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\begin{document}

\title{Effectiveness of A Theory-based Intervention to Promote Diabetes
Management Behaviours Among Adults with Type 2 Diabetes in Iran: A
Randomized Control Trial}



\author[1]{Reza Daryabeygi}%
\author[2]{Katherine White}%
\author[3]{kurosh djafarian}%
\author[1,1]{Sheikh Mohammed  Shariful Islam}%
\author[4]{Susie Cartledge}%
\author[5]{Mohammad Payam Ghaffari}%
\author[3]{Seyed Ali Keshavarz}%
\affil[1]{Deakin University}%
\affil[2]{Queensland University of Technology}%
\affil[3]{Tehran University of Medical Sciences}%
\affil[4]{Flinders University}%
\affil[5]{Islamic Azad University Science and Research Branch}%


\vspace{-1em}



  
  \date{May 20, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background: The prevalence of diabetes has been increasing, imposing
massive costs on nations. Diet and physical activity are recommended for
diabetes management. Evidence suggests theory-based interventions are
more efficacious than non-theory approaches. This study aimed to test
the effectiveness of an integrated theoretical model-based intervention
to encourage compliance for low-fat food consumption, carbohydrate
counting, and physical activity in adults with type 2 diabetes. Methods:
A 4-week parallel randomized control trial was conducted in Iran. Data
were collected using a self-report questionnaire at baseline and 8-weeks
post-intervention. This survey assessed the Theory of Planned Behaviour
(TPB) constructs of attitude, subjective norm (others' approval), and
perceived behavioural control (PBC). We also assessed risk perceptions
(motivational) and planning (volitional) from the Health Action Process
Approach (HAPA). Furthermore, weight, body mass index, triglyceride
(TG), and LDL-cholesterol were measured. Results: For both low-fat food
consumption and physical activity, only planning revealed a significant
improvement over time for intervention rather than control participants
(F=8.78, p[?]0.001 for low-fat vs. F=11.26, p[?]0.001 for physical
activity). For carbohydrate counting, significant effects were found for
behavior (F=4.37, p=0.03), intention (F=8.14, p[?]0.001), PBC (F=7.52,
p[?]0.001), and planning (F=4.54, p=0.03), reflecting improvements over
time in the intervention participants compared to controls. Further, the
effects of the intervention on behavior were partially mediated via
participants' degree of planning (B=0.10, SE=0.06, CI=0.01 to 0.26). The
serum TG level was significantly reduced from pre to post-intervention
for intervention rather than for control participants (F=18.69,
p[?]0.001). Conclusions: This intervention was promising for carbohydrate
counting but did not show improvements for low-fat diet nor physical
activity. Given the improvement in psychological measures and
self-reported behaviour for carbohydrate counting, coupled with the
findings for TG, future research is needed to demonstrate longer-term
improvements. Current theory-based educational strategies can be adopted
for the management of carbohydrate intake in diabetes.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Diabetes is a significant health challenge and dramatically growing
worldwide, especially in low-and middle-income countries (Guariguata et
al., 2014). Diabetes prevalence in Iran is estimated to be 10.1\% in
2015, which translates to 4.6 million adults (20-79 years) with diabetes
(IDF, 2015). Uncontrolled diabetes in the long-term can progressively
lead to complications, including retinopathy, nephropathy, neuropathy,
and cardiovascular effects (Alberti and Zimmet, 1998, IDF, 2013).
Maintenance of blood glucose, cholesterol, and blood pressure to near
normal levels can delay or prevent diabetes complications (IDF, 2013).
The American Diabetes Association (ADA) recommends lower saturated fat
intake in people with diabetes. It considers carbohydrate monitoring
using carbohydrate counting as a critical strategy in the management of
blood glucose (ADA, 2019). Furthermore, physical activity is an
influential component in diabetes management (Clark et al., 2004,
Wheeler et al., 2012). Definitions of the included behaviours are as
follows: Low-fat food/meal consumption was defined as ``reducing
saturated fat intake by low-fat dairy products, using polyunsaturated
and monounsaturated oils {[}plant based{]}, avoiding fried foods and
trimming the fat from meat {[}Lean meat{]}'' (Diabetes Australia, 2015).
Carbohydrate counting was defined as ``identifying which foods contain
carbohydrate, then assessing how much carbohydrate a serving of food (or
an entire meal) contains concerning recommendations and if you use
insulin, match with insulin dose'' (ADA, 2017; Joslin Diabetes Center,
2019) and physical activity was defined as ``engaging in moderate
physical activity for at least 150 minutes per week {[}half an hour/day,
most of the days of week{]}'' (Diabetes Australia, 2015).

\textbf{Background}

Behavioural interventions have demonstrated effectiveness in diabetes
management, and theory-based interventions are more efficacious than
non-theoretical approaches (Michie and Abraham, 2004). Several theories
have been used to inform health behaviour change, including the Theory
of Planned Behaviour (TPB) (Ajzen, 1991) and the Health Action Process
Approach (HAPA) (Schwarzer, 2008). According to the TPB, a person's
behaviour is a function of intention to engage in the behaviour. In
turn, the intention is directly determined by attitude (positive or
negative evaluation of the behaviour), subjective norm (perceived
pressure to perform a behaviour), and perceived behavioural control
(PBC; perception of control over performing the behaviour, also said to
influence behaviour directly). The direct TPB constructs also have a
belief basis with advantages/disadvantages underlying attitude,
important specific referents underlying subjective norm, and
barriers/facilitators underlying PBC. The recent debate about the
utility of the TPB in health psychology applications (Sniehotta et al.,
2014) highlights the need to test the validity of the TPB. A recent
meta-analysis demonstrated the effectiveness of TPB-based interventions
in people with type 2 diabetes (T2D) (Steinmetz et al., 2016), with the
effect size of 0.5 (CI=0.24 to 0.75) for behaviour. A TPB-based
intervention among older T2D adults with cardiovascular disease found an
extended TPB (with planning as an extra measure) intervention effective
for improving physical activity but not healthy eating (White et al.,
2012).

The HAPA is another theoretical framework focusing on motivational and
volitional processes in health behaviour change (Schwarzer, 2008).
Preintentional (motivational) processes target behavioural intention,
and post intentional (volitional) processes lead to actual behaviour
change. Perceived risk and outcome expectancies drive contemplation in
the motivation phase, with self-efficacy also influencing intention.
Schwarzer considers a minimum level of perceived risk necessary for the
initiation of contemplation regarding the benefits of behaviour and
one's competence to perform the behaviour. For intentions to translate
into behaviour, a volitional phase involving planning occurs, also
underpinned by self-efficacy.

Moreover, perceived risk is a core motivational concept in many health
behaviour theories, such as the Health Belief Model {[}HBM{]}
(Rosenstock, 1974). It has been included in TPB studies (Brewer et al.,
2007) to influence motivation for the initiation of mental preparedness
for health change. Typical inclusions of risk perception measurement are
risk severity (feeling the seriousness of contracting an illness or of
leaving it untreated) and risk susceptibility (beliefs about the
likelihood of getting a disease). Risk severity has been effective in
predicting hemoglobin A1C (HbA1c) reductions in T2D participants (Daniel
and Messer, 2002) and those with higher perceived severity and
susceptibility were more adherent to self-care behaviours (Ayele et al.,
2012, Tan, 2004).

In addition, planning has been suggested as a key volitional variable in
behaviour change interventions (Sniehotta et al., 2005b, Norman and
Conner, 2005), especially as it can mediate the intention-behaviour
relationship (Norman and Conner, 2005, Luszczynska, 2006, White et al.,
2012). According to the HAPA, the intention should be transformed into
detailed instructions on how to perform the intended behaviour (action
plans), which is necessary to prevent impulsive actions. Further,
maintaining the initiated behaviour should be protected from barriers
and obstacles by consideration, for example, of effective coping
strategies (coping plans). It is believed that, via planning,
individuals form a subjective instance of the target situation, which
makes the target cue related to the situation more accessible and
critical conditions easily identifiable (Gollwitzer, 1999). Planning has
been previously assessed via action planning (enabling practical
behaviour initiation via specifying a time, location, and how to perform
the behaviour (Leventhal et al., 1965)) and coping planning (overcoming
barriers by utilizing coping responses to deal with difficulties
(Sniehotta et al., 2006)). Similarly, objective measures such as weight,
body mass index (BMI), triglycerides (TG), and LDL-cholesterol (LDL-c)
are essential components of comprehensive diabetes evaluation (ADA,
2019).

In a previous formative qualitative study among T2D (Blinded for review)
we showed the inclusion of the standard TPB constructs (attitude,
subjective norm, PBC), as well as risk perceptions and planning factors
from the HAPA, which appeared viable as the focus of a behaviour change
intervention among this cohort. To our knowledge, there is no TPB-based
intervention among diabetes patients in Iran. Thus, the present study
employed an integrated theoretical framework for informing and
evaluating a behavioural intervention to promote low-fat food
consumption, carbohydrate counting, and physical activity, three
critical behaviours in diabetes management. We hypothesized that
intervention participants would demonstrate an improvement compared to
control participants at the end of the study, for (a) attitude,
subjective norm, perceived behavioural control, intention, planning and
risk perception for the three diabetes management behaviours, (b) the
self-reported diabetes management behaviour, and (c) the behavioural
indicators of weight, BMI, TG, and LDL-c.

\textbf{Methods}

A parallel randomized control trial was designed to assess the
effectiveness of a 4-week intervention based on the integrated
theoretical model in adults with T2D. The factors and underlying beliefs
identified for intervention focus were based on an initial, formative
qualitative study among 30 T2D participants. Ethical approval was
obtained from the Ethics Committee of ``Blinded for Review''. Given
there are no national dietary or educational guidelines for diabetes in
Iran, target behaviours were adapted from international guidelines such
as the American Diabetes Associations (ADA, 2017), Diabetes Australia
(Diabetes Australia, 2015), and the Joslin Diabetes Center (Joslin
Diabetes Center, 2019). The study was registered with the Iranian
Registry of Clinical Trials (IRCT2013040912961N1).

\textbf{Participants}

T2D adults were recruited from the Iranian Diabetes Society (IDS),
Tehran, Iran. Inclusion criteria were adults (aged 25-60 years)
diagnosed with T2D for at least two years on lipid-lowering medications
(e.g., Statins, Nicotinic acid). They had taken part in regular diabetes
education classes, including carbohydrate counting. People with diabetes
complications (retinopathy, nephropathy, and neuropathy), CVD
(cardiovascular diseases) or other conditions that may prevent class
attendance, taking medications that affect lipid levels such as
antipsychotics (e.g., Clozapine), and alcohol drinkers (consume beer,
wine, liquor, or hard liquor beverages at least once per week for a
minimum of 6 months (Lee et al., 2007)) were ineligible.

\textbf{Design and Procedure}

The present study employed a 2 (Condition: intervention vs. control) by
2 (Time: preintervention vs. 8-week post-intervention) mixed measure
design, with Time a repeated measure factor. A power calculation was
undertaken using G*power 3.1 software (Faul et al., 2009), whereby a
repeated measure MANOVA is used with a power of 80\%, an effect size of
0.4 and \selectlanguage{greek}α\selectlanguage{english}=0.05, yielding a minimum sample size required of 97
participants (that was minimally exceeded following exclusions, etc.). A
total of 220 adults diagnosed with T2D were selected from a list
provided by IDS using permuted block randomization (a 1:1 allocation
ratio) and contacted. As a result, the exclusion could only take place
after the randomization process. Permuted block pattern was obtained via
an Internet-generated pattern with a block size of 8. The participant's
list was provided by IDS staff, and the researchers were blinded. Figure
1 shows the flow of participants in the study. Written informed consent
was obtained from each participant before the experiment. The study
conforms to CONSORT guidelines (Schulz et al., 2010) (see Supplementary
File 1).

\textbf{Control Group}

Participants in the control group completed pre- and post-intervention
measures but received no intervention. They were offered one month of
nutrition counseling after completion of the study.

\textbf{Intervention Group}

The intervention participants attended weekly 2-hour group-based
intervention classes (each comprising 10 to 12 individuals) over four
weeks, facilitated by two nutritionists. No outside of class contact was
initiated between participants and educators during the study (see Table
1 for intervention strategies).

\textbf{Measures}

At Time 1, one-week before the intervention, participants completed a
demographic questionnaire at arrival, then via a face-to-face interview
by a trained health care provider completed an integrated questionnaire.
Bodyweight and height were measured, and fasting blood lipids were
assessed (Table 2). Time 2 (follow-up) measurements were performed eight
weeks post-intervention during which time the integrated questionnaire
measures were completed, weight and height measurements repeated, and
lipid profile determined. While HbA1c would have been a desirable
measurement for this study, we were unable to measure HbA1c since the
measurements were performed eight weeks after the intervention. HbA1c is
measurable after three consecutive months. Of the pre-intervention
individuals, 107 (control, \emph{n} =54; intervention, \emph{n} =53)
participated in all sessions and completed second measurements at week-8
(Figure 1).

The questionnaires were constructed broadly according to guidelines and
examples outlined in previous research (Ajzen, 1991, Sniehotta, 2009,
Schwarzer, 2008), with some negatively-worded items to reduce response
bias. (see Table 3). The first draft questionnaires were translated from
English into Persian under the supervision of an expert panel (11
Professors of nutrition, psychology, and health promotion) familiar with
both Persian and English. The content validity of questionnaires was
evaluated by a content validity index (CVI) and content validity ratio
(CVR) and satisfactory scores (\textgreater{}0.82 and
\textgreater{}0.75) for all measures. The questionnaires were then
completed by five individuals with T2D to assess wording and their
comprehension of items. The integrated questionnaires are available as
supplementary (Supplementary File 2-4).

\textbf{Data Analysis}

The impact of the intervention across all three behaviours was assessed
using 2 (intervention, control) \selectlanguage{ngerman}× 2 (pre, post-intervention) MANOVAs.
The integrated model constructs were considered as predictor variables
for low-fat food/meal consumption, carbohydrate counting, and physical
activity. In another MANOVA analysis, BMI, weight, TG, and LDL-c were
considered as predictors to show the effects of the intervention on
clinical and biochemical outcomes. Mediation analyses were also
conducted to assess whether any of the (significant) extended TPB
variables mediated the impact of the intervention on behaviour. \selectlanguage{greek}α \selectlanguage{english}was
set at 0.05 as the significance level. Baseline differences for age,
gender, education level, job, marital status, years since diagnosis,
weight, height, BMI, TG, and LDL-c were examined by Chi-square and
independent t-tests.

\textbf{Results:}

The demographic characteristics of participants and baseline measures
are shown in Table 2. The majority of participants were female (65.3\%),
full-time employees (53\%), and married (90.3\%). The mean age of the
participants was 49.4 years\selectlanguage{ngerman}±SD=6.15 in the control group and 48.05 years
±5.69 in the intervention. No baseline differences were observed in any
variables. The mean number of years since diagnosis was 5.85 years
(SD=2.89; range=2-14 years).

To assess any baseline differences for the integrated model measures,
MANOVAs revealed no significant differences between groups (intervention
or control) on the constructs for all three behaviours.
Intention-to-treat analysis showed similar findings to those with the
full participation of the study.

For the 2×2 MANOVA analysis, the between-subject factor was the
intervention condition (intervention vs. control), and the
within-subject factor was the time (Time 1: pre-intervention and Time 2:
8 weeks post-intervention). The integrated model variables and behaviour
were entered as dependent variables (see Tables 4-7 for Ms, SDs, and
significance levels).

\emph{Impact of Intervention on Low-fat Food Consumption}

No significant Condition effects were observed for low-fat food
consumption. There were, however, significant Time and Time by Condition
effects: \selectlanguage{greek}\emph{F(11,80)=3.01, p=0.002, partial
η\textsuperscript{2}=0.29}\selectlanguage{english} and \selectlanguage{greek}\emph{F(11,80)=2.65, p=0.006, partial
η\textsuperscript{2}=0.26}\selectlanguage{english} , respectively. Univariate results revealed a
significant Time effect for risk severity with a significant increase
for risk severity over time. There was no significant Time by Condition
effect, except for planning, with a significant increase for planning
scores over time in the intervention group, but not the control
condition.

\emph{Impact of Intervention on Carbohydrate Counting}

For carbohydrate counting, there were no significant main effects for
Condition, but significant Time and Time by Condition effects
with\selectlanguage{greek}\emph{F(11,80)=3.67, p=0.000, partial η\textsuperscript{2}=0.33}\selectlanguage{english}
and\selectlanguage{greek}\emph{F(11,80)=3.21, p=0.001, partial η\textsuperscript{2}=0.30}\selectlanguage{english} ,
respectively. Univariate Time effects showed significant results for
behavior attitude\emph{,} risk severity\emph{,} and
planning\emph{,}which all increased over time. Further, significant Time
by Condition effects was observed for behaviour, intention, and
planning. Pairwise comparisons for the simple effects of time within the
intervention conditions were then conducted. Participants in the
intervention condition revealed significant increases in
behaviour\emph{,}intention\emph{,} PBC, and planning, at follow-up
compared to pre-intervention. There were no significant changes in the
control condition across time on these constructs.

\emph{Mediation Analyses for Carbohydrate Counting}

As participants in the intervention condition showed significant
improvement over time for carbohydrate counting, a mediation analysis
(Hayes, 2013) was conducted to assess whether the integrated model
variables mediated the impact of the intervention on carbohydrate
counting. In addition to pre-intervention carbohydrate counting
behavior, all potential mediators, along with Condition, were entered
into the model. The direct effect of Condition on behavior was
significant, \emph{B=0.54, SE=0.14, p\textless{}0.01} . Bootstrapping
analyses resulted in the total significant mediated (indirect) effect as
\emph{B=0.26, SE=0.12, CI=0.01 to 0.51} . The indirect effect was
significant only via planning, \emph{B=0.10, SE=0.06, CI=0.01 to 0.26} .
Further, inspection revealed that, after the inclusion of potential
mediators (integrated model variables), the direct effect of the
intervention on carbohydrate counting behaviour remained significant,
\emph{B=0.37, SE=0.16, CI=0.03 to 0.71,}indicating a partial mediation
effect.

\emph{Impact of Intervention on Physical Activity}

For physical activity, there were no significant effects for Condition
but significant effects for Time and Time by
Condition,\selectlanguage{greek}\emph{F(11,80)=4.01, p=0.000, partial
η\textsuperscript{2}=0.35}\selectlanguage{english} and\selectlanguage{greek}\emph{F(11,81)=3.83, p=0.000, partial
η\textsuperscript{2}=0.34}\selectlanguage{english} , respectively. For Time, there were
significant increases for intention, attitude, risk susceptibility, and
risk severity\emph{.} There was a single significant Time by Condition
interaction for planning whereby intervention participants indicated a
significant improvement over time, with no significant change in the
control condition.

\emph{Effects of Intervention on Anthropometric Measures and Lipid
Profiles}

Weight, BMI, TG, and LDL-c changes were assessed by a 2 (condition:
intervention, control) x 2 (time: pre, post-intervention) MANOVA. There
were significant main effects for Time and Time by
Condition,\selectlanguage{greek}\emph{F(1,92)=4.16, p=0.002, partial
η\textsuperscript{2}=0.19}\selectlanguage{english} and\selectlanguage{greek}\emph{F(1,92)=4.81, p=0.001, partial
η\textsuperscript{2}=0.21,}\selectlanguage{english}respectively. Univariate results of Time
indicated that TG increased significantly. The Time by Condition
findings showed a significant effect on TG. There was a significant
reduction of TG within the intervention condition with a significant
reduction of approximately 10 mg/dl over time but no significant effect
in the control condition\emph{.}

\textbf{Discussion:}

The current study tested the utility of an intervention informed by an
integrated theoretical model to promote low-fat food consumption,
carbohydrate counting, and physical activity among adults with T2D in
Iran. This theory-based intervention could be adopted by chronic disease
health care professionals. Based on the results of earlier qualitative
formative research among the target population (Blinded for review), the
intervention focused on constructs from the TPB and risk perceptions and
planning factors from the HAPA. To our knowledge, this study comprises
the first formal evaluation of a theory-based intervention to promote
carbohydrate counting - a critical behaviour in diabetes management. The
results suggest that the intervention was successful in changing
behaviour and cognitions related to carbohydrate counting, showing some
support for the integrated model to inform behaviour change in this
context. However, it did not produce the expected changes for either
low-fat food consumption or physical activity. The intervention was only
successful in promoting the volitional construct of planning, but
behaviour and other cognitions remained unchanged. This may be due to
the nature of diabetes as a disease highly concerned with nutrition
behaviours such that people already associate a diabetes diagnosis with
a need to address any unhealthy eating patterns (Yannakoulia, 2006) .

For carbohydrate counting, the intervention showed promise by
significant increases in behaviour, intention, PBC a,nd planning for
intervention participants over time compared to control participants,
with the improvements in carbohydrate counting behaviour at least
partially due to enhanced planning. These findings emphasize the
importance of including planning as a self-regulatory component in
behavioural interventions, reinforcing assertions that the impact of
cognitions on behaviour is mediated by planning (Norman and Conner,
2005, Sniehotta et al., 2005a, Gellert et al., 2012). There were no
improvements over time among intervention participants for the
motivational phase constructs of attitudes, subjective norm, and risk
perceptions, but there was a significant change for PBC. This highlights
the importance of control perceptions and the need to consider barriers
to performance at both motivational and volitional stages of decision
making for carbohydrate counting.

The observed differences for carbohydrate counting, as opposed to
low-fat food consumption, may be due to the specificity of the
carbohydrate counting behaviour for diabetes management and to the
short-term acute symptoms resulting from non-compliance (leading to
abnormal blood glucose and, consequently, clinical outcomes). Further,
participants had completed the study inclusion requirement of diabetes
education, incorporating carbohydrate counting training. They so were at
least primed as to the importance and positive benefits of this crucial
diabetes management behaviour.

For physical activity, intervention participants showed an improvement
in their degree of planning for physical activity compared to control
groups but no other cognitions or activity behaviours. These findings
are in contrast to other TPB-related studies showing it to be an
effective basis for a physical activity behaviour change intervention
among adults with T2D (White et al., 2012). The current study may have
faced challenges in promoting physical activity due to: (a) most
participants were females who are culturally less involved in physical
activities, especially outdoor exercise, and (b) \textgreater{}60\% of
participants had full-time jobs. The commute in Tehran is often 3-4
hours/day, both barriers which may benefit from direct targeting in
future interventions.

The intervention significantly reduced TG levels by approximately 10
mg/dl but had no effects on LDL-c, weight, and BMI. Low-carbohydrate
diets have been shown to reduce TG rather than LDL-c (Mahan et al.,
2012, Santos et al., 2012). As the only behaviour change observed in
this study was for carbohydrate counting. Because carbohydrate counting
restricts carbohydrate intake explicitly, it seems reasonable to relate
the reduction of TG to carbohydrate counting behaviour. Congruently, a
meta-analysis by Santos et al., showed that low-carbohydrate diets did
not affect LDL-c (Santos et al., 2012).

Strengths and Limitations

The study's strengths include the strong theory base of the intervention
informed by an initial pilot study (see Blinded for review) and the
mapping of these identified constructs on the intervention content.
Further, this study provided one of the first theory-based interventions
tackling the fundamental diabetes management behaviour of carbohydrate
counting. It offered a test of the utility of the integrated theoretical
model in informing health behaviour change in a cultural context where
there is a dearth of theory-based health behaviour change interventions
for T2D adults. Additional strengths were RCT design and the use of
face-to-face interviews by trained health care providers allowing
participants to check their understanding of the questions posed, likely
improving the reliability of responses.

The relatively small sample size and lack of objective measurements of
HbA1c and fasting blood sugar (FBS) is a limitation of the current
study. FBS and HbA1c are common outcome measures in diabetes. FBS
fluctuates in a short period, and regular test are required for judgment
purposes. With respect to limited resources and participants'
possibility of nonattendance for regular visits, we did not measure
blood sugar. However, HbA1c changes are measurable after three months.
Our post-intervention measurements were performed two months following
the latest intervention session, due to time restrictions. Thus, we
thought that two months might not accurately demonstrate the effects of
TPB intervention on HbA1c.

Our post-intervention measurements were scheduled to be only performed
on one occasion, two months post-intervention. Besides, study
participants were all IDS members. These individuals are usually highly
motivated about their diabetes management, bringing into question the
generalizability of the findings to other adults diagnosed with T2D not
aligned with a national diabetes organization.

Future research directions

Further research is needed to confirm the present findings, especially
for carbohydrate counting and longer-term follow-up designs to assess
other biochemical outcomes to validate self-reported behaviour.
Longer-term designs, potentially integrated into health care clinics,
would also be useful to check for intervention effects given common
health complications and to establish the cost-effectiveness of
behaviour change interventions in this context. Also, the utility of an
integrated theory intervention approach for other key self-management
practices such as self-monitoring of blood glucose and medication
adherence should be assessed. Considering the growing prevalence of T2D
in adolescents and children, further studies are needed to promote
diabetes management behaviours in these age groups including parental
adherence to carbohydrate counting and individuals diagnosed with type 1
diabetes (Kawamura, 2007, Scavone et al., 2010, Bell et al., 2014).

\textbf{Conclusion:}

Overall, the present study provided support for the utility of an
intervention informed by an integrated theoretical model, incorporating
aspects of both the TPB and the HAPA, targeting a critical behaviour
(i.e., carbohydrate counting) for a clinical population of individuals
with T2D. These results were encouraging not only in improving
cognitions and showing promise, especially for constructs in the
volitional phase of decision making, but also self-reported behaviour
and a biochemical outcome. The intervention did not support a change in
low-fat food consumption nor physical activity. Further research is
needed to assess the more prolonged-term effects of interventions with a
focus on carbohydrate counting and to attain generalized adherence to
key management behaviours among this at-risk group to reduce the
negative health consequences for people with a T2D diagnosis.

\textbf{Relevance to clinical practice:}

Health care staff including nurses, dietitians, diabetes educators,
general physicians as well as specialists can use current health
promotion approaches for the management of carbohydrate intake as a
diabetes-specific behaviour, especially as they potentially render
clinical benefits (i.e. reduced TG).

\textbf{References:}

ADA, 2017. Available:
http://www.diabetes.org/food-and-fitness/food/what-can-i-eat/understanding-carbohydrates/carbohydrate-counting.html?referrer=https://www.google.com/
{[}Accessed 2019{]}.

Diabetes Australia, 2015. Available: An electronic version of these
guidelines is available at www.diabetesaustralia.com.au {[}Accessed
2019{]}.

Joslin Diabetes Center, 2019. Available:
http://www.joslin.org/info/Carbohydrate\_Counting\_101.html
{[}Accessed{]}.

ADA 2019. American Diabetes Association: Standards of Medical Care in
Diabetes. \emph{Diabetes Care} .

AJZEN, I. 1991. The Theory of Planned Behavior. \emph{Organizational
Behavior and Human Decision Processes,}50\textbf{,} 179-211.

ALBERTI, K. G. M. M. \& ZIMMET, P. F. 1998. Definition, diagnosis and
classification of diabetes mellitus and its complications. Part 1:
Diagnosis and classification of diabetes mellitus. Provisional report of
a WHO consultation. \emph{Diabetic Medicine,} 15\textbf{,} 539-553.

AYELE, K., TESFA, B., ABEBE, L., TILAHUN, T. \& GIRMA, E. 2012. Self
care behavior among patients with diabetes in Harari, Eastern Ethiopia:
The Health Belief Model perspective. \emph{PLoS One,} 7\textbf{,}
e35515.

BELL, K. J., BARCLAY, A. W., PETOCZ, P., COLAGIURI, S. \& BRAND-MILLER,
J. C. 2014. Efficacy of carbohydrate counting in type 1 diabetes: a
systematic review and meta-analysis.\emph{The Lancet Diabetes \&
Endocrinology,} 2\textbf{,} 133-140.

CLARK, M., HAMPSON, S. E., AVERY, L. \& SIMPSON, R. 2004. Effects of a
tailored lifestyle self-management intervention in patients with type 2
diabetes. \emph{British Journal of Health Psychology,} 9\textbf{,}
365-379.

DANIEL, M. \& MESSER, L. C. 2002. Perceptions of disease severity and
barriers to self-care predict glycemic control in Aboriginal persons
with type 2 diabetes mellitus.\emph{Chronic Diseases and Injuries in
Canada,} 23\textbf{,} 130.

FAUL, F., ERDFELDER, E., BUCHNER, A. \& LANG, A.-G. 2009. Statistical
power analyses using G* Power 3.1: Tests for correlation and regression
analyses. \emph{Behavior research methods,} 41\textbf{,} 1149-1160.

GELLERT, P., ZIEGELMANN, J. P., LIPPKE, S. \& SCHWARZER, R. 2012. Future
time perspective and health behaviors: Temporal framing of
self-regulatory processes in physical exercise and dietary behaviors.
\emph{Annals of Behavioral Medicine,}43\textbf{,} 208-218.

GOLLWITZER, P. M. 1999. Implementation intentions: strong effects of
simple plans.\emph{American Psychologist,} 54\textbf{,} 493.

GUARIGUATA, L., WHITING, D., HAMBLETON, I., BEAGLEY, J., LINNENKAMP, U.
\& SHAW, J. 2014. Global estimates of diabetes prevalence for 2013 and
projections for 2035.\emph{Diabetes Research and Clinical Practice,}
103\textbf{,} 137-149.

HAYES, A. F. 2013. \emph{Introduction to mediation, moderation, and
conditional process analysis: A regression-based approach} , Guilford
Press.

IDF 2013. International Diabetes Federation: Diabetes Atlas, 6th ed.
\emph{Facts and Figures} .

IDF 2015. International Diabetes Federation: Diabetes Atlas, 7th ed.

KAWAMURA, T. 2007. The importance of carbohydrate counting in the
treatment of children with diabetes.\emph{Pediatric Diabetes,}
8\textbf{,} 57-62.

LEE, C. H., WU, D. C., LEE, J. M., WU, I., GOAN, Y. G., KAO, E. L.,
HUANG, H. L., CHAN, T. F., CHOU, S. H. \& CHOU, Y. P. 2007. Anatomical
subsite discrepancy in relation to the impact of the consumption of
alcohol, tobacco and betel quid on esophageal cancer.
\emph{International Journal of Cancer,} 120\textbf{,}1755-1762.

LEVENTHAL, H., SINGER, R. \& JONES, S. 1965. Effects of fear and
specificity of recommendation upon attitudes and behavior. \emph{Journal
of Personality and Social Psychology,} 2\textbf{,} 20.

LUSZCZYNSKA, A. 2006. An implementation intentions intervention, the use
of a planning strategy, and physical activity after myocardial
infarction. \emph{Social Science \& Medicine,} 62\textbf{,} 900-908.

MAHAN, L. K., ESCOTT-STUMP, S., RAYMOND, J. L. \& KRAUSE, M. V. 2012.
\emph{Krause's food \& the nutrition care process} , Elsevier Health
Sciences.

MICHIE, S. \& ABRAHAM, C. 2004. Interventions to change health
behaviours: evidence-based or evidence-inspired? \emph{Psychology \&
Health,} 19\textbf{,} 29-49.

NORMAN, P. \& CONNER, M. 2005. The theory of planned behavior and
exercise: Evidence for the mediating and moderating roles of planning on
intention-behavior relationships.\emph{Journal of Sport \& Exercise
Psychology} .

SANTOS, F., ESTEVES, S., DA COSTA PEREIRA, A., YANCY JR, W. \& NUNES, J.
2012. Systematic review and meta-analysis of clinical trials of the
effects of low carbohydrate diets on cardiovascular risk factors.
\emph{Obesity Reviews,}13\textbf{,} 1048-1066.

SCAVONE, G., MANTO, A., PITOCCO, D., GAGLIARDI, L., CAPUTO, S., MANCINI,
L., ZACCARDI, F. \& GHIRLANDA, G. 2010. Effect of carbohydrate counting
and medical nutritional therapy on glycaemic control in type 1 diabetic
subjects: a pilot study.\emph{Diabetic Medicine,} 27\textbf{,} 477-479.

SCHULZ, K. F., ALTMAN, D. G. \& MOHER, D. 2010. CONSORT 2010 statement:
updated guidelines for reporting parallel group randomised trials.
\emph{BMC medicine,} 8\textbf{,} 18.

SCHWARZER, R. 2008. Modeling health behavior change: How to predict and
modify the adoption and maintenance of health behaviors. \emph{Applied
Psychology,} 57\textbf{,} 1-29.

SNIEHOTTA, F. F. 2009. Towards a theory of intentional behaviour change:
Plans, planning, and self-regulation. \emph{British Journal of Health
Psychology,}14\textbf{,} 261-273.

SNIEHOTTA, F. F., PRESSEAU, J. \& ARA\selectlanguage{ngerman}ÚJO-SOARES, V. 2014. Time to retire
the theory of planned behaviour.\emph{Health Psychology Review,}
8\textbf{,} 1-7.

SNIEHOTTA, F. F., SCHOLZ, U. \& SCHWARZER, R. 2005a. Bridging the
intention--behaviour gap: Planning, self-efficacy, and action control in
the adoption and maintenance of physical exercise. \emph{Psychology \&
Health,} 20\textbf{,} 143-160.

SNIEHOTTA, F. F., SCHOLZ, U. \& SCHWARZER, R. 2006. Action plans and
coping plans for physical exercise: A longitudinal intervention study in
cardiac rehabilitation.\emph{British Journal of Health Psychology,}
11\textbf{,} 23-37.

SNIEHOTTA, F. F., SCHWARZER, R., SCHOLZ, U. \& SCHÜZ, B. 2005b. Action
planning and coping planning for long\selectlanguage{english}-term lifestyle change: theory and
assessment. \emph{European Journal of Social Psychology,} 35\textbf{,}
565-576.

STEINMETZ, H., KNAPPSTEIN, M., AJZEN, I., SCHMIDT, P. \& KABST, R. 2016.
How Effective are Behavior Change Interventions Based on the Theory of
Planned Behavior? \emph{Zeitschrift fur Psychologie} .

TAN, M. Y. 2004. The relationship of health beliefs and complication
prevention behaviors of Chinese individuals with Type 2 Diabetes
Mellitus. \emph{Diabetes Research and Clinical Practice,} 66\textbf{,}
71-77.

WHEELER, G., MONTGOMERY, S. B., BEESON, L., BAHJRI, K., SHULZ, E.,
FIREK, A., DE LEON, M. \& CORDERO-MACINTYRE, Z. 2012. En Balance The
Effects of Spanish Diabetes Education on Physical Activity Changes and
Diabetes Control. \emph{The Diabetes Educator,} 38\textbf{,} 723-732.

WHITE, TERRY, TROUP, REMPEL, NORMAN, MUMMERY, RILEY, POSNER \& KENARDY
2012. An extended theory of planned behavior intervention for older
adults with type 2 diabetes and cardiovascular disease. \emph{Journal of
Aging and Physical Activity,}20\textbf{,} 281.

YANNAKOULIA, M. 2006. Eating behavior among type 2 diabetic patients: a
poorly recognized aspect in a poorly controlled disease. \emph{Rev
Diabet Stud,} 3\textbf{,} 11-16.

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