Influence of personality traits on self-efficacy and self-management in gestational diabetes: a cross-sectional study

Abstract

Background

Given the rising prevalence of gestational diabetes and its substantial impact on both fetal/neonatal outcomes and maternal postpartum health, understanding factors that influence effective management of the condition is of paramount importance.

Methods

This multicentre cross-sectional study was conducted between February and November 2023 on 319 women with gestational diabetes mellitus at three tertiary hospitals in Fujian, China. The participants completed self-report questionnaires on their demographic characteristics, personality traits, self-efficacy, self-management, and social support. A structural equation model identified associations among personality traits, self-efficacy, and self-management.

Results

Among the 303 (94.98%) women included, education level, household income, abortion history, and prematurity history were associated with self-efficacy, while education level and household income were linked to self-management. Conscientiousness and extraversion positively influenced self-efficacy (β = 0.291, 0.378, P = 0.001), with conscientiousness also affecting self-management (β = 0.285, P < 0.05). Social support mediated the relationships between personality traits, self-efficacy, and self-management.

Conclusion

Efforts to improve self-efficacy and self-management behaviours in women with gestational diabetes may benefit from focusing on personal traits and increasing social support.

Background

The prevalence of diabetes has increased dramatically, reaching alarming levels. Gestational diabetes mellitus (GDM), characterised by impaired glucose tolerance, is a transient condition and a significant contributor to the overall prevalence of diabetes cases [1]. As a major cause of unfavourable perinatal outcomes, GDM not only affects foetal/neonatal growth but also maternal postpartum metabolism [2, 3]. Recent data from the International Diabetes Federation indicate that approximately 21.1 million live births in 2021 were affected by hyperglycaemia during pregnancy, with GDM accounting for 80.3% of the cases [4]. Notably, elevated maternal blood glucose levels (BGLs) strongly correlate with negative outcomes [2].

The primary approach for managing BGLs in GDM involves lifestyle interventions, including dietary changes, weight management, exercise, and blood glucose monitoring [5, 6]. Many previous studies have employed various strategies to enhance the health management behaviours of pregnant women with GDM [7–9]. Despite this, many women still encounter difficulties in effectively managing their blood glucose levels, with persistent suboptimal self-management (SM) behaviours continuing [10]. For example, only 30.37% of women with GDM in China exhibited good SM behaviour, which is unsatisfactory [11].

Interventions for pregnant women with GDM are significantly influenced by individuals’ self-efficacy (SE) and SM behaviours [12]. SM is recognised as an effective strategy for managing chronic conditions by promoting patient responsibility and collaboration with healthcare providers [13]. According to Bandura’s social cognitive theory, SE is the belief in one’s ability to achieve desired outcomes, influencing motivation to engage in SM behaviours [14]. The theory’s core concept, triadic reciprocal determinism, highlights the dynamic interaction between personal factors (including personality and self-efficacy), environmental influences (such as social support), and behavioral patterns, with varying interaction intensities [15].

Once diagnosed with GDM, women require immediate and drastic lifestyle changes [16]. If a certain level of SE and SM is achieved, a diagnosis offers behavioural benefits, allowing women to take control of their diet and body weight [12]. However, many women struggle to implement these changes and often experience stigma and psychological distress [17]. A lack of social support complicates achieving recommended behaviour changes [18]. In addition, cultural beliefs, familial obligations, and traditional dietary practices influence a woman’s capacity to sustain effective SM and SE behaviours [19], while socioeconomic factors may also affect maternal blood glucose control [20]. Therefore, identifying the factors that influence SM behaviours in women with GDM is essential.

Personality traits represent stable factors of individual behaviour and psychological tendencies [21]. Various psychological models have been developed to measure personality traits, including the five-factor model [22], Cattell’s sixteen personality factors [23], the Myers-Briggs Type Indicator [24], and the Minnesota Multiphasic Personality Inventory–2 [25]. Among these, the five-factor model is the most widely utilised and assesses an individual’s personality via five dipole scales of the NEO Personality Inventory: neuroticism, conscientiousness, agreeableness, openness, and extraversion. Studies have demonstrated that personality traits influence health behaviours. For instance, higher levels of extraversion have been associated with increased smoking abstinence and cessation [26]. In addition, it has been reported that conscientiousness predicts decreased alcohol intake among men [27]; openness is associated with more physical activity and less inactivity [28]; and extraversion, neuroticism, conscientiousness, and agreeableness have been demonstrated to impact eating behaviours, which in turn, influence weight gain during pregnancy [29]. Therefore, certain personality traits may correlate with health-friendly behaviours, partially affecting perinatal outcomes. However, few studies have investigated the potential relationship between personality traits, SE and SM in pregnant women.

To bridge this knowledge gap, our study, ground in social cognitive theory, aimed to identify the key factors influencing SE and SM in women with GDM, as well as the complex network of interactions between these elements. Based on the literature reviewed, we proposed the following hypotheses:

• Hypothesis 1: Personality traits are significantly associated with SE and SM, either positively or negatively.

• Hypothesis 2: Social support is significantly and positively associated with SE and SM.

• Hypothesis 3: SE is significantly and positively associated with SM.

• Hypothesis 4: Personality traits are significantly associated with social support, either positively or negatively.

Methods

Aim

To understand the association between personality traits, SE, and SM in women with GDM and test whether social support mediates this relationship based on the social cognitive theory.

Design and setting

In this multicentre cross-sectional study, both on-site and online investigations were conducted between 22 February and 28 November 2023. Initially, trained investigators explained the purpose, significance, and survey guidelines on-site. Subsequently, quick response codes linking to the online questionnaire were distributed for participants to fill out via the social media platform WeChat. To prevent duplication, each IP address was permitted a single entry. No time constraint was imposed for questionnaire completion, but all sections were required to be filled out prior to submission. On-site investigators then verified the completeness of each questionnaire and acknowledged participants’ contributions. Midway through the questionnaire, we inserted a question asking, ‘Did you provide truthful answers?’ Responses indicating ‘no’ were excluded during data cleaning.

Participants were recruited through convenience sampling at three tertiary maternity hospitals in Fujian province, southeast China. These hospitals recorded more than 10,000 births in 2023, and recruitment was conducted via prenatal diabetes outpatient clinics. This study was approved by the Provincial Maternity and Child Health Hospital Ethics Committee (No. 2022YJ073; December 20, 2022). The study protocol complied with the Declaration of Helsinki. Data were collected anonymously and used solely only for research purposes. Participants received an invitation letter at the beginning of the questionnaire and were informed that they could withdraw at any time. Participants clicking ‘Agree, Next’ indicate their agreement to participate. Questionnaire quality was ensured through self-report diligence and long-string analyses to exclude invalid submissions. Figure 1 summarizes the participant recruitment process.

Fig. 1.

The participant recruitment process

Participants

Inclusion criteria were as follows: (1) adults aged 18 years or older; (2) diagnosed with GDM through a 75 g oral glucose tolerance test where at least one of the following thresholds was met: fasting blood glucose ≥ 92 mg/dL, 1 h blood glucose ≥ 180 mg/dL, 2 h blood glucose ≥ 153 mg/dL [30]; (3) full-term singleton pregnancy; and (4) cognitive ability to independently complete questionnaires. As a subset of women requires pharmacologic therapy when glycemic control cannot be maintained through lifestyle modifications [31], we excluded this group to avoid potential confounding by medication efficacy. Similarly, individuals with other pregnancy-related complications or comorbidities were excluded to ensure clarity of results.

The sample size was calculated using two-sided confidence intervals for one proportion using the PASS 2021 Sample Size Software for Windows, based on a good SM rate of 25.69% in Chinese women with GDM [32]. A sample size of 313 was required to achieve reasonable estimates with a 95% confidence interval and a 10% margin of error.

Measures

Demographic characteristics

The participants’ demographic characteristics collected included age, education, employment status, place of residence, household income, method of medical payment, and pre-pregnancy body mass index (pre-pregnancy BMI, calculated by dividing a woman’s weight at conceptionin kilogram (kg) by the square of the height in meters [33], and was classified into four categories based on the Chinese criteria [34]: underweight [BMI < 18.5 kg/m²], healthy weight [18.5–23.9 kg/m²], overweight [24.0–27.9 kg/m²], and obesity [≥ 28.0 kg/m²]), planned conception (couples made conscious choices about the timing of conception through active planning and preparation), method of conception (specific ways to achieve pregnancy), parity (the number of offspring a woman has born), history of abortion (spontaneous miscar- riage, elective termination of pregnancy, or either), history of prematurity (born prior to 37 weeks’ gestation), and family history of diabetes(defined as diabetes in parents or siblings).

Chinese big five personality inventory brief version

The Chinese Big Five Personality Inventory (Brief Version) (CBF-PI-B) was designed to assess personality traits. Wang et al. [35] developed a simplified version of this scale, adapted from the NEO Personality Inventory [36]. It comprises 40 items rated on a Likert scale from 0 to 5, measuring five personality dimensions. Scoring higher on a dimension indicates that the respondent is more likely to exhibit that trait. It has demonstrated good validity and reliability with Cronbach’s alpha values above 0.75. Its internal consistency reliabilities range from 0.764 to 0.814, and test-retest reliabilities over ten weeks vary from 0.672 to 0.811. The CBF-PI-B has been widely utilised in various Chinese populations [37, 38].

General self-efficacy scale

The General Self-Efficacy Scale (GSES) measures an individual’s overall efficacy, reflecting self-confidence in facing setbacks or difficulties, and is one of the most widely used instruments. Developed by Schwarzer et al. in 1981 [39], this scale consists of 10 items, each rated on a four-point Likert scale. The higher the score, the stronger the self-confidence. The Cronbach’s alpha coefficients of the GSES in different countries range from 0.75 to 0.94, and the retest reliability ranges from 0.55 to 0.75 [39].

Self-management scale for Chinese gestational diabetes mellitus

The Self-management Scale for Chinese Gestational Diabetes Mellitus (SMS-GDM) developed by Zhang et al. [40] assesses the SM behaviour of patients with gestational diabetes. Traditional diabetes SM tools, which include oral hypoglycemic agents, smoking, and foot care, are less applicable to pregnant women with GDM. This instrument is the first Chinese version of a GDM self-management evaluation scale. It includes 32 forward-scored items, each rated from 1 to 5 points. It evaluates four dimensions: SM awareness, pregnancy SM, glycaemic SM, and resource utilisation. The standard score is calculated by dividing the raw total score by the highest possible score and multiplying it by 100. This scoring classifies SM levels into < 60 points for poor SM, 60–80 points for moderate SM, and > 80 points for good SM. It exhibits a Cronbach’s alpha coefficient of 0.939, Spearman-Brown coefficient of 0.780, test-retest reliability of 0.903, and content validity of 0.927.

Perceived social support scale

Developed by Zimet et al. in 1988 [41], the Perceived Social Support Scale (PSSS) comprises 12 items across three dimensions: family support, friend support, and support from a significant other. Using a seven-point Likert scale, the respondents were asked to rank the items according to their perceived social support, with higher scores indicating greater perceived support. This scale demonstrated good reliability with a Cronbach’s alpha of 0.87. This scale was found to be a reliable scale for pregnant women with GDM [42].

Data analysis

Data cleaning and statistical analysis were conducted using SPSS (version 25.0 for Windows; IBM Corp., Armonk, NY, USA). Descriptive statistics were utilised, with categorical variables reported as frequencies and percentages. The Shapiro-Wilk test assessed the normality of the distribution. Student’s t-test and a one-way analysis of variance were applied to analyse factors individually. A Spearman correlation analysis was used to examine the relationships between variables. Duplicate data and data with missing information were excluded. Outlier detection, facilitated by boxplot analysis, was employed to eliminate anomalous data interference.

A structural equation model (SEM) was constructed in AMOS (version 24.0 for Windows; IBM Corp., Armonk, NY, USA) to perform a path analysis of personality traits, social support, and SE influencing SM in GDM. Fit indices, including the chi-squared to degrees of freedom ratio (CMIN/DF) < 3, goodness-of-fit index (GFI) > 0.90, normed fit index (NFI) > 0.90, incremental fit index (IFI) > 0.90, comparative fit index (CFI) > 0.90, and the root mean square error of approximation (RMSEA) < 0.080, were examined to determine whether the assumed model fit the observed data. The Bias-Corrected Bootstrap method tested for mediating effects and repeated random sampling was used to draw 5000 bootstrap samples from the original dataset to form an approximate sampling distribution. A 95% confidence interval was used to assess the presence of a mediating effect. Statistical significance was set at P < 0.05.

Results

Demographic characteristics

Of the 319 participants recruited for this study, valid responses were collected from 303 participants, resulting in a valid response rate of 95.0%. There were 127 (41.9%) primiparous and 176 (58.1%) multiparous women, with non-advanced maternal age accounting for the majority (age<35, 81.8%). The baseline demographic characteristics are presented in Table 1. Variations in SE were observed based on education level, household income, history of abortion, and history of prematurity. Disparities in SM levels were also noted among participants with differing education levels and household incomes.

Table 1.

Participant characteristics

Variables	N (%)	SE			SM
		Mean ± SD	t/F	P-value	Mean ± SD	t/F	P-value
Age (years)			−1.908	0.057		−0.225	0.822
<35	248 (81.8)	30.18 ± 4.82			84.77 ± 11.80
≥ 35	55 (18.2)	31.51 ± 4.01			85.16 ± 10.92
Education			7.176	0.001		3.223	0.041
High education or below	65 (21.5)	28.51 ± 5.18			81.92 ± 14.05
College	77 (25.4)	30.79 ± 4.67			86.80 ± 10.28
Bachelor or above	161 (53.1)	31.01 ± 4.33			85.08 ± 10.98
Employment status			0.471	0.638		−0.597	0.551
Yes	216 (71.3)	30.50 ± 4.62			84.21 ± 13.07
No	87 (28.7)	30.22 ± 4.92			85.09 ± 11.01
Place of residence			0.389	0.700
Urban areas	267 (88.1)	30.46 ± 4.57			84.73 ± 11.24	−0.375	0.710
Rural areas	36 (11.9)	30.08 ± 5.64			85.66 ± 14.33
Method of medical payment			−1.049	0.295		−1.188	0.236
Own expense	21 (6.9)	29.38 ± 4.66			81.93 ± 16.59
Health insurance	280 (92.4)	30.50 ± 4.70			85.06 ± 11.18
Household income (month, RMB)			7.780	0.001		8.792	<0.001
< 5000	28 (9.2)	29.32 ± 5.55			76.79 ± 15.61
5000–10,000	131 (43.2)	29.45 ± 4.60			84.64 ± 11.46
> 10,000	144 (47.5)	31.51 ± 4.40			86.58 ± 10.21
Pre-pregnancy BMI			0.227	0.878		0.283	0.837
< 18.5	17 (5.6)	31.06 ± 4.07			86.43 ± 10.08
18.5–23.9	203 (67.0)	30.28 ± 4.69			85.10 ± 10.61
24-27.9	68 (22.4)	30.65 ± 4.97			83.94 ± 13.76
≥ 28	15 (5.0)	30.60 ± 4.66			83.58 ± 16.06
Planned conception			−1.081	0.281		−0.308	0.748
Yes	234 (77.2)	30.26 ± 4.68			84.72 ± 11.98
No	69 (22.8)	30.96 ± 4.78			85.24 ± 10.41
Method of conception			0.144	0.886		1.147	0.252
Natural	266 (87.8)	30.44 ± 4.57			85.12 ± 11.65
Assisted reproductive	37 (12.2)	30.30 ± 5.63			82.79 ± 11.35
Parity			0.192	0.105		−0.431	0.667
Primiparous	127 (41.9)	30.48 ± 4.83			84.49 ± 13.03
Multiparous	176 (58.1)	30.38 ± 4.62			85.09 ± 10.53
History of abortion			−1.997	0.047		0.655	0.513
Yes	106 (35.0)	29.71 ± 4.39			85.44 ± 8.98
No	197 (65.0)	30.80 ± 4.83			84.52 ± 12.83
History of prematurity			−3.086	0.002		−0.109	0.914
Yes	16 (5.3)	26.94 ± 5.41			84.53 ± 9.19
No	287 (94.7)	30.61 ± 4.59			84.86 ± 11.76
Family history of diabetes			1.876	0.062		1.463	0.145
Yes	86 (28.4)	31.22 ± 4.73			86.39 ± 10.64
No	217 (71.6)	30.10 ± 4.66			84.23 ± 11.96

Status of SE, SM, social support, and personality traits

As illustrated in Table 2, 34.3% of participants did not achieve a good level of SM. Only 62.7% reported experiencing high level of social support. The general SE score was 30.4 ± 4.7. Scores for the personality traits, neuroticism, conscientiousness, agreeableness, openness, and extraversion, were 22.6 ± 6.3, 33.4 ± 5.5, 31.6 ± 4.3, 30.6 ± 5.7, and 27.6 ± 5.2, respectively.

Table 2.

Status of self-efficacy, self-management, social support, and personality traits

Variables	Categories	Count	Percentage	Mean ± SD
Self-efficacy	-	-	-	30.4 ± 4.7
Self-management				84.8 ± 11.6
	Good	199	65.7
	Moderate	90	29.7
	Poor	14	4.6
Social support				65.1 ± 11.2
	High	190	62.7
	Moderate	112	37.0
	Low	1	0.3
Personality traits
	Neuroticism	-	-	22.6 ± 6.3
	Conscientiousness	-	-	33.4 ± 5.5
	Agreeableness	-	-	31.6 ± 4.3
	Openness	-	-	30.6 ± 5.7
	Extroversion	-	-	27.6 ± 5.2

Correlations between self-efficacy, self-management, social support, and personality traits

Table 3 illustrates the correlations among SM, SE, social support, and personality traits. SE, SM, and social support were positively correlated (r_s: 0.485, 0.495, and 0.536, respectively). Most personality traits also showed positive correlations with SE, SM, and social support (r_s range: 0.263–0.432). However, neuroticism demonstrated negative correlations with these variables (r_s range: −0.164 −0.261).

Table 3.

Correlations between self-efficacy, self-management, social support, and personality traits

	1	2	3	4.1	4.2	4.3	4.4	4.5
1 Self-efficacy	1	0.485**	0.495**	−0.164**	0.380**	0.304**	0.263**	0.276**
2 Self-management		1	0.536**	−0.171**	0.381**	0.206**	0.420**	0.480**
3 Social support			1	−0.261**	0.432**	0.402**	0.349**	0.403**
4 Personality traits
4.1 Neuroticism				1	−0.126*	−0.277**	−0.048	−0.215**
4.2 Conscientiousness					1	0.571**	0.592**	0.473**
4.3 Agreeableness						1	0.348**	0.337**
4.4 Openness							1	0.664**
4.5 Extroversion								1

* P < 0.05. ** P < 0.001. r_s

Structural equation model for self-efficacy, self-management, social support, and personality traits

The SEM was fitted using the maximum likelihood method. The model fit the data well, as indicated by the following indices: CMIN/DF = 2.628 (P < 0.001), GFI = 0.943, NFI = 0.947, IFI = 0.967, CFI = 0.966, and RMSEA = 0.073. Furthermore, 5000 repetitions of the bias-corrected percentile bootstrap method were employed to test the mediation effect. Figure 2 presents the final SEM after correction.

Fig. 2.

The final structural equation model diagram

As illustrated in Table 4, conscientiousness and extraversion positively influenced SE (β = 0.291, P = 0.001; β = 0.378, P = 0.001), with conscientiousness also having a significant impact on SM (β = 0.285, P < 0.05). The path coefficients for conscientiousness and extraversion on SE were significantly reduced when social support acted as a mediator (β = 0.165, P < 0.05; β = 0.285, P = 0.001), unveiling the effect of agreeableness on SE (β = −0.118, P < 0.05). Moreover, social support and SE significantly mediated the relationship between conscientiousness and SM (a*b = 0.133, 95% CI: 0.070–0.214, P < 0.001). Neither neuroticism nor openness directly affected SE or SM (P > 0.05).

Table 4.

The mediating effect between self-efficacy, self-management, and their influences

Path ends	Effect coefficient [95%CI]	Path beginnings: personality traits
		Conscientiousness	Agreeableness	Extroversion
Self-efficacy	Total	0.291* [0.147, 0.431]	−0.054 [−0.153, 0.057]	0.378* [0.261, 0.486]
	Direct	0.165* [0.026, 0.303]	−0.118* [−0.216, −0.012]	0.285* [0.170, 0.406]
	Indirect	0.125** [0.055,0.214]	0.064* [0.009, 0.128]	0.094* [0.038, 0.166]
Self-management	Total	0.285* [0.175,0.393]	-	-
	Direct	0.152* [0.050, 0.254]	-	-
	Indirect	0.133** [0.070, 0.214]	-	-

* P < 0.05. ** P < 0.001

Discussion

This multicentre cross-sectional study, grounded in social cognitive theory, examined the influence of personality traits on SE and SM in women with GDM. The SEM demonstrated a good fit to the data and revealed a positive indirect effect of conscientiousness on SM, mediated by social support and SE. Social support partially mediated the relationship between conscientiousness and SE, while extraversion also exerted a positive influence on SE through the mediating role of social support. Moreover, agreeableness initially exhibited a negative effect on SE, which was reversed to a positive effect due to the suppression effect of social support.

The positive association observed between conscientiousness and both SE and SM is consistent with previous research [43, 44]. Conscientiousness is characterised by traits such as dutifulness, achievement orientation, orderliness, and adherence to rules [45]. Therefore, individuals with this trait are more likely to adhere to health management activities, which may account for the consistency in these findings. Among women exhibiting high conscientiousness, SE appears to facilitate proactive personal behaviour, with environmental influences such as social support acting as a mediating pathway. These results support Bandura’s theory of SE [14].

Conversely, our study also found that neuroticism and openness did not significantly effect SE and SM. Individuals with high levels of neuroticism often exhibit impulsive and maladaptive coping strategies, whereas those with high levels of openness tend to embrace novel and innovative ideas [36]. While previous studies have primarily explored the effects of neuroticism and openness on emotional states and mental health, few have examined their impact on behavioural responses, particularly in pregnant populations [46–48]. Underlying psychological mechanisms may explain the non-significant correlations observed in these two groups. The origins of emotional instability in women with high neuroticism should be investigated, and reliance on negative coping behaviours should be discouraged. The perspectives of women with high openness should be acknowledged, and personalised self-management strategies should be enhanced by integrating innovative approaches aligning with their disposition.

Our results are in line with those of a previous study in adults with type-2 diabetes, showing no correlation between extraversion and SM [49], a finding that diverges from studies in the general population [50]. However, our findings also indicated that extraversion is significantly and positively related to SE. These discrepancies may arise from differences in the surveyed populations. In addition, given that extroverts tend to be impulsive and expressive, they may have higher SE [29, 51]. Nonetheless, hormonal and psychological changes during pregnancy might affect extroverts [29], which could hinder the successful translation of SE into health-friendly behaviours.

Another noteworthy observation is the apparent lack of correlation between agreeableness and SE, attributed to the suppression effect of social support in the influence pathway. In contrast to other studies, our study revealed that an excessively high degree of agreeableness is not beneficial for improving SE after controlling for social support [52, 53]. Agreeableness contributes to harmony in interpersonal relationships and altruistic tendencies [22]. However, this may lead to an overly concerned attitude towards the judgment of others, overshadowing one’s self-assessment. Further studies are required to better understand the influence of suppressor variables.

Lifestyle behaviour change is undeniably a complex, multifaceted process. SE has been demonstrated to positively correlate with women’s SM, whereas social support is an available external resource that can influence individual behaviour, either objectively or subjectively [15]. Furthermore, factors such as education level and income—identified as predictors of self-efficacy and self-management—may interact differently in low-resource settings or populations with lower socioeconomic status. A history of miscarriage or premature birth may further influence a woman’s SE.

These findings resonate with the social cognitive theory, providing healthcare professionals with valuable insights into modifiable psychosocial factors, including personality traits, SE, and social support. Our proposed path serves as a bridge between biomedical and behavioral strategies for GDM management, advocating for an integrative approach that considers biological, psychological, and social determinants of health. Future research efforts should prioritize culturally tailored interventions, such as social support programs or counseling informed by individuals’ personality traits, with the aim of enhancing maternal and neonatal outcomes. As an illustration, interventions could be designed based on personality traits, using digital tools for conscientious individuals and group activities for extroverts to boost motivation.

Limitations

This study has several limitations that should be considered when interpreting the results. First, the use of convenience sampling from prenatal diabetes outpatient clinics at three tertiary hospitals in southeast China may have introduced selection bias. Participants were more likely to be urban residents with higher socioeconomic status, potentially underrepresenting rural populations, low-income groups, or those with limited access to healthcare. This may limit the generalisability of our findings to primary care settings or regions with fewer resources. Second, as this was a cross-sectional study, causal relationships among the variables should be interpreted with caution. Last, although mitigation strategies were implemented—including a multicentre design to capture regional diversity, IP restrictions to prevent duplicate submissions, mid-survey truthfulness checks, and long-string analysis to exclude insincere responses—residual selection and self-report biases may still be present. Consequently, associations involving key predictors, such as education and income, may have been overestimated. To improve the study’s rigour and applicability, future research should focus on several key areas: validating these findings through longitudinal studies across diverse populations; integrating objective indicators alongside self-reported data; and developing culturally sensitive instruments to better capture domain-specific SE and social support dynamics. These improvements could significantly strengthen the generalisability and causal interpretation of the psychosocial pathways proposed in this study.

Conclusion

This study revealed the relationship between personality traits, SE and SM in pregnant women with GDM. It also highlights how other individual differences, such as education level, economic status, and histories of miscarriage and preterm labour, as well as external environmental factors like social support, may play important roles in shaping these relationships.These findings fill a gap in the field but require further investigation, particularly with regard to how SE can be effectively translated into SM behaviours and its role in preventing adverse outcomes in GDM.

Abbreviations

GDM

Gestational diabetes mellitus

BGLs

Blood glucose levels

SE

Self-efficacy

SM

Self-management

CBF-PI-B

Chinese Big Five Personality Inventory brief version

GSES

General Self-Efficacy Scale

PSSS

Perceived Social Support Scale

SEM

Structural equation model

Author contributions

Huimin Lin: Conceptualisation, Funding acquisition, Investigation, Roles/Writing - original draft. Xiuwu Liu: Data curation, Validation, Writing - review & editing. Guihua Liu: Methodology, Writing - review & editing. Jing Zeng: Conceptualization, Formal analysis. Shengbin Guo: Project administration, Supervision, Writing - review & editing.

Funding

This work was supported by Startup Fund for Scientific Research, Fujian Medical University (Grant number: 2023QH1200). The funder had no role in the study design, collection, analysis, interpretation of data, writing, or decision to submit the manuscript for publication.

Data availability

Data will be made available on request.

Declarations

Ethics approval and consent to participate

Ethical approval was granted by Fujian Maternity and Child Health Hospital Ethics Committee (No. 2022YJ073; date: 2022/12/20). The participants received an invitation letter on the front page of the questionnaire and were informed that they could withdraw at any point in the process and that submission of the questionnaire would be regarded as voluntary consent to participate.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.