Given the high and growing prevalence of obesity among adults in the United States, obesity treatment and prevention are important topics in biomedical and public health research. Although researchers recognize the significance of this problem, much remains unknown about safe and effective prevention and treatment of obesity in adults. In response to the worsening obesity epidemic and the many unknowns regarding the disease, a group of key scientific and program staff members of the National Institutes of Health (NIH) and other federal and non-government agencies gathered virtually in September 2021 to discuss the current state of obesity research, research gaps, and opportunities for future research in adult obesity prevention and treatment. The current article synthesizes presentations given by attendees and shares their organizations' current initiatives and identified gaps and opportunities. By integrating the information discussed in the meeting and current initiatives, we identify potential targets and overlapping priorities for future research, including health equity and disparities in obesity, the heterogeneity of obesity, and the use of technological and innovative approaches in interventions.

Objective: Pregnancy-induced nausea and vomiting are common maladies during early pregnancy and may be related to physical activity (PA). Our objective was to determine relations among work-related PA (work PA), leisure-time physical activity (LTPA), and nausea during the first trimester. Study design: Online or mailed surveys with questions on pregnancy-related nausea, work PA, and LTPA were completed by 70 women at 15 to 30 months postpartum. Women recalled nausea during the first trimester (none, ≤1 h/d, 2-3 h/d, 4-6 h/d, ≥6 h/d) as well as LTPA frequency, duration, and type. Women also recalled total working hours in their first trimester and percentage of time sitting, standing, and walking at work. Results: A total of 42 women (60%) were categorized as having high nausea (≥2 h/d). Mann-Whitney U tests showed that women with low nausea had significantly more MET minutes per week of LTPA (P = .05) and hours per week spent standing at work (P = .03). Logistic regression analyses showed standing for ≥20 h/wk at work was related to reduced odds of high nausea (adjusted odds ratio = 0.23; 95% CI = 0.06-0.96), whereas meeting LTPA guidelines was nonsignificantly related to reduced odds. Conclusion: These findings suggest an inverse relationship between first trimester PA and level of nausea. Further investigation is needed to determine the directionality of these relations.

Low-income pregnant women are less likely to meet physical activity (PA) recommendations compared to higher income counterparts. Some studies suggest that the difference in activity levels is diminished when household (HPA) and job physical activities (JPA) are considered but little is known about factors that may influence HPA and JPA levels. PURPOSE: To examine personal, social, and environmental factors impacting HPA and JPA during pregnancy in low-income women based on the Ecological Model. METHODS: Low-income pregnant and postpartum women were recruited nationwide using an online platform. Participants (n=109) recalled pregnancy HPA and JPA using the International Physical Activity Questionnaire (IPAQ) and answered a survey on personal (demographics, self-efficacy, lifestyle beliefs), social (social support, social perceptions, social roles strain) and environmental factors (safety, community involvement). Descriptive statistics were assessed for all variables. Median split was used to categorize HPA and JPA. Correlation matrices were created for personal, social, and environmental factors. Based on established criteria, significant variables were selected to be included in confirmatory factor analysis (CFA). A CFA model for each personal, social, and environmental latent factor and two structural equation models were created for predicting HPA and JPA. RESULTS: Participants’ mean age was 29.5 years (±5.6) and 51.9% of women were on Medicaid. Median, range HPA was 28, 0-354 MET-hrs/wk and JPA was 0.2, 0-367 MET-hrs/wk. Latent personal, social, and environmental factors were not significantly related to HPA or JPA. However, significant interactions occurred between personal and environmental factors (-0.218, p<0.05) and social and environmental factors (-0.207, p<0.05) in the HPA model as well as personal and environmental factors (-0.221, p<0.05) in the JPA model. CONCLUSIONS: Reported HPA and JPA levels were low, varied widely, and could indicate a lack of understanding of PA questions. Latent factors were not related to HPA or JPA, but the interactions among latent factors indicate that this analysis might not capture the complexity of PA behaviors. Other correlates not included in this study, such as job type, may have greater influence on HPA and JPA among low-income pregnant women.

I1 World Congress for Integrative Medicine & Health 2017 - A global forum for exploring the future of comprehensive patient care Benno Brinkhaus1, Torkel Falkenberg2,3, Aviad Haramati4,5, and Stefan N. Willich1 1Institute for Social Medicine, Epidemiology and Health Economics, Charite – Universitatsmedizin Berlin, Berlin, Germany; 2Department of Neurobiology Care Sciences and Society, Division of Nursing, Research Group Integrative Care, Karolinska Institutet, Stockholm, Sweden; 3I C – The Integrative Care Science Center, Jarna, Sweden; 4Department of Biochemistry, Molecular and Cellular Biology, Georgetown University, Medical Center, Washington, DC, USA; 5Department of Medicine, Georgetown University Medical Center, Washington, DC, USA We are excited to present the abstracts of the keynote speakers, parallel sessions and oral and poster presentations of the World Congress on Integrative Medicine & Health (WCIMH 2017; http://www.ecim-iccmr.org/2017/) to be held in Berlin on May 3-5, 2017, which will be jointly convened by the European Society of Integrative Medicine (ESIM) and the International Society for Complementary Medicine Research (ISCMR). The Congress will take place in association with a number of national and international organizations from North America and other continents. Consequently, the congress will provide the most comprehensive global forum and perspective in the field of Complementary and Integrative Medicine in 2017. The congress goal is reflected in its tag line: The Future of Comprehensive Patient Care - Strengthening the Alliance of Researchers, Educators and Providers. We believe that by bringing together researchers, educators and providers, who are addressing various aspects of Integrative Medicine and health, we can build on the evidence obtained through research to inform clinical education and practice and thereby create a better platform for comprehensive patient care. The main themes of the Congress are: Clinical care: The practice of Integrative Medicine should be based on distinct definitions, should be informed by evidence and evolve from guidelines that are developed by experts from conventional and complementary medicine. Education: Academic leaders and health officials have called for future clinicians to possess the knowledge and skills to understand how Integrative Medicine can be incorporated into conventional care to improve the health of the public. Therefore, it is essential to share best practices in how to create robust curricular opportunities for medical students to experience systematic teaching of the principles, strengths and limitations of Integrative Medicine. Research: Within this Congress scientists will showcase the highest quality research worldwide in this field and will provide the state-of-the-science evidence base through plenary lectures, symposia and abstract presentations. Traditional healing systems (THS): Traditional healing practices and practitioners are an important and often underestimated part of health care. THS is found in almost every country in the world and the demand for its services is increasing. Research contributing to evidence informed decision making is imperative to develop a cohesive and integrative approach to health care that allows governments, health care practitioners and, most importantly, those who use health care services, to access THS in a safe, respectful, cost-efficient and effective manner. Arts and medicine: For the first time at a research congress, this theme will explore the important contributions of the arts (music, visual arts, dancing, etc,) for integrative therapeutic interventions to achieve optimal health and healing. Given the ambitious scope of this worldwide international congress, the four authors of the present editorial serve as co-presidents and they are guided by the International Organizing Committee consisting of many experts from around the world including Myeong S. Lee, Jianping Liu, Kenji Watanabe (from Far East Asia), Renee Street (Africa), Amie. Steel (Australia), Paulo Arturo Caceres Guido, Chin An Lin (South America), Heather Boon, Josephine Briggs, John Weeks (North America) and Abdullah Al-Bedah, Mohamed Khalil, Elad Schiff (Middle East and Israel). The programming for each of the five themes is directed by WCIMH 2017 theme subcommittees involving some of the most highly regarded clinicians, educators and researchers in the world in this field (in alphabetic order): Linda Balneaves, Lesley Braun, Eva Bojner Horwitz, Gustav Dobos, Jeffery Dusik, David Eisenberg, Iva Fattorini, Eckhart G. Hahn, Suzanne B. Hanser, Frederick Hecht, George Lewith, Harald Matthes, Andreas Michalsen, Judy Rollins, Volker Scheid, Michael Teut, Robert Saper, Claudia M. Witt, Merlin Wilcox and Darong Wu. The Local Organizing Board is coordinated by M. Cree. We are very grateful to all organisations and individuals working diligently to making this first World Congress for Integrative Medicine & Health in 2017 a great success. We are also pleased to announce that the opening welcome will include the Director General for the World Health Organization, Dr Margaret Chan (on video). All plenary speakers are internationally recognized experts in the field of Complementary and Integrative Medicine such as Josephine B Briggs (US) and Merlin Willcox (UK) as keynote speakers for the theme traditional healing systems; Klaus Linde (Ger) and Michael Moore (UK) for the research theme; Lisa M Wong (US) and Tores Theorell (Sweden) will address the theme of arts and medicine; Darong Wu (China) and Jeffery A Dusek (US) are presenting on the theme of clinical care; and Aviad Haramati and David Eisenberg (both US) will close the Congress with presentations on education. In addition, more than 100 oral presentations in over 40 parallel sessions will be in the program to provide newly emerging data from recent research projects, experiences from new treatment aspects in clinical care, descriptions of new models of education in medicine, information about integration of traditional healing systems in health care systems and new aspects on the integration of arts in medicine. In addition, more than 400 posters will be presented in guided poster sessions during the three days of the Congress. To translate the congress goals and objectives into a tangible action for the field, a Berlin Agreement is being developed. With the title ‘Social and Self-responsibility in practicing and fostering Integrate Health and Medicine Globally,’ this document is meant to help shape the future of comprehensive patient care in Integrative Medicine, and addresses the responsibilities of all participants, including patients and citizens, physicians and all colleagues working in the healthcare system. The Berlin Agreement has been developed by the WCIMH 2017 congress presidents and the International Organizing Committee to create a document for further distribution to the scientific and clinical community and to health care stakeholders, decision makers, and politicians. We anticipate having the final version of the Berlin Agreement endorsed by a number of organizations prior to the Congress and also soliciting the support of congress at the WCIMH 2017 in Berlin. Our hope is that this document will provide an important impetus for further engagement world-wide after the Congress has concluded. Immediately before the start of WCIMH 2017 on Wednesday May 3rd 2017 there will be several high-quality pre-conference workshops covering all congress topics. Reflecting the political situation in recent years, especially in Europe, we have arranged for a unique half-day workshop on the topic: “Refugees with Chronic Diseases between the Middle-East and Europe: The Role of Traditional and Integrative Medicine in Bridging Gaps”, The speakers are all from the Middle East and Europe and will address how Integrative Medicine may serve as an important element to overcome the problematic health situation of refugees around the world. We are convinced that the field of Complementary and Integrative Medicine, including traditional healing systems and medicine and the arts, will benefit from The 2017 World Congress on Integrative Medicine & Health—a preeminent scientific international forum that is focused on highlighting advances in these thematic areas. We invite all practitioners, educators and researchers in the field of Integrative Medicine to come together, participate and engage together to make this Congress an exciting meeting for the successful advancement of Integrative Medicine across the globe.

Objectives: Evaluate accuracy of the activPAL and its proprietary software for prediction of time spent in physical activity (PA) intensities (sedentary, light, and moderate-to-vigorous) and energy expenditure (EE) and compare its accuracy to that of a machine learning model (ANN) developed from raw activPAL data. Design: Semi-structured accelerometer validation in a laboratory setting. Methods: Participants (n = 41 [20 male]; age = 22.0 +/- 4.2) completed a 90-min protocol performing 13 activities for 3-10 min each and choosing activity order, duration, and intensity. Participants wore an activPAL accelerometer (right thigh) and a portable metabolic analyzer. Criterion measures of time spent in sedentary, light, and moderate-to-vigorous PA were determined using measured MET values of <= 1.5, 1.6-2.9, and >= 3.0, respectively. Estimated times in each PA intensity from the activPAL software and ANN were compared with the criterion using repeated measures ANOVA. Window-by-window EE prediction was assessed using correlations and root mean square error. Results: activPAL software-estimated sedentary time was not different from the criterion, but light PA was overestimated (6.2 min) and moderate- to vigorous PA was underestimated (4.3 min). ANN-estimated sedentary time and light PA were not different from the criterion, but moderate- to vigorous PA was overestimated (1.8 min). For EE estimation, the activPAL software had lower correlations (r = 0.76 vs. r = 0.89) and higher error (1.74 vs. 1.07 METs) than the ANN. Conclusions: The ANN had higher accuracy for estimation of EE and PA than the activPAL software in this semi-structured laboratory setting, indicating potential for the ANN to be used in PA assessment. (C) 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

The purpose of this article is to compare accuracy of activity type prediction models for accelerometers worn on the hip, wrists, and thigh. Forty-four adults performed sedentary, ambulatory, lifestyle, and exercise activities (14 total, 10 categories) for 3-10 minutes each in a 90-minute semi-structured laboratory protocol. Artificial neural networks (ANNs) were developed for four accelerometers (right hip, both wrists, and right thigh,) to predict individual activities and activity categories, with direct observation (DO) as criterion. The wrist-mounted accelerometers achieved the highest accuracy for individual activities (80.9%-81.1%) and activity categories (86.6%-86.7%); accuracy was not different between wrists. The hip-mounted accelerometer had the lowest accuracy (66.2% individual activities, 72.5% activity categories); thigh-mounted accelerometer accuracy (71.4% individual activities, 84.0% activity categories) fell between the wrist-and hip-mounted accelerometers. ANNs developed for accelerometers worn on the wrists and thigh provided high accuracy for activity type prediction and represent a potential approach to physical activity (PA) assessment.

During adolescence, sedentary time tends to increase while physical activity (PA) declines. Previous self-report measures of adolescent’s activities can be prone to recall bias and are limited by their inability to detect activities that occur simultaneously. Real-time measurements (Experienced Sampling Method, ESM), may address these limitations by assessing activities as they are taking place. PURPOSE: The primary purpose of this study was to examine the feasibility and acceptability for adolescents to track their after-school PA and sedentary time through a mobile application (app) using ESM. The secondary purpose was to describe adolescents’ after-school behaviors. METHODS: Participants completed surveys on type and amount of time spent in sedentary behaviors and PA during the previous 30 minutes using an app on their mobile device. The surveys occurred randomly, six times, from 3-7pm, for three days (18 surveys total). Participants also completed a 15-minute follow-up interview over the phone to assess ease and likeability of using the app on 5-pt scales (1= very easy, 5= very hard; 1= disliked a lot, 5= liked a lot). RESULTS: A total of 12 adolescents, 11-15 years old, completed the study. The number of surveys answered among participants ranged from 2 to 17 over the three-day period, and the average number of surveys taken per day was 3.2 (53% daily completion rate). Common reasons for not answering surveys were sports practices, traveling without WiFi access, or simply forgetting. Participants indicated the app was very easy to use (mean=1.4), and that they liked using the app (mean=3.8). No participants said they disliked using the app. Adolescents most frequently reported engaging in “mobile device use” (31.7%), “reading, writing, drawing, or doing homework” (31.7%), and “other activities” (30.1%). While it was not the most frequently reported activity, adolescents spent the greatest amount of time doing “other activities” (6.9 hours). CONCLUSIONS: While the mobile app appears to be appealing and easy to use among adolescents, the feasibility was low. Some of the reasons for low feasibility are addressable (forgetting) while others are not (sports practices). The ESM methodology may need to be modified in order to improve feasibility and to account for these periods of unavailability in adolescents.

PURPOSES:(1) Develop artificial neural network (ANN) models for wrist accelerometer data which can predict energy expenditure (EE) using data collected from either wrist. (2) Develop ANNs for detecting the wrist on which the accelerometer was worn. Forty-four adults wore GENEActiv accelerometers on the left and right wrists and a portable metabolic analyzer while participating in a 90 min semi-structured activity protocol. Participants performed 14 sedentary, lifestyle, exercise, and ambulatory activities and were allowed to choose activity order, duration, and intensity. ANNs were created to predict EE and wrist detection using a leave-one-out cross-validation. In total, 12 combinations of feature sets (mean and variance of raw, vector magnitude, and absolute value data), training methods (left- and right- wrist), and testing methods (left- and right-wrist data) were used to develop EE prediction ANNs. Accuracy of the ANNs was evaluated using correlations, root mean square error (RMSE), and bias, using metabolic analyzer data as the criterion for EE. ANNs using raw data from the same wrist (e.g. EE predicted from right wrist ANNs using accelerometer data from right wrist) had the highest accuracy for EE prediction (r  =  0.84, RMSE  =  1.25-1.26 METs); conversely, opposite-wrist prediction accuracy (e.g. EE predicted from right wrist ANNs using accelerometer data from left wrist) was lower (r  =  0.60-0.64, RMSE  =  1.93-2.01 METs). Preprocessing into absolute values prior to ANN development allowed for, high EE prediction accuracy, with no difference in accuracy for same- versus opposite-wrist prediction (r  =  0.80-0.83, RMSE  =  1.30-1.49 METs). Wrist detection ANNs correctly determined wrist placement 100% of the time. Highly accurate, wrist-independent EE prediction ANNs were developed by computing absolute values of raw acceleration data prior to ANN development. This method provides a potential approach for advancing predictive accuracy of wrist-worn accelerometers.

Background: Recent evidence suggests that physical activity (PA) and sedentary behavior (SB) exert independent effects on health. Therefore, measurement methods that can accurately assess both constructs are needed. Objective: To compare the accuracy of accelerometers placed on the hip, thigh, and wrists, coupled with machine learning models, for measurement of PA intensity category (SB, light-intensity PA [LPA], and moderate- to vigorous-intensity PA [MVPA]) and breaks in SB. Methods: Forty young adults (21 female; age 22.0 ± 4.2 years) participated in a 90-minute semi-structured protocol, performing 13 activities (three sedentary, 10 non-sedentary) for 3–10 minutes each. Participants chose activity order, duration, and intensity. Direct observation (DO) was used as a criterion measure of PA intensity category, and transitions from SB to a non-sedentary activity were breaks in SB. Participants wore four accelerometers (right hip, right thigh, and both wrists), and a machine learning model was created for each accelerometer to predict PA intensity category. Sensitivity and specificity for PA intensity category classification were calculated and compared across accelerometers using repeated measures analysis of variance, and the number of breaks in SB was compared using repeated measures analysis of variance. Results: Sensitivity and specificity values for the thigh-worn accelerometer were higher than for wrist- or hip-worn accelerometers, > 99% for all PA intensity categories. Sensitivity and specificity for the hip-worn accelerometer were 87–95% and 93–97%. The left wrist-worn accelerometer had sensitivities and specificities of > 97% for SB and LPA and 91–95% for MVPA, whereas the right wrist-worn accelerometer had sensitivities and specificities of 93–99% for SB and LPA but 67–84% for MVPA. The thigh-worn accelerometer had high accuracy for breaks in SB; all other accelerometers overestimated breaks in SB. Conclusion: Coupled with machine learning modeling, the thigh-worn accelerometer should be considered when objectively assessing PA and SB.

An inclinometer-based accelerometer (PAL) worn on the thigh, coupled with proprietary software, may provide accurate estimates of sedentary time. However, its accuracy for estimating other physical activity (PA) intensities and energy expenditure (EE) is unknown. PURPOSE: To evaluate accuracy of the PAL software for prediction of time spent in PA intensities [sedentary, light, and moderate-to-vigorous (MVPA)] and EE and compare its accuracy to that of a machine learning model (ANN) developed from raw PAL data. METHODS: Participants (n=39 [19 male]; age=22.1±4.3) completed a 90-min, semi-structured protocol in which they performed 13 activities for 3-10 min each, choosing activity order, duration, and intensity. Participants wore a PAL accelerometer on the right thigh and a portable metabolic analyzer (OXY). Time spent in sedentary, light, and MVPA was determined using MET values of ≤1.5, 1.6-2.9, and ≥3.0, respectively, calculated from OXY. Estimated times in each PA intensity from the PAL software and ANN were compared with OXY using difference scores and 95% confidence intervals; non-overlap of confidence intervals with zero indicated significant differences from OXY. Window-by-window EE prediction was assessed using correlations and root mean square error (RMSE). RESULTS: PAL software-predicted sedentary time was not different from OXY (-1.6 min; 95% CI: -3.6 - 0.4 min), but light PA was over-predicted (6.2 min; 95% CI: 4.1 - 8.3 min) and MVPA was under-predicted (-4.6 min; 95% CI: -6.5 - -2.7 min). ANN-predicted sedentary time and light PA were not different from OXY, (-0.7 min, 95% CI: -2.3 - 0.8 min; -0.9 min; 95% CI: -2.8 - 1.1 min, respectively), but MVPA was over-predicted (1.6 min; 95% CI: 0.1 - 3.2 min). For EE prediction, the PAL software had lower correlations (r=0.76 vs. r =0.89) and higher RMSE (1.74 vs. 1.07 METs) than the ANN. Under-prediction by the PAL software was more pronounced at intensities >5.0 METs. CONCLUSIONS: The PAL software distinguished between sedentary and non-sedentary activities but had high error for EE prediction, especially for higher-intensity PA. The ANN had high accuracy for prediction of sedentary and light PA and EE prediction, indicating strong potential for raw data from a thigh-worn PAL to be used in PA assessment. Supported by Blue Cross Blue Shield of Michigan Foundation.

Children are recommended to participate in at least 60 minutes of physical activity (PA) each day, consume at least 1.5 cups/day of fruits and 2 cups/day of vegetables, and limit screen time (ST) to 2 hours/day. While healthy behaviors tend to cluster in adults, it is unknown whether these health behaviors are inter-related among children. PURPOSE: To determine relations among PA, fruit and vegetable consumption, and ST among low income, urban children. METHODS: Participants included 2,859 4th-6th graders from elementary schools located around Flint, Michigan. PA was measured retrospectively over the past week using the Physical Activity Questionnaire for Older Children (PAQ-C, scored 1 (low activity) -5 (high activity)). Fruit and vegetable consumption were reported as 0, 1, 2, or 3/more times on the previous day. Hours spent watching TV, playing video games, and using a computer (non-academic purposes) were separately reported for a typical school day and weekend day, and then summed to calculate hours/day of total ST for a school day and weekend day. Pearson correlations evaluated relations among PAQ-C scores, fruit and vegetable consumption, and ST for the whole sample and stratified by gender. RESULTS: Complete data were available for 372 children (49.5% girls). Average PAQ-C scores were 3.1 ± 0.7, fruit and vegetable consumption were 0 (34.8%, 22.0%), 1 (30.0%, 32.2%), 2 (18.4%, 16.0%), and 3 or more (8.2%, 20.2%) servings per day, respectively, and average ST was 4.9 ± 3.2 hours/school day and 8.0 ± 4.9 hours/weekend day. PA was not associated with other health behaviors. Fruit and vegetable consumption were positively correlated (r=0.33, p=0.001). Fruit consumption was inversely correlated with total ST on weekend days (r=−0.17, p=0.001). When stratified by gender, fruit consumption remained inversely associated with weekend ST in boys (r=−0.23, p=0.002), but not in girls (r= −0.09, p=0.214). CONCLUSIONS: Fruit and vegetable intake were significantly and moderately associated. A weak, but significant, inverse correlation was found between fruit consumption and total ST on weekend days. These data suggest that PA, fruit and vegetable intake, and ST are independent behaviors among children; however, further study with more detailed measures is warranted.

PURPOSE: A number of studies have suggested that lifestyle factors during pregnancy, including diet and cigarette smoking, may influence the long term health of offspring by altering the epigenome. Whether maternal leisure-time physical activity (LTPA) during pregnancy might have a similar effect is unknown. The purpose of this study was to determine the relationship between maternal LTPA during pregnancy and offspring DNA methylation, both globally and in metabolism-related candidate genes. METHODS: The Archive for Research on Child Health (ARCH) study has been ongoing since 2008. At enrollment, subjects’ demographic information and self-reported LTPA during pregnancy were determined via questionnaire. High active subjects (averaged 637.5 minutes per week of moderate or vigorous physical activity (MVPA); n = 14) were matched by age and race to low active subjects (averaged 60.4 minutes per week MVPA; n = 28). The child’s blood spot was obtained at birth. Following DNA isolation and bisulfite treatment, pyrosequencing was used to determine methylation levels of long interspersed nucleotide elements (LINE-1) (global methylation) and PPARγ, PGC1-α, IGF2, PDK4, and TCF7L2. RESULTS: We found no differences between high active and low active groups for LINE-1 methylation, an indicator of global methylation. The only differences in candidate gene methylation between the two groups were at two CpG sites in the P2 promoter of IGF2. In both instances the low active group had significantly higher DNA methylation than the high active group (57.95% ±5.61% versus 52.98% ±6.67%; p = 0.020 and 72.35% ± 2.89% versus 69.86% ±3.95%; p = 0.047). Mean methylation over eight P2 promoter sites for IGF2 was significantly higher in the low active group (74.70% ±2.25% versus 72.83% ±2.85%; p = 0.045). CONCLUSION: Our results suggest no effect of maternal LTPA on global and candidate gene methylation profiles, with the exception of IGF2 methylation. IGF2 differences were in the opposite direction of what was expected; further research is needed to understand implications of these results. Possible reasons for the mostly null findings include our choice of biologic sample (blood spots), lack of transfer of methylation profile from mother to offspring, measurement of methylation rather than gene expression, and inadequate LTPA stimulus.

To describe associations between maternal lipids and birthweight and to determine whether pre-pregnancy body mass index (BMI) modifies these associations. Cohort study. Multiple communities in Michigan, USA. Participants were a sub-cohort of women from the multi-community Pregnancy Outcomes and Community Health (POUCH) study (1998–2004). Maternal total cholesterol, high-density lipoprotein (HDLc), and low-density lipoprotein (LDLc) cholesterol, and triglycerides were assessed at 16–27 weeks' gestation. Women were classified as having normal (<25 kg/m 2 ) or overweight/obese (≥25 kg/m 2 ) pre-pregnancy BMI. Sex- and gestational-age-specific body weight z -score (BWz). Regression models examined associations among lipids (low: 1st quartile, referent: middle quartiles, high: 4th quartile) and BWz for the total sample and stratified by pre-pregnancy BMI. In adjusted analyses ( n  = 1207), low HDLc was associated with lower BWz (β = −0.23, 95% CI −0.40 to −0.06), whereas high triglycerides were associated with higher BWz (β = 0.23, 95% CI 0.06–0.41). Once stratified by pre-pregnancy BMI, low total cholesterol was associated with lower BWz in normal BMI women (β = −0.25, 95% CI −0.47 to −0.03), whereas in overweight/obese BMI women, high HDLc was inversely (β = −0.29, 95% CI −0.54 to −0.04) and high triglycerides were directly associated with BWz (β = 0.32, 95% CI 0.07–0.54). Removing women with gestational diabetes/hypertensive disorders did not alter the results. The associations between maternal lipids and BWz vary by lipid measure and pre-pregnancy BMI. Future work should examine whether lipids and pre-pregnancy BMI make unique contributions to the fetal programming of disease.

Background: Pregnancy risk perceptions and physical activity efficacy beliefs may facilitate or impede pregnancy leisure-time physical activity (LTPA). We examined the separate and joint influence of these variables on LTPA behavior among pregnant women. Methods: Pregnant women (n = 302) completed a survey containing questions on LTPA efficacy beliefs and behavior, as well as pregnancy risk perceptions with respect to the health of the unborn baby. As stipulated by the Risk Perception Attitude (RPA) Framework, 4 attitudinal groups were created: Responsive (High Risk+High Efficacy), Proactive (Low+High), Avoidant (High+Low), and Indifferent (Low+Low). Moderate LTPA and vigorous LTPA were dichotomized for study analyses. Results: A total of 82 women (27.2%) met the moderate physical activity guideline and 90 women (30.1%) performed any vigorous LTPA. Responsive and proactive pregnant women (those with high efficacy) were most likely to meet the moderate guideline and participate in vigorous LTPA. Hierarchical logistic regression did not reveal an interactive effect of pregnancy risk perceptions and LTPA efficacy beliefs for meeting the moderate guideline (OR = 0.94, 95% CI = 0.66-1.36) or any vigorous LTPA participation (OR = 1.41, 95% CI = 0.86-2.29). Conclusions: LTPA efficacy beliefs appear important in facilitating greater levels of pregnancy LTPA. Significant interactive effects between pregnancy risk perceptions and LTPA efficacy beliefs were not found.

Preeclampsia is diagnosed in women presenting with new onset hypertension accompanied by proteinuria or other signs of severe organ dysfunction in the second half of pregnancy. Preeclampsia risk is increased 2- to 4-fold among women with type 1 or type 2 diabetes. The limited number of pregnant women with preexisting diabetes and the difficulties associated with diagnosing preeclampsia in women with proteinuria prior to pregnancy are significant barriers to research in this high-risk population. Gestational diabetes mellitus (GDM) also increases preeclampsia risk, although it is unclear whether these two conditions share a common pathophysiological pathway. Nondiabetic women who have had preeclampsia are more likely to develop type 2 diabetes later in life. Among women with type 1 diabetes, a history of preeclampsia is associated with an increased risk of retinopathy and nephropathy. More research examining the pathophysiology, treatment, and the long-term health implications of preeclampsia among women with preexisting and gestational diabetes is needed.

PurposeThe purpose of this study was to develop, validate, and compare energy expenditure (EE) prediction models for accelerometers placed on the hip, thigh, and wrists using simple accelerometer features as input variables in EE prediction models.MethodsForty-four healthy adults participated in a 90-min, semistructured, simulated free-living activity protocol. During the protocol, participants engaged in 14 different sedentary, ambulatory, lifestyle, and exercise activities for 3-10 min each. Participants chose the order, duration, and intensity of activities. Four accelerometers were worn (right hip, right thigh, as well as right and left wrists) to predict EE compared with that measured by the criterion measure (portable metabolic analyzer). Artificial neural networks (ANNs) were created to predict EE from each accelerometer using a leave-one-out cross-validation approach. Accuracy of the ANN was evaluated using Pearson correlations, root mean square error, and bias. Several ANNs were developed using different input features to determine those most relevant for use in the models.ResultsThe ANNs for all four accelerometers achieved high measurement accuracy, with correlations of r > 0.80 for predicting EE. The thigh accelerometer provided the highest overall accuracy (r = 0.90) and lowest root mean square error (1.04 METs), and the differences between the thigh and the other monitors were more pronounced when fewer input variables were used in the predictive models. None of the predictive models had an overall bias for prediction of EE.ConclusionsA single accelerometer placed on the thigh provided the highest accuracy for EE prediction, although monitors worn on the wrists or hip can also be used with high measurement accuracy.

ObjectiveTo describe associations between maternal lipids and birthweight and to determine whether pre-pregnancy body mass index (BMI) modifies these associations.DesignCohort study.SettingMultiple communities in Michigan, USA.PopulationParticipants were a sub-cohort of women from the multi-community Pregnancy Outcomes and Community Health (POUCH) study (1998-2004).MethodsMaternal total cholesterol, high-density lipoprotein (HDLc), and low-density lipoprotein (LDLc) cholesterol, and triglycerides were assessed at 16-27weeks' gestation. Women were classified as having normal (<25kg/m(2)) or overweight/obese (25kg/m(2)) pre-pregnancy BMI.Main outcome measuresSex- and gestational-age-specific body weight z-score (BWz).ResultsRegression models examined associations among lipids (low: 1st quartile, referent: middle quartiles, high: 4th quartile) and BWz for the total sample and stratified by pre-pregnancy BMI. In adjusted analyses (n=1207), low HDLc was associated with lower BWz (=-0.23, 95% CI -0.40 to -0.06), whereas high triglycerides were associated with higher BWz (=0.23, 95% CI 0.06-0.41). Once stratified by pre-pregnancy BMI, low total cholesterol was associated with lower BWz in normal BMI women (=-0.25, 95% CI -0.47 to -0.03), whereas in overweight/obese BMI women, high HDLc was inversely (=-0.29, 95% CI -0.54 to -0.04) and high triglycerides were directly associated with BWz (=0.32, 95% CI 0.07-0.54). Removing women with gestational diabetes/hypertensive disorders did not alter the results.ConclusionsThe associations between maternal lipids and BWz vary by lipid measure and pre-pregnancy BMI. Future work should examine whether lipids and pre-pregnancy BMI make unique contributions to the fetal programming of disease.

Exercise physical activity (ExPA) during pregnancy has been consistently related to healthier birth weight (BW); however, associations between BW and other domains of physical activity, such as household (HPA) and job-related (JPA), are less well understood. PURPOSE: To determine the associations among domains of maternal physical activity (ExPA, HPA and JPA) in the 2nd and 3rd trimester and birth outcomes such as: birth weight (BW) and gestational age (GA) in low-income pregnant women. METHODS: Pregnant women in the 2nd and 3rd trimesters were recruited from an OBGYN clinic and completed the validated Pregnancy Physical Activity Questionnaire (PPAQ). MET-min/wk spent performing JPA, HPA, and ExPA were calculated. PA variables were categorized for JPA (Any/None), HPA (High/Low based on median splits), and ExPA (Any/None). Medical record abstraction provided infant BW (g) and GA (wks). All data analyses were conducted separately by trimester. Spearman correlation examined the associations among PA domains, BW and GA. Any significant correlations were additionally examined using independent t-test for mean differences in birth outcomes variables by categorical PA variables. RESULTS: Seventy-six women with birth outcome data were included in the analysis. Participant’s mean age was 25 years (±4.9) and most were on Medicaid (85.5%). There were no significant associations between 2nd trimester PA and birth outcomes. Third trimester JPA was significantly correlated with BW (rs=0.38, p=0.038). No significant relations were found between other 3rd trimester PA variables and birth outcomes. Subsequent t-test results showed that women reporting any JPA had significantly higher BW (mean ± standard deviation: 3493±259g) compared to women reporting no JPA (3195±452g, p=0.038). CONCLUSIONS: We found that 2nd trimester PA was not associated with birth outcomes, while only 3rd trimester JPA was directly associated with BW, and this result appears to be driven by women reporting any vs. no JPA. Low rates of PA, particularly ExPA, in this sample may have influenced results. Longitudinal studies are needed to evaluate the associations among the types and intensities of PA and in mid- to late gestation with birth outcomes in low-income women.