physical activity: F = 5.346, p = .023; total physical activity: F = 8.570, p = .004).

INSERT TABLE 1

Mediation

Because significant 3 (time) x 2 (groups) interaction effects emerged on strenuous and total

physical activity, mediation effects of changes in SOSU, SE and AUM on changes in strenuous and

total physical activity were tested (table 2). The paths from the intervention to pre- to post-test change

scores in SE and AUM (α-paths) were significant, indicating that participants in COACH increased

more in SE and AUM from pre- to post-test than participants in CONTR (Pillai’s Trace: F = 3.421, p

= .020). By contrast, the α-path on SOSU failed to reach significance, indicating no differences in

SOSU between COACH and CONTR from pre- to post-test. Furthermore, the β-paths from pre- to

post-test changes in the proposed mediators to changes in strenuous and total physical activity were

not significant. Finally, the confidence intervals of the indirect pathways via the proposed mediators

(i.e. SOSU, SE and AUM) (αβ-paths) showed that ∆prepo in SE significantly mediated the effect of

the intervention on changes in strenuous and total physical activity. Moreover, ∆prepo in AUM

significantly mediated the intervention effect on changes in strenuous physical activity. The overall

mediation models accounted for respectively 19.2% (F = 6.780, p = .000) and 21.4% (F = 7.775, p =

.000) of the variance in ∆prepo in strenuous and total physical activity.

No significant effects were found from the intervention on changes in the proposed mediators

from pre- to follow-up-test (α-path) (Pillai’s Trace: F = 0.935, p = .427). Nevertheless, the significant

direct effects of ∆prefu in SE and AUM on ∆prefu in strenuous physical activity (β-path) indicated

that the more participants increased in SE and AUM from pre- to follow-up, the more they increased

their level of strenuous physical activity from pre- to follow-up. Finally, ∆prefu in SOSU significantly

contributed to the indirect effect of the intervention on ∆prefu in total physical activity (αβ-paths).

The overall models explained 19.0% (F = 5.968, p = .000) and 16.5% (F = 5.024, p = .001) of the

variance in ∆prefu in strenuous and total physical activity respectively.

INSERT TABLE 2

Discussion

The results of this study showed that need-supportive coaching with a limited number of contact

moments was effective in increasing physical activity. This finding is in line with previous SDT-based

intervention studies (e.g., Chatzisarantis et al., 2009; Edmunds et al., 2008; Silva et al., 2010), but

extends earlier research by demonstrating long-term effectiveness. The increased physical activity

level from pre-to post-test in COACH on the one hand and the stable physical activity level in

COACH and CONTR from post-test to one-year follow-up-test on the other hand correspond with

research of Opdenacker et al. (2008). It provides evidence for exercise engagement and persistence as

a result of a need-supportive coaching environment.

As suggested previously (Edmunds et al., 2006; Silva et al., 2010), different intensities of

physical activity were considered as separate outcomes. While mild physical activities are often more

regarded as habitual lifestyle activities (e.g., walking for transportation), moderate and strenuous

physical activities are considered to be more purposeful and structured. Consequently, higher intensity

physical activities might require more cognitive processing (Edmunds et al., 2006; Silva et al., 2010).

The increase in higher intensity physical activity from pre- to post-test in participants of COACH

reflects the fact that during the intervention, the coaches mainly encouraged the participants to

incorporate structured physical activities of moderate to high intensity into their individualized

physical activity program (e.g., following dancing classes or going for a swim once a week).

Furthermore, because of the higher level of cognitive processing associated with higher intensity

physical activities, moderate and strenuous physical activities were possibly easier to remember

compared with mild physical activities, and therefore more likely to be reported in the physical

activity questionnaire. The increased engagement in moderate and strenuous intensity physical

activity in COACH is especially important, because earlier studies have demonstrated that a higher

intensity of performed physical activity results in greater aerobic and cardioprotective benefits (Swain

and Franklin, 2006; Williams, 1998).

The long-term results, i.e. from pre- to follow-up-test, indicate that participants seemed to

integrate mild physical activities (e.g., active transportation) in their daily life as well, in addition to

the structured higher intensity physical activities. This means that in the long term, mild physical

activities that had become a habit were also experienced as self-determined in COACH. Mild physical

activities like active transportation can not only be advanced by a physical activity coach, but are also

important to consider at policy level (e.g., ensure safe environment). Moreover, assuming that the

post-intervention increases in moderate and strenuous physical activities resulted in higher levels of

physical fitness, participants might have incorporated more mild physical activities in their habitual

lifestyle because they require less effort the more one is physically fit. Additionally, because CONTR

also demonstrated small, albeit non-significant, increases in moderate physical activity from pre- to

follow-up-test, seasonal reasons (i.e. spring and summer) can partly explain the increase in moderate

physical activities. However, the significant time x group interaction effect from pre- to follow-up for

strenuous and total physical activity underscores the value of SDT-based coaching for facilitating

persistence in structured high intensity physical activities.

The impact of the need-supportive intervention on the proportion of people who attain the

physical activity health norm should also be noted (Pate et al., 1995). Although the GLTEQ does not

provide a precise measure of minutes that participants engage in physical activity, the overall physical

activity score obtained by this questionnaire can be considered as a general guideline to determine

whether or not one attains the recommended physical activity level. According to Haskell et al.

(2007), health benefits can be obtained by performing twenty minutes of strenuous physical activity

on at least three days a week. This corresponds to an overall score of 27 on the GLTEQ (Scheerder et

al., 2011). The results of this study showed that the number of participants in COACH that attained a

GLTE-score of 27, and thus obtained the physical activity health norm, increased from 8.7% in pre-

test to 41.9% and 48.8% in respectively post- and follow-up-test. Percentages in CONTR changed

from 18.2% over 27.3% and 24.1%, respectively. This resulted in a significant difference in the long

term (i.e. at follow-up) between COACH and CONTR (2 = 5.286, p = .017). Furthermore, One-Way-

analyses revealed that participants who increased their physical activity level were less physically

active at pre-test compared with participants who decreased or did not change their physical activity

level. This trend emerged for moderate, strenuous and total physical activity from pre- to post-test and

for all intensities of physical activity from pre- to follow-up-test. The appearance of this trend

corresponds with the identified significant time effects in COACH, suggesting that the significant

effects are mainly resulting from the increases in physical activity in the most sedentary participants.

These results highlight the potential of a need-supportive physical activity coaching as a public health

initiative, especially when targeting the most sedentary population.

Because understanding the role of underlying psychosocial constructs enlarges the insight in how

an intervention works (Hillsdon et al., 2005), mediation effects of changes in social support, self-

efficacy and autonomous motivation on changes in physical activity were also studied. Mediation

analyses were conducted only on strenuous physical activity and on total physical activity because

these require a higher level of cognitive processing compared with less intensive physical activity.

The results are in line with earlier SDT-based studies, in which (changes in) physical activity could

partly be explained by (changes in) self-efficacy and competence, which are closely related concepts

(Edmunds et al., 2006; Fortier et al., 2007; Haerens et al., 2008). Moreover, previous research found

positive relations between autonomous motivation on the one hand and strenuous and total physical

activity on the other hand (Edmunds et al., 2006; Silva et al., 2010; Silva et al., 2011). Although the

components of autonomous motivation (i.e. identified regulation an intrinsic motivation) have been

shown to contribute differently to the prediction of physical activity, both are proposed to be

beneficial to optimal and continued exercise engagement (Edmunds et al., 2006). While identified

regulation appears to be relevant for engagement in physical activity, intrinsic motivation is important

for exercise persistence (Pelletier et al., 2001). Physical activity promotion should thus focus on

increasing both motivational components in order to be effective. The mediation by social support in

the long-term is consistent with previous research of De Bourdeaudhuij et al. (2002). This finding

emphasizes the importance of interpersonal relationships as a determinant of physical activity and

indicates the high value of the coach’s fulfillment of the need for relatedness during the intervention,

e.g. by active listening, conveying empathy and using non-judgmental language. Furthermore, it

highlights the importance of continued social support from significant others (e.g., family, friends,

colleagues) during and after the intervention in order to enhance sustained physical activity.

In conclusion, the results partially supported our hypotheses, suggesting that SDT-based

behaviour change techniques (Abraham et al., 2008) such as providing options and focusing on task

goals (i.e. autonomy support), setting realistic goals and providing positive feedback (i.e. competence

support) and showing empathy and motivational interviewing (i.e. relatedness support), are successful

in promoting (long-term) physical activity through various psychosocial constructs.

The present study had its particular strengths and weaknesses. First, although coaches were

recently graduated with a bachelor’s degree in kinesiology and thus less experienced, the intervention

demonstrated long-term effects on physical activity. This strengthens the effectiveness of a SDT-

based intervention in the promotion of physical activity at community level. Second, the limited

number of need-supportive contact moments between coach and client enhances the efficiency and

large-scale implementation of physical activity initiatives, especially because previous research (Conn

et al., 2002; Fortier et al. 2007) indicated that intense and regular contact with a physical activity

coach is more effective than limited contact. Individual need-supportive contact seems to be essential

in the beginning of physical activity coaching. This is because need-supportive coaching is considered

to increase the client’s feelings of competence and as a result to enhance long-term engagement.

However, it should be noted that need-supportive coaching remains time and labour intensive, even

with a limited number of contact moments. Therefore, alternative coaching strategies or multisectoral

and integrated approaches appear inevitable to advance large-scale physical activity promotion.

Replacing individual contact moments by group sessions (e.g. in a socio-cultural organization) once

the individual’s competence level is sufficient could increase the feasibility of need-supportive

coaching at community level. Furthermore, in addition to this midstream (i.e. lifestyle) approach that

aims to directly influence behaviour (e.g. increase physical activity), upstream approaches are needed

as well to obtain effective population-based prevention. More specifically, there is vast need for

promotion actions within the broader socio-ecological and economic domain (e.g. education, finance,

appropriate physical environment). The high level of physical inactivity within the population requires

a policy response. Structural environmental changes (e.g. safe bicycle and footpaths) have been found

to be promising steps to make healthy choices the easy choices (Östlin et al., 2006; Sacks et al.,

2009).

A third strength of the present study is the inclusion of a control group within a long-term design.

This allows us to consider increases in physical activity as an intervention effect rather than as an

attention effect.

Although the study included a control group in which participants did not receive any coaching,

the lack of strict randomization between the control and the intervention group constitutes a first

limitation. Because of ethical, educational and social reasons, the ‘first come – first served’ principle

was applied. However, first responders (who were involved in COACH) did not demonstrate higher

pre-test levels of autonomous motivation nor self-efficacy compared with slow responders (who were

involved in CONTR). Therefore, it seems unlikely that the lack of strict randomization was

responsible for the significant differences in strenuous and total physical activity at pre-test between

COACH and CONTR. Second, physical activity was based on self-reports, which can lead to social

desirability (Motl et al., 2005) and overestimation (Fogelholm et al., 2006). On the other hand, the

questionnaire used in this study has been shown to have reasonable validity and reliability (Godin et

al., 1985). Moreover, the GLTE-score has been found to be positively correlated with maximum

oxygen consumption (Jacobs et al., 1993) and activity monitoring (Kriska and Caspersen, 1997).

Third, because more than half of the participants in this study were (emeritus) professors or research

employees, the participants were highly educated compared with the Flemish population. Therefore,

the results should be generalized with caution to the entire Flemish population. On the other hand,

both the gender distribution and age of the participants (48.0% of men; 41.3 years) were

representative for the Flemish population (48.9% of men; 41.0 years) (General Direction of Statistical

and Economical Information Belgium, 2006).

It can be concluded that the findings provide support for the long-term effectiveness of a need-

supportive physical activity coaching with limited contact through various psychosocial constructs.

The study can be considered as a preliminary but promising step within the domain of efficient

physical activity promotion at community level.

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Table 1. Means (SD) at pre-test, pre- to post-test and pre- to follow-up-test change scores for physical activity in COACH and

CONTR, 3 (time) x 2 (groups) interaction effects and One-Way-ANOVAs for physical activity change scores from pre- to post-test

and from pre- to follow-up-test.

COACH CONTR 3 x 2 INTERACTION One-Way-ANOVA

Mean (SD) Mean (SD) F *** η² F *** η²

Mild PA

(times a week for ≥ 20

minutes)

pre 1.01 (1.75) 1.35 (1.74) 1.952 *** .018

∆prepo + 0.20 + 0.10 0.067 *** .001

∆prefu + 0.82 - 0.25 3.228 *** .029

Moderate PA

(times a week for ≥ 20

minutes)

pre 0.92 (1.64) 1.38 (1.37) 2.122 *** .020

∆prepo + 1.45 + 0.39 5.765 *** .047

∆prefu + 1.98 + 0.78 2.302 *** .021

Strenuous PA

(times a week for ≥ 20

minutes)

pre 0.22 (0.56) 0.66 (1.04) 5.882 *** .054

∆prepo + 0.96 - 0.01 14.291 *** .109

∆prefu + 0.85 + 0.12 5.852 *** .052

Total PA

(GLTEQ)

pre 9.61 (9.58) 16.83 (12.72) 8.998 *** .080

∆prepo + 16.46 + 2.17 23.277 *** .166

∆prefu + 19.95 + 4.24 9.849 *** .084

Note. PA, physical activity; COACH, intervention group; CONTR, control group; GLTEQ, Godin Leisure-Time Exercise

Questionnaire; SD, standard deviation; pre, pre-test value; ∆prepo, change score from pre- to post-test; ∆prefu, change score

from pre- to follow-up-test; 3 x 2 INTERACTION, interaction effect over time (3) and between groups (2) for mild, moderate,

strenuous and total physical activity; One-Way-ANOVA, difference between the intervention and control group for change

scores in physical activity; *, p < .05; **, p < .01; ***, p < .001

Table 2. Mediation effects of changes in social support, self-efficacy and autonomous motivation on changes in strenuous and total

physical activity.

α-path β-path αβ-path

coefficient (SE) * coefficient (SE) * coefficient (SE) 95% CI of αβ

∆prepo

Strenuous PA

(times a week

for ≥ 20 minutes)

∆prepo SOSU - 0.208 (0.123) * 0.274 (0.182) * - 0.055 (0.068) - 0.290 to - 0.022

∆prepo SE - 0.267 (0.119) * 0.274 (0.206) * - 0.089 (0.056) - 0.238 to - 0.014

∆prepo AUM - 0.257 (0.102) * 0.373 (0.240) * - 0.092 (0.064) - 0.280 to - 0.005

Total - 0.236 (0.096) - 0.502 to - 0.094

Total PA

(GLTEQ) ∆prepo SOSU - 0.208 (0.123) * 1.698 (2.292) * - 0.312 (0.668) - 2.494 to - 0.566

∆prepo SE - 0.267 (0.120) * 4.903 (2.596) * - 1.323 (0.786) - 3.380 to - 0.175

∆prepo AUM - 0.257 (0.102) * 2.191 (3.022) * - 0.531 (0.808) - 2.529 to - 0.720

Total - 2.166 (1.057) - 4.728 to - 4.000

∆prefu

Strenuous PA

(times a week

for ≥ 20 minutes)

∆prefu SOSU - 0.231 (0.137) * 0.113 (0.197) * - 0.028 (0.052) - 0.190 to - 0.044

∆prefu SE - 0.022 (0.154) * 0.465 (0.190) * - 0.011 (0.059) - 0.147 to - 0.097

∆prefu AUM - 0.028 (0.128) * 0.460 (0.227) * - 0.016 (0.060) - 0.179 to - 0.083

Total - 0.055 (0.113) - 0.308 to - 0.137

Total PA

(GLTEQ) ∆prefu SOSU - 0.231 (0.137) * 5.421 (3.338) * - 1.172 (0.929) - 3.989 to - 0.011

∆prefu SE - 0.022 (0.154) * 6.211 (3.213) * - 0.063 (0.812) - 2.079 to - 1.394

∆prefu AUM - 0.028 (0.128) * 2.046 (3.840) * - 0.014 (0.520) - 1.760 to - 0.671

Total - 1.249 (1.454) - 5.243 to - 0.983

Note. PA, physical activity; GLTEQ, Godin Leisure-Time Exercise Questionnaire; SOSU, social support; SE, self-efficacy; AUM,

autonomous motivation; ∆prepo, change score from pre- to post-test; ∆prefu, change score from pre- to follow-up-test; α, estimate

of the intervention effect on changes in the proposed mediators; β, estimate of the direct effect of changes in the proposed

mediators on changes in physical activity while controlling for the intervention effect; αβ, estimate of the indirect effect of the

intervention on changes in physical activity through the proposed mediators; 95% CI, 95% bias corrected confidence interval with

the empirically derived bootstrapped sampling distribution of αβ (result of 2000 bootstrap resamples); *, p < .05