Pragmatic Randomized Clinical Trial to Limit Weight Gain in Pregnancy and Prevent Obesity: Complete Statistical Analysis

1. Trial at a Glance

This page covers the registry results for Pragmatic Randomized Clinical Trial to Limit Weight Gain in Pregnancy and Prevent Obesity (NCT04724330), a randomized na study. The primary outcome is Observed Total Gestational Weight Gain.

2. Clinical Question

3. Trial Design

The registry describes a randomized, single-masked, parallel-group trial with an enrollment of 534. The listed phase is NA. Johns Hopkins University is the sponsor. The record gives a start date of 2021-03-15, a primary completion date of 2025-01-31, and a status of COMPLETED. These registry descriptors identify the broad design and study timeline; they do not provide a full protocol or explain how masking was implemented.

Participants were assigned to H42/H4U or the mHIP usual-care comparison. H42/H4U combined remote health coaching during pregnancy and 12 weeks postpartum with phone, web, and mobile-phone components. The comparison was described as typical evidence- and guideline-based prenatal care. The pragmatic aim was to embed the intervention into existing Johns Hopkins prenatal practices and test effectiveness and implementation in that setting. The record does not report whether intervention delivery, adherence, or implementation differed across clinics or participants.

The primary endpoint was a continuous measure of total gestational weight gain at 37 weeks. Its posted analysis population comprised complete cases from the intention-to-treat population: randomized participants with non-missing outcome data at that time. The outcome counts were 177.0 in H42/H4U and 176.0 in mHIP. The record does not explain why these counts differ from the overall enrollment, or describe the extent and reasons for missing primary outcomes. For the secondary gestational diabetes outcome, the posted group counts were 187.0 and 188.0, respectively; the record does not reconcile these with the primary outcome population.

The record includes group summaries and comparison estimates for the primary and secondary outcomes, but reports no formal analysis entries. Details such as the trial’s original sample-size justification, prespecified analysis model, handling of missing data, and any interim analyses are not reported in the registry record. The planning illustrations accompanying the posted effects are not the original design justification.

4. Participant Flow and Accounting

The registry reports how participants moved through each stage of the study. All counts below are exactly as posted; missing counts are not inferred from other stages.

Before assignment: Of 534 pregnant women consented, 384 met the inclusion criteria and were randomized.

Full Maternal Follow-up Period

Reasons for not completing this period:

Postpartum Infant Follow-up Period

Reasons for not completing this period:

Analysis population for the primary outcome, as defined in the registry: Complete cases from the intention-to-treat population (all participants randomized to H42/H4U or mHIP with non-missing outcome data) at 37 weeks of gestation.

5. Baseline Characteristics

The table reproduces the baseline characteristics posted in the registry, which describe the participants analysed at baseline and help show whether the randomized groups were comparable.

Population: Data collected for pregnant women only

6. Primary Endpoint

The registry lists the primary outcome as Observed Total Gestational Weight Gain, measured over At 37 weeks in pregnancy.

The primary endpoint, observed total gestational weight gain, measures the change in weight from baseline in the first trimester to the measurement at 37 weeks of pregnancy. Baseline weight was measured prior to 15 weeks of gestation. The registry reports this outcome as a mean in pounds, with a standard deviation for each group. The comparison therefore concerns average weight change, rather than the number of participants who crossed a weight-gain threshold.

A continuous outcome can retain information about the range of observed weight changes and support a comparison of group means. In this trial, the endpoint was selected to address the stated aim of limiting gestational weight gain. It gives a direct measure of weight change over pregnancy up to the specified assessment time, but does not by itself describe postpartum weight retention, infant outcomes, or the distribution of weight gain across individuals.

The timing and definition matter when interpreting the result. The primary outcome is based on weight recorded at 37 weeks, not a measurement at delivery or a postpartum follow-up. The registry’s primary population includes randomized participants with non-missing outcome data at 37 weeks. The number and characteristics of participants without such data, and the methods used to address any missing outcomes, are not reported in the registry record. Consequently, the estimate describes the posted complete-case comparison and should not be assumed to represent every randomized participant without considering missingness.

The standard deviation describes variability among participants within each group; it is not the uncertainty around the difference in group means. The confidence interval for the mean difference addresses uncertainty in the comparison itself. Both kinds of information help characterize the outcome, but they answer different questions.

7. Results Posted in the Registry

• Healthy for Two/Healthy for You (H42/H4U): mean 22.40 versus 22.20 on control; difference 0.20 (95% CI -2.66 to 3.06), standardized effect d = 0.01; z-test p = 0.891.

8. Secondary Efficacy Endpoints

The registry also posts results for secondary outcomes. For each outcome below, the endpoint type, the test applied to the posted summary statistics, the result and its interpretation are given in turn. Secondary endpoints are usually not adjusted for multiple testing, so they support rather than confirm the primary finding.

Observed Incidence of Gestational Diabetes Mellitus

• Endpoint type: Binary: the number of participants with the event, compared as proportions.
• Test: Unadjusted two-proportion z-test, with a Wald 95% confidence interval for the risk difference.
• Result:
  • Healthy for Two/Healthy for You (H42/H4U): 14.4% (27/187) versus 11.7% (22/188) on control; risk difference 2.7 percentage points (95% CI -4.1 to 9.6), risk ratio 1.23 (0.73–2.09); unadjusted two-proportion z-test p = 0.432.
• Interpretation:
  • The 95% confidence interval includes no difference, so these data do not show a statistically significant difference between Healthy for Two/Healthy for You (H42/H4U) and the control. The interval gives the range of effects that remain compatible with the data.

The registry posts one secondary endpoint: observed incidence of gestational diabetes mellitus at 37 weeks in pregnancy. It presents group counts and a comparison summary, but the record lists no formal analysis entries for secondary outcomes. This means the secondary result can provide context about another trial outcome, but the registry does not establish a broader confirmatory analysis framework for it.

The endpoint relates to the primary result because both concern pregnancy outcomes in the randomized groups, but they measure different things. Gestational weight gain is a continuous change in weight; gestational diabetes is a binary outcome recorded as participant counts. Their scales, underlying variability, and statistical interpretations are therefore not interchangeable. A pattern in one endpoint cannot by itself confirm or explain a pattern in the other.

Secondary results generally carry less weight than a primary result when they are unadjusted and when the number of outcomes considered is relevant to interpretation. Looking at multiple outcomes creates more opportunities for apparently notable differences, even when no underlying effect exists. Adjustment for multiplicity or a prespecified hierarchy can address that issue, but the registry record does not report such a plan or correction. The absence of reported analysis entries also limits assessment of how the secondary comparison was selected or interpreted.

Accordingly, the secondary endpoint should be treated as supportive descriptive information rather than as an independent basis for a definitive conclusion. It does not substitute for the primary comparison, and it does not establish the trial’s other stated aims concerning low birth weight or implementation. Those outcomes are not included in the posted outcome details described in the registry record.

Secondary outcomes registered for this trial, for reference (only those above were recalculated):

• Observed Incidence of Gestational Diabetes Mellitus

9. Statistical Interpretation

10. Statistical Methods Explained

The primary outcome is continuous, so the reported group summaries are means and standard deviations. A mean describes the average observed weight gain in a group. A standard deviation describes how spread out individual measurements are around that group mean. It is not a confidence interval and should not be interpreted as a measure of precision for the mean difference.

What does the mean difference represent?

The mean difference compares the H42/H4U average with the mHIP average, with the registry’s comparison oriented as intervention minus comparison. The posted estimate is 0.2 lb. A positive value means the intervention-group mean was higher in this comparison; a negative value would mean it was lower. The estimate is a point estimate: one value summarizing the observed difference, not a guarantee of the true difference in the population.

A difference in means can be useful because it remains in the outcome’s original units, pounds. That makes its scale directly interpretable as a difference in average weight change. It does not indicate how many individuals experienced a particular outcome, whether the distributions overlap, or whether individual participants gained more or less weight than another participant.

How should the confidence interval be read?

A confidence interval expresses uncertainty around an estimate. In general, a wider interval indicates less precision, while a narrower interval indicates greater precision. The primary 95% confidence interval extends from -2.658 to 3.058 lb. Since it includes zero, the data are compatible with no mean difference as well as with differences in either direction within the interval. The interval is not a range containing every participant’s weight gain, and it does not mean that the true effect has a 95% probability of lying within the particular interval.

Confidence intervals rely on assumptions about the sampling process and the statistical method. The registry record does not identify the model used for its primary comparison or provide a detailed analysis plan. The stated precision should therefore be understood as attached to the posted estimate, not as confirmation that all relevant sources of uncertainty were addressed.

What does the p-value add?

The unadjusted p-value for the primary comparison is 0.891. In general, a p-value measures how unusual data at least as discrepant as those observed would be under a specified null hypothesis and statistical model. It does not tell readers whether a difference is clinically important, and it is not the probability that an observed result occurred by chance. A high p-value does not prove that the groups are equivalent. Equivalence or non-inferiority questions require suitable prespecified margins and analyses; those details are not reported in the registry record.

The p-value and confidence interval address related aspects of the comparison, but the interval gives a more direct view of the range and direction of effects that remain compatible with the estimate. Neither statistic resolves concerns about missing observations, outcome measurement, adherence, or generalizability.

Why does variability matter?

Weight gain varied within each trial group, as reflected by the reported standard deviations. In general, substantial variation among individuals can make a group-average contrast harder to estimate precisely, particularly when the contrast is small relative to that variation. This is one reason to consider both the estimated difference and its confidence interval rather than focusing only on whether the group means look similar.

General statistical methods may also need to account for features such as skewed distributions, clustering, or over-dispersion when they are present. Over-dispersion means that observed variability is greater than a model assumes; if not accounted for, uncertainty can be understated. The registry record does not report whether any of these features were assessed or whether the comparison used a particular adjustment. They are general considerations, not claims about this trial’s data.

What is unadjusted analysis?

An unadjusted comparison does not account for other covariates in the estimate, in contrast to an adjusted model that includes selected participant or design characteristics. Random assignment is intended to make groups comparable on average, but adjustment can be used for prespecified reasons, such as improving precision or accounting for design features. The registry’s posted p-values are identified as unadjusted. The record does not report a full model specification, a covariate list, or whether other analyses were conducted outside the registry record.

Finally, the primary outcome analysis is described as complete cases from the intention-to-treat population: randomized participants with non-missing outcome data at 37 weeks. This retains the assignment groups for participants with an observed endpoint, but it does not include participants whose primary outcome is missing. If missingness is associated with assignment or outcomes, a complete-case estimate may differ from the estimate for all randomized participants. The registry record does not report missing-data methods or sensitivity analyses, so that potential limitation cannot be assessed from the posted information.

11. Confidence Intervals and Precision

The primary mean difference is 0.2 lb, and its 95% confidence interval runs from -2.658 to 3.058 lb. The interval crosses zero and includes both a lower mean in H42/H4U and a higher mean in H42/H4U. It is therefore important not to interpret the small point estimate as proof that the true difference is exactly zero. The interval conveys uncertainty that the point estimate alone does not.

In general, a confidence interval gives a range of values compatible with the observed data and the assumptions of the statistical method. The endpoints do not describe the spread of individual participant outcomes; the group standard deviations of 13.7 lb describe that spread. Nor does the interval identify which value is most likely. It helps readers assess precision and direction while leaving the clinical relevance of possible differences to the context and a prespecified interpretation framework.

The registry does not report a clinically meaningful difference threshold or an equivalence margin. Without such a threshold, the interval cannot be used to claim equivalence. The estimate also comes from the stated complete-case population, and the record does not report the reasons for missing primary outcomes or sensitivity analyses examining their potential effect.

The secondary gestational diabetes comparison also has an interval that includes no difference, but it is an unadjusted secondary comparison and should be considered in that context. Its precision does not change the primary endpoint’s estimate. More broadly, confidence intervals quantify sampling uncertainty under their method; they do not capture every uncertainty about measurement, missingness, implementation, or transfer to other care settings. The registry record does not report enough analysis detail to evaluate all such sources.

12. Planning a Similar Trial: Sample Size

This is a planning illustration, not a re-evaluation of the original trial. It asks: if a new study wanted to detect a difference like the one observed, or a smaller one, how large would it need to be? It is deliberately not “observed power” of the finished trial, which is not a meaningful quantity.

The sample-size illustrations translate the observed effects into hypothetical planning requirements. For both the continuous primary outcome and the binary secondary outcome, figures are participants per arm, not total participants. The record’s planning assumptions are a two-sided significance level of 0.05, equal allocation between the two arms, and a normal approximation. Each planning row uses the stated fraction of the observed effect as the effect to detect. The record’s note that time-to-event tables use total events does not apply to these outcomes.

The 80% power column describes the number of participants per arm that the planning calculation associates with an 80% chance of detecting the specified effect under its assumptions, if that effect is real. The 90% power column uses a higher chance of detection for the same assumed effect and therefore calls for a larger planned sample. These are design concepts, not guarantees about what a future study will find.

Rows that plan for a smaller fraction of the observed effect require more participants, because smaller differences are generally harder to distinguish from sampling variation. The values are illustrations based on the observed effect, not the trial’s own design assumptions or a record of what was planned before enrollment. The original trial sample-size justification is not reported in the registry record. This is not observed power; it is a planning illustration under stated assumptions.

The required numbers can be especially sensitive when the observed estimate is small, because a small target difference is harder to detect with precision. The illustrations do not account for unreported factors such as loss to follow-up, departures from equal allocation, or analysis choices. They should be read as conditional calculations rather than as a retrospective verdict on whether the trial was designed appropriately.

13. How the Sample Size Is Derived

Power is the probability that a study of a given size rejects the null hypothesis when a specific true effect exists. It depends on four things: the effect to detect, the variability of the outcome, the significance level (here a two-sided α of 0.05, so z_1−α/2 = 1.960) and the desired power (z_1−β = 0.842 for 80% and 1.282 for 90%).

Formula

n is the number of participants in each arm, Δ is the difference in means to detect and σ is the common standard deviation.

Worked calculation for this trial

1. Observed difference Δ = 0.200; pooled standard deviation σ = 13.700.
2. n = 2 × (1.96 + 0.842)² × 13.700² / 0.200² = 73,657.8, rounded up to 73,658 participants per arm for 80% power.

The smaller-effect rows in the table above repeat the same calculation with the effect multiplied by 0.75 and 0.5 (for ratios, applied to the logarithm of the ratio).

R code

alpha <- 0.05
z_a   <- qnorm(1 - alpha / 2)          # two-sided
delta <- 0.199999999999999                      # observed difference in means
sigma <- 13.7                     # pooled standard deviation
n_per_arm <- function(power, frac = 1) {
  ceiling(round(2 * (z_a + qnorm(power))^2 * sigma^2 / (delta * frac)^2, 6))
}
for (f in c(1, 0.75, 0.5))
  cat(sprintf("%3.0f%% of observed effect: n per arm = %s (80%% power), %s (90%% power)\n", 100 * f,
              format(n_per_arm(0.80, f), big.mark = ","), format(n_per_arm(0.90, f), big.mark = ",")))

# Check against R's built-in function (t-based, so very slightly larger)
cat("power.t.test:", round(power.t.test(delta = delta, sd = sigma, power = 0.80)$n, 1), "\n")

# Simulation: exact two-sample t-test using simulated means and variances
set.seed(4724330)
n    <- n_per_arm(0.80)
reps <- 20000
m_diff <- rnorm(reps, delta, sigma * sqrt(2 / n))             # difference in sample means
s2     <- sigma^2 * rchisq(reps, 2 * (n - 1)) / (2 * (n - 1))   # pooled sample variance
tstat  <- m_diff / sqrt(s2 * 2 / n)
cat(sprintf("Simulated power at n = %s per arm: %.3f (%d replicates)\n", format(n, big.mark = ","), mean(abs(tstat) > qt(0.975, 2 * (n - 1))), reps))

Output from running the code

100% of observed effect: n per arm = 73,658 (80% power), 98,607 (90% power)
 75% of observed effect: n per arm = 130,948 (80% power), 175,302 (90% power)
 50% of observed effect: n per arm = 294,632 (80% power), 394,428 (90% power)
power.t.test: 73658.8 
Simulated power at n = 73,658 per arm: 0.798 (20000 replicates)

The simulation draws many sets of group means and variances at the planned sample size and counts how often the two-sample t-test rejects the null. It should land close to the target power; at very small required sample sizes it can fall below it, because the normal approximation is rough there.

14. Multiplicity, Missing Data and Analysis Populations

Multiplicity arises when several outcomes, comparisons, subgroups, or analyses are examined. In general, the more statistical tests considered, the greater the chance that at least one result will appear unusual even if none reflects a true effect. Researchers may address this through a prespecified hierarchy of outcomes, adjusted significance thresholds, or other strategies. The registry record lists a primary continuous outcome and one secondary outcome, but does not report a multiplicity plan, a hierarchy beyond the primary and secondary labels, or an adjustment for multiple testing. The unadjusted comparisons should therefore not be interpreted as though multiplicity had been addressed.

The registry identifies the primary population as complete cases from the intention-to-treat population: participants randomized to H42/H4U or mHIP with non-missing outcome data at 37 weeks. This wording retains the original assignment categories for those with observed primary outcomes, but it does not make the analysis a complete analysis of every randomized participant. The record does not report the reasons primary outcomes were missing, whether missingness differed by group, or whether imputation or sensitivity analyses were performed. These details are important because complete-case results can be biased if the availability of an outcome is related to the outcome itself or to trial assignment.

The posted primary endpoint counts differ from overall enrollment, while the secondary outcome counts also have a different denominator. The registry record does not explain these differences or whether they result from outcome availability, eligibility for the particular measure, or another reason. No explanation should be inferred from the counts alone.

The record reports no formal analysis entries. The original sample-size justification, statistical model, covariate adjustment, treatment of protocol deviations, and interim analysis plan are not reported in the registry record. The planning illustrations are based on observed effects and stated planning assumptions, rather than documentation of the trial’s original design. These omissions constrain assessment of prespecification and analytic robustness. They do not demonstrate that such procedures were absent; they mean that their details are not reported in the registry record.

15. Safety Results

The registry reports serious adverse events as affected participants divided by participants at risk.

Deaths

Other (non-serious) adverse events

Participants with at least one non-serious event at or above the registry's 5% reporting threshold.

Most frequent serious adverse event terms

Most frequent non-serious adverse event terms

Serious events with the highest arm-level rate: congenital heart defects in infant (0.3%), very pre-term infant at 24 weeks gestation (0.3%), placenta previa and postpartum hemorrhage (0.3%), intrauterine fetal death at 37 weeks gestation (0.3%).

The registry lists serious adverse events for groups at risk of 374 in H42/H4U and 380 in mHIP. Serious adverse events affected 3 participants in H42/H4U and 1 in mHIP. The record lists other adverse events affecting 24 participants in each group. It also records 0 deaths in H42/H4U and 1 in mHIP. These are posted counts, not evidence by themselves that an event was caused by the intervention or comparison care.

The listed serious-event terms include congenital heart defects in infant, very pre-term infant at 24 weeks gestation, placenta previa and postpartum hemorrhage, and intrauterine fetal death at 37 weeks gestation; each term is associated with 0.3% in the record. These descriptions should be read as reported event categories, not as a causal assessment. The registry does not provide sufficient detail to determine relatedness, timing relative to intervention exposure, or whether participants experienced more than one event.

Safety counts do not establish comparative risk without considering how events were defined, collected, and attributed, and without a suitable analysis. The record’s counts should be treated descriptively. They do not establish that either approach is safer, and they do not replace a full safety assessment.

16. What the Result Does, and Does Not, Mean

The primary result is a comparison of average gestational weight gain at 37 weeks among participants with non-missing outcome data. The observed means were close, and the estimated difference was small relative to the reported group variability. The confidence interval nevertheless includes differences in either direction, so the result is not proof of no difference or of equivalence. It describes uncertainty in this comparison under the posted analysis.

The endpoint is specific: change from first-trimester baseline weight, measured prior to 15 weeks of gestation, to weight at 37 weeks. It does not measure total weight change through delivery, postpartum weight retention, or childhood obesity. The trial title and stated aims refer to broader prevention goals, but a result for gestational weight gain cannot establish those other outcomes.

Random assignment is a design strength for comparing intervention and usual-care groups, but the analyzed primary population consists of complete cases. Because the registry does not report missing-data reasons or sensitivity analyses, the extent to which the posted estimate represents all randomized participants cannot be assessed. The record also does not report a detailed analysis plan, original sample-size justification, or multiplicity approach. Those gaps should temper certainty about the scope of inference, without being mistaken for proof that analyses were not conducted.

The intervention was designed for integration into prenatal clinics and included remote coaching and digital components. The posted primary estimate does not establish whether the intervention was delivered consistently, feasible to scale, or effective in other settings. The registry’s description of an aim to test implementation is not itself evidence of successful implementation. Similarly, the posted outcome information does not resolve the stated hypothesis concerning low birth weight.

These results are a statistical description of the registry’s posted comparisons, not medical advice and not a recommendation about prenatal care. They do not establish that H42/H4U works or does not work. The interpretation is limited to the specified outcomes, time points, analyzed participants, and statistical summaries in the registry record.

17. Why This Trial Matters Statistically

This trial raises a common biostatistical question: how should a randomized study be interpreted when the point estimate is small but the uncertainty interval spans differences in both directions? The answer is not to treat a non-small p-value as proof of no effect. The estimate, interval, outcome variability, analysis population, and design assumptions all contribute to interpretation. Distinguishing an absence of precise evidence from evidence of equivalence is essential in clinical research.

The study also illustrates why outcome definition matters. Gestational weight gain is measured as a continuous change at a specified pregnancy time point, while gestational diabetes is a binary event. Different outcome types call for different summaries and interpretations. A mean comparison conveys an average difference, whereas event counts describe how many participants had an outcome. They cannot be combined into a single claim about overall benefit.

Pragmatic trials seek to evaluate interventions in ordinary care settings and can make implementation questions central to the scientific purpose. Here, the intervention was intended to be embedded in prenatal clinics using existing staff as trained coaches, with remote delivery components. Statistical results for a clinical endpoint do not alone answer whether delivery was feasible, scalable, or equitable. Those questions require implementation outcomes and sufficient reporting about delivery and reach.

Finally, the registry record distinguishes posted estimates from formal analysis detail. It provides outcome summaries and unadjusted comparisons, while key information about the original analysis plan, missing data, and multiplicity is not reported. Recognizing those boundaries is part of sound statistical reading: the available estimates can be described, but their interpretation should not extend beyond the design and information documented in the record.

18. Limitations

• The posted summary statistics are the only data these results rest on; patient-level data, full protocols and statistical analysis plans are not available.
• Recalculated results are simple, unadjusted calculations from posted summaries. They do not replace the sponsor's prespecified analysis.

Several limitations are specific to what is reported for this trial. The primary comparison uses complete cases with non-missing weight-gain data at 37 weeks, but the registry record does not describe missingness by group, reasons for missing outcomes, or sensitivity analyses. The primary outcome counts therefore cannot be reconciled with overall enrollment from the registry information alone. The secondary outcome has its own group counts, also not reconciled with the primary population.

The record provides the randomization, masking, and parallel-group descriptors, but does not explain allocation procedures, the identity of masked parties, or how masking was maintained. It does not report adherence to coaching, intervention exposure, contamination between groups, or the extent of routine-care variation. These matters could affect interpretation of a pragmatic intervention comparison, but their status cannot be inferred from the outcome summaries.

The primary and secondary comparisons are unadjusted in the posted summaries, and no formal analysis entries are listed. The registry record does not report a full statistical analysis plan, covariate strategy, multiplicity control, original sample-size justification, or interim analyses. The planning illustrations are based on observed effects and cannot stand in for the trial’s original design documentation.

The posted outcome details cover gestational weight gain and gestational diabetes, while the investigators’ stated aims also mentioned low birth weight and implementation feasibility and scalability. The information summarized in the registry record does not establish results for those aims. Adverse-event counts are descriptive and do not provide a causal assessment. Finally, the trial’s single institutional setting may limit transfer to other care systems, but the registry description alone cannot determine how results would differ elsewhere.

19. Related Tutorials

20. Related Statistical Calculators

21. Sources

• ClinicalTrials.gov: Pragmatic Randomized Clinical Trial to Limit Weight Gain in Pregnancy and Prevent Obesity — NCT04724330.
• Registry record last updated 2026-07-02; status last verified 2026-06.
• Results first posted 2026-07-02.
• Data retrieved from ClinicalTrials.gov on 2026-11-09.
• Group sizes, means, event counts and sponsor-reported p-values are reproduced as posted. Effect estimates, confidence intervals and p-values not attributed to the sponsor are independently calculated from the posted summary statistics.

22. Record Summary

NCT04724330 is a completed, randomized, single-masked, parallel-group trial sponsored by Johns Hopkins University, with enrollment of 534. It compared the remotely delivered Healthy for Two/Healthy for You coaching intervention with the Maintain Health in Pregnancy usual-care comparison in Johns Hopkins prenatal practices.

The primary endpoint was mean total gestational weight gain at 37 weeks, defined as weight change from first-trimester baseline measured prior to 15 weeks. The posted complete-case group means were 22.4 lb and 22.2 lb, with a mean difference of 0.2 lb and a 95% confidence interval from -2.658 to 3.058 lb. The record also posts gestational diabetes as a secondary outcome.

Interpretation is constrained by unreported details about missing outcomes, analysis methods, multiplicity, and the original sample-size justification. The planning illustrations are not the trial’s original design assumptions. The registry record’s posted comparisons describe specific outcomes and populations and do not resolve every stated clinical or implementation aim.