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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: [email protected]
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