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Healthy Mom Zone Intervention components. Components in light blue are in baseline intervention and delivered throughout the duration of the intervention. Active Learning component is adapted depending on decision rules and gestational weight gain (GWG) evaluations. BMI: body mass index.
Source publication
Background:
High gestational weight gain is a major public health concern as it independently predicts adverse maternal and infant outcomes. Past interventions have had only limited success in effectively managing pregnancy weight gain, especially among women with overweight and obesity. Well-designed interventions are needed that take an individu...
Contexts in source publication
Context 1
... intervention components (see Figure 4) are informed by past research and our own pilot data [5][6][7][8][9][10]31,32,38,39]. Evidence from model lifestyle interventions [8][9][10]38], GWG interventions [5][6][7], and our research on promoting healthy behaviors [39][40][41] shows education, goal-setting and action planning, and self-monitoring can effectively manage weight. Our past studies have shown that when people are taught how to set appropriate plans and goals, self-monitor, and manage their time, they are more likely to achieve their goals and see positive behavioral outcomes (eg, engage in healthy eating and exercise to manage weight) [39,[42][43][44]. Furthermore, convincing evidence from past research [39,44,[45][46][47][48][49] shows that healthy eating and physical activity active learning (eg, active participation in strategies to reduce energy density such as food preparation and planning, portion size control, increasing intake of fruits and vegetables, meeting physical activity goals, and engaging in guided exercise sessions) are effective for lowering energy intake and managing body weight. We also learned from our pilot study [50] that women wanted to know more about how the target intervention behaviors (eg, weight, physical activity, dietary intake) were related to their baby's growth, so we developed brief modules to inform women about the following: (1) current research studies (Featured Evidence and Baby's Health) and (2) unique aspects of their baby's growth (Baby Fun Facts); content is delivered weekly in this study via ...
Context 2
... intervention components (see Figure 4) are informed by past research and our own pilot data [5][6][7][8][9][10]31,32,38,39]. Evidence from model lifestyle interventions [8][9][10]38], GWG interventions [5][6][7], and our research on promoting healthy behaviors [39][40][41] shows education, goal-setting and action planning, and self-monitoring can effectively manage weight. Our past studies have shown that when people are taught how to set appropriate plans and goals, self-monitor, and manage their time, they are more likely to achieve their goals and see positive behavioral outcomes (eg, engage in healthy eating and exercise to manage weight) [39,[42][43][44]. Furthermore, convincing evidence from past research [39,44,[45][46][47][48][49] shows that healthy eating and physical activity active learning (eg, active participation in strategies to reduce energy density such as food preparation and planning, portion size control, increasing intake of fruits and vegetables, meeting physical activity goals, and engaging in guided exercise sessions) are effective for lowering energy intake and managing body weight. We also learned from our pilot study [50] that women wanted to know more about how the target intervention behaviors (eg, weight, physical activity, dietary intake) were related to their baby's growth, so we developed brief modules to inform women about the following: (1) current research studies (Featured Evidence and Baby's Health) and (2) unique aspects of their baby's growth (Baby Fun Facts); content is delivered weekly in this study via ...
Citations
... A third study by Rosinger and colleagues [16] found evidence to suggest that pregnant women may be at risk for under-hydration in the second and third trimesters when water needs begin to increase. Rosinger and colleagues [16] examined urine osmolality and underhydration levels in pregnant women with overweight/obesity participating in the Healthy Mom Zone gestational weight gain regulation intervention [17,18]. As part of a larger, multicomponent intervention, women in the intervention group were encouraged to track their water intake, increase intake of water-dense foods, and meet the hydration guidelines of consuming at least 2.4 L of water from~8-36 weeks gestation [7,17,18]. ...
... Rosinger and colleagues [16] examined urine osmolality and underhydration levels in pregnant women with overweight/obesity participating in the Healthy Mom Zone gestational weight gain regulation intervention [17,18]. As part of a larger, multicomponent intervention, women in the intervention group were encouraged to track their water intake, increase intake of water-dense foods, and meet the hydration guidelines of consuming at least 2.4 L of water from~8-36 weeks gestation [7,17,18]. Women used the MyFit-nessPal app (a smartphone app used to track daily food and beverage intake, and exercise behaviors; [19]) to track their daily hydration behaviors and biomarkers of hydration levels (i.e., weekly urine samples) were collected and analyzed to examine hydration levels [16]. These analyses found that the intervention was successful in promoting proper hydration in pregnant women with overweight/obesity [16]. ...
Maintaining adequate hydration over the course of pregnancy is critical for maternal and fetal health and reducing risks for adverse pregnancy outcomes (e.g., preeclampsia, low placental and amniotic fluid volume). Recent evidence suggests that women may be at risk for under-hydration in the second and third trimesters when water needs begin to increase. Scant research has examined pregnant women’s knowledge of hydration recommendations, water intake behaviors, and willingness to use digital tools to promote water intake. This study aimed to: 1) describe hydration recommendation knowledge and behaviors by the overall sample and early vs late pregnancy, and 2) identify habits and barriers of using digital tools. Pregnant women ( N = 137; M age = 30.9 years; M gestational age = 20.9) completed a one-time, 45-minute online survey. Descriptive statistics quantified women’s knowledge of hydration recommendations, behaviors, and attitudes about utilizing digital tools to promote adequate intake, and Mann-Whitney U and chi-squared tests were used to determine group differences. Most women lacked knowledge of and were not meeting hydration recommendations (63%, 67%, respectively) and were not tracking their fluid consumption (59%). Knowledge of hydration recommendations differed by time of pregnancy, such that women in later pregnancy reported 82 ounces compared to women in early pregnancy (49 ounces). Common barriers included: forgetting to drink (47%), not feeling thirsty (47%), and increased urination (33%). Most were willing to use digital tools (69%) and believed a smart water bottle would help them achieve daily fluid recommendations (67%). These initial findings suggest that pregnant women may benefit from useful strategies to increase knowledge, decrease barriers, and maintain adequate hydration, specifically earlier in pregnancy. These findings will inform the design of a behavioral intervention incorporating smart connected water bottles, wearables for gesture detection, and behavior modification strategies to overcome barriers, promote proper hydration and examine its impact on maternal and infant health outcomes.
... Data for this analysis were from two samples that were combined. Most participants (n=27) were from the Healthy Mom Zone (HMZ) study, a randomized-controlled trial designed to manage gestational weight gain in pregnant women with overweight or obesity (26). Women were eligible if they were >8 weeks pregnant with a single fetus, Englishspeaking, non-smoking, free of significant pregnancy complications or medical conditions, and had a BMI ranging from 24.5 to 45 kg/m 2 (>40 kg/m 2 with physician consultation). ...
... Participants were randomized to a standard of care or the HMZ adaptive intervention, and all participants completed an intensive longitudinal data collection protocol, including an ancillary fetal growth study. Further details of the intervention and data collection procedures have been previously published (26). The remaining participants (n=5) included pregnant women with a BMI ≥ 18.5 enrolled into an observation only group to increase sample size for the fetal growth study and incorporate a greater BMI range. ...
Background/objectives
Although cortisol levels increase during normal pregnancy, particularly high levels of cortisol or stress have been associated with adverse maternal/child outcomes. Obesity is associated with altered cortisol metabolism, but there is limited information on pregnancy-related changes in cortisol in pregnant women with overweight/obesity. The objective of this study was to examine weekly measures of urinary cortisol and perceived stress throughout ~10-36 weeks gestation, if levels differ by pre-pregnancy BMI categories, and whether concurrent measures of urinary cortisol and perceived stress are associated.
Methods
Longitudinal observational data from Healthy Mom Zone, a gestational weight management intervention, and an ancillary fetal growth study were combined. Pregnant women with normal (n=7), overweight (n=11), or obese (n=14) pre-pregnancy BMI were recruited at >8 weeks gestation. Overnight urinary cortisol and Perceived Stress Scale were measured weekly from ~10-36 weeks gestation.
Results
Higher pre-pregnancy BMI was associated with overall lower urinary cortisol throughout gestation, but rate of increase in urinary cortisol across pregnancy was similar across weight status groups. Women with obesity reported higher levels of overall perceived stress than normal weight women. Regardless of weight status, perceived stress was not associated with gestational age or cortisol.
Conclusions
Although women with obesity reported higher perceived stress, they had lower urinary cortisol than women with normal BMI, and gestation-related increases in cortisol were similar across weight groups and unrelated to perceived stress, suggesting that physiological factors that drive increases in cortisol as pregnancy may outweigh effects of stress and adiposity.
Clinical trial registration
https://clinicaltrials.gov/ct2/show/NCT03945266 , identifier (NCT03945266)
... Future studies might wish to compare telephone only versus podcast only for effectiveness. An adaptive design, such as the stepped care model being tested by Symons Downs et al. [31], could be employed that begins with podcasts (less staff burden) and introduces telephone calls only if participants are not meeting study goals. Alternatively, a hybrid approach with low personnel burden could be tested that combines podcasts with an automated phone intervention (e.g., interactive voice response system) that allows for scheduled calls, interactivity, accountability, and the use of phone contacts in a cost-effective manner [32][33][34]. ...
Background
Excessive gestational weight gain has increased over time and is resistant to intervention, especially in women living with overweight or obesity. This study described the process evaluation methods and findings from a behavioral lifestyle intervention for African American and white women living with overweight and obesity that spanned pregnancy (≤ 16 weeks gestation) through 6 months postpartum.
Methods
The Health in Pregnancy and Postpartum (HIPP) study tested a theory-based behavioral intervention (vs. standard care) to help women (N = 219; 44% African American, 29.1 ± 4.8 years) living with overweight or obesity meet weight gain guidelines in pregnancy and lose weight in postpartum. Participants completed process evaluation surveys at 32 weeks gestation (n = 183) and 6 months postpartum (n = 168) regarding their perceptions of most and least helpful aspects of the intervention. A database tracked delivery and receipt of intervention components (in-depth counseling session, telephone calls, podcasts). Descriptive statistics are used to report fidelity, dose, and participants’ perceptions. We also tested whether dose of behavioral intervention components was associated with gestational weight gain and 6-month postpartum weight retention with linear regression models controlling for baseline age and gestational weeks, receipt of Medicaid, race, parity, and marital status. A content analysis was used to code and analyze responses to open-ended survey questions.
Results
Over 90% of participants (both groups) would recommend the program to a friend. Implementation fidelity was moderately high and greater in pregnancy than postpartum for all intervention components. Dose received and participants’ ratings of the in-depth counseling session and telephone calls were more favorable than podcasts. The Facebook group was not perceived to be very helpful, likely because of low participant interaction. Although podcasts were created to reinforce call topics, this redundancy was viewed negatively by some. More calls completed and more podcasts downloaded related to lower gestational weight gain (p < .05).
Conclusion
Study findings underscore challenges in engaging this important but busy population, especially during the postpartum period.
Trial registration
The study was registered at clinicaltrials.gov (NCT02260518) on 10/09/2014. https://clinicaltrials.gov/ct2/show/NCT02260518 .
... This was an optimization trial within the multiphase optimization strategy (MOST) framework [31]. Details of the Healthy Mom Zone study intervention have been published previously [32]. Participants were recruited from 2016-2017 through flyers, online platforms, and referrals by local obstetricians at first prenatal appointment. ...
(1) Background: Energy intake (EI) underreporting is a widespread problem of great relevance to public health, yet is poorly described among pregnant women. This study aimed to describe and predict error in self-reported EI across pregnancy among women with overweight or obesity. (2) Methods: Participants were from the Healthy Mom Zone study, an adaptive intervention to regulate gestational weight gain (GWG) tested in a feasibility RCT and followed women (n = 21) with body mass index (BMI) ≥25 from 8–12 weeks to ~36 weeks gestation. Mobile health technology was used to measure daily weight (Wi-Fi Smart Scale), physical activity (activity monitor), and self-reported EI (MyFitnessPal App). Estimated EI was back-calculated daily from measured weight and physical activity data. Associations between underreporting and gestational age, demographics, pre-pregnancy BMI, GWG, perceived stress, and eating behaviors were tested. (3) Results: On average, women were 30.7 years old and primiparous (62%); reporting error was −38% ± 26 (range: −134% (underreporting) to 97% (overreporting)), representing an ~1134 kcal daily underestimation of EI (1404 observations). Estimated (back-calculated), but not self-reported, EI increased across gestation (p < 0.0001). Higher pre-pregnancy BMI (p = 0.01) and weekly GWG (p = 0.0007) was associated with greater underreporting. Underreporting was lower when participants reported higher stress (p = 0.02) and emotional eating (p < 0.0001) compared with their own average. (4) Conclusions: These findings suggest systemic underreporting in pregnant women with elevated BMI using a popular mobile app to monitor diet. Advances in technology that allow estimation of EI from weight and physical activity data may provide more accurate dietary self-monitoring during pregnancy.
... To this end, we developed a theoretically driven, behavioral intervention that uses intensive data (e.g., daily weight, physical activity, energy intake) to adapt the dosage to the unique needs of pregnant women with overweight/obesity in an effort to regulate GWG (Dong et al., 2012(Dong et al., , 2013(Dong et al., , 2014Guo et al., 2016). The intervention was designed with the Multiphase Optimization Strategy (Collins, 2018), is based on principles of control systems engineering (Hekler et al., 2018;Rivera et al., 2018) and adaptive interventions (Almirall et al., 2014), and produced a dynamical, mathematical model of energy balance (Dong et al., 2012(Dong et al., , 2013(Dong et al., , 2014Guo et al., 2016Guo et al., , 2018Guo et al., , 2020Pauley et al., 2018;Symons Downs et al., 2018;Thomas et al., 2012). This model includes the Theories of Planned Behavior (Ajzen, 1991) and Self-Regulation (Carver & Scheier, 1998) constructs targeting physical activity and energy intake/healthy eating, which in turn, are predicted to influence GWG. ...
... EI was estimated with a back-calculation method (Guo et al., , 2020Symons Downs et al., 2018) as a function of maternal weight (W), PA, and resting metabolic rate (RMR) as: ...
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.
... The intervention content was adapted from interventions for gravida with obesity and grounded in social cognitive theory and behavior change principles based on evidence from a prior successful lifestyle/behavior change intervention. 23,25 Four Geisinger-employed RDNs who were experienced with prenatal nutrition and behavioral counseling and trained in motivational interviewing interacted with the EC participants to provide counseling that was consistent with standard practice. 21 For the purpose of this study, RDNs were encouraged to work in partnership with each participant to set and achieve personal nutritional goals for appropriate GWG. ...
Trial design
Excessive gestational weight gain (GWG) can increase pregnancy morbidity and is particularly problematic for women with pregestational obesity. A lifestyle modification intervention was introduced to gravida with obesity to decrease excessive GWG as compared to usual care.
Methods
A randomized controlled trial was conducted to improve healthy lifestyle behaviors to manage appropriate GWG. Consenting participants with pre-pregnancy obesity and singletons <17weeks were randomized to: 1) Usual Care (UC): usual written educational materials and counseling by obstetric provider or 2) Enhanced Care (EC): usual care plus a) personalized letter from physician detailing appropriate GWG; b) access to individualized GWG chart; c) ongoing counseling with registered dietitian/nutritionist (RDN). The primary outcome was proportion with GWG ≤9.1 kilograms (kgs), as this is upper limit recommended by Institute of Medicine (IOM). Total GWG and GWG as less than/within/greater than IOM recommendations (in aggregate and stratified by obesity class), and pregnancy/neonatal outcomes were evaluated as secondary outcomes.
Results
Analyses included 105 participants in EC and 109 in UC arms. The groups had similar demographics: 46% with class I obesity, 26% class II and 28% class III. There were no group differences for any GWG, pregnancy or neonatal outcomes when analyzed in aggregate. As compared to those randomized to the EC arm, participants in UC arm with class I obesity gained 1.4 kg less and those with class II obesity were significantly more likely to gain within IOM guidelines (14.8% vs 40.0%, adjusted p = 0.04). Participants with class III obesity randomized to EC arm were more likely to gain within IOM guidelines as compared to participants randomized to UC arm (29.0% vs 6.7%, adjusted p=0.02).
Conclusion
There were no differences in GWG observed between groups when analyzing participants in aggregate. However, a physician’s letter detailing appropriate GWG, patient portal access to a personalized GWG chart, and RDN consultation were helpful for encouraging GWG within IOM guidelines for women with pre-pregnancy class III obesity. Women with class I or II obesity had better GWG outcomes without these additional interventions.
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... Regulating GWG has largely focused on moderating energy intake and increasing physical activity behaviors. As such, Symons Downs and colleagues expanded on a pregnancy energy balance model to also include planned and self-regulatory behaviors to predict GWG ( Figure 1) [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. However, there is emerging interest in understanding the extent to which prenatal sleep behaviors relate to components of this energy balance model to explain GWG. ...
... Given the small amount of GWG that is recommended each week for PW-OW/OB (i.e., OW: 0.5-0.7 or OB: 0.4-0.6 lb/week), a critical aspect of regulating GWG is identifying modifiable factors (such as sleep behaviors) that may impact GWG on a weekly basis [35,36]. This study provides a novel opportunity to examine actigraphy measured prenatal sleep behaviors within the conceptual framework of the energy balance model to predict GWG over the course of pregnancy [18][19][20]37,38]. ...
... The 29-week intervention was tested in a feasibility randomized control trial. This study aimed to: (1) describe means of sleep behaviors across the total sample and by study group assignment (i.e., intervention vs. control), and (2) examine between-and within-person effects of weekly sleep behaviors on weekly energy intake, physical activity, and GWG among all women and by study group assignment [18][19][20]37,38]. Based on existing literature, it was hypothesized that: (1) decreases in weekly nighttime sleep duration, increases in the number of weekly nighttime awakenings, and increases in weekly daytime nap duration would predict increases in weekly energy intake and GWG, and decreases in weekly physical activity [4,29,31]. ...
Pregnant women are at a high risk for experiencing sleep disturbances, excess energy intake, low physical activity, and excessive gestational weight gain (GWG). Scant research has examined how sleep behaviors influence energy intake, physical activity, and GWG over the course of pregnancy. This study conducted secondary analyses from the Healthy Mom Zone Study to examine between- and within-person effects of weekly sleep behaviors on energy intake, physical activity, and GWG in pregnant women with overweight/obesity (PW-OW/OB) participating in an adaptive intervention to manage GWG. The overall sample of N = 24 (M age = 30.6 years, SD = 3.2) had an average nighttime sleep duration of 7.2 h/night. In the total sample, there was a significant between-person effect of nighttime awakenings on physical activity; women with >1 weekly nighttime awakening expended 167.56 less physical activity kcals than women with <1 nighttime awakening. A significant within-person effect was also found for GWG such that for every increase in one weekly nighttime awakening there was a 0.76 pound increase in GWG. There was also a significant within-person effect for study group assignment; study group appeared to moderate the effect of nighttime awakenings on GWG such that for every one increase in weekly nighttime awakening, the control group gained 0.20 pounds more than the intervention group. There were no significant between- or within-person effects of sleep behaviors on energy intake. These findings illustrate an important need to consider the influence of sleep behaviors on prenatal physical activity and GWG in PW-OW/OB. Future studies may consider intervention strategies to reduce prenatal nighttime awakenings.
... Women (N = 31) were PW-OW/OB participating in the Healthy Mom Zone study, a GWG regulation intervention. 12 Women were randomized to an intervention or control group from ∼8 to 36 weeks gestation. Women were recruited via on-site clinic, community-based, and web-based strategies. ...
... A more detailed explanation of the intervention has been described elsewhere. 12 Women were excluded from this current study if they were non-compliant with wearing the Acti-Graph GT3x+ (n = 1) or completing self-report logs (n = 10), or had an extreme value of wear time from the ActiGraph GT3x + monitor or the self-report log (i.e., >3 standard deviations from the mean; n = 1). Thus, a final sample of N = 19 (collapsed across intervention group) was used for the study analyses. ...
Objectives
Non-wear time algorithms have not been validated in pregnant women with overweight/obesity (PW-OW/OB), potentially leading to misclassification of sedentary/activity data, and inaccurate estimates of how physical activity is associated with pregnancy outcomes. We examined: (1) validity/reliability of non-wear time algorithms in PW-OW/OB by comparing wear time from five algorithms to a self-report criterion and (2) whether these algorithms over- or underestimated sedentary behaviors.
Design
PW-OW/OB (N = 19) from the [anonymous] randomized controlled trial wore an ActiGraph GT3x + for 7 consecutive days between 8–12 weeks gestation.
Methods
Non-wear algorithms (i.e., consecutive strings of zero acceleration in 60-second epochs) were tested at 60, 90, 120, 150, and 180-min. The monitor registered sedentary minutes as activity counts 0−99. Women completed daily self-report logs to report wear time.
Results
Intraclass correlation coefficients for each algorithm were 0.96−0.97; Bland–Altman plots revealed no bias; mean absolute percent errors were <10%. Compared to self-report (M = 829.5, SD = 62.1), equivalency testing revealed algorithm wear times (minutes/day) were equivalent: 60- (M = 816.4, SD = 58.4), 90- (M = 827.5, SD = 61.4), 120- (M = 830.8, SD = 65.2), 150- (M = 833.8, SD = 64.6) and 180-min (M = 837.4, SD = 65.4). Repeated measures ANOVA showed 60- and 90-min algorithms may underestimate sedentary minutes compared to 150- and 180-min algorithms.
Conclusions
The 60, 90, 120, 150, and 180-min algorithms are valid and reliable for estimating wear time in PW-OW/OB. However, implementing algorithms with a higher threshold for consecutive zero counts (i.e., ≥150-min) can avoid the risk of misclassifying sedentary data.
... Data for this analysis were collected as part of an ancillary study to measure fetal growth in women participating in Healthy Mom Zone, an optimization trial within the multiphase optimization strategy (MOST) framework [46], of an adaptive intervention to manage gestational weight gain among pregnant women with overweight or obesity [18]. This study was approved by the Pennsylvania State University Institutional Review Board (study ID #00003752, initial approval date 12/1/2015), and participants provided written consent for their participation. ...
... The study was powered on the primary outcome of gestational weight gain; a sample size of 24 participants (12 per group) was determined to yield 80% power to detect a standardized effect size for gestational weight gain of 1.2 using a two-sided test with a significance level of p = 0.05. We aimed to recruit 30 participants, accounting for up to 25% drop out [18]. Participants (n = 31) were randomized to the intervention or a standard care control group. ...
... This optimization trial was built within the multiphase optimization strategy (MOST) framework [46] with the aim of developing an intervention to efficiently and effectively manage weight gain over pregnancy. Details of the Healthy Mom Zone intervention are published elsewhere [18], but in short, principles from the theory of planned behavior (TPB) [47] and self-regulation [48] provide the conceptual framework for the intervention. ...
Excess maternal weight gain during pregnancy elevates infants’ risk for macrosomia and early-onset obesity. Eating behavior is also related to weight gain, but the relationship to fetal growth is unclear. We examined whether Healthy Mom Zone, an individually tailored, adaptive gestational weight gain intervention, and maternal eating behaviors affected fetal growth in pregnant women (n = 27) with a BMI > 24. At study enrollment (6–13 weeks gestation) and monthly thereafter, the Three-Factor Eating Questionnaire was completed. Ultrasounds were obtained monthly from 14–34 weeks gestation. Data were analyzed using multilevel modeling. Higher baseline levels of uncontrolled eating predicted faster rates of fetal growth in late gestation. Cognitive restraint was not associated with fetal growth, but moderated the effect of uncontrolled eating on fetal growth. Emotional eating was not associated with fetal growth. Among women with higher baseline levels of uncontrolled eating, fetuses of women in the control group grew faster and were larger in later gestation than those in the intervention group (study group × baseline uncontrolled eating × gestational week interaction, p = 0.03). This is one of the first intervention studies to use an individually tailored, adaptive design to manage weight gain in pregnancy to demonstrate potential effects on fetal growth. Results also suggest that it may be important to develop intervention content and strategies specific to pregnant women with high vs. low levels of disinhibited eating.
... These are mobile applications designed to provide users with personalized treatment, generally aimed at improving lifestyle, through surveys and sensors. JITAIs have been developed for, among others: gestational weight management [6], insomnia [7], anxiety in children [8], sedentary behavior in the elderly [9], diabetes self-management [10], and smoking cessation [11]. ...
Daily questionnaires from mobile applications allow large amounts of data to be collected with relative ease. However, these data almost always suffer from missing data, be it due to unanswered questions, or simply skipping the survey some days. These missing data need to be addressed before the data can be used for inferential or predictive purposes. Several strategies for dealing with missing data are available, but most are prohibitively computationally intensive for larger models, such as a recurrent neural network (RNN). Perhaps even more important, few methods allow for data that are missing not at random (MNAR). Hence, we propose a simple strategy for dealing with missing data in longitudinal surveys from mobile applications, using a long-term-short-term-memory (LSTM) network with a count of the missing values in each survey entry and a lagged response variable included in the input. We then propose additional simplifications for padding the days a user has skipped the survey entirely. Finally, we compare our strategy with previously suggested methods on a large daily survey with data that are MNAR and conclude that our method worked best, both in terms of prediction accuracy and computational cost.
