Understanding patient perspectives on glycemic control: A mixed methods study of diverse patients with gestational diabetes

Abstract

Background:

Gestational diabetes mellitus (GDM) increases risk for adverse health outcomes and disproportionately affects racial and ethnic minority populations. Achieving optimal glycemic control during pregnancy mitigates risks but requires consistent blood glucose monitoring, diet, physical activity, and, when needed, medication adherence.

Objective:

To identify barriers and facilitators of GDM treatment adherence among racially and ethnically diverse patients with suboptimal glycemic control (<80% of glucose values/week within goal) or monitoring (<14 measurements/week) in their most recent GDM-affected pregnancy.

Methods:

Using a convergent parallel mixed methods design guided by the Capability, Opportunity, Motivation-Behavior (COM-B) framework, we collected quantitative data using validated online survey tools and conducted racially concordant virtual focus groups.

Results:

Forty participants (25% Asian, 18% Black, 20% Hispanic, 23% White, 15% multiracial) completed surveys; 28 joined focus groups. Self-reported adherence was 55% for glucose monitoring, 70% for diet, and 45% for physical activity. Among those prescribed medication (n=26), 85% reported taking it as prescribed, with higher adherence for insulin (90%) than oral agents (67%). Barriers included unsupportive workplaces, discomfort with fingersticks, limited time for activity, and frustration when goals were unmet despite perceived adherence. While 85% reported food security, participants wanted greater nutritional support from family and community. Preferences for holistic and less invasive management, including continuous glucose monitors, were expressed.

Conclusion:

Patients with suboptimal glycemic control or monitoring reported barriers such as workplace constraints, limited nutritional resources, and invasive treatments. Patient-centered education, community support, and expanded treatment options may improve adherence and outcomes in diverse populations with GDM.

X Summary:

Suboptimal glycemic control in GDM may not just be about behavior. Patients point to structural barriers, competing demands, and emotional burden, highlighting the need for more equitable, patient-centered care.

Introduction

Gestational diabetes mellitus (GDM), or diabetes first diagnosed during pregnancy (typically between 24–28 weeks’ gestation), affects approximately 9% of U.S pregnancies and 14% worldwide each year[1]. GDM increases the risk for preterm birth, fetal abnormalities, macrosomia, neonatal hypoglycemia, and neonatal respiratory distress syndrome[2]. Maternal complications of GDM include cesarean section (c-section), pre-eclampsia, and longer-term risk of progression to type 2 diabetes, heart disease, stroke and other cardiometabolic disease[3, 4].

Glycemic control during pregnancy (i.e., meeting recommended glycemic goals) is critical to mitigating short- and long-term risks associated with GDM. To achieve optimal glycemic control during pregnancy, the American Diabetes Association (ADA) recommends 4-times daily self-monitoring (pre- and postprandial tests), medical nutrition therapy (diet), physical activity, and when necessary, medication [2, 5, 6]. The diet plan should be developed in collaboration with the patient, provider, and experienced dietitian; continuous adjustments should be made to the nutritional plan based on feasibility, glycemic goals, appetite, and weight gain. Recommended physical activity is moderate exercise for 15–30 minutes, 3–4 times a week[7].

Optimal glycemic control reduces the risk of adverse pregnancy outcomes[8, 9] and neonatal outcomes such as shoulder dystocia, macrosomia, birth injury, and NICU admissions[10, 11]. However, a population-based study analyzing self-monitored blood glucose measurements over an average of 12 weeks identified that over 60% of patients with GDM struggle to achieve and maintain blood glucose levels within recommended ranges[12]. Moreover, unfavorable glycemic control trajectories are 17–24% more likely to be experienced by Black patients[12]. There is a clear need for research to understand and improve disparities in glycemic control among diverse racial and ethnic groups with GDM.

Previous studies have found that disease knowledge, self-efficacy, and social support levels influence self-management for glycemic control[13]. A recent systematic review of qualitative literature found that as awareness and knowledge of GDM increases, patients’ confidence and ability to adhere to self-management behaviors also increases[14]. However, the included studies were not specific to populations with a history of suboptimal glycemic control, leaving open questions about how healthcare providers can support those most at risk for poor outcomes. Moreover, few studies examined racially and ethnically diverse populations burdened by health disparities.

This study sought to add to the knowledge base about patient perspectives on the factors affecting glycemic control. We conducted a convergent parallel mixed methods study to describe barriers and facilitators of GDM glycemic control, including adherence to treatment recommendations for blood glucose monitoring, diet, physical activity, and medication, among racially and ethnically diverse individuals with a history of suboptimal glucose control or monitoring in their most recent GDM-affected pregnancy.

Methods

Theoretical Framework

According to the COM-B model[15], Capability (C), Opportunity (O), and Motivation (M) together influence Behavior (B). Capability refers to an individual’s physical and psychological ability to engage in a behavior (e.g., physical strength or knowledge). Opportunity encompasses physical and social external factors that enable or constrain behavior (e.g., access to resources or social support). Motivation includes both automatic and reflective processes that drive decision-making and action (e.g., personal beliefs and emotions). We used the COM-B framework to guide the study design and analysis, leading towards identifying influences on the four behaviors relevant to glycemic control (Fig. 1). Because the COM-B framework explicitly situates behavior within social and environmental contexts, social determinants of health were implicitly embedded within the analytic approach. Open-ended interview questions were used to allow participants to organically identify barriers and facilitators related to their lived contexts, which were then interpreted within the COM-B domains.

Figure 1.

Application of the COM-B Framework to Understand Glycemic Control Behaviors

The figure illustrates how the COM-B model was adapted to examine the influences on four behaviors related to glycemic control (blood glucose monitoring, diet, physical activity, and medication). Social and structural determinants may influence one’s ability, opportunity or motivation to adhere to healthy behaviors.

Design

We used a convergent parallel mixed methods study design[16]. Quantitative data from validated surveys and qualitative data from focus groups were collected and analyzed independently before being integrated through joint displays to provide a comprehensive interpretation of the results.

Setting

The study was conducted within Kaiser Permanente Northern California (KPNC), an integrated healthcare delivery system serving 4.6 million members who are highly representative of the underlying population, except at the extremes of income[17, 18]. Participants were identified by the KPNC Regional Perinatal Service Center (RPSC), a nurse-based management program providing supplemental care via telemedicine to high-risk pregnancies, including those complicated by GDM.

Participants and Procedure

Eligible participants were individuals previously diagnosed with GDM who delivered their infants between December 1, 2022 - November 1, 2023; were enrolled in the RPSC program; and had suboptimal glycemic control (<80% of weekly values meeting ADA targets) or suboptimal monitoring (<14 finger sticks/week) based on the electronic health record (EHR). We used stratified sampling to identify individuals from four racial/ethnic groups: Black, Hispanic, Asian, and White. Other eligibility criteria included being within 12 months of delivery of the index pregnancy, age ≥18 years, and access to the internet.

Recruitment included an e-mail and phone call invitations for a 10-minute online survey and 60-minute online focus group. The invitation to participate was emailed through REDCap[19], where participants completed eligibility screening, consent, and the survey. Participants enrolled in the study were compensated up to $100 in electronic gift cards: $15 after survey completion and $85 after attendance at the focus group. The study protocol and instruments were approved by the KPNC Institutional Review Board.

Data Collection

Participant characteristics

Data on individual-level sociodemographic and clinical characteristics were obtained from the EHR and self-report survey. Sociodemographic factors from the EHR included age at delivery, race/ethnicity, and glycemic control and monitoring values. Race/ethnicity was also self-reported via survey and categorized as Asian/Pacific Islander, Hispanic, Non-Hispanic Black, Non-Hispanic White, or multi-racial. Race/ethnicity was not conceptualized as a biological risk factor, but instead served as representations of social drivers that shape health outcomes through systemic inequities, lived experiences, and differential access to resources. Other self-reported sociodemographic factors included education level, household income, employment status, marital status, and number of children in the household at survey completion.

Quantitative Data

Data on four behavioral domains relevant to GDM self-management— blood glucose monitoring, diet, physical activity, and medication adherence— were self-reported using the Diabetes Self-Care Activities Questionnaire[20], which was adapted to GDM for the purpose of this study (Supplemental Material 1). We used an abbreviated 6-item version which asked respondents to consider the time during their most recent GDM-affected pregnancy. First, self-reported adherence to recommended blood glucose monitoring was measured using two items capturing frequency per week and adherence to the provider-recommended 4-times per day schedule. Second, self-reported dietary adherence was measured using one item on frequency of following the prescribed meal plan. Third, self-reported physical activity adherence was measured using one item on physical activity after meals. Finally, self-reported medication adherence was measured using two items on prescription type and adherence.

Food insecurity was assessed using the validated Hunger as a Vital Sign[21] 2-item questionnaire (i.e., “I worried whether our food would run out before I got money to buy more”; “The food I bought just didn’t last and I didn’t have money to get more”); responses of sometimes or often true to either item signaled risk of food insecurity. Household food and nutrition assistance (e.g., CalFresh, Supplemental Nutrition Assistance Program [SNAP]) were assessed using a single item: “Did your household receive benefits from any of the following food programs?”

Qualitative Data

We conducted three web-based, 60-minute semi-structured focus groups in February 2024 to explore barriers and facilitators to glycemic management[22]. The focus groups were racially-concordant (Black/African American, Hispanic, and combined Asian/White) and facilitated by trained researchers. Sessions were video-recorded with consent and followed an interview guide formulated by the research team, including a clinician who provides direct patient care. Key questions explored participants’ perspectives and ability to adhere to recommendations regarding blood glucose monitoring, diet, physical activity, and medication adherence (Supplemental Material 2).

Analyses

Quantitative data were analyzed descriptively using SAS 9.4. By design, all participants either had suboptimal glycemic control or monitoring per data collected via EHR. Participants were categorized as having moderately high adherence for each behavior separately using the following thresholds: monitoring blood glucose as recommended (4 times per day), or taking medication as prescribed, ≥5 days per week on average; and reporting often or always adhering to diet or physical activity recommendations in an average week. Differences between racial and ethnic groups were not analyzed due to the small sample size.

Qualitative focus group data were analyzed using thematic analysis to derive themes from the data. Two master’s-level researchers (BPS, AS) used Dedoose to independently coded transcripts using line-by-line coding. We used a hybrid deductive/inductive approach, with initial codes developed a priori based on the research questions and key constructs identified in the literature, and additional codes added as they emerged from the data. After coding each transcript, researchers met to discuss code definitions, collaboratively assess underlying patterns and insights, and reconcile any discrepancies. The codes were then organized by behavior, and mapped to the COM-B framework to explore how barriers were related to capabilities, opportunities, and motivations influencing behaviors for glycemic management. Finally, qualitative and quantitative data were integrated using joint displays and joint analyses across sources.

Results

Participant Characteristics

Of 375 invited individuals, 40 completed a survey; and 28 attended the focus groups (70% retention; Table 1). Of the entire sample, 75% met the inclusion criteria by having suboptimal glycemic control only; 5% had suboptimal blood glucose monitoring only; and 20% had both. The sample was racially/ethnically diverse (25% Asian, 17.5% Black, 20% Hispanic, 22.5% White, 15% multi-racial). Most participants (≥85%) had more than a high school education, were partnered, had more than 1 child, and were food secure; 42.5% (n=17) received some form of household food assistance.

Table 1:

Participant Characteristics (N=40)

	n (%)
Race/ethnicity
Asian/Pacific Islander	10 (25)
Black/African American	7 (17.5)
Hispanic	8 (20)
Non-Hispanic White	9 (22.5)
More than one race/ethnicity	6 (15)
Education
High school or less	3 (7.5)
Some college	11 (27.5)
College graduate	15 (37.5)
Postgraduate	11 (27.5)
Marital status
Married/civil union	24 (60)
Living with partner	11 (27.5)
Single	5 (12.5)
Household income
<$50,000	10 (25)
$50,000–99,000	8 (20)
$100,000–149,999	11 (27.5)
≥$150,000	11 (27.5)
Children in household
1	13 (32.50)
2	14 (35)
≥3	13 (32.5)
Glycemic control and monitoring a
Suboptimal control only	30 (75)
Suboptimal monitoring only	2 (5)
Suboptimal control and monitoring	8 (20)
GDM medication prescribed
No medication	14 (35)
Oral medication	6 (15)
Insulin 1 or 2 times per day	16 (40)
Insulin 3 or more times a day	4 (10)
Food insecure
Yes	6 (15)
No	34 (85)
Household food assistance
CalFresh/Supplemental Nutrition Assistance Program (SNAP)	9 (22.5)
Other (e.g., WIC)	12 (30)
No food assistance	23 (57.5)

^aDerived from the EHR. Suboptimal glycemic control was defined as <80% of glucose values per week meeting the ADA glycemic goals: <95 mg/dL for fasting and <140 mg/dL for 1-hour postprandial glucose. Suboptimal blood glucose monitoring was defined as an average of <14 fingerstick measurements/week.

Quantitative Findings

Participants reported monitoring blood glucose on 6.6 days/week (SD=0.34). Participants reported monitoring at the provider recommended frequency (4 times per day) an average of 5.6 days/week (SD=0.63), with 55% (n=22) reaching the level of moderately high adherence. For diet, 70% (n=28) of participants reported moderately high adherence, whereas for physical activity, 75% (n=30) reported moderately high adherence. Among those prescribed medication (n=26), participants reported taking their medication as prescribed 5.7 days/week (SD=1), with 85% (n=22) reporting moderately high adherence. Moderately high adherence for medication was more common among those prescribed insulin (n=18/20, 90%; taken as prescribed a mean of 6.2 days) as compared to oral medication (n=4/6, 67%; taken as prescribed a mean of 4.8 days).

Qualitative Findings

Twelve broad themes emerged reflecting barriers and facilitators of blood glucose monitoring, diet, physical activity, and medication adherence, as well as perspectives on glycemic control. Themes, their descriptions, sample focus groups quotes, and how the themes relate to components of the COM-B model appear in Table 2.

Table 2.

Qualitative themes and illustrative focus group quotes, mapped to COM-B components

Theme	Description	Illustrative Quote	COM-B Componenta	Barrier or Facilitator
Blood Glucose Monitoring
Unsupportive work environments	The nature of certain jobs, such as office work and having minimal breaks and privacy, make it hard to check blood sugar.	“When I had gestational diabetes, I was still working and I was in management, so I wasn’t really sitting down and having meals.”	Physical Opportunity (O)	Barrier
Difficulty remembering and using glucometers	Monitoring methods can be cumbersome and hard to remember.	“I would leave the machine at home when I’m at work.” “It was just kind of like difficult to just keep doing it consistently and it would be helpful if I have like a tool that would just do it automatically for me”	Psychological capability (C)	Barrier
Efficiency of testing methods for adherence	Perception that alternative monitoring methods such as continuous glucose monitors would be more convenient.	“One of my coworkers has diabetes and she has like a little patch on her arm… so I think, maybe, implementing that would be much easier for moms.”	Physical opportunity (O)	Facilitator
Diet
Difficulty understanding and implementing GDM-friendly diet	Lack of knowledge and meal planning make adherence difficult.	“I didn’t see any, but like… a little more… recipes or different meal plans to kind of [diet plan]. Cause I literally was eating the same thing for the last like four months of my pregnancy.”	Psychological capability (C)	Barrier
Access to meal plans and recipes	Desire for premade meal plans and recipes following a GDM diagnosis.	“But just having, maybe, premade meals for us will be helpful.”	Physical opportunity (O)	Facilitator
Physical Activity
Challenges incorporating structured exercise	Recommended physical activity after every meal are hard to fit in with daily activities.	“That I was out and about, and if I had gone out to eat then it’s like I can’t just get up from where I’m at, like somebody else’s house or restaurant and, like, start doing stuff.”	Reflective motivation (M)	Barrier
Integrating physical activity into daily routines	Finding ways to incorporate physical activity into routine activities.	“I noticed when I vacuumed, [I] work up a little sweat, that my blood sugar would drop…walking wasn’t realistic.”	Reflective motivation (M)	Facilitator
Medication
Perception of treatment as invasive	Considering medication, whether insulin or oral medication, as too invasive.	“For the Chinese community, we feel that taking the medicine is kind of like taking poison, somehow, in some way.”	Reflective motivation (M)	Barrier
Preference for lifestyle changes over medication treatment	Preferring to manage blood sugar levels through diet and physical activity lifestyle changes.	“Medication just gives me kind of anxiety and I do my best not to…just manage whatever is going on with like diet and exercise.”	Reflective motivation (M)	Facilitator
Perspectives on glycemic management
Distress due to unmet expectations	Feeling discouraged with self for not being able to meet recommendations.	“It just made me feel guilty, like I wasn’t doing the right thing even though I was.”	Automatic motivation (M)	Barrier
Lack of empathetic and supportive provider communication	Negative interactions with providers can hinder motivation and adherence to recommendations.	“A little bit of empathy and understanding from the nurses that check in with you would have been a little nicer.”	Automatic motivation (M)	Barrier
Role of social support in managing GDM effectively	Seeking guidance from others, particularly regarding healthy meal options.	“Another thing that would be helpful is… creating a sense of community around this because it is hard when… you might not know other people that have gone through it.”	Social opportunity (O)	Facilitator

^aCOM-B components: Capability (C), Opportunity (O), Motivation (M)

Blood Glucose Monitoring

Barrier: Unsupportive work environments.

Participants’ work schedules, lack of breaks, and lack of privacy made it difficult to monitor blood glucose regularly. As one explained, “when I had gestational diabetes, I was still working and I was in management, so I wasn’t really sitting down and having meals.” Others noted the stress of balancing professional responsibilities with GDM recommendations. These concerns reflect physical opportunity (O) as a barrier, as participants described workplace environmental factors as constraints to monitoring (illustrative quotes in Table 2).

Barrier: Difficulty remembering and using glucometers.

Participants commented on difficulty remembering to use and carry their glucometers throughout their daily routines, presenting a barrier to consistent monitoring. Participant experiences reflect psychological capability (C) as a barrier, as participants faced challenges with the cognitive effort for regular glucose monitoring (illustrative quotes in Table 2).

Facilitator: Efficiency of testing methods for adherence.

Although none had used them during their GDM pregnancy, participants hypothesized that the ability to check blood glucose levels via CGM without disrupting their daily routines would facilitate adherence. Others expressed that access to CGM would help reduce the burden of having to remember to pack the glucometer and transport it as they continue with their daily activities. This reflected the physical opportunity (O) component of COM-B, as participants perceived that having access to easier-to-use devices would remove barriers (illustrative quotes in Table 2).

Diet

Barrier: Difficulty understanding and implementing GDM-friendly diet.

Participants expressed challenges in understanding and adhering to GDM dietary recommendations. Many reported frustrations with meal planning, often resorting to repetitive food choices due to uncertainty about what was appropriate. Others mentioned feeling discouraged when dietary adherence did not yield expected improvements in their blood sugar levels, leading to uncertainty about what foods would help them meet their glycemic goals. These challenges reflect the psychological capability (C) component of the COM-B model, as they highlight the cognitive burden associated with dietary planning and decision-making (illustrative quotes in Table 2).

Facilitator: Access to meal plans and recipes.

Participants emphasized the importance of external support, including access to meal plans, recipes, and nutritious and enjoyable prepared meals that align with GDM guidelines. One participant shared, “If they would have given us more of a layout -- like this is what you can have for breakfast, lunch, dinner, snacks--I would have been more excited to do it.” This theme aligns with physical opportunity (O) in the COM-B model, as participants described structured meal options could positively influence their ability to adhere to dietary recommendations (illustrative quotes in Table 2).

Physical Activity

Barrier: Challenges incorporating structured exercise.

Participants expressed difficulties adhering to provider recommendations for structured physical activity, especially after meals. Many found it challenging to fit dedicated exercise into their daily routines, particularly when outside their home environment. These challenges reflect reflective motivation (M) in the COM-B model, as participants struggled with balancing social and environmental constraints, making it difficult to engage in structured exercise (illustrative quotes in Table 2).

Facilitator: Integrating physical activity into daily routines.

Rather than engaging in structured workouts, some participants found success incorporating physical activity into their daily routines. Activities such as housework were seen as a practical alternative to traditional exercise. This theme aligned with reflective motivation (M), in that participants identified conscious decisions and beliefs that influenced their physical activity behavior (illustrative quotes in Table 2).

Medication

Barrier: Perception of treatment as invasive.

Some participants expressed concerns that medication, including insulin injections or oral medication, felt too invasive, despite its medical necessity. Cultural beliefs and personal perceptions influenced these attitudes. One participant stated, “I really didn’t like taking the insulin. It kind of made me feel like I’m putting some kind of poison or something into my body.” This barrier aligns with reflective motivation (M) in the COM-B model, as participants’ beliefs and emotional responses to medication impacted adherence to prescribed treatments.

Facilitator: Preference for lifestyle changes over medication treatment.

Participants preferred managing blood sugar levels through diet and physical activity rather than medication, expressing anxiety and negative emotions about insulin use. One participant shared, “Taking the medication was what had the negative…I had like negative emotions towards that…and what I knew was gonna make me feel better was like learning how to manage it with diet and exercise.” This theme aligns with reflective motivation (M), as participants’ beliefs about medication influenced their treatment preferences and adherence.

Perspectives on Glycemic Management

Barrier: Distress due to unmet expectations.

Participants described emotional distress when they struggled to meet GDM management recommendations, leading to feelings of guilt and self-doubt. Some participants expressed frustration with providers’ reactions, especially when blood sugar levels were not optimal, leading to a sense of discouragement and self-blame.

Other participants reported feeling pressured by healthcare providers to explain fluctuations in their blood sugar levels, which intensified their emotional distress. This theme aligned with automatic motivation (M), as participants described their emotional responses to the management process (illustrative quotes in Table 2).

Barrier: Lack of Empathetic and Supportive Provider Communication.

Participants expressed that more understanding and patience from providers would improve their experience with GDM management. As one participant stated, “I just feel like a little bit more understanding and time to process the information you’re giving women during.” First-time birthing parents emphasized the emotional burden of GDM management while newly navigating pregnancy, stating, “As first-time moms especially, we have so many worries and so many questions.” This theme reflects automatic motivation (M), as provider communication influenced their emotional responses and engagement.

Facilitator: Role of Social support in Managing GDM Effectively.

Participants described the positive influence of social support from family, friends, and online communities in motivating healthier lifestyle choices and improving their understanding of GDM. Social media and support groups offered reassurance additional management strategies, as one participant shared: “Something else that helped was social media, I followed people who were nutritionists, that also had gestational diabetes.” Another noted: “I talked to other people and got ideas from a support group off Facebook…talking to others who were going through the same thing at the same time [really helped].” This theme aligns with social opportunity (O), as social networks supported behaviors and strategies for GDM management.

Integration of Qualitative and Quantitative Findings

Between 55–85% of participants reported moderately high adherence to the four behaviors. The joint display (Table 3) illustrates areas where self-reported adherence aligns with qualitative facilitators, and where motivational and structural barriers presented challenges.

Table 3.

Joint Display of Quantitative Proportions Adherent to Treatment Recommendations and Related Qualitative Themes

Behavior	Quantitative Findings	Qualitative Findings	COM-B component	Alignment of Quantitative and Qualitative Data	Inferences
Blood glucose monitoring	55% achieved moderately high adherence	Barrier: Unsupportive work environments; difficulty remembering and using glucometers	Physical Opportunity, Psychological Capability	Aligned: Self-reported adherence was lowest of all behaviors; qualitative data highlight workplace and testing barriers.	Despite some reporting moderate adherence, qualitative findings highlight workplace barriers and suggest CGMs could improve monitoring.
		Facilitator: More efficient testing methods (i.e., CGM)
Diet	70% achieved moderately high adherence	Barrier: Difficulty understanding and implementing GDM-friendly diet	Physical Opportunity, Social Opportunity	Partially aligned: Self-reported adherence is high, but qualitative data suggest areas for improvement with education and support	While many report moderately high adherence, qualitative data reveal challenges in understanding and following the diet, suggesting a need for structured education and meal plans.
		Facilitator: Support from friends and family for meal plans and recipes
Physical activity	75% achieved moderately high adherence	Barrier: Difficulty incorporating structured exercise into daily routine	Reflective Motivation	Aligned: Self-reported adherence is high, and qualitative data highlight ways individuals incorporated physical activity into daily routine	While many report moderately high adherence, motivation remains a barrier. Integrating physical activity into daily routines may improve adherence.
		Facilitators: Integrating physical activity into chores or errands
Medication	85% achieved moderately high adherence	Barrier: Perception of insulin medication as invasive	Reflective Motivation	Partially aligned: Both data sources indicate adherence, but qualitative data highlight areas to improve patients’ comfort with medication	Adherence is highest of all behaviors, but concerns about invasiveness and a preference for lifestyle changes suggest a need for better communication on treatment options.
		Facilitator: Preference for lifestyle behaviors over medication treatment
Perspectives on glycemic management	Not directly measured	Barrier: Distress from unmet glucose goals; lack of provider empathy	Automatic Motivation, Social Opportunity	Not applicable	Emotional distress from unmet glucose goals and lack of provider empathy may hinder adherence. Support groups and provider reassurance could help.
		Facilitator: Social support

Note: For quantitative data, proportions for moderately high adherence correspond to following treatment recommendations for blood glucose monitoring (4 times per day on ≥5 days/week), diet (often or always), physical activity (often or always), and medication (taken as prescribed on ≥5 days/week). N=40 survey respondents; n=28 focus group participants.

Abbreviations: CGM, continuous glucose monitor; GDM, gestational diabetes mellitus. COM-B, Capability, Opportunity, Motivation for Behavioral Change Model

Blood glucose monitoring had the lowest proportion of participants reporting moderately high adherence (55%), hindered by workplace constraints and inconvenience, suggesting potential benefits of CGMs and workplace accommodations. While 70% reported moderately high dietary adherence, qualitative findings revealed gaps in nutritional knowledge. Physical activity adherence was 75%, though motivation remained a barrier. Medication adherence was highest (85%), yet concerns about treatment invasiveness persisted, bringing attention to the need for better communication on lifestyle versus medication as treatment. Perspectives on glycemic management, though not measured quantitatively, emerged as a key barrier, while support groups and provider reassurance served as facilitators.

Discussion

Using the COM-B framework, this study identified barriers and facilitators to GDM treatment adherence among individuals with suboptimal management. Twelve themes highlighted modifiable influences on blood glucose monitoring, diet, physical activity and medication. Monitoring was hindered by unsupportive work environments and inconvenient methods. Diet was hindered by lack of clear nutritional resources, signaling a need for structured dietary education and medically tailored meals. Busy schedules and time constraints limited physical activity, suggesting integration into daily routines may be effective. Medication adherence was high but affected by perceptions of treatment as invasive, suggesting a need for better communication on the rationale for treatment via lifestyle modifications alone versus the addition of medication. By identifying factors related to capability, opportunity, and motivation, these findings build on existing literature by identifying specific, potentially modifiable barriers to blood glucose monitoring, diet, physical activity, and medication adherence. Integration results suggest that addressing motivation, structural challenges, and emotional distress could improve GDM management. These findings highlight the multifaceted challenges faced by individuals with suboptimal GDM management, and align with previous research demonstrating that enhanced disease knowledge, self-efficacy, and social support are critical for effective glycemic self-management[13].

Capability: Addressing Psychological and Physical Barriers

Our findings highlight the impact of psychological capability on adherence to blood glucose monitoring. While 55% of participants self-reported moderately high adherence to provider recommendations, qualitative findings revealed challenges related to memory, convenience, and perceived inefficiency of traditional glucometers. Participants perceived that other blood glucose monitoring methods, such as CGMs, would be more efficient and could mitigate these barriers. This aligns with results seen by Kusinski and colleagues, who reported that CGMs were easy to use, painless and provided extra reassurance among pregnant individuals[23–25].

Opportunity: Environmental and Social Barriers

Barriers related to physical opportunity were most evident in workplace environments (such as offices), where structured schedules, limited breaks, and limited privacy made it difficult to adhere to glucose monitoring as recommended. Rigid work environments emphasizes the need for employer accommodations that allow for greater privacy and time to engage in regular glucose monitoring. CGMs may address some of these concerns, though their integration into routine GDM care remains limited.

Social opportunity also influenced diet adherence. Participants expressed a strong desire for meal preparation guidance from surrounding networks such as family, friends, or healthcare providers. Participants desired emotional support from family and online communities, with many participants emphasizing the importance of community and shared experiences in managing GDM. Digital platforms, particularly GDM-focused social media groups, were viewed as important sources of informational and emotional support, consistent with prior studies[26, 27]. These findings suggest social support networks, in-person and online, may enhance adherence to GDM treatment recommendations.

Motivation: Automatic and Reflective Barriers

Motivation to adhere to GDM treatment was influenced by emotional responses, provider interactions, treatment preferences, and social support. Although perceived adherence was stronger than in some previous studies, many participants experienced guilt and frustration when unable to meet targets[14]. This emotional distress illustrates the need for psychological support and self-compassion strategies in GDM management. A perceived lack of empathy from providers and clinicians discouraged adherence. Effective support strategies should aim to minimize fear, promote autonomy, and empower individuals to take control of their health during this transitional period.

Participants preferred lifestyle changes over medication; however, individuals were prescribed medication in cases where lifestyle changes alone were insufficient. This emphasizes the importance of medical counseling to support informed decision-making and adherence. While a GDM diagnosis can encourage healthy behavior, awareness of the long-term risks associated with suboptimal glucose control remains limited[28]. Patient-centered discussions may help bridge this gap and improve treatment acceptance.

Strengths and Limitations

This study’s strengths include its use of a mixed methods approach, which integrated both quantitative and qualitative data to provide a comprehensive understanding of GDM management, and the focus on individuals with a history with suboptimal glycemic control or monitoring. This is the first study to examine barriers and facilitators among those with suboptimal glycemic management; the study should be considered hypothesis-generating, to inform future research. The racially/ethnically diverse sample, and the use of racially-concordant focus groups may have promoted a comfortable environment, allowing participants to openly share their experiences and feel a greater sense of community.

Limitations include the relatively high level of education of participants, and recruitment from a single health system, which may limit generalizability to more structurally disadvantaged populations. However, participants within this predominantly higher advantage sample still reported substantial barriers to glycemic management, highlighting the necessity of further characterizing and understanding the barriers to glycemic control and management. An additional limitation is the lack of a comparison group of individuals with optimal glycemic control or monitoring; therefore, findings should be interpreted as descriptive rather than comparative. While each focus group was racially-concordant, the sample size was insufficient to draw racial/ethnic-specific conclusions. However, such data may provide insight to racial and ethnic disparities in GDM treatment adherence, and adverse perinatal health outcomes. Future research would benefit from larger, adequately powered studies to compare racial and ethnic-specific trends in GDM management. This study design provided qualitative context to understand the quantitative results; both should be considered as hypothesis-generating rather than confirmatory, and should be followed with hypothesis-testing in future research. Another limitation is the duration of time between participants’ GDM-affected pregnancy and data collection for this study; findings may be subject to recall bias, as their memory and perception of events may have changed over time. Future research could address this limitation by incorporating prospective methods to improve recall accuracy.

Implications for Clinical Practice and Future Research

Though this study was unable to draw racial/ethnic-specific conclusions, the barriers and facilitators identified here begin to fill gaps in the literature on diverse patient populations. Future research testing behavioral interventions to improve GDM treatment adherence and glycemic control should engage diverse patient populations burdened by health disparities.

This study demonstrates the need for targeted interventions to address the multifaceted barriers to GDM management. In clinical practice, patients with poor glycemic control often require intensive nutrition therapy, education, and real-time support that extend beyond what is feasible during standard obstetric visits. Therefore, strategies such as evaluating the effectiveness of CGM as an alternative monitoring strategy, integrating medically-tailored meal programs into GDM care, enhancing workplace accommodations through healthcare provider support letters, and fostering social support networks could improve adherence and reduce risks associated with poorly managed GDM. While CGM was frequently perceived as beneficial, access remains constrained by insurance coverage policies, particularly for individuals who are diet-controlled or not using insulin, which may limit equitable uptake. Furthermore, the necessary CGM coaching support by dietitians and commercial programs is often not available within routine care, limiting the accessibility for proper CGM use. Additionally, integrating personalized educational approaches may enhance patient motivation and long-term engagement with treatment plans. Moreover, investigations into the effectiveness of digital-based interventions could provide insight into how digital peer support influences adherence behaviors.

Conclusion

Using the COM-B framework, this study identified psychological, environmental, and motivational factors that influence adherence to GDM treatment. Individuals with suboptimal glycemic control or monitoring reported perceived barriers to GDM treatment adherence, such as limited workplace support, inadequate nutritional resources, structured exercise incompatible with daily routines, and treatment options perceived as invasive. Addressing these perceptions through patient-centered education and tailored support, this study raises offers practical strategies that may improve care, reducing the risk of adverse outcomes, for individuals affected by GDM.

Article Highlights.

Why did we undertake this study?

We undertook this study to describe perspectives on gestational diabetes treatment among individuals with a history of suboptimal glycemic control or monitoring.

What is the specific question(s) we wanted to answer?

What barriers and facilitators influenced gestational diabetes treatment adherence among diverse patients with suboptimal glycemic control or monitoring?

What did we find?

Patients reported barriers such as workplace constraints, limited nutritional resources, and perception of treatments being invasive. Facilitators included the acceptability of lifestyle-based strategies over medication, access to meal plans, and social support.

What are the implications of our findings?

These findings highlight the need for patient-centered education and tailored support, which may inform clinical care to improve glycemic management and reduce the risk of adverse outcomes among individuals with gestational diabetes.

Availability of data and materials:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.