Practice and evaluation of digital therapy technologies for nutritional management in high-risk groups of gestational diabetes: a real-world study

Abstract

Background

Overweight and obesity among women of childbearing age have become a major public health challenge affecting reproductive health worldwide. Excessive gestational weight gain and obesity during pregnancy substantially elevate the risk of gestational complications, particularly gestational diabetes mellitus (GDM), which poses threats to maternal and infant safety. Consequently scientific and effective intervention strategies for gestational weight management are urgently required.

Methods

Pregnant women at high risk for GDM who met the inclusion and exclusion criteria were enrolled in the study. Participants were allocated into an intervention group and a control group based on their preference. The intervention group received digital therapy management, whereas the control group received routine gestational care. Intervention effectiveness was assessed based on appropriate gestational weight gain (GWG), GDM incidence, and adherence.

Results

A total of 700 pregnant women at high risk for GDM were included, comprising 504 in the intervention group and 196 in the control group. Baseline characteristics were generally comparable between the groups, although significant differences were noted in gravidity, parity, and educational level. A higher proportion of participants in the intervention group achieved appropriate GWG during the second and third trimesters compared with in the control group, with a statistically significant between-group difference in the third trimester (P = 0.047). No statistically significant difference in the incidence of GDM was observed between the two groups.

Conclusions

Digital therapeutics during pregnancy effectively improved gestational weight management, with greater benefits observed over time. However, this improvement did not reduce GDM incidence.

Trial registration

Chinese Clinical Trial Registry, ChiCTR2300071496, registered 16 May 2023.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12884-026-09726-6.

Introductiom

Gestational weight management is a critical intervention during pregnancy, and its standardized, evidence-based implementation is clinically important for improving maternal and neonatal health outcomes. Overweight or obesity during pregnancy increases the risk of complications including gestational diabetes mellitus (GDM), gestational hypertension, and adverse birth outcomes such as preterm birth, macrosomia, congenital malformations, and childhood obesity [1–3]. A systematic review and meta-analysis of 1,416,915 women from 29 countries found that, according to the 2009 gestational weight gain standards of the Institute of Medicine, 39.4% and 27.8% of pregnant women worldwide had excessive and inadequate gestational weight gain (GWG), respectively [4, 5]. Consistent with global trends, a national cohort study in China demonstrated that 38.2% of pregnant women exceeded guideline-recommended GWG, whereas 25% fell below the recommended values, further highlighting the urgency of gestational weight management [6]. Emerging evidence indicates that structured dietary guidance and physical activity-based lifestyle interventions can effectively control GWG and reduce the risk of subsequent maternal and neonatal adverse outcomes [7–9].

GDM is defined as any degree of glucose intolerance with onset or first recognition during pregnancy, excluding pre-existing type 1 or type 2 diabetes mellitus diagnosed before or during gestation. Over the past decade, GDM prevalence has increased markedly, rising from 4% in 2010 to 21% in 2020 [10]. Delayed childbearing age, rising obesity rates, and other related factors are key drivers of this trend [11]. GDM is closely associated with adverse pregnancy outcomes, including macrosomia, elevated cesarean delivery rates, and neonatal hypoglycemia [12]. Furthermore, women with a history of GDM have an increased risk of developing type 2 diabetes mellitus postpartum, and their offspring face an elevated risk of metabolic disorders, including childhood obesity and cardiovascular disease in later life [13, 14]. The Chinese Guidelines for the Diagnosis and Treatment of Hyperglycemia in Pregnancy (2022) recommend that regular exercise before and during pregnancy significantly reduces GDM risk, with a particularly pronounced protective effect among overweight and obese women [15].

Although traditional interventions, such as face-to-face counseling and group-based education, have demonstrated effectiveness in improving gestational weight management, they are resource-intensive and have limited accessibility and long-term sustainability, making it challenging to provide continuous behavioral support at the population level. Digital therapeutics (DTx) are an emerging form of software-based medical intervention that delivers evidence-based therapeutic interventions to support disease prevention, management, and improved health outcomes [16]. DTx interventions typically integrate digital technologies, such as smartphone applications, wearable devices, and remote monitoring systems, to provide continuous, personalized, and remotely accessible healthcare services. These technologies have been widely applied in the management of chronic diseases and mental health conditions [17, 18]. Previous studies in non-pregnant populations have shown that digital health interventions delivered through mobile applications, wearable monitoring devices, and other digital tools can improve lifestyle behaviors, support weight loss, and improve metabolic outcomes [19, 20]. Encouraged by these findings, researchers have increasingly explored the application of digital technologies for gestational weight management. A systematic review and meta-analysis demonstrated that digital interventions were effective in improving maternal weight management during pregnancy [21]. Similarly, the HealthyMoms randomized controlled trial conducted in Sweden showed that a smartphone application-based intervention effectively improved gestational weight gain (GWG), dietary quality, and physical activity during pregnancy [22]. In China, WeChat-assisted lifestyle interventions have also been reported to reduce the incidence of excessive GWG and GDM among overweight and obese pregnant women [23].

Despite these encouraging findings, existing interventions are often limited by relatively homogeneous strategies and insufficient personalization. Moreover, most available evidence comes from randomized controlled trials, and data on the effectiveness of DTx in routine clinical practice remains limited. Therefore, real-world studies are needed to evaluate the effectiveness of DTx for gestational weight management and GDM prevention in clinical practice.

This study aims to implement continuous, individualized DTx-based intervention and management for women at high risk of GDM, and to systematically evaluate its efficacy in a real-world clinical setting. Furthermore, the study focuses on GWG control, glycemic management, and improvement of maternal adherence, with the goal of providing robust real-world evidence to support the promotion and application of DTx in routine pregnancy care.

Methods

Pregnant women in the first trimester were recruited from the nutrition clinic of our hospital and assigned to two groups according to the intervention regimen: the routine management group and the intervention group receiving routine management combined with DTx. The efficacy of the DTx intervention evaluated by comparing between-group differences in core outcome measures, including the achievement of appropriate GWG, the incidence of GDM, and maternal adherence to the intervention.

Study design and population

Included and excluded criteria

Pregnant women who met the inclusion and exclusion criteria and consented to participation self-selected their intervention after being informed of the study procedures. The recruitment process and inclusion/exclusion criteria are detailed in Fig. 1.

Fig. 1.

Recruitment process and inclusion/exclusion criteria

Study design

In this study, pregnant women were divided into two groups based on the intervention received: the intervention group and the control group. The control group received routine nutritional management during pregnancy, whereas the intervention group received a DTx-based management program in addition to routine nutritional management [24]. GWG and GDM outcomes were subsequently assessed throughout pregnancy.

The DTx intervention consisted of a physician portal and a smartphone-based mini-program. Through the physician portal, clinicians collected baseline information, physical examination, and laboratory results; provided dietary and lifestyle recommendations; and prescribed individualized medical nutrition therapy. At next visited, clinicians reviewed participants’ self-monitoring data, including dietary records, body weight, and blood glucose measurements, through the physician portal. In contrast, corresponding information from participants in the control group was obtained from paper-based record forms.

Participants in the intervention group used the smartphone mini-program for home-based management. The application allowed participants to record dietary intake, body weight, and blood glucose levels, and provided dietary guidance, physical activity recommendations, health education materials, nutrition prescriptions, and reminders for assigned tasks. Participants’ records were synchronized with the physician portal, allowing clinicians to review changes in relevant parameters.

Participants in the control group received routine nutritional management during pregnancy, including standard clinical procedures such as baseline information, physical examination, laboratory results, as well as health education, dietary counseling, exercise guidance, and regular follow-up visits. Participants were instructed to monitor relevant indicators using paper-based record forms and received guidance on gestational weight management, dietary adjustment, physical activity, and glycemic control.

The clinical workflow for both groups is shown in Fig. 2. At the first visit, specialist nurses guided participants in completing baseline information and dietary adherence questionnaires. Participants in the intervention group also received instructions on how to use the smartphone mini-program. Subsequently, clinicians performed physical examinations, including measurements of blood pressure, weight, and waist circumference, recorded clinical information, and reviewed or ordered laboratory tests as required. For participants in the intervention group, clinicians reviewed home monitoring records before providing dietary counseling and updating individualized management plans. Prescribed tasks and health education materials were delivered through the smartphone mini-program, whereas participants in the control group received corresponding instructions via face-to-face counselling and recorded relevant information using paper forms. The next visits were scheduled at intervals of approximately 2–4 weeks according to clinical needs.

Fig. 2.

Outpatient visit process for participants

The primary outcomes were appropriate GWG achievement during pregnancy and the incidence of GDM, and the secondary outcome was dietary adherence. The effectiveness of the DTx intervention was comprehensively evaluated based on the above indicators.

Data collection and processing

Baseline information of enrolled pregnant women were collected, including general demographic information, obstetric history, family medical history, past medical history, and relevant information of the current pregnancy. Meanwhile, body weight measured at the final antenatal visit of the first, second, and third trimesters, as well as relevant clinical indicators such as oral glucose tolerance test (OGTT) results, were recorded.

Data cleaning were performed on the dataset of the enrolled cohort. Participants who did not complete regular antenatal care as required at our hospital, and those who did not complete GDM screening at our hospital were exclude from the final analysis, to ensure the integrity of the data and the reliability of the analytical results.

After data processing, 700 pregnant women who met the inclusion and exclusion criteria were included in the final analysis, with 504 in the intervention group and 196 in the control group. The sample size met the prespecified requirements for statistical analysis.

Measurement of adequate GWG, GDM and dietary adherence

GDM is diagnosed in pregnant women if any one of the following glycemic thresholds is met on the 75 g OGTT performed between 24 and 28 weeks of gestation [25]: (1) FPG ≥ 5.1 mmol/L; (2) 1-hour plasma glucose ≥ 10.0 mmol/L; (3) 2-hour plasma glucose ≥ 8.5 mmol/L.

Participants were stratified according to pre-pregnancy BMI based on the Chinese adult BMI classification standard (underweight, normal weight, overweight, and obese). The recommended ranges of GWG during the second and third trimesters for each BMI category were determined according to the Chinese guidelines [26, 27]. The calculation of weight gain during the second and third trimesters and the corresponding recommended ranges are presented in Supplemental Tables S1 and S2.

Dietary adherence was assessed using the Dietary Guideline Adherence Index for Pregnant Women, which was developed based on the Chinese Dietary Guidelines (2016) for pregnant women and the Chinese Balanced Dietary Pagoda for pregnant women [28]. The questionnaire consists of 13 dimensions, including dietary diversity, staple food, folic acid, green vegetable, household use of iodized salt, milk and dairy products, soybean and soybean products, nuts, animal blood/liver, seafood, livestock and poultry meat and eggs, and personal dietary habits. Different scoring criteria were applied for early pregnancy and mid-to-late pregnancy. The total score ranges from 0 to 100, with higher scores indicating better adherence to the dietary guidelines.

Statistical Analysis

Baseline characteristics of the participants were compared between groups. Normality tests were performed for continuous variables. Normally distributed data were presented as mean ± standard deviation (SD), whereas non-normally distributed data were presented as median (interquartile range, IQR). Categorical variables were described using frequencies and proportions. For group comparisons, continuous variables with a normal distribution were analyzed using the t-test or analysis of variance, while non-normally distributed variables were compared using non-parametric rank-sum tests. Differences in categorical variables between groups were assessed using the chi-square test. Multivariate logistic regression models were applied to examine the associations between intervention strategies and GDM, as well as appropriate GWG compliance rate. Furthermore, multiple linear regression was used to evaluate the intervention’s effect on GWG. All statistical analyses were conducted using R (v4.2.1, R Development Core Team, Vienna, Austria). A p-value < 0.05 was considered a statistically significant difference.

Result

Baseline characteristics

The baseline characteristics of the participants are presented in Table 1. The two groups were comparable with respect to age, pre-pregnancy BMI, annual household income, smoking status, alcohol consumption, history of GDM, and family history of diabetes in first-degree relatives (all P > 0.05). However, statistically significant differences were observed in gravidity, parity, and educational level between the two groups (all P < 0.05).

Table 1.

Baseline characteristics of the intervention and control groups

	Intervention Group (n = 504)	Control Group (n = 196)	P value
Age, years, median (IQR)	33.0 [31.0, 36.0]	34.0 [31.0, 36.0]	0.490
Parity, median (IQR)	0.0 [0.0, 1.0]	1.0 [0.0, 1.0]	0.029
Gravidity, median (IQR)	2.0 [1.0, 2.2]	2.0 [1.0, 3.0]	0.007
pre-pregnancy BMI(kg/m2), median (IQR)	22.4 [20.3, 25.0]	22.5 [20.1, 25.1]	0.981
Education level, n (%)			0.002
Below college/university level	71 (14.1)	49 (25.0)
College/university degree or above	432 (85.7)	146 (74.5)
Unknown	1 (0.2)	1 (0.5)
Annual household income(RMB/year), n (%)			0.113
< 10,000	25 (5.0)	12 (6.1)
10,000–30,000	39 (7.7)	27 (13.8)
31,000–100,000	94 (18.7)	35 (17.9)
> 100,00	290 (57.5)	98 (50.0)
Unknown	56 (11.1)	24 (12.2)
Smoking status, n (%)			0.368
Yes	499 (99.0)	196 (100.0)
no	5 (1.0)	0 (0.0)
Alcohol consumption, n (%)			1
Yes	503 (99.8)	195 (99.5)
no	1 (0.2)	1 (0.5)
History of GDM, n (%)			0.617
Yes	17 (5.7)	10 (7.5)
no	283 (94.3)	124 (92.5)
Family history of diabetes (first-degree relatives), n (%)			0.54
Yes	86 (17.1)	29 (14.8)
no	418 (82.9)	167 (85.2)

Abbreviation: BMI Body Mass Index, GDM Gestational diabetes mellitusx, IQR Interquartile range

P < 0.05 was considered statistically significant

Adequate GWG in the second trimester

During the second trimester, weekly GWG was lower in the intervention group than in the control group (0.47 kg vs. 0.52 kg), and this difference was statistically significant (P = 0.039). Furthermore, in the subgroup with pre-pregnancy BMI < 24.0 kg/m², the intervention group showed significantly lower weekly weight gain than the control group (0.49 kg vs. 0.56 kg, P < 0.05). In contrast, no significant difference was observed between the two groups among those with BMI ≥ 24.0 kg/m². Regarding the adequate GWG rate in the second trimester, the intervention group had a higher compliance rate than the control group, but the between-group difference was not statistically significant (Table 2).

Table 2.

Adequate GWG in the second trimester

	Variables	Intervention Group	Control Group	P value
Weekly GWG in the second trimester(kg)
Total n	Median (IQR)	0.47 (0.34, 0.60)	0.52 (0.38, 0.63)	0.039
pre-pregnancy  BMI<24.0 kg/m2		0.49 [0.38, 0.63]	0.56 (0.47, 0.69)	0.002
pre-pregnancy BMI ≥ 24.0 kg/m2		0.40 (0.24, 0.55)	0.39 (0.24, 0.56)	0.891
The rate of adequate GWG
Total n	Frequency (Percentage, %)	159 (31.5)	53 (27.0)	0.283
pre-pregnancy  BMI<24.0 kg/m2		123 (37.8)	36 (29.8)	0.140
pre-pregnancy BMI ≥ 24.0 kg/m2		36 (20.1)	17 (22.7)	0.773

Abbreviation: n Total participants, BMI Body Mass Index, GWG Gestational weight gain

P < 0.05 was considered statistically significant

Logistic regression was used to evaluate the association between intervention strategies and adequate GWG compliance rate (Table 3). For second-trimester adequate GWG compliance rate, there was no significantly difference between groups in the crude model (OR 0.80, 95% CI 0.55–1.56; P = 0.244). After adjustment for maternal age, pre-pregnancy BMI, gravidity, parity, educational level, annual household income, smoking status, alcohol consumption, history of GDM, and family history of diabetes in first-degree relatives, the association remained non-significant (OR = 0.77, 95% CI: 0.47–1.22, P = 0.268).

Table 3.

Results of logistic regression analysis on the rate of adequate GWG in the second and third trimesters

	Group	Crude model OR (95%CI)	P value	Full-adjusted modela OR (95%CI)	P value
The rate of Adequate GWG in the second trimester (0 = adequate, 1 = inadequate)	Control group	1 (ref)	—	1 (ref)	—
	Intervention group	0.80 (0.55, 1.56)	0.244	0.77 (0.47, 1.22)	0.268
The rate of Adequate GWG in the third trimester (0 = adequate, 1 = inadequate)	Control group	1 (ref)	—	1 (ref)	—
	Intervention group	0.69 (0.49, 0.98)	0.039	0.55 (0.35, 0.86)	0.009

Abbreviation: GWG Gestational weight gain, OR Odds ratio, CI Confidence interval

^a Adjusted for maternal age, pre-pregnancy BMI, gravidity, parity, educational level, annual household income, smoking status, alcohol consumption, history of GDM, and family history of diabetes in first-degree relatives

P < 0.05 was considered statistically significant

Similarly, linear regression analysis showed no significant difference in weekly GWG between the two groups in either the crude model (β = −0.045, 95% CI: −0.121 to 0.031, P = 0.245) or the fully adjusted model (β = −0.053, 95% CI: −0.148 to 0.041, P = 0.269; Table 4).

Table 4.

Results of linear regression analysis on GWG rate in the second and third trimesters

	Group	Crude model β (95% CI)	P value	Full-adjusted modela β (95% CI)	P value
GWG rate in the second trimester (continuous variable)	Control group	0 (ref)	—	0 (ref)	—
	Intervention group	-0.045 (-0.121, 0.031)	0.245	-0.053 (-0.148, 0.041)	0.269
GWG rate in the third trimester (continuous variable)	Control group	0 (ref)	—	0 (ref)	—
	Intervention group	-0.085 (-0.166, -0.004)	0.039	-0.133 (-0.233, -0.032)	0.010

Abbreviations: GWG Gestational weight gain, β Regression coefficient, CI Confidence interval

^a Adjusted for maternal age, pre-pregnancy BMI, gravidity, parity, educational level, annual household income, smoking status, alcohol consumption, history of GDM, and family history of diabetes in first-degree relatives

P < 0.05 was considered statistically significant

Adequate GWG in the third trimester

During the third trimester, weekly GWG was lower in the intervention group than in the control group (0.45 kg vs. 0.48 kg); however, the difference did not reach statistical significance (P = 0.075). Stratified analysis indicated that among women with pre-pregnancy BMI < 24.0 kg/m², weekly weight gain in the intervention group was significantly lower than that in the control group (0.47 vs. 0.51, P = 0.007). In contrast, no statistically significant difference was observed between the two groups among those with BMI ≥ 24.0 kg/m².Regarding the adequate GWG, the intervention group showed a significantly higher rate than the control group (41.7% vs. 33.2%, P < 0.05). Subgroup analysis further demonstrated that this difference remained significant among women with BMI < 24.0 kg/m² (50.2% vs. 38%, P < 0.05), whereas no significant difference was observed in those with BMI ≥ 24.0 kg/m² (Table 5).

Table 5.

Adequate GWG in the third trimester

	Variables	Intervention Group	Control Group	P value
Weekly GWG(kg)
Total n	Median (IQR)	0.45 [0.35, 0.56]	0.48 [0.38, 0.60]	0.075
pre-pregnancy  BMI<24.0 kg/m2		0.47 (0.38, 0.57)	0.51 (0.43, 0.62)	0.007
pre-pregnancy BMI ≥ 24.0 kg/m2		0.42 (0.31, 0.55)	0.43 (0.28, 0.53)	0.817
The rate of Adequate GWG
Total n	Frequency (Percentage, %)	210 (41.7)	65 (33.2)	0.047
pre-pregnancy  BMI<24.0 kg/m2		163 (50.2)	46 (38.0)	0.029
pre-pregnancy BMI ≥ 24.0 kg/m2		47 (26.3)	19 (25.3)	0.999

Abbreviation: n Total participants, BMI Body Mass Index, GWG Gestational weight gain

P < 0.05 was considered statistically significant

During the third trimester, participants in the intervention group had significantly higher rate of adequate GWG than those in the control group (OR 0.69, 95% CI 0.49–0.98; P = 0.039). After full adjustment for the same set of covariates, the association remained significant (adjusted OR = 0.55, 95% CI: 0.35–0.86, P = 0.009; Table 3).

Similarly, linear regression analysis showed a significantly lower weekly GWG compared with control groups in the crude analysis (β = −0.085, 95% CI − 0.166 to − 0.004; P = 0.039). After full adjustment, the association remained significant (adjusted β = −0.133, 95% CI − 0.233 to − 0.032; P = 0.010; Table 4).

Incidence of GDM and dietary adherence score

The incidence of GDM was 22.6% in the intervention group and 15.8% in the control group, with no statistically significant difference between the groups (P = 0.059) (Table 6). To account for potential confounding by baseline demographic characteristics and metabolic risk factors, a multivariable logistic regression model was fitted, adjusting for the same set of covariates. After adjustment, there was still no statistically significant difference in the incidence of GDM between the intervention and the control group (adjusted OR = 1.54, 95% CI: 0.90–2.72, P = 0.126) (Table 7).

Table 6.

Incidence of GDM

	Variables	Intervention Group	Control Group	P value
GDM	Frequency (Percentage, %)	114 (22.6)	31 (15.8)	0.059

Abbreviation: GDM Gestational diabetes mellitus

Table 7.

Results of logistic regression analysis on the incidence of GDM

Group	Crudemodel	Pvalue	Full-adjustedmodela	P value
Control Group	1 (ref)	—	1 (ref)	—
Intervention Group	1.56 (1.02, 2.44)	0.047	1.54 (0.90, 2.72)	0.126

Abbreviation: GDM Gestational diabetes mellitus

^a Adjusted for maternal age, pre-pregnancy BMI, gravidity, parity, educational level, annual household income, smoking status, alcohol consumption, history of GDM, and family history of diabetes in first-degree relatives

P < 0.05 was considered statistically significant

Among the 700 enrolled participants, 108 completed both baseline and follow-up dietary adherence questionnaires and were included in this analysis, comprising 84 participants in the intervention group and 24 in the control group. Changes in dietary adherence scores (post-intervention values minus baseline values) were used to evaluate the effect of the intervention. The median change in dietary adherence score was + 4.0 in the intervention group and − 2.0 in the control group. The between-group difference in score changes was statistically significant (P = 0.028) (Table 8).

Table 8.

Change in dietary adherence score

	Variables	Intervention Group	Control Group	P value
D-value	Median (IQR)	4.0 [-5.8, 13.5]	-2.0 [-11.0, 2.2]	0.028

Abbreviation: D-value Different value

Figure 3 presents violin plots of dietary adherence scores change distributions for both groups. Compared with the control group, the intervention group showed an upward shift in distribution, with most score changes above zero, reflecting improved adherence. Conversely, the control group was primarily below zero, suggesting a decline for most participants.

Fig. 3.

Comparison of difference scores by group

Discussion

In this real-world study, the intervention and control groups exhibited balanced baseline characteristics, with significant between-group differences only observed in gravidity, parity, and educational level. Overall, the DTx intervention demonstrated favorable effects on gestational weight management. Participants in the intervention group achieved a higher proportion of adequate GWG rate and GWG rate in the third trimester. However, the DTx intervention did not reduce the incidence of GDM, even after adjusting for baseline demographic characteristics and metabolic risk factors. Furthermore, in the subset with completed dietary adherence data, participants receiving DTx intervention showed significantly greater improvements in dietary adherence compared with those who received routine care.

The present study demonstrated that the DTx intervention improved gestational weight management. The beneficial effects of the intervention on gestational weight management during the third trimester remained significant after adjustment for covariates, with consistent findings observed in both logistic and linear regression analyses. Specifically, the intervention was associated with a significantly higher likelihood of adequate GWG in the third trimester, whereas no significant between-group difference was observed during the second trimester after full adjustment. These findings were consistent with previous evidence showing that digital health interventions can effectively support weight management during pregnancy through continuous lifestyle monitoring and personalized behavioral guidance [29, 30]. Furthermore, the higher proportion of women achieving adequate GWG observed in the intervention group during late pregnancy may suggest that the effects of DTx gradually accumulate over time. This finding may support the cumulative benefits of DTx for gestational weight management [31].

Unlike traditional face-to-face health education, the DTx intervention evaluated in this study was implemented using the Pregnancy Nutrition Digital Therapeutics Mini Program together with the Pregnancy Nutrition Standardized Management System, which were jointly developed by our hospital and an industry partner. The platform incorporateed a patented personalized pregnancy nutrition recommendation method and provided individualized dietary guidance, home-based health monitoring, and continuous behavioral support throughout pregnancy. Through a web-based management system, clinicians were able to remotely monitor participants’ dietary intake, physical activity, and gestational weight changes in real time and provide timely feedback when necessary. To our knowledge, this platform is the first DTx-based pregnancy nutrition management platform to be implemented in Fujian Province. Although the technology has obtained a national invention patent and software copyrights, its clinical application is still at an exploratory stage and has not yet been widely adopted in China. Collectively, the observed improvements in DTx-mediated gestational weight control provide clinical implications for future antenatal intervention practice.

Previous studies have shown that achieving appropriate GWG was associated with a lower risk of adverse pregnancy outcomes and improved maternal and neonatal outcomes [32–34]. Consistent with the importance of maternal weight management, Denison et al. reported that abnormal maternal BMI was associated with increased maternal and neonatal morbidity, longer and more frequent hospital admissions, and higher health service costs [35]. Therefore, interventions that support healthy weight management during pregnancy may have the potential to improve clinical outcomes while reducing healthcare utilization and the associated economic burden. Furthermore, as a digitally delivered intervention, this model has the potential to improve access to ongoing pregnancy management. However, its clinical effectiveness, cost-effectiveness, and impact on healthcare resource use warrant further evaluation in larger, multicenter studies.

No statistically significant between-group difference in GDM incidence was observed before or after covariate adjustment. This finding was consistent with several previous studies showing that improvements in GWG did not necessarily translate into a reduced risk of GDM [36, 37]. In contrast, other studies have reported that intensive digital lifestyle interventions can reduce the incidence of GDM, particularly among women with overweight or obesity who receive comprehensive dietary and physical activity support [38, 39]. Several factors may account for this discrepancy. First, the present study was conducted in a real-world clinical setting, where participants self-selected whether to receive the DTx intervention. Consequently, women with previous GDM or a family history of diabetes were more likely to receive digital health management, resulting in a higher baseline metabolic risk. Although multivariable adjustment was performed, residual confounding cannot be completely ruled out. Second, GDM is a multifactorial metabolic disorder influenced by genetic susceptibility, insulin resistance, β-cell function, and environmental factors. Therefore, optimizing GWG alone may be insufficient to substantially reduce GDM risk, particularly among women with elevated baseline metabolic risk.

The improved dietary adherence observed among participants receiving the DTx intervention provides a potential explanation for the favorable gestational weight outcomes. Through individualized dietary recommendations, self-monitoring, and continuous feedback, DTx may promote healthier dietary behaviors and strengthen long-term adherence to lifestyle recommendations. Nevertheless, dietary adherence was evaluated in only a subset of participants who completed both assessments, resulting in limited statistical power and the possibility of response bias. Therefore, further exploration is needed and should be confirmed in larger prospective studies with more comprehensive dietary assessments.

Strengths & limitations

This study has several strengths. First, unlike previous randomized controlled trials, it evaluated the DTx intervention in a real-world clinical setting, offering evidence that may better reflect routine practice. Second, the intervention was delivered through a widely used smartphone-based mini-program in China, supporting its scalability. However, several limitations should be acknowledged. First, the non-randomized design may have introduced selection bias and residual confounding despite statistical adjustment. Second, dietary adherence was assessed in only a small proportion of participants, which limited the robustness and generalizability of these findings. Third, the study was designed to focus on the antenatal period and did not evaluate maternal postpartum or neonatal outcomes. Finally, the intervention was implemented at a single center, which may limit the generalizability of the findings to other healthcare settings.

Future implications

Future studies should include multicenter randomized or pragmatic trials with larger sample sizes and longer follow-up periods to evaluate both maternal and neonatal outcomes. Integrating DTx with metabolic risk stratification, continuous glucose monitoring, and individualized nutritional management may further improve the effectiveness of digital interventions for GDM prevention. In addition, future research should evaluate the cost-effectiveness and long-term implementation of DTx.

Conclusion

This real-world study suggests that DTx may improve gestational weight management during pregnancy, particularly with sustained intervention. However, no significant association was observed between the DTx intervention and the incidence of GDM after adjustment for baseline characteristics. These findings support the potential role of DTx as an adjunct to routine antenatal care for gestational weight management, while further well-designed studies are needed to evaluate its effectiveness in reducing GDM risk.

Abbreviations

BMI

Body Mass Index

DTx

Digital Therapeutics

FPG

Fasting Plasma Glucose

GDM

Gestational Diabetes Mellitus

GWG

Gestational Weight Gain

IQR

Interquartile Range

OGTT

Oral Glucose Tolerance Test

Authors' contributions

JL, CM and JD designed the study protocol and were responsible for participant recruitment. JD and YS designed the intervention protocol. BS performed the data analysis and revised the manuscript. TF collected and organized the data and drafted the manuscript. LX and CM revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was partially supported by AI Medical Device Innovation Task Unveiling Program (2024-AIZSSJ-08004), Science and Technology Planning Project of Fujian Provincial Health Commission (2025TG01010056), Joint Funds for Scientific and Technological Innovation of Fujian Provincial Health Commission (2025Y9619).

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the personal sensitive information of the data but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Written informed consent was obtained from all participants prior to their enrollment in the study. This study was approved by the Ethics Review Committee (Institutional Review Board, IRB) of Fujian Maternity and Child Health Hospital, with the ethical approval number: 2023KY046. This trial was registered in the Chinese Clinical Trial Registry (ChiCTR2300071496) on May 16, 2023. All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.