Cluster Randomized Trial of a Mobile Health Intervention to Achieve Appropriate Gestational Weight Gain in Overweight/Obese Women
Phase: NA
Conditions: Gestational Weight Gain
Sponsor: Kaiser Permanente

A Mobile Health Intervention to Achieve Appropriate Gestational Weight Gain in 
Overweight/Obese Women 
 
NCT03880461 
 
Document date: 3/26/2025 
 
 

A Mobile Health Intervention to Achieve Appropriate Gestational Weight Gain in 
Overweight/Obese Women 
CLINICAL TRIAL PROTOCOL 
Abbreviations: 
BMI:             body mass index 
GWG:         Gestational weight gain 
IOM:         Institute of Medicine 
IRB: 
        Institutional Review Board 
KPNC:        Kaiser Permanente Northern California 
mHealth:   mobileHealth 
PA:                physical activity 
RCT: 
        randomized controlled trial 
 
OBJECTIVE: 
We conducted a cluster RCT of an mHealth intervention promoting appropriate GWG in 
pregnant patients with overweight or obesity in KPNC, an integrated healthcare system. 
Randomization was at the clinician level. The primary aim of this clinical trial is to evaluate 
whether an mHealth intervention designed to help pregnant patients with overweight or 
obesity and their clinicians manage GWG will improve GWG (Primary Outcome) in 
comparison to usual care. 
 
BACKGROUND: 
Sixty percent of pregnant patients in the United States are overweight or obese. Over half of 
pregnant patients with overweight or obesity exceed the IOM guidelines for GWG. which further 
increases their elevated risk of gestational diabetes, cesarean delivery, postpartum weight 
retention, severe obesity later in life, having a large-for-gestational-age infant and a child at risk of 
being overweight or obese. Thus, improving GWG among patients with overweight or obesity is a 
public health priority. Pregnancy is a unique window when women are in frequent contact with 
the health system and are motivated to make healthful changes out of concern for their baby, 
which presents an unparalleled opportunity to intervene on weight management. High-intensity 
behavior change interventions have signiﬁcantly improved the percent of women meeting the IOM 
GWG guidelines. However, these interventions are too time-consuming for some patients. In 
addition, while patients view their clinicians as a persuasive, trusted source of behavior change 
advice, many clinicians lack experience and the tools to provide behavior change advice 
necessary to manage GWG. Interventions to manage GWG that support both patients’ and 
clinicians’ barriers, and can be implemented in the health system, could maximize their 
effectiveness and reach.  
 
MHealth interventions offer a promising scalable solution to overcome patients’ and clinicians’ 
barriers to GWG management but have yielded mixed results. We propose that mHealth 
interventions must include key elements to impact weight. These include interactive technologies 

such as “smart” wireless scales and wearable activity trackers that facilitate self-monitoring and 
real-time feedback. These elements have proven efficacious in weight management outside of 
pregnancy but were not previously included in mHealth GWG interventions. We also propose that 
tools and training for clinicians are essential. 
 
STUDY SETTING: 
Research activities took place at the (3) locations listed below: 
1. Kaiser Permanente Northern California (KPNC), Division of Research, Oakland, California 
2. University of Hawaii at Manoa School of Nursing and Dental Hygiene, Honolulu, Hawaii 
3. School of Medicine, University of California, Davis, Sacramento, California 
 
STUDY DESIGN: 
This study conducted a cluster randomized trial to evaluate an mHealth intervention 
promoting appropriate GWG in an integrated healthcare system, with randomization at the 
clinician level. Consented patients received usual care or usual care plus mHealth 
intervention per their clinician’s randomization. The patient adaptive intervention 
provided personalized, automated feedback on GWG and physical activity using 1) a 
smartphone application, 2) a Wi-Fi scale and activity tracker; 3) weekly educational topics; 
and 4) step-wise support (added when GWG is >75th percentile of the GWG guidelines). 
Intervention clinicians received newsletters with motivational interviewing tips to facilitate 
discussing GWG. 
 
STUDY POPULATION: 
a. NUMBER OF SUBJECTS 
58 total clinicians 
1,335 total patients 
 
b. INCLUSION AND EXCLUSION CRITERIA 
Inclusion Criteria: 
• 
Age 21 years or older 
• 
Pregnant 
• 
Women receiving prenatal care at Kaiser Permanente San Francisco and Oakland and 
whose obstetric care clinicians consent to participate 
• 
Pregravid BMI 25 to <40 (as determined from a measured pregravid weight in electronic 
medical record) 
• 
Has access to a smartphone and Wi-Fi 
• 
Provides informed consent to participate 
 
Exclusion Criteria: 
• 
Multiple births 
• 
Planning to move out of the area during the study period 
• 
Inability to speak, read, or understand English 
• 
Placed on bed rest at time of enrollment 
• 
> 15 weeks' gestation at enrollment. 

 
WAIVER OF INFORMED CONSENT and HIPPA PRIVACY RULE AUTHORIZATION: 
A waiver of informed consent and HIPPA Privacy Rule Authorization was granted from KPNC IRB 
(IRB of record for this study) for clinicians and patients in the intervention arm and in the usual 
care arm. The only known risk is disclosure of conﬁdential information. Safeguards were be put in 
place to prevent the loss of privacy and to prevent breaches of conﬁdentiality. This study 
represented no more than minimal risk participants. 
 
STUDY PROCEDURES: 
Eligible clinicians were randomized into (2) arms: 1) an intervention arm and 2) a usual care 
arm. Obstetric clinicians randomized to the intervention arm received quarterly 
newsletters containing study updates on recruitment and retention efforts plus quarterly 
intervention newsletters containing tips on how to use motivational interviewing 
techniques to discuss GWG with their pregnant patients. Obstetric clinicians randomized 
to the usual care arm received quarterly newsletters containing study updates on 
recruitment and retention efforts. 
 
Pregnant patients receiving prenatal care from consented clinicians were eligible to 
participate in the trial. Eligible patients of consented clinicians in both arm were invited to 
complete a trial survey. Patients in both arms were informed they might be invited to an 
mHealth program. Within one week of completing the survey, patients receiving care from 
clinicians in the intervention condition were subsequently invited to join the adaptive 
mHealth intervention and invited to consent to sharing app, activity, and weight data with 
the study team. 
 
DATA ANALYSIS: 
All data analyses were intent-to-treat, including all randomized clinicians and patients. 
Analysis of treatment arm differences was by original treatment group assignment, 
regardless of adherence. Log binomial regression was used to estimate the population 
average mHealth intervention effect on meeting the IOM GWG guide lines with estimation 
via generalized estimating equations (GEE; marginal model), accounting for the within-
clinician correlation among patients to obtain valid estimates of treatment effects and 
associated standard errors. The probability of appropriate total GWG was modeled as a 
function of treatment arm and covariates used in the randomization procedure: clinician 
age, patient race/ethnicity, and patient BMI. In addition, this a priori speciﬁed set of model 
covariates included patient age and parity in the regression models given their strong 
association with GWG.  
 
Linear regression was used to provide point and interval estimates of the between-arm 
difference in mean rate of GWG. The approach to estimation of model parameters (GEE) 
and inclusion of model covariates was as speciﬁed above for the analysis of our primary 
binary outcome. Linear regression with GEE was chosen rather than linear mixed effects 
regression for estimation of population average intervention effects given freedom from 

distribution assumptions on cluster random effects, and to parallel the analyses of our 
binary outcome. 
 
The approach to the analyses of trimester-speciﬁc GWG (kg/week) paralleled that 
described above for the total rate of GWG. Linear mixed effects models were used in 
analyses of the intervention in relation to GWG trajectory, accounting for the within-person 
correlation among repeated measurements (not necessarily equal numbers per subject) 
and within-clinician correlation between patients, to obtain valid estimates of treatment 
effects and associated standard errors. We used latent trajectory class modeling to identify 
and categorize patients with respect to patterns of GWG during pregnancy. Group-based 
trajectory analysis, an application of ﬁnite mixture modeling, is designed to identify 
clusters of individuals with similar patterns of change over time. Examination of the 
intervention in relation to pattern of change category utilized multinomial logistic 
regression with estimation via GEE to account for nesting within clinician. The approach to 
the analyses of postpartum weight retention at 6 weeks, change in moderate to vigorous 
physical activity (12 weeks to 33 weeks; MET hrs/week), overall diet quality at 12 weeks 
(Healthy Eating Index-2015), and birthweight (all continuous), and proportion of infants 
with appropriate size for gestational age (binary) paralleled that described for the primary 
outcomes or rate of GWG and meeting the IOM GWG guidelines. The approach to the 
analyses of the intervention in relation to 12-month infant growth (BMI Z-score) trajectory 
paralleled that described for analyses of GWG trajectory.