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INTRODUCTION
Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy.1234 Managing GDM is crucial, as the condition not only elevates the risk of maternal and perinatal complications but also has negative psychological impacts, including mental distress. Qualitative studies on the psychology of women with GDM have shown that they experience a range of negative psychological effects throughout different aspects of life and suffer from internalized stigma, often felt as guilt and self-blame, along with distal stigma in both medical and non-medical settings.56 Eventually, the pressure to make lifestyle changes, adhere to dietary restrictions, and manage the condition medically, including self-monitoring of blood glucose (SMBG), drives negative mental health effects such as anxiety and depression.7
Understanding the quality of life (QoL) of people with diabetes is crucial, as the goal of treatment is not only to manage blood glucose levels but also to enhance QoL by addressing mental stress.8 Specifically, women with GDM experience a notable decline in their QoL, which, in turn, can affect their glycemic control as well as the mental health of mother and offspring.910 Recent advances in continuous glucose monitoring (CGM) may help alleviate the mental stress associated with SMBG, which has been demonstrated in non-pregnant individuals.11
The use of advanced glucose monitoring systems, such as flash glucose monitoring and CGM, in GDM women is still debated,12 but studies have found their potential as substitutes for SMBG. Previous studies suggest that these systems could be effective alternatives for SMBG, with positive impacts on glycemic control, modifying diet habits, gestational weight management, reducing birth weight, and identifying the risk of adverse pregnancy outcomes.13141516 Additionally, these systems have been technically validated, confirming their safety and accuracy.17 However, there is a lack of investigation into treatment satisfaction for applying CGM in women with GDM. Furthermore, women with GDM have at least seven times higher risk of developing postpartum diabetes,1819 however, whether QoL during pregnancy influences long-term glycemic control after pregnancy remains unclear. Therefore, in this study, we evaluated the QoL and treatment satisfaction using intermittently-scanned continuous glucose monitoring (isCGM) in Korean women with GDM. Additionally, we aimed to investigate the impact of GDM-dependent QoL on long-term glycemic control.
METHODS
Study design and data collection
This prospective observational study enrolled women with GDM between 2020 and 2023 at the Seoul National University Bundang Hospital, a tertiary hospital in South Korea (n = 298). We excluded 13 women with pregestational diabetes (n = 285). Among them, a total of 189 patients who completed the QoL surveys were included in the analysis.
Demographic information, medical history, and biochemical data were collected by clinical research coordinator (registered nurse) upon enrollment. Participants have completed the electronic QoL and treatment satisfaction questionnaires by themselves at the last prenatal visit, which was after 34 weeks of gestation. The registered nurse who enrolled the participants addressed any questions they had. Obstetric history including pre-pregnancy body mass index (BMI), gestational weight gain, adverse pregnancy outcomes, and neonatal data were retrieved from electronic medical records. For postpartum follow-up assessment, a standard 75-g oral glucose tolerance test (OGTT) was performed at 2 months and 1 year after delivery.
Diagnosis of GDM
The diagnosis of GDM was based on a two-step approach. An initial 50-g oral glucose load with a 1-hour glucose value of 140 mg/dL or higher underwent a confirmatory 100-g 3-h OGTT. Two or more of the following criteria were required for the diagnosis of GDM based on Carpenter-Coustan criteria2: fasting plasma glucose of 95 mg/dL or greater, 1-hour glucose of 180 mg/dL or greater, 2-hour glucose of 155 mg/dL or greater, or 3-hour glucose of 145 mg/dL or greater.
Assessment of QoL
To evaluate the diabetes-related QoL, we employed the Korean version of the Audit of Diabetes-Dependent Quality of Life (K-ADDQoL). The ADDQoL is a validated questionnaire designed specifically to measure the impact of diabetes on QoL.20212223 The ADDQoL comprises two overview items and 19 domain-specific items (Supplementary Table 1). The two overview items “In general, my QoL during pregnancy was:”, and “If I had not had GDM, my QoL would have been:” inquire about the general QoL and GDM-dependent QoL during pregnancy, respectively. Next, for each of the 19 domains–leisure activities, working life, travels, vacation, physical health, family life, friendship and social life, personal relationship, sex life, physical appearance, self-confidence, motivation, people’s reactions, feelings about the future, financial situation, living conditions, dependence on others, freedom to eat, and freedom to drink–two questions assessed the impact of diabetes on each domain (impact score) and the importance of that domain (importance score). The impact score ranged from −3 (maximum negative impact) to +1 (positive impact), and the importance score ranged from +3 (very important) to 0 (not at all important). Weighted impact (WI) score was calculated by multiplying the impact score and the importance score, ranging from −9 to +3. An average WI (AWI) score was calculated for each person. Lower AWI scores indicate worse QoL, whereas higher AWI scores reflect better QoL.
CGM and assessment of treatment satisfaction
A total of 25 women voluntarily applied isCGM. FreeStyle Libre CGM (Abbott Diabetes Care, Alameda, CA, USA) was applied between gestational weeks 30 and 34.
The Korean version of the Diabetes Treatment Satisfaction Questionnaire change version (K-DTSQc) was used to assess satisfaction with isCGM during pregnancy. The DTSQc reflects satisfaction with the current treatment compared to the prior treatment.2425 The questionnaire consists of eight items scored on a seven-point scale (Supplementary Table 2). Six items measured the change in treatment satisfaction: satisfaction with the current treatment, convenience of the treatment, flexibility of the treatment, satisfaction with one’s understanding of diabetes mellitus, willingness to recommend the treatment to others, and satisfaction with continuing the treatment. The response option for each of the items ranges from “much more satisfied now (+3)” to “much less satisfied now (−3).” A score of zero indicated “no change.” The sum of the scores of the six aforementioned questions was regarded as the overall treatment satisfaction score, ranging from +18 to −18, with higher scores indicating greater improvement in satisfaction with the treatment. Additionally, two items assess perceived hypoglycemia and perceived hyperglycemia, with response scales ranging from “much more of the time now (+3)” to “much less of the time now (−3),” where a score of 0 indicating “no change.” Lower scores for these two indicated better glucose control.
Statistical analysis
Categorical variables are presented as numbers and percentages, while continuous variables are presented as means and standard deviations (SD). A one-sample t-test was used to compare the calculated means with zero, representing “neither good nor bad” in the general QoL question; “the same” in the GDM-dependent QoL question, impact score, WI score, and AWI score; “not at all important” in the importance score; and “no change” in the K-DTSQc questionnaire. We employed the t-test, Mann–Whitney U test, and Jonckheere-Terpstra test to compare the QoL score of the K-ADDQoL and clinical characteristics across various subgroups. Additionally, Spearman’s correlation was used to assess the relationship between HbA1c levels and QoL. Statistical analyses were performed using SPSS 29.0.2.0 (IBM Corp., Armonk, NY, USA). Statistical significance at P < 0.05 (two-tailed) was considered statistically significant.
RESULTS
Demographics and clinical characteristics
A total of 189 women with GDM were included in this study. The demographic and clinical characteristics are presented in Table 1. The mean age was 36.0 ± 3.8. Of these, 65.6% were over 35 years old, and 68.4% were nullipara. The mean pre-pregnancy BMI was 24.5 ± 4.8 kg/m2, with 14.4% being overweight and 39.3% being obese. The mean HbA1c was 5.4 ± 0.4% and the mean fasting glucose was 89.8 ± 13.1 mg/dL. Moreover, 28.6% were treated with insulin, and 13.2% applied isCGM.
Table 1
Baseline characteristics of women with gestational diabetes mellitus
QoL in women with GDM
We assessed general QoL, as well as GDM-dependent QoL, considering how their QoL would be in the absence of GDM (Fig. 1). General QoL during pregnancy was 0.16 ± 1.61 (P = 0.177) and GDM-dependent QoL was −1.75 ± 1.10 (P < 0.001), suggesting that QoL of women with GDM was neither good nor bad, while 89.4% believed that GDM had a negative impact and their QoL would have been better without diabetes.
Fig. 1
Results of the ADDQoL: General quality of life during pregnancy and GDM–dependent quality of life (A) and 19 domain–specific items (B). (A) The general quality of life during pregnancy was assessed using the question “In general, my present quality of life is:”, and GDM–dependent quality of life was evaluated by “If I did not have diabetes, my QoL would be:”. (B) Weighted impact score is calculated by multiplying the impact score and the importance score, ranging from –9 to +3. A lower score indicates a worse quality of life. The range for the impact score is from –3 (maximum negative impact) to +1 (positive impact). The range for the importance score is from +3 (very important) to 0 (not at all important).
QoL = quality of life, ADDQoL = Audit of Diabetes-Dependent Quality of Life Questionnaire, SD = standard deviation, GDM = gestational diabetes mellitus.
All 19 QoL domains of the K-ADDQoL were adversely influenced by GDM (Fig. 1). The mean AWI score was −2.51 ± 1.59 (P < 0.001), indicating that diabetes was perceived as having a considerable negative impact on the QoL. The impact score in the domains of QoL ranged from −2.72 to −0.13. Furthermore, GDM had the greatest impact on “freedom to eat” (−2.72 ± 0.56, P < 0.001) and “freedom to drink” (−2.51 ± 0.81, P < 0.001). The least impact was noted on “sex life” (−0.13 ± 0.41, P = 0.007). The importance score ranged from 1.65 to 2.64, with the greatest importance observed in “personal relationship” (2.64 ± 0.60, P < 0.001) and the least importance noted in “sex life” (1.65 ± 0.76, P < 0.001). The WI scores ranged from −6.98 to −0.25. “Freedom to eat” was the most negatively impacted domain (−6.98 ± 2.49, P < 0.001), followed by “freedom to drink” (−6.20 ± 3.03, P < 0.001) and “feelings about future” (−4.29 ± 3.25, P < 0.001). The least impacted domain was “sex life” (−0.25 ± 0.80, P = 0.008), followed by “physical appearance” (−0.37 ± 2.14, P = 0.020) and “people’s reaction” (−0.68 ± 1.68, P < 0.001).
In the subgroup analyses based on clinical characteristics, including age and BMI, no statistically significant differences were observed in the general QoL and AWI scores (Table 2). In contrast, GDM-dependent QoL scores were lower in younger women and women treated with insulin, suggesting that these subgroups perceived themselves as having a more affected QoL due to GDM. In specific QoL domains, women who worked or were treated with insulin had a negative outlook on their future. No notable differences were observed in the freedom to eat and drink across clinical subgroups.
Table 2
Comparisons of QoL between subgroups based on clinical characteristics
Perceived QoL and glycemic control
We assessed whether the glycemic status was associated with QoL. Higher HbA1c level was slightly correlated with low GDM-dependent QoL suggesting that women with poor glucose control have lower perceived QoL (r = −0.155, P = 0.047; Supplementary Table 3). Next, we compared the QoL domains and their glycemic control based on the degree of impact of GDM on QoL (Table 3 and Supplementary Table 4). Women were divided into two groups; those whose GDM-dependent QoL score was −2 or −3 were categorized as the “more affected” group (mean, −2.4 ± 0.5), meanwhile, those with scores ranging from −1 to +1 were categorized as “less affected” group (mean, −0.4 ± 0.9).
Table 3
Comparison of the changes in body mass index and HbA1c over one year after delivery between the women more affected by the QoL due to gestational diabetes and those less affected
No significant differences were identified in age, gestational weight gain, or pregestational BMI between the two groups. Intriguingly, both groups had the greatest impact on “freedom to eat” and “freedom to drink” but the scores did not differ between the two groups (Supplementary Table 4; WI: −7.4 ± 2.1 vs. −6.7 ± 2.9, P = 0.503 and −6.3 ± 3.0 vs. −5.3 ± 3.2, P = 0.261). On the other hand, the “more affected” group demonstrated a greater impact on non-dietary domains, including feelings about the future, motivation, dependence on others, living conditions, self-confidence, physical appearance, leisure activities, working life, travels, vacation, physical health, and family life.
We compared the differences in BMI and HbA1c levels between the two groups for up to 1 year after delivery (Table 3). No differences were noted in the BMI before and after delivery and HbA1c levels during pregnancy. However, notably, the changes in HbA1c at 1 year postpartum from that of pregnancy were significantly higher in the “less affected” group than those in the “more affected” group (0.28 ± 0.37 vs. 0.01 ± 0.39, P = 0.013). This suggests that women who perceived themselves as having less of an effect on their QoL due to GDM experience worsened glycemic control after delivery.
Satisfaction to isCGM
Of the total 189 participants, 25 (13.2%) utilized isCGM during pregnancy and responded to the K-DTSQc questionnaire, which assessed satisfaction with the modality (Table 4). Women who used isCGM had a demographic profile comparable to non-isCGM users, though their 2-hour glucose concentration was higher (Supplementary Table 5). Overall, significant improvements in treatment satisfaction were observed after using isCGM, except for perceived hypoglycemia and perceived hyperglycemia. The overall treatment satisfaction score was 10.36 ± 9.21 (P < 0.001). However, no significant correlations were identified between glucose management indicators or average glucose levels and K-DTSQc scores (Supplementary Table 6), suggesting satisfaction with applying isCGM was independent of glycemic control levels. Glucose variability was marginally and positively associated with satisfaction with current treatment (r = 0.372, P = 0.067).
Table 4
Results of the K-DTSQc: satisfaction of using intermittently-scanned continuous glucose monitoring during pregnancy
DISCUSSION
In this study, women with GDM perceived that their current QoL was neither good nor bad, but acknowledged a significant effect of GDM on their QoL. Younger women and women who underwent insulin treatment were more affected in their QoL by GDM. Among the different domains of QoL, freedom to eat and drink was the most affected by GDM. Paying greater attention to GDM may have a potentially positive impact on long-term glycemic control, as less-affected women had a higher increase in their HbA1c level at 1 year postpartum than that observed in more affected women. We also demonstrated that the application of isCGM was satisfactory in women with GDM.
In this study, nearly 90% of the patients reported experiencing a negative impact on their QoL due to GDM, which is consistent with findings from previous studies that utilized varying QoL measures.1026 The most affected life domain was the freedom to eat and drink, which is consistent with the results of a study involving Korean participants with type 2 diabetes (non-pregnant).23 However, the WI score was considerably higher among women with GDM compared to the score in individuals with type 2 diabetes indicating that diabetes during pregnancy induces a higher level of stress than that experienced in the non-pregnant state (WI score for freedom to eat, −6.98 ± 2.49 vs. −4.07 ± 2.87; freedom to drink, −6.20 vs. −3.69; GDM (this study) vs. type 2 diabetes23). It is speculated that the diagnosis of GDM may trigger anxiety concerning both maternal and fetal health.2728
There has been a controversial effect of the treatment modalities on the QoL in women with GDM.26282930 In this study, women treated with insulin had lower GDM-dependent QoL scores. Kopec et al.30 reported that treatment with insulin was associated with distress. This partially contrasts with the findings of Pantzartzis et al.,26 who observed no differences in the responses of the ADDQoL among women based on their therapeutic approach, whether it be diet or insulin. However, these opposing outcomes may be elucidated by the findings of Trutnovsky et al.,28 which suggested that women undergoing additional insulin therapy may experience initial psychological challenges such as fear or resentment about injections during the early stages of treatment; however, these concerns tend to be overcome at late pregnancy.
To explore how QoL affects long-term postpartum glycemic control, we compared two subgroups categorized according to GDM-dependent QoL. The change in HbA1c levels from pregnancy to 1 year after delivery was significantly higher in the less affected group than in the more affected group. Interestingly, both groups had a comparably high level of impact on diet-related domains such as freedom to eat and drink. However, the impacts on non-dietary domains, such as “feelings about the future,” “motivation,” and “living conditions,” were significantly altered in the more affected group, which may contribute to sustaining glycemic control and possibly healthier lifestyles after delivery.
The effectiveness of CGM in managing diabetes during pregnancy has long been controversial. The use of CGM in type 1 diabetes during pregnancy has proven beneficial in improving glycemic control during pregnancy as well as neonatal outcomes, including large-for-gestational age.1231 Although guidelines, such as those from the American Diabetes Association and Korean Diabetes Association, recommend the use of CGM during pregnancy in women with type 1 diabetes, evidence for its effectiveness in type 2 diabetes and GDM remains insufficient.323334 One meta-analysis of the efficacy of CGM in GDM, including six studies, reported that women with GDM using CGM may achieve lower average blood glucose levels, and lower maternal weight gain and infant birth weight than those using self-monitored blood glucose monitoring.35
The maternal or perinatal benefits of utilizing CGM in GDM has been controversial; however, satisfaction with using isCGM was observed in this study. This is consistent with the findings of the studies involving non-pregnant individuals with type 2 diabetes who received multiple daily insulin injections, which improved treatment satisfaction and glycemic control by applying CGM.11 Few studies have assessed the QoL and treatment satisfaction with isCGM or real-time CGM in women with GDM. A German study compared 37 women with GDM managed by isCGM to 74 matched women managed by SMBG using DTSQc, and showed higher satisfaction level in those using isCGM, which is consistent with the findings of our study.13 A randomized controlled trial evaluating the efficacy of CGM in GDM by Lane et al.36 demonstrated that all participants in the CGM group reported that continuous feedback helped them make better food choices to manage their GDM and 90% reported that this feedback outweighed the inconvenience of wearing the device. Considering the substantial impact of GDM on patient’s glycemic control and the resulting distress, the use of CGM may alleviate their stress.
Our study has several strengths. To the best of our knowledge, this is the first study to assess the QoL in individuals with GDM along with prospective postpartum glycemic measures in an Asian population. We demonstrated that GDM-dependent QoL was impaired, and women who experienced a greater impact on their QoL due to GDM exhibited improved glycemic control at 1 year postpartum, possibly attributable to their heightened awareness of metabolic health. We also demonstrated that women with GDM were satisfied with isCGM, citing its convenience, improved understanding of diabetes, and their willingness to recommend it to others. This holds significance considering that isCGM or real-time CGM frequently fails to provide efficacy in mitigating adverse pregnancy outcomes. This is particularly crucial given that mental distress constitutes a significant domain of concern for individuals with GDM.
This study had several limitations. First, the small sample size limited further analysis aimed at identifying the clinical factors that lead to impaired QoL and evaluating the different domains of ADDQoL. However, GDM is speculated to cause significant distress in all patients beyond the clinical characteristics, and we demonstrated that the impact of GDM on QoL is significantly pronounced in younger women and those who received insulin treatment. Evaluation of the efficacy of and satisfaction with CGM in women needs further investigation on a large scale. Second, we lacked data on socioeconomic factors such as household income, housing type, and education, which may affect QoL.37 Beyond evaluating QoL, conducting a thorough assessment of mental distress would be helpful to gain a comprehensive understanding of mental health in individuals with GDM and to identify effective strategies for its improvement. Third, the QoL assessment was conducted at a single point at the final prenatal visit which may not capture the full variability of QoL throughout pregnancy and the postpartum period. A longitudinal approach could provide a more comprehensive view of QoL changes over time. Fourth, this study was conducted in a tertiary hospital setting, and careful interpretation is necessary when generalizing our findings to the community level.
In conclusion, GDM has a negative impact on QoL. Even though education on lifestyle modification is crucial for glycemic control in treating individuals with GDM, it is imperative for doctors not to overlook their QoL and mental health and how it affects their dietary and non-dietary habits. Furthermore, the use of CGM can enhance the convenience, understanding of glycemic control, and overall treatment satisfaction in those with GDM. This approach has the potential to alleviate mental stress during pregnancy.
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