Being active in pregnancy: theory-based predictors of physical activity among pregnant women

Article (PDF Available)inWomen & Health · March 2018with 197 Reads
DOI: 10.1080/03630242.2018.1452835
Abstract
Although regular physical activity is recommended for pregnant women, compared to pre-pregnancy, antenatal physical activity often reduces or ceases completely. Drawing from the theory of planned behavior, self-determination theory, and theory on self-control, we aimed to test an integrative model of physical activity in a sample of pregnant women. The current study was conducted in Brisbane, Australia, in 2014–2015 using a prospective-correlational design with a one-week follow-up. Participants (N = 207, Time 1; Meanage = 30.03 years, SDage = 4.49 years) completed an initial survey measuring: intrinsic motivation from the self-determination theory, social cognitive constructs from the theory of planned behavior, and self-control from the self-control theory, followed by a self-report measure of physical activity one-week later (n = 117, Time 2). A well-fitting structural equation model accounted for 73 and 42 percent of the variance in intention and physical activity behavior, respectively. Perceived behavioral control and attitude, but not subjective norm, mediated the effect of intrinsic motivation on intention. Intention, perceived behavioral control, and self-control were positively associated with physical activity behavior. Future behavioral interventions aiming to promote physical activity during pregnancy, a period when the physical activity levels typically decline, should consider the multiple processes advocated in the integrative model as necessary for motivated action.
Running head: Physical Activity and Pregnancy
Being active in pregnancy: Theory-based predictors of physical activity among pregnant
women
Kyra Hamilton1,2*, Lena Fleig3, Joanna Henderson1, & Martin S. Hagger1,2,4
1School of Applied Psychology, Menzies Health Institute Queensland, Griffith University,
Brisbane, Australia
2School of Psychology, Health Psychology and Behavioural Medicine Research Group,
Curtin University, Perth, Australia
3Department of Educational Science and Psychology, Freie Universitat Berlin, Berlin,
Germany.
4Faculty of Sport and Health Sciences, University of Jyvaskyla, Jyvaskyla, Finland
*For correspondence contact: Dr. Kyra Hamilton, School of Applied Psychology, Griffith
University, 176 Messines Ridge Road, Mt Gravatt, QLD 4122.
Email: [email protected]; Ph: +61 7 373 53334; Fax: +61 (7) 373 53388
Suggested citation: Hamilton, K., Fleig, L., Henderson, J., & Hagger, M. S. (2018). Being
active in pregnancy: theory-based predictors of physical activity among pregnant women.
Women & Health. Advanced online publication.doi:10.1080/03630242.2018.1452835
Abstract
Although regular physical activity is recommended for pregnant women, compared to pre-
pregnancy, antenatal physical activity often reduces or ceases completely. Drawing from the
theory of planned behavior, self-determination theory, and theory on self-control, we aimed to
test an integrative model of physical activity in a sample of pregnant women. The current
study was conducted in Brisbane, Australia in 2014-2015 using a prospective-correlational
design with a one-week follow-up. Participants (N=207, Time 1; Meanage = 30.03 years, SDage
= 4.49 years) completed an initial survey measuring: intrinsic motivation from self-
determination theory, social cognitive constructs from the theory of planned behavior, and
self-control from self-control theory; followed by a self-report measure of physical activity
one-week later (n=117, Time 2). A well-fitting structural equation model accounted for 73%
and 42% of the variance in intention and physical activity behavior, respectively. Perceived
behavioral control and attitude, but not subjective norm, mediated the effect of intrinsic
motivation on intention. Intention, perceived behavioral control, and self-control were
positively associated with physical activity behavior. Future behavioral interventions aiming
to promote physical activity during pregnancy, a period when physical activity levels
typically decline, should consider the multiple processes advocated in the integrative model as
necessary for motivated action.
Key words: Theory of planned behavior, self-determination theory, self-control, physical
activity, pregnancy
Introduction
Regular physical activity (PA) has been associated with optimal pregnancy and
maternal outcomes (Brown, 2002; Nascimento et al., 2012). Despite the benefits, consistent
evidence has shown that compared to pre-pregnancy, antenatal PA is often reduced or ceases
completely (Abbasi & van den Akker, 2015), and only 32% of Australian women meet PA
guidelines during pregnancy (Wilkinson, Miller, & Watson, 2009). While some studies have
shown that demographic factors (e.g., increased age, body mass index [BMI], gestational
age) are negatively associated with women’s PA participation in pregnancy, the findings
have often been descriptive and inconclusive (Gaston & Cramp, 2011). The application of
social cognitive and motivational theories to identify the factors associated with PA in
pregnancy and potentially modifiable targets for behavior change interventions has therefore
been advocated (Connelly et al., 2015; Currie et al., 2013; Gaston & Cramp, 2011).
Theoretical Integration and Physical Activity in Pregnancy
Building on previous research which has tended to adhere to one particular theory or
approach, researchers (Arnautovska et al., 2017; Brown et al., 2018; Hagger & Chatzisarantis,
2014; Hagger et al., 2017; Hamilton, Cox, & White, 2012a; Hamilton et al., 2017a;
McEachan et al., 2016; Montaño & Kasprzyk, 2008) have recently attempted to integrate and
extend social cognitive models, such as the theory of planned behavior (TPB; Ajzen 1991),
which has been well-validated across a range of health behaviors (McEachan et al., 2011;
Rich et al., 2015), including PA (Downs & Hausenblas, 2005), with complementary theories
to build more comprehensive models of human behavior.
The TPB proposes intention as the proximal antecedent of behavior, with intention
conceptualized as a function of attitude (overall evaluations of the behavior), subjective norm
(perceived social pressure to perform the behavior), and perceived behavioral control
(perceived capacity to carry out the behavior), with perceived behavioral control further
hypothesized to be a direct predictor of behavior. While TPB constructs have been associated
with health behaviors in multiple populations (Epton et al., 2015; French & Cooke, 2011;
Hamilton et al., 2017b; Hamilton et al., 2016; Hamilton et al., 2012b; Vayro & Hamilton,
2016), the origins of these constructs have not been comprehensively identified. Theories that
focus on the quality of motivation, such as self-determination theory (SDT; Deci & Ryan,
1985, 2002), may complement the TPB to provide a better understanding of the processes
underpinning TPB constructs (Hagger & Chatzisarantis, 2009; Hagger et al., 2016).
SDT is a theory of motivation which focuses on the quality rather than quantity of
motivation. Central to the theory is the distinction between self-determined or autonomous and
non-self-determined or controlled forms of motivation. Intrinsic motivation is considered the
prototypical form of autonomous motivation, involving behavior that is performed in the
absence of external contingencies and out of inherent choice and interest. Intrinsic motivation
has been associated with adherence to health behaviors, including PA (Teixeira et al., 2012).
In contrast, controlled forms of motivation represent engaging in behaviors out of obligation
or for external contingencies, such as rewards or deadlines. Such contingencies are motivating,
but only as long as the external contingencies are present; once removed, the behavior is likely
to desist. A meta-analysis examining the integration of the TPB and SDT in health behavior
indicated that autonomous forms of motivation from the SDT were related to health behavior
directly as well as indirectly through the TPB constructs (Hagger & Chatzisarantis, 2009).
These findings corroborate more recent studies on PA among general adult populations
(Hagger & Chatzisarantis, 2014) and targeted at-risk groups for physical inactivity, such as
parents of young children (Hamilton et al., 2012a).
The higher demands placed on women during pregnancy, in addition to the physical
(e.g., fatigue, increased body weight) and psychological (e.g., altered moods) effects of
pregnancy, may result in any good intentions to engage in regular PA that do not always
translate into actual participation behavior. Recent research has shown that the translation of
intention into behavior is related to a person’s self-regulatory skills (Junger & Van Kampen,
2010; Reyes Fernández et al., 2016; Zhou et al., 2015). Thus, having the necessary self-
control, considered a quintessential feature of self-regulatory behavior (de Ridder et al., 2012;
Hofmann & Kotabe, 2012; Tangney, Baumeister, & Boone, 2004), to engage in desirable
health behaviors, such as PA, during pregnancy may also be important to consider.
Self-control is conceptualized as an individual difference which enables individuals to
direct their actions toward approaching desirable and inhibiting undesirable behavioral
tendencies (de Ridder et al., 2012). Individuals with high self-control are likely to be more
effective in structuring their long-term goals and recognizing and predicting costs and
consequences of action (Gottfredson & Hirschi, 1990). Self-control is proposed to be
associated with behavior through two routes: directly and indirectly mediated by intentions
(Hagger, 2013, 2014; Hankonen, Kinnunen, Absetz, & Jallinoja, 2014). The direct path
reflects capacity to inhibit impulse-driven non-intentional responses while the indirect
pathway reflects strategic alignment of behavioral intentions to attain long-term goals. Self-
control may also lead individuals to be more effective in fulfilling their intentions by
directing their attention to relevant cues to action (Hagger, 2013, 2014). As a consequence,
self-control may moderate the intention-behavior relationship.
The Current Study and Hypotheses
Drawing from the TPB, SDT, and theory on self-control, the aim of the current study
was to test an integrative model of PA in a sample of pregnant women. The hypothesized
model, developed in line with the existing empirical and theoretical evidence, is depicted in
Figure 1. First, in line with SDT (Deci & Ryan, 1985; Hagger & Chatzisarantis, 2009),
intrinsic motivation was hypothesized to serve as distally related to the social cognitive
antecedents of behavior from the TPB: attitude, subjective norm, and perceived behavioral
control (H1). Consistent with the TPB (Ajzen, 1991; McEachan et al., 2011), attitude,
subjective norm, and perceived behavioral control were proposed to be associated with
intention (H2). Indirect associations of intrinsic motivation from SDT with intention through
attitude, subjective norm, and perceived behavioral control were expected (H3). In addition,
intention and perceived behavioral control were proposed as factors directly associated with
behavior (H4). We also proposed a direct relationship between intrinsic motivation and
behavior (H5). Finally, in accord with theory on self-control (Hagger, 2013, 2014), it was
proposed that self-control would have a direct relation to intention (H6) and behavior (H7),
and moderate the intention-behavior relationship (H8). The mediation of the relationship
between self-control and behavior via intention was also tested (H9).
Method
Participants and Procedure
Participants were pregnant women (N = 207) aged 18 years and older and recruited in
Australia, with the majority residing in the states of Queensland and New South Wales (n =
171, 66%) between October 2014 and March 2015. Women were eligible to participate if they
had not been diagnosed with a medical condition preventing them from engaging in PA in the
antenatal period. Participants were recruited via face-to-face contact at mother/baby groups
and general practice surgeries, along with advertisements at antenatal classes, childcare
centers, and on social media. These recruitment sites were selected to optimize sample size,
given the higher proportion of pregnant women in these settings. However, due to the
different recruitment methods, it was not possible to compute participation rates, although
most of those approached face-to-face participated in the study. As an incentive, participants
were informed of the opportunity to enter a prize draw to win one of three double movie
vouchers (each valued at AUD50). Ethical approval for the study protocol was granted by the
University Human Research Ethics Committee.
The study used a prospective design with a one-week follow-up. At Time 1 (T1),
participants completed an initial questionnaire either face-to-face (n = 48; 23%) or online (n =
159; 77%) to assess TPB constructs (attitude, subjective norm, perceived behavioral control,
and intention) as well as measures of intrinsic motivation from SDT and self-control. Data on
demographics were also collected. At Time 2 (T2), participants completed a follow-up
questionnaire either over the phone (n = 14; 12%) or online (n = 103; 88%) that assessed their
self-reported PA behavior in the previous week. To ensure informed consent, an information
sheet was provided to participants containing all required information on the nature of the
research and outlining confidentiality. Informed consent was gained through the completion
of the T1 questionnaire, and consent to contact participants for the T2 follow-up was given
through the provision of contact details. Providing written consent was deemed not necessary
by the University Human Research Ethics Committee. Data across each time points were able
to be de-identified and matched using a unique code identifier created by the participant.
Measures
Demographic variables. Participants self reported responses to a series of
demographic characteristics that were expected to be related to PA in pregnant women based
on previous research (Gaston & Cramp, 2011) and, therefore, used as covariates in subsequent
analyses: age (in years), self-reported weight and height to calculate BMI in kg/m2, and
gestational age (in weeks; embryonic age plus 2 weeks, which approximately corresponds to
the duration since the last menstrual period began).
Behavior. The target behavior or outcome, was performing the recommended level
of moderate-intensity physical activity over the next week”, following the Australian’s
Physical Activity and Sedentary Behavior Guidelines for Adults (Department of Health,
2014), which recommend accumulating 150-300 minutes (2.5 to 5 hours) of moderate-
intensity PA each week. Moderate-intensity was operationalized as “physical activity which
takes effort, but where you are still able to talk while doing such activity”. To improve
understanding of moderate-intensity PA, examples of activities were presented (e.g., brisk
walking, recreational swimming, household tasks, such as cleaning windows or raking
leaves). PA behavior was assessed by self-report using two 7-point scales: “In the previous
week, on how many days did you perform physical activity following the recommended
guidelines” and “In the previous week, how often did you perform physical activity following
the recommended guidelines”; scored never (1) to very often (7). The responses to these two
items were summed and averaged to provide a single score, with a score range in the current
study of 1-7. The two items were significantly correlated (r = 0.89, p < 0.001).
Social cognitive variables. We used previously-validated measures of the social
cognitive variables used in multiple studies and based on published guidelines (Ajzen, 2006).
The measures were adapted to refer to the target behavior and follow-up period (one week)
relevant to the current study. Attitude was assessed by two 7-point items on a semantic
differential scale: “For me to perform the recommended level of moderate intensity physical
activity over the next week would be …”, unpleasant (1) to pleasant (7) and “For me to
perform the recommended level of moderate intensity physical activity over the next week
would be …”, undesirable (1) to desirable (7). The responses to these two items were
summed and averaged to provide a single score, with a score range in the current study of 1-7.
The two items were significantly correlated (r = 0.71, p < 0.001).
Subjective norm was measured by three items: Most people who are important to me
would approve of me performing the recommended level of moderate intensity physical
activity over the next week”, “Those people who are important to me think I should perform
the recommended level of moderate intensity physical activity over the next week”, and “Most
people like me would perform the recommended level of physical activity in the next week;
scored strongly disagree (1) to strongly agree (7). These three items were summed and
averaged to provide a single score, with a score range in the current study of 2-7. The scale
scores in the current study were internally consistent (α = 0.93).
Two items assessed perceived behavioral control: It would be easy for me to perform
the recommended level of physical activity in the next week” and “I am confident that I could
perform the recommended level of physical activity in the next week”; scored strongly
disagree (1) to strongly agree (7). These two items were summed and averaged to provide a
single score, with a score range in the current study of 1-7. The two items were significantly
correlated (r = 0.83, p < .001).
Two items assessed intention to perform the target behavior: “I expect that I will
perform the recommended level of physical activity in the next week” and “I plan to perform
the recommended level of physical activity in the next week; scored strongly disagree (1) to
strongly agree (7). These two items were summed and averaged to provide a single score,
with a score range in the current study of 1-7. The two items were significantly correlated (r =
0.96, p < 0.001).
Intrinsic motivation. Intrinsic motivation was measured using an adapted version of
Ryan and Connell’s (1989) measure. Participants were presented with a common stem: “The
reason I would perform the recommended level of physical activity over the next week…”
followed by two reasons relating to autonomous motives on a 7-point scale: “Because I
personally believe it is the best thing for my health…” and “Because I personally believe it is
the best thing for the health of my baby…”; scored not at all true (1) to extremely true (7).
These three items were summed and averaged to provide a single score, with a score range in
the current study of 3-7. The items were correlated significantly (r = 0.71, p < .001).
Self-control. General self-control was measured using the Brief Self Control measure
(Tangney, Baumeister, & Boone, 2004) (e.g., “I am good at resisting temptation”; scored not
at all (1) to very much (5). The items were summed and averaged to provide a single score,
with a score range in the current study of 2-5. The measure has demonstrated good
psychometric properties (α = 0.83 and 0.85 in the two validation samples, and test-retest
reliability was 0.87) (Tangney et al., 2004). The scale scores in the current study were
internally consistent (α = 0.76).
Data Analysis
The proposed model (Figure 1) was estimated using structural equation modelling
using Mplus 7. The hypothesized model comprised seven latent variables: intrinsic
motivation, attitude, subjective norm, perceived behavioral control, intention, self-control,
and PA. All latent variables were indicated by the questionnaire items pertaining to each
construct. As the self-control scale comprised a large number of items, item parcelling was
applied using random allocation of items to four parcels (Little et al., 2002). We also
examined correlations between demographic variables, age, BMI, and gestational age, and PA
behavior. Given that increased age, BMI, and gestational age have been shown to affect
women’s PA participation negatively in pregnancy (Gaston & Cramp, 2011) and were also
revealed as significant bivariate correlations with PA, we included these demographic factors
as covariates on PA in the model. Model fit was assessed based on a combination of fit
indices: the comparative fit index (CFI) and the Tucker-Lewis-Index (TLI), which should
approach or exceed 0.95 for good fit; the root-mean-square error of approximation (RMSEA),
which should be less than 0.05 for good fit (Hu & Bentler, 1999). Missing data (< 5%) were
imputed using the full information maximum likelihood (FIML) algorithm (Enders &
Bandalos, 2001). Indirect effects were estimated using 95% bias-corrected bootstrap
confidence intervals (CI) with 5,000 replications.
Results
Attrition Analysis
Data at the one-week follow-up were missing for 90 participants, resulting in a final
sample of 117 participants (Table 1). Attrition analyses showed no significant differences in
PA, age, BMI, gestation age, and levels on the manifest (averaged) social cognitive variables
(intention, perceived behavioral control, self-control) measured at the first time point between
participants who dropped out of the study and those who completed the follow-up assessment
(p > 0.05). Further analyses, however, indicated significant differences in attitude (t(205) =
2.14, p = 0.03, d = 0.31), intrinsic motivation (t(205) = 2.25, p = 0.03, d = 0.31), and
subjective norm (t(205) = 2.55, p = 0.01, d = 0.32). Participants who remained in the study
reported higher levels of attitude, intrinsic motivation, and subjective norm compared to
participants who dropped out of the study.
Structural Equation Model
The structural equation model had a good model fit with the data (χ2(145) = 205.50;
RMSEA = 0.06, CFI = 0.95, TLI = 0.95). In addition, factor loadings for the manifest
indicators of each latent variable were within acceptable ranges, supporting the construct
validity of the measures adopted. The covariates of age, BMI, and gestational age were
retained in the final structural equation model (Table 2). Intrinsic motivation was statistically
significantly directly related to attitude, subjective norm, and perceived behavioral control, as
hypothesized (H1; Figure 1). Attitude and perceived behavioral control, but not subjective
norm, were statistically significantly and positively associated with intention (H2). The
indirect relations of intrinsic motivation to PA intention through attitude (β = 0.22, p = 0.01;
95% CI [-0.02, 0.43]) and perceived behavioral control (β = 0.18, p = 0.01; 95% CI [-0.02,
0.32]) reached level of significance, but not the indirect relation of intrinsic motivation to
intention through subjective norm (β = -0.01, p = 0.81; 95% CI [-0.07, 0.05]) (H3). As
hypothesized (H4), intention and perceived behavioral control showed statistically significant
and positive associations with behavior. Contrary to assumptions (H5), intrinsic motivation
was not statistically significantly directly related to behavior.
Self-control was statistically significantly and positively associated with behavior
(H7); so, this hypothesis was supported. Self-control was not directly associated with intention
(H6) and self-control did not moderate (H8; ß = -0.08; p =0.72; 95% CI [-0.28, 0.52]) or
mediate (H9; β = -0.01, p =0.68; 95% CI [-0.09, 0.07]) the intention-behavior relationship, so
these hypotheses were rejected. Overall, the model accounted for 73% and 42% of the
variance in intention and PA behavior, respectively. Zero-order correlations indicated that
age, BMI, and gestational age were statistically significantly and negatively associated with
PA behavior; however, in the final model only intention, self-control, and perceived
behavioral control were statistically significantly and positively related to PA. These results
indicate that the social cognitive factors were the most prominent factors related to PA and
overrode any associations of age, BMI, or gestational age.
Discussion
The current findings provide confirmation of the multiple pathways by which
psychological constructs were related to PA behavior of pregnant women. Specifically, the
association of a distal motivational factor (intrinsic motivation) with intention was mediated
by belief-based constructs (attitudes, perceived behavioral control), and that intention and
perceived behavioral control were directly associated with PA behavior. These findings are in
line with our hypotheses and consistent with the motivational and social cognitive
components that comprise SDT and TPB, respectively, as well as empirical evidence
supporting the integration of the two psychological theories (Hagger & Chatzisarantis, 2009).
The mechanism underpinning this association was based on the function of intrinsic
motivation in stimulating future action. Consistent with SDT, experiencing activities as
intrinsically motivated is likely to lead individuals to engage in the behavior in future as it is
intrinsically gratifying and associated with adaptive outcomes, including enjoyment and
positive affect. As a consequence, individuals will strategically align their cognition and
beliefs with their motives. In doing so, they leverage the deliberative processes that underpin
action to make participation in the behavior more likely. Pregnant women who experience PA
as intrinsically motivating and enjoyable will, therefore, be more likely to report positive
attitudes and perceived control and intend to participate in PA in future.
Contrary to hypotheses, subjective norm had no significant relation to intention. This
finding is consistent with earlier theoretical explanations of integrating the TPB and SDT.
Such a mediation path was not originally hypothesized, based on the reasoning that subjective
norm is defined as reflecting controlling, rather than autonomous, beliefs (Hagger &
Chatzisarantis, 2009). The current findings provide support for this original theorizing and
corroborate previous research indicating that the association of subjective norm with PA
intention is smaller than that of attitude and perceived behavioral control (Downs &
Hausenblas, 2005). Further, lack of an association of subjective norm with intention could be
due to an increased tendency of pregnant women to make decisions based on their personal
beliefs, rather than their beliefs about others. The myriad of information and general advice
provided to pregnant women from various sources has been shown to produce confusion
(Connelly et al., 2015) and may therefore result in a decreased reliance on others’ approval.
Consistent with this observation, research has shown that self-efficacy beliefs are often
reported as the most salient factors associated with PA in pregnancy (Gaston & Cramp, 2011).
In addition, the current findings indicated that self-control processes accounted for
significant variance in behavior, independent of the motivational and social cognitive
components. However, self-control did not moderate or mediate the intention-behavior
relation as hypothesized in theory on self-control and previous research (Hagger, 2013, 2014;
Hankonen et al., 2014). Theoretical explanations (Tangney et al., 2004) and empirical
evidence (de Ridder, 2015) have supported a direct relation of self-control to behavior.
Pregnant women are likely faced with additional demands on their time as well as the
increased physical demands as a result of their pregnancy. Those with high levels of self-
control are likely to have sufficient self-regulatory skills and resources to exert the necessary
effort to participate in regular PA, which suggests that they are potentially more effective in
resisting alternative immediately gratifying actions, such as sitting down and watching
television, in favor of engaging in effortful activities likely to lead to health benefits, such as
participating in PA. The direct relation of self-control to behavior, and the absence of an
intention-mediated path, is likely to reflect this capacity to resist impulses that is independent
of deliberative processing (Hofmann & Kotabe, 2012). The lack of an interactive effect
implies that self-control may be less important when it comes to more deliberative modes of
acting; enactment of intentions appears not to depend on self-control levels in this population.
The current results also have potential ramifications for improving the PA of pregnant
women. Given that multiple motivational and social cognitive factors are key influences
related to behavior in this context, future interventions and campaigns should target these
range of factors to promote PA behavior in pregnant women. Specifically, interventions at the
individual and community levels should recognize the importance of changing personal
beliefs with respect to PA in this context as pregnant women may be especially amenable to
making health improving changes (Jepson, Harris, Platt, & Tannahill, 2010). Our findings can
be translated into practice by linking the factors related to PA in this population with matched
behavior change methods that have been shown to change these factors (Kok et al., 2016).
This will lead to the development of behavior change interventions that may be optimally
effective in changing behavior (see Michie, van Stralen, & West, 2011).
Based on current findings, some specific behavioral strategies could be considered in
this context. First, pregnant women could be prompted to make choices and set intrinsic goals
for performing PA to instil a sense of intrinsic value and interest toward PA, thus promoting
intrinsic motivation. Second, strategies to increase women’s attitudes (e.g., providing
information targeting salient beliefs and adopting gain-framed messages; Gallagher &
Updegraff, 2012) and perceptions of control (e.g., prompting successful behavior practice and
providing feedback; Ashford, Edmunds, & David, 2010) might be important to consider in
improving PA intentions, the strongest determinant of behavior. Third, prompting pregnant
women to adopt self-regulatory strategies such as monitoring of behavior (e.g., recognizing
situations in which they might lapse from a healthy behavior), developing implementation
plans and identifying cues to action, and engaging in tasks which may train or promote
capacity to inhibit responses (Allom, Mullan, & Hagger, 2016; Friese, Frankenbach, Job, &
Loschelder, 2016) may facilitate greater self-control capacity with respect to PA.
Strengths and Limitations
The current study was the first of which we are aware to apply a comprehensive
integrative theoretical model to the area of PA in pregnant women. Although, in general, the
tenants of the model were supported, future research that attempts to manipulate theoretical
constructs and measures their influence on behavior change is essential in supporting the
model for this behavior in this at-risk target group. The current prospective research does,
however, highlight important multiple pathways to behavioral engagement, which can be used
as a basis for interventions that may be efficacious in eliciting behavior change.
Results should also be considered in light of some limitations. First, although
measures were undertaken to prevent participant drop out (e.g., offers of incentives, a brief
follow-up measure), the attrition rate was 40% from the main study to the follow-up sample.
Thus, the possibility of unmeasured retention biases must be considered. Second, despite
using recruitment methods frequently adopted in research using correlational designs, the
sample consisted predominately of Caucasian women, limiting generalizability of the findings
to other cultural, racial, and ethnic groups. However, research has demonstrated that patterns
of effects among constructs of the TPB generalize across national and cultural groups in the
context of PA (Hagger et al., 2007). Third, although participants who completed measures at
baseline and follow-up did not differ from those who dropped out by demographic variables,
some evidence of selection bias was apparent in that mean levels of social psychological
variables (attitude, intrinsic motivation, and subjective norms) were higher for those
completing both time points and those that dropped out. Fourth, although effect sizes for the
differences were small, we must acknowledge that current findings may have been affected by
the tendency of participants with higher motivation to remain in the study, making the
findings less generalizable.
Furthermore, the measurement of PA was via self report and assessed over a one-week
time period; thus, the current results may reflect some reporting error and cannot be applied to
questions about maintaining PA behavior over a more extended period, which may be more
important and relevant for positive pregnancy and health outcomes. Although self-reports are
a frequently used practice in research on PA and have been shown to be reliable and valid for
assessing PA (Hamilton, White, & Cuddihy, 2012; Milton et al., 2010), to investigate changes
in naturally occurring PA over time, baseline measures of behavior as well as longer follow-
ups and objective measures of PA would be advisable. In addition, some of the measures
adopted exhibited slightly lower psychometric properties, as have been found in previous
research (e.g., self-control, Tangney et al., 2004). The lower reliabilities should be considered
when making comparisons between the findings from the current study using these measures
and research in other contexts adopting similar measures. However, the concerns may be
mitigated somewhat by the use of a latent variable approach in our analysis. This allowed us
explicitly to model measurement error and, as a consequence, the constructs and associated
parameter estimates can be considered relatively error-free (Huba & Harlow, 1987).
The current findings have high value as they provide the first proof-of-concept
evidence in support of the integrated model for PA in pregnant women albeit over a relatively
short behavioral follow-up period. Although our data highlight the potential relevance of the
different pathways to action derived from the integrated model to long-term explanation of
variance in PA, future studies with longer behavioral follow-up are needed to verify this
potential. In addition, research that includes multiple measures of PA at follow-up is needed
to evaluate the effectiveness of the integrated model in accounting for variance in PA across
the life course of pregnancy, from prenatal to antenatal through to postnatal. While this study
investigated an important health behavior for pregnant women, future research might benefit
from a continued examination of this integrative model to determine its utility to other key
health-related behaviors that are also shown to be important in pregnancy (e.g., smoking,
alcohol use, healthy eating practices, sedentary behavior).
Conclusion
The current study tested an integrative model incorporating three psychological
theories (TPB, SDT, and self-control theory) applied to PA in pregnant women, a group that
is at risk of low levels of PA. Overall, the majority of the core associations among the
motivational and social cognitive factors proposed in the model were supported. Future
research should investigate possible moderation and mediation effects to determine which
processes predominate in determining action and manipulate the theoretical constructs and
measure their influences on behavior change to support the tenets of the model. Future
research should also undertake longer term longitudinal investigations to address questions
about PA maintenance during pregnancy. Despite the correlational design of the current
study, the findings do suggest important potential routes to behavioral performance that
researchers can use to ensure the design of future PA interventions for pregnant women that
are efficacious in eliciting behavior change. Future interventions aimed at improving the PA
of pregnant women should therefore consider the multiple processes advocated in the
integrative model as necessary for motivated action.
References
Abbasi, M., and van den Akker, O. 2015. A systematic review of changes in women’s
physical activity before and during pregnancy and the postnatal period. Journal of
Reproductive and Infant Psychology, 33, 325-358.
Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50, 179-211.
Ajzen, I. 2006. Constructing a TPB questionnaire: conceptual and methodological
considerations. Retreived from http://www.uni-
bielefeld.de/ikg/zick/ajzen%20construction%20a%20tpb%20questionnaire.pdf
Allom, V., Mullan, B. A., and Hagger, M. S. 2016. Does inhibitory control training improve
health behavior? A meta-analysis. Health Psychology Review, 10, 168-186.
Arnautovska, U., Fleig, L., O’Callaghan, F., and Hamilton, K. 2017. A longitudinal
investigation of older adults’ physical activity: testing an integrated dual-process
model. Psychology & Health, 32, 166-185.
Ashford, S., Edmunds, J., and French, D. P. 2010. What is the best way to change self-
efficacy to promote lifestyle and recreational physical activity? A systematic review
with meta-analysis. British Journal of Health Psychology, 15, 265-288.
Brown, W. 2002. The benefits of physical activity during pregnancy. Journal of Science and
Medicine in Sport, 5, 37-45.
Brown, D., Hagger, M. S., Morrissey, S., and Hamilton, K. 2018. Predicting fruit and
vegetable consumption in long-haul heavy goods vehicle drivers: application of a
multi-theory, dual-phase model and the contribution of past behavior. Appetite, 121,
326-336.
Connelly, M., Brown, H., van der Pligt, P., and Teychenne, M. 2015. Modifiable barriers to
leisure-time physical activity during pregnancy: a qualitative study investigating first
time mother’s views and experiences. BMC Pregnancy and Childbirth, 15, 100.
Currie, S., Sinclair, M., Murphy, M. H., Madden, E., Dunwoody, L., and Liddle, D. 2013.
Reducing the decline in physical activity during pregnancy: a systematic review of
behavior change interventions. PLoS One, 8(6): e66385.
de Ridder, D. T. D., Lensvelt-Mulders, G., Finkenauer, C., Stok, F. M., and Baumeister, R. F.
2012. Taking stock of self-control: a meta-analysis of how trait self-control relates to a
wide range of behaviors. Personality and Social Psychology Review, 16, 76-99.
Deci, E. L., and Ryan, R. M. 1985. Intrinsic motivation and self-determination in human
behavior. New York: Plenum Press.
Deci, E. L., and Ryan, R. M. 2002. Overview of self-determination theory: an organismic
dialectical perspective. In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-
determination research (pp. 3-33). New York: University of Rochester Press.
Department of Health. 2014. Australia's physical activity and sedentary behavior guidelines
for adults (18-64 years). Retrieved from
http://www.health.gov.au/internet/main/publishing.nsf/content/health-pubhlth-strateg-
phys-act-guidelines
Downs, D. S., and Hausenblas, H. A. 2005. The theories of reasoned action and planned
behavior applied to exercise: a meta-analytic update. Journal of Physical Activity and
Health, 2, 76-97.
Enders, C. K., and Bandalos, D. L. 2001. The relative performance of full information
maximum likelihood estimation for missing data in structural equation models.
Structural Equation Modeling, 8, 430-457.
Epton, T., Norman, P., Harris, P., Webb, T., Snowsill, F. A., and Sheeran, P. 2015.
Development of theory-based health messages: three-phase programme of formative
research. Health Promotion International, 30, 756-768.
Friese, M., Frankenbach, J., Job, V., and Loschelder, D. 2016. Does selfcontrol training
improve selfcontrol? A metaanalysis. Perspectives on Psychological Science, 12,
1077-1099.
French, D. P., and Cooke, R. 2011. Using the theory of planned behavior to understand binge
drinking: the importance of beliefs for developing interventions. British Journal of
Health Behavior, 17, 1-12.
Gallagher, K. M., and Updegraff, J. A. 2012. Health message framing effects on attitudes,
intentions, and behavior: a meta-analytic review. Annals of Behavioral Medicine, 43,
101-116.
Gaston, A., and Cramp, A. 2011. Exercise during pregnancy: a review of patterns and
determinants. Journal of Science and Medicine in Sport, 14, 299-305.
Gottfredson, M. R., and Hirschi, T. 1990. A general theory of crime. Stanford, CA: Stanford
University Press.
Hagger, M. S. 2009. Theoretical integration in health psychology: Unifying ideas and
complementary explanations. British Journal of Health Psychology, 14, 189-194.
Hagger, M. S. 2013. The multiple pathways by which self-control predicts behavior.
Frontiers in Psychology, 4, 849.
Hagger, M. S. 2014. The multiple pathways by which trait self-control predicts health
behavior. Annals of Behavioral Medicine, 48, 282-283.
Hagger, M. S., and Chatzisarantis, N. L. D. 2009. Integrating the theory of planned behavior
and self-determination theory in health behavior: a meta-analysis. British Journal of
Health Psychology, 14, 275-302.
Hagger, M. S., and Chatzisarantis, N. L. D. 2014. An integrated behavior change model for
physical activity. Exercise and Sport Sciences Reviews, 42, 62-69.
Hagger, M. S., Chatzisarantis, N. L. D., Barkoukis, V., Wang, J. C. K., Hein, V., Pihu, M.,…
Karsai, I. 2007. Cross-cultural generalizability of the theory of planned behavior among
young people in a physical activity context. Journal of Sport and Exercise Psychology,
29, 2-20.
Hagger, M. S., Sultan, S., Hardcastle, S. J., Reeve, J., Patall, E. A., Fraser, B., . . .
Chatzisarantis, N. L. D. 2016. Applying the integrated trans-contextual model to
mathematics activities in the classroom and homework behavior and attainment.
Learning and Individual Differences, 45, 166-175.
Hagger, M. S., Trost, N., Keech, J., Chan, D. K. C., and Hamilton, K. 2017. Predicting sugar
consumption: application of an integrated dual-process, dual-phase model. Appetite, 116,
147-156.
Huba, G. J., & Harlow, L. L. (1987). Robust structural equation models: Implications for
developmental psychology. Child Development, 58, 147-166.
Hamilton, K., Cox, S., and White, K. M. 2012a. Testing a model of physical activity among
mothers and fathers of young children: integrating self-determined motivation, planning,
and the theory of planned behavior. Journal of Sport and Exercise Psychology, 34, 124-
145.
Hamilton, K., Peden, A.E., Pearson, M., and Hagger, M. S. 2016. Stop there's water on the
road! Identifying key beliefs guiding people’s willingness to drive through flooded
waterways. Safety Science, 86, 308-314.
Hamilton, K., Kirkpatrick, A., Rebar, A., and Hagger, M. S. 2017a. Child sun safety:
application of an Integrated Behavior Change model. Health Psychology, 36(6), 916-
926.
Hamilton, K., Kirkpatrick, A., Rebar, A., White, K.M. and Hagger, M. S. 2017b. Protecting
young children against skin cancer: Parental beliefs, roles, and regret. Psycho-
Oncology, 26, 2135-2141. doi:10.1002/pon.4434
Hamilton, K., White, K. M., and Cuddihy, T. 2012. Using a single-item physical activity
measure to describe and validate parents' physical activity patterns. Research
Quarterly for Exercise and Sport, 83, 340-345.
Hamilton, K., White, K.M., Young, R., Hawkes, A., Starfelt, L.C., and Leske, S. 2012b.
Identifying critical sun-protective beliefs among Australian adults. Health Education
Research, 27, 834-843.
Hankonen, N., Kinnunen, M., Absetz, P., & Jallinoja, P. (2014). Why do people high in self-
control eat more healthily? Social cognitions as mediators. Annals of Behavioral
Medicine, 47, 242-248.
Higgins, G. E., and Marcum, C. D. 2005. Can the theory of planned behavior medicate the
effects of low self-control on alcohol use? College Student Journal, 39, 90-103.
Hofmann, W., and Kotabe, H. 2012. A general model of preventive and interventive self-
control. Social and Personality Psychology Compass, 6, 707-722.
Hu, L. T., and Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure
analysis: conventional criteria versus new alternatives. Structural Equation Modeling -
A Multidisciplinary Journal, 6, 1-55.
Jepson, R. G., Harris, F. M., Platt, S., and Tannahill, C. 2010. The effectiveness of
interventions to change six health behaviors: a review of reviews. BMC Public Health,
10:538. Retreived from http://www.biomedcentral.com/1471-2458/10/538
Junger, M., and Van Kampen, M. 2010. Cognitive ability and self-control in relation to
dietary habits, physical activity, and bodyweight in adolescents. International Journal
of Behavioral Nutrition and Physical Activity, 7, 112.
Little, T. D., Cunningham, W. A., Shahar, G., and Widaman, K. F. 2002. To parcel or not to
parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9,
151-173.
Kok, G., Gottlieb, N. H., Peters, G.-J. Y., Mullen, P. D., Parcel, G. S., Ruiter, R. A. C., . . .
Bartholomew, L. K. 2016. A taxonomy of behavior change methods: An intervention
mapping approach. Health Psychology Review, 10, 297-312.
McEachan, R. R. C., Conner, M., Taylor, N. J., and Lawton, R. J. 2011. Prospective
prediction of health-related behaviors with the theory of planned behavior: a meta-
analysis. Health Psychology Review, 5, 97-144.
McEachan, R., Taylor, N., Harrison, R., Lawton, R., Gardner, P., and Conner, M. 2016. Meta-
analysis of the reasoned action approach (RAA) to understanding health behaviors. Annals
of Behavioral Medicine, 50, 592-612.
Michie, S., van Stralen, M. M., and West, R. 2011. The behavior change wheel: a new
method for characterising and designing behavior change interventions.
Implementation Science, 6, 42.
Milton, K., Bull, F., and Bauman, A. 2010. Reliability and validity testing of a single-item
physical activity measure. British Journal of Sports Medicine, 45, 203-208.
Montaño, D. E., & Kasprzyk, D. 2008. Theory of reasoned action, theory of planned
behavior, and the integrated behavioral model. In K. Glanz, B. K. Rimer & K.
Viswanath (Eds.), Health behavior and health education: Theory, research, and
practice (4th ed., pp. 67-96). San Francisco, CA: Jossey-Bass.
Nascimento, S.L., Surita, F.G, Godoy, A.C., Kasawara, K. T., and Morais, S. S. 2015.
Physical activity patterns and factors related to exercise during pregnancy: a cross-
sectional study. PloS One, 10(6):e0128953. doi:10.1371/journal.pone.0128953.
Nascimento, S. L., Surita, F. G., and Cecatt, J. G. 2012. Physical exercise during pregnancy: a
systematic review. Current Opinion in Obstetrics & Gynecology, 24, 387-394.
Reyes Fernández , B., Knoll, N., Hamilton, K., and Schwarzer, R. 2016. Social-cognitive
antecedents of hand washing: action control bridges the planning-behavior gap.
Psychology & Health, 31, 993-1004.
Rich, A., Brandes, K., Mullan, B. A., and Hagger, M. S. 2015. Theory of planned behavior
and adherence in chronic illness: A meta-analysis. Journal of Behavioral Medicine, 38,
673-688.
Ryan, R. M., & Connell, J. P. 1989. Perceived locus of causality and internalization:
examining reasons for acting in two domains. Journal of Personality and Social
Psychology, 57, 749.
Tangney, J. P., Baumeister, R. F., and Boone, A. L. 2004. High self-control predicts good
adjustment, less pathology, better grades, and interpersonal success. Journal of
Personality, 72, 271-324.
Teixeira, P. J., Carraça, E. V., Markland, D., Silva, M. N., and Ryan, R. M. 2012. Exercise,
physical activity, and self-determination theory: a systematic review. International
Journal of Behavioral Nutrition and Physical Activity, 9, 78-86.
Vayro, C., and Hamilton, K. 2016. Using three-phase theory-based formative research to
explore healthy eating in Australian truck drivers. Appetite, 98, 41-48.
White, K.M., O’Connor, E.L., & Hamilton, K. (2011). Ingroup and role identity influences on
the initiation and maintenance of students' voluntary attendance at peer study sessions
for statistics. British Journal of Educational Psychology, 81, 325-343.
Wilkinson, S.A., Miller, Y.D., and Watson, B. 2009. Prevalence of health behaviors in
pregnancy at service entry in a Queensland health service district. Australian and New
Zealand Journal of Public Health, 33, 228-233.
Zhou, G., Gan, Y., Miao, M., Hamilton, K., Knoll, N., and Schwarzer, R. 2015. The role of
action control and action planning on fruit and vegetable consumption. Appetite, 91,
64-68.
Table 1
Physical activity and pregnancy: Demographic data and descriptive statistics for study
variables across time points
Variable
Time 1
Time 2
Participants, n
207
117
Age in years, Mean (SD)
30.03(4.49)
30.53 (4.42)
Gestational age in weeks, Mean (SD)
25.02 (8.70)
24.32 (8.81)
BMI in kg/m2, Mean (SD)
27.49 (5.74)
27.34 (5.63)a
Employment status n (%)
currently unemployed/home duties
57 (27.5%)
29 (24.8%)
currently employed full-time
81 (39.1%)
49 (41.9%)
part-time/casual employed
69 (33.4%)
39 (33.3%)
Ethnicity n(%)b
Caucasian
196 (95.6%)
111 (96.5%)
Indigenous/Torres Strait Islander
2 (1.0%)
1 (0.9%)
Other
7 (3.4%)
3 (2.6%)
Annual household income n (%)
AU$0-$18,200 (US$0-$13946)
12 (5.8%)
3 (2.6%)
AU$18201- $37,000 (US$13947- $28352)
9 (4.3%)
6 (5.1%)
AU$37001- $80,000 (US$28353 - $61302)
58 (28.0%)
32 (27.3%)
AU$80,001- $180,000 (US$61303 - $137930)
102 (49.3%)
62 (53.0%)
AU$180,000+ (US$137931+)
26 (12.6%)
14 (12.0%)
Education level attained n (%)
Junior school
12 (5.8%)
4 (3.4%)
Senior school
28 (13.5%)
13 (11.1%)
TAFE (technical and further education) /diploma
50 (24.2%)
28 (23.9%)
University undergraduate degree
73 (35.2%)
42 (36.0%)
University postgraduate degree
44 (21.3%)
30 (25.6%)
Psychological variables, Mean (SD)
Attitude
5.43 (1.53)
5.63 (1.40)
Subjective norm
5.82 (1.35)
6.03 (1.10)
Perceived behavioral control
4.96 (1.70)
5.12 (1.71)
Intention
5.15 (1.78)
5.39 (1.61)
Intrinsic motivation
5.92 (1.13)
6.07 (0.90)
Self-control
3.48 (0.52)
3.52 (0.50)
Behavior
3.89 (1.86)
4.02 (1.75)
Note. Age expressed in years; Gestation age expressed in weeks; BMI expressed as weight
(kg)/height (m)2; Psychological variables measured on 1 to 7 scale; aOne participant did not
report their BMI; bTwo participants did not report their ethnicity; Time 1 = baseline data,
Time 2 = follow-up data.
1
Table 2
Physical activity and pregnancy: Estimated means (M), standard deviations (SD), and intercorrelations of latent variables (N=117)
1
2
3
4
5
6
7
8
9
10
1. Intrinsic motivation T1
1.00
2. Attitude T1
0.54**
1.00
3. Subjective norm T1
0.35*
0.41*
1.00
4. PBC T1
0.37*
0.68**
0.42*
1.00
5. Intention T1
0.47**
0.78**
0.39*
0.78**
1.00
6. Self-control T1
0.45*
0.24*
0.16*
0.17*
0.19*
1.00
7. PA behavior T2
0.23*
0.49*
0.26*
0.57*
0.59*
0.27*
1.00
8. Age
-0.03
-0.02
-0.01
-0.01
-0.01
-0.07
-0.17*
1.00
9. BMI
-0.13*
-0.07
-0.05
-0.05
-0.06
-0.16*
-0.12*
0.10
1.00
10. Gestational age
0.01
0.02
0.01
0.03
0.01
0.08
-0.12*
-0.01
0.12*
1.00
Factor loadings for manifest
indicators within construct
0.91;
0.77
0.95;
0.70
0.77;
0.90;
0.94
0.95;
0.91
0.98;
0.97
0.60;
0.63;
0.50;
0.67
0.91;
0.96
Ma
6.07
5.63
6.03
5.12
5.39
3.51
4.01
30.53
27.34
24.32
SDa
0.90
1.30
1.10
1.71
1.61
0.51
1.74
4.42
5.63
8.81
Alphaa
0.71b
0.71b
0.93
0.83b
0.96b
0.76
0.89b
Note. Age expressed in years; Gestation age expressed in weeks; BMI expressed as weight (kg)/height (m)2; Psychological variables measured on
1 to 7 scale; PBC = perceived behavioral control; aManifest scale means, standard deviations, and internal consistency; bPearson correlation (two
items only); T1 = baseline data, T2 = follow-up data. *p < 0.05; **p < 0.01.
2
Figure 1. Structural model for predicting PA in pregnant women (N = 117). Fully standardised beta coefficients are reported. Of the covariates
entered (i.e., age, BMI, gestation age), none emerged as having significant associations (p > 0.05) over and above the social-cognitive variables
in the tested model. BMI = Body-mass index. Significance levels were *p < 0.05, **p < 0.01.
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    http://www.sciencedirect.com/science/article/pii/S0925753516301382?np=y Floods are among the most widespread of natural disasters and exposure to floodwaters increases drowning risk. A leading cause of flood related drowning deaths is driving through flooded waterways. Drawing on the Theory of Planned Behaviour, a two-phased research program was conducted. Phase 1 (N = 25; Mage = 32.38, SD = 11.46) identified common beliefs about driving through a flooded waterway. Phase 2 (N = 174; Mage = 27.43, SD = 10.76) adopted a cross-sectional design to examine the belief predictors of drivers’ willingness to drive through a flooded waterway. Given differences in consequences due to the depth of water, scenarios of low (road covered in 20 cm of water) and high (road covered in 60 cm of water) risk situations were investigated. A range of beliefs emerged as predicting drivers’ willingness to engage in this unsafe driving behaviour. These included attitudinal beliefs (e.g., sustain vehicle damage, become stuck/stranded), beliefs of social expectations (e.g., pressure from friends, family members, police), and efficacy beliefs (e.g., small distance of water to drive through, presence of signage). The results of the current study support using a Theory of Planned Behaviour belief-based approach to the understanding of risky transport-related aquatic activities. The findings highlight the role that specific key beliefs play in guiding people’s willingness to drive through flooded waterways and, in turn, provide possible targets for future interventions to curb this risky and potentially fatal driving behaviour.
  • Article
    Online health behaviour interventions have great potential but their effectiveness may be hindered by a lack of formative and theoretical work. This paper describes the process of formative research to develop theoretically and empirically based health messages that are culturally relevant and can be used in an online intervention to promote healthy lifestyle behaviours among new university students. Drawing on the Theory of Planned Behaviour, a three-phase programme of formative research was conducted with prospective and current undergraduate students to identify (i) modal salient beliefs (the most commonly held beliefs) about fruit and vegetable intake, physical activity, binge drinking and smoking, (ii) which beliefs predicted intentions/behaviour and (iii) reasons underlying each of the beliefs that could be targeted in health messages. Phase 1, conducted with 96 pre-university college students, elicited 56 beliefs about the behaviours. Phase 2, conducted with 3026 incoming university students, identified 32 of these beliefs that predicted intentions/behaviour. Phase 3, conducted with 627 current university students, elicited 102 reasons underlying the 32 beliefs to be used to construct health messages to bolster or challenge these beliefs. The three-phase programme of formative research provides researchers with an example of how to develop health messages with a strong theoretical- and empirical base for use in health behaviour change interventions.