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| ID | Type | Description | Link |
|---|---|---|---|
| 1R01DK141526-01 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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Food insecurity affects up to 30% of pregnancies and leads to worse health in pregnant people and their children, including an increased risk of gestational diabetes, pre-term birth, and future cardiometabolic chronic conditions (e.g., type 2 diabetes and obesity). Interventions are being utilized to address food insecurity in clinical care settings, but patients differ in the support needed to reduce food insecurity and health systems have limited resources to invest in these interventions. Rather than a single intervention, adaptively allocating interventions could be a more effective, equitable, and efficient approach to improve food security; the objectives of this pilot study are to determine the feasibility of recruiting, retaining, and adaptively providing food insecurity interventions to pregnant patients in anticipation of a large, definitive trial in the future.
The US is facing a maternal and infant health crisis. Each year in the US there are >700 maternal deaths and >60,000 life-threatening pregnancy events. Despite decreasing in other high-income countries, maternal mortality has increased in recent years in the US with a staggering 1210 maternal deaths in 2019. Importantly, maternal deaths in the US are now less likely to be due to direct complications of childbirth, and are increasingly due to endocrine (e.g. gestational diabetes (GDM)) or cardiovascular (e.g. pre-eclampsia) conditions directly related to obesity and other nutrition-related chronic diseases. One significant contributor to maternal mortality is food insecurity (FI), or the lack of consistent access to the food needed for a healthy life. In 2023, 13.5% of US households, (>40 million people), were food insecure. Households with young children have higher rates of FI. Also, up to 30% of pregnancies are impacted by FI. Pregnant and postpartum women are especially vulnerable to the impacts of FI as they have increased nutritional requirements for the growing fetus and while breastfeeding. FI has been associated with inadequate or excessive gestational weight gain, GDM, and pregnancy-induced hypertension. Excess retained weight after pregnancy has important health consequences including development of type 2 diabetes. GDM and gestational weight retention also confer higher risk of complications in subsequent pregnancies and future cardiovascular disease. Additionally, FI is associated with increased risk of preterm birth and infants being born low birth weight, affecting children's growth trajectories and future risk of developing obesity.
To address the high prevalence of FI and its impact on health, national healthcare organizations, including the Centers for Medicare and Medicaid (CMS) and The American College of Obstetricians and Gynecologists, have recommended that health systems address FI as a routine part of clinical care. The integration of interventions to address FI in different populations, particularly those with nutrition-related conditions, has been termed "Food is Medicine". CMS has been piloting Food is Medicine interventions as part of Medicaid reform in several states, including North Carolina. Three "Food is Medicine" interventions that are being studied and used by health systems and insurers include: 1) referring patients to government benefits intended to support nutrition or directly providing food through the use of 2) produce prescriptions and 3) medically-tailored meals. Despite the growing use of FI interventions in clinical care settings, a 2023 systematic review highlighted the need for more research on healthcare system-based interventions to reduce FI in pregnancy.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Electronic Health Record (EHR) referral to Women, Infants and Children (WIC) | Active Comparator | Participants randomized to this intervention will be referred to their county WIC program through an already developed electronic referral process. To enable WIC offices to receive referrals and easily communicate with healthcare teams, our EHR also offers a community provider-facing, read-only EHR version. We have already successfully provided WIC staff with access and training for our ongoing WIC screening and referral pilot in pediatrics. |
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| Electronic Health Record (EHR) referral to Women, Infants and Children (WIC) + care navigation | Experimental | Participants will receive the same intervention as the electronic WIC referral. In addition, a patient care navigator will meet with the participant at enrollment to discuss any anticipated barriers to accessing WIC. The purpose of the visit is to build rapport and trust and to identify any social and structural barriers to enrolling in WIC. The navigator will also contact participants at 2 weeks to discuss any additional barriers reported and as necessary after the baseline visit. Specific counseling will be tailored based on individual's needs, for example difficulty with paperwork. The navigator will also assess any additional community resources to assist the participant with FI (e.g., local food pantries). |
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| Produce prescriptions | Experimental | Participants randomized to this arm will receive $10 worth of produce delivered to their home weekly. Participants will receive a weekly delivery of produce for 3 months. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Produce prescription | Behavioral | Participants randomized to this arm will receive $10 worth of produce delivered to their home weekly. Participants will receive a weekly delivery of produce for 3 months. |
| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of recruitment - Proportion of eligible patients who enroll | Proportion of eligible patients who enroll in the study based on study logs | Baseline |
| Feasibility of retention - Proportion of eligible participants | The proportion of eligible participants who complete 3-month and 6-month follow-up data collection based on study log | Month 6 |
| Feasibility of re-randomization - Proportion of eligible participants | The proportion of eligible participants who are re-randomized to a stage 2 intervention based on study log. | Month 3 |
| Food insecurity Scores | Survey participants using the validated 10-item USDA Adult FSSM, with a 30-day look back period. The tool measures food security over the prior 30 days. Using the standardized scoring provided by the USDA to assess participants responses. This produces a raw score that ranges from 0 to 10 with higher scores indicating worse FI. High Food Security: Raw score of 0. Marginal Food Security: Raw score of 1-2. Low Food Security: Raw score of 3-5. Very Low Food Security: Raw score of 6-1 | Month 6 |
| Measure | Description | Time Frame |
|---|---|---|
| Incidence of Gestational diabetes | Differences in the incidence of gestational diabetes over time based on data extraction from the electronic health record. | Month 6 |
| Gestational weight gain | We will determine the proportion of individuals with excess gestational weight gain. Will be determined by data extraction from the electronic health record and defined as the following: If pre-pregnancy BMI ≥30, gaining ≥20lbs; if BMI 25-29.9, gaining ≥25lbs; if BMI <25, gaining ≥35lbs. |
| Measure | Description | Time Frame |
|---|---|---|
| Food expenditures | Report of out-of-pocket monthly expenditures on food based on study survey | Month 6 |
| Fruit and vegetable intake | Change fruit and vegetable intake over time based on the National Cancer Institute's (NCI) Fruit and Vegetable screener. The NCI Fruit and Vegetable screener measure the frequency and usual portion size for 9 fruit and vegetable components over the prior month. |
Inclusion Criteria:
Exclusion Criteria:
Confirmed viable pregnancy
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Deepak Palakshappa, MD, MSHP | Contact | 336-716-1795 | deepak.palakshappa@wfusm.edu | |
| Rebecca Stone, MPH | Contact | 336-713-5544 | rebecca.j.stone@advocatehealth.org |
| Name | Affiliation | Role |
|---|---|---|
| Deepak Palakshappa, MD, MSHP | Wake Forest University Health Sciences | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Wake Forest University Health Sciences | Recruiting | Winston-Salem | North Carolina | 27157 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40975433 | Derived | Palakshappa D, Stone RJ, Ramirez B, White SE, Rigdon J, Bundy R, Eagleton SG, Caudill N, Martin H, Grundseth M, Best S, Mongraw-Chaffin M, Lewis KH, Montez K. Feasibility of an ADAPTive intervention to improve food security and Maternal-Child Health (ADAPT-MCH): Protocol for a pilot sequential multiple assignment randomized trial. Contemp Clin Trials. 2025 Nov;158:108086. doi: 10.1016/j.cct.2025.108086. Epub 2025 Sep 18. |
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All of the individual participant data collected during the trial after deidentification
Immediately following publication - no end date
anyone wishing access
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Electronic Health Record (EHR) referral to Women, Infants and Children (WIC) or EHR referral to WIC + care navigation
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| Medically tailored meals | Experimental | Medically tailored meals will be delivered weekly to participant's homes for 3 months. During the 3 months, participants will receive 10 medically-tailored refrigerated or frozen meals (5 lunches and 5 dinners) delivered to their home weekly. All meals are planned by a registered dietician. Meals have minimal preparation time, can be heated by stove, oven, or microwave, and will be provided free-of-charge. Because the meals are medically tailored, participants are asked not to share them. Adherence to meals and food sharing will be measured using food consumption diaries. |
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| Medically tailored meals | Behavioral | Medically tailored meals will be delivered weekly to participant's homes for 3 months. During the 3 months, participants will receive 10 medically-tailored refrigerated or frozen meals (5 lunches and 5 dinners) delivered to their home weekly. All meals are planned by a registered dietician. Meals have minimal preparation time, can be heated by stove, oven, or microwave, and will be provided free-of-charge. Because the meals are medically tailored, participants are asked not to share them. Adherence to meals and food sharing will be measured using food consumption diaries |
|
| Electronic health record WIC referral | Behavioral | Participants randomized to this intervention will be referred to their county WIC program through an already developed electronic referral process. To enable WIC offices to receive referrals and easily communicate with healthcare teams, our EHR also offers a community provider-facing, read-only EHR version. We have already successfully provided WIC staff with access and training for our ongoing WIC screening and referral pilot in pediatrics. |
|
| Electronic health record WIC referral + care navigation | Behavioral | Participants will receive the same intervention as the electronic WIC referral. In addition, a patient care navigator will meet with the participant at enrollment to discuss any anticipated barriers to accessing WIC. The purpose of the visit is to build rapport and trust and to identify any social and structural barriers to enrolling in WIC. The navigator will also contact participants at 2 weeks to discuss any additional barriers reported and as necessary after the baseline visit. Specific counseling will be tailored based on individual's needs, for example difficulty with paperwork. The navigator will also assess any additional community resources to assist the participant with FI (e.g., local food pantries). |
|
| Post-delivery |
| Incidence of Pre-eclampsia | Incident number of diagnoses at outpatient, emergency department or hospital encounter based on ICD-10 codes through data extraction from the electronic health record | Post-delivery |
| Number of Community resources uses | Change in the number of community resources used (e.g. food pantries, supplemental nutrition assistance program) based on self-report in the study survey. | month 6 |
| Infant birth weight at the time of delivery | Infant birth weight at the time of delivery based on data extraction from the electronic health record. | Baseline |
| Infant gestational age at the time of delivery | Infant gestational age at the time of delivery based on data extraction from the electronic health record | Baseline |
| Post delivery outcomes - Number of vaginal versus c-section Deliveries | Number of Deliveries based on data extraction from the EHR. | Baseline |
| Post delivery outcomes - infant APGARS Scores | Infant APGARS, Scores based on data extraction from the EHR. The Apgar score is a quick assessment of a newborn's health, evaluating five key areas: heart rate, respiratory effort, muscle tone, reflex irritability, and skin color. Each area is scored from 0 to 2, with a total score ranging from 0 to 10. A score of 7 or above is considered good, indicating the baby is in generally good health. Lower scores may indicate the need for medical assistance, but do not necessarily predict long-term health problems. 7-10: Normal, indicating good health and usually requiring only routine post-delivery care. 4-6: May require some assistance with breathing or other interventions. 0-3: Critical, requiring immediate and potentially life-saving medical attention. | Baseline |
| Post delivery outcomes - breastfeeding status | breastfeeding status based on data extraction from the EHR | Baseline |
| Number of Glucose homeostasis episodes | Episodes of hypo- or hyperglycemic episodes based on 14-days continuous glucose monitoring at baseline and 6 months. | Baseline and month 6 |
| Month 6 |
| Depressive symptoms Scores | Change in depressive symptoms over time based on data extraction from the electronic health record. Depressive systems are collected at each OB visit using the validated Patient Health Questionnaire-2 (PHQ-2). Those with a positive PHQ-2 are reflexively assessed using the full PHQ-9. Scores range from 0-27 with higher scores representing a higher number of depressive systems. | Month 6 |
| Stress Scores | Change in stress based on the Perceived Stress Scale as part of the study survey. Scores range from 0-40 with higher scores representing higher perceived stress. | Month 6 |
| Healthcare use | Number of missed appointments, emergency department use, and hospitalizations based on data extraction from the EHR. | Baseline |