![CFA-based relations between the items and the factors and between the factors. All relations between the factors and items and between the factors were statistically significant (all p < .05). F1 ¼ Attitude; F2 ¼ Intention; F3 ¼ Subjective Norms; F4 ¼ Perceived Behavioral Control; F5 ¼ Weight Controlling Behaviors. Fit indices: v2 [139] ¼ 245.835, p <.001, CFI ¼ 0.950, NFI ¼ 0.901, IFI ¼ 0.950, RMSEA ¼ 0.057 [(0.045-0.068].](https://www.researchgate.net/profile/Haidar-Nadrian/publication/334640474/figure/fig1/AS:846767960621056@1578896622238/CFA-based-relations-between-the-items-and-the-factors-and-between-the-factors-All_Q320.jpg)
Content uploaded by Haidar Nadrian
Author content
All content in this area was uploaded by Haidar Nadrian on Jan 13, 2020
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=uhcw20
Health Care for Women International
ISSN: 0739-9332 (Print) 1096-4665 (Online) Journal homepage: https://www.tandfonline.com/loi/uhcw20
Development and validation of a theory of
planned behavior-based weight control behavior
questionnaire among postmenopausal women
with osteoporosis
Hossein Hajizadeh, Haidar Nadrian, Nazila Farin, Mohammad Asghari
Jafarabadi, Seyed Jamal Ghaemmaghami Hezaveh, Sousan Kolahi, Pouria
Sefid Mooye Azar & Sharon Brennan-Olsen
To cite this article: Hossein Hajizadeh, Haidar Nadrian, Nazila Farin, Mohammad Asghari
Jafarabadi, Seyed Jamal Ghaemmaghami Hezaveh, Sousan Kolahi, Pouria Sefid Mooye Azar &
Sharon Brennan-Olsen (2019) Development and validation of a theory of planned behavior-based
weight control behavior questionnaire among postmenopausal women with osteoporosis, Health
Care for Women International, 40:10, 1101-1116, DOI: 10.1080/07399332.2019.1640700
To link to this article: https://doi.org/10.1080/07399332.2019.1640700
Published online: 23 Jul 2019.
Submit your article to this journal
Article views: 9
View related articles
View Crossmark data
Development and validation of a theory of planned
behavior-based weight control behavior questionnaire
among postmenopausal women with osteoporosis
Hossein Hajizadeh
a
, Haidar Nadrian
b
, Nazila Farin
c
,
Mohammad Asghari Jafarabadi
d
, Seyed Jamal Ghaemmaghami Hezaveh
e
,
Sousan Kolahi
f
, Pouria Sefid Mooye Azar
g
, and Sharon Brennan-Olsen
h,i
a
Department of Nutrition, Faculty of Nutrition and Food Sciences, Tabriz University of Medical
Sciences, Tabriz, Iran;
b
Medical Education Research Center, Health Management and Safety
Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran;
c
Nutrition
Research Center, Tabriz University of Medical Sciences, Tabriz, Iran;
d
Road Traffic Injury Research
Center, Tabriz University of Medical Sciences, Tabriz, Iran;
e
Department of Biochemistry and Diet
Therapy, Tabriz University of Medical Sciences, Tabriz, Iran;
f
Connective Tissue Diseases Research
Center, Tabriz University of Medical Sciences, Tabriz, Iran;
g
Department of Nutrition, Faculty of
Nutrition and Food Sciences, Tabriz University of Medical Sciences, Tabriz, Iran;
h
Department of
Medicine-Western Health, University of Melbourne, St Albans, Australia;
i
Australian Institute for
Musculoskeletal Science (AIMSS), University of Melbourne and Western Health, St
Albans, Australia
ABSTRACT
Our aim was to develop a framework-based weight control
behavior questionnaire (Weight-CuRB) and test its psychomet-
ric properties among a non-probability sample of 240 postme-
nopausal women with osteoporosis. Appropriate validity,
simplicity, functionality and reliability were observed for the
Weight-CuRB. The explanatory model fits the data well (v2
[139] ¼245.835, p<.001, CFI ¼0.950, NFI ¼0.901, IFI ¼
0.950, RMSEA ¼0.057[(0.045–0.068]). To our knowledge, this
was the first study to develop and validate a framework-based
instrument aiming at cognitive needs assessment of postme-
nopausal women with osteoporosis. The weight-CuRB may be
useful in addressing the core cognitive determinants of weight
control among the patients.
ARTICLE HISTORY
Received 14 July 2018
Accepted 3 July 2019
Osteoporosis is a systemic disease characterized by decreased bone mass
and degraded bone microarchitecture, which leads to an increased risk of
bone fragility (Lampropoulou-Adamidou, Karampinas, Chronopoulos,
Vlamis, & Korres, 2014). Based on the World Health Organization (WHO)
definition, osteoporosis is a bone mineral density (BMD) of 2.5 standard
deviations or more below the young adult mean BMD (Zhao et al., 2007).
It is a major public health problem in the people aged 50 years and older
(Shahla & Charesaz, 2007): this also extends to countries such as Iran,
CONTACT Haidar Nadrian haidarnadrian@gmail.com Medical Education Research Center, Health
Management and Safety Promotion Research Institute, Tabriz University of Medical Sciences, Tabriz, Iran
ß2019 Taylor & Francis Group, LLC
HEALTH CARE FOR WOMEN INTERNATIONAL
2019, VOL. 40, NO. 10, 1101–1116
https://doi.org/10.1080/07399332.2019.1640700
whereby the prevalence rate among Iranian women is more than 41%
(Naz, Ozgoli, Aghdashi, & Salmani, 2016; Shahla & Charesaz, 2007). In the
United States (US), Europe and Japan, osteoporosis affects about 75 million
people (Kanis, 2007), and in Iran, the prevalence of osteoporosis among
men and women are reported to be 12.4% and 55.7%, respectively
(Mahdaviroshan & Ebrahimimameghani, 2014).
Postmenopausal women, deprived of the protective effects of endogenous
estrogen are predisposed to increases in weight. For instance, women, but
not men, have exhibited a sharp increase in obesity between the ages of 45
and 54 years, as a result of declines in endogenous estrogen, together with
physical inactivity (Dubnov, Brzezinski, & Berry, 2003). Overweight or high
body mass index (BMI) are also reported as risk factors for vertebral frac-
ture among postmenopausal osteoporotic women (Pirro et al., 2010). On
the other hand, and while not fully understood but potentially due to the
reduced level of BMD and loss of protective tissue against pressure and
force (Nielson, Srikanth, & Orwoll, 2012), postmenopausal women with a
BMI below 18.5 kg/m
2
are more prone to hip fractures, compared to those
with normal BMI (18.5–25 kg/m
2
) (Mpalaris, Anagnostis, Goulis, &
Iakovou, 2015). The mean of BMI among postmenopausal women with
fracture in a study in the UK was 27.4 kg/m
2
, and about 28% of the cases
were obese (BMI 30 kg/m
2
) (Premaor, Pilbrow, Tonkin, Parker, &
Compston, 2010). Moreover, about one-half of all postmenopausal hip frac-
tures occur among overweight (BMI ¼25–29.9 kg/m
2
) (40%) or obese
(BMI 30 kg/m
2
) (9%) women (Armstrong et al., 2011).
Considering the above-mentioned associations between body weight and
risk of fracture among postmenopausal women with osteoporosis (Mpalaris
et al., 2015), weight control appears imperative to reducing the risk
(McConnon et al., 2012). However, limited effectiveness has been shown in
the common approaches to long-term overweight and obesity management,
which highlights the difficulty in effective weight control (Elfhag &
Rossner, 2005; Garcia Ulen, Huizinga, Beech, & Elasy, 2008; McConnon
et al., 2012). In order to better understand reasons that underpin weight
control behaviors and its determinants among postmenopausal women
with osteoporosis, there is a need for valid and reliable instruments.
Conducting pre-experimental studies applying such instruments will enable
future researchers to develop more targeted treatment programs aimed at
supporting weight control efforts among women with osteoporosis.
In order to understand the health behaviors and its determinants, there
exist a number of behavioral models: the Theory of Planned Behavior
(TPB) is one of the more widely-used frameworks to explain health behav-
iors (Ajzen 1985; Godin & Kok, 1996). Based on assumptions in the TPB,
an actual behavior is mostly determined by intention, which in turn is
1102 H. HAJIZADEH ET AL.
predicted by three distinct constructs: attitude, subjective norms and per-
ceived behavioral control (PBC). Attitude is the evaluation of an individu-
al’s behavior, subjective norms are an individuals’perceptions regarding
how important others expect him/her to practice a behavior, and PBC is an
individuals’perception regarding how much control he/she has over a
behavior (McConnon et al., 2012). PBC also encompasses the level of diffi-
culty perceived by an individual to perform a behavior (Ajzen 1991), which
may determine intention and actual behavior (McConnon et al., 2012).
Several researchers have applied the TPB as a theoretical framework for
a majority of diet and weight related studies including ‘healthier’eating
behaviors (Fila & Smith 2006;Jun&Arendt,2016), fruit and vegetable
consumption (Jalilian et al., 2016), fat intake (White, Terry, Troup, Rempel,
&Norman,2010) and weight control (McConnon et al., 2012). However,
the number of valid and reliable TPB-based instruments to be applied in
such studies is few. Given the importance of weight control in postmenau-
posal women with osteoporosis, and the lack of validated TPB-based scales
to investigate weight-control behaviors and its determinants, our aim in the
present study was to develop a TPB-based weight control behavior ques-
tionnaire (Weight-CuRB) and investigate its psychometric properties among
postmenopausal women with osteoporosis in Tabriz, Iran.
Material and methods
Participants
We conducted this cross-sectional study from July to December, 2017. We
invited a convenience sample of 270 postmenopausal women with osteopor-
osis reffering to two densitometry centers, in Tabriz, Iran, to participate in
the study. Eligibility criteria included postmenopausal women with primary
osteoporosis (with T-score score ˗2.5 at the mean lumbar spine (L1-4),
femoral neck, or total). Exclusion criteria included: taking antidepressants
and psychotropic drugs, immunosuppressive agents (kidney transplantation,
liver, autoimmune, cancers), or corticosteroids (betamethasone, prednisone,
hydrocortisone, dexamethasone), and those with type 2 diabetes mellitus,
rheumatoid arthritis, history of rheumatism and/or lupus, ankylosing spon-
dylitis, spondylitis arthritis. Among 270 invited patients, 21 cases declined to
participate in the study, and a further 9 cases did not fulfill inclusion criteria
and were thus excluded, leaving a total of 240 study participants.
Design and item generation
In order to develop the instrument and create an initial item pool, we con-
ducted a comprehensive review of the literature (Choyhirun, Suchaxaya,
HEALTH CARE FOR WOMEN INTERNATIONAL 1103
Chontawan, & Kantawang, 2008; McConnon et al., 2012;Schifter&
Ajzen, 1985; Waleekhachonloet, Limwattananon, Limwattananon, &
Gross, 2007). We searched the common search engines of PubMed/
MEDLINE, Science Direct, Scopus, and Google Scholar using the key-
words of weight control, weight change, weight management, women,
osteoporosis, theory of planned behavior, theory of reasoned action, and
(bone) fracture. An independent researcher cross-checked the derived
data and in total, 35 items were generated. We grouped these items sub-
sequently into the five subscales of attitude (7 items), intention (4 items),
subjective norms (6 items), pbc (7 items), and weight control behaviors
(11 items).
We developed the attitude scale to measure the attitude of participants
toward weight control and toward controlling diet and physical activity to
control weight. The intention scale was developed to assess if the partici-
pants intend to control their weight or not. In the subjective norms scale,
the participants were asked on how their family members, friends and rela-
tives expect them to control their weight. In the perceived behavior control
(PeBeC) scale, the patients were investigated on how much control they
have over their weight. In the weight control behaviors scale (WecBes), the
patients were asked on how often they perform weight controlling behav-
iors like physical activity, sedentary behaviors, fruit and vegetable consump-
tion and daily weighing.
Response format for the items of attitude, intention and subjective norms
scales were based on a five-point Likert scale (completely disagree [0],
disagree [1], no idea [2], agree [3], and completely agree [4]). A higher
score was indicative of a more positive attitude, and higher levels of inten-
tion and subjective norms in terms of perceptions toward weight con-
trol behaviors.
The response format for the items of PeBeC was based on a five-point
Likert scale (totally incorrect [0], incorrect [1], no idea [2], correct [3], and
totally correct [4]). A higher score was indicative of a greater level of con-
trol perceived by the participants toward weight control behaviors.
In the WecBes scale, a 5-point Likert scale (never ¼0, rarely ¼1, some-
times ¼2, often ¼3, always ¼4) was used as response format. A higher
score was indicative of a higher level of control perceived by the partici-
pants regarding their weight-related behaviors.
Participants provided us with demographic data pertaining to: age,
marital status (single, married, or widowed), perceived level of income
(low/moderate/high/very high), physical comorbidities, and family his-
tory of osteoporosis, occupation type (housewife, worker/farmer/self-
employed, employee, retired), use of supplements therapy, and age
at menopause.
1104 H. HAJIZADEH ET AL.
Content validity
We used quantitative and qualitative methods to assess content validity of
the scale. In the qualitative phase, we consulted a panel of six specialists in
the fields of health education and behaviors, a rheumatologist, and four
specialists in the fields of nutrition and community nutrition to evaluate
the content validity of the scales. All items were examined and surveyed by
the expert panel in terms of content coverage and the wording and phras-
ing. For quantitative evaluation, we measured the content validity ratio
(CVR) and content validity index (CVI) (Lindahl, Sattorov, Boqvist, &
Magnusson, 2015).
To assess CVR, we delivered the questionnaire to 10 scholars in the fields
of nutrition, rheumatology, rheumatology nursing, health education and
behavior and psychology. We asked them to determine the necessity of
each item based on a three-point scale (necessary, useful but not necessary,
unnecessary). Based on the Lawshe table (Lawshe 1975), a CVR score
>0.62 was considered as acceptable. To investigate CVI, we examined the
questionnaire in terms of relevancy, clarity, and simplicity. A CVI score >
0.79 was considered as acceptable (Hajari, Shams, Afrooghi, Fadaei Nobari,
& Abaspoor Najafabadi, 2016).
Face validity
We interviewed the panel of experts face-to-face to examine the difficulty
level of items with the patients. We asked them to report the level of
importance of each item based on a 5-point Likert scale (not important at
all, a little important, moderately important, important, and absolutely
important). We then assessed the impact score of each item by multiplying
the frequency of an item by its mean importance impact score ¼
½
frequency ð%Þimportance:Finally, the items with impact score1.5
were considered for the next analysis phase (Goldberg et al., 1997; Juniper,
Guyatt, Cox, Ferrie, & King, 1999).
Ethical considerations
This research was approved by the Iranian Medical University Ethics
Committee. All participants provided signed, informed consent.
Statistical analysis
We used the measures of central tendency and variability to summarize
and organize the data. We used the IBM Statistical Package for Social
Sciences (SPSS) version 22 for Windows and the Analysis of Moment
HEALTH CARE FOR WOMEN INTERNATIONAL 1105
Structures (AMOS), version 10.0 to conduct both Exploratory Factor
Analysis (EFA) and conducted confirmatory factor analysis (CFA),
respectively.
Factor structure (construct validity)
We performed EFA to determine the construct validity and factor structure
of the instrument. To do so, we applied principal component factor ana-
lysis with varimax rotation. In factor analysis, at least five samples per item
are required (Osborne, Costello, & Kellow, 2008). We conducted the EFA
based on a randomized split of data in the sample. We randomly selected a
sample of 120 participants using the randomization function on SPSS v. 22.
We considered factor-item loading values greater than 0.3 as acceptable to
allocate an item to a factor. We administered Kaiser-Meyer-Olkin (KMO)
and Bartlett’s test of sphericity to investigate the suitability of data. The
KMO test more than 0.7 is acceptable for conducting factor analysis (Shen
& Chen, 2007). To evaluate how well the model derived in the EFA fits the
data, we conducted CFA on the remaining 120 data of the larger overall
sample. We used the method of weighted least squares, whereby we meas-
ured the root mean square error of approximation (RMSEA), incremental
fit index (IFI), normed fit index (NFI), and comparative fit index (CFI),
(Schreiber, Nora, Stage, Barlow, & King, 2006).
Convergent validity
We used Pearson’s correlation coefficient to demonstrate the nature of
associations between the domains (the extracted factors) of the question-
naire and also to assess the associations between the factors and the
Persian version of Morisky, Green, and Levine (MGL) adherence scale
(Hezomi & Nadrian, 2019).
The MGL is one of the most widely used questionnaires to assess
patients’adherence to recommended treatment regimens (P
erez-Escamilla,
Franco-Trigo, Moullin, Mart
ınez-Mart
ınez, & Garc
ıa-Corpas, 2015). This
questionnaire comprises four items, using a binary (Yes/NO) response
format. The total score for the scale ranges from 0 to 4, whereby higher
scores indicate lower compliance with medication (more than 2 ¼low,
1–2¼medium, and zero ¼high levels of adherence to treatment).
Reliability
To assess internal consistency of the scale, we used Cronbach’s alpha coeffi-
cient (Gliem & Gliem, 2003), and intra-class correlation coefficients (ICC)
1106 H. HAJIZADEH ET AL.
with 95% confidence intervals (CI). We considered an ICC 0.7 (Bartko
1966), and a Cronbach’s alpha coefficient of 0.7 to be satisfactory.
Results
Participants
The mean (± standard deviation [SD]) of age, t-score, z-score, and BMD at
spine (L1-4) among the participants were 60.13 (±6.57), -2.96 (±.50), -1.22
(±.77) and .56 (±.16), respectively (Table 1). The proportion of participants
who were married, 66 years of age and older, and with a BMI 25 kg/m
2
were 85%, 22% and 50%, respectively.
Based on the qualitative analyses, we finalized 35 items and entered them
into the CVI and CVR processes. In the CVR process, we removed one
Table 1. Summary of demographic characteristics of total study population.
Variables
N¼
(total ¼240) % F1
¥
F2 F3 F4 F5
Age (years) 45 61 25.4 .228 .045.035.796 .228
46–55 59 24.6
56–65 67 27.9
66þ53 22.1
Body mass
index (kg/m
2
)
<18.5 61 25.4 .611 .024.007.032.000
18.5–24.9 59 24.6
25–29.9 60 25
30þ60 25
Marital status Single 5 2.1 .065.454 .025.479 .000
Married 204 85
Widow 31 12.9
Education status Illiterate and
primary school
146 60.8 .001.034.000 .041.237
Diploma 53 22.1
University education 41 17.1
Occupation Housewife 162 67.5 .029.006.004.315 .006
Worker/farmer/
self-employed
14 5.9
Employee 15 6.3
Retired 49 20.4
BMD T-score
At either spine (L1-4),
femoral neck, or total
>˗3.98 10 4.2 .025,016.405.046.002
˗.296 to ˗3.98 87 36.2
<˗2.95 143 59.6
Income Low 132 55 .002.039.000 .312 .626
Moderate 46 19.2
High 14 5.8
Very high 48 20
Supplement
therapy
Ca þD 140 58.3 .039.697 .034.041.021
D 57 23.8
B and others 28 18.7
Age at
menopause (years)
40 30 12.5 .312 .030.010.173 .028
41–50 187 77.9
51 23 9.6
¥F1 ¼attitude; F2 ¼intention; F3 ¼subjective norms; F4 ¼perceived behavioral control; F5 ¼weight control-
ling behaviors.
Correlation is significant at the level of .05 (two-tailed).
Correlation is significant at the level of .01 (two-tailed).
HEALTH CARE FOR WOMEN INTERNATIONAL 1107
item from the subjective norms scale, due to low CVR value (less than
0.62). In the CVI process no item was deleted. The mean CVI scores for
attitude, intention, subjective norms scales, PeBeC and WeCBes, were 92.8
(±7.7), .94.6 (±4.5), 90.2 (±5.7), 97.4 (±1.7), and 94.8 (±4.1), respectively. In
total, we approved 34 items for content and face validity, and we thus
entered them into the factor analysis process.
Factor structure
Applying a model-based factor analysis, we simultaneously put all five sub-
scales of the questionnaire into the EFA process. Five factors were extracted
with eigenvalues greater than 1, which altogether accounted for 64.103% of
the total variance. KMO (0.716) and Bartlett’s test of sphericity (v
2
¼
2164.46, df ¼170, p<.0001) confirmed the sufficiency of data. The number
of items, range, means, standard deviations, skewness and kurtosis for the
factors are presented in Table 2. To evaluate the factors, we considered the
factor pattern coefficient values (Table 3). We then entered this explanatory
model, containing all five factors (F1 ¼Attitude; F2 ¼Intention;
F3 ¼Subjective Norms; F4 ¼Perceived Behavioral Control; F5 ¼Weight
Controlling Behaviors) into the CFA process.
In the CFA (Figure 1), Chi- square/df was 1.769 (Chi-square ¼245.835,
df ¼139, p<.001), and RMSEA, CFI, NFI and IFI were .057 (.045, .068),
.95, .90 and .95, respectively.
Convergent validity
In the convergent validity process, we found significant correlation between
subjective norms scale and the MGL adherence scale (r ¼.314, p.01).
Applying Pearson correlation coefficient test, we found significant relation-
ships between factor 1 (attitude) and factors 2 (intention) (r ¼.305, p
.01) and 4 (PeBeC) (r ¼0.485, p.01), and between factor 2 (intention)
and factors 3 (subjective norms) (r ¼.136, p.05) and 4 (PeBeC) (r ¼
.142, p.05) (Table 4).
Table 2. Summary of characteristics of the subscales used in factor analyses.
Factors
(Subscales)
Number
of items Range
M
(SD) Kurtosis Skewness
Floor
effect (%)
Ceiling
effect (%)
F160–24 14.96 (7.8) .687 .800 0 0
F2 4 0–16 7.89 (3.8) .939 .220 .4 2.3
F3 3 0–12 7.50 (3.5) 1.495 .066 0 .2
F4 3 0–12 5.04 (2.5) .162 .386 3.3 .2
F5 3 1–12 6.50 (3.1) .943 .464 .7 .4
F1 ¼attitude; F2 ¼intention; F3 ¼subjective norms; F4 ¼perceived behavioral control; F5 ¼weight control-
ling behaviors.
1108 H. HAJIZADEH ET AL.
Table 3. Rotated factor pattern coefficients for variables (19 items) of weight control question.
N FactorsF1 F2 F3 F4 F5
1 I believe that a proper diet has an important role in controlling my weight. .909
2 I believe that sitting too much to watch TV results in gaining weight. .856
3 I can avoid prolonged sitting (such as long sitting to watch TV)
during my daily activities
.824
4 Controlling weight is important for me to prevent bone fracture .793
5 I can exercise daily for at least 30 min (like walking, swimming, etc.),
even if others are opposed
.653
6 I believe that weight control is useless because my weight changes are not very high .484
7 From the next week, I intend to weigh myself 2 to 3 times per week. .810
8 I intend to have a healthier diet in the next six months (such as higher
amount of milk and dairy products, and lower amounts of pizza and sandwiches).
.773
9 From the next week, I intend to sit down for less than 1 h per day to perform
recreational activities (like watching TV)
.478
10 From the next week, I intend to have at least 30 min of daily exercise
(like walking, swimming, etc.)
.448
11 If I get overweight and obese, my husband will remind me .876
12 My family’s perception on my weight and appearance is important to me .873
13 My close friends think that it is not necessary to be worry about weight control. .824
14 It is difficult for me to eat boiled foods, because I do not like it. .827
15 I can avoid too much sleeping (9 h and more in 24 h) .797
16 As we do not have weighing scale at home, I cannot control my weight weekly. .663
17 I do exercise (such as walking or swimming) for at least 30 min a day. .842
18 Every day, I use walking instead of bus, elevators or car driving to
perform my activities of daily living.
.792
19 In the last month, how long have you exercised per day
(walking, swimming, running, etc.)?
.641
Initial Eigenvalues 4.42 2.43 1.95 1.74 1.62
Percent of variance explained 23.3%12.8%10.2%9.1%8.5%
Cronbach a.862 .744 .860 .722 .712
ICC (95%CI) .860 (.715
to .953)
.784 (.533
to .927)
.717 (.397
to .904)
.730 (.427
to .909)
.805 (.600
to .933)
F1 ¼attitude; F2 ¼intention; F3 ¼subjective norms; F4 ¼perceived behavioral control; F5 ¼weight controlling behaviors.
Extraction method: principal component analysis;
Rotation method: Varimax with Kaiser Normalization;
ICC: intra-class correlation coeffiecient; CI: confidence interval.
HEALTH CARE FOR WOMEN INTERNATIONAL 1109
Reliability
Cronbach’s alpha coefficient for all subscales was greater than 0.71. ICC
with 95% CIs was also higher than .717 (.397–.904) for all subscales, which
supported the internal consistency of the scales (Table 3).
Discussion
In the present study, we reported the development and psychometric prop-
erties of a TPB-based instrument designed to investigate the determinants
of weight control among postmenopausal women with osteoporosis.
Construct validity of the instrument was approved as we found the five-fac-
tor solution as the clearest pattern of factor loadings, which accounted for
a large proportion (64%) of all variance between the items. These findings
support the conceptual framework of the TPB which encompasses the five
concepts of attitude, intention, subjective norms, PBC, and behavior.
Our results in the EFA showed PBC and intention as the most significant
predictors of weight control behavior. Similarly, Armitage et al. (Armitage
Figure 1. CFA-based relations between the items and the factors and between the factors. All
relations between the factors and items and between the factors were statistically significant
(all p<.05). F1 ¼Attitude; F2 ¼Intention; F3 ¼Subjective Norms; F4 ¼Perceived Behavioral
Control; F5 ¼Weight Controlling Behaviors. Fit indices: v2 [139] ¼245.835, p<.001, CFI ¼
0.950, NFI ¼0.901, IFI ¼0.950, RMSEA ¼0.057 [(0.045–0.068].
1110 H. HAJIZADEH ET AL.
& Conner, 2001) and McConnon et al. (2012), also showed the related
behaviors in close association with intention and PBC.
In order to investigate convergent validity of the scale, we applied the
MGL. Factor 3 (subjective norms) was significantly associated with adher-
ence, which suggests that the higher the level of adherence the higher the
level of perceived subjective norms among the patients. We found a range
of no association to moderate associations between the factors. We identi-
fied the strongest association between attitude and PBC, which was a posi-
tive relationship, thus suggesting convergent validity of the scale.
We also found a satisfactory internal consistency in the factors derived
from TPB questionnaire. The Cronbach’s alpha for the factors ranged from
moderate to very high, which are comparable to the ranges proposed by
previous researchers (DeVellis, 2016; Sim & Wright, 2000). Several
researchers in previous psychometric studies (Haghighi et al., 2017;
Rezakhani Moghaddam et al., 2018; Toopchian, Safieh, Babazadeh,
Allahverdipour, & Nadrian, 2017; Yari et al., 2014) have applied
Cronbach’s alpha to confirm the internal consistency of instruments.
Furthermore, our ICC supports the reliability of the Weight-CuRB, suggest-
ing that the questionnaire can reliably discriminate between subjects. Our
findings in the CVI process as well as the face and content validity
approved the clarity, simplicity, and relevancy of Weight-CuRB.
Researchers in previous studies reported intention and PBC as the most
important determinants of weight loss (Palmeira et al., 2007; Schifter &
Ajzen, 1985). Hausenblas et al. who applied TPB in a previous study, con-
sidered intention as the main predictor of behavior, followed by PBC
(Hausenblas, Carron, & Mack, 1997). Due to poor perceptions regarding
behavior control, individuals, despite strong intentions, may plausibly not
have positive expectations of being able to perform the behavior (Paisley &
Sparks, 1998). Moreover, this complexity may explain why we did not
observe any relationship between intention and behavior in our study. As
noted by Bagozzi and Edwards, identifying associations between those two
Table 4. The Pearson’s correlation coeffiecients between the extracted factors and the
domains of Morinsky medication non-adherence scale (N¼240).
Factors/constructs
Morisky 4-item medication non-adherence
Overall
non-adherence
Unintentional
non-adherence
Intentional
non-adherence
F1: Attitude .039 .014 .021
F2: Intention .025 .083 .021
F3: Subjective norms .019 .010 .009
F4: Perceived behavioral control .215 .186 .048
F5: Weight control behaviors .055 .128.102
Correlation is significant at the pvalue of <.05 (two-tailed).
Correlation is significant at the pvalue of <.01 (two-tailed).
HEALTH CARE FOR WOMEN INTERNATIONAL 1111
scales may be complex, due to the non-volitional nature of weight control
behavior (Bagozzi & Edwards, 1998).
As a limitation for our study, we were unable to compare the constructs
of our developed instrument with other similar scales, due to a paucity of
available comparable scales. However, to the best of our knowledge, this is
the first framework-based instrument aimed at assessing the cognitive needs
of postmenopausal women with osteoporosis in terms of weight control.
Conclusion
We reported the Weight-CuRB with appropriate validity, reliability, simpli-
city, and functionality. As the TPB is a cross-culturally applicable theory,
and considering that the weight gain among menopausal women is likely
to be global, we speculate that the instrument would be useful to the schol-
ars and practitioners throughout the world. We, therefore, suggest the rep-
lication of work elsewhere on similar populations of menopausal
women.This TPB-based questionnaire may also be useful while designing
weight control interventions for Persian-language patients with osteopor-
osis, as it can help the researchers in addressing the core cognitive determi-
nants of weight control among the patients. Health practitioners, health
care providers and researchers in the field of osteoporosis may apply this
instrument to find a comprehensive understanding of the cognitive barriers
and enablers of weight control, prior to planning health promotion pro-
grams for postmenopausal women with osteoporosis in Persian-language
communities like Iran, Tajikistan, and some regions of Afghanistan.
Further research on this scale is recommended to assess its psychometric
characteristics in different communities with different contexts.
Acknowledgments
We thank all patients participated in the study. The participants were told about the aim of
study and were assured on the confidentiality of data. All participants signed a consent
form before data collection.
Disclosure statement
The authors declare no competing financial interests. Hossein Hajizadeh, Haidar Nadrian,
Nazila Farin, Mohammad Asghari Jafarabadi, seyed Jamal Ghaemmaghami hezaveh, Sousan
Kolahi, Pouria Sefid Mooye Azar, and Sharon Lee-Brennan declare that they have no con-
flict of interest.
1112 H. HAJIZADEH ET AL.
Authors’contributions
Study design: H. N., S. J. G. H., H. H., and S. K. Study conduct: H. N., H. H., and N. F.
Data collection: P. S. M. A., H. H., and H. N. Data analysis: H. H. and M. A. J. Data inter-
pretation: H. N., H. H., S. B.-O. and M. A. J. Drafting manuscript: H. H. Revising manu-
script and content: H. N., S. B.-O., H. H., S. K., and N. F. Approving final version of
manuscript: All authors. H. N. takes responsibility for the integrity of the data analysis.
Ethical approval
This research was approved by the Ethics Committee in Tabriz University of Medical
Sciences (ethical approval code: IR.TBZMED.REC.920).
Funding
Sharon Brennan-Olsen is supported by a National Health and Medical Research Council of
Australia Career Development Fellowship (1107510).
ORCID
Haidar Nadrian http://orcid.org/0000-0003-3129-2475
Mohammad Asghari Jafarabadi http://orcid.org/0000-0003-3284-9749
References
Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. Action control
(pp. 11–39). Berlin, Heidelberg: Springer.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes,50(2), 179–211. doi:10.1016/0749-5978(91)90020-T
Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-
analytic review. British Journal of Social Psychology,40(4), 471–499. doi:10.1348/
014466601164939
Armstrong, M. E., Spencer, E. A., Cairns, B. J., Banks, E., Pirie, K., Green, J., …M. W. S.
Collaborators (2011). Body mass index and physical activity in relation to the incidence
of hip fracture in postmenopausal women. Journal of Bone and Mineral Research,26(6),
1330–1338. doi:10.1002/jbmr.315
Bagozzi, R. P., & Edwards, E. A. (1998). Goal setting and goal pursuit in the regulation of
body weight. Psychology and Health,13(4), 593–621. doi:10.1080/08870449808407421
Bartko, J. J. (1966). The intraclass correlation coefficient as a measure of reliability.
Psychological Reports,19(1), 3–11. doi:10.2466/pr0.1966.19.1.3
Choyhirun, T., Suchaxaya, P., Chontawan, R., & Kantawang, S. (2008). Predictors of eating
behaviors for weight control among overweight early adolescents. Pacific Rim
International Journal of Nursing Research,12(2), 107–120. Retrieved from https://www.
tci-thaijo.org/index.php/PRIJNR/article/view/5882/5088
DeVellis, R. F. (2016). Scale development: Theory and applications. North Carolina: Sage
Publications.
HEALTH CARE FOR WOMEN INTERNATIONAL 1113
Dubnov, G., Brzezinski, A., & Berry, E. M. (2003). Weight control and the management of
obesity after menopause: The role of physical activity. Maturitas,44(2), 89–101. doi:10.
1016/S0378-5122(02)00328-6
Elfhag, K., & Rossner, S. (2005). Who succeeds in maintaining weight loss? A conceptual
review of factors associated with weight loss maintenance and weight regain. Obesity
Reviews,6(1), 67–85. doi:10.1111/j.1467-789X.2005.00170.x
Fila, S. A., & Smith, C. (2006). Applying the theory of planned behavior to healthy eating
behaviors in urban native American youth. International Journal of Behavioral Nutrition
and Physical Activity,3(1), 11. doi:10.1186/1479-5868-3-11
Garcia Ulen, C., Huizinga, M. M., Beech, B., & Elasy, T. A. (2008). Weight regain preven-
tion. Clinical Diabetes,26(3), 100–113. doi:10.2337/diaclin.26.3.100
Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s
alpha reliability coefficient for Likert-type scales. Paper presented at the Midwest
Research-to-Practice Conference in Adult, Continuing, and Community Education,
Columbus, OH.
Godin, G., & Kok, G. (1996). The theory of planned behavior. A review of its applications
to health-related behaviors. American Journal of Health Promotion,11(2), 87–98. doi:10.
4278/0890-1171-11.2.87
Goldberg, D. P., Gater, R., Sartorius, N., Ustun, T. B., Piccinelli, M., Gureje, O., & Rutter,
C. (1997). The validity of two versions of the GHQ in the WHO study of mental illness
in general health care. Psychological Medicine,27(1), 191–197. doi:10.1017/
S0033291796004242
Haghighi, M., Taghdisi, M. H., Nadrian, H., Moghaddam, H. R., Mahmoodi, H., &
Alimohammadi, I. (2017). Safety culture promotion intervention program (SCPIP) in an
oil refinery factory: An integrated application of Geller and health belief models. Safety
Science,93,76–85. doi:10.1016/j.ssci.2016.11.019
Hajari, A., Shams, M., Afrooghi, S., Fadaei Nobari, R., & Abaspoor Najafabadi, R. (2016).
Using the precede-proceed model in needs assessment for the prevention of brucellosis
in rural areas of Isfahan, Iran. Armaghane Danesh,21(4), 396–409. Retrieved from
http://armaghanj.yums.ac.ir/article-1-1418-en.html
Hausenblas, H. A., Carron, A. V., & Mack, D. E. (1997). Application of the theories of rea-
soned action and planned behavior to exercise behavior: A meta-analysis. Journal of
Sport and Exercise Psychology,19(1), 36–51. doi:10.1123/jsep.19.1.36
Hezomi, H., & Nadrian, H. (2019). Health literacy as a determinant for hypertension medi-
cation adherence: What are the differences in determinants by the level of adherence?
Health Promotion Perspectives, Under Review.
Jalilian, F., Matin, A., Mahboubi, M., Rahimi, S., Karimi, G., Kanani, S., & Khashij, S.
(2016). Factors related to fruit and vegetable consumption: A cross-sectional study based
on theory of planned behavior. International Journal of Tropical Medicine,11(5),
149–153. Retrieved from http://docsdrive.com/pdfs/medwelljournals/ijtmed/2016/149-
153.pdf
Jun, J., & Arendt, S. W. (2016). Understanding healthy eating behaviors at casual dining
restaurants using the extended theory of planned behavior. International Journal of
Hospitality Management,53, 106–115. doi:10.1016/j.ijhm.2015.12.002
Juniper, E., Guyatt, G., Cox, F., Ferrie, P., & King, D. (1999). Development and validation
of the mini asthma quality of life questionnaire. European Respiratory Journal,14(1),
32–38. doi:10.1034/j.1399-3003.1999.14a08.x
1114 H. HAJIZADEH ET AL.
Kanis, J. (2007). Assessment of osteoporosis at the primary health-care level. Technical
Report. University of Sheffield, UK: World Health Organization Collaborating Centre for
Metabolic Bone Diseases.
Lampropoulou-Adamidou, K., Karampinas, P. K., Chronopoulos, E., Vlamis, J., & Korres,
D. S. (2014). Currents of plate osteosynthesis in osteoporotic bone. European Journal of
Orthopaedic Surgery & Traumatology,24(4), 427–433. doi:10.1007/s00590-013-1215-0
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology,
28(4), 563–575. doi:10.1111/j.1744-6570.1975.tb01393.x
Lindahl, E., Sattorov, N., Boqvist, S., & Magnusson, U. (2015). A study of knowledge, atti-
tudes and practices relating to brucellosis among small-scale dairy farmers in an urban
and peri-urban area of Tajikistan. PloS One,10(2), e0117318. doi:10.1371/journal.pone.
0117318
Mahdaviroshan, M., & Ebrahimimameghani, M. (2014). Assessments of dietary pattern and
nutritional intake in osteoporotic patients in Tabriz. Journal of Paramedical Sciences,
5(3), 27–30. Retrieved from https://pdfs.semanticscholar.org/09a5/93fa3c964fd2ab194-
b06040209a71c5ca443.pdf
McConnon, A., Raats, M., Astrup, A., Bajzov
a, M., Handjieva-Darlenska, T., Lindroos,
A. K., …Pfeiffer, A. (2012). Application of the theory of planned behaviour to weight
control in an overweight cohort. Results from a pan-European dietary intervention trial
(DiOGenes). Appetite,58(1), 313–318. doi:10.1016/j.appet.2011.10.017
Mpalaris, V., Anagnostis, P., Goulis, D., & Iakovou, I. (2015). Complex association between
body weight and fracture risk in postmenopausal women. Obesity Reviews,16(3),
225–233. doi:10.1111/obr.12244
Naz, M. S. G., Ozgoli, G., Aghdashi, M. A., & Salmani, F. (2016). Prevalence and risk fac-
tors of osteoporosis in women referring to the bone densitometry academic center in
Urmia, Iran. Global Journal of Health Science,8(7), 135. doi:10.5539/gjhs.v8n7p135
Nielson, C. M., Srikanth, P., & Orwoll, E. S. (2012). Obesity and fracture in men and
women: An epidemiologic perspective. Journal of Bone and Mineral Research,27(1),
1–10. doi:10.1002/jbmr.1486
Osborne, J., Costello, A., & Kellow, J. (2008). Best practices in exploratory factor analysis.
Best Practices in Quantitative Methods, Part 1, Chapter 6, 86–99. Retrieved from https://
methods.sagepub.com/book/best-practices-in-quantitative-methods/d8.xml
Paisley, C. M., & Sparks, P. (1998). Expectations of reducing fat intake: The role of per-
ceived need within the theory of planned behaviour. Psychology and Health,13(2),
341–353. doi:10.1080/08870449808406755
Palmeira, A. L., Teixeira, P. J., Branco, T. L., Martins, S. S., Minderico, C. S., Barata, J. T.,
Serpa, S. O., & Sardinha, L. B. (2007). Predicting short-term weight loss using four lead-
ing health behavior change theories. International Journal of Behavioral Nutrition and
Physical Activity,4(1), 14. doi:10.1186/1479-5868-4-14
P
erez-Escamilla, B., Franco-Trigo, L., Moullin, J., Mart
ınez-Mart
ınez, F., & Garc
ıa-
Corpas, J. (2015). Identification of validated questionnaires to measure adherence to
pharmacological antihypertensive treatments. Patient Preference and Adherence,9, 569.
doi:10.2147/PPA.S76139
Pirro, M., Fabbriciani, G., Leli, C., Callarelli, L., Manfredelli, M. R., Fioroni, C., Mannarino,
M. R., Scarponi, A. M., & Mannarino, E. (2010). High weight or body mass index
increase the risk of vertebral fractures in postmenopausal osteoporotic women. Journal
of Bone and Mineral Metabolism,28(1), 88–93. doi:10.1007/s00774-009-0108-0
HEALTH CARE FOR WOMEN INTERNATIONAL 1115
Premaor, M. O., Pilbrow, L., Tonkin, C., Parker, R. A., & Compston, J. (2010). Obesity and
fractures in postmenopausal women. Journal of Bone and Mineral Research,25(2),
292–297. doi:10.1359/jbmr.091004
Rezakhani Moghaddam, H., Nadrian, H., Abbagolizadeh, N., Babazadeh, T., Aghemiri, M.,
& Fathipour, A. (2018). Mental health-ill health differences in disease severity and its
sociodemographic biobehavioral predictors among patients with knee osteoarthritis.
Clinical Nursing Research, 105477381775152. Jan 1:1054773817751527. doi:10.1177/
1054773817751527
Schifter, D. E., & Ajzen, I. (1985). Intention, perceived control, and weight loss: An applica-
tion of the theory of planned behavior. Journal of Personality and Social Psychology,
49(3), 843. doi:10.1037//0022-3514.49.3.843
Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural
equation modeling and confirmatory factor analysis results: A review. The Journal of
Educational Research,99(6), 323–338. doi:10.3200/JOER.99.6.323-338
Shahla, A., & Charesaz, S. (2007). Recurrent hip fracture prevention with osteoporosis man-
agement. Medical Journal of the Islamic Republic Of Iran (MJIRI),20(4), 181–183.
Retrieved from http://mjiri.iums.ac.ir/browse.php?a_id=385&sid=1&slc_lang=fa
Shen, M.-J., & Chen, M.-C. (2007). The relationship of leadership, team trust and team per-
formance: A comparison of the service and manufacturing industries. Social Behavior
and Personality: An International Journal,35(5), 643–658. doi:10.2224/sbp.2007.35.5.643
Sim, J., & Wright, C. H. (2000). Research in health care: Concepts, designs and methods.
Cheltenham: Nelson Thornes.
Toopchian, A., Safieh, K., Babazadeh, T., Allahverdipour, H., & Nadrian, H. (2017).
Development and psychometric properties of a condom use and its cognitive determi-
nants questionnaire (CUCDQ). Open Access Macedonian Journal of Medical Sciences,
5(1), 79. doi:10.3889/oamjms.2017.001
Waleekhachonloet, O.-A., Limwattananon, C., Limwattananon, S., & Gross, C. R. (2007).
Group behavior therapy versus individual behavior therapy for healthy dieting and
weight control management in overweight and obese women living in rural community.
Obesity Research & Clinical Practice,1(4), 223–232. doi:10.1016/j.orcp.2007.07.005
White, K. M., Terry, D. J., Troup, C., Rempel, L. A., & Norman, P. (2010). Predicting the
consumption of foods low in saturated fats among people diagnosed with Type 2 dia-
betes and cardiovascular disease. The role of planning in the theory of planned behav-
iour. Appetite,55(2), 348–354. doi:10.1016/j.appet.2010.07.011
Yari, A., Nadrian, H., Rashidian, H., Nedjat, S., Esmaeilnasab, N., Doroudi, R., & Hoursan,
H. (2014). Psychometric properties of the Persian version of Social Capital Questionnaire
in Iran. Medical Journal of the Islamic Republic of Iran,28, 17. Retrieved from https://
www.ncbi.nlm.nih.gov/pmc/articles/PMC4153523/
Zhao, L. J., Jiang, H., Papasian, C. J., Maulik, D., Drees, B., Hamilton, J., & Deng, H. W.
(2007). Correlation of obesity and osteoporosis: Effect of fat mass on the determination
of osteoporosis. Journal of Bone and Mineral Research,23(1), 17–29. doi:10.1359/jbmr.
070813
1116 H. HAJIZADEH ET AL.
