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The goal of this clinical trial is to examine how daily behavioral patterns in early pregnancy, including sleep, physical activity, and meal timing, influence continuous glucose dynamics and subsequent risk of gestational diabetes mellitus (GDM) in pregnant women without pre-existing diabetes.
The main questions it aims to answer are:
This study is a prospective, nested randomized pilot trial embedded within the ongoing Towards Optimal Fertility, Fathering and Fatherhood studY (TOFFFY) cohort (NCT06293235) at KK Women's and Children's Hospital, Singapore. A total of 140 pregnant women without pre-existing diabetes, recruited at ≤13 weeks gestation, will be randomized in a 1:1 ratio to either a pilot arm (wearable-based self-monitoring) or a control arm (usual care).
Participants in the pilot arm (n=70) will undergo intensive behavioral and metabolic monitoring over a 14-day period in early pregnancy, including continuous glucose monitoring using a CGM device, wrist actigraphy to assess sleep-wake and rest-activity patterns, and an AI-supported mobile application to record meal timing and dietary intake. Participants will have real-time access to their glucose data and behavioral feedback, enabling self-monitoring and potential behavioral adjustments.
Circadian disruption during pregnancy is increasingly recognized as an important, yet understudied, contributor to impaired glucose regulation and gestational diabetes mellitus (GDM). Emerging evidence suggests that nocturnal eating, irregular sleep timing, reduced rest-activity rhythm (RAR) stability, and greater behavioral variability may impair glucose homeostasis through pathways involving reduced insulin sensitivity, altered β-cell stress, and inflammation.
Most studies assess chronobehaviors using questionnaires, which are limited by recall bias and poor temporal granularity. Recent technological advances enable high-resolution measurement of circadian and metabolic physiology using wrist actigraphy and continuous glucose monitor (CGM). These tools allow objective quantification of sleep timing, RAR, activity fragmentation, and 24-hour glycaemic patterns. Integrating these data in early pregnancy may enable earlier identification of at-risk women, allowing intervention before the onset of overt hyperglycaemia.
The ongoing TOFFFY study (NCT 06293235) provides a unique opportunity to embed such a pilot study among well-phenotyped Singaporean pregnant women. Leveraging this cohort will support mechanistic insights into the interplay between circadian rhythms, meal timing, and glucose regulation, and provide preliminary data to power a larger mother-fetus chronometabolic project. Findings from this pilot will provide high-resolution insight into how early-pregnancy circadian, behavioral, and glycemic patterns interact to shape metabolic physiology. By capturing glucose responses such as glucose AUC, insulin resistance, and C-peptide, the study will identify early mechanistic pathways through which chronobehavioral disruption contributes to dysglycemia.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pilot arm | Experimental | Participants in this arm (n=70) will undergo a 14-day monitoring period in early pregnancy (≤13 weeks gestation). Participants will have real-time access to their glucose data and food logging feedback, enabling self-monitoring and potential behavioural adjustments. Participants will also continue to receive routine antenatal care and standard clinical assessments. |
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| Control Arm | No Intervention | Participants (n=70) receive routine antenatal care only, with no additional devices, monitoring, or feedback introduced by the study. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Continuous glucose monitor (CGM) | Device | Participants will wear a continuous glucose monitor for 14 days in early pregnancy. |
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| Measure | Description | Time Frame |
|---|---|---|
| 24-hour glucose area under the curve (AUC) | Continuous glucose monitor (CGM) will be used to assess 24-hour glucose exposure, expressed as area under the curve (AUC) (unit: mmol/L h). Higher AUC values indicate greater overall glucose exposure and poorer glycemic control. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Nocturnal glucose levels | CGM will be used to assess mean nocturnal glucose levels during the sleep period. The ideal nocturnal glucose range is 3.9-10mmol/L). Higher values indicate poorer nocturnal glycemic control. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Glycemic variability - standard deviation (SD) | CGM will be used to assess glycemic variability using the standard deviation (SD) of glucose values (unit: mmol/L), reflecting the dispersion from the average blood glucose level. Higher values indicate greater glycemic variability and poorer glycemic control. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Glycemic variability - coefficient of variation (CV) | CV is an accepted index for evaluating within-day glycemic variability, where CV = (SD) / (mean glucose) × 100%. Higher values indicate greater glycemic variability and poorer glycemic control. A CV of ≥36% is commonly used to define high glycemic variability. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Rest-activity rhythm (RAR) - intra-daily variability (IV) | Wrist actigraphy will be used to derive RAR by calculating the intra-daily variability (IV). IV reflects the degree of fragmentation in circadian activity patterns by assessing fluctuations in activity frequency and intensity within a given time period, capturing the extent of transitions between periods of rest and activity over time. IV values range from 0 to 1. Higher IV scores indicating poorer outcomes with greater disruption and fragmentation of the RAR. |
| Measure | Description | Time Frame |
|---|---|---|
| Glucose tolerance status during pregnancy | Glucose tolerance status will be assessed using a 75 g oral glucose tolerance test (OGTT), which reflects dynamic glucose response at 0, 60, and 120 minutes (unit: mmol/L). Higher values indicate poorer glucose tolerance. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
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Inclusion Criteria:
Exclusion Criteria:
Eligibility is restricted to pregnant individuals in early gestation, which requires female biological reproductive capacity.
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Benjarat Oh | Contact | +6563948105 | 65 | benjarat.oh@kkh.com.sg |
| Name | Affiliation | Role |
|---|---|---|
| See Ling Loy | KK Women's and Children's Hospital | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| KK Women's and Children's Hospital | Singapore | Singapore | 229899 | Singapore |
Individual participant data will not be shared due to ethical and privacy considerations, as the dataset contains sensitive health and behavioural information that could potentially allow participant re-identification.
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| Wrist actigraphy device | Device | A wrist actigraphy device will be used to assess sleep-wake patterns and physical activity over 14 days. |
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| AI-based dietary and meal timing logging mobile application | Behavioral | Participants will record their dietary intake, meal timing logging, and feedback-based self-monitoring using an AI-based food logging mobile application. |
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| From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Rest-activity rhythm (RAR) - inter-daily stability (IS) | Wrist actigraphy will be used to derive RAR by calculating the inter-daily variability (IS). IS reflects the stability of 24-hour circadian activity variations and the balance between RAR and the circadian cycle. IV values range from 0 to 1. IS values close to 1 indicating a better RAR outcome with greater rhythm stability. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Chrononutrition behavior - meal timing | AI-based food logging will be used to assess meal timing, defined as the timing of caloric intake, expressed as clock time of energy consumption. Later or more irregular intake timing indicates less favorable chrononutrition alignment. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Chrononutrition behavior - eating jetlag | AI-based food logging will be used to assess eating jetlag, defined as the difference in timing of the caloric midpoint between weekdays and weekends (unit: hours). Higher values indicate greater circadian misalignment in eating behavior. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Chrononutrition behavior - frequency of night-eating | AI-based food logging will be used to assess frequency of night-eating episodes, defined as the number of eating events occurring during the biological night or habitual sleep period. Higher night-eating frequency indicates poorer chrononutrition behavior. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Total glycemic exposure during pregnancy | Total glycemic exposure during the 75 g OGTT will be calculated as area under the glucose curve (unit: mmol/L h). Higher values indicate poorer glucose tolerance. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
| Glycemic marker - fasting insulin | Serum insulin concentration measured in blood (unit: pmol/L, SI unit) following the 75 g OGTT at 0, 60, and 120 minutes. Higher values indicate greater circulating insulin levels. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
| Glycemic marker - C-peptide | C-peptide concentration is assessed (unit: pmol/L, SI unit) following the 75 g OGTT at 0, 60, and 120 minutes. Higher values indicate increased endogenous insulin secretion. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
| Maternal glycemic control index - Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) | HOMA-IR is derived from fasting glucose and fasting insulin to measure insulin resistance. Higher values indicate more insulin is needed to maintain glucose levels. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
| Glycemic control index - Homeostatic Model Assessment for Beta Cell Function (HOMA-β) | HOMA-β is derived from fasting glucose and fasting insulin to assess the function of beta cells in the pancreas, which are responsible for producing insulin. Higher value indicates better beta-cell functionality, while a lower value suggests impaired beta-cell function, which can be a sign of insulin resistance or diabetes. The glycemic outcomes will be compared between participants receiving the pilot intervention and those receiving usual care. | From 24 weeks till 28 weeks of gestation |
| Sleep timing | Wrist actigraphy will be used to assess sleep timing, including sleep onset time and wake time (unit: clock time, hh:mm). Later sleep timing indicates delayed circadian phase. Changes across the monitoring period will be evaluated to assess potential behavioral effects of real-time self-monitoring. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| Physical activity level | Wrist actigraphy will be used to assess physical activity pattern based on daily step counts. Higher values indicate greater physical activity levels. Changes across the monitoring period will be evaluated to assess potential behavioral effects of real-time self-monitoring. | From enrollment in first trimester (≤13 weeks gestation), over 14 days |
| ID | Term |
|---|---|
| D016640 | Diabetes, Gestational |
| D008659 | Metabolic Diseases |
| ID | Term |
|---|---|
| D011248 | Pregnancy Complications |
| D005261 | Female Urogenital Diseases and Pregnancy Complications |
| D000091642 | Urogenital Diseases |
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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