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Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity

Journal of Behavioral Medicine volume 44, pages 605–621 (2021)Cite this article

Abstract

Interventions have modest impact on reducing excessive gestational weight gain (GWG) in pregnant women with overweight/obesity. This two-arm feasibility randomized control trial tested delivery of and compliance with an intervention using adapted dosages to regulate GWG, and examined pre-post change in GWG and secondary outcomes (physical activity: PA, energy intake: EI, theories of planned behavior/self-regulation constructs) compared to a usual care group. Pregnant women with overweight/obesity (N = 31) were randomized to a usual care control group or usual care + intervention group from 8 to 2 weeks gestation and completed the intervention through 36 weeks gestation. Intervention women received weekly evidence-based education/counseling (e.g., GWG, PA, EI) delivered by a registered dietitian in a 60-min face-to-face session. GWG was monitored weekly; women within weight goals continued with education while women exceeding goals received more intensive dosages (e.g., additional hands-on EI/PA sessions). All participants used mHealth tools to complete daily measures of weight (Wi-Fi scale) and PA (activity monitor), weekly evaluation of diet quality (MyFitnessPal app), and weekly/monthly online surveys of motivational determinants/self-regulation. Daily EI was estimated with a validated back-calculation method as a function of maternal weight, PA, and resting metabolic rate. Sixty-five percent of eligible women were randomized; study completion was 87%; 10% partially completed the study and drop-out was 3%. Compliance with using the mHealth tools for intensive data collection ranged from 77 to 97%; intervention women attended > 90% education/counseling sessions, and 68–93% dosage step-up sessions. The intervention group (6.9 kg) had 21% lower GWG than controls (8.8 kg) although this difference was not significant. Exploratory analyses also showed the intervention group had significantly lower EI kcals at post-intervention than controls. A theoretical, adaptive intervention with varied dosages to regulate GWG is feasible to deliver to pregnant women with overweight/obesity.

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References

  • ActiGraph, LLC. Retrieved November 17, 2019, from https://www.actigraphcorp.com/

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision, 50, 179–211

    Article  Google Scholar 

  • Almirall, D., Nahum-Shani, I., Sherwood, N. E., & Murphy, S. A. (2014). Introduction to SMART designs for the development of adaptive interventions: with application to weight loss research. Translational Behavioral Medicine, 4, 260–274

    Article  PubMed  PubMed Central  Google Scholar 

  • American College of Obstetrics and Gynecologists (ACOG). (2015). ACOG committee opinion No. 650: Physical activity and exercise during pregnancy and the postpartum period. Obstetrics and Gynecology, 126, e135–e142

    Article  Google Scholar 

  • Blanchard, C. M., Fisher, J., Sparling, P. B., Shanks, T. H., Nehl, E., Rhodes, R. E., Courneya, K. S., & Baker, F. (2009). Understanding adherence to 5 servings of fruits and vegetables per day: A theory of planned behavior perspective. Journal of Nutrition Education and Behavior, 41, 3–10

    Article  PubMed  PubMed Central  Google Scholar 

  • Butte, N. F., Ellis, K. J., Wong, W. W., Hopkinson, J. M., & Smith, E. O. (2003). Composition of gestational weight gain impacts maternal fat retention and infant birth weight. American Journal of Obstetrics and Gynecology, 189, 1423–1432

    Article  PubMed  Google Scholar 

  • Butte, N. F., Wong, W. W., Treuth, M. S., Ellis, K. J., & O’Brian Smith, E. (2004). Energy requirements during pregnancy based on total energy expenditure and energy deposition. American Journal of Clinical Nutrition, 79, 1078–1087

    Article  CAS  PubMed  Google Scholar 

  • Carpenter, R. E., Emery, S. J., Rassi, D., Uzun, O., & Lewis, M. J. (2016). Recruitment of pregnant women to an exercise-intervention study. Randomized Controlled Trial, 36, 200–207

    CAS  Google Scholar 

  • Carver, C., & Scheier, M. F. (1998). On the self-regulation of behavior. Cambridge University Press.

  • Chang, M., Nitzke, S., Buist, D., Cain, D., Horning, S., & Eghtedary, K. (2015). I am pregnant and want to do better but I can’t: Focus groups with low income overweight and obese pregnant women. Maternal and Child Health Journal, 19, 1060–1070

    Article  PubMed  Google Scholar 

  • Coleman-Phox, K., Laraia, B., Alder, N., Vieten, C., Thomas, M., & Epel, E. (2013). Recruitment and retention of pregnant women for a behavioral intervention: Lessons from the maternal adiposity, metabolism, and stress (MAMAS) study. Preventing Chronic Disease, 10, e31

    Article  PubMed Central  Google Scholar 

  • Collins, L. M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: The multiphase optimization strategy (MOST). Springer.

  • Diabetes Prevention Program (DPP) Research Group. (2002). The diabetes prevention program (DPP) description of lifestyle intervention. Diabetes Care, 25, 2165–2171

    Article  Google Scholar 

  • Dong, Y., Deshpande, S., Rivera, D. E., Downs, D. S., & Savage, J. S. (2014). Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. In Proceedings of the American control conference (pp. 4198–4203).

  • Dong, Y., Rivera, D. E., Downs, D. S., Savage, J. S., Thomas, D. M., & Collins, L. M. (2013). Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. In Proceedings of the American control conference (pp. 1970–1975).

  • Dong, Y., Rivera, D. E., Thomas, D. M., Navarro-Barrientos, J. E., Symons Downs, D., Savage, J. S., & Collins, L. M. (2012). A dynamical systems model for improving gestational weight gain behavioral interventions. In Proceedings of the American Control Conference (pp. 4059–4064).

  • Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015). Systematic review of the validity and reliability of consumer-wearable activity trackers. International Journal of Behavioral Nutrition and Physical Activity, 12, 159

    Article  PubMed  PubMed Central  Google Scholar 

  • Farpour-Lambert, N. J., Ells, L. J., Martinez de Tejada, B., & Scott, C. (2018). Obesity and weight gain in pregnancy and postpartum: An evidence review of lifestyle interventions to inform maternal and child health policies. Frontiers in Endocrinology, 9, 546. https://doi.org/10.3389/fendo.2018.00546

    Article  PubMed  PubMed Central  Google Scholar 

  • Ferguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015). The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: A cross-sectional study. International Journal of Behavioral Nutrition and Physical Activity, 12, 42

    Article  PubMed  PubMed Central  Google Scholar 

  • Fitbit Inc. Retrieved November 17, 2019, from https://fitbit.com/aria

  • Gollwitzer, P. M., & Sheeran, P. (2006). Implementation intentions and goal achievement: A meta-analysis of effects and processes. Advances in Experimental Psychology, 38, 69–119

    Article  Google Scholar 

  • Gunderson, E. P., & Abrams, B. (1999). Epidemiology of gestational weight gain and body weight changes after pregnancy. Epidemiologic Reviews, 21, 261–275

    Article  CAS  PubMed  Google Scholar 

  • Guo, P., Rivera, D. E., Pauley, A. M., Leonard, K. S., Savage, J. S., & Symons Downs, D. (2018). A “Model-on-Demand” methodology for energy intake estimation to improve gestational weight control interventions. In Proceedings of the IFAC World Congress (Vol. 51, pp. 144–149).

  • Guo, P., Rivera, D. E., Savage, J. S., Hohman, E. E., Pauley, A. M., Leonard, K. S., & Symons Downs, D. (2020). System identification approaches for energy intake estimation: Enhancing interventions for managing gestational weight gain. IEEE Transactions on Control Systems Technology, 28, 63–78. https://doi.org/10.1109/TCST.2018.2871871

  • Guo, P., Rivera, D. E., Symons Downs, D., & Savage, J. S. (2016). Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. In Proceedings of the American control conference (pp. 1271–1276). https://doi.org/10.1109/ACC.2016.7525092

  • Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: U.S., 2015–2016. National Center for Health Statistics Data Brief, 288, 1–8

    Google Scholar 

  • Hausenblas, H. A., & Symons Downs, D. (2004). Prospective examination of the Theory of Planned Behavior applied to physical activity behavior during women’s first trimester of pregnancy. Journal of Reproductive and Infant Psychology, 22, 199–210

    Article  Google Scholar 

  • Harris, P. A., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. G. (2009). Research electronic data capture (REDCap): A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42, 377–381

    Article  PubMed  Google Scholar 

  • Hekler, E. B., Rivera, D. E., Martin, C. A., Phatak, S. S., Freigoun, M. T., Korinek, E., Klasnja, P., Adams, M. A., & Buman, M. P. (2018). Tutorial for using control systems engineering to optimize adaptive mobile health interventions. Journal of Medical Internet Research, 20, e214. https://doi.org/10.2196/jmir.8622

    Article  PubMed  PubMed Central  Google Scholar 

  • Herring, S. J., Cruice, J. F., Bennett, G. G., Rose, M. Z., Davey, A., & Foster, G. D. (2016). Preventing excessive gestational weight gain among African American women: A randomized clinical trial. Obesity (Silver Spring), 24, 30–36

    Article  Google Scholar 

  • Hood, K. M., Marr, C., Kirk-Sorrow, J., Farmer, J., IV., Lee, M., & Kern, M. (2019). Validity and reliability of a Wi-Fi smart scale to estimated body composition. Health and Technology, 9, 839–856

    Article  Google Scholar 

  • James, B. L., Roe, L. S., Loken, E., & Rolls, B. J. (2018). Early predictors of weight loss in a 1-year behavioural weight-loss programme. Obesity Science and Practice, 4, 20–28

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Julios, S. A. (2005). Sample size of 12 per group rule of thumb for a pilot study. Pharmaceutical Statistics, 4, 287–291

    Article  Google Scholar 

  • Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983–997

    Article  CAS  PubMed  Google Scholar 

  • Leonard, K. S., Guo, P., Pauley, A. M., Hohman, E. E., McNitt, K. M., Rivera, D. E., Savage, J. S., & Symons Downs D. (2019). Accuracy of back-calculated energy intake: Comparing estimated vs. measured resting metabolic rate. In Poster presentation at The Obesity Society, Obesity Week Annual Meeting, 3-7 November 2019, Las Vegas, Nevada.

  • Lindsay, K. L., Heneghan, C., McNulty, B., Brennan, L., & McAuliffe, F. M. (2015). Lifestyle and dietary habits of an obese pregnant cohort. Maternal and Child Health Journal, 19, 25–32

    Article  PubMed  Google Scholar 

  • Meldrum, D. R., Morris, M. A., & Gambone, J. C. (2017). Obesity pandemic: causes, consequences, and solutions: But do we have the will? Fertility and Sterility, 107, 833–839

    Article  PubMed  Google Scholar 

  • McClung, H. L., Ptomey, L. T., Shook, R. P., Aggarwal, A., Gorczyca, A. M., Sazonov, E. S., Becofsky, K., Weiss, R., & Das, S. K. (2018). Dietary intake and physical activity assessment: Current tools, techniques, and technologies for use in adult populations. American Journal of Preventive Medicine, 55, e93-104

    Article  PubMed  Google Scholar 

  • Millar, B. M. (2017). Clocking self-regulation: Why time of day matters for health psychology. Health Psychology Review, 11, 345–357

    Article  PubMed  Google Scholar 

  • Murnaghan, D. A., Blanchard, C. M., Rodgers, W. M., LaRosa, J. N., MacQuarrie, C. R., MacLellan, D. L., & Gray, B. J. (2010). Predictors of physical activity, healthy eating and being smoke-free in teens: A theory of planned behaviour approach. Psychology and Health, 25, 925–941

    Article  PubMed  Google Scholar 

  • Nagourney, E. M., Goodman, D., Lam, Y., Hurley, K. M., Henderson, J., & Surkan, P. J. (2019). Obese women’s perceptions of weight gain during pregnancy: A theory-based analysis. Public Health Nutrition, 22, 2228–2236. https://doi.org/10.1017/S1368980019000703

    Article  PubMed  Google Scholar 

  • Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52, 446–462

    Article  PubMed  Google Scholar 

  • Pauley, A. M., Hohman, E., Savage, J. S., Rivera, D. E., Guo, P., Leonard, K. S., & Symons Downs, D. (2018). Gestational weight gain intervention impacts determinants of healthy eating and exercise in overweight/obese pregnant women. Journal of Obesity, 6469170.

  • Peaceman, A. M., Clifton, R. G., Phelan, S., Gallagher, D., Evans, M., Redman, L. M., Knowler, W. C., Joshipura, K., Haire-Joshu, D., Yanovski, S. Z., Couch, K. A., Drews, K. L., Franks, P. W., Klein, S., Martin, C. K., Pi-Sunyer, X., Thom, E. A., Van Horn, L., Wing, R. R., …, LIFE-Moms Research Group. (2018). Lifestyle interventions limit gestational weight gain in women with overweight or obesity: LIFE-Moms prospective meta-analysis. Obesity, 26, 1396–1404.

  • Phelan, S., Phipps, M. G., Abrams, B., Darroch, F., Schaffner, A., & Wing, R. R. (2011). Randomized trial of a behavioral intervention to prevent excessive gestational weight gain: The fit for delivery study. The American Journal of Clinical Nutrition, 93, 772–779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Phelan, S., Wing, R. R., Brannen, A., McHugh, A., Hagobian, T. A., Schaffner, A., Jelalian, E., Hart, C. N., Scholl, T. O., Munoz-Christian, K., Yin, E., Phipps, M. G., Keadle, S., & Abrams, B. (2018). Randomized controlled clinical trial of behavioral lifestyle intervention with partial meal replacement to reduce excessive gestational weight gain. American Journal of Clinical Nutrition, 107, 183–194

    Article  PubMed  PubMed Central  Google Scholar 

  • Rasmussen, K. M., & Yaktine, A. L. (Eds.). (2009). Weight gain during pregnancy: Reexamining the guidelines. National Academies Press (US).

  • Rivera, D. E., Heckler, E., Savage, J. S., & Symons Downs, D. (2018). Intensively adaptive interventions using control systems engineering: Two illustrative examples. In L. M. Collins & K. Kugler (Eds.), Optimization of Behavioral, biobehavioral, and biomedical interventions (pp. 121–173). Cham, Switzerland: Springer.

  • Rivera, D. E., Pew, M. D., & Collins, L. M. (2007). Using engineering control principles to inform the design of adaptive interventions: A conceptual introduction. Drug and Alcohol Dependence, 88, S31–S40

    Article  PubMed  Google Scholar 

  • Sniehotta, F. F., Schwarzer, R., Scholz, U., & Schüz, B. (2005). Action planning and coping planning for long-term lifestyle change: Theory and assessment. European Journal of Social Psychology, 35, 565–576

    Article  Google Scholar 

  • Symons Downs, D., & Hausenblas, H. A. (2003). Exercising for two: Examining pregnant women’s second trimester physical activity intention and behavior using the framework of the theory of planned behavior. Women’s Health Issues, 13, 222–228

    Article  Google Scholar 

  • Symons Downs, D., & Hausenblas, H. A. (2007). Pregnant women’s third trimester physical activity behaviors, body mass index, and pregnancy outcomes. Psychology and Health, 22, 545–549

    Article  Google Scholar 

  • Symons Downs, D., & Singer, R. N. (2004). Goal setting and implementation intentions: Preliminary support for increasing exercise behavior. Journal of Human Movement Studies, 45, 419–432

    Google Scholar 

  • Symons Downs, D., DiNallo, J. M., Birch, L. L., Paul, I. M., & Ulbrecht, J. S. (2017a). Randomized face-to-face vs. home exercise interventions in pregnant women with gestational diabetes. Psychology of Sport and Exercise, 30, 73–81.

  • Symons Downs, D., Feinberg, M., Hillemeier, M. H., Weisman, C. S., Chase, G. A., Chuang, C. H., Parrott, R., & Francis, L. A. (2009). Design of the Central Pennsylvania Women’s Health Study (CePAWHS) strong healthy women intervention: Improving preconceptional health. Maternal and Child Health Journal, 13, 18–28.

  • Symons Downs, D., Nigg, C. R., Hausenblas, H. A., & Rauff, E. L. (2013). Understanding of why people change physical activity behavior. In C. R. Nigg (Ed.), American college of sports medicine behavioral aspects of exercise.

  • Symons Downs, D., Savage, J. S., & Rauff, E. L. (2014). Falling short of guidelines? Nutrition and weight gain knowledge in pregnancy. Journal of Women’s Health, 3, 1000184. https://doi.org/10.4172/2167-0420.1000184

  • Symons Downs, D., Savage, J. S., Rivera, D. E., Pauley, A. M., Hess, L., & Guo, P. (2016). Design and preliminary findings of an individually-tailored “just-in-time” adaptive intervention to manage prenatal weight gain. Annals of Behavioral Medicine, 50, S162.

    Google Scholar 

  • Symons Downs, D., Savage, J. S., Rivera, D. E., Leonard, K. S., Pauley, A. M., Hohman, E. E., & Guo, P. (2017b). Using mHealth and eHealth methods to promote weight control among women. Annals of Behavioral Medicine, 51(S1), 1587–1588.

    Google Scholar 

  • Symons Downs, D., Savage, J. S., Rivera, D. E., Leonard, K. S., Pauley, A. M., Hohman, E. E., & Guo, P. (2017c). Influence of a feasibility study on the design of an individually-tailored, adaptive intervention to manage weight in overweight and obese pregnant women. Annals of Behavioral Medicine, 51(S1), 1451–1452.

    Google Scholar 

  • Symons Downs, D., Savage, J. S., Rivera, D. E., Smyth, J. M., Rolls, B. J., Hohman, E. E., McNitt, K. M., Kunselman, A. R., Stetter, C., Pauley, A. M., Leonard, K. S., & Guo, P. (2018). Individually tailored, adaptive intervention to manage gestational weight gain: Protocol for a randomized controlled trial in women with overweight and obesity. JMIR Research Protocols, 7, e150. https://doi.org/10.2196/resprot.9220

    Article  PubMed  PubMed Central  Google Scholar 

  • Symons Downs, D., Smyth, J. M., Heron, K. E., Feinberg, M. E., Hillemeier, M., & Materia, F. T. (2019). Beliefs about using smartphones for health behavior change: An elicitation study with overweight and obese rural women. Journal of Technology in Behavioral Science, 4, 33–41.

    Article  Google Scholar 

  • Thabane, L., Ma, J., Chu, R., Cheng, J., Ismaila, A., Rios, L. P., Robson, R., Thabane, M., Giangregorio, L., & Goldsmith, C. H. (2010). A tutorial on pilot studies: The what, why and how. BMC Medical Research Methodology, 10, 1. https://doi.org/10.1186/1471-2288-10-1

    Article  PubMed  PubMed Central  Google Scholar 

  • Thomas, D. M. (2009). A dynamical fetal-maternal model of gestational weight gain. Personal Communication.

  • Thomas, D. M., Navarro-Barrientos, J. E., Rivera, D. E., Heymsfield, S. B., Bredlau, C., Redman, L. M., Martin, C. K., Lederman, S. A., Collins, L. M., & Butte, N. F. (2012). Dynamic energy-balance model predicting gestational weight gain. The American Journal of Clinical Nutrition, 95, 115. https://doi.org/10.3945/ajcn.111.024307

    Article  CAS  PubMed  Google Scholar 

  • Umstattd, M. R., Motl, R., Wilcox, S., Saunders, R., & Watford, M. (2009). Measuring physical activity self-regulation strategies in older adults. Journal of Physical Activity and Health, 6, S105–S112

    Article  PubMed  Google Scholar 

  • Unick, J. L., Pellegrini, C. A., Demos, K. E., & Dorfman, L. (2017). Initial weight loss response as an indicator for providing early rescue efforts to improve long-term treatment outcomes. Current Diabetes Report, 17, 69. https://doi.org/10.1007/s11892-017-0904-1

    Article  Google Scholar 

  • Urrutia, R. P., Berger, A. A., Ivins, A. A., Beckham, A. J., Thorp, J. M., Jr., & Nicholson, W. K. (2015). Internet use and access among pregnant women via computer and mobile phone: Implications for delivery of perinatal care. JMIR mHealth and uHealth, 3, e25

    Article  Google Scholar 

  • Vesco, K. K., Karanja, N., King, J. C., Gillman, M. W., Leo, M. C., Perrin, N., McEvoy, C. T., Eckhardt, C. L., Smith, K. S., & Stevens, V. J. (2014). Efficacy of a group-based dietary intervention for limiting gestational weight gain among obese women: A randomized trial. Obesity (Silver Spring), 22, 1989–1996

    Article  CAS  Google Scholar 

  • Vincze, L., Rollo, M., Hutchesson, M., Hauck, Y., MacDonald-Wicks, L., Wood, L., Callister, R., & Collins, C. (2019). Interventions including a nutrition component aimed at managing gestational weight gain or postpartum weight retention: A systematic review and meta-analysis. JBI Database of Systematic Reviews and Implementation Reports, 17, 297–364

    Article  PubMed  Google Scholar 

  • Zwebe, A., Fucito, K. M., & O’Malley, S. S. (2009). Effective strategies for maintaining research participation in clinical trials. Drug Information Journal, 43, 459–467

    Article  Google Scholar 

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Acknowledgements

We would like to acknowledge the assistance of the Healthy Mom Zone team who assisted with participant recruitment and data collection for this study. The project described was supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health through grant 1 R01 HL119245-01 and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000127 and TR002014. The content is solely responsibility of the authors and does not necessarily represent the official views of the NIH.

Funding

The project described was supported by the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health through grant 1 R01 HL119245-01 and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000127 and TR002014.

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Authors and Affiliations

  1. Exercise Psychology Laboratory, Department of Kinesiology, The Pennsylvania State University, University Park, PA, 16802, USA

    Danielle Symons Downs, Abigail M. Pauley & Krista S. Leonard

  2. Department of Obstetrics and Gynecology, Penn State College of Medicine, Hershey, PA, USA

    Danielle Symons Downs

  3. Department of Nutritional Sciences and Center for Childhood Obesity Research, The Pennsylvania State University, University Park, PA, USA

    Jennifer S. Savage, Emily E. Hohman & Katherine M. McNitt

  4. School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA

    Daniel E. Rivera & Penghong Guo

  5. Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, USA

    Christy Stetter & Allen Kunselman

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. DSD, Dr. JSS, Dr. DER, Dr. AMP, Dr. KSL, Dr. EH, Dr. PG, CS, and AK. The first draft of the manuscript was written by Dr. DSD and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Danielle Symons Downs.

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This study was performed in line with principles of the Declaration of Helsinki. Approval was granted by the Pennsylvania State Institutional Review Board (Date: 12/1/2015, Study# 00003752).

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Informed consent was obtained from all individual participants included in the study.

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Downs, D.S., Savage, J.S., Rivera, D.E. et al. Adaptive, behavioral intervention impact on weight gain, physical activity, energy intake, and motivational determinants: results of a feasibility trial in pregnant women with overweight/obesity. J Behav Med 44, 605–621 (2021). https://doi.org/10.1007/s10865-021-00227-9

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