Clinical trial NCT03945266: Healthy Mom Zone: A Gestational Weight Gain Management Intervention
Official title: Control Systems Engineering for Optimizing a Prenatal Weight Gain Intervention
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Overweight and Obesity
Physical Activity
Weight Change, Body
Eating Behavior
Keywords: gestational weight gain
pregnancy weight regulation
individually tailored intervention
Lead sponsor: Penn State University
Sponsor class: OTHER
Collaborators: name: National Heart, Lung, and Blood Institute (NHLBI)
name: Arizona State University
First posted: 2019-05-10
Enrollment: 31
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 40
Standard ages: ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: Participants are randomly assigned to an intervention or control group
Primary purpose: TREATMENT

Brief summary: The purpose is to establish feasibility of delivering an individually-tailored, behavioral intervention to manage gestational weight gain \[GWG\] that adapts to the unique needs and challenges of overweight/obese pregnant women \[OW/OBPW\] and will utilize control systems engineering to optimize this intervention; in other words, make this intervention manage GWG in OW/OBPW as effectively and efficiently as possible.

Detailed description: The research proposed here will establish the dosage of components needed to impact GWG and develop an efficient (optimized) intervention to effectively manage GWG in OW/OBPW before a randomized controlled trial (RCT) can be implemented. The aims of the proposed research are: to establish feasibility of delivering an individually-tailored behavioral intervention for managing GWG in OW/OBPW. Two studies will be conducted to establish feasibility. Study 1 will examine viability of delivering dosages/sequencing of components (education, goal-setting, self-monitoring, HE/PA), GWG/HE/PA self-monitoring using e-health technology mechanisms, and data collection by delivering varied dosages to OW/OBPW over a brief, 4-week period followed by focus groups to evaluate user acceptability. The investigators will then make necessary revisions to the intervention. Study 2 will pilot test intervention delivery with decision rules for when/how to adapt dosages for an individual and randomization/retention/data collection procedures with treatment and control groups in a new cohort of OW/OBPW. Also, the investigators aim to use feasibility data collected from Aim 1 and control systems engineering to build a model that characterizes the effects of energy balance and planned/self-regulatory behaviors on GWG over time and use this model to develop an optimized intervention. The investigators will identify a dynamical model from feasibility data collected in Aim 1 that considers how changes in GWG responds to changes in energy intake, PA, and planned/self-regulatory behaviors. Model predictive control (a decision-making method from control systems engineering) will inform how the dosage adaptations are decided. The investigators will then identify a customized intervention plan for each woman based on her levels of energy intake, PA, planned/self-regulatory behaviors and the extent to which she is meeting GWG goals over pregnancy. This will lead to final program modifications and result in an individually-tailored, optimized intervention. The investigators will test the efficacy of this optimized intervention for managing GWG in OW/OBPW in a future RCT. This innovative research will develop an individually-tailored, optimized intervention that effectively and efficiently manages GWG in OW/OBPW and that will eventually be available to all pregnant women (via e-health technology) to improve the health of mothers and infants and impact the etiology of obesity and cardiovascular disease at a critical time in the life cycle.

Eligibility criteria: Inclusion Criteria:

* Pregnant women
* Overweight or obese \[body mass index range 24- \>40 (if BMI (kg/m\^2) is \> 40, consultation with woman's health care provider (PCP/OBGYN) will be made to determine eligibility and ensure she does not have any contraindications to exercise) PI Danielle Downs will have communication with Dr. Hovick from MNPG to give information on the woman. The investigators also have a physician consent form (see Physician Patient Consent to Participate in documents) that the physician will complete as to whether the woman is eligible or not eligible to participate.
* Normal weight women with a BMI range of 18.0 to 23.9 can be enrolled in to the study as control participants (same measures of data collection, no opportunity for intervention).
* Ages 18-40 years \[based on pilot data this group comprises \>85% of the live births in Central Pennsylvania\]
* 1st, 2nd or 3rd pregnancy 6-16 weeks gestation
* Able to read, understand, and speak English
* Residing in and around State College, PA
* 1st and 2nd time pregnant mothers \[none or one other live or still born, biological children \> 25 weeks gestation prior to this pregnancy; it may be conceivable that a woman has a blended family due to a mixed marriage and she will not be excluded if she is a parent to a guardian, foster child, or step child\]
* Access to a computer or willingness to come onsite to complete study materials
* Infants born to participants who are 6-10 weeks old

Exclusion Criteria:

* Having more than one live or stillborn child \> 25 weeks gestation; late-term pregnancy loss
* Diabetes at study entry \[while future adaptations of this study will target women with diabetes, for the pilot study, they will be excluded to control for this confound\]
* Contraindications to exercise in pregnancy \[Hemodynamically significant heart disease, Restrictive lung disease, Incompetent cervix/cerclage, Persistent second \[or third\] trimester bleeding, Placenta previa after 26 weeks of gestation, Premature labor during the current pregnancy, Ruptured membranes, Preeclampsia/pregnancy-induced hypertension\] per the ACOG guidelines \[ACOG Committee on Obstetric Practice. \[(2015, December). ACOG Committee opinion. Number 650: Physical Activity and Exercise During Pregnancy and the Postpartum Period. Obstetrics and Gynecology, 126,(6), 135-142\].
* Having a body mass index less than 18 or over 40 (exclusion only if physician doesn't provide consent for BMI is over 40)
* Not planning to live in the area for the study duration
* Severe allergies or dietary restrictions that would preclude eating healthy foods
* Not able to read, understand, and/or speak English
* Cognitively impaired
* Currently smoking
* Infants not born to participants
* Infants younger than 6 weeks old

Primary outcomes:
measure: Gestational Weight Change
timeFrame: Daily data was collected starting at baseline (pre-intervention), through study completion Month 6 (post-intervention). Also measured daily for one week at 6-weeks postpartum. Outcome measure reported at mean change from pre- to post-intervention.
Secondary outcomes:
measure: Theory of Planned Behavior (Motivational Determinants of Healthy Eating)
timeFrame: Data was collected each month starting at baseline (pre-intervention), through study completion at Month 6 (post-intervention). Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, post-intervention.
measure: Theory of Planned Behavior (Motivational Determinants of Physical Activity)
timeFrame: Data was collected each month starting at baseline (pre-intervention), through study completion at post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, post-intervention.
measure: Self-regulation of Healthy Eating
timeFrame: Data was collected bi-weekly starting at baseline (pre-intervention), through study completion at Month 6 (post-intervention). Pre-intervention week, week 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, and post-intervention week.
measure: Self-regulation of Physical Activity
timeFrame: Data was collected bi-weekly starting at baseline (pre-intervention), through study completion at Month 6 (post-intervention). Pre-intervention week, week 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, and post-intervention week.
measure: Objective Activity Monitor UP Jawbone
timeFrame: Daily data was collected starting at pre-intervention, through study completion post-intervention. Daily for a total of 6 months. Outcome measure reported at mean change from pre- to post-intervention.
measure: Energy Intake
timeFrame: Daily energy intake was estimated starting at pre-intervention through post-intervention. Each day for 6 months. Outcome measure reported as mean change of energy intake calories from pre to post-intervention.
measure: MyFitnessPal
timeFrame: Data was collected 3 days a week, each week from pre-intervention through post-intervention. A total of 72 days over 6 months. Outcome measure reported as mean change of energy intake calories from pre-intervention week to post-intervention week.
measure: Three Factor Eating Questionnaire
timeFrame: Data was collected monthly starting at pre-intervention, through post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, post-intervention.
measure: Leisure Time Exercise Questionnaire
timeFrame: Data was collected monthly starting at pre-intervention, through post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, post-intervention.
measure: Pittsburgh Sleep Quality Index
timeFrame: Data was collected monthly starting at pre-intervention, through post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, post-intervention.
measure: Sleep Behaviors (Activity Monitor) - Nighttime Sleep Duration
timeFrame: Data was collected each day starting at pre-intervention, through post-intervention - a total of 6 months of daily data collection.
measure: Fetal Measure: Ultrasounds [Fetal Growth]
timeFrame: Each month - Month 1, Month 2, Month 3, Month 4, Month 5, and Month 6. Data reported is the sum of all flagged ultrasounds over the study duration.
measure: Infant Birthweight
timeFrame: Assessed at time of birth - data abstracted at 6-week postpartum session.
measure: Infant Length
timeFrame: Assessed at time of birth - data abstracted at 6-week postpartum session.
measure: Resting Metabolic Rate
timeFrame: Data was collected weekly from pre-intervention to post-intervention - Pre-intervention, week 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 ,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, post-intervention.
measure: Body Area Satisfaction Scale
timeFrame: Data was collected monthly starting at pre-intervention, through study post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, and post-intervention. Outcome measure is reported as the score at post-intervention.
measure: Center for Epidemiological Studies Depression Scale
timeFrame: Data was collected monthly starting at pre-intervention, through study post-intervention - Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, and post-intervention. Results are the mean scores at the post-intervention time period.
measure: State-Trait Anxiety Inventory
timeFrame: Data was collected monthly starting at pre-intervention through post-intervention: Pre-intervention, Month 1, Month 2, Month 3, Month 4, Month 5, Month 6, and Post-intervention. Results reported reflect mean scores at the post-intervention time period.
measure: Perceived Stress Scale
timeFrame: Assessed weekly at pre-intervention week, week 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, and post-intervention. Post-intervention mean scores reported.
measure: Sleep Behaviors (Activity Monitor) - Nighttime Awakenings
timeFrame: Data was collected each day starting at pre-intervention, through post-intervention - a total of 6 months of daily data collection.
Other outcomes:
Locations:
city: University Park
country: United States
facility: Noll Laboratory
state: Pennsylvania
Study officials:
affiliation: The Pennsylvania State University
name: Danielle S Downs, Ph.D.
role: PRINCIPAL_INVESTIGATOR
References:
citation: Symons Downs D, Savage JS, Rivera DE, Smyth JM, Rolls BJ, Hohman EE, McNitt KM, Kunselman AR, Stetter C, Pauley AM, Leonard KS, Guo P. Individually Tailored, Adaptive Intervention to Manage Gestational Weight Gain: Protocol for a Randomized Controlled Trial in Women With Overweight and Obesity. JMIR Res Protoc. 2018 Jun 8;7(6):e150. doi: 10.2196/resprot.9220.
citation: Guo P, Rivera DE, Pauley AM, Leonard KS, Savage JS, Downs DS. A "Model-on-Demand" Methodology For Energy Intake Estimation to Improve Gestational Weight Control Interventions. IFAC Pap OnLine. 2018;51(15):144-149. doi: 10.1016/j.ifacol.2018.09.105. Epub 2018 Oct 8.
citation: Rauff EL, Downs DS. A Prospective Examination of Physical Activity Predictors in Pregnant Women with Normal Weight and Overweight/Obesity. Womens Health Issues. 2018 Nov-Dec;28(6):502-508. doi: 10.1016/j.whi.2018.09.003. Epub 2018 Oct 15.
citation: Rauff EL, Downs DS. Mobile Health Technology in Prenatal Care: Understanding OBGYN Providers' Beliefs about Using Technology to Manage Gestational Weight Gain. J Technol Behav Sci. 2019 Mar;4(1):17-24. doi: 10.1007/s41347-018-0068-0. Epub 2018 Aug 15.
citation: Pauley AM, Hohman E, Savage JS, Rivera DE, Guo P, Leonard KS, Symons Downs D. Gestational Weight Gain Intervention Impacts Determinants of Healthy Eating and Exercise in Overweight/Obese Pregnant Women. J Obes. 2018 Oct 1;2018:6469170. doi: 10.1155/2018/6469170. eCollection 2018.
citation: Guo P, Rivera DE, Savage JS, Hohman EE, Pauley AM, Leonard KS, Downs DS. System Identification Approaches For Energy Intake Estimation: Enhancing Interventions For Managing Gestational Weight Gain. IEEE Trans Control Syst Technol. 2020 Jan;28(1):63-78. doi: 10.1109/TCST.2018.2871871. Epub 2018 Oct 12.
citation: Freigoun MT, Rivera DE, Guo P, Hohman EE, Gernand AD, Downs DS, Savage JS. A Dynamical Systems Model of Intrauterine Fetal Growth. Math Comput Model Dyn Syst. 2018;24(6):661-687. doi: 10.1080/13873954.2018.1524387. Epub 2018 Oct 7.
citation: Guo P, Rivera DE, Savage JS, Downs DS. State Estimation Under Correlated Partial Measurement Losses: Implications for Weight Control Interventions. IFAC Pap OnLine. 2017 Jul;50(1):13532-13537. doi: 10.1016/j.ifacol.2017.08.2347. Epub 2017 Oct 18.
citation: Downs DS. Obesity in Special Populations: Pregnancy. Prim Care. 2016 Mar;43(1):109-20, ix. doi: 10.1016/j.pop.2015.09.003. Epub 2016 Jan 12.
citation: Devlin CA, Huberty J, Downs DS. Influences of prior miscarriage and weight status on perinatal psychological well-being, exercise motivation and behavior. Midwifery. 2016 Dec;43:29-36. doi: 10.1016/j.midw.2016.10.010. Epub 2016 Oct 29.
citation: Guo P, Rivera DE, Downs DS, Savage JS. Semi-physical Identification and State Estimation of Energy Intake for Interventions to Manage Gestational Weight Gain. Proc Am Control Conf. 2016 Jul;2016:1271-1276. doi: 10.1109/ACC.2016.7525092. Epub 2016 Aug 1.
citation: Downs DS, Devlin CA, Rhodes RE. The Power of Believing: Salient Belief Predictors of Exercise Behavior in Normal Weight, Overweight, and Obese Pregnant Women. J Phys Act Health. 2015 Aug;12(8):1168-76. doi: 10.1123/jpah.2014-0262. Epub 2014 Nov 19.
citation: Savage JS, Downs DS, Dong Y, Rivera DE. Control systems engineering for optimizing a prenatal weight gain intervention to regulate infant birth weight. Am J Public Health. 2014 Jul;104(7):1247-54. doi: 10.2105/AJPH.2014.301959. Epub 2014 May 15.
citation: Dong Y, Deshpande S, Rivera DE, Downs DS, Savage JS. Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions. Proc Am Control Conf. 2014 Jun;2014:4198-4203. doi: 10.1109/ACC.2014.6859462.
citation: Downs DS, Savage JS, Rauff EL. Falling Short of Guidelines? Nutrition and Weight Gain Knowledge in Pregnancy. J Womens Health Care. 2014;3:1000184. doi: 10.4172/2167-0420.1000184.
citation: Dong Y, Rivera DE, Downs DS, Savage JS, Thomas DM, Collins LM. Hybrid Model Predictive Control for Optimizing Gestational Weight Gain Behavioral Interventions. Proc Am Control Conf. 2013:1970-1975. doi: 10.1109/acc.2013.6580124.
citation: Dong Y, Rivera DE, Thomas DM, Navarro-Barrientos JE, Downs DS, Savage JS, Collins LM. A Dynamical Systems Model for Improving Gestational Weight Gain Behavioral Interventions. Proc Am Control Conf. 2012:4059-4064. doi: 10.1109/acc.2012.6315424.
citation: McNitt KM, Hohman EE, Rivera DE, Guo P, Pauley AM, Gernand AD, Symons Downs D, Savage JS. Underreporting of Energy Intake Increases over Pregnancy: An Intensive Longitudinal Study of Women with Overweight and Obesity. Nutrients. 2022 Jun 1;14(11):2326. doi: 10.3390/nu14112326.
citation: Rosinger AY, Bethancourt HJ, Pauley AM, Latona C, John J, Kelyman A, Leonard KS, Hohman EE, McNitt K, Gernand AD, Downs DS, Savage JS. Variation in urine osmolality throughout pregnancy: a longitudinal, randomized-control trial among women with overweight and obesity. Eur J Nutr. 2022 Feb;61(1):127-140. doi: 10.1007/s00394-021-02616-x. Epub 2021 Jul 3.
citation: Guo P, Rivera DE, Dong Y, Deshpande S, Savage JS, Hohman EE, Pauley AM, Leonard KS, Downs DS. Optimizing Behavioral Interventions to Regulate Gestational Weight Gain With Sequential Decision Policies Using Hybrid Model Predictive Control. Comput Chem Eng. 2022 Apr;160:107721. doi: 10.1016/j.compchemeng.2022.107721. Epub 2022 Feb 8.
citation: Leonard KS, Pauley AM, Hohman EE, Guo P, Rivera DE, Savage JS, Buman MP, Symons Downs D. Identifying ActiGraph non-wear time in pregnant women with overweight or obesity. J Sci Med Sport. 2020 Dec;23(12):1197-1201. doi: 10.1016/j.jsams.2020.08.003. Epub 2020 Aug 11.
citation: Ranghetti L, Rivera DE, Guo P, Visioli A, Savage JS, Symons Downs D. A control-based observer approach for estimating energy intake during pregnancy. Int J Robust Nonlinear Control. 2023 Jun;33(9):5105-5127. doi: 10.1002/rnc.6019. Epub 2022 Jan 30.
citation: Savage JS, Hohman EE, McNitt KM, Pauley AM, Leonard KS, Turner T, Pauli JM, Gernand AD, Rivera DE, Symons Downs D. Uncontrolled Eating during Pregnancy Predicts Fetal Growth: The Healthy Mom Zone Trial. Nutrients. 2019 Apr 21;11(4):899. doi: 10.3390/nu11040899.
citation: Downs DS, Savage JS, Rivera DE, Pauley AM, Leonard KS, Hohman EE, Guo P, McNitt KM, Stetter C, Kunselman A. 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. 2021 Oct;44(5):605-621. doi: 10.1007/s10865-021-00227-9. Epub 2021 May 5.
citation: Leonard KS, Symons Downs D. Low prenatal resting energy expenditure and high energy intake predict high gestational weight gain in pregnant women with overweight/obesity. Obes Res Clin Pract. 2022 Jul-Aug;16(4):281-287. doi: 10.1016/j.orcp.2022.07.003. Epub 2022 Jul 13.
citation: Pauley AM, Hohman EE, Leonard KS, Guo P, McNitt KM, Rivera DE, Savage JS, Downs DS. Short Nighttime Sleep Duration and High Number of Nighttime Awakenings Explain Increases in Gestational Weight Gain and Decreases in Physical Activity but Not Energy Intake among Pregnant Women with Overweight/Obesity. Clocks Sleep. 2020 Nov 14;2(4):487-501. doi: 10.3390/clockssleep2040036.