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The effect of a personalized intervention on weight gain and physical activity among pregnant women in China
International Journal of Gynecology and Obstetrics 129 (2015) 138–141
Contents lists available at ScienceDirect
International Journal of Gynecology and Obstetrics journal homepage: www.elsevier.com/locate/ijgo
CLINICAL ARTICLE
The effect of a personalized intervention on weight gain and physical activity among pregnant women in China Wenjuan Jing 1, Yan Huang 1, Xinghui Liu, Biru Luo ⁎, Yi Yang, Shujuan Liao Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
a r t i c l e
i n f o
Article history: Received 15 May 2014 Received in revised form 7 November 2014 Accepted 14 January 2015 Keywords: Excessive weight gain Gestational diabetes mellitus Health belief model Intervention Physical activity
a b s t r a c t Objective: To examine whether personalized interventions could improve dietary intake and physical activity among pregnant women. Methods: A randomized controlled trial was conducted at a center in Chengdu, China, between September 2012 and February 2013. Women with a singleton pregnancy (aged ≥18 years, could understand written Chinese, did not have pre-existing diabetes) were enrolled at approximately 12 weeks of pregnancy. They were randomly assigned (1:1) to an intervention group (received personalized educational materials) or a control group (conventional interventions only). Data for dietary intake and physical activity were recorded via questionnaires. Only pregnant women who completed the study were included in the analysis. Results: Analyses included 106 women in the control group and 115 in the intervention group. After intervention, the intake of energy, protein, fruit, milk, seafood, and nuts differed significantly between groups (P b 0.05), with intakes closer to the recommended amounts in the intervention group. Women in the intervention group spent significantly less time resting (P = 0.033) and more time doing mild activity (P = 0.016). Mean weight gain per week was significantly lower in the intervention group (P = 0.023), and significantly fewer women in this group developed gestational diabetes (P = 0.043). Conclusion: Personalized educational interventions can improve dietary behavior and physical activity levels, and reduce prevalence of gestational diabetes among pregnant women in China. Chinese Clinical Trial Register: ChiCTR-IPR-15005809. © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.
1. Introduction Gestational diabetes mellitus (GDM) is a heterogeneous group of metabolic disorders that result in varying degrees of maternal hyperglycemia and pregnancy-associated risk [1]. It affects 1%–14.2% pregnant women globally [2] and 3%–7% [3] in China. Epidemiological studies have found that women with GDM have an increased risk of hypertension [4], polyhydramnios [4], type 2 diabetes [5], spontaneous abortion [6], preterm delivery [7], and macrosomia [8]. Moreover, GDM increases the incidence of type 2 diabetes, obesity, and mental disorders in the offspring [9]. Many risk factors for GDM have been identified, such as ethnic origin [1], age [1], excessive gestational weight gain (GWG) [10], reduction of physical activity [11], diet [12], family history [1], and obesity [13]. Body fat percentage, physical activity, and possibly diet quality are important modifiable risk factors for GDM [14]. Many studies have been conducted to examine the effect of lifestyle interventions on GWG, but the results have been inconsistent. Lifestyle interventions during pregnancy have been shown to increase physical activity, improve dietary habits, and reduce GWG [15]. Healthy lifestyle behavior related to eating and physical activity can prevent excessive GWG [16]. ⁎ Corresponding author at: No. 20, Section 3, Renmin Nanlu, Chengdu, Sichuan 610041, China. Tel.: +86 28 85503852; fax: +86 28 85503776. E-mail address: [email protected] (B. Luo). 1 These authors contributed equally.
However, interventions in previous studies were not comprehensive because they were implemented after the women were diagnosed with GDM and because many had methodological limitations such as lack of theoretical basis. Additionally, few studies have reported the effect of interventions on diet and exercise behavior among pregnant women in China—a middle-income country with a culture that is substantially different from that of high-income countries. Over the past few decades, the lifestyle of pregnant women in China has changed greatly. Although undernutrition no longer seems to be an issue in urban areas, overnutrition, obesity during pregnancy, and GDM now seem to be the main problems [17]. Furthermore, 73% of women gain more than the national recommended amount of weight [18]. The aim of the present study was to investigate whether personalized interventions based on the health belief model (HBM) could improve pregnant women’s behavior with regard to dietary intake and physical activity, and lower the frequency of excessive GWG and GDM in China. The HBM was developed to predict compliance with preventive health recommendations [19]. According to HBM, when a person’s perceived value of an outcome is added to his or her expectation that a certain behavior will lead to an outcome, behavior change occurs [20]. 2. Materials and methods The present randomized controlled trial was conducted among women with singleton pregnancies at West China Second University
http://dx.doi.org/10.1016/j.ijgo.2014.11.014 0020-7292/© 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.
W. Jing et al. / International Journal of Gynecology and Obstetrics 129 (2015) 138–141
Hospital, Chengdu, China, between September 3, 2012, and February 1, 2013. Eligible women were aged at least 18 years, could understand the written Chinese language, and did not have pre-existing diabetes. Women with any pregnancy-related complications or general medical disorders not associated with pregnancy were excluded. The study was approved by the institutional review board of the study center and participants signed an informed consent form. Women were approached at their first prenatal check (after approximately 12 weeks of pregnancy) and introduced to the aim of the present study. After enrollment, they were randomly assigned (1:1) to the control group or intervention group. The participants were divided according to the sequence of time and randomized numbers produced by SAS version 11.0 (SAS Institute Inc, Raleigh, NC, USA). Participants and data analysts were masked to group assignment. The investigators were not masked to the assignment so that they could implement the personalized intervention for women in the intervention group. After a preliminary investigation, an intervention model had been developed on the basis of the HBM theory. The key points of education included the harm of GWG and GDM, the benefit of encouraged behaviors, the difficulty to change habits, and the importance of belief in the efficacy of the interventions. In addition to standard health education manuals, women in the intervention group received an education manual on diet and physical activity (written by the research team) after randomization, and one-to-one counseling for at least 20 minutes in a private room with a trained graduate student (W.J.) after group assignment and at 16–20 and 20–24 weeks. The same graduate student was also available to answer questions and provide feedback on diet and physical activity behavior until 20–24 weeks either over the phone or via a group established on Tecent instant messenger (generally known as QQ in China) specifically for the present study. By contrast, pregnant women in the control group received only conventional interventions, such as standard health education manuals produced by the hospital. At baseline (12 weeks of pregnancy), demographic and obstetric characteristics, and data for dietary intake and physical activity in early pregnancy were recorded for all participants. Research tools included a general information questionnaire, a food frequency questionnaire (FFQ), and a physical activity form. Data were subsequently recorded at 16–20 and 20–24 weeks of pregnancy. The FFQ is an inventory that was originally developed through literature review, preliminary investigation, and expert evaluation to measure the dietary intake of pregnant women. It was amended for the present study on the basis of a standard version of the FFQ. It records food types, frequency, and intake amount at each meal. The participants completed the part on intake frequency. After consulting the participants, W.J. completed the part on the intake of food on the basis
of the food model book containing photos of kinds of food and amounts. Intake of nine kinds of food was measured in the present study: grains, vegetables, fruit, meat, seafood, eggs, milk, beans, and nuts. The Cronbach’s α coefficient of FFQ in the present study was 0.756, suggesting good internal consistency. The Pregnancy Physical Activity Questionnaire (PPAQ) was previously introduced to China [21]: after translation, back-translation, and revision, the PPAQ was used in Chinese pregnant women to measure their physical activity with regard to the intensity, time spent, and energy expenditure of various categories. The PPAQ classifies the physical activity done by pregnant women into nine different levels from A to I, with the metabolic equivalent (MET) increasing gradually in each one (0.9, 1, 1.5, 2, 3, 4, 5, 6, and N6). On the basis of the MET, the activities are grouped into four categories: 1) resting (levels A and B [MET b 1.0]); 2) mild physical activity (levels C and D [MET 1.0–2.9]); 3) moderate physical activity (levels E, F, G, and H [MET 3.0–6.0]); and 4) intense physical activity (level I [MET N6.0]). The reliability and validity of the PPAQ was confirmed in a previous study [21]. The Cronbach’s α coefficient of PPAQ in the present study was 0.718, suggesting good internal consistency. Sample size calculation indicated that 262 participants were needed for a power of 0.8 at a significance level of 0.05 (Supplementary Material S1). EpiData (EpiData Association, Odense, Denmark) was used to establish the database, and SAS version 9.1 (SAS Institute Inc, Raleigh, NC, USA) was used for statistical analysis. Only women who finished the whole study were included in the analysis. Rate, constituent ratio, and mean were used to describe social and demographic data of pregnant women, and t test and χ2test were used to compare dietary intake and physical activity. P b 0.05 was considered statistically significant.
3. Results Altogether 262 women were recruited and underwent randomization, but only 221 participants (106 in the control group and 115 in the intervention group) completed the study and were analyzed (Fig. 1). The two groups did not differ significantly in age, pre-pregnancy weight, body mass index, or fasting blood glucose at baseline (Table 1). Before implementation of the intervention, dietary intake did not differ between groups (Table 2). After implementation, the intake of energy, protein, grains, fruit, milk, seafood, and nuts differed significantly between the two groups (P b 0.05) (Table 2). The intake of foods in these categories was closer to the recommended amount for pregnant women (according to the dietary guidelines for Chinese residents developed by Ministry of
Eligible (n=262)
Randomly assigned (n=262)
Control group (n=131) Did not complete study (n=25) Abnormal blood sugar (n=1) Spontaneous abortion (n=2) Relocation (n=13) Loss to follow up (n=9) Analyzed (n=106)
139
Intervention group (n=131)
Did not complete study (n=16) Abnormal blood sugar (n=1) Spontaneous abortion (n=2) Relocation (n=11) Loss to follow-up (n=2) Analyzed (n=115)
Fig. 1. Flow of participants through the study.
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W. Jing et al. / International Journal of Gynecology and Obstetrics 129 (2015) 138–141
Table 1 Baseline characteristics.a
Table 4 Weight gain.a
Variables
Intervention group (n = 115)
Control group (n = 106)
P value
Age, y Pre-pregnancy weight, kg Pre-pregnancy BMI Fasting blood glucose, mmol/L
29.57 ± 4.13 52.23 ± 6.84 20.44 ± 2.54 4.43 ± 0.34
29.89 ± 3.86 52.86 ± 6.08 20.74 ± 2.43 4.46 ± 0.34
0.56 0.48 0.38 0.56
Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters). a Values are given as mean ± SD unless indicated otherwise.
Weight gain
Intervention group (n = 115)
Control group (n = 106)
P value
Total gestational weight gain between baseline and 20–24 weeks, kg Weight gain per week, kg/wk Women above IOM recommendationb
9.24 ± 3.99
9.69 ± 3.85
0.400
0.65 ± 0.20 102 (88.7)
0.71 ± 0.22 97 (91.5)
0.023 0.489
Abbreviation: IOM, Institute of Medicine. a Values are given as mean ± SD or number (percentage), unless indicated otherwise. b Recommended weight gain per week is 0.41 kg [23].
Health of China in 2008 [22]) in the intervention group than in the control group. In addition, women in the intervention group spent significantly less time resting (P = 0.033) and more time doing mild activity (P = 0.016) than did women in the control group (Table 3). The Institute of Medicine has recommended that women should gain 0.41 kg per week during pregnancy [23], which corresponds to a recommended total GWG for the participants in the present study of 5.46 kg. For participants in both groups, total GWG and average weight gain per week were higher than these recommendations (Table 4). Although the intervention and the control groups did not differ
significantly in total GWG, they did differ significantly in weight gain per week (P = 0.023) (Table 4). In both groups, most women had a weekly weight gain that was higher than recommended by the IOM (Table 4). Finally, 26 (22.6%) women in the intervention group developed GDM, compared with 37 (34.9%) in the control group (P = 0.043).
4. Discussion The present study has shown that personalized health education based on the HBM can improve pregnant women’s dietary behavior.
Table 2 Dietary intake. Variables
Energy, Kcal Protein, g Percentage of daily intake, % Carbohydrate, g Percentage of daily intake, % Fat, g Percentage of daily intake, % Grain, g Meat, g Vegetables, g Fruit, g Eggs, g Milk, g Seafood, g Beans, g Nuts, g
Baseline
After intervention(20–24 weeks)
Intervention group (n = 115)a
Control group (n = 106)a
RNI
P value
Intervention group (n = 115)a
Control group (n = 106)a
RNI
P value
1829.55 ± 476.71 60.84 ± 18.99 13.3 251.94 ± 72.82 55.1 76.70 ± 28.67 37.7 250.90 ± 84.67 85.62 ± 70.15 475.96 ± 266.61 499.87 ± 283.23 46.70 ± 32.26 252.25 ± 185.12 24.00 ± 30.40 13.53 ± 11.28 18.09 ± 14.56
1787.35 ± 466.29 59.50 ± 20.77 13.3 239.91 ± 60.73 53.7 79.28 ± 31.07 39.9 235.73 ± 73.33 92.09 ± 74.32 430.59 ± 275.86 517.65 ± 264.42 41.84 ± 24.63 227.86 ± 159.88 26.62 ± 34.72 14.92 ± 13.25 20.29 ± 14.92
2100 – 16.6 – 51.7 – 31.7 250 75 400 150 50 225 50 50 15
0.503 0.567 – 0.137 – 0.354 – 0.089 0.378 0.322 0.507 0.271 0.723 0.968 0.454 0.168
2255.24 ± 401.41 75.26 ± 14.93 13.1 309.60 ± 72.86 54.1 80.90 ± 16.06 33.3 338.38 ± 94.29 111.74 ± 46.85 353.91 ± 120.52 461.88 ± 213.08 52.62 ± 23.74 336.17 ± 164.00 41.50 ± 28.46 11.67 ± 9.24 15.35 ± 8.78
2129.90 ± 413.34 69.35 ± 14.32 13.1 290.35 ± 77.06 53.5 78.17 ± 16.61 34.0 305.98 ± 96.97 104.05 ± 48.98 361.48 ± 117.21 526.05 ± 266.17 52.80 ± 31.00 289.00 ± 159.50 33.72 ± 24.55 11.64 ± 9.73 18.92 ± 15.23
2300 – 15 – 55 – 30 400 87.5 400 300 50 375 87.5 40 15
0.024 0.003 – 0.058 – 0.216 – 0.013 0.235 0.637 0.048 0.962 0.060 0.031 0.982 0.036
Abbreviation: RNI, recommended nutrition intake. a Values are given as mean ± SD unless indicated otherwise.
Table 3 Time spent (h/day) on different intensities and categories of physical activity.a Intensity/category
A B C D E F G H I Resting Mild Moderate Severe a
Baseline
After intervention (20–24 weeks)
Intervention group (n = 115)
Control group (n = 106)
P value
Intervention group (n = 115)
Control group (n = 106)
P value
9.71 ± 1.55 5.68 ± 3.16 4.68 ± 2.63 2.54 ± 1.22 0.93 ± 1.04 0.24 ± 0.56 0.13 ± 0.30 0.09 ± 0.28 0 15.39 ± 3.19 7.22 ± 2.78 1.38 ± 1.45 0
9.53 ± 1.52 5.93 ± 3.20 4.87 ± 2.79 2.35 ± 1.25 0.83 ± 1.02 0.25 ± 0.52 0.09 ± 0.24 0.12 ± 0.31 0 15.46 ± 3.42 7.22 ± 2.97 1.29 ± 1.55 0
0.362 0.221 0.664 0.262 0.820 0.955 0.320 0.235 – 0.444 0.367 0.906
9.19 ± 1.31 4.63 ± 1.84 5.36 ± 2.22 3.53 ± 1.38 0.88 ± 0.98 0.20 ± 0.53 0.22 ± 0.32 0.05 ± 0.21 0 13.82 ± 1.98 8.89 ± 2.12 1.35 ± 1.35 0
9.45 ± 1.41 5.05 ± 2.49 5.01 ± 2.26 3.12 ± 1.20 0.96 ± 1.19 0.26 ± 0.58 0.17 ± 0.27 0.01 ± 0.10 0 14.50 ± 2.63 8.13 ± 2.52 1.40 ± 1.52 0
0.150 0.167 0.254 0.020 0.565 0.446 0.187 0.079 – 0.033 0.016 0.824 –
Values are given as mean ± SD unless indicated otherwise.
W. Jing et al. / International Journal of Gynecology and Obstetrics 129 (2015) 138–141
The women in the intervention group had intakes of energy, protein, grains, fruits, milk, seafood, and nuts that were closer to the amounts recommended by the Chinese Ministry of Health. A previous study [24] also showed that education was positively associated with the pregnant women’s dietary behaviors. Additionally, women in the intervention group spent more time on physical activity than did those in the control group, with a higher intensity. Therefore, improvements in knowledge can lead to behavior changes among pregnant women. Women in both groups ate much more fruit than is recommended, both at baseline and after those in the intervention group had received education. However, too much fruit can increase the risk of obesity and can even induce GDM [25]. The nut intake was higher than recommended values at baseline in both groups, but it subsequently fell among women who had received the intervention. Women in the control group might have continued to eat too many nuts because they were unaware that excessive intake would result in excessive weight gain. The amount of seafood eaten in both groups was half the recommended value both at baseline and after implementation of the intervention, and the amount of beans eaten was less than half that suggested in guidelines, which suggests these food types are not popular in China. Future interventions should emphasize the benefits of these two kinds of food to pregnant women. The present study has also shown that personalized health education based on HBM could encourage pregnant women to do appropriate exercise. The American College of Obstetricians and Gynecologists published exercise guidelines for pregnancy [26], which recommended at least 30 minutes of moderate exercise a day in the absence of medical or obstetric complications [27]. Unfortunately, previous studies [21,28,29] have shown that pregnant women have a low level of daily physical activity and regular recreational exercise. In the present study, pregnant women were told that physical activity could reduce the risk of obesity, type 2 diabetes, cardiovascular disease, osteoporosis, and depression [30]. Additionally, they were told how to do physical activity safely, given a health education handbook, and provided with timely feedback concerning their energy consumption from the activity to instruct them on doing exercise reasonably. The present study has also shown that personalized intervention based on the HBM could significantly reduce the number of pregnant women who develop GDM. After implementation of the intervention, some risk factors were improved, such as dietary intake, physical activity and excessive GWG. Previous studies [11,12] have revealed that unreasonable dietary structure and lack of physical activity among pregnant women could lead to GDM. Hedderson et al. [10] found that excessive GWG during early pregnancy can increase risk of GDM. Using HBM as the theoretical basis, the present intervention could be comprehensively designed. However, one limitation of the present study should be considered in the interpretation of its findings. The participants were from one hospital, so the results might not be readily generalizable to other areas. Further research, with more representative samples from different areas, is needed to substantiate the results. In conclusion, the present study has shown that personalized health education can control excessive dietary intake and increase physical activity in China, which could ultimately reduce the incidence of GDM. Further research, with more representative samples, is needed to substantiate the results. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijgo.2014.11.014.
Conflict of interest The authors have no conflicts of interest.
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Contents lists available at ScienceDirect
International Journal of Gynecology and Obstetrics journal homepage: www.elsevier.com/locate/ijgo
CLINICAL ARTICLE
The effect of a personalized intervention on weight gain and physical activity among pregnant women in China Wenjuan Jing 1, Yan Huang 1, Xinghui Liu, Biru Luo ⁎, Yi Yang, Shujuan Liao Department of Obstetrics, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
a r t i c l e
i n f o
Article history: Received 15 May 2014 Received in revised form 7 November 2014 Accepted 14 January 2015 Keywords: Excessive weight gain Gestational diabetes mellitus Health belief model Intervention Physical activity
a b s t r a c t Objective: To examine whether personalized interventions could improve dietary intake and physical activity among pregnant women. Methods: A randomized controlled trial was conducted at a center in Chengdu, China, between September 2012 and February 2013. Women with a singleton pregnancy (aged ≥18 years, could understand written Chinese, did not have pre-existing diabetes) were enrolled at approximately 12 weeks of pregnancy. They were randomly assigned (1:1) to an intervention group (received personalized educational materials) or a control group (conventional interventions only). Data for dietary intake and physical activity were recorded via questionnaires. Only pregnant women who completed the study were included in the analysis. Results: Analyses included 106 women in the control group and 115 in the intervention group. After intervention, the intake of energy, protein, fruit, milk, seafood, and nuts differed significantly between groups (P b 0.05), with intakes closer to the recommended amounts in the intervention group. Women in the intervention group spent significantly less time resting (P = 0.033) and more time doing mild activity (P = 0.016). Mean weight gain per week was significantly lower in the intervention group (P = 0.023), and significantly fewer women in this group developed gestational diabetes (P = 0.043). Conclusion: Personalized educational interventions can improve dietary behavior and physical activity levels, and reduce prevalence of gestational diabetes among pregnant women in China. Chinese Clinical Trial Register: ChiCTR-IPR-15005809. © 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.
1. Introduction Gestational diabetes mellitus (GDM) is a heterogeneous group of metabolic disorders that result in varying degrees of maternal hyperglycemia and pregnancy-associated risk [1]. It affects 1%–14.2% pregnant women globally [2] and 3%–7% [3] in China. Epidemiological studies have found that women with GDM have an increased risk of hypertension [4], polyhydramnios [4], type 2 diabetes [5], spontaneous abortion [6], preterm delivery [7], and macrosomia [8]. Moreover, GDM increases the incidence of type 2 diabetes, obesity, and mental disorders in the offspring [9]. Many risk factors for GDM have been identified, such as ethnic origin [1], age [1], excessive gestational weight gain (GWG) [10], reduction of physical activity [11], diet [12], family history [1], and obesity [13]. Body fat percentage, physical activity, and possibly diet quality are important modifiable risk factors for GDM [14]. Many studies have been conducted to examine the effect of lifestyle interventions on GWG, but the results have been inconsistent. Lifestyle interventions during pregnancy have been shown to increase physical activity, improve dietary habits, and reduce GWG [15]. Healthy lifestyle behavior related to eating and physical activity can prevent excessive GWG [16]. ⁎ Corresponding author at: No. 20, Section 3, Renmin Nanlu, Chengdu, Sichuan 610041, China. Tel.: +86 28 85503852; fax: +86 28 85503776. E-mail address: [email protected] (B. Luo). 1 These authors contributed equally.
However, interventions in previous studies were not comprehensive because they were implemented after the women were diagnosed with GDM and because many had methodological limitations such as lack of theoretical basis. Additionally, few studies have reported the effect of interventions on diet and exercise behavior among pregnant women in China—a middle-income country with a culture that is substantially different from that of high-income countries. Over the past few decades, the lifestyle of pregnant women in China has changed greatly. Although undernutrition no longer seems to be an issue in urban areas, overnutrition, obesity during pregnancy, and GDM now seem to be the main problems [17]. Furthermore, 73% of women gain more than the national recommended amount of weight [18]. The aim of the present study was to investigate whether personalized interventions based on the health belief model (HBM) could improve pregnant women’s behavior with regard to dietary intake and physical activity, and lower the frequency of excessive GWG and GDM in China. The HBM was developed to predict compliance with preventive health recommendations [19]. According to HBM, when a person’s perceived value of an outcome is added to his or her expectation that a certain behavior will lead to an outcome, behavior change occurs [20]. 2. Materials and methods The present randomized controlled trial was conducted among women with singleton pregnancies at West China Second University
http://dx.doi.org/10.1016/j.ijgo.2014.11.014 0020-7292/© 2015 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.
W. Jing et al. / International Journal of Gynecology and Obstetrics 129 (2015) 138–141
Hospital, Chengdu, China, between September 3, 2012, and February 1, 2013. Eligible women were aged at least 18 years, could understand the written Chinese language, and did not have pre-existing diabetes. Women with any pregnancy-related complications or general medical disorders not associated with pregnancy were excluded. The study was approved by the institutional review board of the study center and participants signed an informed consent form. Women were approached at their first prenatal check (after approximately 12 weeks of pregnancy) and introduced to the aim of the present study. After enrollment, they were randomly assigned (1:1) to the control group or intervention group. The participants were divided according to the sequence of time and randomized numbers produced by SAS version 11.0 (SAS Institute Inc, Raleigh, NC, USA). Participants and data analysts were masked to group assignment. The investigators were not masked to the assignment so that they could implement the personalized intervention for women in the intervention group. After a preliminary investigation, an intervention model had been developed on the basis of the HBM theory. The key points of education included the harm of GWG and GDM, the benefit of encouraged behaviors, the difficulty to change habits, and the importance of belief in the efficacy of the interventions. In addition to standard health education manuals, women in the intervention group received an education manual on diet and physical activity (written by the research team) after randomization, and one-to-one counseling for at least 20 minutes in a private room with a trained graduate student (W.J.) after group assignment and at 16–20 and 20–24 weeks. The same graduate student was also available to answer questions and provide feedback on diet and physical activity behavior until 20–24 weeks either over the phone or via a group established on Tecent instant messenger (generally known as QQ in China) specifically for the present study. By contrast, pregnant women in the control group received only conventional interventions, such as standard health education manuals produced by the hospital. At baseline (12 weeks of pregnancy), demographic and obstetric characteristics, and data for dietary intake and physical activity in early pregnancy were recorded for all participants. Research tools included a general information questionnaire, a food frequency questionnaire (FFQ), and a physical activity form. Data were subsequently recorded at 16–20 and 20–24 weeks of pregnancy. The FFQ is an inventory that was originally developed through literature review, preliminary investigation, and expert evaluation to measure the dietary intake of pregnant women. It was amended for the present study on the basis of a standard version of the FFQ. It records food types, frequency, and intake amount at each meal. The participants completed the part on intake frequency. After consulting the participants, W.J. completed the part on the intake of food on the basis
of the food model book containing photos of kinds of food and amounts. Intake of nine kinds of food was measured in the present study: grains, vegetables, fruit, meat, seafood, eggs, milk, beans, and nuts. The Cronbach’s α coefficient of FFQ in the present study was 0.756, suggesting good internal consistency. The Pregnancy Physical Activity Questionnaire (PPAQ) was previously introduced to China [21]: after translation, back-translation, and revision, the PPAQ was used in Chinese pregnant women to measure their physical activity with regard to the intensity, time spent, and energy expenditure of various categories. The PPAQ classifies the physical activity done by pregnant women into nine different levels from A to I, with the metabolic equivalent (MET) increasing gradually in each one (0.9, 1, 1.5, 2, 3, 4, 5, 6, and N6). On the basis of the MET, the activities are grouped into four categories: 1) resting (levels A and B [MET b 1.0]); 2) mild physical activity (levels C and D [MET 1.0–2.9]); 3) moderate physical activity (levels E, F, G, and H [MET 3.0–6.0]); and 4) intense physical activity (level I [MET N6.0]). The reliability and validity of the PPAQ was confirmed in a previous study [21]. The Cronbach’s α coefficient of PPAQ in the present study was 0.718, suggesting good internal consistency. Sample size calculation indicated that 262 participants were needed for a power of 0.8 at a significance level of 0.05 (Supplementary Material S1). EpiData (EpiData Association, Odense, Denmark) was used to establish the database, and SAS version 9.1 (SAS Institute Inc, Raleigh, NC, USA) was used for statistical analysis. Only women who finished the whole study were included in the analysis. Rate, constituent ratio, and mean were used to describe social and demographic data of pregnant women, and t test and χ2test were used to compare dietary intake and physical activity. P b 0.05 was considered statistically significant.
3. Results Altogether 262 women were recruited and underwent randomization, but only 221 participants (106 in the control group and 115 in the intervention group) completed the study and were analyzed (Fig. 1). The two groups did not differ significantly in age, pre-pregnancy weight, body mass index, or fasting blood glucose at baseline (Table 1). Before implementation of the intervention, dietary intake did not differ between groups (Table 2). After implementation, the intake of energy, protein, grains, fruit, milk, seafood, and nuts differed significantly between the two groups (P b 0.05) (Table 2). The intake of foods in these categories was closer to the recommended amount for pregnant women (according to the dietary guidelines for Chinese residents developed by Ministry of
Eligible (n=262)
Randomly assigned (n=262)
Control group (n=131) Did not complete study (n=25) Abnormal blood sugar (n=1) Spontaneous abortion (n=2) Relocation (n=13) Loss to follow up (n=9) Analyzed (n=106)
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Intervention group (n=131)
Did not complete study (n=16) Abnormal blood sugar (n=1) Spontaneous abortion (n=2) Relocation (n=11) Loss to follow-up (n=2) Analyzed (n=115)
Fig. 1. Flow of participants through the study.
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Table 1 Baseline characteristics.a
Table 4 Weight gain.a
Variables
Intervention group (n = 115)
Control group (n = 106)
P value
Age, y Pre-pregnancy weight, kg Pre-pregnancy BMI Fasting blood glucose, mmol/L
29.57 ± 4.13 52.23 ± 6.84 20.44 ± 2.54 4.43 ± 0.34
29.89 ± 3.86 52.86 ± 6.08 20.74 ± 2.43 4.46 ± 0.34
0.56 0.48 0.38 0.56
Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by the square of height in meters). a Values are given as mean ± SD unless indicated otherwise.
Weight gain
Intervention group (n = 115)
Control group (n = 106)
P value
Total gestational weight gain between baseline and 20–24 weeks, kg Weight gain per week, kg/wk Women above IOM recommendationb
9.24 ± 3.99
9.69 ± 3.85
0.400
0.65 ± 0.20 102 (88.7)
0.71 ± 0.22 97 (91.5)
0.023 0.489
Abbreviation: IOM, Institute of Medicine. a Values are given as mean ± SD or number (percentage), unless indicated otherwise. b Recommended weight gain per week is 0.41 kg [23].
Health of China in 2008 [22]) in the intervention group than in the control group. In addition, women in the intervention group spent significantly less time resting (P = 0.033) and more time doing mild activity (P = 0.016) than did women in the control group (Table 3). The Institute of Medicine has recommended that women should gain 0.41 kg per week during pregnancy [23], which corresponds to a recommended total GWG for the participants in the present study of 5.46 kg. For participants in both groups, total GWG and average weight gain per week were higher than these recommendations (Table 4). Although the intervention and the control groups did not differ
significantly in total GWG, they did differ significantly in weight gain per week (P = 0.023) (Table 4). In both groups, most women had a weekly weight gain that was higher than recommended by the IOM (Table 4). Finally, 26 (22.6%) women in the intervention group developed GDM, compared with 37 (34.9%) in the control group (P = 0.043).
4. Discussion The present study has shown that personalized health education based on the HBM can improve pregnant women’s dietary behavior.
Table 2 Dietary intake. Variables
Energy, Kcal Protein, g Percentage of daily intake, % Carbohydrate, g Percentage of daily intake, % Fat, g Percentage of daily intake, % Grain, g Meat, g Vegetables, g Fruit, g Eggs, g Milk, g Seafood, g Beans, g Nuts, g
Baseline
After intervention(20–24 weeks)
Intervention group (n = 115)a
Control group (n = 106)a
RNI
P value
Intervention group (n = 115)a
Control group (n = 106)a
RNI
P value
1829.55 ± 476.71 60.84 ± 18.99 13.3 251.94 ± 72.82 55.1 76.70 ± 28.67 37.7 250.90 ± 84.67 85.62 ± 70.15 475.96 ± 266.61 499.87 ± 283.23 46.70 ± 32.26 252.25 ± 185.12 24.00 ± 30.40 13.53 ± 11.28 18.09 ± 14.56
1787.35 ± 466.29 59.50 ± 20.77 13.3 239.91 ± 60.73 53.7 79.28 ± 31.07 39.9 235.73 ± 73.33 92.09 ± 74.32 430.59 ± 275.86 517.65 ± 264.42 41.84 ± 24.63 227.86 ± 159.88 26.62 ± 34.72 14.92 ± 13.25 20.29 ± 14.92
2100 – 16.6 – 51.7 – 31.7 250 75 400 150 50 225 50 50 15
0.503 0.567 – 0.137 – 0.354 – 0.089 0.378 0.322 0.507 0.271 0.723 0.968 0.454 0.168
2255.24 ± 401.41 75.26 ± 14.93 13.1 309.60 ± 72.86 54.1 80.90 ± 16.06 33.3 338.38 ± 94.29 111.74 ± 46.85 353.91 ± 120.52 461.88 ± 213.08 52.62 ± 23.74 336.17 ± 164.00 41.50 ± 28.46 11.67 ± 9.24 15.35 ± 8.78
2129.90 ± 413.34 69.35 ± 14.32 13.1 290.35 ± 77.06 53.5 78.17 ± 16.61 34.0 305.98 ± 96.97 104.05 ± 48.98 361.48 ± 117.21 526.05 ± 266.17 52.80 ± 31.00 289.00 ± 159.50 33.72 ± 24.55 11.64 ± 9.73 18.92 ± 15.23
2300 – 15 – 55 – 30 400 87.5 400 300 50 375 87.5 40 15
0.024 0.003 – 0.058 – 0.216 – 0.013 0.235 0.637 0.048 0.962 0.060 0.031 0.982 0.036
Abbreviation: RNI, recommended nutrition intake. a Values are given as mean ± SD unless indicated otherwise.
Table 3 Time spent (h/day) on different intensities and categories of physical activity.a Intensity/category
A B C D E F G H I Resting Mild Moderate Severe a
Baseline
After intervention (20–24 weeks)
Intervention group (n = 115)
Control group (n = 106)
P value
Intervention group (n = 115)
Control group (n = 106)
P value
9.71 ± 1.55 5.68 ± 3.16 4.68 ± 2.63 2.54 ± 1.22 0.93 ± 1.04 0.24 ± 0.56 0.13 ± 0.30 0.09 ± 0.28 0 15.39 ± 3.19 7.22 ± 2.78 1.38 ± 1.45 0
9.53 ± 1.52 5.93 ± 3.20 4.87 ± 2.79 2.35 ± 1.25 0.83 ± 1.02 0.25 ± 0.52 0.09 ± 0.24 0.12 ± 0.31 0 15.46 ± 3.42 7.22 ± 2.97 1.29 ± 1.55 0
0.362 0.221 0.664 0.262 0.820 0.955 0.320 0.235 – 0.444 0.367 0.906
9.19 ± 1.31 4.63 ± 1.84 5.36 ± 2.22 3.53 ± 1.38 0.88 ± 0.98 0.20 ± 0.53 0.22 ± 0.32 0.05 ± 0.21 0 13.82 ± 1.98 8.89 ± 2.12 1.35 ± 1.35 0
9.45 ± 1.41 5.05 ± 2.49 5.01 ± 2.26 3.12 ± 1.20 0.96 ± 1.19 0.26 ± 0.58 0.17 ± 0.27 0.01 ± 0.10 0 14.50 ± 2.63 8.13 ± 2.52 1.40 ± 1.52 0
0.150 0.167 0.254 0.020 0.565 0.446 0.187 0.079 – 0.033 0.016 0.824 –
Values are given as mean ± SD unless indicated otherwise.
W. Jing et al. / International Journal of Gynecology and Obstetrics 129 (2015) 138–141
The women in the intervention group had intakes of energy, protein, grains, fruits, milk, seafood, and nuts that were closer to the amounts recommended by the Chinese Ministry of Health. A previous study [24] also showed that education was positively associated with the pregnant women’s dietary behaviors. Additionally, women in the intervention group spent more time on physical activity than did those in the control group, with a higher intensity. Therefore, improvements in knowledge can lead to behavior changes among pregnant women. Women in both groups ate much more fruit than is recommended, both at baseline and after those in the intervention group had received education. However, too much fruit can increase the risk of obesity and can even induce GDM [25]. The nut intake was higher than recommended values at baseline in both groups, but it subsequently fell among women who had received the intervention. Women in the control group might have continued to eat too many nuts because they were unaware that excessive intake would result in excessive weight gain. The amount of seafood eaten in both groups was half the recommended value both at baseline and after implementation of the intervention, and the amount of beans eaten was less than half that suggested in guidelines, which suggests these food types are not popular in China. Future interventions should emphasize the benefits of these two kinds of food to pregnant women. The present study has also shown that personalized health education based on HBM could encourage pregnant women to do appropriate exercise. The American College of Obstetricians and Gynecologists published exercise guidelines for pregnancy [26], which recommended at least 30 minutes of moderate exercise a day in the absence of medical or obstetric complications [27]. Unfortunately, previous studies [21,28,29] have shown that pregnant women have a low level of daily physical activity and regular recreational exercise. In the present study, pregnant women were told that physical activity could reduce the risk of obesity, type 2 diabetes, cardiovascular disease, osteoporosis, and depression [30]. Additionally, they were told how to do physical activity safely, given a health education handbook, and provided with timely feedback concerning their energy consumption from the activity to instruct them on doing exercise reasonably. The present study has also shown that personalized intervention based on the HBM could significantly reduce the number of pregnant women who develop GDM. After implementation of the intervention, some risk factors were improved, such as dietary intake, physical activity and excessive GWG. Previous studies [11,12] have revealed that unreasonable dietary structure and lack of physical activity among pregnant women could lead to GDM. Hedderson et al. [10] found that excessive GWG during early pregnancy can increase risk of GDM. Using HBM as the theoretical basis, the present intervention could be comprehensively designed. However, one limitation of the present study should be considered in the interpretation of its findings. The participants were from one hospital, so the results might not be readily generalizable to other areas. Further research, with more representative samples from different areas, is needed to substantiate the results. In conclusion, the present study has shown that personalized health education can control excessive dietary intake and increase physical activity in China, which could ultimately reduce the incidence of GDM. Further research, with more representative samples, is needed to substantiate the results. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ijgo.2014.11.014.
Conflict of interest The authors have no conflicts of interest.
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