Intensive lifestyle interventions in pregnancy have shown success in limiting gestational weight gain, but the effects on mood and quality of life in pregnancy and postpartum are less known. The purpose was to quantify changes in mental and physical quality of life and depressive symptoms across pregnancy and the postpartum period, to determine the association between gestational weight gain and change in mood and quality of life, and to assess the effect of a behavioral intervention targeting excess gestational weight gain on these outcomes. A three group parallel-arm randomized controlled pilot trial of 54 pregnant women who were overweight or obese was conducted to test whether the SmartMoms® intervention decreased the proportion of women with excess gestational weight gain. Individuals randomized to Usual Care (n = 17) did not receive any weight management services from interventionists. Individuals randomized to the SmartMoms® intervention (n = 37) were provided with behavioral weight management counseling by interventionists either in clinic (In-Person, n = 18) or remotely through a smartphone application (Phone, n = 19). In a subset of 43 women, mood and mental and physical quality of life were assessed with the Beck Depression Inventory-II and the Rand 12-Item short form, respectively, in early pregnancy, late pregnancy, 1–2 months postpartum, and 12 months postpartum. The SmartMoms® intervention and Usual Care groups had higher depressive symptoms (p < 0.03 for SmartMoms® intervention, p < 0.01 for Usual Care) and decreased physical health (p < 0.01) from early to late pregnancy. Both groups returned to early pregnancy mood and physical quality of life postpartum. Mental health did not change from early to late pregnancy (p = 0.8), from early pregnancy to 1–2 months (p = 0.5), or from early pregnancy to 12 months postpartum (p = 0.9), respectively. There were no significant intervention effects. Higher gestational weight gain was associated with worsened mood and lower physical quality of life across pregnancy. High depressive symptoms and poor quality of life may be interrelated with the incidence of excess gestational weight gain. The behavioral gestational weight gain intervention did not significantly impact these outcomes, but mood and quality of life should be considered within future interventions and clinical practice to effectively limit excess gestational weight gain. NCT01610752 , Expecting Success, Registered 31 May 2012.

Background To improve weight management in pregnant women, there is a need to deliver specific, data-based recommendations on energy intake. Objective This cross-sectional study evaluated the accuracy of an electronic reporting method to measure daily energy intake in pregnant women compared with total daily energy expenditure (TDEE). Methods Twenty-three obese [mean ± SEM body mass index (kg/m2): 36.9 ± 1.3] pregnant women (aged 28.3 ±1.1 y) used a smartphone application to capture images of their food selection and plate waste in free-living conditions for ≥6 d in early (13-16 wk) and late (35-37 wk) pregnancy. Energy intake was evaluated by the smartphone application SmartIntake and compared with simultaneous assessment of TDEE obtained by doubly labeled water. Accuracy was defined as reported energy intake compared with TDEE (percentage of TDEE). Ecological momentary assessment prompts were used to enhance data reporting. Two-one-sided t tests for the 2 methods were used to assess equivalency, which was considered significant when accuracy was >80%. Results Energy intake reported by the SmartIntake application was 63.4% ± 2.3% of TDEE measured by doubly labeled water (P = 1.00). Energy intake reported as snacks accounted for 17% ± 2% of reported energy intake. Participants who used their own phones compared with participants who used borrowed phones captured more images (P = 0.04) and had higher accuracy (73% ± 3% compared with 60% ± 3% of TDEE; P = 0.01). Reported energy intake as snacks was significantly associated with the accuracy of SmartIntake (P = 0.03). To improve data quality, excluding erroneous days of likely underreporting (<60% TDEE) improved the accuracy of SmartIntake, yet this was not equivalent to TDEE (-22% ± 1% of TDEE; P = 1.00). Conclusions Energy intake in obese, pregnant women obtained with the use of an electronic reporting method (SmartIntake) does not accurately estimate energy intake compared with doubly labeled water. However, accuracy improves by applying criteria to eliminate erroneous data. Further evaluation of electronic reporting in this population is needed to improve compliance, specifically for reporting frequent intake of small meals. This trial was registered at www.clinicaltrials.gov as NCT01954342.

To improve weight management of pregnant women, there is a need to deliver specific, data‐based recommendations on energy intake or physical activity. This study investigated how to use an electronic reporting method of energy intake in pregnant women and evaluated its validity compared to total daily energy expenditure (TDEE).23 obese (BMI: 36.9±1.3 kg/m2) pregnant women (28.3±1.1 years) used a Smartphone application to capture images of their food selection and plate waste in free‐living conditions for at least 6 days in early (13–16 weeks) and late (35–37 weeks) pregnancy. Energy intake by Remote Food Photography Method (RFPM) was evaluated compared to TDEE obtained by doubly labeled water over the same 7‐day period.During 45 observation periods (n=23 × 2 visits, one abnormal TDEE excluded), pictures were captured on 290 (99%) days (6.4±0.1 days captured per observation, 9.0±0.4 pictures*d−1). Average energy intake reported by the RFPM was 1755±59 kcal*d−1 (range: 804 to 2729 kcal*d−1) which equals a 37%±2% (−68 to −11%) underestimate compared to TDEE (2807±51 kcal*d−1), consistent with earlier data from non‐pregnant adults where non‐personalized ecological momentary assessment (EMA) prompts were utilized. Meals (breakfast, lunch, dinner) were reported on 82% (reported meals vs total days) of possible occasions and accounted for 83±2% of total energy intake, while energy intake from snacks was 17±2%.To investigate which factors contribute to compliance, we differentiated reported energy intake by days of the week (week vs weekend), type of phone (own vs study‐provided) and meal type (meals vs snacks). While we found no significant difference in the reported energy intake between week and weekend days, participants that captured pictures on their own phone compared to the study‐provided phone had significantly more pictures (P=.04) and consequently a higher energy intake (P=.13). Reported energy intake as snacks (%energy snack/total) was significantly associated with % reporting (% RFPM/DLW, R2=0.19, P=.03), indicating that reporting of snacks increased accuracy.To understand how to improve data quality, we excluded days of likely underreporting by three criteria: <1000 kcal*d−1, <2 main meals*d−1 or <60% of TDEE. The accuracy of energy intake in comparison to TDEE increased significantly (+7±2%, +5±3% and +24±4%), yet 9, 9 and 45% of the days were excluded by these criteria, respectively. Despite these improvements none of the criteria increased the RFPM to an accuracy within 10% bounds (Two One‐Sided Test for Equivalence, −33±2%, −34±2% and −22±1%, all: P=1.00).In summary, using an electronic reporting method (RFPM), energy intake in obese pregnant women does not accurately estimate energy intake as compared to DLW. However, data quality improves when participants use their own phones and by applying criteria to eliminate unrealistic data. Further evaluation of EMA prompting in this population is needed to improve compliance, specifically for snacks and frequent small meals.Support or Funding InformationNIH R01DK099175, LaCats U54GM104940 and NORC P30DK094418

Clinical research in the pregnant population allows for delivery of quality, evidence-based care in obstetrics. However, in recent years, the field of obstetrics has faced severe challenges in the recruitment of the pregnant population into clinical trials, a struggle also shared by several other medical disciplines. We candidly describe our failure to recruit a healthy population of overweight and obese pregnant women in their first trimester. We were then able to glean unsuccessful and successful recruitment approaches and improve our recruitment effort by autopsy of failed strategies and with guidance from a survey disseminated to improve our understanding of community feelings about participating in research while pregnant. These "lessons learned" taught us that active recruitment within this population is a necessity; that is, direct (face-to-face discussions at obstetric appointments) compared with indirect (flyers and general emails) modalities and that prenatal care provider support of the proposed research study is vital to a patient's willingness to participate. By implementation of "lessons learned," we describe how we successfully recruited a similar pregnant population 1 year later. The Clinical Trials related to our article are as follows: 1) Expecting Success: NCT01610752, https://clinicaltrials.gov/ct2/show/NCT01610752; 2) MomEE: NCT01954342, https://clinicaltrials.gov/ct2/show/NCT01954342; and 3) Participate While Pregnant Survey: NCT02699632, https://clinicaltrials.gov/ct2/show/NCT02699632.

Background Two-thirds of pregnant women exceed gestational weight gain (GWG) recommendations. Because excess GWG is associated with adverse outcomes for mother and child, development of scalable and cost-effective approaches to deliver intensive lifestyle programs during pregnancy is urgent. Objective The aim of this study was to decrease the proportion of women who exceed the Institute of Medicine (IOM) 2009 GWG guidelines. Methods In a parallel-arm randomized controlled trial, 54 pregnant women (age 18-40 years) who were overweight (n=25) or obese (n=29) were enrolled to test whether an intensive lifestyle intervention (called SmartMoms) decreased the proportion of women with excess GWG, defined as exceeding the 2009 IOM guidelines, compared to no intervention (usual care group). The SmartMoms intervention was delivered through mobile phone (remote group) or in a traditional in-person, clinic-based setting (in-person group), and included a personalized dietary intake prescription, self-monitoring weight against a personalized weight graph, activity tracking with a pedometer, receipt of health information, and continuous personalized feedback from counselors. Results A significantly smaller proportion of women exceeded the IOM 2009 GWG guidelines in the SmartMoms intervention groups (in-person: 56%, 10/18; remote: 58%, 11/19) compared to usual care (85%, 11/13; P=.02). The remote intervention was a lower cost to participants (mean US $97, SD $6 vs mean US $347, SD $40 per participant; P<.001) and clinics (US $215 vs US $419 per participant) and with increased intervention adherence (76.5% vs 60.8%; P=.049). Conclusions An intensive lifestyle intervention for GWG can be effectively delivered via a mobile phone, which is both cost-effective and scalable. Trial Registration Clinicaltrials.gov NCT01610752; https://clinicaltrials.gov/ct2/show/NCT01610752 (Archived by WebCite at http://www.webcitation.org/6sarNB4iW)